# Patent

## Title
Diagnosis of sepsis

## Abstract

Methods for predicting the development of sepsis in a subject at risk for developing sepsis are provided. In one method, features in a biomarker profile of the subject are evaluated. The subject is likely to develop sepsis if these features satisfy a particular value set. Methods for predicting the development of a stage of sepsis in a subject at risk for developing a stage of sepsis are provided. In one method, a plurality of features in a biomarker profile of the subject is evaluated. The subject is likely to have the stage of sepsis if these feature values satisfy a particular value set. Methods of diagnosing sepsis in a subject are provided. In one such method, a plurality of features in a biomarker profile of the subject is evaluated. The subject is likely to develop sepsis when the plurality of features satisfies a particular value set.

## Background
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims benefit, under 35 U.S.C. §119(e), of U.S. Provisional Patent Application No. 60/671,620, filed on Apr. 15, 2005, which hereby is incorporated herein, by reference, in its entirety. This application also claims benefit, under 35 U.S.C. §119(e), of U.S. Provisional Patent Application No. 60/674,046, filed on Apr. 22, 2005, which is hereby incorporated herein, by reference, in its entirety.


1. FIELD OF THE INVENTION
The present invention relates to methods and compositions for diagnosing or predicting sepsis and/or its stages of progression in a subject. The present invention also relates to methods and compositions for diagnosing systemic inflammatory response syndrome in a subject.
2. BACKGROUND OF THE INVENTION
Early detection of a disease condition typically allows for a more effective therapeutic treatment with a correspondingly more favorable clinical outcome. In many cases, however, early detection of disease symptoms is problematic due to the complexity of the disease; hence, a disease may become relatively advanced before diagnosis is possible. Systemic inflammatory conditions represent one such class of diseases. These conditions, particularly sepsis, typically, but not always, result from an interaction between a pathogenic microorganism and the host's defense system that triggers an excessive and dysregulated inflammatory response in the host. The complexity of the host's response during the systemic inflammatory response has complicated efforts towards understanding disease pathogenesis (reviewed in Healy, 2002, Annul. Pharmacother. 36:648-54). An incomplete understanding of the disease pathogenesis, in turn, contributes to the difficulty in finding useful diagnostic biomarkers. Early and reliable diagnosis is imperative, however, because of the remarkably rapid progression of sepsis into a life-threatening condition.
The development of sepsis in a subject follows a well-described course, progressing from systemic inflammatory response syndrome (“SIRS”)-negative, to SIRS-positive, and then to sepsis, which may then progress to severe sepsis, septic shock, multiple organ dysfunction (“MOD”), and ultimately death. Sepsis may also arise in an infected subject when the subject subsequently develops SIRS. “Sepsis” is commonly defined as the systemic host response to infection with SIRS plus a documented infection. “Severe sepsis” is associated with MOD, hypotension, disseminated intravascular coagulation (“DIC”) or hypoperfusion abnormalities, including lactic acidosis, oliguria, and changes in mental status. “Septic shock” is commonly defined as sepsis-induced hypotension that is resistant to fluid resuscitation with the additional presence of hypoperfusion abnormalities.
Documenting the presence of the pathogenic microorganisms that are clinically significant to sepsis has proven difficult. Causative microorganisms typically are detected by culturing a subject's blood, sputum, urine, wound secretion, in-dwelling line catheter surfaces, etc. Causative microorganisms, however, may reside only in certain body microenvironments such that the particular material that is cultured may not contain the contaminating microorganisms. Detection may be complicated further by low numbers of microorganisms at the site of infection. Low numbers of pathogens in blood present a particular problem for diagnosing sepsis by culturing blood. In one study, for example, positive culture results were obtained in only 17% of subjects presenting clinical manifestations of sepsis (Rangel-Frausto et al., 1995, JAMA 273:117-123). Diagnosis can be further complicated by contamination of samples by non-pathogenic microorganisms. For example, only 12.4% of detected microorganisms were clinically significant in a study of 707 subjects with septicemia (Weinstein et al., 1997, Clinical Infectious Diseases 24:584-602).
The difficulty in early diagnosis of sepsis is reflected by the high morbidity and mortality associated with the disease. Sepsis currently is the tenth leading cause of death in the United States and is especially prevalent among hospitalized patients in non-coronary intensive care units (ICUs), where it is the most common cause of death. The overall rate of mortality is as high as 35%, with an estimated 750,000 cases per year occurring in the United States alone. The annual cost to treat sepsis in the United States alone is on the order of billions of dollars.
A need, therefore, exists for a method of diagnosing sepsis, using techniques that have satisfactory specificity and sensitivity performance, sufficiently early to allow effective intervention and prevention.
3. SUMMARY OF THE INVENTION
The present invention relates to methods and compositions for diagnosing sepsis, including the onset of sepsis, in a test subject. The present invention also relates to methods and compositions for predicting sepsis in a test subject.
The present invention further relates to methods and compositions for diagnosing or predicting stages of sepsis progression in a test subject. The present invention still further relates to methods and compositions for diagnosing systemic inflammatory response syndrome (SIRS) in a test subject.
In one aspect, the present invention provides a method of predicting the development of sepsis in a test subject at risk for developing sepsis. This method comprises evaluating whether a plurality of features in a biomarker profile of the test subject satisfies a value set, wherein satisfying the value set means that the test subject will develop sepsis with a likelihood that is determined by the accuracy of the decision rule to which the plurality of features are applied in order to determine whether they satisfy the value set. In some embodiments, the accuracy of the decision rule is at least 60%. Therefore, correspondingly, the likelihood that the test subject will develop sepsis when the plurality of features satisfies the value set is at least 60%.
Yet another aspect of the invention comprises a method of diagnosing sepsis in a test subject. These methods comprise evaluating whether a plurality of features in a biomarker profile of the test subject satisfies a value set, wherein satisfying the value set predicts that the test subject has sepsis with a likelihood that is determined by the accuracy of the decision rule to which the plurality of features are applied in order to determine whether they satisfy the value set. In some embodiments, the accuracy of the decision rule is at least 60%. Therefore, correspondingly, the likelihood that the test subject has sepsis when the plurality of features satisfies the value set is at least 60%.
In a particular embodiment, the biomarker profile comprises at least two features, each feature representing a feature of a corresponding biomarker listed in column four or five of Table 30. In one embodiment, the biomarker profile comprises at least two different biomarkers listed in column four or five of Table 30. In such an embodiment, the biomarker profile can comprise a respective corresponding feature for the at least two biomarkers. Generally, the at least two biomarkers are derived from at least two different genes. In the case where a biomarker in the at least two different biomarkers is listed in column four of Table 30, the biomarker can be, for example, a transcript made by the listed gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein listed in column five of Table 30, or a discriminating fragment of the protein, or an indication of any of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein. In accordance with this embodiment, the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker. Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in Table 30). In one embodiment, such an assay utilizes a nucleic acid microarray. In some embodiments, the biomarker profile comprises at least two different biomarkers from column four or five of Table 32. In some embodiments, the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, or 50 different biomarkers from Table 30.
In a particular embodiment, the biomarker profile comprises at least two different biomarkers that each contain one of the probesets listed in column 2 of Table 30, biomarkers that contain the complement of one of the probesets of Table 30, or biomarkers that contain an amino acid sequence encoded by a gene that either contains one of the probesets of Table 30 or the complement of one of the probesets of Table 30. Such biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example amplified nucleic acid, or proteins. The biomarker profile further comprises a respective corresponding feature for the at least two biomarkers. Generally, the at least two biomarkers are derived from at least two different genes. In the case where a biomarker is based upon a gene that includes the sequence of a probeset listed in Table 30, the biomarker can be, for example, a transcript made by the gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein encoded by a gene that includes a probeset sequence described in Table 30, or a discriminating fragment of the protein, or an indication of any of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein. In some embodiments, the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers from any one of Table 31, 32, 33, 34, or 36.
In a particular embodiment, the biomarker profile comprises at least two different biomarkers listed in column three of Table 31. The biomarker profile further comprises a respective corresponding feature for the at least two biomarkers. Generally, the at least two biomarkers are derived from at least two different genes. The biomarker can be, for example, a transcript made by gene listed in Table 31, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein encoded by a gened listed in column three of Table 31, or a discriminating fragment of the protein, or an indication of any of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein. In accordance with this embodiment, the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker. Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in Table 31). In one embodiment, such an assay utilizes a nucleic acid microarray.
In a particular embodiment, the biomarker profile comprises at least two different biomarkers that each contain one of the probesets listed in column 2 of Table 31, biomarkers that contain the complement of one of the probesets of Table 31, or biomarkers that contain an amino acid sequence encoded by a gene that either contains one of the probesets of Table 31 or the complement of one of the probesets of Table 31. Such biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example amplified nucleic acid, or proteins. The biomarker profile further comprises a respective corresponding feature for the at least two biomarkers. Generally, the at least two biomarkers are derived from at least two different genes. In the case where a biomarker is based upon a gene that includes the sequence of a probeset listed in Table 31, the biomarker can be, for example, a transcript made by the gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein encoded by a gene that includes a probeset sequence described in Table 31, or a discriminating fragment of the protein, or an indication of any of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein. In some embodiments, the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, or 50 different biomarkers from Table 31.
In a particular embodiment, the biomarker profile comprises at least three features, each feature representing a feature of a corresponding biomarker listed in column 3 or four of Table I. In one embodiment, the biomarker profile comprises at least three different biomarkers listed in column three or four of Table I. In such an embodiment, the biomarker profile can comprise a respective corresponding feature for the at least three biomarkers. Generally, the at least three biomarkers are derived from at least three different genes listed in Table I. In the case where a biomarker in the at least three different biomarkers is listed in column three of Table I, the biomarker can be, for example, a transcript made by the listed gene, a complement thereof, a splice variant thereof, a complement of a splice variant thereof, or a discriminating fragment or complement of any of the foregoing, a cDNA of any of the forgoing, a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein listed in column four of Table I, or a discriminating fragment of the protein, or an indication of any of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, splice-variant thereof or protein. In accordance with this embodiment, the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker. Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in Table I). In one embodiment, such an assay utilizes a nucleic acid microarray. In some embodiments, the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, or 50 different biomarkers from Table I.
In a particular embodiment, the biomarker profile comprises at least three features, each feature representing a feature of a corresponding biomarker listed in column 3 or four of Table J. In one embodiment, the biomarker profile comprises at least three different biomarkers listed in column three or four of Table J. In such an embodiment, the biomarker profile can comprise a respective corresponding feature for the at least three biomarkers. Generally, the at least three biomarkers are derived from at least three different genes. In the case where a biomarker in the at least three different biomarkers is listed in column three of Table J, the biomarker can be, for example, a transcript made by the listed gene, a complement thereof, a splice variant thereof, a complement of a splice variant thereof, or a discriminating fragment or complement of any of the foregoing, a cDNA of any of the forgoing, a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein listed in column four of Table J, or a discriminating fragment of the protein, or an indication of any of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, splice-variant thereof or protein. In accordance with this embodiment, the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker. Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in Table J). In one embodiment, such an assay utilizes a nucleic acid microarray. In some embodiments, the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40 different biomarkers from Table J.
In a particular embodiment, the biomarker profile comprises at least three features, each feature representing a feature of a corresponding biomarker listed in column 3 or four of Table K. In one embodiment, the biomarker profile comprises at least three different biomarkers listed in column three or four of Table K. In such an embodiment, the biomarker profile can comprise a respective corresponding feature for the at least three biomarkers. Generally, the at least two or three biomarkers are derived from at least two or three different genes, respectively. In the case where a biomarker in the at least two or three different biomarkers is listed in column three of Table K, the biomarker can be, for example, a transcript made by the listed gene, a complement thereof, a splice variant thereof, a complement of a splice variant thereof, or a discriminating fragment or complement of any of the foregoing, a cDNA of any of the forgoing, a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein listed in column four of Table K, or a discriminating fragment of the protein, or an indication of any of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, splice-variant thereof or protein. In accordance with this embodiment, the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker. Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in Table K). In one embodiment, such an assay utilizes a nucleic acid microarray. In some embodiments, the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10 different biomarkers from Table K.
Although the methods of the present invention are particularly useful for detecting or predicting the onset of sepsis in SIRS subjects, one of skill in the art will understand that the present methods may be used for any subject: including, but not limited to, subjects suspected of having SIRS or of being at any stage of sepsis. For example, a biological sample can be taken from a subject, and a profile of biomarkers in the sample can be evaluated in light of biomarker profiles obtained from several different types of training populations. Representative training populations variously include, for example, populations that include subjects who are SIRS-negative, populations that include subjects who are SIRS-positive, and/or populations that include subjects at a particular stage of sepsis. Evaluation of the biomarker profile in light of each of these different training populations can be used to determine whether the test subject is SIRS-negative, SIRS-positive, is likely to become septic, or has a particular stage of sepsis. Based on the diagnosis resulting from the methods of the present invention, an appropriate treatment regimen can then be initiated.
In particular embodiments, the invention also provides kits that are useful in diagnosing or predicting the development of sepsis or SIRS in a subject (see Section 5.3, infra). The kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more biomarkers and/or reagents used to detect the presence or abundance of such biomarkers. In some embodiments, each of these biomarkers is from Table 30. In some embodiments, each of these biomarkers is from Table 31. In some embodiments, each of these biomarkers is from Table 32. In some embodiments, each of these biomarkers is from Table 33. In some embodiments, each of these biomarkers is from Table 36. In some embodiments, each of these biomarkers is from FIG. 39, FIG. 43, FIG. 52, FIG. 53, or FIG. 56. In another embodiment, the kits of the present invention comprise at least two, but as many as several hundred or more biomarkers and/or reagents used to detect the presence or abundance of such biomarkers.
In a specific embodiment, the kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more reagents that specifically bind the biomarkers of the present invention. For example, such kits can comprise nucleic acid molecules and/or antibody molecules that specifically bind to biomarkers of the present invention.
Specific exemplary biomarkers that are useful in the present invention are set forth in Section 5.6, Section 5.11, as well as Tables 30, 31, 32, 34 and 36 of Section 6. The biomarkers of the kit can be used to generate biomarker profiles according to the present invention. Examples of types of biomarkers and/or reagents within such kits include, but are not limited to, proteins and fragments thereof, peptides, polypeptides, antibodies, proteoglycans, glycoproteins, lipoproteins, carbohydrates, lipids, nucleic acids (mRNA, DNA, cDNA), organic and inorganic chemicals, and natural and synthetic polymers or a discriminating molecule or fragment thereof.
In particular embodiments, the invention also provides still other kits that are useful in diagnosing or predicting the development of sepsis or SIRS in a subject (see Section 5.3, infra). The kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50 or more biomarkers. In some embodiments, each of these biomarkers is from Table I. In some embodiments, each of these biomarkers is from Table J. In some embodiments, each of these biomarkers is from Table K. In some embodiments, each of these biomarkers is found in Table I or Table 30. In some embodiments, each of these biomarkers is found in Table I or Table 31. In some embodiments, each of these biomarkers is from FIG. 39, FIG. 43, FIG. 52, FIG. 53, or FIG. 56. In another embodiment, the kits of the present invention comprise at least two, but as many as 50 or more biomarkers. In a specific embodiment, the kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more reagents that specifically bind the biomarkers of the present invention. Specific biomarkers that are useful in the present invention are set forth in Section 5.6, Section 5.11, as well as Tables I, J, K, L, M, N, and O. The biomarkers of the kits can be used to generate biomarker profiles according to the present invention. Examples of classes of compounds of the kits include, but are not limited to, proteins and fragments thereof, peptides, polypeptides, proteoglycans, glycoproteins, lipoproteins, carbohydrates, lipids, nucleic acids (mRNA, DNA, cDNA), organic and inorganic chemicals, and natural and synthetic polymers or a discriminating molecule or fragment thereof.
Still another aspect of the present invention comprises computers and computer readable media for evaluating whether a test subject is likely to develop sepsis or SIRS. For instance, one embodiment of the present invention provides a computer program product for use in conjunction with a computer system. The computer program product comprises a computer readable storage medium and a computer program mechanism embedded therein. The computer program mechanism comprises instructions for evaluating whether a plurality of features in a biomarker profile of a test subject at risk for developing sepsis satisfies a first value set. Satisfaction of the first value set predicts that the test subject is likely to develop sepsis. The features are measurable aspects of a plurality of biomarkers comprising at least three biomarkers listed in Table I. In some embodiments, the computer program product further comprises instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a second value set. Satisfaction of the second value set predicts that the test subject is not likely to develop sepsis. In some embodiments, the biomarker profile has between 3 and 50 biomarkers listed in Table I, between 3 and 40 biomarkers listed in Table I, at least four biomarkers listed in Table I, or at least six biomarkers listed in Table I.
Another computer embodiment of the present invention comprises a central processing unit and a memory coupled to the central processing unit. The memory stores instructions for evaluating whether a plurality of features in a biomarker profile of a test subject at risk for developing sepsis satisfies a first value set. Satisfaction of the first value set predicts that the test subject is likely to develop sepsis. The features are measurable aspects of a plurality of biomarkers. This plurality of biomarkers comprises at least three biomarkers from Table I. In some embodiments, the memory further stores instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a second value set, wherein satisfying the second value set predicts that the test subject is not likely to develop sepsis. In some embodiments, the biomarker profile consists of between 3 and 50 biomarkers listed in Table I, between 3 and 40 biomarkers listed in Table I, at least four biomarkers listed in Table I, or at least eight biomarkers listed in Table I.
Another computer embodiment in accordance with the present invention comprises a computer system for determining whether a subject is likely to develop sepsis. The computer system comprises a central processing unit and a memory, coupled to the central processing unit. The memory stores instructions for obtaining a biomarker profile of a test subject. The biomarker profile comprises a plurality of features. The plurality of biomarkers comprises at least three biomarkers listed in Table I. The memory further comprises instructions for transmitting the biomarker profile to a remote computer. The remote computer includes instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a first value set. Satisfaction of the first value set predicts that the test subject is likely to develop sepsis. The memory further comprises instructions for receiving a determination, from the remote computer, as to whether the plurality of features in the biomarker profile of the test subject satisfies the first value set. The memory also comprises instructions for reporting whether the plurality of features in the biomarker profile of the test subject satisfies the first value set. In some embodiments, the remote computer further comprises instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a second value set. Satisfaction of the second value set predicts that the test subject is not likely to develop sepsis. In such embodiments, the memory further comprises instructions for receiving a determination, from the remote computer, as to whether the plurality of features in the biomarker profile of the test subject satisfies the second set as well as instructions for reporting whether the plurality of features in the biomarker profile of the test subject satisfies the second value set. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least six biomarkers listed in Table I.
Still another embodiment of the present invention comprises a digital signal embodied on a carrier wave comprising a respective value for each of a plurality of features in a biomarker profile. The features are measurable aspects of a plurality of biomarkers. The plurality of biomarkers comprises at least three biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
Still another aspect of the present invention provides a digital signal embodied on a carrier wave comprising a determination as to whether a plurality of features in a biomarker profile of a test subject satisfies a value set. The features are measurable aspects of a plurality of biomarkers. This plurality of biomarkers comprises at least three biomarkers listed in Table I. Satisfying the value set predicts that the test subject is likely to develop sepsis. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
Still another embodiment provides a digital signal embodied on a carrier wave comprising a determination as to whether a plurality of features in a biomarker profile of a test subject satisfies a value set. The features are measurable aspects of a plurality of biomarkers. The plurality of biomarkers comprises at least three biomarkers listed in Table I. Satisfaction of the value set predicts that the test subject is not likely to develop sepsis. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
Still another embodiment of the present invention provides a graphical user interface for determining whether a subject is likely to develop sepsis. The graphical user interface comprises a display field for a displaying a result encoded in a digital signal embodied on a carrier wave received from a remote computer. The features are measurable aspects of a plurality of biomarkers. The plurality of biomarkers comprises at least three biomarkers listed in Table I. The result has a first value when a plurality of features in a biomarker profile of a test subject satisfies a first value set. The result has a second value when a plurality of features in a biomarker profile of a test subject satisfies a second value set. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
Yet another aspect of the present invention provides a computer system for determining whether a subject is likely to develop sepsis. The computer system comprises a central processing unit and a memory, coupled to the central processing unit. The memory stores instructions for obtaining a biomarker profile of a test subject. The biomarker profile comprises a plurality of features. The features are measurable aspects of a plurality of biomarkers. The plurality of biomarkers comprise at least three biomarkers listed in Table I. The memory further stores instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a first value set. Satisfying the first value set predicts that the test subject is likely to develop sepsis. The memory also stores instructions for reporting whether the plurality of features in the biomarker profile of the test subject satisfies the first value set. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least eight biomarkers listed in Table I.



4. BRIEF DESCRIPTION OF THE FIGURES
FIG. 1 illustrates a classification and regression tree for discriminating between a SIRS phenotypic state characterized by the onset of sepsis and a SIRS phenotypic state characterized by the absence of sepsis using T−36 static data obtained from a training population in accordance with an embodiment of the present invention.
FIG. 2 shows the distribution of feature values for five biomarkers used in the decision tree of FIG. 1 across T−36 static data obtained from a training population in accordance with an embodiment of the present invention. The biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
FIG. 3 illustrates the overall accuracy, sensitivity, and specificity of 500 trees used to train a decision tree using the Random Forests method based upon T−36 static data obtained from a training population in accordance with an embodiment of the present invention.
FIG. 4 illustrates the biomarker importance in the decision rule trained using the trees of FIG. 3.
FIG. 5 illustrates the overall accuracy, with 95% confidence interval bars, specificity, and sensitivity of a decision rule developed with predictive analysis of microarrays (PAM) using the biomarkers of the present invention across T−36 static data obtained from a training population.
FIG. 6 is a list of biomarkers, rank-ordered by their respective degrees of discriminatory power, identified by PAM using T−36 static data obtained from a training population. The biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
FIG. 7 illustrates CART, PAM, and random forests classification algorithm performance data, and associated 95% confidence intervals, for T−36 static data obtained from a training population.
FIG. 8 illustrates the number of times that common biomarkers were found to be important across the decision rules developed using (i) CART, (ii) PAM, (iii) random forests, and (iv) the Wilcoxon (adjusted) test, for T−36 static data obtained from a training population.
FIG. 9 illustrates an overall ranking of biomarkers for T−36 static data obtained from a training population. The biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
FIG. 10 illustrates a classification and regression tree for discriminating between a SIRS phenotypic state characterized by the onset of sepsis and a SIRS phenotypic state characterized by the absence of sepsis using data using T−12 static data obtained from a training population in accordance with an embodiment of the present invention.
FIG. 11 shows the distribution of feature values for four biomarkers used in the decision tree of FIG. 10 using T−12 static data obtained from a training population in accordance with an embodiment of the present invention. The biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
FIG. 12 illustrates the overall accuracy, sensitivity, and specificity of 500 trees used to train a decision tree using the Random Forests method based upon T−12 static data obtained from a training population in accordance with an embodiment of the present invention.
FIG. 13 illustrates the biomarker importance in the decision rule trained using the trees of FIG. 12. The biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
FIG. 14 illustrates a calculation of biomarker importance, summing to 100%, determined by a multiple additive regression tree (MART) approach using T−12 static data obtained from a training population. The biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
FIG. 15 illustrates the distribution of feature values of the biomarkers selected by the MART approach illustrated in FIG. 14 between the Sepsis and SIRS groups using T−12 static data obtained from a training population. The biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
FIG. 16 illustrates the overall accuracy, with 95% confidence interval bars, specificity, and sensitivity of a decision rule developed with predictive analysis of microarrays (PAM) using the biomarkers of the present invention using T−12 static data obtained from a training population.
FIG. 17 is a list of biomarkers, rank-ordered by their respective degrees of discriminatory power, identified by PAM using T−12 static data obtained from a training population. The biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
FIG. 18 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T−12 static data obtained from a training population.
FIG. 19 illustrates the number of times that common biomarkers were found to be important across the decision rules developed using (i) CART, (ii) MART, (iii) PAM, (iv) random forests, and (v) the Wilcoxon (adjusted) test using T−12 static data obtained from a training population. The biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
FIG. 20 illustrates an overall ranking of biomarkers using T−12 static data obtained from a training population.
FIG. 21 illustrates a classification and regression tree for discriminating between a SIRS phenotypic state characterized by the onset of sepsis and a SIRS phenotypic state characterized by the absence of sepsis using T−12 baseline data obtained from a training population in accordance with an embodiment of the present invention.
FIG. 22 shows the distribution of the feature values of five biomarkers used in the decision tree of FIG. 21 using T−12 baseline data obtained from a training population in accordance with an embodiment of the present invention. The biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
FIG. 23 illustrates the overall accuracy, sensitivity, and specificity of 500 trees used to train a decision tree using the Random Forests method using T−12 baseline data obtained from a training population in accordance with an embodiment of the present invention.
FIG. 24 illustrates the biomarker importance in the decision rule trained using the trees of FIG. 23. The biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
FIG. 25 illustrates the overall accuracy, with 95% confidence interval bars, specificity, and sensitivity of a decision rule developed with predictive analysis of microarrays (PAM) using select biomarkers of the present invention and T−12 baseline data obtained from a training population.
FIG. 26 is a list of biomarkers, rank-ordered by their respective degrees of discriminatory power, identified by PAM using T−12 baseline data obtained from a training population. The biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
FIG. 27 illustrates CART, PAM, and random forests classification algorithm (decision rule) performance data, and associated 95% confidence intervals, using T−12 baseline data obtained from a training population in accordance with an embodiment of the present invention.
FIG. 28 illustrates the number of times that common biomarkers were found to be important across the decision rules developed using (i) CART, (ii) PAM, (iii) random forests, and (iv) the Wilcoxon (adjusted) test using T−12 baseline data obtained from a training population.
FIG. 29 illustrates an overall ranking of biomarkers for data obtained using T−12 baseline data obtained from a training population. The biomarkers are referenced by their corresponding Affymetrix U133 plus 2.0 probeset names given in Table 30.
FIG. 30 illustrates the filters applied to identify biomarkers that discriminate between subjects that will get sepsis during a defined time period and subjects that will not get sepsis during the defined time period using data obtained from a training population, in accordance with an embodiment of the present invention. Other combinations of biomarkers are disclosed herein including, for example, in Section 5.3 and in Section 6.
FIG. 31 shows the correlation between IL18R1 expression, as determined by RT-PCR, and the intensity of the X206618_at probeset, as determined using Affymetrix U133 plus 2.0 microarray measurements, across a training population.
FIG. 32 shows the correlation between FCGR1A expression, as determined by RT-PCR, and the intensity of the X214511_x_at, X216950_s_at and X216951_at probesets, as determined using Affymetrix U133 plus 2.0 microarray measurements, across a training population.
FIG. 33 shows the correlation between MMP9 expression, as determined by RT-PCR, and the intensity of the X203936_s_at probeset, as determined using Affymetrix U133 plus 2.0 microarray measurements, across a training population.
FIG. 34 shows the correlation between CD86 expression, as determined by RT-PCR, and the intensity of the X205685_at, X205686_s_at, and X210895_s_at probesets, as determined using Affymetrix U133 plus 2.0 microarray measurements, across a training population.
FIG. 35 shows a computer system in accordance with the present invention.
FIG. 36 illustrates a classification and regression tree for discriminating between a SIRS phenotypic state characterized by the onset of sepsis and a SIRS phenotypic state characterized by the absence of sepsis using T−12 static data obtained from an RT-PCR discovery training population in accordance with an embodiment of the present invention.
FIG. 37 shows the distribution of feature values for seven biomarkers used in the decision tree of FIG. 36 across T−12 static data obtained from an RT-PCR discovery training population in accordance with an embodiment of the present invention.
FIG. 38 illustrates the overall accuracy, sensitivity, and specificity of 462 trees used to train a decision tree using the Random Forests method based upon T−12 static data obtained from an RT-PCR discovery training population in accordance with an embodiment of the present invention.
FIG. 39 illustrates the biomarker importance in the decision rule trained using the trees of FIG. 38.
FIG. 40 illustrates a calculation of biomarker importance, summing to 100%, determined by a multiple additive regression tree (MART) approach using T−12 static data obtained from an RT-PCR discovery training population.
FIG. 41 illustrates the distribution of feature values of the biomarkers selected by the MART approach illustrated in FIG. 40 between the Sepsis and SIRS groups using T−12 static data obtained from an RT-PCR discovery training population.
FIG. 42 illustrates the overall accuracy, with 95% confidence interval bars, specificity, and sensitivity of a decision rule developed with predictive analysis of microarrays (PAM) using the biomarkers of the present invention using T−12 static data obtained from an RT-PCR discovery training population.
FIG. 43 is a list of biomarkers, rank-ordered by their respective degrees of discriminatory power, identified by PAM using T−12 static data obtained from an RT-PCR discovery training population.
FIG. 44 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T−12 static data obtained from an RT-PCR discovery training population.
FIG. 45 identified fifty selected biomarkers selected based on the decision rule performance summarized in FIG. 44.
FIG. 46 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T−12 static data obtained from an Affymetrix gene chip discovery training population.
FIG. 47 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T−12 static data obtained from an RT-PCR confimatory training population.
FIG. 48 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T−12 static data obtained from a combined pool of a Affymetrix gene chip confirmatory training population and an RT-PCR confirmatory training population.
FIG. 49 illustrates a classification and regression tree for discriminating between a SIRS phenotypic state characterized by the onset of sepsis and a SIRS phenotypic state characterized by the absence of sepsis using T−12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
FIG. 50 shows the distribution of feature values for ten biomarkers used in the decision tree of FIG. 49 across T−12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
FIG. 51 illustrates the overall accuracy, sensitivity, and specificity of 64 trees used to train a decision tree using the Random Forests method based upon T−12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
FIG. 52 illustrates the biomarker importance in the decision rule trained using the trees of FIG. 51.
FIG. 53 illustrates a calculation of biomarker importance, summing to 100%, determined by a multiple additive regression tree (MART) approach using T−12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
FIG. 54 illustrates the distribution of feature values of the biomarkers selected by the MART approach illustrated in FIG. 53 between the Sepsis and SIRS groups using T−12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
FIG. 55 illustrates the overall accuracy, with 95% confidence interval bars, specificity, and sensitivity of a decision rule developed with predictive analysis of microarrays (PAM) using the biomarkers of the present invention using T−12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
FIG. 56 is a list of biomarkers, rank-ordered by their respective degrees of discriminatory power, identified by PAM using T−12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
FIG. 57 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T−12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
FIG. 58 illustrates the number of times that common biomarkers were found to be important across the decision rules developed using (i) CART, (ii) MART, (iii) PAM, (iv) random forests, and (v) the Wilcoxon (adjusted) test using T−12 static data obtained from a bead-based protein discovery training population in accordance with an embodiment of the present invention.
FIG. 59 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T−12 static data obtained from a bead-based protein confirmation training population in accordance with an embodiment of the present invention.’
FIG. 60 plots the sepsis predicting accuracy of each of 24 families of subcombinations from Table J, using T−12 nucleic acid data, in a bar graph fashion, in accordance with an embodiment of the present invention.
FIG. 61 plots the sepsis predicting performance (accuracy) of each individual subcombination in each of 24 families of subcombinations, for a total of 4800 subcombinations from Table J, using T−12 nucleic acid data, in accordance with an embodiment of the present invention.
FIG. 62 plots the sepsis predicting accuracy of each of 8 families of subcombinations from Table K, using T−12 protein data, in a bar graph fashion, in accordance with an embodiment of the present invention.
FIG. 63 plots the sepsis predicting performance (accuracy) of each individual subcombination in each of 8 families of subcombinations, for a total of 1600 subcombinations from Table K, using T−12 protein data, in accordance with an embodiment of the present invention.
FIG. 64 plots the sepsis predicting accuracy of each of 8 families of subcombinations from Table K, using T−36 protein data, in a bar graph fashion, in accordance with an embodiment of the present invention.
FIG. 65 plots the sepsis predicting performance (accuracy) of each individual subcombination in each of 8 families of subcombinations, for a total of 1600 subcombinations from Table K, using T−36 protein data, in accordance with an embodiment of the present invention.
FIG. 66 plots the sepsis predicting accuracy of each of 23 families of subcombinations from Table J, using T−36 nucleic acid data, in a bar graph fashion, in accordance with an embodiment of the present invention.
FIG. 67 plots the sepsis predicting performance (accuracy) of each individual subcombination in each of 23 families of subcombinations, for a total of 4600 subcombinations from Table J, using T−36 nucleic acid data, in accordance with an embodiment of the present invention.
FIG. 68 plots the sepsis predicting accuracy of each of 23 families of subcombinations from Table I, using T−12 combined protein and nucleic acid data, in a bar graph fashion, in accordance with an embodiment of the present invention.
FIG. 69 plots the sepsis predicting performance (accuracy) of each individual subcombination in each of 23 families of subcombinations, for a total of 4600 subcombinations from Table I, using T−12 combined protein and nucleic acid data, in accordance with an embodiment of the present invention.
FIG. 70 plots the sepsis predicting accuracy of each of 23 families of subcombinations from Table I, using T−36 combined protein and nucleic acid data, in a bar graph fashion, in accordance with an embodiment of the present invention.
FIG. 71 plots the sepsis predicting performance (accuracy) of each individual subcombination in each of 23 families of subcombinations, for a total of 4600 subcombinations from Table I, using T−36 combined protein and nucleic acid data, in accordance with an embodiment of the present invention.



5. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
The present invention allows for the rapid and accurate diagnosis or prediction of sepsis by evaluating biomarker features in biomarker profiles. These biomarker profiles can be constructed from one or more biological samples of subjects at a single time point (“snapshot”), or multiple such time points, during the course of time the subject is at risk for developing sepsis. Advantageously, sepsis can be diagnosed or predicted prior to the onset of conventional clinical sepsis symptoms, thereby allowing for more effective therapeutic intervention.
5.1 Definitions
“Systemic inflammatory response syndrome,” or “SIRS,” refers to a clinical response to a variety of severe clinical insults, as manifested by two or more of the following conditions within a 24-hour period:

    
    
        body temperature greater than 38° C. (100.4° F.) or less than 36° C. (96.8° F.);
        heart rate (HR) greater than 90 beats/minute;
        respiratory rate (RR) greater than 20 breaths/minute, or PCO2 less than 32 mmHg, or requiring mechanical ventilation; and
        white blood cell count (WBC) either greater than 12.0×109/L or less than 4.0×109/L.
    
    


These symptoms of SIRS represent a consensus definition of SIRS that can be modified or supplanted by other definitions in the future. The present definition is used to clarify current clinical practice and does not represent a critical aspect of the invention (see, e.g., American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: Definitions for Sepsis and Organ Failure and Guidelines for the Use of Innovative Therapies in Sepsis, 1992, Crit. Care. Med. 20, 864-874, the entire contents of which are herein incorporated by reference).
A subject with SIRS has a clinical presentation that is classified as SIRS, as defined above, but is not clinically deemed to be septic. Methods for determining which subjects are at risk of developing sepsis are well known to those in the art. Such subjects include, for example, those in an ICU and those who have otherwise suffered from a physiological trauma, such as a burn, surgery or other insult. A hallmark of SIRS is the creation of a proinflammatory state that can be marked by tachycardia, tachypnea or hyperpnea, hypotension, hypoperfusion, oliguria, leukocytosis or leukopenia, pyrexia or hypothermia and the need for volume infusion. SIRS characteristically does not include a documented source of infection (e.g., bacteremia).
“Sepsis” refers to a systemic host response to infection with SIRS plus a documented infection (e.g., a subsequent laboratory confirmation of a clinically significant infection such as a positive culture for an organism). Thus, sepsis refers to the systemic inflammatory response to a documented infection (see, e.g., American College of Chest Physicians Society of Critical Care Medicine, Chest, 1997, 101:1644-1655, the entire contents of which are herein incorporated by reference). As used herein, “sepsis” includes all stages of sepsis including, but not limited to, the onset of sepsis, severe sepsis, septic shock and multiple organ dysfunction (“MOD”) associated with the end stages of sepsis.
The “onset of sepsis” refers to an early stage of sepsis, e.g., prior to a stage when conventional clinical manifestations are sufficient to support a clinical suspicion of sepsis. Because the methods of the present invention are used to detect sepsis prior to a time that sepsis would be suspected using conventional techniques, the subject's disease status at early sepsis can only be confirmed retrospectively, when the manifestation of sepsis is more clinically obvious. The exact mechanism by which a subject becomes septic is not a critical aspect of the invention. The methods of the present invention can detect the onset of sepsis independent of the origin of the infectious process.
“Severe sepsis” refers to sepsis associated with organ dysfunction, hypoperfusion abnormalities, or sepsis-induced hypotension. Hypoperfusion abnormalities include, but are not limited to, lactic acidosis, oliguria, or an acute alteration in mental status.
“Septic shock” refers to sepsis-induced hypotension that is not responsive to adequate intravenous fluid challenge and with manifestations of peripheral hypoperfusion.
A “converter” or “converter subject” refers to a SIRS-positive subject who progresses to clinical suspicion of sepsis during the period the subject is monitored, typically during an ICU stay.
A “non-converter” or “non-converter subject” refers to a SIRS-positive subject who does not progress to clinical suspicion of sepsis during the period the subject is monitored, typically during an ICU stay.
A “biomarker” is virtually any detectable compound, such as a protein, a peptide, a proteoglycan, a glycoprotein, a lipoprotein, a carbohydrate, a lipid, a nucleic acid (e.g., DNA, such as cDNA or amplified DNA, or RNA, such as mRNA), an organic or inorganic chemical, a natural or synthetic polymer, a small molecule (e.g., a metabolite), or a discriminating molecule or discriminating fragment of any of the foregoing, that is present in or derived from a biological sample. “Derived from” as used in this context refers to a compound that, when detected, is indicative of a particular molecule being present in the biological sample. For example, detection of a particular cDNA can be indicative of the presence of a particular RNA transcript in the biological sample. As another example, detection of or binding to a particular antibody can be indicative of the presence of a particular antigen (e.g., protein) in the biological sample. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of an above-identified compound.
A biomarker can, for example, be isolated from the biological sample, directly measured in the biological sample, or detected in or determined to be in the biological sample. A biomarker can, for example, be functional, partially functional, or non-functional. In one embodiment of the present invention, a biomarker is isolated and used, for example, to raise a specifically-binding antibody that can facilitate biomarker detection in a variety of diagnostic assays. Any immunoassay may use any antibodies, antibody fragment or derivative thereof capable of binding the biomarker molecules (e.g., Fab, F(ab′)2, Fv, or scFv fragments). Such immunoassays are well-known in the art. In addition, if the biomarker is a protein or fragment thereof, it can be sequenced and its encoding gene can be cloned using well-established techniques.
As used herein, the term “a species of a biomarker” refers to any discriminating portion or discriminating fragment of a biomarker described herein, such as a splice variant of a particular gene described herein (e.g., a gene listed in Table 30, or Table I, or Table J, or Table K, infra). Here, a discriminating portion or discriminating fragment is a portion or fragment of a molecule that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein.
As used herein, the terms “protein”, “peptide”, and “polypeptide” are, unless otherwise indicated, interchangeable.
A “biomarker profile” comprises a plurality of one or more types of biomarkers (e.g., an mRNA molecule, a cDNA molecule, a protein and/or a carbohydrate, etc.), or an indication thereof, together with a feature, such as a measurable aspect (e.g., abundance) of the biomarkers. A biomarker profile comprises at least two such biomarkers or indications thereof, where the biomarkers can be in the same or different classes, such as, for example, a nucleic acid and a carbohydrate. A biomarker profile may also comprise at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more biomarkers or indications thereof. In one embodiment, a biomarker profile comprises hundreds, or even thousands, of biomarkers or indications thereof. A biomarker profile can further comprise one or more controls or internal standards. In one embodiment, the biomarker profile comprises at least one biomarker, or indication thereof, that serves as an internal standard. In another embodiment, a biomarker profile comprises an indication of one or more types of biomarkers. The term “indication” as used herein in this context merely refers to a situation where the biomarker profile contains symbols, data, abbreviations or other similar indicia for a biomarker, rather than the biomarker molecular entity itself. For instance, consider an exemplary biomarker profile of the present invention that comprises the Affymetrix (Santa Clara, Calif.) U133 plus 2.0 205013_s_at and 209369_at probesets. Another exemplary biomarker profile of the present invention comprises the name of genes used to derive the Affymetrix (Santa Clara, Calif.) U133 plus 2.0 205013_s_at and 209369_at probesets. In still another exemplary biomarker profile of the present invention, the biomarker profile comprises a physical quantity of a transcript of a gene from which the 205013_s_at probeset was derived, and a physical quantity of a transcript of a gene from which the 209369_at probeset was derived. In another embodiment, the biomarker profile comprises a nominal indication of the quantity of a transcript of a gene from which the 205013_s_at probeset was derived and a nominal indication of the quantity of transcript of a gene from which the 209369_at probeset was derived. Still another exemplary biomarker profile of the present invention comprises a microarray to which a physical quantity of a gene transcript from which the 205013_s_at probeset was derived is bound at a first probe spot on the microarray and an abundance of a gene transcript from which the 209369_at probeset was derived is bound to a second probe spot on the microarray. In this last exemplary biomarker profile, at least twenty percent, forty percent, or more than forty percent of the probes spots are based on sequences in the probesets given in Table 30. In another exemplary biomarker profile, at least twenty percent, forty percent, or more than forty percent of the probes spots are based on sequences in the probesets given in Table 31.
Each biomarker in a biomarker profile includes a corresponding “feature.” A “feature”, as used herein, refers to a measurable aspect of a biomarker. A feature can include, for example, the presence or absence of biomarkers in the biological sample from the subject as illustrated in exemplary biomarker profile 1:
Exemplary Biomarker Profile 1.















Feature



Biomarker
Presence in sample









transcript of gene A
Present



transcript of gene B
Absent










In exemplary biomarker profile 1, the feature value for the transcript of gene A is “presence” and the feature value for the transcript of gene B is “absence.”
A feature can include, for example, the abundance of a biomarker in the biological sample from a subject as illustrated in exemplary biomarker profile 2:
Exemplary biomarker profile 2.















Feature




Abundance in sample in relative



Biomarker
units









transcript of gene A
300



transcript of gene B
400










In exemplary biomarker profile 2, the feature value for the transcript of gene A is 300 units and the feature value for the transcript of gene B is 400 units.
A feature can also be a ratio of two or more measurable aspects of a biomarker as illustrated in exemplary biomarker profile 3:
Exemplary Biomarker Profile 3.















Feature




Ratio of abundance of transcript of



Biomarker
gene A/transcript of gene B









transcript of gene A
300/400



transcript of gene B










In exemplary biomarker profile 3, the feature value for the transcript of gene A and the feature value for the transcript of gene B is 0.75 (300/400).
A feature may also be the difference between a measurable aspect of the corresponding biomarker that is taken from two samples, where the two samples are collected from a subject at two different time points. For example, consider the case where the biomarker is a transcript of a gene A and the “measurable aspect” is abundance of the transcript, in samples obtained from a test subject as determined by, e.g., RT-PCR or microarray analysis. In this example, the abundance of the transcript of gene A is measured in a first sample as well as a second sample. The first sample is taken from the test subject a number of hours before the second sample. To compute the feature for gene A, the abundance of the transcript of gene A in one sample is subtracted from the abundance of the transcript of gene A in the second sample. A feature can also be an indication as to whether an abundance of a biomarker is increasing in biological samples obtained from a subject over time and/or an indication as to whether an abundance of a biomarker is decreasing in biological samples obtained from a subject over time.
In some embodiments, there is a one-to-one correspondence between features and biomarkers in a biomarker profile as illustrated in exemplary biomarker profile 1, above. In some embodiments, the relationship between features and biomarkers in a biomarker profile of the present invention is more complex, as illustrated in Exemplary biomarker profile 3, above.
Those of skill in the art will appreciate that other methods of computation of a feature can be devised and all such methods are within the scope of the present invention. For example, a feature can represent the average of an abundance of a biomarker across biological samples collected from a subject at two or more time points. Furthermore, a feature can be the difference or ratio of the abundance of two or more biomarkers from a biological sample obtained from a subject in a single time point. A biomarker profile may also comprise at least three, four, five, 10, 20, 30 or more features. In one embodiment, a biomarker profile comprises hundreds, or even thousands, of features.
In some embodiments, features of biomarkers are measured using microarrays. The construction of microarrays and the techniques used to process microarrays in order to obtain abundance data is well known, and is described, for example, by Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman & Hall/CRC, and international publication number WO 03/061564, each of which is hereby incorporated by reference in its entirety. A microarray comprises a plurality of probes. In some instances, each probe recognizes, e.g., binds to, a different biomarker. In some instances, two or more different probes on a microarray recognize, e.g., bind to, the same biomarker. Thus, typically, the relationship between probe spots on the microarray and a subject biomarker is a two to one correspondence, a three to one correspondence, or some other form of correspondence. However, it can be the case that there is a unique one-to-one correspondence between probes on a microarray and biomarkers.
A “phenotypic change” is a detectable change in a parameter associated with a given state of the subject. For instance, a phenotypic change can include an increase or decrease of a biomarker in a bodily fluid, where the change is associated with SIRS, sepsis, the onset of sepsis or with a particular stage in the progression of sepsis. A phenotypic change can further include a change in a detectable aspect of a given state of the subject that is not a change in a measurable aspect of a biomarker. For example, a change in phenotype can include a detectable change in body temperature, respiration rate, pulse, blood pressure, or other physiological parameter. Such changes can be determined via clinical observation and measurement using conventional techniques that are well-known to the skilled artisan.
As used herein, the term “complementary,” in the context of a nucleic acid sequence (e.g., a nucleotide sequence encoding a gene described herein), refers to the chemical affinity between specific nitrogenous bases as a result of their hydrogen bonding properties. For example, guanine (G) forms a hydrogen bond with only cytosine (C), while adenine forms a hydrogen bond only with thymine (T) in the case of DNA, and uracil (U) in the case of RNA. These reactions are described as base pairing, and the paired bases (G with C, or A with T/U) are said to be complementary. Thus, two nucleic acid sequences may be complementary if their nitrogenous bases are able to form hydrogen bonds. Such sequences are referred to as “complements” of each other. Such complement sequences can be naturally occurring, or, they can be chemically synthesized by any method known to those skilled in the art, as for example, in the case of antisense nucleic acid molecules which are complementary to the sense strand of a DNA molecule or an RNA molecule (e.g., an mRNA transcript). See, e.g., Lewin, 2002, Genes VII. Oxford University Press Inc., New York, N.Y., which is hereby incorporated by reference.
As used herein, “conventional techniques” in the context of diagnosing or predicting sepsis or SIRS are those techniques that classify a subject based on phenotypic changes without obtaining a biomarker profile according to the present invention.
A “decision rule” is a method used to evaluate biomarker profiles. Such decision rules can take on one or more forms that are known in the art, as exemplified in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, which is hereby incorporated by reference in its entirety. A decision rule may be used to act on a data set of features to, inter alia, predict the onset of sepsis, to determine the progression of sepsis, or to diagnose sepsis. Exemplary decision rules that can be used in some embodiments of the present invention are described in further detail in Section 5.5, below.
“Predicting the development of sepsis” is the determination as to whether a subject will develop sepsis. Any such prediction is limited by the accuracy of the means used to make this determination. The present invention provides a method, e.g., by utilizing a decision rule(s), for making this determination with an accuracy that is 60% or greater. As used herein, the terms “predicting the development of sepsis” and “predicting sepsis” are interchangeable. In some embodiments, the act of predicting the development of sepsis (predicting sepsis) is accomplished by evaluating one or more biomarker profiles from a subject using a decision rule that is indicative of the development of sepsis and, as a result of this evaluation, receiving a result from the decision rule that indicates that the subject will become septic. Such an evaluation of one or more biomarker profiles from a test subject using a decision rule uses some or all the features in the one or more biomarker profiles to obtain such a result.
The terms “obtain” and “obtaining,” as used herein, mean “to come into possession of,” or “coming into possession of,” respectively. This can be done, for example, by retrieving data from a data store in a computer system. This can also be done, for example, by direct measurement.
As used herein, the term “specifically,” and analogous terms, in the context of an antibody, refers to peptides, polypeptides, and antibodies or fragments thereof that specifically bind to an antigen or a fragment and do not specifically bind to other antigens or other fragments. A peptide or polypeptide that specifically binds to an antigen may bind to other peptides or polypeptides with lower affinity, as determined by standard experimental techniques, for example, by any immunoassay well-known to those skilled in the art. Such immunoassays include, but are not limited to, radioimmunoassays (RIAs) and enzyme-linked immunosorbent assays (ELISAs). Antibodies or fragments that specifically bind to an antigen may be cross-reactive with related antigens. Preferably, antibodies or fragments thereof that specifically bind to an antigen do not cross-react with other antigens. See, e.g., Paul, ed., 2003, Fundamental Immunology, 5th ed., Raven Press, New York at pages 69-105, which is incorporated by reference herein, for a discussion regarding antigen-antibody interactions, specificity and cross-reactivity, and methods for determining all of the above.
As used herein, a “subject” is an animal, preferably a mammal, more preferably a non-human primate, and most preferably a human. The terms “subject” “individual” and “patient” are used interchangeably herein.
As used herein, a “test subject,” typically, is any subject that is not in a training population used to construct a decision rule. A test subject can optionally be suspected of either having sepsis at risk of developing sepsis.
As used herein, a “tissue type,” is a type of tissue. A tissue is an association of cells of a multicellular organism, with a common embryoloical origin or pathway and similar structure and function. Often, cells of a tissue are contiguous at cell membranes but occasionally the tissue may be fluid (e.g., blood). Cells of a tissue may be all of one type (a simple tissue, e.g., squamous epithelium, plant parentchyma) or of more than one type (a mixed tissue, e.g., connective tissue).
As used herein, a “training population” is a set of samples from a population of subjects used to construct a decision rule, using a data analysis algorithm, for evaluation of the biomarker profiles of subjects at risk for developing sepsis. In a preferred embodiment, a training population includes samples from subjects that are converters and subjects that are nonconverters.
As used herein, a “data analysis algorithm” is an algorithm used to construct a decision rule using biomarker profiles of subjects in a training population. Representative data analysis algorithms are described in Section 5.5. A “decision rule” is the final product of a data analysis algorithm, and is characterized by one or more value sets, where each of these value sets is indicative of an aspect of SIRS, the onset of sepsis, sepsis, or a prediction that a subject will acquire sepsis. In one specific example, a value set represents a prediction that a subject will develop sepsis. In another example, a value set represents a prediction that a subject will not develop sepsis.
As used herein, a “validation population” is a set of samples from a population of subjects used to determine the accuracy of a decision rule. In a preferred embodiment, a validation population includes samples from subjects that are converters and subjects that are nonconverters. In a preferred embodiment, a validation population does not include subjects that are part of the training population used to train the decision rule for which an accuracy measurement is sought.
As used herein, a “value set” is a combination of values, or ranges of values for features in a biomarker profile. The nature of this value set and the values therein is dependent upon the type of features present in the biomarker profile and the data analysis algorithm used to construct the decision rule that dictates the value set. To illustrate, reconsider exemplary biomarker profile 2:
Exemplary Biomarker Profile 2.















Feature




Abundance in sample in relative



Biomarker
units









transcript of gene A
300



transcript of gene B
400










In this example, the biomarker profile of each member of a training population is obtained. Each such biomarker profile includes a measured feature, here abundance, for the transcript of gene A, and a measured feature, here abundance, for the transcript of gene B. These feature values, here abundance values, are used by a data analysis algorithm to construct a decision rule. In this example, the data analysis algorithm is a decision tree, described in Section 5.5.1 and the final product of this data analysis algorithm, the decision rule, is a decision tree. An exemplary decision tree is illustrated in FIG. 1. The decision rule defines value sets. One such value set is predictive of the onset of sepsis. A subject whose biomarker feature values satisfy this value set is likely to become septic. An exemplary value set of this class is exemplary value set 1:
Exemplary Value Set 1.















Value set component




(Abundance in sample in relative



Biomarker
units)









transcript of gene A
<400



transcript of gene B
<600










Another such value set is predictive of a septic-free state. A subject whose biomarker feature values satisfy this value set is not likely to become septic. An exemplary value set of this class is exemplary value set 2:
Exemplary Value Set 2.















Value set component




(Abundance in sample in relative



Biomarker
units)









transcript of gene A
>400



transcript of gene B
>600










In the case where the data analysis algorithm is a neural network analysis and the final product of this neural network analysis is an appropriately weighted neural network, one value set is those ranges of biomarker profile feature values that will cause the weighted neural network to indicate that onset of sepsis is likely. Another value set is those ranges of biomarker profile feature values that will cause the weighted neural network to indicate that onset of sepsis is not likely.
As used herein, the term “probe spot” in the context of a microarray refers to a single stranded DNA molecule (e.g., a single stranded cDNA molecule or synthetic DNA oligomer), referred to herein as a “probe,” that is used to determine the abundance of a particular nucleic acid in a sample. For example, a probe spot can be used to determine the level of mRNA in a biological sample (e.g., a collection of cells) from a test subject. In a specific embodiment, a typical microarray comprises multiple probe spots that are placed onto a glass slide (or other substrate) in known locations on a grid. The nucleic acid for each probe spot is a single stranded contiguous portion of the sequence of a gene or gene of interest (e.g., a 10-mer, 11-mer, 12-mer, 13-mer, 14-mer, 15-mer, 16-mer, 17-mer, 18-mer, 19-mer, 20-mer, 21-mer, 22-mer, 23-mer, 24-mer, 25-mer or larger) and is a probe for the mRNA encoded by the particular gene or gene of interest. Each probe spot is characterized by a single nucleic acid sequence, and is hybridized under conditions that cause it to hybridize only to its complementary DNA strand or mRNA molecule. As such, there can be many probe spots on a substrate, and each can represent a unique gene or sequence of interest. In addition, two or more probe spots can represent the same gene sequence. In some embodiments, a labeled nucleic sample is hybridized to a probe spot, and the amount of labeled nucleic acid specifically hybridized to a probe spot can be quantified to determine the levels of that specific nucleic acid (e.g., mRNA transcript of a particular gene) in a particular biological sample. Probes, probe spots, and microarrays, generally, are described in Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman & Hall/CRC, Chapter, 2, which is hereby incorporated by reference in its entirety.
As used herein, the term “annotation data” refers to any type of data that describes a property of a biomarker. Annotation data includes, but is not limited to, biological pathway membership, enzymatic class (e.g., phosphodiesterase, kinase, metalloproteinase, etc.), protein domain information, enzymatic substrate information, enzymatic reaction information, protein interaction data, disease association, cellular localization, tissue type localization, and cell type localization.
As used herein, the term “T−12” refers to the last time blood was obtained from a subject before the subject is clinically diagnosed with sepsis. Since, in the present invention, blood is collected from subjects each 24 hour period, T−12 references the average time period prior to the onset of sepsis for a pool of patients, with some patients turning septic prior to the 12 hour mark and some patients turning septic after the 12 hour mark. However, across a pool of subjects, the average time period for this last blood sample is the 12 hour mark, hence the name “T−12.”
5.2 Methods for Screening Subjects
The present invention allows for accurate, rapid prediction and/or diagnosis of sepsis through detection of two or more features of a biomarker profile of a test individual suspected of or at risk for developing sepsis in each of one or more biological samples from a test subject. In one embodiment, only a single biological sample taken at a single point in time from the test subject is needed to construct a biomarker profile that is used to make this prediction or diagnosis of sepsis. In another embodiment, multiple biological samples taken at different points in time from the test subject are used to construct a biomarker profile that is used to make this prediction or diagnosis of sepsis.
In specific embodiments of the invention, subjects at risk for developing sepsis or SIRS are screened using the methods of the present invention. In accordance with these embodiments, the methods of the present invention can be employed to screen, for example, subjects admitted to an ICU and/or those who have experienced some sort of trauma (such as, e.g., surgery, vehicular accident, gunshot wound, etc.).
In specific embodiments, a biological sample such as, for example, blood, is taken upon admission. In some embodiments, a biological sample is blood, plasma, serum, saliva, sputum, urine, cerebral spinal fluid, cells, a cellular extract, a tissue specimen, a tissue biopsy, or a stool specimen. In some embodiments a biological sample is whole blood and this whole blood is used to obtain measurements for a biomarker profile. In some embodiments a biological sample is some component of whole blood. For example, in some embodiments some portion of the mixture of proteins, nucleic acid, and/or other molecules (e.g., metabolites) within a cellular fraction or within a liquid (e.g., plasma or serum fraction) of the blood is resolved as a biomarker profile. This can be accomplished by measuring features of the biomarkers in the biomarker profile. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers in a specific cell type that is isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in monocytes that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in red blood cells that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in platelets that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in neutriphils that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in eosinophils that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in basophils that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in lymphocytes that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from biomarkers expressed or otherwise found in monocytes that are isolated from the whole blood. In some embodiments, the biological sample is whole blood but the biomarker profile is resolved from one, two, three, four, five, six, or seven cell types from the group of cells types consisting of red blood cells, platelets, neutrophils, eosinophils, basophils, lymphocytes, and monocytes.
A biomarker profile comprises a plurality of one or more types of biomarkers (e.g., an mRNA molecule, a cDNA molecule, a protein and/or a carbohydrate, etc.), or an indication thereof, together with features, such as a measurable aspect (e.g., abundance) of the biomarkers. A biomarker profile can comprise at least two such biomarkers or indications thereof, where the biomarkers can be in the same or different classes, such as, for example, a nucleic acid and a carbohydrate. In some embodiments, a biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more biomarkers or indications thereof. In one embodiment, a biomarker profile comprises hundreds, or even thousands, of biomarkers or indications thereof. In some embodiments, a biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more biomarkers or indications thereof. In one example, in some embodiments, a biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more biomarkers selected from Table I of Section 5.11, or indications thereof. In another example, in some embodiments, a biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40 or more biomarkers selected from Table J of Section 5.11, or indications thereof. In another example, in some embodiments, a biomarker profile comprises any 2, 3, 4, 5, 6, 7, 8, 9, or all ten biomarkers in Table K of Section 5.11, or indications thereof.
In typical embodiments, each biomarker in the biomarker profile is represented by a feature. In other words, there is a correspondence between biomarkers and features. In some embodiments, the correspondence between biomarkers and features is 1:1, meaning that for each biomarker there is a feature. In some embodiments, there is more than one feature for each biomarker. In some embodiments the number of features corresponding to one biomarker in the biomarker profile is different than then number of features corresponding to another biomarker in the biomarker profile. As such, in some embodiments, a biomarker profile can include at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more features, provided that there are at least 2, 3, 4, 5, 6, or 7 or more biomarkers in the biomarker profile. In some embodiments, a biomarker profile can include at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more features. Regardless of embodiment, these features can be determined through the use of any reproducible measurement technique or combination of measurement techniques. Such techniques include those that are well known in the art including any technique described herein or, for example, any technique disclosed in Section 5.4, infra. Typically, such techniques are used to measure feature values using a biological sample taken from a subject at a single point in time or multiple samples taken at multiple points in time. In one embodiment, an exemplary technique to obtain a biomarker profile from a sample taken from a subject is a cDNA microarray (see, e.g., Section 5.4.1.2 and Section 6, infra). In another embodiment, an exemplary technique to obtain a biomarker profile from a sample taken from a subject is a protein-based assay or other form of protein-based technique such as described in the BD Cytometric Bead Array (CBA) Human Inflammation Kit Instruction Manual (BD Biosciences) or the bead assay described in U.S. Pat. No. 5,981,180, each of which is incorporated herein by reference in their entirety, and in particular for their teachings of various methods of assay protein concentrations in biological samples. In still another embodiment, the biomarker profile is mixed, meaning that it comprises some biomarkers that are nucleic acids, or indications thereof, and some biomarkers that are proteins, or indications thereof. In such embodiments, both protein based and nucleic acid based techniques are used to obtain a biomarker profile from one or more samples taken from a subject. In other words, the feature values for the features associated with the biomarkers in the biomarker profile that are nucleic acids are obtained by nucleic acid based measurement techniques (e.g., a nucleic acid microarray) and the feature values for the features associated with the biomarkers in the biomarker profile that are proteins are obtained by protein based measurement techniques. In some embodiments biomarker profiles can be obtained using a kit, such as a kit described in Section 5.3 below.
In specific embodiments, a subject is screened using the methods and compositions of the invention as frequently as necessary (e.g., during their stay in the ICU) to diagnose or predict sepsis or SIRS in a subject. In a preferred embodiment, the subject is screened soon after they arrive in the ICU. In some embodiments, the subject is screened daily after they arrive in the ICU. In some embodiments, the subject is screened every 1 to 4 hours, 1 to 8 hours, 8 to 12 hours, 12 to 16 hours, or 16 to 24 hours after they arrive in the ICU.
5.3 Kits
The invention also provides kits that are useful in diagnosing or predicting the development of sepsis or diagnosing SIRS in a subject. In some embodiments, the kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more biomarkers and/or reagents to detect the presence or abundance of such biomarkers. In other embodiments, the kits of the present invention comprise at least 2, but as many as several hundred or more biomarkers. In some embodiments, the kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50 or more biomarkers selected from Table I of Section 5.11. In some embodiments, the kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40 or more biomarkers selected from Table J of Section 5.11. In some embodiments, the kits of the present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, or all 10 of the biomarkers in Table K of Section 5.11. In accordance with the definition of biomarkers given in Section 5.1, in some instances, a biomarker is in fact a discriminating molecule of, for example, a gene, mRNA, or protein rather than the gene, mRNA, or protein itself. Thus, a biomarker could be a molecule that indicates the presence or abundance of a particular gene or protein, or fragment thereof, identified in any one of Tables I, J, or K of Section 5.11 rather than the actual gene or protein itself. Such discriminating molecules are sometimes referred to in the art as “reagents.” In some embodiments, the kits of the present invention comprise at least 2, but as many as several hundred or more biomarkers.
The biomarkers of the kits of the present invention can be used to generate biomarker profiles according to the present invention. Examples of classes of compounds of the kit include, but are not limited to, proteins and fragments thereof, peptides, proteoglycans, glycoproteins, lipoproteins, carbohydrates, lipids, nucleic acids (e.g., DNA, such as cDNA or amplified DNA, or RNA, such as mRNA), organic or inorganic chemicals, natural or synthetic polymers, small molecules (e.g., metabolites), or discriminating molecules or discriminating fragments of any of the foregoing. In a specific embodiment, a biomarker is of a particular size, (e.g., at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 1000, 2000, 3000, 5000, 10 k, 20 k, 100 k Daltons or greater). The biomarker(s) may be part of an array, or the biomarker(s) may be packaged separately and/or individually. The kit may also comprise at least one internal standard to be used in generating the biomarker profiles of the present invention. Likewise, the internal standard or standards can be any of the classes of compounds described above.
In one embodiment, the invention provides kits comprising probes and/or primers that may or may not be immobilized at an addressable position on a substrate, such as found, for example, in a microarray. In a particular embodiment, the invention provides such a microarray.
In a specific embodiment, the invention provides a kit for predicting the development of sepsis in a test subject that comprises a plurality of antibodies that specifically bind the protein-based biomarkers listed in any one of Tables 30, 31, 32, 33, 34, 36, I, J, or K. In such embodiments, the antibodies themselves are biomarkers within the scope of the present invention. In accordance with this embodiment, the kit may comprise a set of antibodies or functional fragments or derivatives thereof (e.g., Fab, F(ab′)2, Fv, or scFv fragments) that specifically bind at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more of the protein-based biomarkers set forth in any one of Tables 30, 31, 32, 33, 34, 36, I, J, or K. In accordance with this embodiment, the kit may include antibodies, fragments or derivatives thereof (e.g., Fab, F(ab′)2, Fv, or scFv fragments) that are specific for the biomarkers of the present invention. In one embodiment, the antibodies may be detectably labeled.
In a specific embodiment, the invention provides a kit for predicting the development of sepsis in a test subject comprises a plurality of antibodies that specifically bind a plurality of the protein-based biomarkers listed in Table I of Section 5.11. In accordance with this embodiment, the kit may comprise a set of antibodies or functional fragments or derivatives thereof (e.g., Fab, F(ab′)2, Fv, or scFv fragments) that specifically bind at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50 or more of the biomarkers set forth in Table I. In accordance with this embodiment, the kit may include antibodies, fragments or derivatives thereof (e.g., Fab, F(ab′)2, Fv, or scFv fragments) that are specific for the biomarkers of the present invention. In one embodiment, the antibodies may be detectably labeled.
In other embodiments of the invention, a kit may comprise a specific biomarker binding component, such as an aptamer. If the biomarkers comprise a nucleic acid, the kit may provide an oligonucleotide probe that is capable of forming a duplex with the biomarker or with a complementary strand of a biomarker. The oligonucleotide probe may be detectably labeled. In such embodiments, the probes are themselves biomarkers that fall within the scope of the present invention.
The kits of the present invention may also include additional compositions, such as buffers, that can be used in constructing the biomarker profile. Prevention of the action of microorganisms can be ensured by the inclusion of various antibacterial and antifungal agents, for example, paraben, chlorobutanol, phenol sorbic acid, and the like. It may also be desirable to include isotonic agents such as sugars, sodium chloride, and the like.
Some kits of the present invention comprise a microarray. In one embodiment this microarray comprises a plurality of probe spots, wherein at least twenty percent of the probe spots in the plurality of probe spots correspond to biomarkers in any one of Tables 30, 31, 32, 33, 34, 36, I, J, or K. In some embodiments, at least twenty-five percent, at least thirty percent, at least thirty-five percent, at least forty percent, or at least sixty percent, or at least eighty percent of the probe spots in the plurality of probe spots correspond to biomarkers in any one of Tables 30, 31, 32, 33, 34, 36, I, J, or K. Such probe spots are biomarkers within the scope of the present invention. In some embodiments, the microarray consists of between about three and about one hundred probe spots on a substrate. In some embodiments, the microarray consists of between about three and about one hundred probe spots on a substrate. As used in this context, the term “about” means within five percent of the stated value, within ten percent of the stated value, or within twenty-five percent of the stated value. In some embodiments, such microarrays contain one or more probe spots for inter-microarray calibration or for calibration with other microarrays such as reference microarrays using techniques that are known to those of skill in the art. In some embodiments such microarrays are nucleic acid microarrays. In some embodiments, such microarrays are protein microarrays.
Some kits of the invention may further comprise a computer program product for use in conjunction with a computer system, wherein the computer program product comprises a computer readable storage medium and a computer program mechanism embedded therein. In such kits, the computer program mechanism comprises instructions for evaluating whether a plurality of features in a biomarker profile of a test subject at risk for developing sepsis satisfies a first value set. Satisfying the first value set predicts that the test subject is likely to develop sepsis. In one embodiment, the plurality of features corresponds to biomarkers listed in any one of Tables 30, 31, 32, 33, 34, 36, I, J, or K. In some kits, the computer program product further comprises instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a second value set. Satisfying the second value set predicts that the test subject is not likely to develop sepsis.
Some kits of the present invention comprise a computer having a central processing unit and a memory coupled to the central processing unit. The memory stores instructions for evaluating whether a plurality of features in a biomarker profile of a test subject at risk for developing sepsis satisfies a first value set. Satisfying the first value set predicts that the test subject is likely to develop sepsis. In one embodiment, the plurality of features corresponds to biomarkers listed in any one of Tables 30, 31, 32, 33, 34, 36, I, J, or K.
FIG. 35 details an exemplary system that supports the functionality described above. The system is preferably a computer system 10 having:

    
    
        a central processing unit 22;
        a main non-volatile storage unit 14, for example, a hard disk drive, for storing software and data, the storage unit 14 controlled by storage controller 12;
        a system memory 36, preferably high speed random-access memory (RAM), for storing system control programs, data, and application programs, comprising programs and data loaded from non-volatile storage unit 14; system memory 36 may also include read-only memory (ROM);
        a user interface 32, comprising one or more input devices (e.g., keyboard 28) and a display 26 or other output device;
        a network interface card 20 for connecting to any wired or wireless communication network 34 (e.g., a wide area network such as the Internet);
        an internal bus 30 for interconnecting the aforementioned elements of the system; and
        a power source 24 to power the aforementioned elements.
    
    


Operation of computer 10 is controlled primarily by operating system 40, which is executed by central processing unit 22. Operating system 40 can be stored in system memory 36. In addition to operating system 40, in a typical implementation, system memory 36 includes:

    
    
        file system 42 for controlling access to the various files and data structures used by the present invention;
        a training data set 44 for use in construction one or more decision rules in accordance with the present invention;
        a data analysis algorithm module 54 for processing training data and constructing decision rules;
        one or more decision rules 56;
        a biomarker profile evaluation module 60 for determining whether a plurality of features in a biomarker profile of a test subject satisfies a first value set or a second value set;
        a test subject biomarker profile 62 comprising biomarkers 64 and, for each such biomarkers, features 66; and
        a database 68 of select biomarkers of the present invention (e.g., Table 30 and/or Table I and/or Table J and/or Table K, and/or Table L and/or Table M and/or Table N and/or Table O etc.) and/or one or features for each of these select biomarkers.
    
    


Training data set 46 comprises data for a plurality of subjects 46. For each subject 46 there is a subject identifier 48 and a plurality of biomarkers 50. For each biomarker 50, there is at least one feature 52. Although not shown in FIG. 35, for each feature 52, there is a feature value. For each decision rule 56 constructed using data analysis algorithms, there is at least one decision rule value set 58.
As illustrated in FIG. 35, computer 10 comprises software program modules and data structures. The data structures stored in computer 10 include training data set 44, decision rules 56, test subject biomarker profile 62, and biomarker database 68. Each of these data structures can comprise any form of data storage system including, but not limited to, a flat ASCII or binary file, an Excel spreadsheet, a relational database (SQL), or an on-line analytical processing (OLAP) database (MDX and/or variants thereof). In some specific embodiments, such data structures are each in the form of one or more databases that include hierarchical structure (e.g., a star schema). In some embodiments, such data structures are each in the form of databases that do not have explicit hierarchy (e.g., dimension tables that are not hierarchically arranged).
In some embodiments, each of the data structures stored or accessible to system 10 are single data structures. In other embodiments, such data structures in fact comprise a plurality of data structures (e.g., databases, files, archives) that may or may not all be hosted by the same computer 10. For example, in some embodiments, training data set 44 comprises a plurality of Excel spreadsheets that are stored either on computer 10 and/or on computers that are addressable by computer 10 across wide area network 34. In another example, training data set 44 comprises a database that is either stored on computer 10 or is distributed across one or more computers that are addressable by computer 10 across wide area network 34.
It will be appreciated that many of the modules and data structures illustrated in FIG. 35 can be located on one or more remote computers. For example, some embodiments of the present application are web service-type implementations. In such embodiments, biomarker profile evaluation module 60 and/or other modules can reside on a client computer that is in communication with computer 10 via network 34. In some embodiments, for example, biomarker profile evaluation module 60 can be an interactive web page.
In some embodiments, training data set 44, decision rules 56, and/or biomarker database 68 illustrated in FIG. 35 are on a single computer (computer 10) and in other embodiments one or more of such data structures and modules are hosted by one or more remote computers (not shown). Any arrangement of the data structures and software modules illustrated in FIG. 35 on one or more computers is within the scope of the present invention so long as these data structures and software modules are addressable with respect to each other across network 34 or by other electronic means. Thus, the present invention fully encompasses a broad array of computer systems.
Still another kit of the present invention comprises computers and computer readable media for evaluating whether a test subject is likely to develop sepsis or SIRS. For instance, one embodiment of the present invention provides a computer program product for use in conjunction with a computer system. The computer program product comprises a computer readable storage medium and a computer program mechanism embedded therein. The computer program mechanism comprises instructions for evaluating whether a plurality of features in a biomarker profile of a test subject at risk for developing sepsis satisfies a first value set. Satisfaction of the first value set predicts that the test subject is likely to develop sepsis. The plurality of features are measurable aspects of a plurality of biomarkers, the plurality of biomarkers comprising at least three biomarkers listed in Table I. In certain embodiments, the plurality of biomarkers comprises at least six biomarkers listed in Table I, wherein the plurality of biomarkers comprises both IL-6 and IL-8. In some embodiments, the computer program product further comprises instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a second value set. Satisfaction of the second value set predicts that the test subject is not likely to develop sepsis. In some embodiments, the biomarker profile has between 3 and 50 biomarkers listed in Table I, between 3 and 40 biomarkers listed in Table I, at least four biomarkers listed in Table I, or at least eight biomarkers listed in Table I.
Another kit of the present invention comprises a central processing unit and a memory coupled to the central processing unit. The memory stores instructions for evaluating whether a plurality of features in a biomarker profile of a test subject at risk for developing sepsis satisfies a first value set. Satisfaction of the first value set predicts that the test subject is likely to develop sepsis. The plurality of features are measurable aspects of a plurality of biomarkers. This plurality of biomarkers comprises at least three biomarkers from Table I. In some embodiments, the plurality of biomarkers comprises at least six biomarkers listed in Table I when the plurality of biomarkers comprises both IL-6 and IL-8. In some embodiments, the memory further stores instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a second value set, wherein satisfying the second value set predicts that the test subject is not likely to develop sepsis. In some embodiments, the biomarker profile consists of between 3 and 50 biomarkers listed in Table I, between 3 and 40 biomarkers listed in Table I, at least four biomarkers listed in Table I, or at least eight biomarkers listed in Table I.
Another kit in accordance with the present invention comprises a computer system for determining whether a subject is likely to develop sepsis. The computer system comprises a central processing unit and a memory, coupled to the central processing unit. The memory stores instructions for obtaining a biomarker profile of a test subject. The biomarker profile comprises a plurality of features. Each feature in the plurality of features is a measurable aspect of a corresponding biomarker in a plurality of biomarkers. The plurality of biomarkers comprises at least three biomarkers listed in Table I. The memory further comprises instructions for transmitting the biomarker profile to a remote computer. The remote computer includes instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a first value set. Satisfaction of the first value set predicts that the test subject is likely to develop sepsis. The memory further comprises instructions for receiving a determination, from the remote computer, as to whether the plurality of features in the biomarker profile of the test subject satisfies the first value set. The memory also comprises instructions for reporting whether the plurality of features in the biomarker profile of the test subject satisfies the first value set. In some embodiments, the plurality of biomarkers comprises at least six biomarkers listed in Table I when the plurality of biomarkers comprises both IL-6 and IL-8. In some embodiments, the remote computer further comprises instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a second value set. Satisfaction of the second value set predicts that the test subject is not likely to develop sepsis. In such embodiments, the memory further comprises instructions for receiving a determination, from the remote computer, as to whether the plurality of features in the biomarker profile of the test subject satisfies the second set as well as instructions for reporting whether the plurality of features in the biomarker profile of the test subject satisfies the second value set. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least six biomarkers listed in Table I.
Still another aspect of the present invention comprises a digital signal embodied on a carrier wave comprising a respective value for each of a plurality of features in a biomarker profile. The plurality of features are measurable aspects of a plurality of biomarkers. The plurality of biomarkers comprises at least three biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least six biomarkers listed in Table I when the plurality of biomarkers comprises both IL-6 and IL-8. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
Still another aspect of the present invention provides a digital signal embodied on a carrier wave comprising a determination as to whether a plurality of features in a biomarker profile of a test subject satisfies a value set. The plurality of features are measurable aspects of a plurality of biomarkers. This plurality of biomarkers comprises at least three biomarkers listed in Table I. Satisfying the value set predicts that the test subject is likely to develop sepsis. In some embodiments, the plurality of biomarkers comprises at least six biomarkers listed in Table I when the plurality of biomarkers comprises both IL-6 and IL-8. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
Still another embodiment provides a digital signal embodied on a carrier wave comprising a determination as to whether a plurality of features in a biomarker profile of a test subject satisfies a value set. The plurality of features are measurable aspects of a plurality of biomarkers. The plurality of biomarkers comprise at least three biomarkers listed in Table I. Satisfaction of the value set predicts that the test subject is not likely to develop sepsis. In some embodiments, the plurality of biomarkers comprises at least six biomarkers listed in Table I when the plurality of biomarkers comprises both IL-6 and IL-8. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
Still another embodiment of the present invention provides a graphical user interface for determining whether a subject is likely to develop sepsis. The graphical user interface comprises a display field for a displaying a result encoded in a digital signal embodied on a carrier wave received from a remote computer. The plurality of features are measurable aspects of a plurality of biomarkers. The plurality of biomarkers comprise at least three biomarkers listed in Table I. The result has a first value when a plurality of features in a biomarker profile of a test subject satisfies a first value set. The result has a second value when a plurality of features in a biomarker profile of a test subject satisfies a second value set. In some embodiments, the plurality of biomarkers comprises at least six biomarkers listed in Table I when the plurality of biomarkers comprises IL-6 and IL-8. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
Yet another kit of the present invention provides a computer system for determining whether a subject is likely to develop sepsis. The computer system comprises a central processing unit and a memory, coupled to the central processing unit. The memory stores instructions for obtaining a biomarker profile of a test subject. The biomarker profile comprises a plurality of features. The plurality of features are measurable aspects of a plurality of biomarkers. The plurality of biomarkers comprise at least three biomarkers listed in Table I. The memory further stores instructions for evaluating whether the plurality of features in the biomarker profile of the test subject satisfies a first value set. Satisfying the first value set predicts that the test subject is likely to develop sepsis. The memory also stores instructions for reporting whether the plurality of features in the biomarker profile of the test subject satisfies the first value set. In some embodiments, the plurality of biomarkers comprises at least six biomarkers listed in Table I when the plurality of biomarkers comprises both IL-6 and IL-8. In some embodiments, the plurality of biomarkers comprises at least four biomarkers listed in Table I. In some embodiments, the plurality of biomarkers comprises at least eight biomarkers listed in Table I.
5.4 Generation of Biomarker Profiles
According to one embodiment, the methods of the present invention comprise generating a biomarker profile from a biological sample taken from a subject. The biological sample may be, for example, whole blood, plasma, serum, red blood cells, platelets, neutrophils, eosinophils, basophils, lymphocytes, monocytes, saliva, sputum, urine, cerebral spinal fluid, cells, a cellular extract, a tissue sample, a tissue biopsy, a stool sample or any sample that may be obtained from a subject using techniques well known to those of skill in the art. In a specific embodiment, a biomarker profile is determined using samples collected from a subject at one or more separate time points. In another specific embodiment, a biomarker profile is generated using samples obtained from a subject at separate time points. In one example, these samples are obtained from the subject either once or, alternatively, on a daily basis, or more frequently, e.g., every 4, 6, 8 or 12 hours. In a specific embodiment, a biomarker profile is determined using samples collected from a single tissue type. In another specific embodiment, a biomarker profile is determined using samples collected from at least two different tissue types.
5.4.1 Methods of Detecting Nucleic Acid Biomarkers
In specific embodiments of the invention, biomarkers in a biomarker profile are nucleic acids. Such biomarkers and corresponding features of the biomarker profile may be generated, for example, by detecting the expression product (e.g., a polynucleotide or polypeptide) of one or more genes described herein (e.g., a gene listed in Table 30, Table I, Table J, or Table K.). In a specific embodiment, the biomarkers and corresponding features in a biomarker profile are obtained by detecting and/or analyzing one or more nucleic acids expressed from a gene disclosed herein (e.g., a gene listed in Table 30, Table I, Table J, or Table K) using any method well known to those skilled in the art including, but by no means limited to, hybridization, microarray analysis, RT-PCR, nuclease protection assays and Northern blot analysis.
In certain embodiments, nucleic acids detected and/or analyzed by the methods and compositions of the invention include RNA molecules such as, for example, expressed RNA molecules which include messenger RNA (mRNA) molecules, mRNA spliced variants as well as regulatory RNA, cRNA molecules (e.g., RNA molecules prepared from cDNA molecules that are transcribed in vitro) and discriminating fragments thereof. Nucleic acids detected and/or analyzed by the methods and compositions of the present invention can also include, for example, DNA molecules such as genomic DNA molecules, cDNA molecules, and discriminating fragments thereof (e.g., oligonucleotides, ESTs, STSs, etc.).
The nucleic acid molecules detected and/or analyzed by the methods and compositions of the invention may be naturally occurring nucleic acid molecules such as genomic or extragenomic DNA molecules isolated from a sample, or RNA molecules, such as mRNA molecules, present in, isolated from or derived from a biological sample. The sample of nucleic acids detected and/or analyzed by the methods and compositions of the invention comprise, e.g., molecules of DNA, RNA, or copolymers of DNA and RNA. Generally, these nucleic acids correspond to particular genes or alleles of genes, or to particular gene transcripts (e.g., to particular mRNA sequences expressed in specific cell types or to particular cDNA sequences derived from such mRNA sequences). The nucleic acids detected and/or analyzed by the methods and compositions of the invention may correspond to different exons of the same gene, e.g., so that different splice variants of that gene may be detected and/or analyzed.
In specific embodiments, the nucleic acids are prepared in vitro from nucleic acids present in, or isolated or partially isolated from biological a sample. For example, in one embodiment, RNA is extracted from a sample (e.g., total cellular RNA, poly(A)+ messenger RNA, fraction thereof) and messenger RNA is purified from the total extracted RNA. Methods for preparing total and poly(A)+ RNA are well known in the art, and are described generally, e.g., in Sambrook et al., 2001, Molecular Cloning: A Laboratory Manual. 3rd ed. Cold Spring Harbor Laboratory Press (Cold Spring Harbor, N.Y.), which is incorporated by reference herein in its entirety. In one embodiment, RNA is extracted from a sample using guanidinium thiocyanate lysis followed by CsCl centrifugation and an oligo dT purification (Chirgwin et al., 1979, Biochemistry 18:5294-5299). In another embodiment, RNA is extracted from a sample using guanidinium thiocyanate lysis followed by purification on RNeasy columns (Qiagen, Valencia, Calif.). cDNA is then synthesized from the purified mRNA using, e.g., oligo-dT or random primers. In specific embodiments, the target nucleic acids are cRNA prepared from purified messenger RNA extracted from a sample. As used herein, cRNA is defined here as RNA complementary to the source RNA. The extracted RNAs are amplified using a process in which doubled-stranded cDNAs are synthesized from the RNAs using a primer linked to an RNA polymerase promoter in a direction capable of directing transcription of anti-sense RNA. Anti-sense RNAs or cRNAs are then transcribed from the second strand of the double-stranded cDNAs using an RNA polymerase (see, e.g., U.S. Pat. Nos. 5,891,636, 5,716,785; 5,545,522 and 6,132,997, which are hereby incorporated by reference). Both oligo-dT primers (U.S. Pat. Nos. 5,545,522 and 6,132,997, hereby incorporated by reference herein) or random primers that contain an RNA polymerase promoter or complement thereof can be used. In some embodiments the target nucleic acids are short and/or fragmented nucleic acid molecules which are representative of the original nucleic acid population of the sample.
In one embodiment, nucleic acids of the invention can be detectably labeled. For example, cDNA can be labeled directly, e.g., with nucleotide analogs, or indirectly, e.g., by making a second, labeled cDNA strand using the first strand as a template. Alternatively, the double-stranded cDNA can be transcribed into cRNA and labeled.
In some embodiments the detectable label is a fluorescent label, e.g., by incorporation of nucleotide analogs. Other labels suitable for use in the present invention include, but are not limited to, biotin, imminobiotin, antigens, cofactors, dinitrophenol, lipoic acid, olefinic compounds, detectable polypeptides, electron rich molecules, enzymes capable of generating a detectable signal by action upon a substrate, and radioactive isotopes. Suitable radioactive isotopes include 32P, 35S, 14C, 15N and 125I. Fluorescent molecules suitable for the present invention include, but are not limited to, fluorescein and its derivatives, rhodamine and its derivatives, Texas red, 5′carboxy-fluorescein (“FMA”), 6-carboxy-4′,5′-dichloro-2′,7′-dimethoxyfluorescein, succinimidyl ester (“JOE”), 6-carboxytetramethylrhodamine (“TAMRA”), 6Ncarboxy-X-rhodamine (“ROX”), HEX, TET, IRD40, and IRD41. Fluorescent molecules that are suitable for the invention further include, but are not limited to: cyamine dyes, including by not limited to Cy3, Cy3.5 and Cy5; BODIPY dyes including but not limited to BODIPY-FL, BODIPY-TR, BODIPY-TMR, BODIPY-630/650, BODIPY-650/670; and ALEXA dyes, including but not limited to ALEXA-488, ALEXA-532, ALEXA-546, ALEXA-568, and ALEXA-594; as well as other fluorescent dyes which will be known to those who are skilled in the art. Electron-rich indicator molecules suitable for the present invention include, but are not limited to, ferritin, hemocyanin, and colloidal gold. Alternatively, in some embodiments the target nucleic acids may be labeled by specifically complexing a first group to the nucleic acid. A second group, covalently linked to an indicator molecules and which has an affinity for the first group, can be used to indirectly detect the target nucleic acid. In such an embodiment, compounds suitable for use as a first group include, but are not limited to, biotin and iminobiotin. Compounds suitable for use as a second group include, but are not limited to, avidin and streptavidin.
5.4.1.1 Nucleic Acid Arrays
In certain embodiments of the invention, nucleic acid arrays are employed to generate features of biomarkers in a biomarker profile by detecting the expression of any one or more of the genes described herein (e.g., a gene listed in Table 30, Table I, Table J or Table K). In one embodiment of the invention, a microarray, such as a cDNA microarray, is used to determine feature values of biomarkers in a biomarker profile. The diagnostic use of cDNA arrays is well known in the art. (See, e.g., Zou et. al., 2002, Oncogene 21:4855-4862; as well as Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman & Hall/CRC, each of which is hereby incorporated by reference herein in its entirety). Exemplary methods for cDNA microarray analysis are described below, and in the examples in Section 6, infra.
In certain embodiments, the feature values for biomarkers in a biomarker profile are obtained by hybridizing to the array detectably labeled nucleic acids representing or corresponding to the nucleic acid sequences in mRNA transcripts present in a biological sample (e.g., fluorescently labeled cDNA synthesized from the sample) to a microarray comprising one or more probe spots.
Nucleic acid arrays, for example, microarrays, can be made in a number of ways, of which several are described herein below. Preferably, the arrays are reproducible, allowing multiple copies of a given array to be produced and results from said microarrays compared with each other. Preferably, the arrays are made from materials that are stable under binding (e.g., nucleic acid hybridization) conditions. Those skilled in the art will know of suitable supports, substrates or carriers for hybridizing test probes to probe spots on an array, or will be able to ascertain the same by use of routine experimentation.
Arrays, for example, microarrays, used can include one or more test probes. In some embodiments each such test probe comprises a nucleic acid sequence that is complementary to a subsequence of RNA or DNA to be detected. Each probe typically has a different nucleic acid sequence, and the position of each probe on the solid surface of the array is usually known or can be determined. Arrays useful in accordance with the invention can include, for example, oligonucleotide microarrays, cDNA based arrays, SNP arrays, spliced variant arrays and any other array able to provide a qualitative, quantitative or semi-quantitative measurement of expression of a gene described herein (e.g., a gene listed in Table 30, Table I, Table J or Table K). Some types of microarrays are addressable arrays. More specifically, some microarrays are positionally addressable arrays. In some embodiments, each probe of the array is located at a known, predetermined position on the solid support so that the identity (e.g., the sequence) of each probe can be determined from its position on the array (e.g., on the support or surface). In some embodiments, the arrays are ordered arrays. Microarrays are generally described in Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman & Hall/CRC, which is hereby incorporated herein by reference in its entirety.
In some embodiments of the present invention, an expressed transcript (e.g., a transcript of a gene described herein) is represented in the nucleic acid arrays. In such embodiments, a set of binding sites can include probes with different nucleic acids that are complementary to different sequence segments of the expressed transcript. Exemplary nucleic acids that fall within this class can be of length of 15 to 200 bases, 20 to 100 bases, 25 to 50 bases, 40 to 60 bases or some other range of bases. Each probe sequence can also comprise one or more linker sequences in addition to the sequence that is complementary to its target sequence. As used herein, a linker sequence is a sequence between the sequence that is complementary to its target sequence and the surface of support. For example, the nucleic acid arrays of the invention can comprise one probe specific to each target gene or exon. However, if desired, the nucleic acid arrays can contain at least 2, 5, 10, 100, or 1000 or more probes specific to some expressed transcript (e.g., a transcript of a gene described herein, e.g., in Table 30, Table I, Table J, or Table K). For example, the array may contain probes tiled across the sequence of the longest mRNA isoform of a gene.
It will be appreciated that when cDNA complementary to the RNA of a cell, for example, a cell in a biological sample, is made and hybridized to a microarray under suitable hybridization conditions, the level of hybridization to the site in the array corresponding to a gene described herein (e.g., a gene listed in Table 30, Table I, Table J, or Table K) will reflect the prevalence in the cell of mRNA or mRNAs transcribed from that gene. Alternatively, in instances where multiple isoforms or alternate splice variants produced by particular genes are to be distinguished, detectably labeled (e.g., with a fluorophore) cDNA complementary to the total cellular mRNA can be hybridized to a microarray, and the site on the array corresponding to an exon of the gene that is not transcribed or is removed during RNA splicing in the cell will have little or no signal (e.g., fluorescent signal), and a site corresponding to an exon of a gene for which the encoded mRNA expressing the exon is prevalent will have a relatively strong signal. The relative abundance of different mRNAs produced from the same gene by alternative splicing is then determined by the signal strength pattern across the whole set of exons monitored for the gene.
In one embodiment, hybridization levels at different hybridization times are measured separately on different, identical microarrays. For each such measurement, at hybridization time when hybridization level is measured, the microarray is washed briefly, preferably in room temperature in an aqueous solution of high to moderate salt concentration (e.g., 0.5 to 3 M salt concentration) under conditions which retain all bound or hybridized nucleic acids while removing all unbound nucleic acids. The detectable label on the remaining, hybridized nucleic acid molecules on each probe is then measured by a method which is appropriate to the particular labeling method used. The resulting hybridization levels are then combined to form a hybridization curve. In another embodiment, hybridization levels are measured in real time using a single microarray. In this embodiment, the microarray is allowed to hybridize to the sample without interruption and the microarray is interrogated at each hybridization time in a non-invasive manner. In still another embodiment, one can use one array, hybridize for a short time, wash and measure the hybridization level, put back to the same sample, hybridize for another period of time, wash and measure again to get the hybridization time curve.
In some embodiments, nucleic acid hybridization and wash conditions are chosen so that the nucleic acid biomarkers to be analyzed specifically bind or specifically hybridize to the complementary nucleic acid sequences of the array, typically to a specific array site, where its complementary DNA is located.
Arrays containing double-stranded probe DNA situated thereon can be subjected to denaturing conditions to render the DNA single-stranded prior to contacting with the target nucleic acid molecules. Arrays containing single-stranded probe DNA (e.g., synthetic oligodeoxyribonucleic acids) may need to be denatured prior to contacting with the target nucleic acid molecules, e.g., to remove hairpins or dimers which form due to self complementary sequences.
Optimal hybridization conditions will depend on the length (e.g., oligomer versus polynucleotide greater than 200 bases) and type (e.g., RNA, or DNA) of probe and target nucleic acids. General parameters for specific (i.e., stringent) hybridization conditions for nucleic acids are described in Sambrook et al., (supra), and in Ausubel et al., 1988, Current Protocols in Molecular Biology, Greene Publishing and Wiley-Interscience, New York. When the cDNA microarrays of Shena et al. are used, typical hybridization conditions are hybridization in 5×SSC plus 0.2% SDS at 65° C. for four hours, followed by washes at 25° C. in low stringency wash buffer (1×SSC plus 0.2% SDS), followed by 10 minutes at 25° C. in higher stringency wash buffer (0.1×SSC plus 0.2% SDS) (Shena et al., 1996, Proc. Natl. Acad. Sci. U.S.A. 93:10614). Useful hybridization conditions are also provided in, e.g., Tijessen, 1993, Hybridization With Nucleic Acid Probes, Elsevier Science Publishers B.V.; Kricka, 1992, Nonisotopic DNA Probe Techniques, Academic Press, San Diego, Calif.; and Zou et. al., 2002, Oncogene 21:4855-4862; and Draghici, Data Analysis Tools for DNA Microanalysis, 2003, CRC Press LLC, Boca Raton, Fla., pp. 342-343, which are hereby incorporated by reference herein in their entirety.
In a specific embodiment, a microarray can be used to sort out RT-PCR products that have been generated by the methods described, for example, below in Section 5.4.1.2.
5.4.1.2 RT-PCR
In certain embodiments, to determine the feature values of biomarkers in a biomarker profile of the invention, the level of expression of one or more of the genes described herein (e.g., a gene listed in Table 30, Table I, Table J, or Table K) is measured by amplifying RNA from a sample using reverse transcription (RT) in combination with the polymerase chain reaction (PCR). In accordance with this embodiment, the reverse transcription may be quantitative or semi-quantitative. The RT-PCR methods taught herein may be used in conjunction with the microarray methods described above, for example, in Section 5.4.1.1. For example, a bulk PCR reaction may be performed, the PCR products may be resolved and used as probe spots on a microarray. See also Section 6.10, infra.
Total RNA, or mRNA from a sample is used as a template and a primer specific to the transcribed portion of the gene(s) is used to initiate reverse transcription. Methods of reverse transcribing RNA into cDNA are well known and described in Sambrook et al., 2001, supra. Primer design can be accomplished based on known nucleotide sequences that have been published or available from any publicly available sequence database such as GenBank. For example, primers may be designed for any of the genes described herein (see, e.g., in Table 30, Table I, Table J, or Table K). Further, primer design may be accomplished by utilizing commercially available software (e.g., Primer Designer 1.0, Scientific Software etc.). The product of the reverse transcription is subsequently used as a template for PCR.
PCR provides a method for rapidly amplifying a particular nucleic acid sequence by using multiple cycles of DNA replication catalyzed by a thermostable, DNA-dependent DNA polymerase to amplify the target sequence of interest. PCR requires the presence of a nucleic acid to be amplified, two single-stranded oligonucleotide primers flanking the sequence to be amplified, a DNA polymerase, deoxyribonucleoside triphosphates, a buffer and salts. The method of PCR is well known in the art. PCR, is performed, for example, as described in Mullis and Faloona, 1987, Methods Enzymol. 155:335, which is hereby incorporated herein by reference in its entirety.
PCR can be performed using template DNA or cDNA (at least 1 fg; more usefully, 1-1000 ng) and at least 25 pmol of oligonucleotide primers. A typical reaction mixture includes: 2 μl of DNA, 25 pmol of oligonucleotide primer, 2.5 μl of 10 M PCR buffer 1 (Perkin-Elmer, Foster City, Calif.), 0.4 μl of 1.25 M dNTP, 0.15 μl (or 2.5 units) of Taq DNA polymerase (Perkin Elmer, Foster City, Calif.) and deionized water to a total volume of 25 μl. Mineral oil is overlaid and the PCR is performed using a programmable thermal cycler.
The length and temperature of each step of a PCR cycle, as well as the number of cycles, are adjusted according to the stringency requirements in effect. Annealing temperature and timing are determined both by the efficiency with which a primer is expected to anneal to a template and the degree of mismatch that is to be tolerated. The ability to optimize the stringency of primer annealing conditions is well within the knowledge of one of moderate skill in the art. An annealing temperature of between 30° C. and 72° C. is used. Initial denaturation of the template molecules normally occurs at between 92° C. and 99° C. for 4 minutes, followed by 20-40 cycles consisting of denaturation (94-99° C. for 15 seconds to 1 minute), annealing (temperature determined as discussed above; 1-2 minutes), and extension (72° C. for 1 minute). The final extension step is generally carried out for 4 minutes at 72° C., and may be followed by an indefinite (0-24 hour) step at 4° C.
Quantitative RT-PCR (“QRT-PCR”), which is quantitative in nature, can also be performed to provide a quantitative measure of gene expression levels. In QRT-PCR reverse transcription and PCR can be performed in two steps, or reverse transcription combined with PCR can be performed concurrently. One of these techniques, for which there are commercially available kits such as Taqman (Perkin Elmer, Foster City, Calif.) or as provided by Applied Biosystems (Foster City, Calif.) is performed with a transcript-specific antisense probe. This probe is specific for the PCR product (e.g. a nucleic acid fragment derived from a gene) and is prepared with a quencher and fluorescent reporter probe complexed to the 5′ end of the oligonucleotide. Different fluorescent markers are attached to different reporters, allowing for measurement of two products in one reaction. When Taq DNA polymerase is activated, it cleaves off the fluorescent reporters of the probe bound to the template by virtue of its 5′-to-3′ exonuclease activity. In the absence of the quenchers, the reporters now fluoresce. The color change in the reporters is proportional to the amount of each specific product and is measured by a fluorometer; therefore, the amount of each color is measured and the PCR product is quantified. The PCR reactions are performed in 96-well plates so that samples derived from many individuals are processed and measured simultaneously. The Taqman system has the additional advantage of not requiring gel electrophoresis and allows for quantification when used with a standard curve.
A second technique useful for detecting PCR products quantitatively is to use an intercolating dye such as the commercially available QuantiTect SYBR Green PCR (Qiagen, Valencia Calif.). RT-PCR is performed using SYBR green as a fluorescent label which is incorporated into the PCR product during the PCR stage and produces a flourescense proportional to the amount of PCR product.
Both Taqman and QuantiTect SYBR systems can be used subsequent to reverse transcription of RNA. Reverse transcription can either be performed in the same reaction mixture as the PCR step (one-step protocol) or reverse transcription can be performed first prior to amplification utilizing PCR (two-step protocol).
Additionally, other systems to quantitatively measure mRNA expression products are known including Molecular Beacons® which uses a probe having a fluorescent molecule and a quencher molecule, the probe capable of forming a hairpin structure such that when in the hairpin form, the fluorescence molecule is quenched, and when hybridized the fluorescence increases giving a quantitative measurement of gene expression.
Additional techniques to quantitatively measure RNA expression include, but are not limited to, polymerase chain reaction, ligase chain reaction, Qbeta replicase (see, e.g., International Application No. PCT/US87/00880, which is hereby incorporated by reference), isothermal amplification method (see, e.g., Walker et al., 1992, PNAS 89:382-396, which is hereby incorporated herein by reference), strand displacement amplification (SDA), repair chain reaction, Asymmetric Quantitative PCR (see, e.g., U.S. Publication No. US 2003/30134307A1, herein incorporated by reference) and the multiplex microsphere bead assay described in Fuja et al., 2004, Journal of Biotechnology 108:193-205, herein incorporated by reference.
The level of expression of one or more of the genes described herein (e.g., the genes listed in Table 30, Table I, Table J, or Table K) can, for example, be measured by amplifying RNA from a sample using amplification (NASBA). See, e.g., Kwoh et al., 1989, PNAS USA 86:1173; International Publication No. WO 88/10315; and U.S. Pat. No. 6,329,179, each of which is hereby incorporated by reference. In NASBA, the nucleic acids may be prepared for amplification using conventional methods, e.g., phenol/chloroform extraction, heat denaturation, treatment with lysis buffer and minispin columns for isolation of DNA and RNA or guanidinium chloride extraction of RNA. These amplification techniques involve annealing a primer that has target specific sequences. Following polymerization, DNA/RNA hybrids are digested with RNase H while double stranded DNA molecules are heat denatured again. In either case the single stranded DNA is made fully double stranded by addition of second target specific primer, followed by polymerization. The double-stranded DNA molecules are then multiply transcribed by a polymerase such as T7 or SP6. In an isothermal cyclic reaction, the RNA's are reverse transcribed into double stranded DNA, and transcribed once with a polymerase such as T7 or SP6. The resulting products, whether truncated or complete, indicate target specific sequences.
Several techniques may be used to separate amplification products. For example, amplification products may be separated by agarose, agarose-acrylamide or polyacrylamide gel electrophoresis using conventional methods. See Sambrook et al., 2001. Several techniques for detecting PCR products quantitatively without electrophoresis may also be used according to the invention (see, e.g., PCR Protocols, A Guide to Methods and Applications, Innis et al., 1990, Academic Press, Inc. N.Y., which is hereby incorporated by reference). For example, chromatographic techniques may be employed to effect separation. There are many kinds of chromatography which may be used in the present invention: adsorption, partition, ion-exchange and molecular sieve, HPLC, and many specialized techniques for using them including column, paper, thin-layer and gas chromatography (Freifelder, Physical Biochemistry Applications to Biochemistry and Molecular Biology, 2nd ed., Wm. Freeman and Co., New York, N.Y., 1982, which is hereby incorporated by reference).
Another example of a separation methodology is to covalently label the oligonucleotide primers used in a PCR reaction with various types of small molecule ligands. In one such separation, a different ligand is present on each oligonucleotide. A molecule, perhaps an antibody or avidin if the ligand is biotin, that specifically binds to one of the ligands is used to coat the surface of a plate such as a 96 well ELISA plate. Upon application of the PCR reactions to the surface of such a prepared plate, the PCR products are bound with specificity to the surface. After washing the plate to remove unbound reagents, a solution containing a second molecule that binds to the first ligand is added. This second molecule is linked to some kind of reporter system. The second molecule only binds to the plate if a PCR product has been produced whereby both oligonucleotide primers are incorporated into the final PCR products. The amount of the PCR product is then detected and quantified in a commercial plate reader much as ELISA reactions are detected and quantified. An ELISA-like system such as the one described here has been developed by Raggio Italgene (under the C-Track tradename.
Amplification products should be visualized in order to confirm amplification of the nucleic acid sequences of interest, i.e., nucleic acid sequences of one or more of the genes described herein (e.g., a gene listed in Table 30, Table I, Table J, or Table K). One typical visualization method involves staining of a gel with ethidium bromide and visualization under UV light. Alternatively, if the amplification products are integrally labeled with radio- or fluorometrically-labeled nucleotides, the amplification products may then be exposed to x-ray film or visualized under the appropriate stimulating spectra, following separation.
In one embodiment, visualization is achieved indirectly. Following separation of amplification products, a labeled, nucleic acid probe is brought into contact with the amplified nucleic acid sequence of interest, i.e., nucleic acid sequences of one or more of the genes described herein (e.g., a gene listed in Table 30, Table I, Table J, or Table K). The probe preferably is conjugated to a chromophore but may be radiolabeled. In another embodiment, the probe is conjugated to a binding partner, such as an antibody or biotin, where the other member of the binding pair carries a detectable moiety.
In another embodiment, detection is by Southern blotting and hybridization with a labeled probe. The techniques involved in Southern blotting are well known to those of skill in the art and may be found in many standard books on molecular protocols. See Sambrook et al., 2001. Briefly, amplification products are separated by gel electrophoresis. The gel is then contacted with a membrane, such as nitrocellulose, permitting transfer of the nucleic acid and non-covalent binding. Subsequently, the membrane is incubated with a chromophore-conjugated probe that is capable of hybridizing with a target amplification product. Detection is by exposure of the membrane to x-ray film or ion-emitting detection devices. One example of the foregoing is described in U.S. Pat. No. 5,279,721, incorporated by reference herein, which discloses an apparatus and method for the automated electrophoresis and transfer of nucleic acids. The apparatus permits electrophoresis and blotting without external manipulation of the gel and is ideally suited to carrying out methods according to the present invention.
5.4.1.3 Nuclease Protection Assays
In particular embodiments, feature values for biomarkers in a biomarker profile can be obtained by performing nuclease protection assays (including both ribonuclease protection assays and S1 nuclease assays) to detect and quantify specific mRNAs (e.g., mRNAs of a gene described in Table 30, Table I, Table J, or Table K). Such assays are described in, for example, Sambrook et al., 2001, supra. In nuclease protection assays, an antisense probe (labeled with, e.g., radiolabeled or nonisotopic) hybridizes in solution to an RNA sample. Following hybridization, single-stranded, unhybridized probe and RNA are degraded by nucleases. An acrylamide gel is used to separate the remaining protected fragments. Typically, solution hybridization is more efficient than membrane-based hybridization, and it can accommodate up to 100 μg of sample RNA, compared with the 20-30 μg maximum of blot hybridizations.
The ribonuclease protection assay, which is the most common type of nuclease protection assay, requires the use of RNA probes. Oligonucleotides and other single-stranded DNA probes can only be used in assays containing S1 nuclease. The single-stranded, antisense probe must typically be completely homologous to target RNA to prevent cleavage of the probe:target hybrid by nuclease.
5.4.1.4 Northern Blot Assays
Any hybridization technique known to those of skill in the art can be used to generate feature values for biomarkers in a biomarker profile. In other particular embodiments, feature values for biomarkers in a biomarker profile can be obtained by Northern blot analysis (to detect and quantify specific RNA molecules (e.g., RNAs of a gene described in Table 30, Table I, Table J, or Table K). A standard Northern blot assay can be used to ascertain an RNA transcript size, identify alternatively spliced RNA transcripts, and the relative amounts of one or more genes described herein (in particular, mRNA) in a sample, in accordance with conventional Northern hybridization techniques known to those persons of ordinary skill in the art. In Northern blots, RNA samples are first separated by size via electrophoresis in an agarose gel under denaturing conditions. The RNA is then transferred to a membrane, crosslinked and hybridized with a labeled probe. Nonisotopic or high specific activity radiolabeled probes can be used including random-primed, nick-translated, or PCR-generated DNA probes, in vitro transcribed RNA probes, and oligonucleotides. Additionally, sequences with only partial homology (e.g., cDNA from a different species or genomic DNA fragments that might contain an exon) may be used as probes. The labeled probe, e.g., a radiolabelled cDNA, either containing the full-length, single stranded DNA or a fragment of that DNA sequence may be at least 20, at least 30, at least 50, or at least 100 consecutive nucleotides in length. The probe can be labeled by any of the many different methods known to those skilled in this art. The labels most commonly employed for these studies are radioactive elements, enzymes, chemicals that fluoresce when exposed to ultraviolet light, and others. A number of fluorescent materials are known and can be utilized as labels. These include, but are not limited to, fluorescein, rhodamine, auramine, Texas Red, AMCA blue and Lucifer Yellow. The radioactive label can be detected by any of the currently available counting procedures. Non-limiting examples of isotopes include 3H, 14C, 32P, 35S, 36Cl, 51Cr, 57Co, 58Co, 59Fe, 90Y, 125I, 131I, and 186Re. Enzyme labels are likewise useful, and can be detected by any of the presently utilized colorimetric, spectrophotometric, fluorospectrophotometric, amperometric or gasometric techniques. The enzyme is conjugated to the selected particle by reaction with bridging molecules such as carbodiimides, diisocyanates, glutaraldehyde and the like. Any enzymes known to one of skill in the art can be utilized. Examples of such enzymes include, but are not limited to, peroxidase, beta-D-galactosidase, urease, glucose oxidase plus peroxidase and alkaline phosphatase. U.S. Pat. Nos. 3,654,090, 3,850,752, and 4,016,043 are referred to by way of example for their disclosure of alternate labeling material and methods.
5.4.2 Methods of Detecting Proteins
In specific embodiments of the invention, feature values of biomarkers in a biomarker profile can be obtained by detecting proteins, for example, by detecting the expression product (e.g., a nucleic acid or protein) of one or more genes described herein (e.g., a gene listed in Table 30, Table I, Table J, or Table K), or post-translationally modified, or otherwise modified, or processed forms of such proteins. In a specific embodiment, a biomarker profile is generated by detecting and/or analyzing one or more proteins and/or discriminating fragments thereof expressed from a gene disclosed herein (e.g., a gene listed in Table 30, Table I, Table J, or Table K) using any method known to those skilled in the art for detecting proteins including, but not limited to protein microarray analysis, immunohistochemistry and mass spectrometry.
Standard techniques may be utilized for determining the amount of the protein or proteins of interest (e.g., proteins expressed from genes listed in Table 30, Table I, Table J, or Table K) present in a sample. For example, standard techniques can be employed using, e.g., immunoassays such as, for example Western blot, immunoprecipitation followed by sodium dodecyl sulfate polyacrylamide gel electrophoresis, (SDS-PAGE), immunocytochemistry, and the like to determine the amount of protein or proteins of interest present in a sample. One exemplary agent for detecting a protein of interest is an antibody capable of specifically binding to a protein of interest, preferably an antibody detectably labeled, either directly or indirectly.
For such detection methods, if desired a protein from the sample to be analyzed can easily be isolated using techniques which are well known to those of skill in the art. Protein isolation methods can, for example, be such as those described in Harlow and Lane, 1988, Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press (Cold Spring Harbor, N.Y.), which is incorporated by reference herein in its entirety.
In certain embodiments, methods of detection of the protein or proteins of interest involve their detection via interaction with a protein-specific antibody. For example, antibodies directed to a protein of interest (e.g., a protein expressed from a gene described herein, e.g., a protein listed in Table 30, Table I, Table J, or Table K). Antibodies can be generated utilizing standard techniques well known to those of skill in the art. In specific embodiments, antibodies can be polyclonal, or more preferably, monoclonal. An intact antibody, or an antibody fragment (e.g., scFv, Fab or F(ab′)2) can, for example, be used.
For example, antibodies, or fragments of antibodies, specific for a protein of interest can be used to quantitatively or qualitatively detect the presence of a protein. This can be accomplished, for example, by immunofluorescence techniques. Antibodies (or fragments thereof) can, additionally, be employed histologically, as in immunofluorescence or immunoelectron microscopy, for in situ detection of a protein of interest. In situ detection can be accomplished by removing a biological sample (e.g., a biopsy specimen) from a patient, and applying thereto a labeled antibody that is directed to a protein of interest (e.g., a protein expressed from a gene in Table 30, Table I, Table J, or Table K). The antibody (or fragment) is preferably applied by overlaying the antibody (or fragment) onto a biological sample. Through the use of such a procedure, it is possible to determine not only the presence of the protein of interest, but also its distribution, in a particular sample. A wide variety of well-known histological methods (such as staining procedures) can be utilized to achieve such in situ detection.
Immunoassays for a protein of interest typically comprise incubating a biological sample of a detectably labeled antibody capable of identifying a protein of interest, and detecting the bound antibody by any of a number of techniques well-known in the art. As discussed in more detail, below, the term “labeled” can refer to direct labeling of the antibody via, e.g., coupling (i.e., physically linking) a detectable substance to the antibody, and can also refer to indirect labeling of the antibody by reactivity with another reagent that is directly labeled. Examples of indirect labeling include detection of a primary antibody using a fluorescently labeled secondary antibody.
The biological sample can be brought in contact with and immobilized onto a solid phase support or carrier such as nitrocellulose, or other solid support which is capable of immobilizing cells, cell particles or soluble proteins. The support can then be washed with suitable buffers followed by treatment with the detectably labeled fingerprint gene-specific antibody. The solid phase support can then be washed with the buffer a second time to remove unbound antibody. The amount of bound label on solid support can then be detected by conventional methods.
By “solid phase support or carrier” is intended any support capable of binding an antigen or an antibody. Well-known supports or carriers include glass, polystyrene, polypropylene, polyethylene, dextran, nylon, amylases, natural and modified celluloses, polyacrylamides and magnetite. The nature of the carrier can be either soluble to some extent or insoluble for the purposes of the present invention. The support material can have virtually any possible structural configuration so long as the coupled molecule is capable of binding to an antigen or antibody. Thus, the support configuration can be spherical, as in a bead, or cylindrical, as in the inside surface of a test tube, or the external surface of a rod. Alternatively, the surface can be flat such as a sheet, test strip, etc. Preferred supports include polystyrene beads. Those skilled in the art will know many other suitable carriers for binding antibody or antigen, or will be able to ascertain the same by use of routine experimentation.
One of the ways in which an antibody specific for a protein of interest can be detectably labeled is by linking the same to an enzyme and use in an enzyme immunoassay (EIA) (Voller, 1978, “The Enzyme Linked Immunosorbent Assay (ELISA)”, Diagnostic Horizons 2:1-7, Microbiological Associates Quarterly Publication, Walkersville, Md.; Voller et al., 1978, J. Clin. Pathol. 31:507-520; Butler, J. E., 1981, Meth. Enzymol. 73:482-523; Maggio (ed.), 1980, Enzyme Immunoassay, CRC Press, Boca Raton, Fla.; Ishikawa et al., (eds.), 1981, Enzyme Immunoassay, Kgaku Shoin, Tokyo, each of which is hereby incorporated by reference in its entirety). The enzyme which is bound to the antibody will react with an appropriate substrate, preferably a chromogenic substrate, in such a manner as to produce a chemical moiety which can be detected, for example, by spectrophotometric, fluorimetric or by visual means. Enzymes which can be used to detectably label the antibody include, but are not limited to, malate dehydrogenase, staphylococcal nuclease, delta-5-steroid isomerase, yeast alcohol dehydrogenase, alpha-glycerophosphate, dehydrogenase, triose phosphate isomerase, horseradish peroxidase, alkaline phosphatase, asparaginase, glucose oxidase, beta-galactosidase, ribonuclease, urease, catalase, glucose-6-phosphate dehydrogenase, glucoamylase and acetylcholinesterase. The detection can be accomplished by colorimetric methods which employ a chromogenic substrate for the enzyme. Detection can also be accomplished by visual comparison of the extent of enzymatic reaction of a substrate in comparison with similarly prepared standards.
Detection can also be accomplished using any of a variety of other immunoassays. For example, by radioactively labeling the antibodies or antibody fragments, it is possible to detect a protein of interest through the use of a radioimmunoassay (RIA) (see, for example, Weintraub, 1986, Principles of Radioimmunoassays, Seventh Training Course on Radioligand Assay Techniques, The Endocrine Society, which is hereby incorporated by reference herein). The radioactive isotope (e.g., 125I, 131I, 35S or 3H) can be detected by such means as the use of a gamma counter or a scintillation counter or by autoradiography.
It is also possible to label the antibody with a fluorescent compound. When the fluorescently labeled antibody is exposed to light of the proper wavelength, its presence can then be detected due to fluorescence. Among the most commonly used fluorescent labeling compounds are fluorescein isothiocyanate, rhodamine, phycoerythrin, phycocyanin, allophycocyanin, o-phthaldehyde and fluorescamine.
The antibody can also be detectably labeled using fluorescence emitting metals such as 152Eu, or others of the lanthanide series. These metals can be attached to the antibody using such metal chelating groups as diethylenetriaminepentacetic acid (DTPA) or ethylenediaminetetraacetic acid (EDTA).
The antibody also can be detectably labeled by coupling it to a chemiluminescent compound. The presence of the chemiluminescent-tagged antibody is then determined by detecting the presence of luminescence that arises during the course of a chemical reaction. Examples of particularly useful chemiluminescent labeling compounds are luminol, isoluminol, theromatic acridinium ester, imidazole, acridinium salt and oxalate ester.
Likewise, a bioluminescent compound can be used to label the antibody of the present invention. Bioluminescence is a type of chemiluminescence found in biological systems in, which a catalytic protein increases the efficiency of the chemiluminescent reaction. The presence of a bioluminescent protein is determined by detecting the presence of luminescence. Important bioluminescent compounds for purposes of labeling are luciferin, luciferase and aequorin.
In another embodiment, specific binding molecules other than antibodies, such as aptamers, may be used to bind the biomarkers. In yet another embodiment, the biomarker profile may comprise a measurable aspect of an infectious agent (e.g., lipopolysaccharides or viral proteins) or a component thereof.
In some embodiments, a protein chip assay (e.g., The ProteinChip® Biomarker System, Ciphergen, Fremont, Calif.) is used to measure feature values for the biomarkers in the biomarker profile. See also, for example, Lin, 2004, Modern Pathology, 1-9; Li, 2004, Journal of Urology 171, 1782-1787; Wadsworth, 2004, Clinical Cancer Research, 10, 1625-1632; Prieto, 2003, Journal of Liquid Chromatography & Related Technologies 26, 2315-2328; Coombes, 2003, Clinical Chemistry 49, 1615-1623; Mian, 2003, Proteomics 3, 1725-1737; Lehre et al., 2003, BJU International 92, 223-225; and Diamond, 2003, Journal of the American Society for Mass Spectrometry 14, 760-765, each of which is hereby incorporated by reference in its entirety.
In some embodiments, a bead assay is used to measure feature values for the biomarkers in the biomarker profile. One such bead assay is the Becton Dickinson Cytometric Bead Array (CBA). CBA employs a series of particles with discrete fluorescence intensities to simultaneously detect multiple soluble analytes. CBA is combined with flow cytometry to create a multiplexed assay. The Becton Dickinson CBA system, as embodied for example in the Becton Dickinson Human Inflammation Kit, uses the sensitivity of amplified fluorescence detection by flow cytometry to measure soluble analytes in a particle-based immunoassay. Each bead in a CBA provides a capture surface for a specific protein and is analogous to an individually coated well in an ELISA plate. The BD CBA capture bead mixture is in suspension to allow for the detection of multiple analytes in a small volume sample.
In some embodiments the multiplex analysis method described in U.S. Pat. No. 5,981,180 (“the '180 patent”), herein incorporated by reference in its entirety, and in particular for its teachings of the general methodology, bead technology, system hardware and antibody detection, is used to measure feature values for the biomarkers in a biomarker profile. For this analysis, a matrix of microparticles is synthesized, where the matrix consists of different sets of microparticles. Each set of microparticles can have thousands of molecules of a distinct antibody capture reagent immobilized on the microparticle surface and can be color-coded by incorporation of varying amounts of two fluorescent dyes. The ratio of the two fluorescent dyes provides a distinct emission spectrum for each set of microparticles, allowing the identification of a microparticle a set following the pooling of the various sets of microparticles. U.S. Pat. Nos. 6,268,222 and 6,599,331 also are incorporated herein by reference in their entirety, and in particular for their teachings of various methods of labeling microparticles for multiplex analysis.
5.4.3 Use of Other Methods of Detection
In some embodiments, a separation method may be used determine feature values for biomarkers in a biomarker profile, such that only a subset of biomarkers within the sample is analyzed. For example, the biomarkers that are analyzed in a sample may be mRNA species from a cellular extract which has been fractionated to obtain only the nucleic acid biomarkers within the sample, or the biomarkers may be from a fraction of the total complement of proteins within the sample, which have been fractionated by chromatographic techniques.
Feature values for biomarkers in a biomarker profile can also, for example, be generated by the use of one or more of the following methods described below. For example, methods may include nuclear magnetic resonance (NMR) spectroscopy, a mass spectrometry method, such as electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS)n (n is an integer greater than zero), matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), desorption/ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS)n, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS/MS, and APPI-(MS)n. Other mass spectrometry methods may include, inter alia, quadrupole, Fourier transform mass spectrometry (FTMS) and ion trap. Other suitable methods may include chemical extraction partitioning, column chromatography, ion exchange chromatography, hydrophobic (reverse phase) liquid chromatography, isoelectric focusing, one-dimensional polyacrylamide gel electrophoresis (PAGE), two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) or other chromatography, such as thin-layer, gas or liquid chromatography, or any combination thereof. In one embodiment, the biological sample may be fractionated prior to application of the separation method.
In one embodiment, laser desorption/ionization time-of-flight mass spectrometry is used to create determine feature values in a biomarker profile where the biomarkers are proteins or protein fragments that have been ionized and vaporized off an immobilizing support by incident laser radiation and the feature values are the presence or absence of peaks representing these fragments in the mass spectra profile. A variety of laser desorption/ionization techniques are known in the art (see, e.g., Guttman et al., 2001, Anal. Chem. 73:1252-62 and Wei et al., 1999, Nature 399:243-246, each of which is hereby incorporated by herein be reference in its entirety).
Laser desorption/ionization time-of-flight mass spectrometry allows the generation of large amounts of information in a relatively short period of time. A biological sample is applied to one of several varieties of a support that binds all of the biomarkers, or a subset thereof, in the sample. Cell lysates or samples are directly applied to these surfaces in volumes as small as 0.5 μL, with or without prior purification or fractionation. The lysates or sample can be concentrated or diluted prior to application onto the support surface. Laser desorption/ionization is then used to generate mass spectra of the sample, or samples, in as little as three hours.
5.5 Data Analysis Algorithms
Biomarkers whose corresponding feature values are capable of discriminating between converters and nonconverters are identified in the present invention. The identity of these biomarkers and their corresponding features (e.g., expression levels) can be used to develop a decision rule, or plurality of decision rules, that discriminate between converters and nonconverters. Section 6 below illustrates how data analysis algorithms can be used to construct a number of such decision rules. Each of the data analysis algorithms described in Section 6 use features (e.g., expression values) of a subset of the biomarkers identified in the present invention across a training population that includes converters and nonconverters. Typically, a SIRS subject is considered a nonconverter when the subject does not develop sepsis in a defined time period (e.g., observation period). This defined time period can be, for example, twelve hours, twenty four hours, forty-eight hours, a day, a week, a month, or longer. Specific data analysis algorithms for building a decision rule, or plurality of decision rules, that discriminate between subjects that develop sepsis and subjects that do not develop sepsis during a defined period will be described in the subsections below. Once a decision rule has been built using these exemplary data analysis algorithms or other techniques known in the art, the decision rule can be used to classify a test subject into one of the two or more phenotypic classes (e.g. a converter or a nonconverter). This is accomplished by applying the decision rule to a biomarker profile obtained from the test subject. Such decision rules, therefore, have enormous value as diagnostic indicators.
The present invention provides, in one aspect, for the evaluation of a biomarker profile from a test subject to biomarker profiles obtained from a training population. In some embodiments, each biomarker profile obtained from subjects in the training population, as well as the test subject, comprises a feature for each of a plurality of different biomarkers. In some embodiments, this comparison is accomplished by (i) developing a decision rule using the biomarker profiles from the training population and (ii) applying the decision rule to the biomarker profile from the test subject. As such, the decision rules applied in some embodiments of the present invention are used to determine whether a test subject having SIRS will or will not likely acquire sepsis.
In some embodiments of the present invention, when the results of the application of a decision rule indicate that the subject will likely acquire sepsis, the subject is diagnosed as a “sepsis” subject. If the results of an application of a decision rule indicate that the subject will not acquire sepsis, the subject is diagnosed as a “SIRS” subject. Thus, in some embodiments, the result in the above-described binary decision situation has four possible outcomes:
(i) truly septic, where the decision rule indicates that the subject will acquire sepsis and the subject does in fact acquire sepsis during the definite time period (true positive, TP);
(ii) falsely septic, where the decision rule indicates that the subject will acquire sepsis and the subject, in fact, does not acquire sepsis during the definite time period (false positive, FP);
(iii) truly SIRS, where the decision rule indicates that the subject will not acquire sepsis and the subject, in fact, does not acquire sepsis during the definite time period (true negative, TN); or
(iv) falsely SIRS, where the decision rule indicates that the subject will not acquire sepsis and the subject, in fact, does acquire sepsis during the definite time period (false negative, FN).
It will be appreciated that other definitions for TP, FP, TN, FN can be made. For example, TP could have been defined as instances where the decision rule indicates that the subject will not acquire sepsis and the subject, in fact, does not acquire sepsis during the definite time period. While all such alternative definitions are within the scope of the present invention, for ease of understanding the present invention, the definitions for TP, FP, TN, and FN given by definitions (i) through (iv) above will be used herein, unless otherwise stated.
As will be appreciated by those of skill in the art, a number of quantitative criteria can be used to communicate the performance of the comparisons made between a test biomarker profile and reference biomarker profiles (e.g., the application of a decision rule to the biomarker profile from a test subject). These include positive predicted value (PPV), negative predicted value (NPV), specificity, sensitivity, accuracy, and certainty. In addition, other constructs such a receiver operator curves (ROC) can be used to evaluate decision rule performance. As used herein:



  
    PPV
    =
    
      TP
      
        TP
        +
        FP
      
    
  




  
    NPV
    =
    
      TN
      
        TN
        +
        FN
      
    
  




  
    specificity
    =
    
      TN
      
        TN
        +
        FP
      
    
  




  
    sensitivity
    =
    
      TP
      
        TP
        +
        FN
      
    
  




  
    accuracy
    =
    
      certainty
      =
      
        
          TP
          +
          TN
        
        N
      
    
  



Here, N is the number of samples compared (e.g., the number of test samples for which a determination of sepsis or SIRS is sought). For example, consider the case in which there are ten subjects for which SIRS/sepsis classification is sought. Biomarker profiles are constructed for each of the ten test subjects. Then, each of the biomarker profiles is evaluated by applying a decision rule, where the decision rule was developed based upon biomarker profiles obtained from a training population. In this example, N, from the above equations, is equal to 10. Typically, N is a number of samples, where each sample was collected from a different member of a population. This population can, in fact, be of two different types. In one type, the population comprises subjects whose samples and phenotypic data (e.g., feature values of biomarkers and an indication of whether or not the subject acquired sepsis) was used to construct or refine a decision rule. Such a population is referred to herein as a training population. In the other type, the population comprises subjects that were not used to construct the decision rule. Such a population is referred to herein as a validation population. Unless otherwise stated, the population represented by N is either exclusively a training population or exclusively a validation population, as opposed to a mixture of the two population types. It will be appreciated that scores such as accuracy will be higher (closer to unity) when they are based on a training population as opposed to a validation population. Nevertheless, unless otherwise explicitly stated herein, all criteria used to assess the performance of a decision rule (or other forms of evaluation of a biomarker profile from a test subject) including certainty (accuracy) refer to criteria that were measured by applying the decision rule corresponding to the criteria to either a training population or a validation population. Furthermore, the definitions for PPV, NPV, specificity, sensitivity, and accuracy defined above can also be found in Draghici, Data Analysis Tools for DNA Microanalysis, 2003, CRC Press LLC, Boca Raton, Fla., pp. 342-343, which is hereby incorporated herein by reference.
In some embodiments the training population comprises nonconverters and converters. In some embodiments, biomarker profiles are constructed from this population using biological samples collected from the training population at some time period prior to the onset of sepsis by the converters of the population. As such, for the converters of the training population, a biological sample can be collected two week before, one week before, four days before, three days before, one day before, or any other time period before the converters became septic. In practice, such collections are obtained by collecting a biological sample at regular time intervals after admittance into the hospital with a SIRS diagnosis. For example, in one approach, subjects who have been diagnosed with SIRS in a hospital are used as a training population. Once admitted to the hospital with SIRS, the biological samples are collected from the subjects at selected times (e.g., hourly, every eight hours, every twelve hours, daily, etc.). A portion of the subjects acquire sepsis and a portion of the subjects do not acquire sepsis. For the subjects that acquire sepsis, the biological sample taken from the subjects just prior to the onset of sepsis are termed the T−12 biological samples. All other biological samples from the subjects are retroactively indexed relative to these biological samples. For instance, when a biological sample has been taken from a subject on a daily basis, the biological sample taken the day before the T−12 sample is referred to as the T−36 biological sample. Time points for biological samples for a nonconverter in the training population are identified by “time-matching” the nonconverter subject with a converter subject. To illustrate, consider the case in which a subject in the training population became clinically-defined as septic on his sixth day of enrollment. For the sake of illustration, for this subject, T−36 is day four of the study, and the T−36 biological sample is the biological sample that was obtained on day four of the study. Likewise, T−36 for the matched nonconverter subject is deemed to be day four of the study on this paired nonconverter subject.
In some embodiments, N is more than one, more than five, more than ten, more than twenty, between ten and 100, more than 100, or less than 1000 subjects. A decision rule (or other forms of comparison) can have at least about 99% certainty, or even more, in some embodiments, against a training population or a validation population. In other embodiments, the certainty is at least about 97%, at least about 95%, at least about 90%, at least about 85%, at least about 80%, at least about 75%, at least about 70%, at least about 65%, or at least about 60% against a training population or a validation population (and therefore against a single subject that is not part of a training population such as a clinical patient). The useful degree of certainty may vary, depending on the particular method of the present invention. As used herein, “certainty” means “accuracy.” In one embodiment, the sensitivity and/or specificity is at is at least about 97%, at least about 95%, at least about 90%, at least about 85%, at least about 80%, at least about 75%, or at least about 70% against a training population or a validation population. In some embodiments, such decision rules are used to predict the development of sepsis with the stated accuracy. In some embodiments, such decision rules are used to diagnoses sepsis with the stated accuracy. In some embodiments, such decision rules are used to determine a stage of sepsis with the stated accuracy.
The number of features that may be used by a decision rule to classify a test subject with adequate certainty is two or more. In some embodiments, it is three or more, four or more, ten or more, or between 10 and 200. Depending on the degree of certainty sought, however, the number of features used in a decision rule can be more or less, but in all cases is at least two. In one embodiment, the number of features that may be used by a decision rule to classify a test subject is optimized to allow a classification of a test subject with high certainty.
In some of the examples in Section 6 below, microarray data abundance data was collected for a plurality of biomarkers in each subject. That is, for each biomarker in a biomarker profile, a feature, microarray abundance data for the biomarker, was measured. Decision rules are developed from such biomarker profiles from a training population using data analysis algorithms in order to predict sample phenotypes based on observed gene expression patterns. While new and microarray specific classification tools are constantly being developed, the existing body of pattern recognition and prediction algorithms provide effective data analysis algorithms for constructing decision rules. See, for example, National Research Council; Panel on Discriminant Analysis Classification and Clustering, Discriminant Analysis and Clustering, Washington, D.C.: National Academy Press, which is hereby incorporated by reference. Furthermore, the techniques described in Dudoit et al., 2002, “Comparison of discrimination methods for the classification of tumors using gene expression data.” JASA 97; 77-87, hereby incorporated by reference herein in its entirety, can be used to develop such decision rules.
Relevant data analysis algorithms for developing a decision rule include, but are not limited to, discriminant analysis including linear, logistic, and more flexible discrimination techniques (see, e.g., Gnanadesikan, 1977, Methods for Statistical Data Analysis of Multivariate Observations, New York: Wiley 1977, which is hereby incorporated by reference herein in its entirety); tree-based algorithms such as classification and regression trees (CART) and variants (see, e.g., Breiman, 1984, Classification and Regression Trees, Belmont, Calif.: Wadsworth International Group, which is hereby incorporated by reference herein in its entirety, as well as Section 5.1.3, below); generalized additive models (see, e.g., Tibshirani, 1990, Generalized Additive Models, London: Chapman and Hall, which is hereby incorporated by reference herein in its entirety); and neural networks (see, e.g., Neal, 1996, Bayesian Learning for Neural Networks, New York: Springer-Verlag; and Insua, 1998, Feedforward neural networks for nonparametric regression In: Practical Nonparametric and Semiparametric Bayesian Statistics, pp. 181-194, New York: Springer, which is hereby incorporated by reference herein in its entirety, as well as Section 5.5.6, below).
In one embodiment, comparison of a test subject's biomarker profile to a biomarker profiles obtained from a training population is performed, and comprises applying a decision rule. The decision rule is constructed using a data analysis algorithm, such as a computer pattern recognition algorithm. Other suitable data analysis algorithms for constructing decision rules include, but are not limited to, logistic regression (see Section 5.5.10, below) or a nonparametric algorithm that detects differences in the distribution of feature values (e.g., a Wilcoxon Signed Rank Test (unadjusted and adjusted)). The decision rule can be based upon two, three, four, five, 10, 20 or more features, corresponding to measured observables from one, two, three, four, five, 10, 20 or more biomarkers. In one embodiment, the decision rule is based on hundreds of features or more. Decision rules may also be built using a classification tree algorithm. For example, each biomarker profile from a training population can comprise at least three features, where the features are predictors in a classification tree algorithm (see Section 5.5.1, below). The decision rule predicts membership within a population (or class) with an accuracy of at least about at least about 70%, of at least about 75%, of at least about 80%, of at least about 85%, of at least about 90%, of at least about 95%, of at least about 97%, of at least about 98%, of at least about 99%, or about 100%.
Suitable data analysis algorithms are known in the art, some of which are reviewed in Hastie et al., supra. In a specific embodiment, a data analysis algorithm of the invention comprises Classification and Regression Tree (CART; Section 5.5.1, below), Multiple Additive Regression Tree (MART; Section 5.5.4, below), Prediction Analysis for Microarrays (PAM; Section 5.5.2, below) or Random Forest analysis (Section 5.5.1, below). Such algorithms classify complex spectra from biological materials, such as a blood sample, to distinguish subjects as normal or as possessing biomarker expression levels characteristic of a particular disease state. In other embodiments, a data analysis algorithm of the invention comprises ANOVA and nonparametric equivalents, linear discriminant analysis (Section 5.5.10, below), logistic regression analysis (Section 5.5.10, below), nearest neighbor classifier analysis (Section 5.5.9, below), neural networks (Section 5.5.6, below), principal component analysis (Section 5.5.8, below), quadratic discriminant analysis (Section 5.5.11, below), regression classifiers (Section 5.5.5, below) and support vector machines (Section 5.5.12, below). While such algorithms may be used to construct a decision rule and/or increase the speed and efficiency of the application of the decision rule and to avoid investigator bias, one of ordinary skill in the art will realize that computer-based algorithms are not required to carry out the methods of the present invention.
Decision rules can be used to evaluate biomarker profiles, regardless of the method that was used to generate the biomarker profile. For example, suitable decision rules that can be used to evaluate biomarker profiles generated using gas chromatography, as discussed in Harper, “Pyrolysis and GC in Polymer Analysis,” Dekker, New York (1985). Further, Wagner et al., 2002, Anal. Chem. 74:1824-1835 disclose a decision rule that improves the ability to classify subjects based on spectra obtained by static time-of-flight secondary ion mass spectrometry (TOF-SIMS). Additionally, Bright et al., 2002, J. Microbiol. Methods 48:127-38, hereby incorporated by reference herein in its entirety, disclose a method of distinguishing between bacterial strains with high certainty (79-89% correct classification rates) by analysis of MALDI-TOF-MS spectra. Dalluge, 2000, Fresenius J. Anal. Chem. 366:701-711, hereby incorporated by reference herein in its entirety, discusses the use of MALDI-TOF-MS and liquid chromatography-electrospray ionization mass spectrometry (LC/ESI-MS) to classify profiles of biomarkers in complex biological samples.
5.5.1 Decision Trees
One type of decision rule that can be constructed using the feature values of the biomarkers identified in the present invention is a decision tree. Here, the “data analysis algorithm” is any technique that can build the decision tree, whereas the final “decision tree” is the decision rule. A decision tree is constructed using a training population and specific data analysis algorithms. Decision trees are described generally by Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York. pp. 395-396, which is hereby incorporated by reference. Tree-based methods partition the feature space into a set of rectangles, and then fit a model (like a constant) in each one.
The training population data includes the features (e.g., expression values, or some other observable) for the biomarkers of the present invention across a training set population. One specific algorithm that can be used to construct a decision tree is a classification and regression tree (CART). Other specific decision tree algorithms include, but are not limited to, ID3, C4.5, MART, and Random Forests. CART, ID3, and C4.5 are described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York. pp. 396-408 and pp. 411-412, which is hereby incorporated by reference. CART, MART, and C4.5 are described in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, Chapter 9, which is hereby incorporated by reference in its entirety. Random Forests are described in Breiman, 1999, “Random Forests—Random Features,” Technical Report 567, Statistics Department, U.C. Berkeley, September 1999, which is hereby incorporated by reference in its entirety.
In some embodiments of the present invention, decision trees are used to classify subjects using features for combinations of biomarkers of the present invention. Decision tree algorithms belong to the class of supervised learning algorithms. The aim of a decision tree is to induce a classifier (a tree) from real-world example data. This tree can be used to classify unseen examples that have not been used to derive the decision tree. As such, a decision tree is derived from training data. Exemplary training data contains data for a plurality of subjects (the training population). For each respective subject there is a plurality of features the class of the respective subject (e.g., sepsis/SIRS). In one embodiment of the present invention, the training data is expression data for a combination of biomarkers across the training population.
The following algorithm describes an exemplary decision tree derivation:












Tree(Examples,Class,Features)


  Create a root node


  If all Examples have the same Class value, give the root this label


  Else if Features is empty label the root according to the most common


  value


  Else begin


   Calculate the information gain for each Feature


   Select the Feature A with highest information gain and make


   this the root Feature


   For each possible value, v, of this Feature


    Add a new branch below the root, corresponding to A = v


    Let Examples(v) be those examples with A = v


    If Examples(v) is empty, make the new branch a leaf node


    labeled with the most common value among Examples


    Else let the new branch be the tree created by


    Tree(Examples(v),Class,Features - {A })


  end









A more detailed description of the calculation of information gain is shown in the following. If the possible classes vi of the examples have probabilities P(vi) then the information content I of the actual answer is given by:




  
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The I-value shows how much information we need in order to be able to describe the outcome of a classification for the specific dataset used. Supposing that the dataset contains p positive (e.g. will develop sepsis) and n negative (e.g. will not develop sepsis) examples (e.g. subjects), the information contained in a correct answer is:





  
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          +
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where log2 is the logarithm using base two. By testing single features the amount of information needed to make a correct classification can be reduced. The remainder for a specific feature A (e.g. representing a specific biomarker) shows how much the information that is needed can be reduced.





  
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“v” is the number of unique attribute values for feature A in a certain dataset, “i” is a certain attribute value, “pi” is the number of examples for feature A where the classification is positive (e.g. will develop sepsis), “ni” is the number of examples for feature A where the classification is negative (e.g. will not develop sepsis).

The information gain of a specific feature A is calculated as the difference between the information content for the classes and the remainder of feature A:




  
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The information gain is used to evaluate how important the different features are for the classification (how well they split up the examples), and the feature with the highest information.

In general there are a number of different decision tree algorithms, many of which are described in Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc. Decision tree algorithms often require consideration of feature processing, impurity measure, stopping criterion, and pruning. Specific decision tree algorithms include, but are not limited to classification and regression trees (CART), multivariate decision trees, ID3, and C4.5.
In one approach, when a decision tree is used, the gene expression data for a select combination of genes described in the present invention across a training population is standardized to have mean zero and unit variance. The members of the training population are randomly divided into a training set and a test set. For example, in one embodiment, two thirds of the members of the training population are placed in the training set and one third of the members of the training population are placed in the test set. The expression values for a select combination of biomarkers described in the present invention is used to construct the decision tree. Then, the ability for the decision tree to correctly classify members in the test set is determined. In some embodiments, this computation is performed several times for a given combination of biomarkers. In each computational iteration, the members of the training population are randomly assigned to the training set and the test set. Then, the quality of the combination of biomarkers is taken as the average of each such iteration of the decision tree computation.
In addition to univariate decision trees in which each split is based on a feature value for a corresponding biomarker, among the set of biomarkers of the present invention, or the relative feature values of two such biomarkers, multivariate decision trees can be implemented as a decision rule. In such multivariate decision trees, some or all of the decisions actually comprise a linear combination of feature values for a plurality of biomarkers of the present invention. Such a linear combination can be trained using known techniques such as gradient descent on a classification or by the use of a sum-squared-error criterion. To illustrate such a decision tree, consider the expression:

0.04x1+0.16x2<500

Here, x1 and x2 refer to two different features for two different biomarkers from among the biomarkers of the present invention. To poll the decision rule, the values of features x1 and x2 are obtained from the measurements obtained from the unclassified subject. These values are then inserted into the equation. If a value of less than 500 is computed, then a first branch in the decision tree is taken. Otherwise, a second branch in the decision tree is taken. Multivariate decision trees are described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 408-409, which is hereby incorporated by reference.
Another approach that can be used in the present invention is multivariate adaptive regression splines (MARS). MARS is an adaptive procedure for regression, and is well suited for the high-dimensional problems addressed by the present invention. MARS can be viewed as a generalization of stepwise linear regression or a modification of the CART method to improve the performance of CART in the regression setting. MARS is described in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, pp. 283-295, which is hereby incorporated by reference in its entirety.
5.5.2 Predictive Analysis of Microarrays (PAM)
One approach to developing a decision rule using feature values of biomarkers of the present invention is the nearest centroid classifier. Such a technique computes, for each class (sepsis and SIRS), a centroid given by the average feature levels of the biomarkers in the class, and then assigns new samples to the class whose centroid is nearest. This approach is similar to k-means clustering except clusters are replaced by known classes. This algorithm can be sensitive to noise when a large number of biomarkers are used. One enhancement to the technique uses shrinkage: for each biomarker, differences between class centroids are set to zero if they are deemed likely to be due to chance. This approach is implemented in the Prediction Analysis of Microarray, or PAM. See, for example, Tibshirani et al., 2002, Proceedings of the National Academy of Science USA 99; 6567-6572, which is hereby incorporated by reference in its entirety. Shrinkage is controlled by a threshold below which differences are considered noise. Biomarkers that show no difference above the noise level are removed. A threshold can be chosen by cross-validation. As the threshold is decreased, more biomarkers are included and estimated classification errors decrease, until they reach a bottom and start climbing again as a result of noise biomarkers—a phenomenon known as overfitting.
5.5.3 Bagging, Boosting, and the Random Subspace Method
Bagging, boosting, the random subspace method, and additive trees are data analysis algorithms known as combining techniques that can be used to improve weak decision rules. These techniques are designed for, and usually applied to, decision trees, such as the decision trees described in Section 5.5.1, above. In addition, such techniques can also be useful in decision rules developed using other types of data analysis algorithms such as linear discriminant analysis.
In bagging, one samples the training set, generating random independent bootstrap replicates, constructs the decision rule on each of these, and aggregates them by a simple majority vote in the final decision rule. See, for example, Breiman, 1996, Machine Learning 24, 123-140; and Efron & Tibshirani, An Introduction to Boostrap, Chapman & Hall, New York, 1993, which is hereby incorporated by reference in its entirety.
In boosting, decision rules are constructed on weighted versions of the training set, which are dependent on previous classification results. Initially, all features under consideration have equal weights, and the first decision rule is constructed on this data set. Then, weights are changed according to the performance of the decision rule. Erroneously classified features get larger weights, and the next decision rule is boosted on the reweighted training set. In this way, a sequence of training sets and decision rules is obtained, which is then combined by simple majority voting or by weighted majority voting in the final decision rule. See, for example, Freund & Schapire, “Experiments with a new boosting algorithm,” Proceedings 13th International Conference on Machine Learning, 1996, 148-156, which is hereby incorporated by reference in its entirety.
To illustrate boosting, consider the case where there are two phenotypes exhibited by the population under study, phenotype 1 (e.g., acquiring sepsis during a defined time periond), and phenotype 2 (e.g., SIRS only, meaning that the subject does acquire sepsis within a defined time period). Given a vector of predictor biomarkers (e.g., a vector of features that represent such biomarkers) from the training set data, a decision rule G(X) produces a prediction taking one of the type values in the two value set: {phenotype 1, phenotype 2}. The error rate on the training sample is




  
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where N is the number of subjects in the training set (the sum total of the subjects that have either phenotype 1 or phenotype 2). For example, if there are 49 organisms that acquire sepsis and 72 organisms that remain in the SIRS state, N is 121. A weak decision rule is one whose error rate is only slightly better than random guessing. In the boosting algorithm, the weak decision rule is repeatedly applied to modified versions of the data, thereby producing a sequence of weak decision rules Gm(x), m, =1, 2, . . . , M. The predictions from all of the decision rules in this sequence are then combined through a weighted majority vote to produce the final decision rule:




  
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Here α1, α2, . . . , αM are computed by the boosting algorithm and their purpose is to weigh the contribution of each respective decision rule Gm(x). Their effect is to give higher influence to the more accurate decision rules in the sequence.
The data modifications at each boosting step consist of applying weights w1, w2, . . . , wn to each of the training observations (xi, yi), i=1, 2, . . . , N. Initially all the weights are set to wi=1/N, so that the first step simply trains the decision rule on the data in the usual manner. For each successive iteration m=2, 3, . . . , M the observation weights are individually modified and the decision rule is reapplied to the weighted observations. At step m, those observations that were misclassified by the decision rule Gm−1(x) induced at the previous step have their weights increased, whereas the weights are decreased for those that were classified correctly. Thus as iterations proceed, observations that are difficult to correctly classify receive ever-increasing influence. Each successive decision rule is thereby forced to concentrate on those training observations that are missed by previous ones in the sequence.
The exemplary boosting algorithm is summarized as follows:












1. Initialize the observation weights wi = 1/N, i = 1, 2, . . . , N.


2. For m = 1 to M:









(a) Fit a decision rule Gm(X) to the training set using weights wi.



(b) Compute



 













  
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(c) Compute αm=log((1−errm)/errm).



 







  
    
      
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In one embodiment in accordance with this algorithm, each object is, in fact, a factor. Furthermore, in the algorithm, the current decision rule Gm(x) is induced on the weighted observations at line 2a. The resulting weighted error rate is computed at line 2b. Line 2c calculates the weight αm given to Gm(x) in producing the final classifier G(x) (line 3). The individual weights of each of the observations are updated for the next iteration at line 2d. Observations misclassified by Gm(x) have their weights scaled by a factor exp(αm), increasing their relative influence for inducing the next classifier Gm+1(x) in the sequence. In some embodiments, modifications of the Freund and Schapire, 1997, Journal of Computer and System Sciences 55, pp. 119-139, boosting methods are used. See, for example, Hasti et al., The Elements of Statistical Learning, 2001, Springer, New York, Chapter 10, which is hereby incorporated by reference in its entirety. For example, in some embodiments, feature preselection is performed using a technique such as the nonparametric scoring methods of Park et al., 2002, Pac. Symp. Biocomput. 6, 52-63, which is hereby incorporated by reference in its entirety. Feature preselection is a form of dimensionality reduction in which the genes that discriminate between classifications the best are selected for use in the classifier. Then, the LogitBoost procedure introduced by Friedman et al., 2000, Ann Stat 28, 337-407 is used rather than the boosting procedure of Freund and Schapire. In some embodiments, the boosting and other classification methods of Ben-Dor et al., 2000, Journal of Computational Biology 7, 559-583, hereby incorporated by reference in its entirety, are used in the present invention. In some embodiments, the boosting and other classification methods of Freund and Schapire, 1997, Journal of Computer and System Sciences 55, 119-139, hereby incorporated by reference in its entirety, are used.
In the random subspace method, decision rules are constructed in random subspaces of the data feature space. These decision rules are usually combined by simple majority voting in the final decision rule. See, for example, Ho, “The Random subspace method for constructing decision forests,” IEEE Trans Pattern Analysis and Machine Intelligence, 1998; 20(8): 832-844, which is hereby incorporated by reference in its entirety.
5.5.4 Multiple Additive Regression Trees
Multiple additive regression trees (MART) represents another way to construct a decision rule that can be used in the present invention. A generic algorithm for MART is:
















  
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2. For m = 1 to M:









(a) For I = 1, 2, . . . , N compute



 











  
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(b) Fit a regression tree to the targets rim giving terminal regions







Rjm, j   = 1, 2, . . . , Jm.









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Specific algorithms are obtained by inserting different loss criteria L(y,f(x)). The first line of the algorithm initializes to the optimal constant model, which is just a single terminal node tree. The components of the negative gradient computed in line 2(a) are referred to as generalized pseudo residuals, r. Gradients for commonly used loss functions are summarized in Table 10.2, of Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, p. 321, which is hereby incorporated by reference. The algorithm for classification is similar and is described in Hastie et al., Chapter 10, which is hereby incorporated by reference in its entirety. Tuning parameters associated with the MART procedure are the number of iterations M and the sizes of each of the constituent trees Jm, m=1, 2, . . . , M.
5.5.5 Decision Rules Derived by Regression
In some embodiments, a decision rule used to classify subjects is built using regression. In such embodiments, the decision rule can be characterized as a regression classifier, preferably a logistic regression classifier. Such a regression classifier includes a coefficient for each of the biomarkers (e.g., a feature for each such biomarker) used to construct the classifier. In such embodiments, the coefficients for the regression classifier are computed using, for example, a maximum likelihood approach. In such a computation, the features for the biomarkers (e.g., RT-PCR, microarray data) is used. In particular embodiments, molecular marker data from only two trait subgroups is used (e.g., trait subgroup a: will acquire sepsis in a defined time period and trait subgroup b: will not acquire sepsis in a defined time period) and the dependent variable is absence or presence of a particular trait in the subjects for which biomarker data is available.
In another specific embodiment, the training population comprises a plurality of trait subgroups (e.g., three or more trait subgroups, four or more specific trait subgroups, etc.). These multiple trait subgroups can correspond to discrete stages in the phenotypic progression from healthy, to SIRS, to sepsis, to more advanced stages of sepsis in a training population. In this specific embodiment, a generalization of the logistic regression model that handles multicategory responses can be used to develop a decision that discriminates between the various trait subgroups found in the training population. For example, measured data for selected molecular markers can be applied to any of the multi-category logit models described in Agresti, An Introduction to Categorical Data Analysis, 1996, John Wiley & Sons, Inc., New York, Chapter 8, hereby incorporated by reference in its entirety, in order to develop a classifier capable of discriminating between any of a plurality of trait subgroups represented in a training population.
5.5.6 Neural Networks
In some embodiments, the feature data measured for select biomarkers of the present invention (e.g., RT-PCR data, mass spectrometry data, microarray data) can be used to train a neural network. A neural network is a two-stage regression or classification decision rule. A neural network has a layered structure that includes a layer of input units (and the bias) connected by a layer of weights to a layer of output units. For regression, the layer of output units typically includes just one output unit. However, neural networks can handle multiple quantitative responses in a seamless fashion.
In multilayer neural networks, there are input units (input layer), hidden units (hidden layer), and output units (output layer). There is, furthermore, a single bias unit that is connected to each unit other than the input units. Neural networks are described in Duda et al., 2001, Pattern Classification, Second Edition, John Wiley & Sons, Inc., New York; and Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, each of which is hereby incorporated by reference in its entirety. Neural networks are also described in Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman & Hall/CRC; and Mount, 2001, Bioinformatics: sequence and genome analysis, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York, each of which is hereby incorporated by reference in its entirety. What is disclosed below is some exemplary forms of neural networks.
The basic approach to the use of neural networks is to start with an untrained network, present a training pattern to the input layer, and to pass signals through the net and determine the output at the output layer. These outputs are then compared to the target values; any difference corresponds to an error. This error or criterion function is some scalar function of the weights and is minimized when the network outputs match the desired outputs. Thus, the weights are adjusted to reduce this measure of error. For regression, this error can be sum-of-squared errors. For classification, this error can be either squared error or cross-entropy (deviation). See, e.g., Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, which is hereby incorporated by reference in its entirety.
Three commonly used training protocols are stochastic, batch, and on-line. In stochastic training, patterns are chosen randomly from the training set and the network weights are updated for each pattern presentation. Multilayer nonlinear networks trained by gradient descent methods such as stochastic back-propagation perform a maximum-likelihood estimation of the weight values in the classifier defined by the network topology. In batch training, all patterns are presented to the network before learning takes place. Typically, in batch training, several passes are made through the training data. In online training, each pattern is presented once and only once to the net.
In some embodiments, consideration is given to starting values for weights. If the weights are near zero, then the operative part of the sigmoid commonly used in the hidden layer of a neural network (see, e.g., Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, hereby incorporated by reference) is roughly linear, and hence the neural network collapses into an approximately linear classifier. In some embodiments, starting values for weights are chosen to be random values near zero. Hence the classifier starts out nearly linear, and becomes nonlinear as the weights increase. Individual units localize to directions and introduce nonlinearities where needed. Use of exact zero weights leads to zero derivatives and perfect symmetry, and the algorithm never moves. Alternatively, starting with large weights often leads to poor solutions.
Since the scaling of inputs determines the effective scaling of weights in the bottom layer, it can have a large effect on the quality of the final solution. Thus, in some embodiments, at the outset all expression values are standardized to have mean zero and a standard deviation of one. This ensures all inputs are treated equally in the regularization process, and allows one to choose a meaningful range for the random starting weights. With standardization inputs, it is typical to take random uniform weights over the range [−0.7, +0.7].
A recurrent problem in the use of three-layer networks is the optimal number of hidden units to use in the network. The number of inputs and outputs of a three-layer network are determined by the problem to be solved. In the present invention, the number of inputs for a given neural network will equal the number of biomarkers selected from the training population. The number of output for the neural network will typically be just one. However, in some embodiments more than one output is used so that more than just two states can be defined by the network. For example, a multi-output neural network can be used to discriminate between, healthy phenotypes, various stages of SIRS, and/or various stages of sepsis. If too many hidden units are used in a neural network, the network will have too many degrees of freedom and is trained too long, there is a danger that the network will overfit the data. If there are too few hidden units, the training set cannot be learned. Generally speaking, however, it is better to have too many hidden units than too few. With too few hidden units, the classifier might not have enough flexibility to capture the nonlinearities in the date; with too many hidden units, the extra weight can be shrunk towards zero if appropriate regularization or pruning, as described below, is used. In typical embodiments, the number of hidden units is somewhere in the range of 5 to 100, with the number increasing with the number of inputs and number of training cases.
One general approach to determining the number of hidden units to use is to apply a regularization approach. In the regularization approach, a new criterion function is constructed that depends not only on the classical training error, but also on classifier complexity. Specifically, the new criterion function penalizes highly complex classifiers; searching for the minimum in this criterion is to balance error on the training set with error on the training set plus a regularization term, which expresses constraints or desirable properties of solutions:

J=Jpat+λJreg.

The parameter λ is adjusted to impose the regularization more or less strongly. In other words, larger values for λ will tend to shrink weights towards zero: typically cross-validation with a validation set is used to estimate λ. This validation set can be obtained by setting aside a random subset of the training population. Other forms of penalty have been proposed, for example the weight elimination penalty (see, e.g., Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, hereby incorporated by reference).

Another approach to determine the number of hidden units to use is to eliminate—prune—weights that are least needed. In one approach, the weights with the smallest magnitude are eliminated (set to zero). Such magnitude-based pruning can work, but is nonoptimal; sometimes weights with small magnitudes are important for learning and training data. In some embodiments, rather than using a magnitude-based pruning approach, Wald statistics are computed. The fundamental idea in Wald Statistics is that they can be used to estimate the importance of a hidden unit (weight) in a classifier. Then, hidden units having the least importance are eliminated (by setting their input and output weights to zero). Two algorithms in this regard are the Optimal Brain Damage (OBD) and the Optimal Brain Surgeon (OBS) algorithms that use second-order approximation to predict how the training error depends upon a weight, and eliminate the weight that leads to the smallest increase in training error.
Optimal Brain Damage and Optimal Brain Surgeon share the same basic approach of training a network to local minimum error at weight w, and then pruning a weight that leads to the smallest increase in the training error. The predicted functional increase in the error for a change in full weight vector δw is:




  
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The Optimal Brain Damage method is computationally simpler because the calculation of the inverse Hessian matrix in line 3 is particularly simple for a diagonal matrix. The above algorithm terminates when the error is greater than a criterion initialized to be θ. Another approach is to change line 6 to terminate when the change in J(w) due to elimination of a weight is greater than some criterion value. In some embodiments, the back-propagation neural network See, for example Abdi, 1994, “A neural network primer,” J. Biol System. 2, 247-283, hereby incorporated by reference in its entirety.
5.5.7 Clustering
In some embodiments, features for select biomarkers of the present invention are used to cluster a training set. For example, consider the case in which ten features (corresponding to ten biomarkers) described in the present invention is used. Each member m of the training population will have feature values (e.g. expression values) for each of the ten biomarkers. Such values from a member m in the training population define the vector:

X1m X2m X3m X4m X5m X6m X7m X8m X9m X10m 

where X1m is the expression level of the ith biomarker in organism m. If there are m organisms in the training set, selection of i biomarkers will define m vectors. Note that the methods of the present invention do not require that each the expression value of every single biomarker used in the vectors be represented in every single vector m. In other words, data from a subject in which one of the ith biomarkers is not found can still be used for clustering. In such instances, the missing expression value is assigned either a “zero” or some other normalized value. In some embodiments, prior to clustering, the feature values are normalized to have a mean value of zero and unit variance.

Those members of the training population that exhibit similar expression patterns across the training group will tend to cluster together. A particular combination of genes of the present invention is considered to be a good classifier in this aspect of the invention when the vectors cluster into the trait groups found in the training population. For instance, if the training population includes class a: subjects that do not develop sepsis, and class b: subjects that develop sepsis, an ideal clustering classifier will cluster the population into two groups, with one cluster group uniquely representing class a and the other cluster group uniquely representing class b.
Clustering is described on pages 211-256 of Duda and Hart, Pattern Classification and Scene Analysis, 1973, John Wiley & Sons, Inc., New York, (hereinafter “Duda 1973”) which is hereby incorporated by reference in its entirety. As described in Section 6.7 of Duda 1973, the clustering problem is described as one of finding natural groupings in a dataset. To identify natural groupings, two issues are addressed. First, a way to measure similarity (or dissimilarity) between two samples is determined. This metric (similarity measure) is used to ensure that the samples in one cluster are more like one another than they are to samples in other clusters. Second, a mechanism for partitioning the data into clusters using the similarity measure is determined.
Similarity measures are discussed in Section 6.7 of Duda 1973, where it is stated that one way to begin a clustering investigation is to define a distance function and to compute the matrix of distances between all pairs of samples in a dataset. If distance is a good measure of similarity, then the distance between samples in the same cluster will be significantly less than the distance between samples in different clusters. However, as stated on page 215 of Duda 1973, clustering does not require the use of a distance metric. For example, a nonmetric similarity function s(x, x′) can be used to compare two vectors x and x′. Conventionally, s(x, x′) is a symmetric function whose value is large when x and x′ are somehow “similar”. An example of a nonmetric similarity function s(x, x′) is provided on page 216 of Duda 1973.
Once a method for measuring “similarity” or “dissimilarity” between points in a dataset has been selected, clustering requires a criterion function that measures the clustering quality of any partition of the data. Partitions of the data set that extremize the criterion function are used to cluster the data. See page 217 of Duda 1973. Criterion functions are discussed in Section 6.8 of Duda 1973.
More recently, Duda et al., Pattern Classification, 2nd edition, John Wiley & Sons, Inc. New York, has been published. Pages 537-563 describe clustering in detail. More information on clustering techniques can be found in Kaufman and Rousseeuw, 1990, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York, N.Y.; Everitt, 1993, Cluster analysis (3d ed.), Wiley, New York, N.Y.; and Backer, 1995, Computer-Assisted Reasoning in Cluster Analysis, Prentice Hall, Upper Saddle River, N.J. Particular exemplary clustering techniques that can be used in the present invention include, but are not limited to, hierarchical clustering (agglomerative clustering using nearest-neighbor algorithm, farthest-neighbor algorithm, the average linkage algorithm, the centroid algorithm, or the sum-of-squares algorithm), k-means clustering, fuzzy k-means clustering algorithm, and Jarvis-Patrick clustering.
5.5.8 Principle Component Analysis
Principal component analysis (PCA) has been proposed to analyze gene expression data. More generally, PCA can be used to analyze feature value data of biomarkers of the present invention in order to construct a decision rule that discriminates converters from nonconverters. Principal component analysis is a classical technique to reduce the dimensionality of a data set by transforming the data to a new set of variable (principal components) that summarize the features of the data. See, for example, Jolliffe, 1986, Principal Component Analysis, Springer, New York, which is hereby incorporated by reference. Principal component analysis is also described in Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman & Hall/CRC, which is hereby incorporated by reference. What follows is non-limiting examples of principal components analysis.
Principal components (PCs) are uncorrelated and are ordered such that the kth PC has the kth largest variance among PCs. The kth PC can be interpreted as the direction that maximizes the variation of the projections of the data points such that it is orthogonal to the first k−1 PCs. The first few PCs capture most of the variation in the data set. In contrast, the last few PCs are often assumed to capture only the residual ‘noise’ in the data.
PCA can also be used to create a classifier in accordance with the present invention. In such an approach, vectors for the select biomarkers of the present invention can be constructed in the same manner described for clustering above. In fact, the set of vectors, where each vector represents the feature values (e.g., abundance values) for the select genes from a particular member of the training population, can be viewed as a matrix. In some embodiments, this matrix is represented in a Free-Wilson method of qualitative binary description of monomers (Kubinyi, 1990, 3D QSAR in drug design theory methods and applications, Pergamon Press, Oxford, pp 589-638), and distributed in a maximally compressed space using PCA so that the first principal component (PC) captures the largest amount of variance information possible, the second principal component (PC) captures the second largest amount of all variance information, and so forth until all variance information in the matrix has been considered.
Then, each of the vectors (where each vector represents a member of the training population) is plotted. Many different types of plots are possible. In some embodiments, a one-dimensional plot is made. In this one-dimensional plot, the value for the first principal component from each of the members of the training population is plotted. In this form of plot, the expectation is that members of a first subgroup (e.g. those subjects that do not develop sepsis in a determined time period) will cluster in one range of first principal component values and members of a second subgroup (e.g., those subjects that develop sepsis in a determined time period) will cluster in a second range of first principal component values.
In one ideal example, the training population comprises two subgroups: “sepsis” and “SIRS.” The first principal component is computed using the molecular marker expression values for the select biomarkers of the present invention across the entire training population data set. Then, each member of the training set is plotted as a function of the value for the first principal component. In this ideal example, those members of the training population in which the first principal component is positive are the “responders” and those members of the training population in which the first principal component is negative are “subjects with sepsis.”
In some embodiments, the members of the training population are plotted against more than one principal component. For example, in some embodiments, the members of the training population are plotted on a two-dimensional plot in which the first dimension is the first principal component and the second dimension is the second principal component. In such a two-dimensional plot, the expectation is that members of each subgroup represented in the training population will cluster into discrete groups. For example, a first cluster of members in the two-dimensional plot will represent subjects that develop sepsis in a given time period and a second cluster of members in the two-dimensional plot will represent subjects that do not develop sepsis in a given time period.
5.5.9 Nearest Neighbor Analysis
Nearest neighbor classifiers are memory-based and require no classifier to be fit. Given a query point x0, the k training points x(r), r, . . . , k closest in distance to x0 are identified and then the point x0 is classified using the k nearest neighbors. Ties can be broken at random. In some embodiments, Euclidean distance in feature space is used to determine distance as:

d(i)=∥x(i)−x0∥.

Typically, when the nearest neighbor algorithm is used, the expression data used to compute the linear discriminant is standardized to have mean zero and variance 1. In the present invention, the members of the training population are randomly divided into a training set and a test set. For example, in one embodiment, two thirds of the members of the training population are placed in the training set and one third of the members of the training population are placed in the test set. A select combination of biomarkers of the present invention represents the feature space into which members of the test set are plotted. Next, the ability of the training set to correctly characterize the members of the test set is computed. In some embodiments, nearest neighbor computation is performed several times for a given combination of biomarkers of the present invention. In each iteration of the computation, the members of the training population are randomly assigned to the training set and the test set. Then, the quality of the combination of biomarkers is taken as the average of each such iteration of the nearest neighbor computation.

The nearest neighbor rule can be refined to deal with issues of unequal class priors, differential misclassification costs, and feature selection. Many of these refinements involve some form of weighted voting for the neighbors. For more information on nearest neighbor analysis, see Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York, each of which is hereby incorporated by reference in its entirety.
5.5.10 Linear Discriminant Analysis
Linear discriminant analysis (LDA) attempts to classify a subject into one of two categories based on certain object properties. In other words, LDA tests whether object attributes measured in an experiment predict categorization of the objects. LDA typically requires continuous independent variables and a dichotomous categorical dependent variable. In the present invention, the feature values for the select combinations of biomarkers of the present invention across a subset of the training population serve as the requisite continuous independent variables. The trait subgroup classification of each of the members of the training population serves as the dichotomous categorical dependent variable.
LDA seeks the linear combination of variables that maximizes the ratio of between-group variance and within-group variance by using the grouping information. Implicitly, the linear weights used by LDA depend on how the feature values of a molecular marker across the training set separates in the two groups (e.g., a group a that develops sepsis during a defined time period and a group b that does not develop sepsis during a defined time period) and how these feature values correlate with the feature values of other biomarkers. In some embodiments, LDA is applied to the data matrix of the N members in the training sample by K biomarkers in a combination of biomarkers described in the present invention. Then, the linear discriminant of each member of the training population is plotted. Ideally, those members of the training population representing a first subgroup (e.g. those subjects that develop sepsis in a defined time period) will cluster into one range of linear discriminant values (e.g., negative) and those member of the training population representing a second subgroup (e.g. those subjects that will not develop sepsis in a defined time period) will cluster into a second range of linear discriminant values (e.g., positive). The LDA is considered more successful when the separation between the clusters of discriminant values is larger. For more information on linear discriminant analysis, see Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York; and Venables & Ripley, 1997, Modern Applied Statistics with s-plus, Springer, New York, each of which is hereby incorporated by reference in its entirety.
5.5.11 Quadratic Discriminant Analysis
Quadratic discriminant analysis (QDA) takes the same input parameters and returns the same results as LDA. QDA uses quadratic equations, rather than linear equations, to produce results. LDA and QDA are interchangeable, and which to use is a matter of preference and/or availability of software to support the analysis. Logistic regression takes the same input parameters and returns the same results as LDA and QDA.
5.5.12 Support Vector Machines
In some embodiments of the present invention, support vector machines (SVMs) are used to classify subjects using feature values of the genes described in the present invention. SVMs are a relatively new type of learning algorithm. See, for example, Cristianini and Shawe-Taylor, 2000, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge; Boser et al., 1992, “A training algorithm for optimal margin classifiers,” in Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, ACM Press, Pittsburgh, Pa., pp. 142-152; Vapnik, 1998, Statistical Learning Theory, Wiley, New York; Mount, 2001, Bioinformatics: sequence and genome analysis, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc.; and Hastie, 2001, The Elements of Statistical Learning, Springer, New York; and Furey et al., 2000, Bioinformatics 16, 906-914, each of which is hereby incorporated by reference in its entirety. When used for classification, SVMs separate a given set of binary labeled data training data with a hyper-plane that is maximally distance from them. For cases in which no linear separation is possible, SVMs can work in combination with the technique of ‘kernels’, which automatically realizes a non-linear mapping to a feature space. The hyper-plane found by the SVM in feature space corresponds to a non-linear decision boundary in the input space.
In one approach, when a SVM is used, the feature data is standardized to have mean zero and unit variance and the members of a training population are randomly divided into a training set and a test set. For example, in one embodiment, two thirds of the members of the training population are placed in the training set and one third of the members of the training population are placed in the test set. The expression values for a combination of genes described in the present invention is used to train the SVM. Then the ability for the trained SVM to correctly classify members in the test set is determined. In some embodiments, this computation is performed several times for a given combination of molecular markers. In each iteration of the computation, the members of the training population are randomly assigned to the training set and the test set. Then, the quality of the combination of biomarkers is taken as the average of each such iteration of the SVM computation.
5.5.13 Evolutionary Methods
Inspired by the process of biological evolution, evolutionary methods of decision rule design employ a stochastic search for an decision rule. In broad overview, such methods create several decision rules—a population—from a combination of biomarkers described in the present invention. Each decision rule varies somewhat from the other. Next, the decision rules are scored on feature data across the training population. In keeping with the analogy with biological evolution, the resulting (scalar) score is sometimes called the fitness. The decision rules are ranked according to their score and the best decision rules are retained (some portion of the total population of decision rules). Again, in keeping with biological terminology, this is called survival of the fittest. The decision rules are stochastically altered in the next generation—the children or offspring. Some offspring decision rules will have higher scores than their parent in the previous generation, some will have lower scores. The overall process is then repeated for the subsequent generation: the decision rules are scored and the best ones are retained, randomly altered to give yet another generation, and so on. In part, because of the ranking, each generation has, on average, a slightly higher score than the previous one. The process is halted when the single best decision rule in a generation has a score that exceeds a desired criterion value. More information on evolutionary methods is found in, for example, Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc.
5.5.14 Other Data Analysis Algorithms
The data analysis algorithms described above are merely examples of the types of methods that can be used to construct a decision rule for discriminating converters from nonconverters. Moreover, combinations of the techniques described above can be used. Some combinations, such as the use of the combination of decision trees and boosting, have been described. However, many other combinations are possible. In addition, in other techniques in the art such as Projection Pursuit and Weighted Voting can be used to construct decision rules.
5.6 Biomarkers
In specific embodiments, the present invention provides biomarkers that are useful in diagnosing or predicting sepsis and/or its stages of progression in a subject. While the methods of the present invention may use an unbiased approach to identifying predictive biomarkers, it will be clear to the artisan that specific groups of biomarkers associated with physiological responses or with various signaling pathways may be the subject of particular attention. This is particularly the case where biomarkers from a biological sample are contacted with an array that can be used to measure the amount of various biomarkers through direct and specific interaction with the biomarkers (e.g., an antibody array or a nucleic acid array). In this case, the choice of the components of the array may be based on a suggestion that a particular pathway is relevant to the determination of the status of sepsis or SIRS in a subject. The indication that a particular biomarker has a feature that is predictive or diagnostic of sepsis or SIRS may give rise to an expectation that other biomarkers that are physiologically regulated in a concerted fashion likewise may provide a predictive or diagnostic feature. The artisan will appreciate, however, that such an expectation may not be realized because of the complexity of biological systems. For example, if the amount of a specific mRNA biomarker were a predictive feature, a concerted change in mRNA expression of another biomarker might not be measurable, if the expression of the other biomarker was regulated at a post-translational level. Further, the mRNA expression level of a biomarker may be affected by multiple converging pathways that may or may not be involved in a physiological response to sepsis.
Biomarkers can be obtained from any biological sample, which can be, by way of example and not of limitation, whole blood, plasma, saliva, serum, red blood cells, platelets, neutrophils, eosinophils, basophils, lymphocytes, monocytes, urine, cerebral spinal fluid, sputum, stool, cells and cellular extracts, or other biological fluid sample, tissue sample or tissue biopsy from a host or subject. The precise biological sample that is taken from the subject may vary, but the sampling preferably is minimally invasive and is easily performed by conventional techniques.
Measurement of a phenotypic change may be carried out by any conventional technique. Measurement of body temperature, respiration rate, pulse, blood pressure, or other physiological parameters can be achieved via clinical observation and measurement. Measurements of biomarker molecules may include, for example, measurements that indicate the presence, concentration, expression level, or any other value associated with a biomarker molecule. The form of detection of biomarker molecules typically depends on the method used to form a profile of these biomarkers from a biological sample. See Section 5.4, above, and Tables 30, I, J, K, L, and M below.
In a particular embodiment, the biomarker profile comprises at least two different biomarkers listed in column four or five of Table 30. The biomarker profile further comprises a respective corresponding feature for the at least two biomarkers. Such biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example amplified nucleic acid, or proteins. Generally, the at least two biomarkers are derived from at least two different genes. In the case where a biomarker in the at least two different biomarkers is listed in column four of Table 30, the biomarker can be, for example, a transcript made by the listed gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein listed in column five of Table 30, or a discriminating fragment of the protein, or an indication of any of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein. In accordance with this embodiment, the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker. Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in Table 30). In one embodiment, such an assay utilizes a nucleic acid microarray.
In a particular embodiment, the biomarker profile comprises at least two different biomarkers that each contain one of the probesets listed in column 2 of Table 30, biomarkers that contain the complement of one of the probesets of Table 30, or biomarkers that contain an amino acid sequence encoded by a gene that either contains one of the probesets of Table 30 or the complement of one of the probesets of Table 30. Such biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example amplified nucleic acid, or proteins. The biomarker profile further comprises a respective corresponding feature for the at least two biomarkers. Generally, the at least two biomarkers are derived from at least two different genes. In the case where a biomarker is based upon a gene that includes the sequence of a probeset listed in Table 30, the biomarker can be, for example, a transcript made by the gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein encoded by a gene that includes a probeset sequence described in Table 30, or a discriminating fragment of the protein, or an indication of any of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein.
In some embodiments the biomarker profile has between 2 and 626 biomarkers listed in Table 30. In some embodiments, the biomarker profile has between 3 and 50 biomarkers listed in Table 30. In some embodiments, the biomarker profile has between 4 and 25 biomarkers listed in Table 30. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table 30. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table 30. In some embodiments, the biomarker profile has at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, or 100 biomarkers listed in Table 30. In some embodiments, each such biomarker is a nucleic acid. In some embodiments, each such biomarker is a protein.
In some embodiments, some of the biomarkers in the biomarker profile are nucleic acids and some of the biomarkers in the biomarker profile are proteins. In some embodiments the biomarker profile has between 2 and 130 biomarkers listed in Table 31. In some embodiments, the biomarker profile has between 3 and 50 biomarkers listed in Table 31. In some embodiments, the biomarker profile has between 4 and 25 biomarkers listed in Table 31. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table 31. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table 30. In some embodiments, the biomarker profile has at least 6, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, or 100 biomarkers listed in Table 31.
In some embodiments the biomarker profile has between 2 and 10 biomarkers listed in Table 33. In some embodiments, the biomarker profile has between 3 and 10 biomarkers listed in Table 32. In some embodiments, the biomarker profile has between 4 and 10 biomarkers listed in Table 32. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table 32. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table 32. In some embodiments, the biomarker profile has at least 6, 7, 8, 9, or 10 biomarkers listed in Table 32. In some embodiments, each such biomarker is a nucleic acid. In some embodiments, each such biomarker is a protein. In some embodiments, some of the biomarkers in the biomarker profile are nucleic acids and some of the biomarkers in the biomarker profile are proteins.
In some embodiments the biomarker profile has between 2 and 10 biomarkers listed in Table 33. In some embodiments, the biomarker profile has between 3 and 10 biomarkers listed in Table 33. In some embodiments, the biomarker profile has between 4 and 10 biomarkers listed in Table 33. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table 33. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table 33. In some embodiments, the biomarker profile has at least 6, 7, 8, 9, or 10 biomarkers listed in Table 33. In some embodiments, each such biomarker is a nucleic acid. In some embodiments, each such biomarker is a protein. In some embodiments, some of the biomarkers in the biomarker profile are nucleic acids and some of the biomarkers in the biomarker profile are proteins.
In some embodiments the biomarker profile has between 2 and 130 biomarkers listed in Table 34. In some embodiments, the biomarker profile has between 3 and 40 biomarkers listed in Table 34. In some embodiments, the biomarker profile has between 4 and 25 biomarkers listed in Table 34. In some embodiments, the biomarker profile has at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, or 40 biomarkers listed in Table 34. In some embodiments, each such biomarker is a nucleic acid. In some embodiments, each such biomarker is a protein. In some embodiments, some of the biomarkers in the biomarker profile are nucleic acids and some of the biomarkers in the biomarker profile are proteins.
In some embodiments the biomarker profile has between 2 and 7 biomarkers listed in Table 36. In some embodiments, the biomarker profile has between 3 and 6 biomarkers listed in Table 36. In some embodiments, the biomarker profile has between 4 and 7 biomarkers listed in Table 36. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table 36. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table 36. In some embodiments, the biomarker profile has at least 6, 7, 8, 9, or 10 biomarkers listed in Table 36. In some embodiments, each such biomarker is a nucleic acid. In some embodiments, each such biomarker is a protein. In some embodiments, some of the biomarkers in the biomarker profile are nucleic acids and some of the biomarkers in the biomarker profile are proteins.
In some embodiments the biomarker profile has between 2 and 53 biomarkers listed in Table I. In some embodiments, the biomarker profile has between 3 and 50 biomarkers listed in Table I. In some embodiments, the biomarker profile has between 4 and 25 biomarkers listed in Table I. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table I. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table I. In some embodiments, the biomarker profile has at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, or 53 biomarkers listed in Table I. In some embodiments, each of the biomarkers in the biomarker profile is a nucleic acid in Table I. In some embodiments, each of the biomarkers in the biomarker profile is a protein in Table I. In some embodiments, some of the biomarkers in a biomarker profile are proteins in Table I and some of the biomarkers in the same biomarker profile are nucleic acids in Table I.
In some embodiments the biomarker profile has between 2 and 44 biomarkers listed in Table J. In some embodiments, the biomarker profile has between 3 and 44 biomarkers listed in Table J. In some embodiments, the biomarker profile has between 4 and 25 biomarkers listed in Table J. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table J. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table J. In some embodiments, the biomarker profile has at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, or 43 biomarkers listed in Table J. In some embodiments, each of the biomarkers in the biomarker profile is a nucleic acid in Table J. In some embodiments, each of the biomarkers in the biomarker profile is a protein in Table J. In some embodiments, some of the biomarkers in a biomarker profile are proteins in Table J and some of the biomarkers in the same biomarker profile are nucleic acids in Table J.
In some embodiments the biomarker profile has between 2 and 10 biomarkers listed in Table K. In some embodiments, the biomarker profile has between 3 and 10 biomarkers listed in Table K. In some embodiments, the biomarker profile has between 4 and 10 biomarkers listed in Table K. In some embodiments, the biomarker profile has at least 3 biomarkers listed in Table K. In some embodiments, the biomarker profile has at least 4 biomarkers listed in Table K. In some embodiments, the biomarker profile has at least 5, 6, 7, 8, or 9 biomarkers listed in Table K. In some embodiments, each of the biomarkers in the biomarker profile is a nucleic acid in Table K. In some embodiments, each of the biomarkers in the biomarker profile is a protein in Table K. In some embodiments, some of the biomarkers in a biomarker profile are proteins in Table K and some of the biomarkers in the same biomarker profile are nucleic acids in Table K.
5.6.1 Isolation of Useful Biomarkers
The biomarkers of the present invention may, for example, be used to raise antibodies that bind the biomarker if it is a protein (using methods described in Section 5.4.2, supra, or any method well known to those of skill in the art), or they may be used to develop a specific oligonucleotide probe, if it is a nucleic acid, for example, using a method described in Section 5.4.1, supra, or any method well known to those of skill in the art. The skilled artisan will readily appreciate that useful features can be further characterized to determine the molecular structure of the biomarker. Methods for characterizing biomarkers in this fashion are well-known in the art and include X-ray crystallography, high-resolution mass spectrometry, infrared spectrometry, ultraviolet spectrometry and nuclear magnetic resonance. Methods for determining the nucleotide sequence of nucleic acid biomarkers, the amino acid sequence of polypeptide biomarkers, and the composition and sequence of carbohydrate biomarkers also are well-known in the art.
5.7 Application of the Present Invention to SIRS Subjects
In one embodiment, the presently described methods are used to screen SIRS subjects who are at risk for developing sepsis. A biological sample is taken from a SIRS-positive subject and used to construct a biomarker profile. The biomarker profile is then evaluated to determine whether the feature values of the biomarker profile satisfy a first value set associated with a particular decision rule. This evaluation classifies the subject as a converter or a nonconverter. A treatment regimen may then be initiated to forestall or prevent the progression of sepsis when the subject is classified as a converter.
5.8 Application of the Present Invention to Stages of Sepsis
In one embodiment, the presently described methods are used to screen subjects who are particularly at risk for developing a certain stage of sepsis. A biological sample is taken from a subject and used to construct a biomarker profile. The biomarker profile is then evaluated to determine whether the feature values of the biomarker profile satisfy a first value set associated with a particular decision rule. This evaluation classifies the subject as having or not having a particular stage of sepsis. A treatment regimen may then be initiated to treat the specific stage of sepsis. In some embodiments, the stage of sepsis is for example, onset of sepsis, severe sepsis, septic shock, or multiple organ dysfunction.
5.9 Exemplary Embodiments
In some embodiments of the present invention, a biomarker profile is obtained using a biological sample from a test subject, particularly a subject at risk of developing sepsis, having sepsis, or suspected of having sepsis. The biomarker profile in such embodiments is evaluated. This evaluation can be made, for example, by applying a decision rule to the test subject. The decision rule can, for example, be or have been constructed based upon the biomarker profiles obtained from subjects in the training population. The training population, in one embodiment, includes (a) subjects that had SIRS and were then diagnosed as septic during an observation time period as well as (b) subjects that had SIRS and were not diagnosed as septic during an observation time period. If the biomarker profile from the test subject contains appropriately characteristic features, then the test subject is diagnosed as having a more likely chance of becoming septic, as being afflicted with sepsis or as being at the particular stage in the progression of sepsis. Various populations of subjects including those who are suffering from SIRS (e.g., SIRS-positive subjects) or those who are suffering from an infection but who are not suffering from SIRS (e.g., SIRS-negative subjects) can serve as training populations. Accordingly, the present invention allows the clinician to distinguish, inter alia, between those subjects who do not have SIRS, those who have SIRS but are not likely to develop sepsis within a given time frame, those who have SIRS and who are at risk of eventually becoming septic, and those who are suffering from a particular stage in the progression of sepsis. For more details on suitable training populations and suitable data collected from such populations, see Section 5.5, above.
5.10 Use of Annotation Data to Identify Discriminating Biomarkers
In some embodiments, data analysis algorithms identify a large set of biomarkers whose features discriminate between converters and nonconverters. For example, in some embodiments, application of a data analysis algorithm to a training population results in the selection of more than 500 biomarkers, more than 1000 biomarkers, or more than 10,000 biomarkers. In some embodiments, further reduction in the number of biomarkers that are deemed to be discriminating is desired. Accordingly, in some embodiments, filtering rules that are complementary to data analysis algorithms (e.g., the data analysis algorithms of Section 5.5) are used to further reduce the list of discriminating biomarkers identified by the data analysis algorithms. Specifically, the list of biomarkers identified by application of one or more data analysis algorithms to the biomarker profile data measured in a training population is further refined by application of annotation data based filtering rules to the list. In such embodiments, those biomarkers in the set of biomarkers identified by the one or more data analysis algorithms that satisfy the one or more applied annotation data based filtering rules remain in the set of discriminating biomarkers. In some instances, those biomarkers in the set of biomarkers identified by the one or more data analysis algorithms that do not satisfy the one or more applied annotation data based filtering rules are removed from the set. In other instances, those biomarkers in the set of biomarkers identified by the one or more data analysis algorithms that do not satisfy the one or more applied annotation data based filtering rules stay in the set and those that satisfy the one or more applied annotation data based filtering rules are removed from the set. In this way, annotation data can be used to reduce the number of biomarkers in the set of discriminating biomarkers identified by the data analysis algorithms.
Annotation data based filtering rules are rules based upon annotation data. Annotation data refers to any type of data that describes a property of a biomarker. An example of annotation data is the identification of biological pathways to which a given biomarker belongs. Another example of annotation data is enzymatic class (e.g., phosphodiesterases, kinases, metalloproteinases, etc.). Still other examples of annotation data include, but are not limited to, protein domain information, enzymatic substrate information, enzymatic reaction information, and protein interaction data. Yet another example of annotation data is disease association, in other words, which disease process a given biomarker has been linked to or otherwise affects. Another form of annotation data is any type of data that associates biomarker expression, other forms of biomarker abundance, and/or biomarker activity, with cellular localization, tissue type localization, and/or cell type localization.
As the name implies, annotation data is used to construct an annotation data based filtering rule. An example of an annotation data based filtering rule is:
Annotation Rule 1:


    
    
        remove all transcription factors from the training set.

Application of this filtering rule to a set of biomarkers will remove all transcription factors from the set.

    
    


Another type of annotation data based filtering rule is:
Annotation Rule 2:


    keep all biomarkers that are enriched for annotation X in a biomarker list.

Application of this filtering rule will only keep those biomarkers in a given list that are enriched (overrepresented) for annotation X in the list. To more fully appreciate this filtering rule, consider an exemplary biomarker set that has been identified by application of a data analysis algorithm (Section 5.5) to biomarker profiles measured using training population data measured in accordance with a technique disclosed in Section 5.4. This exemplary biomarker set has 500 biomarkers. Assume, for in this illustrative example, that the full set of biomarkers in a human consists of 25,000 biomarkers. Here, the 25,000 biomarkers is a population and the 500 biomarker set is the sample. As used here, the term “population” consists of all possible observable biomarkers. The term “sample” is the data that is actually considered. Now, for this example, let X=kinases. Suppose there are 800 known human kinases and further suppose that the set of 500 biomarkers was randomly selected with respect to kinases. Under these circumstances, the list of 500 biomarkers identified by the data analysis algorithms should select about (500/25,000)*800=16 kinases. Since there are, in fact, 50 kinases in the sample, a conclusion can be reached that kinases are indeed enriched in the sample relative to the population.



More formally, in this example, a determination can be made as to whether kinases are enriched in the set of biomarkers identified by the data analysis algorithm (the sample) relative to the population by analysis of the two-way contingency table that describes the observed sample and population:














Kinase













Group
Yes
No
Total
















Population
800
24,200
25,000



Sample
50
450
500










Following Agresti, 1996, An Introduction to Categorical Data Analysis, John Wiley & Sons, New York, which is hereby incorporated by reference in its entirety, this two-way contingency table can be analyzed by treating each row as an independent bionomial variable. In such instances, the true difference in proportions, termed π1−π2, compares the probabilities in the two rows. This difference falls between −1 and +1. It equals zero when π1=π2; that is, when the selection of kinases in the sample from the population is independent of the kinase annotation. Of the N1=25,000 biomarkers in the population, 800 are kinases, a proportion of p1=800/25,000=0.032. Of the N2=500 biomarkers in the sample identified using a data analysis algorithm, 50 are kinases, a proportion p2 of 5/500=0.10. The sample difference of proportions is 0.032−0.10=−0.068. In accordance with Agresti, when the counts in the two rows are independent binomial samples, the estimated standard error of p1−p2 is:




  
    
      σ
      ⋒
    
    ⁡
    
      (
      
        
          p
          1
        
        -
        
          p
          2
        
      
      )
    
  
  =
  
    
      
        
          
            p
            1
          
          ⁡
          
            (
            
              1
              -
              
                p
                1
              
            
            )
          
        
        
          N
          1
        
      
      +
      
        
          
            p
            2
          
          ⁡
          
            (
            
              1
              -
              
                p
                2
              
            
            )
          
        
        
          N
          2
        
      
    
  




where N1 and N2 are the samples sizes for the population and the sample selected by data analysis algorithm, respectively. The standard error decreases, and hence the estimate of π1−π2 improves, as the sample sizes increase. A large-sample (100(1−α))% confidence interval for π1−π2 is

(p1+p2)±zα/2=z0.025=1.96

Thus, for this example, the estimated error is





  
    
      
        
          0.032
          ⁢
          
            (
            
              1
              -
              0.032
            
            )
          
        
        
          25
          ⁢
          
            ,
          
          ⁢
          000
        
      
      +
      
        
          0.10
          ⁢
          
            (
            
              1
              -
              0.10
            
            )
          
        
        500
      
    
  
  =
  0.013




and a 95% confidence interval for the true difference π1−π2 is −0.068±1.96(0.013), or −0.068±0.025. Since the 95% confidence interval contains only negative values, the conclusion can be reached that kinases are enriched in the sample (the biomarker set produced by the data analysis algorithm) relative to the population of 25,000 biomarkers.

The two-way contingency table in the example above can be analysed using methods known in the art other than the one disclosed above. For example, the chi-square test for independence and/or Fisher's exact test can be used to test the null hypothesis that the row and column classification variables of the two-way contingency table are independent.
The term “X” in annotation rule 2 can be any form of annotation data. In one embodiment, “X” is any biological pathway. As such the annotation data based filtering rule has the following form.
Annotation Rule 3:
Select all biomarkers that are in any biological pathway that is enriched in the biomarker list.
To determine whether a particular biological pathway is enriched, the number of biomarkers in a particular biological pathway in the sample is compared with the number of biomarkers that are in the particular biological pathway in the population using, for example, the two-way contingency table analysis described above, or other techniques known in the art. If the biological pathway is enriched in the sample, then all biomarkers in the sample that are also in the biological pathway are retained for further analysis, in accordance with the annotation data based filtering rule.
An example of enrichment, in which it was shown that the proportion of kinases in the sample was greater than the proportion of kinases in the population across its entire 95% confidence interval has been given. In one embodiment, biomarkers having a given annotation are considered enriched in the sample relative to the population when the proportion of biomarkers having the annotation in the sample is greater than the proportion of biomarkers having the annotation in the population across its entire 95% degree confidence interval as determined by two-way contingency table analysis. In another embodiment, biomarkers having a given annotation are considered enriched in the sample relative to the population if a p value as determined by the Fisher exact test, Chi-square test, or relative algorithms is 0.05 or less, 0.005 or less or 0.0005 or less.
Another form of annotation data based filtering rule has the following form:
Annotation Rule 4:
Select all biomarkers that are in biological pathway X.
In an embodiment, a set of biomarkers is determined using a data analysis algorithm. Exemplary data analysis algorithms are disclosed in Section 5.5. In addition, Section 6 describes certain tests that can also serve as data analysis algorithms. These tests include, but are not limited to a Wilcoxon test and the like with a statistically significant p value (e.g., 0.05 or less, 0.04, or less, etc.), and/or a requirement that a biomarker exhibit a mean differential abundance between biological samples obtained from converters and biological samples obtained from nonconverters in a training population. Upon application of the data analysis algorithm, a set of biomarkers that discriminates between converters and nonconverters is determined. Next, an annotation rule, for example annotation rule 4, is applied to the set of discriminating biomarkers in order to further reduce the set of biomarkers. Those of skill in the art will appreciate that the order in which these rules are applied is generally not important. For example, annotation rule 4 can be applied first and then certain data analysis algorithms can be applied, or vice versa. In some embodiments, biomarkers ultimately deemed as discriminating between converters and nonconverters satisfy each of the following criteria: (i) a p value of 0.05 or less (p<0.05) as determined from a Wilcoxon adjusted test using static (single time point) data; (ii) a mean-fold change of 1.2 or greater between converters and nonconverters across the training set using static (single time point data), and (iii) present in a specific biological pathway. See also, Section 6.7, infra, for a detailed example. In this example, there is no requirement that members of the pathway are enriched in the set of biomarkers identified by the data analysis algorithms. Furthermore, it is noted that criteria (i) and (ii) are forms of data analysis algorithms and criterion (iii) is a annotation data based filtering rule.
In another embodiment, once a list of discriminating biomarkers is identified, the biomarkers can then be used to determine the identity of the particular biological pathways from which the discriminating biomarkers are implicated. In certain embodiments, annotation data-based filtering rules are applied to the list of discriminating biomarkers identified by the methods of the present invention (e.g., the methods described in Sections 5.4, 5.5 and 6). Such annotation data-based filtering rules identify the particular biological pathway or pathways that are enriched in the discriminating list of biomarkers identified by the data analysis algorithms. In an exemplary embodiment of the invention, DAVID 2.0 software, available at apps1.niaid.nih.gov/david/, is used to identify and apply such annotation data-based filtering rules to the set of biomarkers identified by the data analysis algorithms in order to identify pathways that are enriched in the set. In some embodiments, those biomarkers that are in an enriched biological pathway are selected for use as discriminating biomarkers in the kits of the present invention.
In some embodiments of the present invention, biomarkers that are in biological pathways that are enriched in the biomarker set determined by application of a data analysis algorithm to a training population that includes converters and nonconverters can be used as filtering step to reduce the number of biomarkers in the set. In one such approach, biological samples from subjects in a training population are obtained using, e.g., any of one or more of the methods described in Section 5.4, supra, and in Section 6, infra. In accordance with this embodiment, a nucleic acid array, such as a cDNA microarray, may be employed to generate features of biomarkers in a biomarker profile by detecting the expression of any one or more of the genes known to be or suspected to be involved in the selected biological pathways. Data derived from the cDNA microarray analysis may then be analyzed using any one or more of the analysis algorithms described in Section 5.5, supra, to identify biomarkers whose features discriminate between converters and nonconverters. Biomarkers whose corresponding feature values are capable of discriminating, for example, between converters (i.e., SIRS patients who subsequently develop sepsis) and non-converters (i.e., SIRS patients who do not subsequently develop sepsis) can thus be identified and classified as discriminating biomarkers. Biomarkers that are in enriched biological pathways can be selected from this set by applying Annotation rule 3, from above. Representative biological pathways that could be found include, for example, genes involved in the Th1/Th2 cell differentiation pathway). In one embodiments, biomarkers ultimately deemed as discriminating between converters and nonconverters satisfy each of the following criteria: (i) a p value of 0.05 or less (p<0.05) as determined from a Wilcoxon adjusted test; (ii) a mean-fold change of 1.2 or greater between converters and nonconverters across the training set, and (iii) present in a biological pathway that is enriched in the set of biomarkers derived by application of criteria (i) and (ii).
In some embodiments of the present invention, annotation data based filtering rules are used to identify biological pathways that are enriched in a given biomarker set. This biomarker set can be, for example, a set of biomarkers that is identified by application of a data analysis algorithm to training data comprising converters and nonconverters. Then, biomarkers in these enriched biological pathways are analyzed using any of the data analysis algorithms disclosed herein in order to identify biomarkers that discriminate between converters and nonconverters. In some instances, some of the biomarkers analyzed in the enriched biological pathways were not among the biomarkers in the original given biomarker set. In some instances, some of the biomarkers in the enriched biological pathways are among the biomarkers in the original given biomarker set. In some embodiments, a secondary assay is used to collect feature data for biomarkers that are in enriched pathways and it is this data that is used to determine whether the biomarkers in the enriched biological pathways discriminate between converters and nonconverters.
In some embodiments, biomarkers in biological pathways of interest are identified. In one example, genes involved in the Th1/Th2 cell differentiation pathway are identified. Then, these biomarkers are evaluated using the data analysis algorithms disclosed herein to determine whether they discriminate between converters and nonconverters.
5.11 Representative Embodiment in Accordance with the Present Invention
Sections 6.11 through 6.13 identify a number of biomarkers that are of interest in one embodiment in accordance with the present invention. Specifically, one embodiment of the present invention comprises the 10 biomarkers identified in Table 48 of Section 6.11.1, the 34 biomarkers listed in Table 59 of Section 6.11.2, and the 10 biomarkers listed in Table 93 of Section 6.13.1, below. Table 48 and Table 93 each identify MMP9 as a discriminating biomarker. Thus, the total number of biomarkers in Table I is one less than the sum of the biomarkers identified in Tables 48, Table 59, and Table 93, (34+10+10−1) or 53. These biomarkers are reproduced in Table I, below. Section 5.11.1 provides details on each of the individual biomarkers. Section 5.11.2, below, provides more details on select combinations of the biomarkers listed in Tables I, J, and K. Each of the biomarkers listed in Table I were selected based on the experimental results summarized in Sections 6.11 through 6.13. In some experiments, the identified biomarkers were proteins or fragments thereof. Such protein biomarkers, which discriminate between sepsis and SIRS, are listed in Table I with a “P” designation in column 5. In some experiments, the identified biomarkers were nucleic acids or fragment thereof. Such nucleic acid biomarkers, which discriminate between sepsis and SIRS, are listed in Table I with an “N” designation in column 5. As indicated above, one biomarker MMP9, was identified both as a protein and as a nucleic acid biomarker. Table J below lists the biomarkers in accordance with one embodiment of the present invention in which the biomarkers were discovered using nucleic acid based assays described in Section 6, such as RT-PCR. Table K below lists the biomarkers in accordance with one embodiment of the present invention in which the biomarkers were discovered using protein based assays, described in Section 6, such as bead assays. One embodiment of the invention comprises at least 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers from any one of Tables 48, 59, or 93.
Unless indicated in specific embodiments below, the biomarkers of Tables I, J and K are not limited by their physical form in the experiments summarized in Sections 6.11 through 6.13. For example, although the discriminatory nature of a biomarker may have been discovered by the abundance of the biomarker, in nucleic acid form, in a nucleic acid assay such as RT-PCR and accordingly listed in Table I on this basis with an “N” designation in column 5 of Table I, the physical manifestation of the biomarker in the methods, kits, and biomarker profiles of the present invention is not limited to nucleic acids. Rather, any physical manifestation of the biomarker as defined for the term “biomarker” in Section 5.1 is encompassed in the present invention. Column 6 of Table I indicates, based on the data summarized in Section 6 below, whether the biomarker is up-regulated or down-regulated in the subjects that will convert to sepsis (the converters) relative to the subjects that will not convert (the SIRS subjects). Thus, if a biomarker has the designation UP, in column 6, that means that the biomarker, in the form indicated in column 5, was, on average, more abundant in subjects that will convert to sepsis (sepsis subjects) relative to subjects that will not convert to sepsis (SIRS subjects). Furthermore, if a biomarker has the designation DOWN, in column 6, that means that the biomarker, in the form indicated in column 5, was, on average, less abundant in subjects that will convert to sepsis (sepsis subjects), relative to subjects that will not convert to sepsis (SIRS subjects).







TABLE I







Biomarkers in accordance with an embodiment of the present invention.














Gene
Protein




Gene

Accession
Accession

Regulation


Symbol
Gene Name
Number
Number
Source
in SEPSIS


1
2
3
4
5
6





AFP
ALPHA-FETOPROTEIN
NM_001134
CAA79592
P
UP


ANKRD22
ANKYRIN REPEAT
NM_144590
NP_653191
N
UP



DOMAIN 22


ANXA3
ANNEXIN A3
NM_005139
NP_005130
N
UP


APOC3
APOLIPOPROTEIN CIII
NM_000040
CAA25648
P
DOWN


ARG2
ARGINASE TYPE II
NM_001172
CAG38787
N
UP


B2M
BETA-2
NM_004048
AAA51811
P
UP



MICROGLOBULIN


BCL2A1
BCL2-RELATED
NM_004049
NP_004040
N
UP



PROTEIN A1


CCL5
CHEMOKINE (C—C
NM_002985
NP_002976
N
DOWN



MOTIF) LIGAND 5


CD86
CD86 ANTIGEN (CD28
NM_006889
NP_008820
N
DOWN



ANTIGEN LIGAND 2,
NM_175862
NP_787058



B7-2 ANTIGEN)


CEACAM1
CARCINOEMBRYONIC
NM_001712
NP_001703
N
UP



ANTIGEN-RELATED



CELL ADHESION



MOLECULE 1


CRP
C REACTIVE PROTEIN
NM_000567
CAA39671
P
UP


CRTAP
CARTILAGE-
NM_006371
NP_006362
N
DOWN



ASSOCIATED



PROTEIN


CSF1R
COLONY
NM_005211
NP_005202
N
DOWN



STIMULATING



FACTOR 1 RECEPTOR,



FORMERLY



MCDONOUGH FELINE



SARCOMA VIRAL (V-



FMS) ONCOGENE



HOMOLOG


FAD104
FIBRONECTIN TYPE
NM_022763
NP_073600
N
UP



III DOMAIN



CONTAINING 3B



(FNDC3B)


FCGR1A
FC FRAGMENT OF
NM_000566
NP_000557
N
UP



IGG, HIGH AFFINITY



IA


GADD45A
GROWTH ARREST
NM_001924
NP_001915
N
UP



AND DNA-DAMAGE-



INDUCIBLE, ALPHA


GADD45B
GROWTH ARREST-
NM_015675
NP_056490
N
UP



AND DNA DAMAGE-



INDUCIBLE GENE



GADD45


HLA-DRA
MAJOR
NM_002123
NP_002114
N
DOWN



HISTOCOMPATIBILITY



COMPLEX, CLASS



II, DR ALPHA


IFNGR1
INTERFERON GAMMA
NM_000416
NP_000407
N
UP



RECEPTOR 1


IL1RN
INTERLEUKIN = 1
NM_000577,
AAN87150
N
UP



RECEPTOR
NM_173841,



ANTAGONIST GENE
NM_173842,




NM_173843


IL-6
INTERLEUKIN 6
NM_000600
NP_000591
P
UP


IL-8
INTERLEUKIN 8
M28130
AAA59158
P
UP


IL-10
INTERLEUKIN 10
NM_000572
CAH73907
P
UP


IL10RA
INTERLEUKIN 10
NM_001558
NP_001549
N
DOWN



RECEPTOR, ALPHA


IL18R1
INTERLEUKIN 18
NM_003855
NP_003846
N
UP



RECEPTOR 1


INSL3
INSULIN-LIKE 3
NM_005543
NP_005534
N
UP



(LEYDIG CELL)


IRAK2
INTERLEUKIN-1
NM_001570
NP_001561
N
UP



RECEPTOR-



ASSOCIATED KINASE 2


IRAK4
INTERLEUKIN-1
NM_016123
NP_057207
N
UP



RECEPTOR-



ASSOCIATED KINASE 4


ITGAM
INTEGRIN, ALPHA M
NM_000632
NP_000623
N
UP



(COMPLEMENT



COMPONENT



RECEPTOR 3, ALPHA;



ALSO KNOWN AS



CD11B (P170),



MACROPHAGE



ANTIGEN ALPHA



POLYPEPTIDE)


JAK2
JANUS KINASE 2 (A
NM_004972
NP_004963
N
UP



PROTEIN TYROSINE



KINASE)


LDLR
LOW DENSITY
NM_000527
NP_000518
N
UP



LIPOPROTEIN



RECEPTOR


LY96
LYMPHOCYTE
NM_015364
NP_056179
N
UP



ANTIGEN 96


MAP2K6
MITOGEN-
NM_002758
NP_002749
N
UP



ACTIVATED PROTEIN
NM_031988
NP_114365



KINASE KINASE 6


MAPK14
MAPK14 MITOGEN-
NM_001315
NP_001306
N
UP



ACTIVATED PROTEIN
NM_139012
NP_620581



KINASE 14
NM_139013
NP_620582




NM_139014
NP_620583


MCP1
MONOCYTE
AF493698,
AAQ75526
P
UP



CHEMOATTRACTANT
AF493697



PROTEIN 1


MKNK1
MAP KINASE
NM_003684
NP_003675
N
UP



INTERACTING
NM_198973
NP_945324



SERINE/THREONINE



KINASE 1


MMP9
MATRIX
NM_004994
NP_004985
N/P
UP (both



METALLOPROTEINASE



protein and



9 (GELATINASE B,



nucleic



92 KDA GELATINASE,



acid)



92 KDA TYPE IV



COLLAGENASE)


NCR1
NATURAL
NM_004829
NP_004820
N
UP



CYTOTOXICITY



TRIGGERING



RECEPTOR 1


OSM
ONCOSTATIN M
NM_020530
NP_065391
N
UP


PFKFB3
6-PHOSPHOFRUCTO-
NM_004566
NP_004557
N
UP



2-KINASE/FRUCTOSE-



2,6-BISPHOSPHATASE 3


PRV1
NEUTROPHIL-
NM_020406
NP_065139
N
UP



SPECIFIC ANTIGEN 1



(POLYCYTHEMIA



RUBRA VERA 1)


PSTPIP2
PROLINE/SERINE/THREONINE
NM_024430
NP_077748
N
UP



PHOSPHATASE-



INTERACTING



PROTEIN 1 (PROLINE-



SERINE-THREONINE



PHOSPHATASE



INTERACTING



PROTEIN 2)


SOCS3
SUPPRESSOR OF
NM_003955
NP_003946
N
UP



CYTOKINE



SIGNALING 3


SOD2
SUPEROXIDE
NM_000636
NP_000627
N
UP



DISMUTASE 2,



MITOCHONDRIAL


TDRD9
TUDOR DOMAIN
NM_153046
NP_694591
N
UP



CONTAINING 9


TGFBI
TRANSFORMING
NM_000358
NP_000349
N
DOWN



GROWTH FACTOR,



BETA-1



(TRANSFORMING



GROWTH FACTOR,



BETA-INDUCED,



68 KDA)


TIFA
TRAF-INTERACTING
NM_052864
NP_443096
N
UP



PROTEIN WITH A



FORKHEAD-



ASSOCIATED



DOMAIN


TIMP1
TISSUE INHIBITOR OF
NM_003254
AAA75558
P
UP



METALLOPROTEINASE 1


TLR4
TOLL-LIKE
AH009665
AAF05316
N
UP



RECEPTOR 4


TNFRSF6
TUMOR NECROSIS
NM_152877
NP_000034
N
UP



FACTOR RECEPTOR



SUPERFAMILY,



MEMBER 6


TNFSF10
TUMOR NECROSIS
NM_003810
NP_003801
N
UP



FACTOR (LIGAND)



SUPERFAMILY,



MEMBER 10


TNFSF13B
TUMOR NECROSIS
NM_006573
NP_006564
N
UP



FACTOR (LIGAND)



SUPERFAMILY,



MEMBER 13B


VNN1
VANIN 1
NM_004666
NP_004657
N
UP









Each of the sequences, genes, proteins, and probesets identified in Table I is hereby incorporated by reference.







TABLE J







Biomarkers identified based on the ability of the nucleic acid form


of the biomarker to discriminate between SIRS and sepsis












Gene Accession
Protein Accession


Gene Symbol
Gene Name
Number
Number


1
2
3
4





FCGR1A
FC FRAGMENT OF
NM_000566
NP_000557



IGG, HIGH AFFINITY



IA


MMP9
MATRIX
NM_004994
NP_004985



METALLOPROTEINASE 9


IL18R1
INTERLEUKIN 18
NM_003855
NP_003846



RECEPTOR 1


ARG2
ARGINASE TYPE II
NM_001172
CAG38787


IL1RN
INTERLEUKIN-1
NM_000577,
AAN87150



RECEPTOR
NM_173841,



ANTAGONIST GENE
NM_173842,




NM_173843


TNFSF13B
TUMOR NECROSIS
NM_006573
NP_006564



FACTOR



SUPERFAMILY,



MEMBER 13B


ITGAM
INTEGRIN, ALPHA M
NM_000632
NP_000623


TGFB1
TRANSFORMING
NM_000358
NP_000349



GROWTH FACTOR,



BETA-1


CD86
CD86 ANTIGEN
NM_006889
NP_008820




NM_175682
NP_787058


TLR4
TOLL-LIKE
AH009665
AAF05316



RECEPTOR 4



BCL2-RELATED
NM_004049
NP_004040



PROTEIN A1


CCL5
CHEMOKINE (C—C
NM_002985
NP_002976



MOTIF) LIGAND 5


CSF1R
COLONY
NM_005211
NP_005202



STIMULATING



FACTOR 1 RECEPTOR,



FORMERLY



MCDONOUGH FELINE



SARCOMA VIRAL (V-



FMS) ONCOGENE



HOMOLOG


GADD45A
GROWTH ARREST
NM_001924
NP_001915



AND DNA-DAMAGE-



INDUCIBLE, ALPHA


GADD45B
GROWTH ARREST-
NM_015675
NP_056490



AND DNA DAMAGE-



INDUCIBLE GENE



GADD45


IFNGR1
INTERFERON
NM_000416
NP_000407



GAMMA RECEPTOR 1


IL10RA
INTERLEUKIN 10
NM_001558
NP_001549



RECEPTOR, ALPHA


IRAK2
INTERLEUKIN-1
NM_001570
NP_001561



RECEPTOR-



ASSOCIATED KINASE 2


IRAK4
INTERLEUKIN-1
NM_016123
NP_057207



RECEPTOR-



ASSOCIATED KINASE 4


JAK2
JANUS KINASE 2 (A
NM_004972
NP_004963



PROTEIN TYROSINE



KINASE)


LY96
LYMPHOCYTE
NM_015364
NP_056179



ANTIGEN 96


MAP2K6
MITOGEN-
NM_002758
NP_002749



ACTIVATED PROTEIN
NM_031988
NP_114365



KINASE 6


MAPK14
MAPK14 MITOGEN-
NM_001315
NP_001306



ACTIVATED PROTEIN
NM_139012
NP_620581



KINASE 14
NM_139013
NP_620582




NM_139014
NP_620583


MKNK1
MAP KINASE
NM_003684
NP_003675



INTERACTING
NM_198973
NP_945324



SERINE/THREONINE



KINASE 1


OSM
ONCOSTATIN M
NM_020530
NP_065391


SOCS3
SUPPRESSOR OF
NM_003955
NP_003946



CYTOKINE



SIGNALING 3


TDRD9
TUDOR DOMAIN
NM_153046
NP_694591



CONTAINING 9


TNFRSF6
TUMOR NECROSIS
NM_152877
NP_000034



FACTOR RECEPTOR



SUPERFAMILY,



MEMBER 6


TNFSF10
TUMOR NECROSIS
NM_003810
NP_003801



FACTOR (LIGAND)



SUPERFAMILY,



MEMBER 10


ANKRD22
ANKYRIN REPEAT
NM_144590
NP_653191



DOMAIN 22


ANXA3
ANNEXIN A3
NM_005139
NP_005130


CEACAM1
CARCINOEMBRYONIC
NM_001712
NP_001703



ANTIGEN-RELATED



CELL ADHESION



MOLECULE 1


LDLR
LOW DENSITY
NM_000527
NP_000518



LIPOPROTEIN



RECEPTOR


PFKFB3
6-PHOSPHOFRUCTO-
NM_004566
NP_004557



2-KINASE/FRUCTOSE-



2,6-BISPHOSPHATASE 3


PRV1
NEUTROPHIL-
NM_020406
NP_065139



SPECIFIC ANTIGEN 1



(POLYCYTHEMIA



RUBRA VERA 1)


PSTPIP2
PROLINE/SERINE/THREONINE
NM_024430
NP_077748



PHOSPHATASE-



INTERACTING



PROTEIN 1 (PROLINE-



SERINE-THREONINE



PHOSPHATASE



INTERACTING



PROTEIN 2)


TIFA
TRAF-INTERACTING
NM_052864
NP_443096



PROTEIN WITH A



FORKHEAD-



ASSOCIATED



DOMAIN


VNN1
VANIN 1
NM_004666
NP004657


NCR1
NATURAL
NM_004829
NP_004820



CYTOTOXICITY



TRIGGERING



RECEPTOR 1


FAD104
FIBRONECTIN TYPE
NM_022763
NP_073600



III DOMAIN



CONTAINING 3B



(FNDC3B)


INSL3
INSULIN-LIKE 3
NM_005543
NP_005534



(LEYDIG CELL)


CRTAP
CARTILAGE-
NM_006371
NP_006362



ASSOCIATED



PROTEIN


HLA-DRA
MAJOR
NM_002123
NP_002114



HISTOCOMPATIBILITY



COMPLEX, CLASS



II, DR ALPHA


SOD2
SUPEROXIDE
NM_000636
NP_000627



DISMUTASE 2,



MITOCHONDRIAL
















TABLE K







Biomarkers identified based on the ability of the protein form


of the biomarker to discriminate between SIRS and sepsis












Gene
Protein


Gene

Accession
Accession


Symbol
Gene Name
Number
Number


1
2
3
4





IL-6
INTERLEUKIN 6
NM_000600
NP_000591


IL-8
INTERLEUKIN 8
M28130
AAA59158


CRP
C Reactive protein
CAA39671
NM_000567


IL-10
INTERLEUKIN 10
NM_000572
CAH73907


APOC3
APOLIPOPROTEIN CIII
NM_000040
CAA25648


MMP9
MATRIX
NM_004994
NP_004985



METALLOPROTEINASE 9



(GELATINASE B, 92 KDA



GELATINASE, 92 KDA TYPE



IV COLLAGENASE)


TIMP1
TISSUE INHIBITOR OF
NM_003254
AAA75558



METALLOPROTEINASE 1


MCP1
MONOCYTE
AF493698,
AAQ75526



CHEMOATTRACTANT
AF493697



PROTEIN 1


AFP
ALPHA-FETOPROTEIN
NM_001134
CAA79592


B2M
BETA-2 MICROGLOBULIN
NM_004048
AAA51811









5.11.1 Biomarker Descriptions
The references for the biomarkers in this section merely provide exemplary sequences for the biomarkers set forth in the present application.
The nucleotide sequence of AFP (identified by accession no. NM—001134) is disclosed in, e.g., Beattie et al., 1982, “Structure and evolution of human alpha-fetoprotein deduced from partial sequence of cloned cDNA” Gene 20 (3): 415-422, Harper, M. E. et al., 1983, “Linkage of the evolutionarily-related serum albumin and alpha-fetoprotein genes within q11-22 of human chromosome 4,” Am. J. Hum. Genet. 35 (4):565-572, Morinaga, T. et al., 1983, “Primary structures of human alpha-fetoprotein and its mRNA,” Proc. Natl. Acad. Sci. U.S.A. 80 (15):4604-4608, and the amino acid sequence of AFP (identified by accession no. CAA79592) is disclosed in, e.g., McVey, 1993, Direct Submission, Clinical Research Centre, Haemostasis Research Group, Watford Road, Harrow, UK, HA1 3UJ, McVey et al., 1993, “A G-->A substitution in an HNF I binding site in the human alpha-fetoprotein gene is associated with hereditary persistence of alpha-fetoprotein (HPAFP),” Hum. Mol. Genet. 2 (4): 379-384, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of ANKRD22 (identified by accession no. NM—144590) is disclosed in, e.g., Strausberg, 2002, “Homo sapiens ankyrin repeat domain 22, mRNA (cDNA clone MGC:22805 IMAGE:3682099),” unpublished, and the amino acid sequence of ANKRD22 (identified by accession no. NP—653191) is disclosed in, e.g., Strausberg, 2002, “Homo sapiens ankyrin repeat domain 22, mRNA (cDNA clone MGC:22805 IMAGE:3682099),” unpublished, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of ANXA3 (identified by accession no. NM—005139) is disclosed in, e.g., Pepinsky, R. B. et al., 1988,“ Five distinct calcium and phospholipid binding proteins share homology with lipocortin I,” J. Biol. Chem. 263 (22): 10799-10811, Tait, J. F. et al., 1988, “Placental anticoagulant proteins: isolation and comparative characterization four members of the lipocortin family,” Biochemistry 27 (17):6268-6276, Ross, T. S. et al., 1990, “Identity of inositol 1,2-cyclic phosphate 2-phosphohydrolase with lipocortin III,” Science 248 (4955):605-607, and the amino acid sequence of ANXA3 (identified by accession no. NP—005130) is disclosed in, e.g., Pepinsky, R. B et al., 1988,“ Five distinct calcium and phospholipid binding proteins share homology with lipocortin I,” J. Biol. Chem. 263 (22): 10799-10811, Tait, J. F. et al., 1988, “Placental anticoagulant proteins: isolation and comparative characterization four members of the lipocortin family,” Biochemistry 27 (17):6268-6276, Ross, T. S. et al., 1990, “Identity of inositol 1,2-cyclic phosphate 2-phosphohydrolase with lipocortin III,” Science 248 (4955):605-607, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of Apolipoprotein CIII (APOC3) (identified by accession no. NM—000040) is disclosed in, e.g., Ruiz-Narvaez. et al., 2005 “APOC3/A5 haplotypes, lipid levels, and risk of myocardial infarction in the Central Valley of Costa Rica,” J. Lipid Res. 46 (12), 2605-2613; Garenc et al., 2005, “Effect of the APOC3 Sst I SNP on fasting triglyceride levels in men heterozygous for the LPL P207L deficiency,” Eur. J. Hum. Genet. 13, 1159-1165; Baum. et al., 2005, “Effect of hepatic lipase -514C->T polymorphism and its interactions with apolipoprotein C3 -482C->T and apolipoprotein E exon 4 polymorphisms on the risk of nephropathy in chinese type 2 diabetic patients,” Diabetes Care 28, 1704-1709, and the amino acid sequence of APOC3 (identified by accession no. CAA25648) is disclosed in, e.g., Protter et al., 1984, “Isolation and sequence analysis of the human apolipoprotein CIII gene and the intergenic region between the apo AI and apo CIII,” DNA 3, 449-456, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of ARG2 (identified by accession no. NM—001172) is disclosed in, e.g., Gotoh et al., 1996 “Molecular cloning of cDNA for nonhepatic mitochondrial arginase(arginase II) and comparison of its induction with nitric oxide synthase in a murine macrophage-like cell line,” FEBS Lett. 395 (2-3):119-122, Vockley et al., 1996, “Cloning and characterization of the human type II arginase gene,” Genomics 38 (2):118-123, Gotoh et al., 1997, “Chromosomal localization of the human arginase II gene and tissue distribution of its mRNA,” Biochem. Biophys. Res. Commun. 233 (2):487-491, and the amino acid sequence of ARG2 (identified by accession no. CAG38787) is disclosed in, e.g., Halleck et al., 2004, Direct Submission, RZPD Deutsches Ressourcenzentrum fuer Genomforschung GmbH, Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany, Halleck et al., unpublished, “Cloning of human full open reading frames in GatewayTM system entry vector (pDONR201),” each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of B2M (identified by accession no. NM—004048) is disclosed in, e.g., Krangel, M. S. et al., 1979, “Assembly and maturation of HLA-A and HLA-B antigens in vivo,” Cell 18 (4):979-991, Suggs, S. V. et al., 1981, “Use of synthetic oligonucleotides as hybridization probes: isolation of cloned cDNA sequences for human beta 2-microglobulin,” Proc. Natl. Acad. Sci. U.S.A. 78 (11):6613-6617, Rosa, F. et al., 1983, “The beta2-microglobulin mRNA in human Daudi cells has a mutated initiation codon but is still inducible by interferon,” EMBO J. 2 (2):239-243, and the amino acid sequence of B2M (identified by accession no. AAA51811) is disclosed in, e.g., Gussow, D. et al., 1987, “The human beta 2-microglobulin gene. Primary structure and definition of the transcriptional unit,” J. Immunol. 139 (9): 3132-3138, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of BCL2A1 (identified by accession no. NM—004049) is disclosed in, e.g., Lin, E. Y. et al., 1993, “Characterization of A1, a novel hemopoietic-specific early-response gene with sequence similarity to bcl-2,” J. Immunol. 151 (4):1979-1988, Savitsky, K. et al., “The complete sequence of the coding region of the ATM gene reveals similarity to cell cycle regulators in different species,” Hum. Mol. Genet. 4 (11):2025-2032, Choi, S. S. et al., 1995, “A novel Bcl-2 related gene, Bfl-1, is overexpressed in stomach cancer and preferentially expressed in bone marrow,” Oncogene 11 (9):1693-1698, and the amino acid sequence of BCL2A1 (identified by accession no. NP—004040) is disclosed in, e.g., Lin, E. Y. et al., 1993, “Characterization of A1, a novel hemopoietic-specific early-response gene with sequence similarity to bcl-2,” J. Immunol. 151 (4):1979-1988, Savitsky, K. et al., “The complete sequence of the coding region of the ATM gene reveals similarity to cell cycle regulators in different species,” Hum. Mol. Genet. 4 (11):2025-2032, Choi, S. S. et al., 1995, “A novel Bcl-2 related gene, Bfl-1, is overexpressed in stomach cancer and preferentially expressed in bone marrow,” Oncogene 11 (9):1693-1698, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of CCL5 (identified by accession no. NM—002985) is disclosed in, e.g., Schall, T. J. et al., 1988, “A human T cell-specific molecule is a member of a new gene family,” J. Immunol. 141 (3):1018-1025, Donlon, T. A. et al., 1990, “Localization of a human T-cell-specific gene, RANTES (D 17S136E), to chromosome 17q11.2-q12,” Genomics 6 (3):548-553, Kameyoshi, Y. et al., 1992, “Cytokine RANTES released by thrombin-stimulated platelets is a potent attractant for human eosinophils,” J. Exp. Med. 176 (2):587-592, and the amino acid sequence of CCL5 (identified by accession no. NP—002976) is disclosed in, e.g., Schall, T. J. et al., 1988, “A human T cell-specific molecule is a member of a new gene family,” J. Immunol. 141 (3):1018-1025, Donlon, T. A. et al., 1990, “Localization of a human T-cell-specific gene, RANTES (D17S136E), to chromosome 17q11.2-q12,” Genomics 6 (3):548-553, Kameyoshi, Y. et al., 1992, “Cytokine RANTES released by thrombin-stimulated platelets is a potent attractant for human eosinophils,” J. Exp. Med. 176 (2):587-592, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of CD86 (identified by accession nos. NM—006889, NM—175862) is disclosed in, e.g., Azuma, M. et al., 1993, “B70 antigen is a second ligand for CTLA-4 and CD28,” Nature 366 (6450):76-79, Freeman, G. J. et al., 1993, “Cloning of B7-2: a CTLA-4 counter-receptor that costimulates human T cell proliferation,” Science 262 (5135):909-911, Chen, C. et al., 1994, “Molecular cloning and expression of early T cell costimulatory molecule-1 and its characterization as B7-2 molecule,” J. Immunol. 152 (10):4929-4936, and the amino acid sequence of CD86 (identified by accession nos. NP—008820, NP—787058) is disclosed in, e.g., Azuma, M. et al., 1993, “B70 antigen is a second ligand for CTLA-4 and CD28,” Nature 366 (6450):76-79, Azuma, M. et al., 1993, “B70 antigen is a second ligand for CTLA-4 and CD28,” Nature 366 (6450):76-79, Freeman, G. J. et al., 1993, “Cloning of B7-2: a CTLA-4 counter-receptor that costimulates human T cell proliferation,” Science 262 (5135):909-911, Chen, C. et al., 1994, “Molecular cloning and expression of early T cell costimulatory molecule-1 and its characterization as B7-2 molecule,” J. Immunol. 152 (10):4929-4936, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of CEACAM1 (identified by accession no. NM—001712) is disclosed in, e.g., Svenberg, T. et al., 1979, “Immunofluorescence studies on the occurrence and localization of the CEA-related biliary glycoprotein I (BGP I) in normal human gastrointestinal tissues,” Clin. Exp. Immunol. 36 (3):436-441, Hinoda, Y. et al., 1988, “Molecular cloning of a cDNA coding biliary glycoprotein I: primary structure of a glycoprotein immunologically crossreactive with carcinoembryonic antigen,” Proc. Natl. Acad. Sci. U.S.A. 85 (18):6959-6963, Barnett, T. R. et al., 1989, “Carcinoembryonic antigens: alternative splicing accounts for the multiple mRNAs that code for novel members of the carcinoembryonic antigen family,” J. Cell Biol. 108 (2):267-276, and the amino acid sequence of CEACAM1 (identified by accession no. NP—001703) is disclosed in, e.g., Svenberg, T. et al., 1979, “Immunofluorescence studies on the occurrence and localization of the CEA-related biliary glycoprotein I (BGP I) in normal human gastrointestinal tissues,” Clin. Exp. Immunol. 36 (3):436-441, Hinoda, Y. et al., 1988, “Molecular cloning of a cDNA coding biliary glycoprotein I: primary structure of a glycoprotein immunologically crossreactive with carcinoembryonic antigen,” Proc. Natl. Acad. Sci. U.S.A. 85 (18):6959-6963, Barnett, T. R. et al., 1989, “Carcinoembryonic antigens: alternative splicing accounts for the multiple mRNAs that code for novel members of the carcinoembryonic antigen family,” J. Cell Biol. 108 (2):267-276, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of C Reactive Protein (CRP) (identified by accession no. NM—000567) is disclosed in, e.g., Song et al., 2006, “C-reactive protein contributes to the hypercoagulable state in coronary artery disease,” J. Thromb. Haemost. 4 (1), 98-106; Wakugawa et al., 2006, “C-reactive protein and risk of first-ever ischemic and hemorrhagic stroke in a general Japanese population: the Hisayama Study,” Stroke 37, 27-32; Tong et al., 2005, “Association of testosterone, insulin-like growth factor-I, and C-reactive protein with metabolic syndrome in Chinese middle-aged men with a family history of type 2 diabetes,” J. Clin. Endocrinol. Metab. 90, 6418-6423, and the amino acid sequence of CRP (identified by accession no. CAA39671 is described in a direct submissiong by Tenchini et al., 1990, Tenchini M. L., Dipartimento di Biologia e Genetica per le Scienze mediche, via Viotti 3, 20133 Milano, Italy, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of CRTAP (identified by accession no. NM—006371) is disclosed in, e.g., Castagnola, P. et al., 1997, “Cartilage associated protein (CASP) is a novel developmentally regulated chick embryo protein,” J. Cell. Sci. 110 (PT 12):1351-1359; Tonachini, L. et al., 1999, “cDNA cloning, characterization and chromosome mapping of the gene encoding human cartilage associated protein (CRTAP),” Cytogenet. Cell Genet. 87:(3-4); Morello, R. et al., 1999, “cDNA cloning, characterization and chromosome mapping of Crtap encoding the mouse cartilage associated protein,” Matrix Biol. 18 (3):319-324, and the amino acid sequence of CRTAP (identified by accession no. NP—006362) is disclosed in, e.g., Castagnola, P. et al., 1997, “Cartilage associated protein (CASP) is a novel developmentally regulated chick embryo protein,” J. Cell. Sci. 110 (PT 12):1351-1359, Tonachini, L. et al., 1999, “cDNA cloning, characterization and chromosome mapping of the gene encoding human cartilage associated protein (CRTAP),” Cytogenet. Cell Genet. 87:(3-4), Morello, R. et al., 1999, “cDNA cloning, characterization and chromosome mapping of Crtap encoding the mouse cartilage associated protein,” Matrix Biol. 18 (3):319-324, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of CSF1R (identified by accession no. NM—005211) is disclosed in, e.g., Verbeek, J. S. et al., 1985, “Human c-fins proto-oncogene: comparative analysis with an abnormal allele,” Mol. Cell. Biol. 5 (2):422-426; Xu, D. Q. et al., 1985, “Restriction fragment length polymorphism of the human c-fms gene,” Proc. Natl. Acad. Sci. U.S.A. 82 (9):2862-2865; Sherr, C. J. et al., 1985, “The c-fms proto-oncogene product is related to the receptor for the mononuclear phagocyte growth factor, CSF-1,” Cell 41 (3):665-676, and the amino acid sequence of CSF1R (identified by accession no. NP—005202) is disclosed in, e.g., Verbeek, J. S. et al., 1985, “Human c-fms proto-oncogene: comparative analysis with an abnormal allele,” Mol. Cell. Biol. 5 (2):422-426, Xu, D. Q. et al., 1985, “Restriction fragment length polymorphism of the human c-fms gene,” Proc. Natl. Acad. Sci. U.S.A. 82 (9):2862-2865, Sherr, C. J. et al., 1985, “The c-fms proto-oncogene product is related to the receptor for the mononuclear phagocyte growth factor, CSF-1,” Cell 41 (3):665-676, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of FAD104 (identified by accession no. NM—022763) is disclosed in, e.g., Clark, H. F. et al., 2003, “The secreted protein discovery initiative (SPDI), a large-scale effort to identify novel human secreted and transmembrane proteins: a bioinformatics assessment,” Genome Res. 13 (10):2265-2270, Tominaga, K. et al., 2004, “The novel gene fad104, containing a fibronectin type III domain, has a significant role in adipogenesis,” FEBS Lett. 577 (1-2):49-54, and the amino acid sequence of FAD104 (identified by accession no. NP—073600) is disclosed in, e.g., Clark, H. F. et al., 2003, “The secreted protein discovery initiative (SPDI), a large-scale effort to identify novel human secreted and transmembrane proteins:a bioinformatics assessment,” Genome Res. 13 (10):2265-2270, Tominaga, K. et al., 2004, “The novel gene fad104, containing a fibronectin type III domain, has a significant role in adipogenesis,” FEBS Lett. 577 (1-2):49-54, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of FCGR1A (identified by accession no. NM—000566) is disclosed in, e.g., Eizuru, Y. et al., 1988, “Induction of Fc (IgG) receptor(s) by simian cytomegaloviruses in human embryonic lung fibroblasts,” Intervirology 29 (6):339-345, Allen, J. M. et al., 1988, “Nucleotide sequence of three cDNAs for the human high affinity Fc receptor (FcRI),” Nucleic Acids Res. 16 (24):11824, van de Winkel, J. G. et al., 1991, “Gene organization of the human high affinity receptor for IgG, Fc gamma RI (CD64). Characterization and evidence for a second gene,” J. Biol. Chem. 266 (20):13449-1345, and the amino acid sequence of FCGR1A (identified by accession no. NP—000557) is disclosed in, e.g., Eizuru, Y. et al., 1988, “Induction of Fc (IgG) receptor(s) by simian cytomegaloviruses in human embryonic lung fibroblasts,” Intervirology 29 (6):339-345, Allen, J. M. et al., 1988, “Nucleotide sequence of three cDNAs for the human high affinity Fc receptor (FcRI),” Nucleic Acids Res. 16 (24):11824, van de Winkel, J. G. et al., 1991, “Gene organization of the human high affinity receptor for IgG, Fc gamma R1 (CD64). Characterization and evidence for a second gene,” J. Biol. Chem. 266 (20):13449-1345, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of GADD45A (identified by accession no. NM—001924) is disclosed in, e.g., Papathanasiou, M. A. et al., 1991, “Induction by ionizing radiation of the gadd45 gene in cultured human cells: lack of mediation by protein kinase C,” Mol. Cell. Biol. 11 (2): 1009-1016, Hollander, M. C. et al., 1993, “Analysis of the mammalian gadd45 gene and its response to DNA damage,” J. Biol. Chem. 268 (32):24385-24393, Smith, M. L. et al., 1994, “Interaction of the p53-regulated protein Gadd45 with proliferating cell nuclear antigen,” Science 266 (5189):1376-1380, and the amino acid sequence of GADD45A (identified by accession no. NP—001915) is disclosed in, e.g., Papathanasiou, M. A. et al., 1991, “Induction by ionizing radiation of the gadd45 gene in cultured human cells: lack of mediation by protein kinase C,” Mol. Cell. Biol. 11 (2):1009-1016, Hollander, M. C. et al., 1993, “Analysis of the mammalian gadd45 gene and its response to DNA damage,” J. Biol. Chem. 268 (32):24385-24393, Smith, M. L. et al., 1994, “Interaction of the p53-regulated protein Gadd45 with proliferating cell nuclear antigen,” Science 266 (5189):1376-1380, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of GADD45B (identified by accession no. NM—015675) is disclosed in, e.g., Abdollahi, A. et al., 1991, “Sequence and expression of a cDNA encoding MyD118: a novel myeloid differentiation primary response gene induced by multiple cytokines,” Oncogene 6 (1): 165-167, Vairapandi, M. et al., 1996, “The differentiation primary response gene MyD118, related to GADD45, encodes for a nuclear protein which interacts with PCNA and p21WAF1/CIP1,” Oncogene 12 (12):2579-2594, Koonin, E. V., 1997, “Cell cycle and apoptosis: possible roles of Gadd45 and MyD118 proteins inferred from their homology to ribosomal proteins,” J. Mol. Med. 75 (4):236-238, and the amino acid sequence of GADD45B (identified by accession no. NP—056490) is disclosed in, e.g., Abdollahi, A. et al., 1991, “Sequence and expression of a cDNA encoding MyD118: a novel myeloid differentiation primary response gene induced by multiple cytokines,” Oncogene 6 (1):165-167, Vairapandi, M. et al., 1996, “The differentiation primary response gene MyD118, related to GADD45, encodes for a nuclear protein which interacts with PCNA and p21WAF1/CIP1,” Oncogene 12 (12):2579-2594, Koonin, E. V., 1997, “Cell cycle and apoptosis: possible roles of Gadd45 and MyD118 proteins inferred from their homology to ribosomal proteins,” J. Mol. Med. 75 (4):236-238, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of HLA-DRA (identified by accession no. NM—002123) is disclosed in, e.g., Larhammar, D. et al., 1981, Evolutionary relationship between HLA-DR antigen beta-chains, HLA-A, B, C antigen subunits and immunoglobulin chains,” Scand. J. Immunol. 14 (6):617-622, Wiman, K. et al., 1982, “Isolation and identification of a cDNA clone corresponding to an HLA-DR antigen beta chain,” Proc. Natl. Acad. Sci. U.S.A. 79 (6):1703-1707, Larhammar, D. et al., 1982, “Complete amino acid sequence of an HLA-DR antigen-like beta chain as predicted from the nucleotide sequence: similarities with immunoglobulins and HLA-A, -B, and -C antigens,” Proc. Natl. Acad. Sci. U.S.A. 79 (12):3687-3691, and the amino acid sequence of HLA-DRA (identified by accession no. NP—002114) is disclosed in, e.g., Larhammar, D. et al., 1981, Evolutionary relationship between HLA-DR antigen beta-chains, HLA-A, B, C antigen subunits and immunoglobulin chains,” Scand. J. Immunol. 14 (6):617-622, Wiman, K. et al., 1982, “Isolation and identification of a cDNA clone corresponding to an HLA-DR antigen beta chain,” Proc. Natl. Acad. Sci. U.S.A. 79 (6):1703-1707, Larhammar, D. et al., 1982, “Complete amino acid sequence of an HLA-DR antigen-like beta chain as predicted from the nucleotide sequence: similarities with immunoglobulins and HLA-A, -B, and -C antigens,” Proc. Natl. Acad. Sci. U.S.A. 79 (12):3687-3691, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of IFNGR1 (identified by accession no. NM—000416) is disclosed in, e.g., Novick, D. et al., 1987, “The human interferon-gamma receptor. Purification, characterization, and preparation of antibodies, each of which is incorporated by reference herein in its entirety,” J. Biol. Chem. 262 (18): 8483-8487, Aguet, M. et al., 1988, “Molecular cloning and expression of the human interferon-gamma receptor,” Cell 55 (2): 273-280, Le Coniat, M. et al., 1989, “Human interferon gamma receptor 1 (IFNGR1) gene maps to chromosome region 6q23-6q24,” Hum. Genet. 84 (1):92-94, and the amino acid sequence of IFNGR1 (identified by accession no. NP—000407) is disclosed in, e.g., Novick, D. et al., 1987, “The human interferon-gamma receptor. Purification, characterization, and preparation of antibodies,” J. Biol. Chem. 262 (18):8483-8487, Aguet, M. et al., 1988, “Molecular cloning and expression of the human interferon-gamma receptor,” Cell 55 (2): 273-280, Le Coniat, M. et al., 1989, “Human interferon gamma receptor 1 (IFNGR1) gene maps to chromosome region 6q23-6q24,” Hum. Genet. 84 (1):92-94, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of IL1RN (identified by accession nos. NM—000577, NM—173841, NM—173842, NM—173843) is disclosed in, e.g., Eisenberg, S. P. et al., 1990, “Primary structure and functional expression from complementary DNA of a human interleukin-1 receptor antagonist,” Nature 343 (6256):341-346, Carter, D. B. et al., 1990, “Purification, cloning, expression and biological characterization of an interleukin-1 receptor antagonist protein,” Nature 344 (6267):633-638, Seckinger, P. et al., 1990, “Natural and recombinant human IL-1 receptor antagonists block the effects of IL-1 on bone resorption and prostaglandin production,” J. Immunol. 145 (12):4181-4184, and the amino acid sequence of IL1RN (identified by accession no. AAN87150) is disclosed in, e.g., Rieder, M. J. et al., 2002, Direct Submission, Genome Sciences, University of Washington, 1705 NE Pacific, Seattle, Wash. 98195, USA, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of IL-6 (identified by accession no. NM—000600) is disclosed in, e.g., Haegeman, G. et al., 1986, “Structural analysis of the sequence coding for an inducible 26-kDa protein in human fibroblasts,” Eur. J. Biochem. 159 (3):625-632, Zilberstein, A. et al., 1986, “Structure and expression of cDNA and genes for human interferon-beta-2, a distinct species inducible by growth-stimulatory cytokines,” EMBO J. 5 (10):2529-2537, Hirano, T. et al., 1986, “Complementary DNA for a novel human interleukin (BSF-2) that induces B lymphocytes to produce immunoglobulin,” Nature 324 (6092):73-76, and the amino acid sequence of IL-6 (identified by accession no. NP—000591) is disclosed in, e.g., Haegeman, G. et al., 1986, “Structural analysis of the sequence coding for an inducible 26-kDa protein in human fibroblasts,” Eur. J. Biochem. 159 (3):625-632, Zilberstein, A. et al., 1986, “Structure and expression of cDNA and genes for human interferon-beta-2, a distinct species inducible by growth-stimulatory cytokines,” EMBO J. 5 (10):2529-2537, Hirano, T. et al., 1986, “Complementary DNA for a novel human interleukin (BSF-2) that induces B lymphocytes to produce immunoglobulin,” Nature 324. (6092):73-76, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of IL-8 (identified by accession no. M28130) and the amino acid sequence of IL-8 (identified by accession no. AAA59158) are each disclosed in, e.g., Mukaida et al., 1989, “Genomic structure of the human monocyte-derived neutrophil chemotactic factor IL-8,” J. Immunol. 143, 1366-1371 which is incorporated by reference herein in its entirety.
The nucleotide sequence of IL-10 (identified by accession no. NM—000572) is disclosed in, e.g., Ghosh, S. et al., 1975, “Anaerobic acidogenesis of wastewater sludge,” Breast Cancer Res. Treat. 47 (1):30-45, Hsu, D. H. et al., 1990, “Expression of interleukin-10 activity by Epstein-Barr virus protein BCRF1,” Science 250 (4982):830-832, Vieira, P. et al., 1991, “Isolation and expression of human cytokine synthesis inhibitory factor cDNA clones: homology to Epstein-Barr virus open reading frame BCRFI,” Proc. Natl. Acad. Sci. U.S.A. 88 (4):1172-1176, and the amino acid sequence of IL-10 (identified by accession no. CAH73907) is disclosed in, e.g., Tracey, A., 2005, Direct Submission, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB 10 1SA, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of IL10RA (identified by accession no. NM—001558) is disclosed in, e.g., Tan, J. C. et al, 1993, “Characterization of interleukin-10 receptors on human and mouse cells,” J. Biol. Chem. 268 (28):21053-21059, Ho, A. S. et al., 1993, “A receptor for interleukin 10 is related to interferon receptors,” Proc. Natl. Acad. Sci. U.S.A. 90 (23):11267-11271, Liu, Y. et al., 1994, “Expression cloning and characterization of a human IL-10 receptor,” J. Immunol. 152 (4): 1821-1829, and the amino acid sequence of IL10RA (identified by accession no. NP—001549) is disclosed in, e.g., Tan, J. C. et al., 1993, “Characterization of interleukin-10 receptors on human and mouse cells,” J. Biol. Chem. 268 (28):21053-21059, Ho, A. S. et al., 1993, “A receptor for interleukin 10 is related to interferon receptors,” Proc. Natl. Acad. Sci. U.S.A. 90 (23):11267-11271, Liu, Y. et al., 1994, “Expression cloning and characterization of a human IL-10 receptor,” J. Immunol. 152 (4):1821-1829, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of IL18R1 (identified by accession no. NM—003855) is disclosed in, e.g., Parnet, P. et al., 1996, “IL-1Rrp is a novel receptor-like molecule similar to the type I interleukin-1 receptor and its homologues T1/ST2 and IL-1R AcP,” J. Biol. Chem. 271 (8):3967-3970, Lovenberg, T. W. et al., 1996, “Cloning of a cDNA encoding a novel interleukin-1 receptor related protein (IL1R-rp2),” J. Neuroimmunol. 70 (2):113-122, Torigoe, K. et al., 1997, “Purification and characterization of the human interleukin-18 receptor,” J. Biol. Chem. 272 (41):25737-25742, and the amino acid sequence of IL18R1 (identified by accession no. NP—003846) is disclosed in, e.g., Parnet, P. et al., 1996, “IL-1Rrp is a novel receptor-like molecule similar to the type I interleukin-1 receptor and its homologues T1/ST2 and IL-1R AcP,” J. Biol. Chem. 271 (8):3967-3970, Lovenberg, T. W. et al., 1996, “Cloning of a cDNA encoding a novel interleukin-1 receptor related protein (IL1R-rp2),” J. Neuroimmunol. 70 (2): 113-122, Torigoe, K. et al., 1997, “Purification and characterization of the human interleukin-18 receptor,” J. Biol. Chem. 272 (41):25737-25742, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of INSL3 (identified by accession no. NM—005543) is disclosed in, e.g., Adham, I. M. et al., 1993, “Cloning of a cDNA for a novel insulin-like peptide of the testicular Leydig cells,” J. Biol. Chem. 268 (35):26668-26672, Burkhardt, E. et al., 1994, “Structural organization of the porcine and human genes coding for a Leydig cell-specific insulin-like peptide (LEY I-L) and chromosomal localization of the human gene (INSL3),” Genomics 20 (1):13-19, Burkhardt, E. et al., 1994, “A human cDNA coding for the Leydig insulin-like peptide (Ley I-L),” Hum. Genet. 94 (1):91-94, and the amino acid sequence of INSL3 (identified by accession no. NP—005534) is disclosed in, e.g., Adham, I. M. et al., 1993, “Cloning of a cDNA for a novel insulin-like peptide of the testicular Leydig cells,” J. Biol. Chem. 268 (35):26668-26672, Burkhardt, E. et al., 1994, “Structural organization of the porcine and human genes coding for a Leydig cell-specific insulin-like peptide (LEY I-L) and chromosomal localization of the human gene (INSL3),” Genomics 20 (1):13-19, Burkhardt, E. et al., 1994, “A human cDNA coding for the Leydig insulin-like peptide (Ley I-L),” Hum. Genet. 94 (1):91-94, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of IRAK2 (identified by accession no. NM—001570) is disclosed in, e.g., Muzio, M. et al., 1997, “IRAK (Pelle) family member IRAK-2 and MyD88 as proximal mediators of IL-1 signaling,” Science 278 (5343):1612-1615, Auron, P. E., 1998, “The interleukin 1 receptor: ligand interactions and signal transduction,” Cytokine Growth Factor Rev. 9 (3-4):221-237, Maschera, B. et al., 1999, “Overexpression of an enzymically inactive interleukin-1-receptor-associated kinase activates nuclear factor-kappaB,” Biochem. J. 339 (PT 2):227-231, and the amino acid sequence of IRAK2 (identified by accession no. NP—001561) is disclosed in, e.g., Muzio, M. et al., 1997, “IRAK (Pelle) family member IRAK-2 and MyD88 as proximal mediators of IL-1 signaling,” Science 278 (5343):1612-1615, Auron, P. E., 1998, “The interleukin 1 receptor: ligand interactions and signal transduction,” Cytokine Growth Factor Rev. 9 (3-4):221-237, Maschera, B. et al., 1999, “Overexpression of an enzymically inactive interleukin-1-receptor-associated kinase activates nuclear factor-kappaB,” Biochem. J. 339 (PT 2):227-231, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of IRAK4 (identified by accession no. NM—016123) is disclosed in, e.g., Siu, G. et al., 1986, “Analysis of a human V beta gene subfamily,” J. Exp. Med. 164 (5):1600-1614, Scanlan, M. J. et al., 1999, “Antigens recognized by autologous antibody in patients with renal-cell carcinoma,” Int. J. Cancer 83 (4):456-464, Li, S. et al., 2002, “IRAK-4: a novel member of the IRAK family with the properties of an IRAK-kinase,” Proc. Natl. Acad. Sci. U.S.A. 99 (8):5567-5572, and the amino acid sequence of IRAK4 (identified by accession no. NP—057207) is disclosed in, e.g., Siu, G. et al., 1986, “Analysis of a human V beta gene subfamily,” J. Exp. Med. 164 (5):1600-1614, Scanlan, M. J. et al., 1999, “Antigens recognized by autologous antibody in patients with renal-cell carcinoma,” Int. J. Cancer 83 (4):456-464, Li, S. et al., 2002, “IRAK-4: a novel member of the IRAK family with the properties of an IRAK-kinase,” Proc. Natl. Acad. Sci. U.S.A. 99 (8):5567-5572, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of ITGAM (identified by accession no. NM—000632) is disclosed in, e.g., Micklem, K. J. et al., 1985, “Isolation of complement-fragment-iC3b-binding proteins by affinity chromatography. The identification of p150,95 as an iC3b-binding protein,” Biochem. J. 231 (1):233-236, Pierce, M. W. et al., 1986, “N-terminal sequence of human leukocyte glycoprotein Mo1:conservation across species and homology to platelet IIb/IIIa,” Biochim. Biophys. Acta 874 (3):368-371, Arnaout, M. A. et al., 1988, “Molecular cloning of the alpha subunit of human and guinea pig leukocyte adhesion glycoprotein Mo1: chromosomal localization and homology to the alpha subunits of integrins,” Proc. Natl. Acad. Sci. U.S.A. 85 (8):2776-2780, and the amino acid sequence of ITGAM (identified by accession no. NP—000623) is disclosed in, e.g., Micklem, K. J. et al., 1985, “Isolation of complement-fragment-iC3b-binding proteins by affinity chromatography. The identification of p150,95 as an iC3b-binding protein,” Biochem. J. 231 (1):233-236, Pierce, M. W. et al., 1986, “N-terminal sequence of human leukocyte glycoprotein Mo1:conservation across species and homology to platelet IIb/IIIa,” Biochim. Biophys. Acta 874 (3):368-371, Arnaout, M. A. et al., 1988, “Molecular cloning of the alpha subunit of human and guinea pig leukocyte adhesion glycoprotein Mo1: chromosomal localization and homology to the alpha subunits of integrins,” Proc. Natl. Acad. Sci. U.S.A. 85 (8):2776-2780, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of JAK2 (identified by accession no. NM—004972) is disclosed in, e.g., Wilks, A. F. et al., 1991, “Two novel protein-tyrosine kinases, each with a second phosphotransferase-related catalytic domain, define a new class of protein kinase,” Mol. Cell. Biol. 11 (4):2057-2065, Pritchard, M. A. et al., 1992, “Two members of the JAK family of protein tyrosine kinases map to chromosomes 1p31.3 and 9p24,” Mamm. Genome 3 (1):36-38, Witthuhn, B. A. et al., 1993, “JAK2 associates with the erythropoietin receptor and is tyrosine phosphorylated and activated following stimulation with erythropoietin,” Cell 74 (2):227-236, and the amino acid sequence of JAK2 (identified by accession no. NP—004963) is disclosed in, e.g., Wilks, A. F. et al., 1991, “Two novel protein-tyrosine kinases, each with a second phosphotransferase-related catalytic domain, define a new class of protein kinase,” Mol. Cell. Biol. 11 (4):2057-2065, Pritchard, M. A. et al., 1992, “Two members of the JAK family of protein tyrosine kinases map to chromosomes 1p31.3 and 9p24,” Mamm. Genome 3 (1):36-38, Witthuhn, B. A. et al., 1993, “JAK2 associates with the erythropoietin receptor and is tyrosine phosphorylated and activated following stimulation with erythropoietin,” Cell 74 (2):227-236, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of LDLR (identified by accession no. NM—000527) is disclosed in, e.g., Brown, M. S. et al., 1979, “Receptor-mediated endocytosis: insights from the lipoprotein receptor system,” Proc. Natl. Acad. Sci. U.S.A. 76 (7):3330-3337, Goldstein, J. L. et al., 1982, “Receptor-mediated endocytosis and the cellular uptake of low density lipoprotein,” Ciba Found. Symp. 92, 77-95, Tolleshaug H. et al., 1983, “The LDL receptor locus in familial hypercholesterolemia: multiple mutations disrupt transport and processing of a membrane receptor,” Cell 32 (3):941-951, and the amino acid sequence of LDLR (identified by accession no. NP—000518) is disclosed in, e.g., Brown, M. S. et al., 1979, “Receptor-mediated endocytosis: insights from the lipoprotein receptor system,” Proc. Natl. Acad. Sci. U.S.A. 76 (7):3330-3337, Goldstein, J. L. et al., 1982, “Receptor-mediated endocytosis and the cellular uptake of low density lipoprotein,” Ciba Found. Symp. 92, 77-95, Tolleshaug, H. et al., 1983, “The LDL receptor locus in familial hypercholesterolemia: multiple mutations disrupt transport and processing of a membrane receptor,” Cell 32 (3):941-951, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of LY96 (identified by accession no. NM—015364) is disclosed in, e.g., Shimazu, R. et al., 1999, “MD-2, a molecule that confers lipopolysaccharide responsiveness on Toll-like receptor 4,” J. Exp. Med. 189 (11):1777-1782, Kato, K. et al., 2000, “ESOP-1, a secreted protein expressed in the hematopoietic, nervous, and reproductive systems of embryonic and adult mice,” Blood 96 (1):362-364, Dziarski, R. et al., 2001, “MD-2 enables Toll-like receptor 2 (TLR2)-mediated responses to lipopolysaccharide and enhances TLR2-mediated responses to Gram-positive and Gram-negative bacteria and their cell wall components,” J. Immunol. 166 (3):1938-1944, and the amino acid sequence of LY96 (identified by accession no. NP—056179) is disclosed in, e.g., Shimazu, R. et al., 1999, “MD-2, a molecule that confers lipopolysaccharide responsiveness on Toll-like receptor 4,” J. Exp. Med. 189 (11):1777-1782, Kato, K. et al., 2000, “ESOP-1, a secreted protein expressed in the hematopoietic, nervous, and reproductive systems of embryonic and adult mice,” Blood 96 (1):362-364, Dziarski, R. et al., 2001, “MD-2 enables Toll-like receptor 2 (TLR2)-mediated responses to lipopolysaccharide and enhances TLR2-mediated responses to Gram-positive and Gram-negative bacteria and their cell wall components,” J. Immunol. 166 (3):1938-1944, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of MAP2K6 (identified by accession nos. NM—002758, NM—031988) is disclosed in, e.g., Han, J. et al., 1996, “Characterization of the structure and function of a novel MAP kinase kinase (MKK6), J. Biol. Chem. 271 (6):2886-2891, Raingeaud, J. et al., 1996, “MKK3- and MKK6-regulated gene expression is mediated by the p38 mitogen-activated protein kinase signal transduction pathway,” Mol. Cell. Biol. 16 (3), 1247-1255, Stein, B. et al., 1996, “Cloning and characterization of MEK6, a novel member of the mitogen-activated protein kinase kinase cascade,” J. Biol. Chem. 271 (19): 11427-11433, and the amino acid sequence of MAP2K6 (identified by accession nos. NP—002749, NP—114365) is disclosed in, e.g., Han, J. et al., 1996, “Characterization of the structure and function of a novel MAP kinase kinase (MKK6), J. Biol. Chem. 271 (6):2886-2891, Raingeaud, J. et al., 1996, “MKK3- and MKK6-regulated gene expression is mediated by the p38 mitogen-activated protein kinase signal transduction pathway,” Mol. Cell. Biol. 16 (3), 1247-1255, Stein, B. et al., 1996, “Cloning and characterization of MEK6, a novel member of the mitogen-activated protein kinase kinase cascade,” J. Biol. Chem. 271 (19): 11427-11433, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of MAPK14 (identified by accession nos. NM—001315, NM—139012, NM—139013, NM—139014) is disclosed in, e.g., Zhukov-Verezhnikov, N. N. et al., 1976, “Study of the heterogenetic antigens in vaccinal preparations of V. cholerae,” Biochem. Biophys. Res. Commun. 82 (8):961-962, Schultz, S. J. et al., 1993, Identification of 21 novel human protein kinases, including 3 members of a family related to the cell cycle regulator nimA of Aspergillus nidulans,” Cell Growth Differ. 4 (10):821-830, Lee, J. C. et al., 1994, “A protein kinase involved in the regulation of inflammatory cytokine biosynthesis,” Nature 372 (6508):739-746, and the amino acid sequence of MAPK14 (identified by accession nos. NP—001306, NP—620581, NP—620582, NP—620583) is disclosed in, e.g., Zhukov-Verezhnikov, N. N. et al., 1976, “Study of the heterogenetic antigens in vaccinal preparations of V. cholerae,” Biochem. Biophys. Res. Commun. 82 (8):961-962, Schultz, S. J. et al., 1993, Identification of 21 novel human protein kinases, including 3 members of a family related to the cell cycle regulator nimA of Aspergillus nidulans,” Cell Growth Differ. 4 (10):821-830, Lee, J. C. et al., 1994, “A protein kinase involved in the regulation of inflammatory cytokine biosynthesis,” Nature 372 (6508):739-746, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of Monocyte Chemoattractant Protein 1 (MCP1) (identified by accession nos. AF493698 and AF493697) is disclosed in, e.g., Shanmugasundaram et al., 2002, Virology II, National Institute of Immunology, Aruna Asag Ali Marg, J.N.U. Campus, New Delhi 110 067, India, and the amino acid sequence of MCP1 (identified by accession no. AAQ75526) is disclosed in, e.g., Nyquist et al., 2003, direct submission, Medicine, Inova Fairfax, 3300 Gallows Road, Falls Church, Va. 22402-3300, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of MKNK1 (identified by accession nos. NM—003684, NM—198973) is disclosed in, e.g., Fukunaga et al., 1997, “MNK1, a new MAP kinase-activated protein kinase, isolated by a novel expression screening method for identifying protein kinase substrates, EMBO J. 16: 1921-1933; Pyronnet et al., 1999, “Human eukaryotic translation initiation factor 4G (eIF4G) recruits mnk1 to phosphorylate eIF4E,” EMBO J. 18: 270-279; Cuesta et al., 2000, “Chaperone hsp27 inhibits translation during heat shock by binding eIF4G and facilitating dissociation of cap-initiation complexes,” Genes Dev. 14: 1460-1470, and the amino acid sequence of MKNK1 (identified by accession nos. NP—003675, NP—945324) is disclosed in, e.g., Fukunaga et al., 1997, “MNK1, a new MAP kinase-activated protein kinase, isolated by a novel expression screening method for identifying protein kinase substrates,” EMBO J. 16:1921-1933, Pyronnet et al., 1999, “Human eukaryotic translation initiation factor 4G (eIF4G) recruits mnk1 to phosphorylate eIF4E,” EMBO J. 18: 270-279, Cuesta et al., 2000, “Chaperone hsp27 inhibits translation during heat shock by binding eIF4G and facilitating dissociation of cap-initiation complexes,” Genes Dev. 14: 1460-1470, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of MMP9 (identified by accession no. NM—004994) is disclosed in, e.g., Wilhelm et al., 1989, “SV40-transformed human lung fibroblasts secrete a 92-kDa type IV collagenase which is identical to that secreted by normal human macrophages,” J. Biol. Chem. 264: 17213-17221, Huhtala et al., 1990, “Completion of the primary structure of the human type IV collagenase preproenzyme and assignment of the gene (CLG4) to the q21 region of chromosome 16,” Genomics 6: 554-559, Collier et al., 1991, “On the structure and chromosome location of the 72- and 92-kDa human type IV collagenase genes,” Genomics 9: 429-434, and the amino acid sequence of MMP9 (identified by accession no. NP—004985) is disclosed in, e.g., Wilhelm et al., 1989, “SV40-transformed human lung fibroblasts secrete a 92-kDa type IV collagenase which is identical to that secreted by normal human macrophages,” J. Biol. Chem. 264: 17213-17221, Huhtala et al., 1990, “Completion of the primary structure of the human type IV collagenase preproenzyme and assignment of the gene (CLG4) to the q21 region of chromosome 16,” Genomics 6: 554-559, Collier et al., 1991, “On the structure and chromosome location of the 72- and 92-kDa human type IV collagenase genes,” Genomics 9: 429-434, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of NCR1 (identified by accession no. NM—004829) is disclosed in, e.g., Sivori et al., 1997, “p46, a novel natural killer cell-specific surface molecule that mediates cell activation,” J. Exp. Med. 186:1129-1136, Vitale, M. et al., NKp44, 1998, “NKp44, a novel triggering surface molecule specifically expressed by activated natural killer cells, is involved in non-major histocompatibility complex-restricted tumor cell lysis,” J. Exp. Med. 187: 2065-2072, Pessino et al., 1998, “Molecular cloning of NKp46: a novel member of the immunoglobulin superfamily involved in triggering of natural cytotoxicity,” J. Exp. Med. 188: 953-960, and the amino acid sequence of NCR1 (identified by accession no. NP—004820) is disclosed in, e.g., Sivori et al., 1997, “p46, a novel natural killer cell-specific surface molecule that mediates cell activation,” J. Exp. Med. 186:1129-1136, Vitale et al., NKp44, 1998, “NKp44, a novel triggering surface molecule specifically expressed by activated natural killer cells, is involved in non-major histocompatibility complex-restricted tumor cell lysis,” J. Exp. Med. 187: 2065-2072, Pessino et al., 1998, “Molecular cloning of NKp46: a novel member of the immunoglobulin superfamily involved in triggering of natural cytotoxicity,” J. Exp. Med. 188: 953-96, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of OSM (identified by accession no. NM—020530) is disclosed in, e.g., Zarling et al., 1986, “Oncostatin M: a growth regulator produced by differentiated histiocytic lymphoma cells,” Proc. Natl. Acad. Sci. U.S.A. 83 (24):9739-9743, Malik et al., 1989, “Molecular cloning, sequence analysis, and functional expression of a novel growth regulator, oncostatin M,” Mol. Cell. Biol. 9 (7):2847-2853, Linsley, P. S. et al., 1990, “Cleavage of a hydrophilic C-terminal domain increases growth-inhibitory activity of oncostatin M,” Mol. Cell. Biol. 10 (5):1882-1890, and the amino acid sequence of OSM (identified by accession no. NP—065391) is disclosed in, e.g., Zarling, J. M. et al., 1986, “Oncostatin M: a growth regulator produced by differentiated histiocytic lymphoma cells,” Proc. Natl. Acad. Sci. U.S.A. 83 (24):9739-9743, Malik, N. et al., 1989, “Molecular cloning, sequence analysis, and functional expression of a novel growth regulator, oncostatin M,” Mol. Cell. Biol. 9 (7):2847-2853, Linsley, P. S. et al., 1990, “Cleavage of a hydrophilic C-terminal domain increases growth-inhibitory activity of oncostatin M,” Mol. Cell. Biol. 10 (5):1882-1890, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of PFKFB3 (identified by accession no. NM—004566) is disclosed in, e.g., Sakai, A. et al., 1996, “Cloning of cDNA encoding for a novel isozyme of fructose 6-phosphate, 2-kinase/fructose 2,6-bisphosphatase from human placenta,” J. Biochem. 119 (3):506-511, Hamilton, J. A. et al., 1997, “Identification of PRG1, a novel progestin-responsive gene with sequence homology to 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase,”
Mol. Endocrinol. 11 (4):490-502, Nicholl, J. et al., “The third human isoform of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFKFB3) map position 10p14-p15, Chromosome Res. 5 (2):150, and the amino acid sequence of PFKFB3 (identified by accession no. NP—004557) is disclosed in, e.g., Sakai, A. et al., 1996, “Cloning of cDNA encoding for a novel isozyme of fructose 6-phosphate, 2-kinase/fructose 2,6-bisphosphatase from human placenta,” J. Biochem. 119 (3):506-511, Hamilton, J. A. et al., 1997, “Identification of PRG1, a novel progestin-responsive gene with sequence homology to 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase,” Mol. Endocrinol. 11 (4):490-502, Nicholl, J. et al., “The third human isoform of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFKFB3) map position 10p14-p15, Chromosome Res. 5 (2):150, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of PRV1 (identified by accession no. NM—020406) is disclosed in, e.g., Lalezari, P. et al., 1971, “NB 1, a new neutrophil-specific antigen involved in the pathogenesis of neonatal neutropenia,” J. Clin. Invest. 50 (5): 1108-1115, Goldschmeding, R. et al., 1992, “Further characterization of the NB 1 antigen as a variably expressed 56-62 kD GPI-linked glycoprotein of plasma membranes and specific granules of neutrophils,” Br. J. Haematol. 81 (3):336-345, Stroncek, D. F. et al., “Neutrophil-specific antigen NB1 inhibits neutrophil-endothelial cell interactions,” J. Lab. Clin. Med. 123 (2):247-255, and the amino acid sequence of PRV1 (identified by accession no. NP—065139) is disclosed in, e.g., Lalezari, P. et al., 1971, “NB 1, a new neutrophil-specific antigen involved in the pathogenesis of neonatal neutropenia,” J. Clin. Invest. 50 (5): 1108-1115, Goldschmeding, R. et al., 1992, “Further characterization of the NB 1 antigen as a variably expressed 56-62 kD GPI-linked glycoprotein of plasma membranes and specific granules of neutrophils,” Br. J. Haematol. 81 (3):336-345, Stroncek, D. F. et al., “Neutrophil-specific antigen NB 1 inhibits neutrophil-endothelial cell interactions,” J. Lab. Clin. Med. 123 (2):247-255, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of PSTPIP2 (identified by accession no. NM—024430) is disclosed in, e.g., Hillier, L. D. et al., 1996, “Generation and analysis of 280,000 human expressed sequence tags,” Genome Res. 6 (9):807-828, Wu, Y. et al., 1998, “PSTPIP 2, a second tyrosine phosphorylated, cytoskeletal-associated protein that binds a PEST-type protein-tyrosine phosphatase,” J. Biol. Chem. 273 (46):30487-30496, Yeung, Y. G. et al., 1998, “A novel macrophage actin-associated protein (MAYP) is tyrosine-phosphorylated following colony stimulating factor-1 stimulation,” J. Biol. Chem. 273 (46): 30638-30642, and the amino acid sequence of PSTPIP2 (identified by accession no. NP—077748) is disclosed in, e.g., Hillier, L. D. et al., 1996, “Generation and analysis of 280,000 human expressed sequence tags,” Genome Res. 6 (9):807-828, Wu, Y. et al., 1998, “PSTPIP 2, a second tyrosine phosphorylated, cytoskeletal-associated protein that binds a PEST-type protein-tyrosine phosphatase,” J. Biol. Chem. 273 (46):30487-30496, Yeung, Y. G. et al., 1998, “A novel macrophage actin-associated protein (MAYP) is tyrosine-phosphorylated following colony stimulating factor-1 stimulation,” J. Biol. Chem. 273 (46): 30638-30642, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of SOCS3 (identified by accession no. NM—003955) is disclosed in, e.g., Minamoto, S. et al., 1997, “Cloning and functional analysis of new members of STAT induced STAT inhibitor (SSI) family: SSI-2 and SSI-3,” Biochem. Biophys. Res. Commun. 237 (1):79-83, Masuhara, M. et al., 1997, “Cloning and characterization of novel CIS family genes,” Biochem. Biophys. Res. Commun. 239 (2):439-446, Zhang, J. G. et al., 1999, “The conserved SOCS box motif in suppressors of cytokine signaling binds to elongins B and C and may couple bound proteins to proteasomal degradation,” Proc. Natl. Acad. Sci. U.S.A. 96 (5):2071-2076, and the amino acid sequence of SOCS3 (identified by accession no. NP—003946) is disclosed in, e.g., Minamoto, S. et al., 1997, “Cloning and functional analysis of new members of STAT induced STAT inhibitor (SSI) family: SSI-2 and SSI-3,” Biochem. Biophys. Res. Commun. 237 (1):79-83, Masuhara, M. et al., 1997, “Cloning and characterization of novel CIS family genes,” Biochem. Biophys. Res. Commun. 239 (2):439-446, Zhang, J. G. et al., 1999, “The conserved SOCS box motif in suppressors of cytokine signaling binds to elongins B and C and may couple bound proteins to proteasomal degradation,” Proc. Natl. Acad. Sci. U.S.A. 96 (5):2071-2076, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of SOD2 (identified by accession no. NM—000636) is disclosed in, e.g., Smith, M. et al., 1978, “Regional localization of HLA, MES, and SODM on chromosome 6,” Cytogenet. Cell Genet. 22 (1-6):428-433, Beck, Y. et al., 1987, “Human Mn superoxide dismutase cDNA sequence,” Nucleic Acids Res. 15 (21):9076, Ho, Y. S. et al., 1988, “Isolation and characterization of complementary DNAs encoding human manganese-containing superoxide dismutase,” FEBS Lett. 229 (2):256-260, and the amino acid sequence of SOD2 (identified by accession no. NP—000627) is disclosed in, e.g., Smith, M. et al., 1978, “Regional localization of HLA, MES, and SODM on chromosome 6,” Cytogenet. Cell Genet. 22 (1-6):428-433, Beck, Y. et al., 1987, “Human Mn superoxide dismutase cDNA sequence,” Nucleic Acids Res. 15 (21):9076, Ho, Y. S. et al., 1988, “Isolation and characterization of complementary DNAs encoding human manganese-containing superoxide dismutase,” FEBS Lett. 229 (2):256-260, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of TDRD9 (identified by accession no. NM—153046) is disclosed in, e.g., Isogai et al., 2003, “Homo sapiens cDNA FLJ43990 fis, clone TEST14019566, weakly similar to Dosage compensation regulator,” unpublished, and the amino acid sequence of TDRD9 (identified by accession no. NP—694591) is disclosed in, e.g., Isogai et al., 2003, “Homo sapiens cDNA FLJ43990 fis, clone TEST14019566, weakly similar to Dosage compensation regulator,” unpublished, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of TGFBI (identified by accession no. NM—000358) is disclosed in, e.g., Skonier et al., 1992, “cDNA cloning and sequence analysis of beta ig-h3, a novel gene induced in a human adenocarcinoma cell line after treatment with transforming growth factor-beta,” DNA Cell Biol. 11 (7):511-522, Stone et al., 1994, “Three autosomal dominant corneal dystrophies map to chromosome 5q,” Nat. Genet. 6 (1):47-51, Skonier et al., 1994, “beta ig-h3: a transforming growth factor-beta-responsive gene encoding a secreted protein that inhibits cell attachment in vitro and suppresses the growth of CHO cells in nude mice,” DNA Cell Biol. 13 (6):571-584, and the amino acid sequence of TGFBI (identified by accession no. NP—000349) is disclosed in, e.g., Skonier et al., 1992, “cDNA cloning and sequence analysis of beta ig-h3, a novel gene induced in a human adenocarcinoma cell line after treatment with transforming growth factor-beta,” DNA Cell Biol. 11 (7):511-522; Stone et al., 1994, “Three autosomal dominant corneal dystrophies map to chromosome 5q,” Nat. Genet. 6 (1):47-51; Skonier et al., 1994, “beta ig-h3: a transforming growth factor-beta-responsive gene encoding a secreted protein that inhibits cell attachment in vitro and suppresses the growth of CHO cells in nude mice,” DNA Cell Biol. 13: 571-584, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of TIFA (identified by accession no. NM—052864) is disclosed in, e.g., Kanamori, M. et al., 2002, “T2BP, a novel TRAF2 binding protein, can activate NF-kappaB and AP-1 without TNF stimulation,” Biochem. Biophys. Res. Commun. 290 (3):1108-1113, Takatsuna, H. et al., 2003, “Identification of TIFA as an adapter protein that links tumor necrosis factor receptor-associated factor 6 (TRAF6) to interleukin-1 (IL-1) receptor-associated kinase-1 (IRAK-1) in IL-1 receptor signaling,” J. Biol. Chem. 278 (14):12144-12150, Matsuda et al., 2003, “Large-scale identification and characterization of human genes that activate NF-kappaB and MAPK signaling pathways,” Oncogene 22 (21):3307-3318, and the amino acid sequence of TIFA (identified by accession no. NP—443096) is disclosed in, e.g., Kanamori et al., 2002, “T2BP, a novel TRAF2 binding protein, can activate NF-kappaB and AP-1 without TNF stimulation,” Biochem. Biophys. Res. Commun. 290:1108-1113, Takatsuna et al., 2003, “Identification of TIFA as an adapter protein that links tumor necrosis factor receptor-associated factor 6 (TRAF6) to interleukin-1 (IL-1) receptor-associated kinase-1 (IRAK-1) in IL-1 receptor signaling,” J. Biol. Chem. 278 (14):12144-12150, Matsuda et al., 2003, “Large-scale identification and characterization of human genes that activate NF-kappaB and MAPK signaling pathways,” Oncogene 22 (21):3307-3318, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of Tissue Inhibitor of Metalloproteinase 1 (TIMP 1) (identified by accession no. NM—003254) is disclosed in, e.g., Domeij et al., 2005, “ell expression of MMP-1 and TIMP-1 in co-cultures of human gingival fibroblasts and monocytes: the involvement of ICAM-1,” Biochem. Biophys. Res. Commun. 338, 1825-1833; Zureik et al., “Serum tissue inhibitors of metalloproteinases 1 (TIMP-1) and carotid atherosclerosis and aortic arterial stiffness”, J. Hypertens. 23, 2263-2268; Crombez, 2005, “High level production of secreted proteins: example of the human tissue inhibitor of metalloproteinases 1”, Biochem. Biophys. Res. Commun. 337, 908-915 and the amino acid sequence of TIMP1 (identified by accession no. AAA75558) is disclosed in, e.g., Hardcastle et al., 1997, “Genomic organization of the human TIMP-1 gene. Investigation of a causative role in the pathogenesis of X-linked retinitis pigmentosa,” Invest. Ophthalmol. Vis. Sci. 38, 1893-1896, which is incorporated by reference herein in its entirety.
The nucleotide sequence of TLR4 (identified by accession no. AH009665) is disclosed in, e.g., Arbour, N. C. et al., 1999, Direct Submission, Medicine, University of Iowa, 2182 Med Labs, Iowa City, Iowa 52242, USA, Arbour, N. C. et al., A Genetic Basis for a Blunted Response to Endotoxin in Humans, Arbour, N. C. et al., unpublished, “A Genetic Basis for a Blunted Response to Endotoxin in Humans”, and the amino acid sequence of TLR4 (identified by accession no. AAF05316) is disclosed in, e.g., Beutler, 1999, Direct Submission, Department of Internal Medicine, University of Texas Southwestern Medical Center and the Howard Hughes Medical Institute, 5323 Harry Hines Boulevard, Dallas, Tex. 75235-9050, USA, Smirnova, I. et al., 2000, “Phylogenetic variation and polymorphism at the toll-like receptor 4 locus (TLR4),” Genome Biol. 1, res. 002.1-002.10, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of TNFRSF6 (identified by accession no. NM—152877) is disclosed in, e.g., Oehm, A. et al., 1992, “Purification and molecular cloning of the APO-1 cell surface antigen, a member of the tumor necrosis factor/nerve growth factor receptor superfamily. Sequence identity with the Fas antigen,” J. Biol. Chem. 267 (15):10709-10715, Inazawa, J. et al., 1992, “Assignment of the human Fas antigen gene (Fas) to 10q24.1,” Genomics 14 (3):821-822, Cheng, J. et al., 1994, “Protection from Fas-mediated apoptosis by a soluble form of the Fas molecule,” Science 263 (5154):1759-1762, and the amino acid sequence of TNFRSF6 (identified by accession no. NP—000034) is disclosed in, e.g., Oehm, A. et al., 1992, “Purification and molecular cloning of the APO-1 cell surface antigen, a member of the tumor necrosis factor/nerve growth factor receptor superfamily. Sequence identity with the Fas antigen,” J. Biol. Chem. 267 (15):10709-10715, Inazawa, J. et al., 1992, “Assignment of the human Fas antigen gene (Fas) to 10q24.1,” Genomics 14 (3):821-822, Cheng, J. et al., 1994, “Protection from Fas-mediated apoptosis by a soluble form of the Fas molecule,” Science 263 (5154):1759-1762, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of TNFSF10 (identified by accession no. NM—003810) is disclosed in, e.g., Wiley, S. R. et al., 1995, “Identification and characterization of a new member of the TNF family that induces apoptosis,” Immunity 3 (6):673-682, Pitti, R. M. et al., 1996, “Induction of apoptosis by Apo-2 ligand, a new member of the tumor necrosis factor cytokine family,” J. Biol. Chem. 271 (22):12687-12690, Pan, G. et al., 1997, “The receptor for the cytotoxic ligand TRAIL,” Science 276 (5309):111-113, and the amino acid sequence of TNFSF10 (identified by accession no. NP—003801) is disclosed in, e.g., Wiley, S. R. et al., 1995, “Identification and characterization of a new member of the TNF family that induces apoptosis,” Immunity 3 (6):673-682, Pitti, R. M. et al., 1996, “Induction of apoptosis by Apo-2 ligand, a new member of the tumor necrosis factor cytokine family,” J. Biol. Chem. 271 (22): 12687-12690, Pan, G. et al., 1997, “The receptor for the cytotoxic ligand TRAIL,” Science 276 (5309): 111-113, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of TNFSF13B (identified by accession no. NM—006573) is disclosed in, e.g., Shu, H. B. et al., 1999, “TALL-1 is a novel member of the TNF family that is down-regulated by mitogens,” J. Leukoc. Biol. 65 (5): 680-683, Mukhopadhyay, A. et al., 1999, “Identification and characterization of a novel cytokine, THANK, a TNF homologue that activates apoptosis, nuclear factor-kappaB, and c-Jun NH2-terminal kinase,” J. Biol. Chem. 274 (23):15978-15981, Schneider, P. et al., 1999, “BAFF, a novel ligand of the tumor necrosis factor family, stimulates B cell growth,” J. Exp. Med. 189 (11):1747-1756, and the amino acid sequence of TNFSF13B (identified by accession no. NP—006564) is disclosed in, e.g., Shu, H. B. et al., 1999, “TALL-1 is a novel member of the TNF family that is down-regulated by mitogens,” J. Leukoc. Biol. 65 (5): 680-683, Mukhopadhyay, A. et al., 1999, “Identification and characterization of a novel cytokine, THANK, a TNF homologue that activates apoptosis, nuclear factor-kappaB, and c-Jun NH2-terminal kinase,” J. Biol. Chem. 274 (23):15978-15981, Schneider, P. et al., 1999, “BAFF, a novel ligand of the tumor necrosis factor family, stimulates B cell growth,” J. Exp. Med. 189 (11):1747-1756, each of which is incorporated by reference herein in its entirety.
The nucleotide sequence of VNN1 (identified by accession no. NM—004666) is disclosed in, e.g., Aurrand-Lions, M. et al., 1996, “Vanin-1, a novel GPI-linked perivascular molecule involved in thymus homing,” Immunity 5 (5):391-405, Galland, F. et al., 1998, “Two human genes related to murine vanin-1 are located on the long arm of human chromosome 6,” Genomics 53 (2):203-213, Maras, B. et al., 1999, “Is pantetheinase the actual identity of mouse and human vanin-1 proteins?,” FEBS Lett. 461 (3):149-152, and the amino acid sequence of VNN1 (identified by accession no. NP—004657) is disclosed in, e.g., Aurrand-Lions, M. et al., 1996, “Vanin-1, a novel GPI-linked perivascular molecule involved in thymus homing,” Immunity 5 (5):391-405, Galland, F. et al., 1998, “Two human genes related to murine vanin-1 are located on the long arm of human chromosome 6,” Genomics 53 (2):203-213, Maras, B. et al., 1999, “Is pantetheinase the actual identity of mouse and human vanin-1 proteins?,” FEBS Lett. 461 (3):149-152, each of which is incorporated by reference herein in its entirety.
5.11.2 Exemplary Combinations of Biomarkers in Accordance with Embodiments of the Invention
In some embodiments, the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, or more biomarkers selected from Table I regardless of whether each such biomarker has an “N” designation or a “P” designation in Table I. In some nonlimiting exemplary embodiments, between 2 and 53, between 3 and 40, between 4 and 30, or between 5 and 20 such biomarkers are used.
Nucleic acid based kits and methods. In some embodiments, the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, or more biomarkers selected from Table J. Typcially, in these embodiments, each biomarker is a nucleic acid (e.g., DNA, such as cDNA or amplified DNA, or RNA, such as mRNA), or a discriminating molecule or discriminating fragment of a nucleic acid. In some nonlimiting exemplary embodiments, between 2 and 44, between 3 and 35, between 4 and 25, or between 5 and 20 such biomarkers are used.
Protein or peptide based kits and methods. In some embodiments, the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 of the biomarkers selected from Table K. Typcially, such biomarkers are peptide-based (e.g., a peptide, a full length protein, etc.), or a discriminating molecule or discriminating fragment of the foregoing. In some embodiments, the biomarkers in the kit are specific antibodies to two or more of the biomarkers listed in Table K. In some nonlimiting exemplary embodiments, between 2 and 10, between 3 and 10, between 4 and 10, or between 5 and 10 such biomarkers are used.
Homogenous kits and methods. In some embodiments, each of the biomarkers in the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use at least two or more biomarkers selected from Table I where each biomarker used in such methods or kits is in the same physical form. In one example in accordance with such embodiments, each biomarker in a method or kit in accordance Section 5.2 and Section 5.3, respectively, is a biomarker selected from Table I and is a nucleic acid or a discriminating molecule of a nucleic acid in the method or kit. In another example in accordance with such embodiments, each biomarker in a method or kit in accordance Section 5.2 and Section 5.3, respectively, is a biomarker selected from Table I and is peptide-based (e.g., a peptide, a full length protein, etc.) or a discriminating molecule of the forgoing. In these embodiments, biomarkers are selected without regard as to whether they are designated “P” or “N” in Table I. Thus, a kit in accordance with these embodiments can include a biomarker in nucleic acid form, even when the biomarker is designated “P” on Table I. Correspondingly, a kit in accordance with this embodiment can include a biomarker in peptidic form, even when the biomarker is designated “N” on Table I.
Heterogeneous kits and methods. In some embodiments, each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least two or more biomarkers selected from Table I where each such biomarker is in the same physical form that the biomarker was in when identified in Sections 6.11 through 6.13 below. In other words, if the biomarker has an “N” designation in Table I, a nucleic acid form of the biomarker is used in the methods and kits respectively described or referenced in Section 5.2 and 5.3 in accordance with this embodiment of the invention. If the biomarker has a “P” designation in Table I, a peptidic form of the biomarker is used in the methods and kits respectively described or referenced in Section 5.2 and 5.3 in accordance with this embodiment of the invention. Further, there is at least one biomarker used in such methods or kits that has an “N” designation in Table I and at least one biomarker that has a “P” designation. In such embodiments, biomarkers having an N designation in Table I are nucleic acids and biomarkers having a P designation in Table I are peptide-based or protein-based.
A non-limiting exemplary kit in accordance with such mixed embodiments use two biomarkers from among the biomarkers listed in Table J, in nucleic acid form, and three biomarkers from among the biomarkers listed in Table K, in peptidic-based form. In some embodiments, the non-limiting methods and kits respectively described or referenced in Sections 5.2 and 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, or more biomarkers from Table J, in nucleic acid form, and 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers from Table K in peptide-based or protein-based form.
Additional kits and methods. In some embodiments, each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least one biomarkers selected from Table I and at least one different biomarker from Table 31. In some embodiments, each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers selected from Table I and at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers from Table 31.
In some embodiments, each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least one biomarker in, nucleic acid form, selected from Table J and at least one different biomarker from Table 31. In some embodiments, each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers selected from Table I, each in nucleic acid form, and at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers from Table 31.
In some embodiments, each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least one biomarker in, protein form, selected from Table K and at least one different biomarker from Table 31. In some embodiments, each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers selected from Table I, each in protein form, and at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers from Table 31.
In some embodiments, each of the biomarkers in the methods and kits respectively described or referenced in Section 5.2 and Section 5.3 use at least one biomarker from among the biomarkers listed in Table J, in nucleic acid form, and at least one biomarkers from among the biomarkers listed in Table K, in protein form. In some embodiments, the non-limiting methods and kits respectively described or referenced in Sections 5.2 and 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, or more biomarkers from Table J, in nucleic acid form, and 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers from Table K in protein form.
In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-6, IL-8, MMP9, B2M, HLA-DRA, and MCP1 biomarkers are not used in such methods or kits. For example, in embodiments where certain monocytes are isolated from whole blood and tested, such biomarkers are not utilized, especially when such biomarkers are nucleic acids. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-6, IL-8, IL-10, and CRP protein biomarkers are not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-6, IL-8, IL-10, and CRP nucleic acid biomarkers are not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-6 and MAPK biomarkers are not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-6, IL-8, and IL-10 biomarkers are not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the CD86, IL-6, IL-8, IL-10, and CRP biomarkers are not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-6 and IL-10 biomarkers are not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-6 and CRP biomarkers are not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the CRP biomarker is not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the IL-8 biomarker is not used in such methods or kits. In some embodiments, any of the above-described combinations of biomarkers are used in methods or kits in accordance Section 5.2 and Section 5.3 with the exception that the B2M biomarker is not used in such methods or kits.
5.11.3 Exemplary Subcombinations of Biomarkers in Accordance with Embodiments of the Invention
In some embodiments, the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use any one biomarker set listed in Table L. The biomarker sets listed in Table L were identified in the computational experiments described in Section 6.14.1, below, in which 4600 random subcombinations of the biomarkers listed in Table J were tested. Table L, below, lists some of the biomarker sets that provided high accuracy scores against the validation population described in Section 6.14.1. Each row of Table L lists a single biomarker set that can be used in the methods and kits respectively referenced in Sections 5.2 and 5.3. In other words, each row of Table L describes a biomarker set that can be used to discriminate between sepsis and SIRS subjects (e.g., to determine whether a subject is likely to acquire sepsis). In some embodiments, nucleic acid forms of the biomarkers listed in a biomarker set in Table L are used in the methods and kits respectively referenced in Sections 5.2 and 5.3. In some embodiments, protein forms of the biomarkers listed in a biomarker set in Table L are used in the methods and kits respectively referenced in Sections 5.2 and 5.3. In some hybrid embodiments, some of the biomarkers in a biomarker set listed in Table L are in protein form and some of the biomarkers in the same biomarker set from Table L are in nucleic acid form in the methods and kits respectively referenced in Sections 5.2 and 5.3.
In some embodiments, a given biomarker set listed in Table L is used with the addition of one, two, three, four, five, six, seven, eight, or nine or more additional biomarkers listed in Table I that are not within the given set of biomarkers from Table L. In some embodiments, a given biomarker set listed in Table L is used with the addition of one, two, three, four, five, six, seven, eight, or nine or more additional biomarkers from any one of Tables 1, 30, 31, 32, 33, 34, or 36 that are not within the given biomarker set from Table L. In Table L, accuracy, specificity, and senstitivity are described with reference to T−12 time point data described in Section 6.14.1, below.







TABLE L







Exemplary sets of biomarkers used in the methods


or kits referenced in Sections 5.2 and 5.3










BIOMARKER SET
ACCURACY
SPECIFICITY
SENSISTIVITY













INSL3, BCL2A1, CD86
0.82
0.82
0.83


MAP2K6, INSL3, CD86
0.82
0.75
0.87


ARG2, MAP2K6, SOCS3
0.82
0.75
0.87


NCR1, GADD45A, OSM
0.81
0.77
0.85


GADD45B, TNFSF13B, PFKFB3
0.80
0.74
0.87


TLR4, FCGR1A, CSF1R
0.80
0.82
0.78


SOCS3, FCGR1A, PSTPIP2
0.80
0.79
0.81


TGFBI, MAP2K6, PSTPIP2
0.80
0.76
0.83


IFNGR1, JAK2, TNFRSF6, OSM
0.83
0.80
0.87


IRAK2, GADD45A, CD86, JAK2
0.83
0.86
0.81


GADD45A, PRV1, OSM, FCGR1A
0.83
0.80
0.86


IRAK4, CCL5, INSL3, CD86
0.83
0.76
0.90


VNN1, BCL2A1, GADD45B, FAD104
0.82
0.83
0.81


OSM, CD86, PRV1, BCL2A1
0.82
0.78
0.85


VNN1, SOCS3, CSF1R, FCGR1A
0.82
0.78
0.85


VNN1, CCL5, ANKRD22, OSM
0.82
0.77
0.86


LDLR, SOCS3, CD86, IL10alpha
0.81
0.78
0.85


TLR4, SOCS3, IRAK2, CSF1R
0.81
0.76
0.85


IL1RN, SOCS3, ARG2, LDLR
0.81
0.76
0.84


IL18R1, MAP2K6, TGFBI, OSM
0.80
0.86
0.75


FCGR1A, HLA-DRA, IL18R1, PSTPIP2
0.80
0.79
0.82


OSM, IL1RN, SOD2, SOCS3
0.80
0.78
0.82


NCR1, JAK2, TNFSF13B, FCGR1A
0.80
0.76
0.86


TIFA, VNN1, ANXA3, ITGAM
0.80
0.73
0.88


PFKFB3, IRAK2, CSF1R, CD86, PSTPIP2
0.88
0.83
0.91


PSTPIP2, FAD104, TIFA, CD86, LY96
0.84
0.85
0.84


IL1RN, IL10alpha, IFNGR1, OSM, MKNK1
0.83
0.78
0.89


IL18R1, CCL5, JAK2, SOCS3, SOD2
0.83
0.81
0.84


JAK2, MKNK1, TNFSF13B, PRV1, TNFSF10
0.83
0.81
0.84


MAP2K6, ARG2, OSM, ANKRD22,
0.83
0.8
0.85


Gene_MMP9


SOCS3, IL1RN, ARG2, FCGR1A, CCL5
0.83
0.78
0.87


CCL5, INSL3, SOD2, TLR4, ARG2
0.83
0.78
0.87


FCGR1A, ARG2, CD86, MAPK14, TNFRSF6
0.82
0.83
0.82


INSL3, TLR4, SOCS3, CSF1R, FCGR1A
0.82
0.79
0.85


CEACAM1, TNFRSF6, MAPK14, IL10alpha,
0.82
0.79
0.84


CSF1R


ANKRD22, CD86, CRTAP, OSM, PFKFB3
0.82
0.79
0.84


OSM, IL18R1, LDLR, GADD45B, MKNK1
0.82
0.76
0.86


CRTAP, SOCS3, PSTPIP2, TIFA, FAD104
0.81
0.82
0.81


PSTPIP2, ARG2, IL10alpha, TLR4, CSF1R
0.81
0.81
0.82


TIFA, PFKFB3, CSF1R, LDLR, Gene_MMP9
0.81
0.79
0.83


NCR1, PSTPIP2, GADD45A, LY96, MAPK14
0.81
0.77
0.86


ARG2, BCL2A1, NCR1, PSTPIP2, IL10alpha
0.81
0.82
0.8


PFKFB3, OSM, CSF1R, CD86, TIFA
0.81
0.81
0.81


IL10alpha, CD86, SOCS3, GADD45A, TGFBI
0.81
0.78
0.84


PSTPIP2, PFKFB3, INSL3, PRV1, IL1RN
0.81
0.78
0.85


ITGAM, PRV1, IL18R1, INSL3, JAK2
0.81
0.77
0.85


PSTPIP2, OSM, IL18R1, TNFSF13B, ITGAM
0.81
0.72
0.9


CD86, TIFA, CSF1R, FCGR1A, CRTAP
0.81
0.84
0.78


LY96, TGFBI, SOCS3, ANKRD22, MAPK14
0.81
0.83
0.79


IL1RN, SOD2, VNN1, OSM, TNFSF10
0.81
0.79
0.82


CRTAP, NCR1, OSM, PRV1, ANXA3
0.81
0.76
0.85


TDRD9, LY96, CEACAM1, OSM, NCR1
0.81
0.72
0.88


TGFB1, INSL3, GADD45A, LDLR, PSTPIP2
0.8
0.75
0.85


PFKFB3, IRAK2, CSF1R, CD86, PSTPIP2
0.88
0.83
0.91


MAP2K6, ARG2, CD86, PRV1, FAD104,
0.85
0.86
0.84


MAPK14


ARG2, LY96, INSL3, MAP2K6, TNFSF10,
0.85
0.86
0.84


NCR1


SOCS3, GADD45B, CSF1R, ARG2, PSTPIP2,
0.85
0.83
0.86


OSM


GADD45B, PFKFB3, PSTPIP2, FCGR1A,
0.85
0.81
0.88


HLA-DRA, ARG2


TIFA, IL18R1, MAPK14, CD86, ARG2,
0.84
0.8
0.88


TNFSF13B


FCGR1A, ARG2, GADD45B, IL10alpha,
0.84
0.81
0.86


NCR1, LDLR


TGFBI, INSL3, IRAK4, GADD45B, SOCS3,
0.84
0.8
0.87


CSF1R


SOCS3, CSF1R, CEACAM1, ARG2,
0.83
0.82
0.85


IL10alpha, IFNGR1


TLR4, PFKFB3, ARG2, PRV1, LDLR,
0.83
0.81
0.85


TNFSF13B


PSTPIP2, OSM, TLR4, INSL3, IRAK4,
0.83
0.8
0.85


IL18R1


GADD45A, CCL5, FCGR1A, PSTPIP2,
0.82
0.83
0.82


MAP2K6, IL1RN


OSM, FAD104, JAK2, CRTAP, TDRD9,
0.82
0.79
0.85


TNFSF13B


FAD104, SOCS3, TNFSF13B, GADD45B,
0.82
0.84
0.81


CRTAP, TGFBI


IL18R1, TNFRSF6, INSL3, CD86, ANXA3,
0.82
0.79
0.84


PSTPIP2


HLA-DRA, INSL3, ARG2, CD86, CCL5,
0.82
0.79
0.84


SOCS3


TNFRSF6, IL18R1, CD86, PFKFB3,
0.81
0.82
0.81


IL10alpha, FAD104


FAD104, TGFBI, TDRD9, CD86, SOD2,
0.81
0.79
0.83


ARG2


CD86, ARG2, GADD45A, TLR4, BCL2A1,
0.81
0.79
0.83


GADD45B


SOD2, CEACAM1, OSM, GADD45A,
0.81
0.74
0.88


PSTPIP2, IL10alpha


FCGR1A, CSF1R, NCR1, ANXA3, SOCS3,
0.81
0.81
0.8


Gene_MMP9


TNFSF10, IL1RN, OSM, CSF1R, PSTPIP2,
0.81
0.78
0.83


JAK2


CD86, VNN1, LDLR, IL1RN, MAP2K6,
0.81
0.76
0.84


TDRD9


ARG2, OSM, CSF1R, ITGAM, CRTAP,
0.81
0.76
0.85


SOCS3


ANXA3, CSF1R, CEACAM1, Gene_MMP9,
0.8
0.8
0.81


CD86, OSM


LY96, VNN1, SOD2, TGFBI, ARG2, CSF1R
0.8
0.78
0.83


GADD45A, PSTPIP2, BCL2A1, ANKRD22,
0.8
0.77
0.83


HLA-DRA, ANXA3


TGFBI, FCGR1A, ARG2, CD86, PFKFB3,
0.86
0.86
0.85


BCL2A1, TNFRSF6


SOCS3, ITGAM, TDRD9, INSL3, PRV1,
0.84
0.81
0.87


TGFBI, ARG2


MKNK1, GADD45B, IRAK2, TIFA, OSM,
0.83
0.81
0.85


VNN1, PSTPIP2


SOCS3, PSTPIP2, TDRD9, IL10alpha, ARG2,
0.83
0.82
0.84


CD86, CCL5


CSF1R, PSTPIP2, MAPK14, INSL3, IL18R1,
0.83
0.78
0.87


JAK2, OSM


MKNK1, PSTPIP2, ARG2, LY96, ANKRD22,
0.82
0.85
0.8


SOCS3, IRAK4


PSTPIP2, FAD104, TNFSF13B, ITGAM,
0.82
0.83
0.82


BCL2A1, FCGR1A, ANXA3


SOCS3, IRAK2, IFNGR1, CD86, OSM,
0.82
0.81
0.83


PSTPIP2, GADD45A


INSL3, NCR1, PSTPIP2, PFKFB3,
0.82
0.8
0.84


ANXRD22, HLA-DRA, MKNK1


FCGR1A, HLA-DRA, CSF1R, SOCS3,
0.82
0.76
0.88


IRAK4, TIFA, ARG2


FAD104, TGFBI, MAP2K6, IRAK4, LY96,
0.82
0.81
0.83


CD86, ANKRD22


LDLR, INSL3, GADD45B, ARG2, PFKFB3,
0.82
0.78
0.86


HLA-DRA, ITGAM


FCGR1A, TIFA, CD86, PFKFB3, TDRD9,
0.82
0.79
0.85


GADD45A, LDLR


SOCS3, CSF1R, SOD2, CD86, MAP2K6,
0.82
0.76
0.86


GADD45B, PSTPIP2


MKNK1, CD86, FAD104, PRV1, SOCS3,
0.82
0.76
0.87


IL10alpha, MAP2K6


TIFA, JAK2, LDLR, IRAK2, VNN1, CD86,
0.81
0.81
0.81


ARG2


ANKRD22, MAPK14, INSL3, BCL2A1,
0.81
0.79
0.83


CRTAP, IRAK2, FCGR1A


FAD104, INSL3, CD86, TNFRSF6,
0.81
0.79
0.83


GADD45A, IFNGR1, JAK2


CSF1R, INSL3, VNN1, TIFA, IFNGR1,
0.81
0.79
0.83


LDLR, ARG2


PFKFB3, BCL2A1, ANIXA3, IL10alpha,
0.81
0.79
0.84


FAD104, VNN1, INSL3


CSF1R, CEACAM1, MAP2K6, GADD45B,
0.81
0.82
0.8


TNFSF10, TNFSF13B, TIFA


FCGR1A, ARG2, IRAK2, GADD45A, CD86,
0.81
0.82
0.79


Gene_MMP9, BCL2A1


CRTAP, CEACAM1, FAD104, MKNK1,
0.81
0.76
0.84


INSL3, ITGAM, SOD2


OSM, TDRD9, BCL2A1, IRAK2, GADD45A,
0.8
0.77
0.84


CD86, LDLR


PFKFB3, CCL5, CSF1R, LDLR, TLR4, LY96,
0.8
0.77
0.84


FAD104


IRAK4, GADD45B, CEACAM1, FAD104,
0.8
0.75
0.85


CSF1R, IRAK2, MAPK14


IFNGR1, FAD104, MAP2K6, TNFRSF6,
0.8
0.74
0.86


FCGR1A, IRAK2, ARG2


TGFBI, IRAK2, CRTAP, BCL2A1, ITGAM,
0.8
0.81
0.79


ANXA3, FCGR1A


SOD2, PFKFB3, GADD45B, IRAK2, PRV1,
0.8
0.77
0.82


SOCS3, FCGR1A


TNFRSF6, TLR4, IRAK2, ITGAM, JAK2,
0.8
0.77
0.83


OSM, NCR1


IL10alpha, ANKRD22, Gene_MMP9, IL1RN,
0.8
0.76
0.84


LY96, FAD104, PSTPIP2


IRAK4, INSL3, CSF1R, ITGAM, VNN1,
0.8
0.74
0.86


HLA-DRA, IL18R1


IRAK2, TGFBI, MAP2K6, IL18R1, IFNGR1,
0.8
0.73
0.87


CRTAP, PSTPIP2


TDRD9, ITGAM, OSM, NCR1, CD86,
0.8
0.73
0.87


MAP2K6, CCL5


ANXA3, FCGR1A, TNFSF10, VNN1,
0.85
0.86
0.84


TNFSF13B, ARG2, 12, CD86


INSL3, PFKFB3, MAPK14, FCGR1A,
0.85
0.84
0.85


TDRD9, CSF1R, 12, IRAK4


VNN1, CSF1R, ANKRD22, OSM,
0.85
0.78
0.9


GADD45A, LY96, 12, MAP2K6


ITGAM, OSM, LY96, TDRD9, ANKRD22,
0.84
0.84
0.83


TLR4, 12, MKNK1


ARG2, ANXA3, MAP2K6, CCL5, CD86,
0.84
0.84
0.83


OSM, 12, LDLR


OSM, IL1RN, FCGR1A, GADD45A, ARG2,
0.84
0.82
0.86


IL10alpha, 12, ITGAM


TIFA, ANKRD22, TNFSF13B, CRTAP,
0.84
0.81
0.86


MAP2K6, IRAK4, 12, ARG2


IRAK2, TLR4, IL10alpha, TGFBI, PRV1,
0.83
0.8
0.86


FAD104, 12, MAP2K6


IL18R1, FAD104, TNFSF13B, MAP2K6,
0.83
0.81
0.85


OSM, SOD2, 12, TNFRSF6


OSM, LDLR, VNN1, LY96, ARG2, MAPK14,
0.83
0.8
0.85


12, IRAK2


PRV1, ITGAM, SOD2, Gene_MMP9, OSM,
0.83
0.79
0.86


JAK2, 12, ARG2


ANXA3, TNFSF10, CEACAM1, FCGR1A,
0.83
0.78
0.86


HLA-DRA, IL10alpha, 12, SOCS3


ITGAM, CD86, CEACAM1, TDRD9,
0.83
0.77
0.88


GADD45A, PFKFB3, 12, SOCS3


SOCS3, PRV1, ARG2, CEACAM1, LDLR,
0.82
0.83
0.82


GADD45A, 12, IL10alpha


INSL3, CSF1R, IL1RN, PSTPIP2, MKNK1,
0.82
0.8
0.84


SOCS3, 12, JAK2


TDRD9, LY96, ITGAM, NCR1, PSTPIP2,
0.82
0.75
0.9


IL10alpha, 12, OSM


PFKFB3, MAP2K6, ARG2, TGFBI, LDLR,
0.82
0.84
0.8


FAD104, 12, MAPK14


ARG2, IL18R1, NCR1, CD86, FCGR1A,
0.82
0.79
0.84


TGFBI, 12, IL1RN


HLA-DRA, CEACAM1, IFNGR1, MKNK1,
0.82
0.76
0.87


LDLR, GADD45B, 12, CSF1R


IL10alpha, IL1RN, OSM, PSTPIP2, INSL3,
0.82
0.81
0.82


TIFA, 12, TLR4


MKNK1, CSF1R, VNN1, OSM, ARG2,
0.81
0.81
0.82


GADD45B, 12, SOCS3


IL18R1, GADD45B, TNFRSF6, TNFSF10,
0.81
0.79
0.84


TIFA, JAK2, 12, GADD45A


LDLR, IL10alpha, PRV1, LY96, ANXA3,
0.81
0.76
0.86


TNFRSF6, 12, CCL5


IL18R1, CD86, PFKFB3, ANKRD22, CSF1R,
0.81
0.8
0.82


SOCS3, 12, TIFA


CCL5, TDRD9, PSTPIP2, ARG2, INSL3,
0.81
0.77
0.85


OSM, 12, CSF1R


CEACAM1, IL10alpha, IL18R1, PSTPIP2,
0.81
0.77
0.85


TGFBI, TIFA, 12, VNN1


GADD45B, Gene_MMP9, TLR4, PFKFB3,
0.81
0.76
0.85


VNN1, JAK2, 12, IL18R1


FAD104, MAPK14, IFNGR1, IL18R1,
0.81
0.76
0.86


TNFSF10, CD86, 12, HLA-DRA


PSTPIP2, SOCS3, OSM, CSF1R, PFKFB3,
0.81
0.75
0.86


NCR1, 12, PRV1


TGFBI, SOCS3, ITGAM, TNFSF13B,
0.81
0.8
0.81


IL18R1, PSTPIP2, 12, ANKRD22


CEACAM1, SOCS3, PFKFB3, TNFRSF6,
0.81
0.79
0.82


PSTPIP2, OSM, 12, BCL2A1


TNFRSF6, ITGAM, BCL2A1, INSL3, CD86,
0.81
0.77
0.84


TIFA, 12, PFKFB3


PFKFB3, PSTPIP2, MAP2K6, IRAK4, OSM,
0.81
0.76
0.85


CCL5, 12, TNFSF10


MKNK1, TIFA, IL1RN, ARG2, SOCS3,
0.81
0.76
0.85


IL10alpha, 12, IFNGR1


FAD104, TNFSF13B, OSM, BCL2A1,
0.8
0.8
0.8


TDRD9, LY96, 12, SOD2


CD86, SOCS3, PSTPIP2, CCL5, OSM, TLR4,
0.8
0.77
0.83


12, MAPK14


TGFBI, LDLR, CRTAP, CSF1R, NCR1,
0.8
0.82
0.78


LY96, 12, PSTPIP2


JAK2, TIFA, TNFSF10, IL18R1, CCL5,
0.8
0.82
0.78


INSL3, 12, VNN1


TIFA, IL1RN, MAP2K6, HLA-DRA, OSM,
0.8
0.8
0.8


FAD104, 12, INSL3


JAK2, IRAK2, PRV1, TNFSF13B, OSM,
0.8
0.8
0.8


HLA-DRA, 12, IFNGR1


ANXA3, CSF1R, TLR4, SOCS3, IRAK4,
0.8
0.8
0.8


PRV1, 12, INSL3


TGFBI, IRAK4, PFKFB3, SOD2, ANXA3,
0.8
0.8
0.8


ITGAM, 12, TDRD9


TIFA, FCGR1A, TNFRSF6, LY96, IL10alpha,
0.8
0.78
0.81


SOCS3, 12, OSM


PRV1, TLR4, CSF1R, IL18R1, PSTPIP2,
0.8
0.78
0.82


TDRD9, 12, HLA-DRA


LY96, TNFSF13B, OSM, TGFBI, TIFA,
0.8
0.77
0.82


FAD104, 12, NCR1


ITGAM, ARG2, IL10alpha, SOD2, LY96,
0.8
0.76
0.83


OSM, 12, FCGR1A


ANKRD22, HLA-DRA, PRV1, NCR1,
0.8
0.76
0.84


CSF1R, PSTPIP2, 12, LY96


Gene_MMP9, PSTPIP2, GADD45B, SOD2,
0.8
0.74
0.86


ANKRD22, TNFSF13B, 12, ITGAM


ANXA3, FCGR1A, TNFSF10, VNN1,
0.85
0.86
0.84


TNFSF13B, ARG2, 12, CD86


CSF1R, PFKFB3, BCL2A1, SOCS3, NCR1,
0.86
0.87
0.85


TNFSF10, FCGR1A, ARG2, CD86


PSTPIP2, ITGAM, IRAK2, OSM, NCR1,
0.84
0.81
0.87


CEACAM1, PFKFB3, TLR4, ANXA3


INSL3, ARG2, LDLR, HLA-DRA, NCR1,
0.84
0.78
0.9


TIFA, LY96, ITGAM, SOCS3


CD86, CSF1R, SOD2, OSM, SOCS3,
0.83
0.79
0.87


BCL2A1, GADD45B, ARG2, HLA-DRA


ARG2, CD86, MAP2K6, HLA-DRA,
0.83
0.82
0.84


IL10alpha, IRAK2, GADD45B, MKNK1,


IL18R1


IFNGR1, BCL2A1, ARG2, TNFSF13B,
0.83
0.8
0.86


GADD45A, FCGR1A, TNFRSF6, CD86,


MAP2K6


OSM, TNFSF10, CSF1R, CCL5, IRAK2,
0.83
0.82
0.83


INSL3, ARG2, TNFSF13B, TNFRSF6


LY96, TNFSF10, GADD45B, CRTAP, ARG2,
0.82
0.87
0.77


ANXA3, CSF1R, CCL5, OSM


NCR1, LY96, FAD104, ANKRD22, BCL2A1,
0.82
0.79
0.84


PSTPIP2, ARG2, PRV1, IL18R1


SOD2, IRAK2, JAK2, CCL5, IL10alpha,
0.82
0.77
0.86


ARG2, BCL2A1, SOCS3, CSF1R


TNFRSF6, TGFBI, FCGR1A, IRAK4,
0.82
0.81
0.83


GADD45A, LDLR, IFNGR1, CSF1R, TIFA


GADD45B, ITGAM, PRV1, SOD2,
0.81
0.79
0.83


TNFSF13B, HLA-DRA, FAD104, TNFRSF6,


TLR4


IRAK2, SOCS3, GADD45B, MAP2K6, PRV1,
0.81
0.83
0.8


PFKFB3, CD86, IFNGR1, ANKRD22


HLA-DRA, GADD45A, FCGR1A,
0.81
0.81
0.81


ANKRD22, ARG2, NCR1, BCL2A1, IRAK2,


SOCS3


IRAK4, SOCS3, MKNK1, JAK2, OSM,
0.81
0.79
0.83


ANXA3, VNN1, ITGAM, TNFRSF6


SOD2, JAK2, FAD104, CD86, ARG2, CCL5,
0.81
0.79
0.83


MAP2K6, IFNGR1, PFKFB3


IL18R1, CSF1R, IRAK2, HLA-DRA,
0.81
0.78
0.83


PFKFB3, CRTAP, CD86, TIFA, TNFSF10


MAP2K6, FAD104, TGFBI, IRAK4, CRTAP,
0.81
0.77
0.84


LDLR, IRAK2, FCGR1A, ARG2


CEACAM1, SOD2, GADD45A, VNN1,
0.81
0.73
0.88


IRAK4, OSM, TDRD9, GADD45B, PSTPIP2


PSTPIP2, ANKRD22, TNFSF10, INSL3,
0.81
0.84
0.78


HLA-DRA, NCR1, TNFSF13B, CSF1R,


Gene_MMP9


JAK2, MAP2K6, CSF1R, IRAK2, TNFSF10,
0.81
0.81
0.8


LDLR, OSM, BCL2A1, ARG2


Gene_MMP9, MAP2K6, IL18R1, VNN1,
0.81
0.8
0.81


INSL3, ANKRD22, CCL5, PFKFB3,


MAPK14


IL18R1, ARG2, FCGR1A, CRTAP,
0.81
0.8
0.82


GADD45B, FAD104, IRAK4, MAPK14,


TDRD9


SOD2, PRV1, MKNK1, FCGR1A, CD86,
0.81
0.78
0.83


GADD45A, IL18R1, TNFSF13B, HLA-DRA


ANXA3, TNFRSF6, MAP2K6, OSM,
0.81
0.78
0.83


ANKRD22, IL18R1, MAPK14, GADD45A,


GADD45B


OSM, IRAK2, ANXA3, TNFSF13B, IL18R1,
0.81
0.78
0.83


ANKRD22, MAP2K6, IL10alpha, FAD104


ITGAM, SOD2, CSF1R, TGFBI, IFNGR1,
0.81
0.73
0.87


TDRD9, JAK2, ARG2, GADD45A


INSL3, ITGAM, OSM, TIFA, IRAK2,
0.8
0.84
0.77


MKNK1, SOCS3, TNFSF10, ANKRD22


GADD45A, PFKFB3, SOD2, IRAK2,
0.8
0.79
0.82


MAPK14, INSL3, IRAK4, ITGAM, ARG2


NCR1, INSL3, ARG2, IFNGR1, LDLR, OSM,
0.8
0.78
0.83


PRV1, GADD45B, CD86


IRAK2, FAD104, TLR4, CSF1R, PRV1, OSM,
0.8
0.77
0.83


MKNK1, BCL2A1, CD86


NCR1, SOCS3, HLA-DRA, PFKFB3,
0.8
0.74
0.86


FAD104, IRAK4, VNN1, CCL5, MAP2K6


CRTAP, TLR4, PFKFB3, CSF1R, TIFA,
0.8
0.82
0.78


PSTPIP2, PRV1, IFNGR1, CCL5


LY96, SOD2, IL18R1, TNFRSF6, TLR4,
0.8
0.81
0.79


MAP2K6, FAD104, Gene_MMP9, NCR1


ITGAM, SOD2, SOCS3, LDLR, MAP2K6,
0.8
0.8
0.8


FAD104, NCR1, CSF1R, CD86


CRTAP, ARG2, SOD2, TDRD9, TNFRSF6,
0.8
0.8
0.8


TIFA, OSM, Gene_MMP9, HLA-DRA


OSM, LY96, CEACAM1, IRAK4, INSL3,
0.8
0.78
0.82


PSTPIP2, PRV1, IRAK2, JAK2


CD86, IL1RN, IFNGR1, ANXA3, CSF1R,
0.8
0.78
0.82


ITGAM, NCR1, TDRD9, MAP2K6


TNFSF13B, JAK2, IRAK4, TDRD9, HLA-
0.8
0.78
0.82


DRA, SOCS3, PSTPIP2, FAD104, SOD2


Gene_MMP9, SOD2, JAK2, CD86, HLA-
0.8
0.74
0.85


DRA, IRAK2, CEACAM1, MAPK14, ANXA3


GADD45B, ITGAM, TLR4, NCR1, CD86,
0.8
0.71
0.88


TNFSF13B, HLA-DRA, FCGR1A, OSM


OSM, GADD45B, CSF1R, CCL5, ANXA3,
0.85
0.85
0.84


CEACAM1, CD86, TNFSF10, ARG2, LY96,


TDRD9


NCR1, HLA-DRA, BCL2A1, ARG2, SOCS3,
0.84
0.84
0.84


IL18R1, PSTPIP2, VNN1, CD86, GADD45A,


CCL5


PFKFB3, SOCS3, TNFRSF6, GADD45A,
0.84
0.8
0.87


OSM, TDRD9, IL18R1, NCR1, CSF1R,


ANXA3, PSTPIP2


ARG2, IFNGR1, MAPK14, Gene_MMP9,
0.84
0.8
0.88


IRAK4, CEACAM1, ITGAM, ANKRD22,


GADD45B, VNN1, OSM


BCL2A1, LY96, GADD45B, IL10alpha,
0.84
0.82
0.86


CRTAP, OSM, IFNGR1, IL1RN, TIFA,


IRAK4, GADD45A


TGFBI, SOCS3, MAP2K6, ANXA3, TLR4,
0.83
0.77
0.89


IL1RN, VNN1, HLA-DRA, TIFA, JAK2,


TDRD9


TNFSF13B, GADD45A, ANXA3, IL18R1,
0.83
0.83
0.83


FCGR1A, JAK2, CD86, SOCS3, INSL3,


CRTAP, NCR1


LY96, INSL3, TNFSF10, MAP2K6, OSM,
0.83
0.78
0.88


ITGAM, JAK2, CD86, FCGR1A, IL10alpha,


CCL5


ARG2, OSM, TLR4, NCR1, CCL5, BCL2A1,
0.83
0.76
0.88


IL1RN, GADD45A, MAPK14, SOCS3,


TDRD9


INSL3, IL18R1, IFNGR1, ARG2, IL10alpha,
0.83
0.82
0.83


LY96, CRTAP, LDLR, JAK2, CSF1R, VNN1


ANXA3, IFNGR1, GADD45A, TNFRSF6,
0.83
0.79
0.86


CCL5, JAK2, FAD104, IL1RN, ARG2,


IL10alpha, INSL3


CRTAP, TNFRSF6, LDLR, VNN1, HLA-
0.82
0.86
0.79


DRA, SOCS3, TGFBI, TNFSF10, IFNGR1,


ARG2, FCGR1A


GADD45A, VNN1, MKNK1, CCL5,
0.82
0.83
0.82


IL10alpha, PSTPIP2, IRAK2, TNFRSF6,


CEACAM1, FAD104, TGFBI


HLA-DRA, BCL2A1, PSTPIP2, PFKFB3,
0.82
0.82
0.83


JAK2, TNFSF10, ARG2, CEACAM1, IL18R1,


MAPK14, CSF1R


GADD45B, TNFSF10, TNFSF13B, OSM,
0.82
0.82
0.83


VNN1, PRV1, MKNK1, Gene_MMP9,


ANXA3, TGFBI, HLA-DRA


GADD45A, IFNGR1, IRAK4, TGFBI, NCR1,
0.82
0.75
0.88


FAD104, INSL3, IL10alpha, OSM, TIFA,


CSF1R


Gene_MMP9, IRAK2, JAK2, TGFBI,
0.82
0.83
0.81


BCL2A1, PSTPIP2, GADD45A, ARG2, OSM,


CEACAM1, IFNGR1


MAP2K6, FCGR1A, TNFSF13B, SOD2,
0.82
0.79
0.85


NCR1, ANXA3, TLR4, CD86, ITGAM,


IRAK2, INSL3


FAD104, ARG2, NCR1, ANKRD22, OSM,
0.82
0.78
0.85


CSF1R, BCL2A1, CRTAP, LY96, SOD2,


TNFRSF6


LY96, TDRD9, CD86, GADD45A, ARG2,
0.82
0.82
0.81


VNN1, IL10alpha, SOD2, CRTAP, TIFA,


FCGR1A


BCL2A1, VNN1, LDLR, TLR4, OSM,
0.82
0.82
0.81


IRAK4, IRAK2, CRTAP, IFNGR1, TGFBI,


CD86


CCL5, IFNGR1, TIFA, SOCS3, INSL3, TLR4,
0.82
0.81
0.82


IRAK4, ANXA3, TGFBI, TDRD9, CSF1R


VNN1, SOD2, CCL5, BCL2A1, HLA-DRA,
0.82
0.79
0.85


ANKRD22, CD86, TDRD9, TLR4, FCGR1A,


TNFSF10


CEACAM1, OSM, IRAK4, MAP2K6,
0.82
0.77
0.85


PSTPIP2, GADD45A, IRAK2, PRV1, IL1RN,


TNFSF10, PFKFB3


TNFSF10, IL1RN, IFNGR1, TIFA, FCGR1A,
0.81
0.82
0.81


PSTPIP2, OSM, ANXA3, TGFBI, INSL3,


CRTAP


LDLR, VNN1, GADD45B, IL18R1,
0.81
0.8
0.83


GADD45A, Gene_MMP9, FAD104, IL1RN,


IRAK4, JAK2, TGFBI


FCGR1A, OSM, GADD45A, IL18R1,
0.81
0.79
0.83


GADD45B, TLR4, MAP2K6, CRTAP, TIFA,


CCL5, BCL2A1


CSF1R, ITGAM, HLA-DRA, MAP2K6,
0.81
0.78
0.85


JAK2, FCGR1A, OSM, LDLR, SOCS3,


TNFRSF6, IL18R1


IL10alpha, IRAK2, OSM, TIFA, TNFSF10,
0.81
0.75
0.87


FAD104, GADD45B, ITGAM, CD86, VNN1,


SOD2


ARG2, GADD45A, LDLR, TNFRSF6,
0.81
0.84
0.78


CEACAM1, ANKRD22, MAPK14, IRAK4,


SOD2, INSL3, PSTPIP2


TGFBI, TNFRSF6, IRAK4, IRAK2, OSM,
0.81
0.82
0.79


TNFSF13B, TIFA, FAD104, ANKRD22,


MAPK14, CD86


VNN1, INSL3, TNFSF10, TGFBI, JAK2,
0.81
0.82
0.8


CRTAP, IRAK2, TNFRSF6, TNFSF13B,


LY96, OSM


GADD45B, OSM, SOD2, FCGR1A, VNN1,
0.81
0.77
0.85


CEACAM1, TIFA, PSTPIP2, IL1RN, TDRD9,


LY96


PFKFB3, LDLR, IL10alpha, IRAK4, ANXA3,
0.81
0.77
0.85


NCR1, IL18R1, VNN1, TDRD9, TNFSF13B,


CSF1R


CD86, TNFRSF6, PFKFB3, MKNK1, OSM,
0.81
0.76
0.85


JAK2, FAD104, IL10alpha, BCL2A1, SOCS3,


IRAK4


OSM, GADD45A, TNFSF10, IFNGR1,
0.81
0.75
0.87


CRTAP, JAK2, ANKRD22, HLA-DRA,


TNFSF13B, SOCS3, FCGR1A


CCL5, CD86, HLA-DRA, SOCS3, TGFBI,
0.84
0.85
0.82


PSTPIP2, ANXA3, GADD45A, CSF1R,


IRAK4, FAD104, MAPK14


IRAK2, CD86, IL1RN, TLR4, ANKRD22,
0.84
0.82
0.85


ANXA3, IL10alpha, GADD45B, BCL2A1,


CSF1R, INSL3, FCGR1A


CD86, TNFRSF6, TIFA, GADD45B,
0.84
0.8
0.87


CEACAM1, TNFSF13B, OSM, IL18R1,


CCL5, ITGAM, TGFBI, FAD104


NCR1, CCL5, BCL2A1, IL18R1, ARG2,
0.83
0.82
0.85


MKNK1, FCGR1A, CD86, GADD45B,


INSL3, IRAK4, ANXA3


TNFSF13B, IFNGR1, Gene_MMP9, SOD2,
0.83
0.8
0.86


LDLR, NCR1, CD86, INSL3, SOCS3, VNN1,


PSTPIP2, CEACAM1


SOD2, INSL3, TDRD9, OSM, TNFSF13B,
0.83
0.82
0.84


BCL2A1, JAK2, CSF1R, ANXA3, TNFSF10,


GADD45A, CRTAP


IL10alpha, MKNK1, GADD45A, TGFBI,
0.83
0.79
0.86


MAPK14, IRAK4, TDRD9, IL1RN,


TNFRSF6, FCGR1A, ITGAM, CD86


TNFRSF6, IL10alpha, PSTPIP2, HLA-DRA,
0.83
0.78
0.87


CRTAP, ARG2, MKNK1, NCR1, OSM,


INSL3, VNN1, FAD104


ANXA3, PRV1, LDLR, TNFSF13B, PFKFB3,
0.82
0.8
0.84


TNFRSF6, VNN1, ARG2, ANKRD22, INSL3,


NCR1, OSM


FCGR1A, HLA-DRA, IFNGR1, CD86, LY96,
0.82
0.76
0.89


ANXA3, MAP2K6, TDRD9, IL18R1, PRV1,


SOCS3, TIFA


GADD45B, OSM, ITGAM, CSF1R, CD86,
0.82
0.84
0.8


CEACAM1, IFNGR1, SOCS3, MAP2K6,


IL1RN, FAD104, CCL5


TGFBI, PRV1, JAK2, FCGR1A, ANKRD22,
0.82
0.83
0.81


TNFSF10, VNN1, SOCS3, PSTPIP2, IRAK2,


INSL3, FAD104


FCGR1A, GADD45A, SOD2, OSM, ARG2,
0.82
0.8
0.83


PFKFB3, ANKRD22, IL10alpha, CCL5,


SOCS3, CD86, ITGAM


LDLR, MAP2K6, INSL3, TDRD9, NCR1,
0.82
0.84
0.79


IL1RN, HLA-DRA, ARG2, MKNK1,


MAPK14, OSM, PFKFB3


IL1RN, PFKFB3, TIFA, OSM, IRAK2,
0.82
0.83
0.81


TGFBI, INSL3, TNFSF13B, TNFRSF6,


MAP2K6, PSTPIP2, CEACAM1


LY96, TNFSF13B, HLA-DRA, IRAK2,
0.82
0.82
0.82


FCGR1A, ANXA3, CEACAM1, FAD104,


TDRD9, IL1RN, ARG2, LDLR


IL1RN, ARG2, IRAK2, IRAK4, SOCS3,
0.82
0.81
0.82


IL10alpha, CCL5, Gene_MMP9, MAPK14,


FAD104, LY96, TGFBI


BCL2A1, LY96, ITGAM, OSM, TNFSF10,
0.81
0.81
0.82


INSL3, CD86, IRAK2, MAP2K6, IFNGR1,


PRV1, TNFRSF6


BCL2A1, ANXA3, LY96, TNFSF10, NCR1,
0.83
0.83
0.83


OSM, MAPK14, MKNK1, IFNGR1,


GADD45A, INSL3, ANKRD22, TNFSF13B


LY96, GADD45B, MAPK14, OSM, MKNK1,
0.83
0.82
0.84


BCL2A1, ARG2, IL1RN, INSL3, PFKFB3,


LDLR, CRTAP, TIFA


OSM, CD86, GADD45B, IRAK4, MAPK14,
0.83
0.82
0.84


SOCS3, VNN1, ARG2, TNFSF13B, TDRD9,


PRV1, IL1RN, IL18R1


OSM, NCR1, HLA-DRA, TNFSF10, PSTPIP2,
0.83
0.82
0.84


IL1RN, SOCS3, INSL3, TNFRSF6, MAPK14,


Gene_MMP9, CEACAM1, IL18R1


CCL5, ARG2, IL10alpha, MAPK14, CSF1R,
0.83
0.8
0.85


GADD45B, LDLR, SOD2, Gene_MMP9,


IFNGR1, IL18R1, CEACAM1, CD86


TDRD9, SOCS3, Gene_MMP9, IL18R1,
0.83
0.85
0.81


CRTAP, ANXA3, PRV1, ARG2, CD86,


ITGAM, OSM, NCR1, VNN1


SOD2, JAK2, PSTPIP2, MAPK14, MAP2K6,
0.83
0.83
0.83


FCGR1A, CCL5, ITGAM, CD86, GADD45B,


IL1RN, HLA-DRA, VNN1


IRAK4, JAK2, SOD2, Gene_MMP9, PSTPIP2,
0.83
0.81
0.84


PFKFB3, HLA-DRA, TNFRSF6, FAD104,


ARG2, IFNGR1, IRAK2, MAP2K6


PSTPIP2, MAPK14, CCL5, Gene_MMP9,
0.83
0.79
0.86


TNFRSF6, IL10alpha, LY96, IL1RN, ARG2,


SOCS3, TLR4, OSM, HLA-DRA


CRTAP, CEACAM1, ARG2, JAK2,
0.83
0.79
0.87


TNFSF10, VNN1, PSTPIP2, IRAK2,


TNFRSF6, ITGAM, SOCS3, OSM, IL18R1


VNN1, PSTPIP2, GADD45B, ITGAM,
0.83
0.77
0.88


IL1RN, FAD104, NCR1, TIFA, OSM,


TDRD9, SOD2, ARG2, TGFBI


TGFBI, IL1RN, INSL3, PSTPIP2, NCR1,
0.82
0.84
0.81


FAD104, HLA-DRA, CD86, IRAK4,


IL10alpha, ARG2, CSF1R, MAP2K6


FAD104, IRAK2, TIFA, TGFBI, IL18R1,
0.82
0.82
0.83


MAPK14, SOCS3, PSTPIP2, CD86, PRV1,


NCR1, FCGR1A, ANXA3


GADD45A, HLA-DRA, INSL3, ANKRD22,
0.82
0.79
0.85


ANXA3, CD86, IRAK4, GADD45B, PFKFB3,


ITGAM, VNN1, NCR1, JAK2


MKNK1, CCL5, PSTPIP2, ANXA3, VNN1,
0.82
0.86
0.78


LY96, IRAK2, IFNGR1, CRTAP, PFKFB3,


IL18R1, LDLR, FAD104


TNFSF10, OSM, FCGR1A, IRAK4, TLR4,
0.82
0.81
0.83


SOCS3, IL18R1, CRTAP, GADD45B, IL1RN,


IL10alpha, PRV1, JAK2


FAD104, ITGAM, ARG2, PSTPIP2, TLR4,
0.82
0.79
0.84


NCR1, IL1RN, MAP2K6, FCGR1A, PFKFB3,


LDLR, IFNGR1, BCL2A1


LDLR, ARG2, NCR1, MKNK1, GADD45B,
0.82
0.79
0.84


GADD45A, CEACAM1, PSTPIP2,


Gene_MMP9, CCL5, BCL2A1, TIFA, TDRD9


IRAK2, Gene_MMP9, INSL3, ARG2, OSM,
0.81
0.81
0.82


ITGAM, PSTPIP2, TNFSF13B, FCGR1A,


BCL2A1, CRTAP, PRV1, MAP2K6


CEACAM1, PSTPIP2, TLR4, IFNGR1,
0.85
0.89
0.83


GADD45B, CSF1R, CD86, VNN1, IL18R1,


ANKRD22, MAPK14, OSM, CCL5, IRAK4


LY96, ANKRD22, Gene_MMP9, ARG2,
0.85
0.84
0.86


GADD45A, MKNK1, CD86, PSTPIP2, OSM,


FAD104, FCGR1A, IL18R1, TIFA, ITGAM


ARG2, ANKRD22, VNN1, TLR4, OSM,
0.84
0.82
0.86


TIFA, TGFBI, TDRD9, ANXA3, CCL5,


TNFRSF6, GADD45B, FAD104, CD86


IFNGR1, TLR4, CRTAP, ANKRD22,
0.84
0.79
0.87


Gene_MMP9, JAK2, INSL3, ITGAM, IRAK4,


HLA-DRA, BCL2A1, OSM, TNFSF10, NCR1


TNFSF10, VNN1, TDRD9, CSF1R, OSM,
0.83
0.83
0.83


IFNGR1, TLR4, PSTPIP2, TIFA, ARG2,


FCGR1A, CD86, MAPK14, MAP2K6


LDLR, IL18R1, BCL2A1, IL1RN, ARG2,
0.83
0.83
0.84


IRAK2, JAK2, GADD45A, ANKRD22,


MAP2K6, OSM, CD86, IRAK4, SOD2


IL1RN, IRAK4, VNN1, CRTAP, TNFSF10,
0.83
0.8
0.86


IFNGR1, FAD104, ARG2, OSM, NCR1,


JAK2, ANXA3, CEACAM1, TDRD9


CD86, FCGR1A, MKNK1, TNFRSF6,
0.83
0.78
0.88


GADD45B, LY96, NCR1, PSTPIP2, HLA-


DRA, VNN1, ANXA3, IRAK4, ARG2, TGFBI


IRAK2, ANKRD22, JAK2, CD86, INSL3,
0.83
0.77
0.88


TNFSF10, OSM, PSTPIP2, IL10alpha, CCL5,


TDRD9, GADD45B, Gene_MMP9, LY96


LY96, FCGR1A, CCL5, IL18R1, VNN1,
0.83
0.83
0.83


TNFSF10, MAP2K6, PRV1, IRAK4, IL1RN,


TLR4, PSTPIP2, PFKFB3, TGFBI


SOD2, IL1RN, JAK2, PRV1, IRAK2, CD86,
0.83
0.83
0.83


TGFBI, CCL5, MAPK14, TLR4, INSL3,


PFKFB3, GADD45B, LY96


TDRD9, FCGR1A, NCR1, IFNGR1, ARG2,
0.83
0.81
0.85


SOD2, TNFRSF6, CD86, PFKFB3, LDLR,


JAK2, CCL5, ANKRD22, FAD104


MAPK14, INSL3, MAP2K6, CCL5, CSF1R,
0.83
0.82
0.83


CD86, GADD45A, SOCS3, GADD45B,


ANXA3, TGFBI, TNFRSF6, IFNGR1,


CRTAP


GADD45B, MAPK14, GADD45A, IL1RN,
0.83
0.81
0.84


CEACAM1, CRTAP, MKNK1, IL18R1,


NCR1, FCGR1A, TIFA, MAP2K6, CD86,


TLR4


ARG2, ANKRD22, OSM, LDLR, CCL5,
0.82
0.83
0.82


IL1RN, FCGR1A, PFKFB3, CSF1R, ANXA3,


HLA-DRA, INSL3, NCR1, TIFA


TNFSF10, ANXA3, OSM, JAK2, VNN1,
0.82
0.81
0.83


ANKRD22, INSL3, IFNGR1, CD86,


MAPK14, GADD45B, TNFRSF6, MAP2K6,


LY96


TGFBI, IL18R1, IFNGR1, TDRD9, ANXA3,
0.82
0.8
0.84


TNFSF10, ANKRD22, CD86, TNFRSF6,


BCL2A1, FAD104, Gene_MMP9, TNFSF13B,


CRTAP


OSM, ANXA3, SOCS3, INSL3, ITGAM,
0.82
0.87
0.79


SOD2, NCR1, TNFSF10, BCL2A1, PSTPIP2,


TLR4, IRAK2, Gene_MMP9, IL18R1


SOD2, IRAK4, TNFRSF6, PRV1, FCGR1A,
0.82
0.84
0.8


LDLR, MAP2K6, TIFA, CEACAM1, IL18R1,


SOCS3, OSM, IL10alpha, MKNK1


TLR4, MKNK1, SOD2, SOCS3, FAD104,
0.85
0.83
0.86


HLA-DRA, PSTPIP2, ANKRD22, TIFA,


TNFRSF6, JAK2, TNFSF10, ARG2, CSF1R,


TLR4


CCL5, MAP2K6, SOCS3, IFNGR1, TGFBI,
0.84
0.85
0.83


ANXA3, OSM, GADD45A, TNFSF10,


Gene_MMP9, TNFRSF6, TIFA, ARG2,


INSL3, SOCS3


ANXA3, IL18R1, VNN1, NCR1, TIFA,
0.84
0.84
0.83


INSL3, TGFBI, MAPK14, CEACAM1,


CRTAP, CSF1R, TDRD9, IL10alpha, CCL5,


MAPK14


TLR4, MKNK1, SOD2, SOCS3, FAD104,
0.85
0.83
0.86


HLA-DRA, PSTPIP2, ANKRD22, TIFA,


TNFRSF6, JAK2, TNFSF10, ARG2, CSF1R,


IRAK4


CCL5, MAP2K6, SOCS3, IFNGR1, TGFBI,
0.84
0.85
0.83


ANXA3, OSM, GADD45A, TNFSF10,


Gene_MMP9, TNFRSF6, TIFA, ARG2,


INSL3, TLR4


ANXA3, IL18R1, VNN1, NCR1, TIFA,
0.84
0.84
0.83


INSL3, TGFBI, MAPK14, CEACAM1,


CRTAP, CSF1R, TDRD9, IL10alpha, CCL5,


SOCS3


IL18R1, MAP2K6, INSL3, IRAK4, CCL5,
0.84
0.79
0.88


PFKFB3, CSF1R, LDLR, ITGAM,


GADD45A, ARG2, PSTPIP2, TLR4, CD86,


MAPK14


SOD2, IFNGR1, CEACAM1, OSM, FAD104,
0.83
0.84
0.83


HLA-DRA, CRTAP, IL10alpha, TGFBI,


GADD45A, ITGAM, IL18R1, CCL5, TLR4,


FCGR1A


SOCS3, OSM, TIFA, TNFRSF6, INSL3,
0.83
0.83
0.84


LDLR, IL18R1, PFKFB3, TGFBI, IL10alpha,


GADD45B, ARG2, TNFSF10, VNN1,


ANXA3


PRV1, PFKFB3, CEACAM1, FCGR1A, TIFA,
0.83
0.82
0.85


MKNK1, ARG2, GADD45B, IL18R1, CD86,


ITGAM, VNN1, IFNGR1, OSM, JAK2


NCR1, INSL3, HLA-DRA, TNFSF10,
0.83
0.82
0.84


TNFRSF6, FCGR1A, OSM, GADD45B,


MKNK1, TNFSF13B, CSF1R, LY96,


MAPK14, PRV1, CCL5


FCGR1A, CD86, CEACAM1, ANXA3,
0.83
0.79
0.87


FAD104, CRTAP, JAK2, MKNK1, MAPK14,


IFNGR1, GADD45A, PFKFB3, ANKRD22,


IL18R1, LY96


IRAK2, IL10alpha, INSL3, FAD104, TIFA,
0.83
0.78
0.87


SOD2, IFNGR1, IL1RN, HLA-DRA, LY96,


IL18R1, CCL5, CD86, TDRD9, TNFSF10


LY96, BCL2A1, Gene_MMP9, OSM, ARG2,
0.83
0.8
0.85


MAP2K6, INSL3, ITGAM, MAPK14, TIFA,


IRAK2, PSTPIP2, FCGR1A, CEACAM1,


IFNGR1


IL18R1, BCL2A1, PFKFB3, Gene_MMP9,
0.83
0.76
0.88


IL1RN, IL10alpha, SOCS3, PSTPIP2, CRTAP,


OSM, CD86, FCGR1A, FAD104, JAK2,


SOD2


MKNK1, CRTAP, PRV1, IL1RN, GADD45A,
0.82
0.82
0.82


TNFRSF6, FAD104, HLA-DRA, CEACAM1,


PSTPIP2, OSM, JAK2, IL18R1, LDLR,


IRAK4


FCGR1A, BCL2A1, IFNGR1, CRTAP, VNN1,
0.82
0.8
0.84


TIFA, CCL5, NCR1, OSM, HLA-DRA,


IRAK4, INSL3, MAP2K6, TNFSF13B, ARG2


FAD104, BCL2A1, PRV1, MKNK1, CRTAP,
0.82
0.77
0.87


IRAK4, PFKFB3, SOD2, CD86, ARG2,


FCGR1A, ANKRD22, INSL3, IFNGR1,


LDLR


SOCS3, CD86, FCGR1A, MAP2K6, TGFBI,
0.82
0.84
0.8


IRAK2, PSTPIP2, CCL5, IL1RN, GADD45B,


TDRD9, OSM, IL10alpha, PFKFB3, FAD104


TIFA, SOD2, LDLR, FCGR1A, BCL2A1,
0.86
0.84
0.87


TNFSF13B, ARG2, PSTPIP2, MAPK14,


LY96, Gene_MMP9 IFNGR1, GADD45B,


ANXA3, PRV1, CD86


HLA-DRA, IRAK2, FCGR1A, ANXA3,
0.84
0.83
0.84


ITGAM, LY96, TDRD9, SOCS3, IL1RN,


PFKFB3, GADD45B, TNFSF13B, TLR4,


ARG2, CSF1R, FAD104


OSM, CRTAP, CEACAM1, NCR1, IRAK4,
0.83
0.81
0.86


TLR4, FAD104, MKNK1, TDRD9, PSTPIP2,


IL1RN, CSF1R, MAP2K6, ITGAM, ARG2,


IFNGR1


TIFA, IL10alpha, VNN1, OSM, MAP2K6,
0.83
0.77
0.9


GADD45B, PSTPIP2, TDRD9, TNFRSF6,


INSL3, IL1RN, FAD104, TNFSF10, TGFBI,


IL18R1, TLR4


GADD45A, CSF1R, INSL3, BCL2A1,
0.83
0.82
0.84


TDRD9, LDLR, HLA-DRA, MAP2K6,


PSTPIP2, CCL5, ANXA3, PRV1, TNFRSF6,


TLR4, CD86, JAK2


TDRD9, PFKFB3, MAPK14, IL1RN,
0.83
0.85
0.8


IFNGR1, FCGR1A, MAP2K6, TNFRSF6,


ARG2, VNN1, CRTAP, LDLR, CEACAM1,


FAD104, NCR1, TNFSF10


ARG2, IL10alpha, TLR4, PRV1, INSL3,
0.83
0.82
0.83


OSM, CD86, TGFBI, SOCS3, GADD45B,


TIFA, LDLR, IRAK2, GADD45A, SOD2,


TNFSF13B


TNFSF10, PRV1, SOCS3, FAD104,
0.83
0.8
0.85


TNFRSF6, ARG2, Gene_MMP9 FCGR1A,


TGFBI, NCR1, CRTAP, MAP2K6, ANXA3,


CSF1R, HLA-DRA, JAK2


TNFRSF6, BCL2A1, VNN1, ANXA3, SOCS3,
0.83
0.8
0.85


GADD45A, CRTAP, CCL5, FAD104,


ANKRD22, MKNK1, FCGR1A, SOD2,


IRAK2, MAPK14, Gene_MMP9


FAD104, OSM, LDLR, TNFSF10, GADD45B,
0.82
0.78
0.87


HLA-DRA, TNFRSF6, GADD45A, CD86,


TDRD9, ITGAM, ANXA3, IFNGR1,


MAPK14, CSF1R, TGFBI


CSF1R, PRV1, ANXA3, SOD2, PSTPIP2,
0.82
0.77
0.87


CEACAM1, IFNGR1, IRAK4, LY96,


MAPK14, IL10alpha, MKNK1, TNFRSF6,


OSM, TGFBI, INSL3


PSTPIP2, ARG2, MAP2K6, INSL3, SOCS3,
0.82
0.76
0.89


JAK2, FAD104, ANKRD22, HLA-DRA,


ITGAM, GADD45B, LY96, IRAK2, PFKFB3,


TNFRSF6, IFNGR1


CRTAP, MKNK1, BCL2A1, PRV1, CD86,
0.82
0.88
0.77


TNFRSF6, PSTPIP2, MAPK14, TNFSF13B,


ARG2, PFKFB3, CEACAM1, FAD104,


Gene_MMP9, OSM, SOD2


MKNK1, SOCS3, CRTAP, FCGR1A, CD86,
0.82
0.82
0.82


IL10alpba, GADD45A, IL18R1, IRAK2,


CCL5, JAK2, ANKRD22, TIFA, TGFBI,


CSF1R, BCL2A1


GADD45B, CEACAM1, ANKRD22, IRAK4,
0.82
0.81
0.82


LDLR, CRTAP, MKNK1, OSM, MAPK14,


MAP2K6, INSL3, GADD45A, PFKFB3,


TNFSF10, CSF1R, TIFA


CSF1R, INSL3, TNFRSF6, BCL2A1, CD86,
0.82
0.79
0.85


CEACAM1, IL10alpha, IL18R1, TLR4,


ITGAM, TNFSF10, OSM, ARG2, SOD2,


FCGR1A, PSTPIP2


NCR1, LDLR, MKNK1, INSL3, BCL2A1,
0.82
0.78
0.85


JAK2, FCGR1A, IL1RN, TNFRSF6, PRV1,


GADD45B, ARG2, MAP2K6, OSM, VNN1,


TDRD9


TLR4, CD86, MAPK14, TNFSF13B, INSL3,
0.82
0.85
0.79


CRTAP, NCR1, ARG2, GADD45A, CSF1R,


TNFRSF6, MAP2K6, JAK2, MKNK1,


ANKRD22, OSM


CSF1R, CCL5, ARG2, BCL2A1, FCGR1A,
0.82
0.85
0.79


MKNK1, TDRD9, IFNGR1, PFKFB3,


ITGAM, JAK2, OSM, GADD45B, FAD104,


NCR1, HLA-DRA


IFNGR1, FCGR1A, TLR4, OSM, PSTPIP2,
0.86
0.82
0.9


IL18R1, NCR1, SOCS3, PFKFB3, INSL3,


LDLR, TNFRSF6, SOD2, GADD45B,


IL10alpha, CCL5, IL1RN


CEACAM1, IL18R1, SOCS3, CRTAP, LDLR,
0.86
0.84
0.88


HLA-DRA, LY96, IL1RN, IL10alpha,


BCL2A1, GADD45A, TIFA, FAD104,


ANKRD22, OSM, CCL5, IFNGR1


FAD104, GADD45B, HLA-DRA, VNN1,
0.85
0.79
0.91


IL10alpha, CD86, JAK2, INSL3, TDRD9,


TLR4, IRAK4, SOD2, LDLR, CCL5,


MKNK1, ARG2, IL18R1


IL18R1, PRV1, IL1RN, TNFSF10, FAD104,
0.85
0.81
0.89


ITGAM, FCGR1A, INSL3, MAP2K6, LDLR,


TNFSF13B, IRAK2, OSM, PFKFB3, TGFBI,


IL10alpha, LY96


IRAK2, HLA-DRA, IFNGR1, MAP2K6,
0.85
0.81
0.89


TLR4, ITGAM, SOCS3, CD86, ARG2, VNN1,


IL18R1, ANXA3, FCGR1A, IL1RN,


Gene_MMP9, TGFBI, IL10alpha


MAP2K6, IL18R1, IL1RN, CSF1R,
0.84
0.81
0.87


TNFRSF6, FCGR1A, NCR1, TDRD9,


TNFSF10, SOCS3, CCL5, IFNGR1, TIFA,


CRTAP, GADD45B, IL10alpha, TGFBI


CD86, CCL5, IRAK4, GADD45A, ANXA3,
0.84
0.8
0.88


OSM, JAK2, INSL3, SOCS3, BCL2A1,


FAD104, MAPK14, TIFA, TLR4, NCR1,


PRV1, TDRD9


BCL2A1, IL18R1, TLR4, OSM, CD86,
0.84
0.8
0.89


FAD104, PRV1, JAK2, MAPK14, TNFRSF6,


CEACAM1, IL1RN, IL10alpha, SOD2,


Gene_MMP9, CSF1R, PFKFB3


LY96, TIFA, IL10alpha, ANXA3, LDLR,
0.84
0.81
0.87


JAK2, IFNGR1, IRAK2, MAP2K6, TGFBI,


MAPK14, TDRD9, FCGR1A, ITGAM,


TNFSF10, GADD45B, SOCS3


TNFSF13B, FAD104, SOD2, SOCS3,
0.84
0.85
0.83


CEACAM1, TDRD9, ARG2, CD86, IRAK2,


PFKFB3, FCGR1A, NCR1, MAPK14,


CRTAP, LDLR, GADD45A, TNFRSF6


IRAK2, MKNK1, PSTPIP2, ANXA3, HLA-
0.84
0.83
0.84


DRA, TNFSF10, IFNGR1, PFKFB3, OSM,


PRV1, IL1RN, IL10alpha, FAD104, CD86,


TIFA, BCL2A1, TNFSF13B


VNN1, IFNGR1, LY96, SOD2, IL18R1,
0.84
0.82
0.86


SOCS3, FCGR1A, ARG2, CSF1R,


Gene_MMP9, IRAK4, MAP2K6, TIFA,


FAD104, HLA-DRA, GADD45B, IL1RN


CD86, Gene_MMP9 IL18R1, TNFSF13B,
0.83
0.82
0.84


FCGR1A, TNFRSF6, INSL3, IL1RN,


PFKFB3, PSTPIP2, NCR1, GADD45B,


VNN1, CRTAP, IRAK4, MAP2K6, OSM


TNFSF13B, FAD104, PRV1, TIFA, SOD2,
0.83
0.79
0.88


TDRD9, TLR4, TNFRSF6, MKNK1, OSM,


MAP2K6, CCL5, ARG2, LDLR, HLA-DRA,


PSTPIP2, IL18R1


IRAK4, MAP2K6, JAK2, LY96, ITGAM,
0.83
0.82
0.84


CCL5, CSF1R, ARG2, FCGR1A, FAD104,


CD86, TNFSF10, IL18R1, CRTAP,


GADD45A, TLR4, Gene_MMP9


IRAK2, OSM, MAP2K6, TNFSF13B,
0.83
0.8
0.86


ANKRD22, HLA-DRA, SOD2, TNFSF10,


VNN1, ARG2, IRAK4, LY96, IFNGR1, JAK2,


BCL2A1, FCGR1A, CSF1R


IL10alpha, FCGR1A, TGFBI, ANKRD22,
0.83
0.78
0.87


IRAK4, CD86, TNFSF13B, TNFRSF6,


IL18R1, JAK2, IL1RN, PSTPIP2, OSM,


MAP2K6, GADD45A, Gene_MMP9,


MAPK14


PRV1, IRAK4, MKNK1, JAK2, OSM,
0.82
0.83
0.82


MAP2K6, BCL2A1, GADD45B,


Gene_MMP9, IL10alpha, FAD104, ARG2,


PSTPIP2, SOD2, TNFRSF6, TNFSF10,


IL1RN


IL1RN, OSM, FAD104, CRTAP, IRAK4,
0.82
0.82
0.83


IL10alpha, LDLR, INSL3, TNFSF10, CCL5,


IL18R1, ANXA3, PRV1, ARG2,


Gene_MMP9, CEACAM1, SOCS3


TNFRSF6, MAP2K6, FCGR1A, MAPK14,
0.82
0.81
0.83


ARG2, INSL3, TNFSF10, NCR1, PRV1,


CEACAM1, ANXA3, IL18R1, TIFA,


IFNGR1, IRAK4, CCL5, VNN1


IRAK2, ANKRD22, MAPK14, TIFA,
0.85
0.83
0.87


GADD45B, OSM, IL10alpha, SOD2, CCL5,


GADD45A, CD86, IRAK4, SOCS3, TDRD9,


MAP2K6, FAD104, PRV1, ANXA3


TLR4, LDLR, OSM, MAP2K6, GADD45A,
0.85
0.84
0.86


TIFA, NCR1, IL18R1, IFNGR1, INSL3,


ANXA3, IL10alpha, IL1RN, CSF1R,


GADD45B, PFKFB3, TGFBI, CRTAP


HLA-DRA, GADD45A, ANXA3, ARG2,
0.84
0.85
0.83


FAD104, PFKFB3, ITGAM, JAK2, MAPK14,


OSM, CD86, LDLR, TIFA, CCL5, NCR1,


IRAK2, SOD2, PRV1


GADD45A, INSL3, IRAK2, TNFSF10,
0.83
0.82
0.85


TGFBI, IRAK4, NCR1, HLA-DRA,


CEACAM1, GADD45B, MAPK14, CD86,


IL18R1, CRTAP, ANKRD22, PSTPIP2, LY96,


PFKFB3


MAP2K6, IL1RN, TIFA, TLR4, OSM, TGFBI,
0.83
0.85
0.81


ANXA3, NCR1, IL18R1, ANKRD22,


GADD45A, TNFSF10, PRV1, IRAK2,


TDRD9, JAK2, Gene_MMP9, CSF1R


CD86, GADD45A, GADD45B, TNFSF13B,
0.83
0.83
0.83


CRTAP, TNFRSF6, NCR1, IL10alpha,


CSF1R, OSM, MKNK1, CEACAM1, TLR4,


IFNGR1, IRAK2, SOCS3, TGFBI,


Gene_MMP9


BCL2A1, ANKRD22, OSM, CD86, ITGAM,
0.83
0.82
0.84


ANXA3, FCGR1A, CCL5, TIFA, IRAK4,


HLA-DRA, NCR1, CRTAP, TLR4,


CEACAM1, FAD104, ARG2, MAP2K6


GADD45A, IFNGR1, MAP2K6, CRTAP,
0.83
0.82
0.84


MAPK14, TNFSF10, LDLR, TIFA, OSM,


SOCS3, CD86, ARG2, PSTPIP2, IL1RN,


LY96, GADD45B, ANKRD22, TGFBI


INSL3, TLR4, BCL2A1, ANKRD22, FAD104,
0.83
0.81
0.85


MAP2K6, GADD45B, ARG2, NCR1,


MKNK1, ITGAM, CSF1R, IL1RN, HLA-


DRA, LDLR, CRTAP, PRV1, LY96


CRTAP, HLA-DRA, ARG2, PSTPIP2,
0.83
0.81
0.86


MKNK1, INSL3, TIFA, CEACAM1, JAK2,


Gene_MMP9, TLR4, IRAK4, CD86, FAD104,


CCL5, TNFSF10, LDLR, IFNGR1


IL18R1, TNFRSF6, PFKFB3, FAD104,
0.83
0.8
0.86


GADD45A, OSM, JAK2, VNN1, MKNK1,


BCL2A1, SOCS3, NCR1, TLR4, FCGR1A,


CSF1R, ITGAM, IRAK4, CRTAP


FAD104, TNFRSF6, OSM, TIFA, PSTPIP2,
0.83
0.78
0.87


ANXA3, TLR4, CD86, IRAK4, TNFSF13B,


IL1RN, IFNGR1, ITGAM, BCL2A1,


CEACAM1, MKNK1, TGFBI, ARG2


TNFSF10, BCL2A1, TGFBI, LY96, PRV1,
0.83
0.85
0.81


MKNK1, SOD2, ARG2, SOCS3, CD86,


IL10alpha, TNFSF13B, ITGAM, OSM,


MAPK14, PSTPIP2, ANXA3, CCL5


SOCS3, OSM, CCL5, JAK2, MAP2K6,
0.83
0.85
0.81


IL18R1, NCR1, CEACAM1, IRAK2, ARG2,


LY96, PRV1, ITGAM, TNFSF13B, TNFSF10,


TGFBI, IL10alpha, LDLR


ARG2, IRAK2, Gene_MMP9, GADD45B,
0.83
0.83
0.82


MKNK1, PFKFB3, MAPK14, IRAK4, CSF1R,


FCGR1A, GADD45A, TDRD9, TIFA, CD86,


IL18R1, BCL2A1, CRTAP, TNFRSF6


FAD104, IL1RN, TGFBI, TLR4, BCL2A1,
0.83
0.82
0.83


IFNGR1, IRAK4, PRV1, ANKRD22, CRTAP,


TNFRSF6, CSF1R, ARG2, OSM, GADD45A,


VNN1, INSL3, CEACAM1


CSF1R, SOCS3, FAD104, TLR4, INSL3,
0.83
0.81
0.84


ANXA3, NCR1, CRTAP, IFNGR1, TIFA,


OSM, MAPK14, TDRD9, IL1RN, ANKRD22,


TNFRSF6, IRAK2, BCL2A1


MAP2K6, IFNGR1, CD86, FCGR1A, IRAK2,
0.83
0.81
0.84


MKNK1, CRTAP, FAD104, IL10alpha,


VNN1, ANXA3, NCR1, IL18R1, CEACAM1,


CCL5, ARG2, MAPK14, SOCS3


IL1RN, IRAK4, JAK2, CD86, BCL2A1,
0.83
0.79
0.86


TGFBI, Gene_MMP9, NCR1, IFNGR1,


VNN1, SOCS3, CCL5, TNFSF13B, TDRD9,


MAPK14, PRV1, OSM, TLR4


MAP2K6, CSF1R, HLA-DRA, ANKRD22,
0.82
0.8
0.85


MKNK1, SOCS3, TNFSF10, LDLR, FAD104,


CEACAM1, TNFSF13B, TDRD9, IRAK4,


VNN1, IL18R1, OSM, PSTPIP2, Gene_MMP9


CCL5, SOD2, JAK2, IRAK4, IRAK2,
0.84
0.79
0.89


Gene_MMP9, IFNGR1, TLR4, GADD45A,


TNFSF10, CSF1R, IL18R1, PRV1,


TNFSF13B, HLA-DRA, LDLR, CD86,


SOCS3, FAD104


MAP2K6, TNFSF13B, SOD2, GADD45B,
0.84
0.84
0.84


HLA-DRA, CSF1R, CCL5, TIFA, NCR1,


IFNGR1, OSM, CD86, SOCS3, ARG2,


IL10alpha, BCL2A1, TDRD9, LDLR,


GADD45A


IFNGR1, OSM, MAPK14, CEACAM1,
0.84
0.83
0.85


PFKFB3, TLR4, CSF1R, JAK2, IL18R1,


TGFBI, CD86, IL10alpha, INSL3, BCL2A1,


FCGR1A, GADD45B, LDLR, PSTPIP2,


FAD104


ARG2, PRV1, IRAK4, TNFRSF6, MAP2K6,
0.84
0.8
0.86


SOCS3, IL18R1, HLA-DRA, IFNGR1,


ANXA3, TNFSF10, JAK2, FCGR1A,


GADD45A, INSL3, IL1RN, TNFSF13B,


ITGAM, CSF1R


LDLR, INSL3, JAK2, TNFRSF6, PRV1,
0.83
0.82
0.84


IFNGR1, OSM, ITGAM, FCGR1A, IL10alpha,


NCR1, TDRD9, MAP2K6, TNFSF13B, TIFA,


HLA-DRA, ANKRD22, GADD45B, IL1RN


MAPK14, SOD2, CSF1R, ITGAM, MAP2K6,
0.83
0.82
0.84


TLR4, ANXA3, BCL2A1, CRTAP, IL10alpha,


IRAK4, CCL5, SOCS3, TNFSF13B, ARG2,


FCGR1A, CEACAM1, OSM, IL1RN


LY96, IL10alpha, GADD45A, GADD45B,
0.83
0.78
0.87


IL1RN, IL18R1, PSTPIP2, ARG2, IRAK2,


CEACAM1, MKNK1, PFKFB3, TNFSF10,


ANKRD22, ANXA3, SOD2, MAP2K6,


IRAK4, SOCS3


IL18R1, MAP2K6, ARG2, CD86, TNFSF13B,
0.82
0.82
0.83


MAPK14, TNFSF10, CRTAP, GADD45A,


NCR1, GADD45B, JAK2, MKNK1,


TNFRSF6, VNN1, FAD104, LY96,


CEACAM1, PRV1


HLA-DRA, CD86, SOCS3, TIFA, TNFSF13B,
0.82
0.79
0.85


FCGR1A, JAK2, PFKFB3, MAP2K6, OSM,


TGFBI, ANKRD22, CEACAM1, IRAK4,


ARG2, IL18R1, SOD2, MKNK1, GADD45B


TDRD9, IRAK2, PFKFB3, CSF1R, TGFBI,
0.82
0.79
0.85


SOCS3, IL10alpha, IFNGR1, TNFRSF6,


VNN1, FCGR1A, PRV1, TNFSF13B,


MAPK14, BCL2A1, CD86, SOD2, INSL3,


ARG2


LDLR, ITGAM, IL18R1, ANXA3,
0.82
0.79
0.86


GADD45A, VNN1, TDRD9, LY96, BCL2A1,


CD86, IRAK2, FAD104, Gene_MMP9, TLR4,


TIFA, OSM, ARG2, CRTAP, PSTPIP2


CCL5, TGFBI, BCL2A1, VNN1, TDRD9,
0.82
0.8
0.84


SOCS3, CRTAP, CD86, TNFRSF6, LDLR,


CSF1R, PRV1, IL18R1, INSL3, GADD45B,


TNFSF13B, PFKFB3, JAK2, SOD2


SOD2, ARG2, HLA-DRA, LY96,
0.82
0.8
0.84


Gene_MMP9, VNN1, CD86, IL10alpha,


CSF1R, PSTPIP2, JAK2, TNFSF13B, IRAK2,


CCL5, ANKRD22, TLR4, IL1RN, OSM,


GADD45B


SOCS3, TGFBI, FCGR1A, TDRD9,
0.82
0.78
0.86


GADD45A, TIFA, IFNGR1, VNN1, ITGAM,


MAPK14, OSM, ANXA3, TNFSF13B,


IL1RN, HLA-DRA, ARG2, MAP2K6, TLR4,


PSTPIP2


CD86, INSL3, MAPK14, TIFA, MAP2K6,
0.82
0.76
0.87


Gene_MMP9, CRTAP, CSF1R, MKNK1,


IL10alpha, FAD104, PRV1, BCL2A1, NCR1,


LDLR, IRAK4, HLA-DRA, IFNGR1, TDRD9


NCR1, LDLR, IRAK2, TNFRSF6, CD86,
0.82
0.74
0.89


SOD2, TNFSF13B, VNN1, GADD45A,


Gene_MMP9, PFKFB3, ANKRD22, PSTPIP2,


PRV1, FCGR1A, IL18R1, TIFA, INSL3,


CRTAP


IL1RN, TLR4, PSTPIP2, IL18R1, GADD45A,
0.82
0.82
0.82


IL10alpha, BCL2A1, MKNK1, IRAK2, HLA-


DRA, ANKRD22, NCR1, CEACAM1,


LRAK4, OSM, TIFA, SOD2, TGFBI,


Gene_MMP9


GADD45A, LY96, ITGAM, CCL5, TNFSF10,
0.82
0.78
0.85


TNFSF13B, HLA-DRA, CSF1R, TIFA,


SOCS3, MKNK1, ARG2, IFNGR1, IL1RN,


BCL2A1, OSM, PFKFB3, PSTPIP2, IRAK2


Gene_MMP9, GADD45A, PSTPIP2, INSL3,
0.82
0.78
0.85


IRAK4, HLA-DRA, CCL5, TGFBI, OSM,


LY96, TDRD9, NCR1, PFKFB3, IFNGR1,


IRAK2, VNN1, CRTAP, TIFA, CD86


LDLR, ARG2, MAP2K6, MAPK14, IL18R1,
0.85
0.81
0.9


CCL5, PSTPIP2, ANKRD22, OSM, TDRD9,


HLA-DRA, SOCS3, ANXA3, TNFRSF6,


TIFA, CD86, FAD104, MKNK1, BCL2A1,


IRAK2


FCGR1A, FAD104, Gene_MMP9, LDLR,
0.85
0.8
0.88


ANKRD22, VNN1, SOCS3, TNFSF13B,


TLR4, TDRD9, CEACAM1, PSTPIP2,


MAPK14, ARG2, IRAK4, OSM, PRV1,


TNFRSF6, IL10alpha, PFKFB3


TNFSF10, IRAK2, TDRD9, TGFBI, PFKFB3,
0.84
0.8
0.88


CD86, OSM, IFNGR1, FAD104, ANXA3,


CCL5, IRAK4, PSTPIP2, GADD45A, SOCS3,


CSF1R, NCR1, CRTAP, IL1RN, BCL2A1


IFNGR1, TIFA, ARG2, IRAK2, CCL5, LDLR,
0.84
0.83
0.85


OSM, SOCS3, SOD2, IL1RN, PSTPIP2,


BCL2A1, FAD104, IL18R1, IL10alpha, CD86,


FCGR1A, ITGAM, JAK2, Gene_MMP9


PSTPIP2, SOCS3, OSM, FCGR1A, IL1RN,
0.84
0.82
0.85


IRAK4, ITGAM, ARG2, TGFBI,


Gene_MMP9, CSF1R, TLR4, GADD45A,


GADD45B, PRV1, IFNGR1, IL18R1, VNN1,


FAD104, PFKFB3


TNFRSF6, TIFA, PFKFB3, PRV1, OSM,
0.84
0.8
0.87


JAK2, TGFBI, IL10alpha, CEACAM1, INSL3,


IRAK2, LY96, ARG2, CD86, FAD104,


MAP2K6, TLR4, SOCS3, IL18R1, ITGAM


FCGR1A, HLA-DRA, ARG2, CRTAP,
0.84
0.83
0.84


CEACAM1, TNFSF13B, OSM, ANXA3,


IL1RN, Gene_MMP9, TNFRSF6, FAD104,


JAK2, IFNGR1, MKNK1, LDLR, IL10alpha,


TGFBI, SOD2, CCL5


GADD45A, MAPK14, ARG2, TDRD9, NCR1,
0.84
0.83
0.85


IL18R1, SOD2, ITGAM, FCGR1A, SOCS3,


HLA-DRA, IRAK4, TNFRSF6, PRV1, CD86,


TGFBI, TNFSF13B, TIFA, VNN1, FAD104


HLA-DRA, ARG2, IL1RN, SOCS3, PSTPIP2,
0.84
0.79
0.88


CCL5, IFNGR1, CD86, TLR4, TGFBI, LY96,


TNFRSF6, OSM, MAP2K6, VNN1, ITGAM,


TNFSF10, NCR1, IRAK4, MAPK14


BCL2A1, ITGAM, ANKRD22, ARG2,
0.84
0.79
0.87


FAD104, OSM, GADD45A, CCL5, TGFBI,


CD86, PSTPIP2, PFKFB3, IFNGR1, IL18R1,


CEACAM1, Gene_MMP9, IRAK2, IL1RN,


NCR1, LY96


JAK2, VNN1, CSF1R, TLR4, OSM, SOCS3,
0.84
0.79
0.88


ANXA3, LY96, MKNK1, TDRD9, ITGAM,


Gene_MMP9, TGFBI, CEACAM1, CD86,


MAP2K6, CCL5, TNFSF10, IL1RN, IL18R1


FAD104, PSTPIP2, CEACAM1, MAP2K6,
0.83
0.84
0.83


TIFA, ANKRD22, INSL3, TLR4, CRTAP,


LY96, SOCS3, MAPK14, JAK2, ARG2,


MKNK1, IL18R1, CSF1R, CD86, PRV1, OSM


CEACAM1, SOCS3, FCGR1A, ARG2,
0.83
0.8
0.87


INSL3, FAD104, IRAK4, GADD45A,


ITGAM, PRV1, TNFSF13B, NCR1,


Gene_MMP9, IL18R1, SOD2, MAPK14,


TIFA, IRAK2, ANKRD22, IL1RN


GADD45B, SOD2, CRTAP, OSM,
0.83
0.78
0.88


TNFSF13B, CCL5, CD86, INSL3, HLA-DRA,


TNFRSF6, TGFBI, GADD45A, FCGR1A,


FAD104, JAK2, IL1RN, PFKFB3, MAP2K6,


CEACAM1, TDRD9


FCGR1A, GADD45A, ANKRD22, IL1RN,
0.83
0.78
0.88


PFKFB3, CCL5, TIFA, IL10alpha, CRTAP,


MKNK1, PSTPIP2, PRV1, CSF1R, ANXA3,


NCR1, JAK2, VNN1, IRAK4, CD86,


MAP2K6


TLR4, GADD45A, JAK2, OSM, CD86,
0.83
0.78
0.88


SOCS3, CEACAM1, IL18R1, MAP2K6,


PRV1, FAD104, BCL2A1, VNN1, INSL3,


PSTPIP2, ANKRD22, TNFSF10, IFNGR1,


CRTAP, HLA-DRA


FAD104, IL18R1, TIFA, TNFRSF6,
0.83
0.77
0.88


Gene_MMP9, ARG2, OSM, TNFSF13B,


FCGR1A, CD86, CEACAM1, LY96, NCR1,


TNFSF10, PFKFB3, PRV1, GADD45A,


SOCS3, HLA-DRA, IRAK2


TDRD9, MKNK1, PFKFB3, IRAK2, INSL3,
0.83
0.82
0.84


ITGAM, MAPK14, JAK2, HLA-DRA,


CSF1R, CRTAP, NCR1, SOD2, TIFA, IRAK4,


CD86, OSM, BCL2A1, LY96, ANKRD22


ANKRD22, CRTAP, NCR1, OSM, INSL3,
0.83
0.82
0.84


CD86, CCL5, JAK2, CSF1R, GADD45B,


ANXA3, SOCS3, PSTPIP2, FCGR1A, HLA-


DRA, IRAK2, IL1RN, IL18R1, PFKFB3,


Gene_MMP9


IL1RN, LY96, ARG2, PRV1, GADD45A,
0.85
0.84
0.85


TNFSF10, FCGR1A, IL10alpha, LDLR,


PFKFB3, CRTAP, SOD2, CEACAM1,


IL18R1, CCL5, PSTPIP2, TLR4, VNN1,


HLA-DRA, JAK2, ANKRD22


CD86, LDLR, CRTAP, OSM, TGFBI,
0.84
0.88
0.82


FCGR1A, NCR1, MAPK14, GADD45A,


ARG2, TLR4, GADD45B, INSL3, TNFSF10,


ANXA3, MKNK1, PSTPIP2, CSF1R, SOD2,


MAP2K6, BCL2A1


IRAK4, GADD45A, MAP2K6, ANKRD22,
0.84
0.82
0.85


Gene_MMP9, TDRD9, PSTPIP2, VNN1,


IL18R1, ARG2, IL1RN, PFKFB3, FCGR1A,


TNFRSF6, JAK2, NCR1, TLR4, FAD104,


SOCS3, IFNGR1, SOD2


SOCS3, ITGAM, Gene_MMP9, MKNK1,
0.84
0.82
0.86


ARG2, CRTAP, BCL2A1, PRV1, NCR1,


HLA-DRA, MAP2K6, FCGR1A, CD86,


FAD104, CCL5, TGFBI, TDRD9, OSM,


GADD45B, IRAK4, LY96


INSL3, BCL2A1, PSTPIP2, OSM, MAP2K6,
0.83
0.84
0.83


CCL5, MKNK1, FAD104, ITGAM, MAPK14,


IL1RN, VNN1, IRAK2, FCGR1A, CD86,


PFKFB3, TDRD9, HLA-DRA, ARG2, TLR4,


CEACAM1


TIFA, MKNK1, TNFSF13B, CSF1R, HLA-
0.83
0.82
0.84


DRA, IL18R1, MAPK14, INSL3, PFKFB3,


ANKRD22, LDLR, ARG2, CCL5, LY96,


PSTPIP2, GADD45A, CEACAM1, JAK2,


TGFBI, VNN1, IL1RN


CRTAP, FAD104, TIFA, BCL2A1, IRAK2,
0.83
0.81
0.85


PSTPIP2, PFKFB3, MKNK1, ANKRD22,


IL18R1, GADD45B, TDRD9, TLR4, INSL3,


CEACAM1, MAP2K6, ARG2, CD86, NCR1,


TNFSF13B, PRV1


JAK2, SOCS3, IFNGR1, IL1RN, OSM,
0.83
0.81
0.85


BCL2A1, SOD2, ITGAM, FAD104, IL18R1,


PSTPIP2, ARG2, PRV1, TNFSF13B,


FCGR1A, IRAK2, IL10alpha, PFKFB3,


MAPK14, INSL3, TGFBI


GADD45A, CCL5, LDLR, ARG2, IRAK2,
0.83
0.86
0.8


SOCS3, SOD2, PRV1, MAP2K6, INSL3,


TNFSF10, IL18R1, IL1RN, MAPK14,


FAD104, IFNGR1, HLA-DRA, PSTPIP2,


ITGAM, CSF1R, IL10alpha


CD86, TGFBI, ITGAM, IL10alpha, JAK2,
0.83
0.79
0.86


TIFA, FAD104, CRTAP, IL1RN, BCL2A1,


CCL5, GADD45B, HLA-DRA, SOD2, OSM,


NCR1, VNN1, IL18R1, ANXA3,


Gene_MMP9, PSTPIP2


IL10alpha, TNFSF13B, GADD45B, MAP2K6,
0.83
0.76
0.89


CCL5, IRAK2, MKNK1, LDLR, VNN1,


GADD45A, ARG2, OSM, IFNGR1, IL18R1,


ANKRD22, JAK2, TLR4, TGFBI, TNFRSF6,


FAD104, PFKFB3


MAPK14, SOD2, PRV1, GADD45B,
0.83
0.83
0.82


MKNK1, IL18R1, INSL3, NCR1, LY96,


IRAK2, CSF1R, TNFRSF6, HLA-DRA,


VNN1, IRAK4, FAD104, CEACAM1,


IFNGR1, FCGR1A, TIFA, CD86


IL1RN, PFKFB3, IL18R1, PRV1, CRTAP,
0.83
0.81
0.84


ITGAM, TNFRSF6, IL10alpha, SOCS3,


VNN1, BCL2A1, MAPK14, GADD45A,


IRAK2, CCL5, ARG2, TLR4, CD86,


ANKRD22, TNFSF10, TGFBI


HLA-DRA, PRV1, GADD45A, IL1RN,
0.83
0.81
0.84


IL18R1, TNFRSF6, LDLR, IRAK4, BCL2A1,


TIFA, PSTPIP2, SOCS3, IL10alpha, FAD104,


MKNK1, TNFSF13B, JAK2, TDRD9,


TNFSF10, FCGR1A, CD86


INSL3, GADD45A, TGFBI, JAK2, IRAK2,
0.82
0.82
0.82


OSM, TIFA, TNFSF13B, HLA-DRA,


FCGR1A, BCL2A1, PRV1, CEACAM1,


SOCS3, MAPK14, IRAK4, ANXA3,


TNFRSF6, FAD104, IFNGR1, Gene_MMP9


BCL2A1, ANKRD22, IL10alpha, HLA-DRA,
0.82
0.8
0.84


VNN1, GADD45B, TNFRSF6, CSF1R,


IRAK4, ITGAM, IL1RN, IRAK2, LY96,


MAPK14, JAK2, Gene_MMP9, TLR4, ARG2,


CCL5, SOCS3, MAP2K6


TDRD9, VNN1, GADD45A, ANKRD22,
0.82
0.79
0.85


PFKFB3, TNFSF13B, SOCS3, IL18R1,


IL1RN, ARG2, CSF1R, HLA-DRA, PRV1,


CEACAM1, CD86, IFNGR1, CCL5,


MAP2K6, TGFBI, IL10alpha, Gene_MMP9


CRTAP, IL1RN, TIFA, IRAK4, ANXA3,
0.82
0.78
0.86


SOCS3, CD86, CSF1R, FCGR1A, FAD104,


ANKRD22, TNFSF13B, PSTPIP2, TDRD9,


ARG2, TGFBI, Gene_MMP9, CCL5,


IL10alpha, GADD45B, TNFRSF6


ANXA3, TNFRSF6, TDRD9, IRAK2,
0.82
0.78
0.87


MAP2K6, INSL3, FCGR1A, GADD45A,


NCR1, ARG2, VNN1, PRV1, MAPK14,


IRAK4, SOCS3, ITGAM, HLA-DRA, CD86,


CEACAM1, LY96, GADD45B


VNN1, CCL5, IFNGR1, LY96, IL10alpha,
0.87
0.84
0.89


ITGAM, FCGR1A, FAD104, NCR1,


TNFRSF6, TNFSF13B, SOCS3, TIFA,


TNFSF10, PSTPIP2, ARG2, IL18R1, CSF1R,


OSM, PFKFB3, LDLR, IRAK2


IL18R1, GADD45A, BCL2A1, HLA-DRA,
0.84
0.79
0.88


PSTPIP2, ANKRD22, CRTAP, FAD104,


CD86, TNFRSF6, Gene_MMP9, IRAK2,


SOD2, IL10alpha, IFNGR1, FCGR1A, TIFA,


OSM, CCL5, GADD45B, TGFBI, TLR4


TNFSF13B, LDLR, GADD45B, MAPK14,
0.84
0.79
0.89


PFKFB3, CRTAP, MAP2K6, NCR1, CCL5,


ARG2, SOD2, BCL2A1, MKNK1, TIFA,


ANKRD22, Gene_MMP9, TGFBI, IL1RN,


HLA-DRA, IL18R1, VNN1, CSF1R


TNFRSF6, PSTPIP2, CD86, VNN1, CCL5,
0.83
0.85
0.82


MAPK14, TLR4, BCL2A1, ANKRD22,


ARG2, ITGAM, IL10alpha, IRAK4, SOCS3,


LY96, CRTAP, JAK2, IL1RN, FCGR1A,


MAP2K6, TNFSF10, GADD45A


TDRD9, CRTAP, ANKRD22, TNFSF13B,
0.83
0.82
0.85


ANXA3, CCL5, FCGR1A, TNFSF10,


TNFRSF6, PRV1, IRAK2, CEACAM1,


SOCS3, CSF1R, FAD104, PSTPIP2, VNN1,


ARG2, IL1RN, HLA-DRA, BCL2A1, INSL3


TNFSF10, TLR4, MAP2K6, PFKFB3,
0.83
0.79
0.86


FCGR1A, INSL3, MAPK14, PSTPIP2,


IFNGR1, CD86, PRV1, IL10alpha, OSM,


FAD104, ITGAM, ANXA3, TIFA,


CEACAM1, IL18R1, TNFRSF6, NCR1,


GADD45A


GADD45B, HLA-DRA, NCR1, TGFBI, OSM,
0.83
0.8
0.86


MKNK1, TLR4, ARG2, CCL5, LDLR,


IFNGR1, SOCS3, INSL3, TIFA, TNFSF10,


CD86, IL10alpha, GADD45A, CSF1R,


TDRD9, BCL2A1, ANXA3


TLR4, ANXA3, IL10alpha, NCR1, JAK2,
0.83
0.76
0.88


TNFSF13B, GADD45A, OSM, SOCS3,


CEACAM1, BCL2A1, MKNK1, ARG2,


CRTAP, TNFRSF6, Gene_MMP9, PSTPIP2,


SOD2, CD86, IL1RN, FCGR1A, CSF1R


LY96, TIFA, TLR4, PSTPIP2, Gene_MMP9,
0.83
0.82
0.83


PRV1, HLA-DRA, CEACAM1, FCGR1A,


ARG2, IRAK4, IL1RN, OSM, IFNGR1,


TNFSF13B, CSF1R, TDRD9, GADD45B,


ANXA3, SOCS3, GADD45A, LDLR


INSL3, PSTPIP2, MKNK1, FCGR1A,
0.83
0.82
0.83


PFKFB3, OSM, TGFBI, MAPK14, IRAK2,


GADD45A, ANKRD22, CCL5, HLA-DRA,


IL10alpha, SOCS3, CD86, IFNGR1, ARG2,


Gene_MMP9, GADD45B, VNN1, IL1RN


IL1RN, IFNGR1, CCL5, GADD45B, VNN1,
0.83
0.78
0.87


CSF1R, TNFSF10, LDLR, TNFRSF6, INSL3,


CD86, OSM, FCGR1A, BCL2A1, CRTAP,


TLR4, NCR1, PSTPIP2, SOCS3, MAP2K6,


TNFSF13B, Gene_MMP9


ARG2, GADD45B, TNFSF10, IRAK2,
0.82
0.83
0.82


MAPK14, IL1RN, MKNK1, CRTAP,


TNFSF13B, PRV1, SOD2, VNN1, IL18R1,


HLA-DRA, MAP2K6, INSL3, CEACAM1,


IL10alpha, LY96, SOCS3, FCGR1A,


ANKRD22


IFNGR1, LDLR, ITGAM, VNN1, IL18R1,
0.82
0.83
0.82


TGFBI, SOCS3, ANKRD22, HLA-DRA,


TIFA, OSM, TLR4, IRAK4, INSL3, SOD2,


TNFSF13B, LY96, IRAK2, BCL2A1,


MAPK14, CCL5, MKNK1


Gene_MMP9, BCL2A1, TDRD9, OSM,
0.82
0.81
0.83


MAPK14, IRAK2, CRTAP, MAP2K6, TGFBI,


IL18R1, TNFSF10, ANXA3, IFNGR1,


GADD45A, TIFA, PSTPIP2, SOCS3, ITGAM,


ARG2, HLA-DRA, FAD104, IRAK4


IRAK2, IL1RN, ITGAM, LY96, IFNGR1,
0.82
0.81
0.84


TGFBI, TIFA, PFKFB3, Gene_MMP9,


FAD104, TNFSF13B, VNN1, LDLR, INSL3,


HLA-DRA, NCR1, TDRD9, TNFRSF6,


ANXA3, CSF1R, SOCS3, IL18R1


TNFRSF6, INSL3, LDLR, CD86, TGFBI,
0.82
0.8
0.84


NCR1, Gene_MMP9, CRTAP, HLA-DRA,


BCL2A1, MKNK1, IL18R1, TLR4,


CEACAM1, PRV1, CCL5, OSM, TDRD9,


PFKFB3, IFNGR1, IRAK2, PSTPIP2


PFKFB3, ITGAM, ANKRD22, MAPK14,
0.82
0.8
0.85


TGFBI, PSTPIP2, BCL2A1, IFNGR1,


MKNK1, NCR1, ARG2, HLA-DRA, INSL3,


CRTAP, FCGR1A, LDLR, CCL5, JAK2,


IRAK4, TLR4, LY96, IL10alpha


TIFA, IFNGR1, HLA-DRA, Gene_MMP9,
0.82
0.8
0.84


PRV1, FAD104, IL10alpha, GADD45B,


IRAK4, IL1RN, TDRD9, IL18R1, BCL2A1,


CD86, GADD45A, CCL5, ANXA3, OSM,


SOCS3, PFKFB3, LDLR, CSF1R


FAD104, NCR1, BCL2A1, IRAK2, TLR4,
0.82
0.8
0.84


IL18R1, SOD2, MAPK14, GADD45B, CD86,


FCGR1A, CSF1R, OSM, MAP2K6, PFKFB3,


LY96, TIFA, MKNK1, PSTPIP2, CRTAP,


TGFBI, GADD45A


GADD45A, CSF1R, IL18R1, TGFBI,
0.85
0.81
0.89


TNFSF13B, ANXA3, OSM, SOCS3, LY96,


TDRD9, ITGAM, FCGR1A, IFNGR1,


FAD104, HLA-DRA, PSTPIP2, MKNK1,


CRTAP, GADD45B, Gene_MMP9, LDLR,


TLR4, VNN1


MAP2K6, TGFBI, HLA-DRA, IL10alpha,
0.85
0.82
0.87


VNN1, GADD45B, CEACAM1, PRV1, OSM,


IRAK4, IRAK2, ITGAM, CSF1R, TDRD9,


NCR1, TNFSF13B, CRTAP, BCL2A1, TIFA,


IFNGR1, GADD45A, IL18R1, SOD2


GADD45B, MAPK14, TDRD9, CCL5, OSM,
0.84
0.83
0.85


TNFSF13B, ANXA3, TIFA, ANKRD22,


TNFRSF6, TNFSF10, PSTPIP2, TLR4, VNN1,


FCGR1A, IL18R1, NCR1, GADD45A, LY96,


INSL3, ITGAM, BCL2A1, IRAK2


HLA-DRA, PFKFB3, IRAK4, MKNK1,
0.84
0.8
0.88


TGFBI, CRTAP, ANXA3, CEACAM1, CCL5,


JAK2, TNFSF10, IL1RN, CSF1R, IFNGR1,


ARG2, LY96, Gene_MMP9, PRV1, CD86,


IRAK2, ITGAM, IL10alpha, OSM


FAD104, LY96, NCR1, TLR4, TNFSF13B,
0.84
0.8
0.89


MAPK14, MAP2K6, HLA-DRA, FCGR1A,


CD86, ANKRD22, LDLR, IL1RN, IFNGR1,


TDRD9, TGFBI, GADD45A, PRV1, PFKFB3,


ITGAM, JAK2, PSTPIP2, CRTAP


BCL2A1, FCGR1A, CRTAP, Gene_MMP9,
0.84
0.82
0.85


TDRD9, CEACAM1, SOCS3, SOD2, LDLR,


GADD45B, LY96, CSF1R, ARG2, TNFRSF6,


PSTPIP2, PFKFB3, IL1RN, IL10alpha, VNN1,


GADD45A, INSL3, JAK2, IFNGR1


Gene_MMP9, LDLR, CEACAM1, MAPK14,
0.84
0.82
0.86


TLR4, ANXA3, IRAK4, FCGR1A,


GADD45B, GADD45A, TGFBI, BCL2A1,


CSF1R, PRV1, TNFRSF6, IFNGR1, TDRD9,


LY96, MAP2K6, OSM, CRTAP, CD86,


FAD104


FCGR1A, ANXA3, MAPK14, TNFRSF6,
0.83
0.82
0.84


PSTPIP2, INSL3, ANKRD22, CD86, CRTAP,


FAD104, GADD45B, IL18R1, TLR4, IRAK2,


ITGAM, JAK2, GADD45A, BCL2A1,


IFNGR1, CSF1R, TIFA, NCR1, IRAK4


CRTAP, OSM, TNFRSF6, IRAK2, VNN1,
0.83
0.8
0.85


IRAK4, ANXA3, SOD2, ANKRD22, ITGAM,


TLR4, MKNK1, IL18R1, CEACAM1, TGFBI,


PRV1, Gene_MMP9, TNFSF13B, BCL2A1,


HLA-DRA, INSL3, NCR1, CSF1R


FAD104, CEACAM1, CCL5, PSTPIP2,
0.83
0.79
0.87


TNFSF10, VNN1, CRTAP, IRAK2, FCGR1A,


TNFSF13B, CD86, IL10alpha, ARG2,


BCL2A1, IFNGR1, PRV1, IL18R1, TNFRSF6,


TIFA, TLR4, JAK2, MAPK14, MAP2K6


ARG2, MAPK14, IRAK4, LDLR, IL10alpha,
0.83
0.84
0.81


Gene_MMP9, NCR1, OSM, CEACAM1,


SOD2, CSF1R, CCL5, GADD45A, ITGAM,


BCL2A1, HLA-DRA, PFKFB3, TNFSF13B,


TNFSF10, IRAK2, VNN1, JAK2, PRV1


FAD104, IFNGR1, INSL3, PFKFB3,
0.83
0.83
0.83


MAP2K6, LDLR, CD86, ARG2, PRV1,


IL1RN, OSM, ITGAM, VNN1, MKNK1,


ANXA3, JAK2, GADD45B, CSF1R,


TNFSF13B, PSTPIP2, FCGR1A, CRTAP,


TGFBI


VNN1, SOCS3, ANKRD22, FAD104, IL18R1,
0.83
0.77
0.89


OSM, ITGAM, CCL5, TGFBI, MAPK14,


MKNK1, HLA-DRA, LDLR, PSTPIP2,


ARG2, CSF1R, IL10alpha, MAP2K6, LY96,


FCGR1A, TNFSF10, JAK2, TLR4


CEACAM1, MAP2K6, IL18R1, TIFA, HLA-
0.82
0.82
0.83


DRA, FAD104, TGFBI, LDLR, ANKRD22,


IL1RN, SOCS3, TNFSF13B, NCR1, CD86,


BCL2A1, IL10alpha, TLR4, CRTAP,


MKNK1, ITGAM, JAK2, OSM, ARG2


VNN1, FCGR1A, SOD2, CRTAP, TGFBI,
0.82
0.81
0.84


LDLR, FAD104, NCR1, TNFRSF6, ARG2,


GADD45A, OSM, ANXA3, ITGAM,


BCL2A1, CSF1R, IFNGR1, TIFA,


CEACAM1, CCL5, SOCS3, ANKRD22,


Gene_MMP9


CCL5, IL1RN, TIFA, PRV1, TNFSF13B,
0.82
0.81
0.84


INSL3, IRAK2, MKNKI, MAPK14,


FCGR1A, SOCS3, JAK2, FAD104, IFNGR1,


CRTAP, IL18R1, GADD45B, SOD2,


TNFSF10, HLA-DRA, TNFRSF6, ANKRD22,


LDLR


PRV1, BCL2A1, SOD2, VNN1, FAD104,
0.82
0.74
0.9


TIFA, IL10alpha, SOCS3, ITGAM, IL18R1,


CEACAM1, MAP2K6, TNFSF13B, JAK2,


IRAK4, TNFRSF6, OSM, CRTAP, PSTPIP2,


TLR4, CSF1R, IL1RN, FCGR1A


TNFRSF6, TNFSF10, CD86, IL10alpha,
0.82
0.81
0.83


ARG2, TLR4, JAK2, MAP2K6, GADD45B,


LDLR, TIFA, IRAK2, BCL2A1, SOD2, LY96,


PFKFB3, HLA-DRA, CSF1R, FAD104,


CRTAP, FCGR1A, ANXA3, SOCS3


TNFSF13B, IRAK4, CD86, LDLR, OSM,
0.82
0.8
0.83


CCL5, ANXA3, IL1RN, GADD45B, SOCS3,


TGFBI, BCL2A1, FAD104, IRAK2,


IL10alpha, NCR1, MAP2K6, INSL3, TIFA,


CEACAM1, MKNK1, MAPK14, JAK2


LY96, ANXA3, TIFA, CSF1R, GADD45B,
0.85
0.8
0.89


PFKFB3, IL1RN, IL18R1, LDLR, TNFRSF6,


OSM, INSL3, CRTAP, MAP2K6, IRAK2,


ARG2, IL10alpha, NCR1, FAD104, IRAK4,


MKNK1, VNN1, IFNGR1, SOD2


NCR1, IL1RN, PRV1, IL18R1, HLA-DRA,
0.84
0.82
0.86


BCL2A1, GADD45A, FAD104, TLR4, OSM,


FCGR1A, TNFSF10, CRTAP, INSL3,


GADD45B, LY96, IRAK2, CD86, VNN1,


CCL5, JAK2, IL10alpha, MKNK1, IRAK4


GADD45A, MKNK1, ANXA3, TLR4,
0.84
0.83
0.85


MAP2K6, TIFA, FCGR1A, IRAK2, TDRD9,


VNN1, CSF1R, GADD45B, LDLR, IL1RN,


ANKRD22, JAK2, HLA-DRA, IL10alpha,


PSTPIP2, Gene_MMP9, CRTAP, IL18R1,


MAPK14, ARG2


FAD104, IL18R1, IRAK2, TIFA, IL10alpha,
0.83
0.83
0.84


ITGAM, SOCS3, TDRD9, PSTPIP2, ARG2,


INSL3, IL1RN, TLR4, IFNGR1, VNN1,


MAPK14, TNFRSF6, SOD2, ANKRD22,


NCR1, ANXA3, FCGR1A, CD86, OSM


TLR4, TGFBI, CEACAM1, OSM, CRTAP,
0.83
0.83
0.83


IL1RN, TNFRSF6, PRV1, SOD2, MKNK1,


VNN1, CSF1R, IL18R1, ANKRD22,


MAPK14, ANXA3, TNFSF10, TDRD9,


BCL2A1, IRAK4, FCGR1A, CCL5,


TNFSF13B, GADD45B


ARG2, JAK2, CSF1R, NCR1, LY96, HLA-
0.83
0.82
0.84


DRA, ANXA3, PSTPIP2, IRAK4, BCL2A1,


IL1RN, IFNGR1, FCGR1A, VNN1, TNFSF10,


MAPK14, TGFBI, GADD45B, INSL3,


IRAK2, OSM, CD86, CRTAP, TNFSF13B


HLA-DRA, INSL3, PRV1, MAP2K6, TIFA,
0.83
0.81
0.84


NCR1, CSF1R, TDRD9, IL18R1, MKNK1,


TNFRSF6, TNFSF10, LDLR, IRAK4,


FAD104, ITGAM, PSTPIP2, MAPK14,


TNFSF13B, GADD45B, CEACAM1, IL1RN,


ANXA3, PFKFB3


INSL3, TDRD9, GADD45A, BCL2A1,
0.83
0.78
0.87


PFKFB3, TNFRSF6, MAP2K6, GADD45B,


TGFBI, IRAK2, CEACAM1, ITGAM,


IL10alpha, ANXA3, JAK2, IL1RN, CRTAP,


PRV1, SOCS3, TIFA, CCL5, LY96,


TNFSF10, OSM


VNN1, LDLR, FAD104, HLA-DRA, ARG2,
0.82
0.85
0.8


IFNGR1, IRAK4, TNFRSF6, TIFA, MAP2K6,


NCR1, OSM, PRV1, CSF1R, INSL3,


TNFSF13B, JAK2, MAPK14, BCL2A1,


IRAK2, TLR4, PSTPIP2, TDRD9, ANXA3


ANXA3, TNFSF10, TGFBI, MKNK1,
0.82
0.83
0.82


PSTPIP2, GADD45A, CRTAP, LDLR, INSL3,


MAPK14, IFNGR1, BCL2A1, TNFSF13B,


GADD45B, Gene_MMP9, IRAK2,


CEACAM1, PRV1, SOD2, FAD104, JAK2,


NCR1, ARG2, IL1RN


TDRD9, LY96, PFKFB3, IRAK2, FAD104,
0.82
0.8
0.85


NCR1, Gene_MMP9, MAPK14, CCL5,


LDLR, PSTPIP2, OSM, VNN1, IRAK4,


BCL2A1, TIFA, GADD45A, TGFBI,


ANKRD22, FCGR1A, IFNGR1, ARG2, CD86,


IL18R1


CD86, TNFSF13B, PSTPIP2, IL10alpha,
0.82
0.8
0.84


HLA-DRA, MAP2K6, FCGR1A,


Gene_MMP9, JAK2, SOCS3, CSF1R,


TDRD9, ARG2, NCR1, OSM, FAD104,


BCL2A1, TNFRSF6, INSL3, VNN1, ITGAM,


PRV1, TLR4, CEACAM1


IL18R1, ARG2, VNN1, TNFRSF6, TIFA,
0.82
0.78
0.86


MKNK1, IL10alpha, CD86, NCR1, OSM,


ANKRD22, TDRD9, PSTPIP2, ITGAM,


IFNGR1, MAP2K6, BCL2A1, IRAK2, TLR4,


LY96, SOCS3, GADD45B, IRAK4, PRV1


TNFSF10, ITGAM, MAP2K6, TIFA, CSF1R,
0.82
0.78
0.86


TDRD9, FAD104, TLR4, GADD45B, HLA-


DRA, IRAK2, IRAK4, OSM, FCGR1A,


CCL5, SOD2, VNN1, MKNK1, ARG2,


Gene_MMP9, TGFBI, TNFSF13B, MAPK14,


PFKFB3


TNFSF10, CEACAM1, IFNGR1, TIFA,
0.82
0.75
0.88


MKNK1, ANXA3, IL1RN, IL10alpha,


IL18R1, HLA-DRA, SOCS3, Gene_MMP9,


MAPK14, TGFBI, JAK2, IRAK2, TLR4,


CSF1R, BCL2A1, PSTPIP2, MAP2K6, CD86,


ITGAM, SOD2


SOD2, PFKFB3, MAP2K6, HLA-DRA,
0.82
0.85
0.79


ANKRD22, IL18R1, Gene_MMP9, LDLR,


ARG2, GADD45A, JAK2, MKNK1, PRV1,


FCGR1A, ITGAM, OSM, NCR1, VNN1,


LY96, IFNGR1, TIFA, PSTPIP2, IL1RN,


TLR4


CSF1R, FCGR1A, IL18R1, ANKRD22,
0.82
0.81
0.83


MKNK1, NCR1, IRAK2, TDRD9, GADD45A,


CRTAP, GADD45B, JAK2, PRV1, SOCS3,


CD86, MAPK14, MAP2K6, IFNGR1, LY96,


FAD104, OSM, SOD2, TLR4, IL10alpha


ARG2, TGFBI, TIFA, IL18R1, TNFRSF6,
0.82
0.8
0.83


CSF1R, CCL5, SOCS3, LY96, MKNK1,


BCL2A1, SOD2, FCGR1A, PSTPIP2,


GADD45B, IFNGR1, NCR1, TNFSF10,


LDLR, PRV1, IL1RN, TDRD9, ANKRD22,


TLR4


IL1RN, IL10alpha, IFNGR1, TDRD9,
0.82
0.8
0.84


PFKFB3, GADD45B, TNFSF10, PSTPIP2,


SOCS3, TIFA, MAPK14, CSF1R, TNFSF13B,


CRTAP, TNFRSF6, ARG2, IL18R1, LY96,


TGFBI, CD86, TLR4, GADD45A, OSM,


Gene_MMP9


SOD2, IRAK4, SOCS3, VNN1, IL1RN,
0.86
0.88
0.85


ITGAM, TNFSF10, GADD45A, CCL5,


CEACAM1, ANKRD22, NCR1, IL18R1,


OSM, ARG2, INSL3, MAPK14, MAP2K6,


TGFBI, TNFSF13B, PFKFB3, MKNK1,


LY96, FCGR1A, CSF1R


LDLR, VNN1, GADD45A, SOCS3, TLR4,
0.85
0.85
0.85


SOD2, BCL2A1, IL18R1, IRAK2, HLA-DRA,


TIFA, CEACAM1, OSM, INSL3, TNFSF13B,


TNFRSF6, Gene_MMP9, CRTAP, ARG2,


LY96, GADD45B, CSF1R, FCGR1A, IL1RN,


PFKFB3


ARG2, PRV1, TNFSF10, FAD104, SOD2,
0.85
0.84
0.86


ANXA3, IL18R1, JAK2, LDLR, OSM,


IFNGR1, PSTPIP2, TNFRSF6, IRAK4,


IL1RN, VNN1, FCGR1A, ITGAM, IL10alpha,


IRAK2, INSL3, CD86, TDRD9, TIFA,


MKNK1


GADD45B, IRAK2, MAPK14, Gene_MMP9,
0.85
0.81
0.89


CD86, CEACAM1, SOD2, SOCS3, ARG2,


ANXA3, LDLR, JAK2, VNN1, IFNGR1,


FAD104, NCR1, PRV1, OSM, TDRD9,


MKNK1, ITGAM, INSL3, IL1RN,


ANKRD22, CCL5


IL10alpha, IRAK2, HLA-DRA, Gene_MMP9,
0.84
0.85
0.84


TGFBI, LDLR, TIFA, GADD45A, ARG2,


CSF1R, MAP2K6, CEACAM1, PRV1, OSM,


CD86, TNFRSF6, LY96, FAD104, PSTPIP2,


ANXA3, IFNGR1, NCR1, CCL5, GADD45B,


PFKFB3


GADD45A, SOCS3, SOD2, TGFBI, HLA-
0.84
0.84
0.85


DRA, VNN1, CD86, CCL5, BCL2A1,


CRTAP, MAP2K6, PRV1, IL18R1, CSF1R,


OSM, IRAK2, PSTPIP2, TLR4, FCGR1A,


ANKRD22, CEACAM1, JAK2, INSL3,


TDRD9, TNFSF10


FCGR1A, TLR4, ANKRD22, CEACAM1,
0.84
0.78
0.9


IRAK4, LY96, TDRD9, ARG2, CRTAP,


ANXA3, LDLR, MAPK14, CD86,


Gene_MMP9, INSL3, GADD45B, TNFSF10,


VNN1, IRAK2, PSTPIP2, TIFA, TNFRSF6,


TGFBI, IL18R1, IL1RN


SOCS3, VNN1, FCGR1A, SOD2, OSM,
0.84
0.83
0.84


TNFSF10, LY96, Gene_MMP9, GADD45B,


CRTAP, PRV1, HLA-DRA, GADD45A,


TLR4, ARG2, IRAK2, FAD104, INSL3,


PSTPIP2, TIFA, TGFBI, IL18R1, MAP2K6,


LDLR, ANXA3


MAP2K6, LDLR, TIFA, TNFSF13B, IL18R1,
0.84
0.83
0.84


ITGAM, SOCS3, OSM, ANXA3, GADD45A,


Gene_MMP9, CD86, IL1RN, IFNGR1, PRV1,


FCGR1A, MAPK14, CCL5, VNN1, ARG2,


PSTPIP2, IRAK2, NCR1, TDRD9, TNFRSF6


TIFA, ANKRD22, TNFSF13B, SOCS3,
0.84
0.82
0.86


NCR1, IRAK4, JAK2, GADD45A, CCL5,


LDLR, MAPK14, IL18R1, SOD2, TGFBI,


CSF1R, IFNGR1, MAP2K6, TNFSF10,


IRAK2, LY96, IL1RN, TNFRSF6, VNN1,


INSL3, PFKFB3


MAP2K6, FAD104, CCL5, IL18R1, NCR1,
0.84
0.81
0.86


VNN1, IL10alpha, ANKRD22, IFNGR1,


MAPK14, CD86, MKNK1, TLR4, LY96,


TIFA, PSTPIP2, TNFRSF6, LDLR, CSF1R,


ARG2, TGFBI, JAK2, PFKFB3, OSM,


TDRD9


JAK2, OSM, IRAK4, VNN1, SOCS3,
0.83
0.84
0.83


GADD45B, IL1RN, FCGR1A, TNFRSF6,


Gene_MMP9, ANKRD22, ARG2, IL10alpha,


CCL5, IL18R1, ANXA3, LY96, PSTPIP2,


TIFA, TNFSF10, FAD104, MAP2K6,


MKNK1, PFKFB3, CRTAP


TIFA, IRAK2, ANKRD22, CCL5, IL10alpha,
0.83
0.85
0.82


INSL3, CEACAM1, TLR4, FCGR1A, NCR1,


CD86, BCL2A1, GADD45A, ITGAM,


MAP2K6, CRTAP, VNN1, TDRD9, SOCS3,


ANXA3, TNFSF10, LY96, MKNK1, JAK2,


ARG2


PSTPIP2, CEACAM1, FAD104, TIFA,
0.83
0.82
0.84


ANKRD22, OSM, TNFSF13B, IRAK4,


INSL3, GADD45A, IL10alpha, CSF1R, HLA-


DRA, SOCS3, GADD45B, CCL5,


Gene_MMP9, LY96, TLR4, IFNGR1, TGFBI,


BCL2A1, MAP2K6, CD86, PFKFB3


IL1RN, JAK2, PFKFB3, OSM, CD86, IL18R1,
0.83
0.82
0.84


SOD2, GADD45B, ITGAM, TNFRSF6,


MAP2K6, LDLR, TLR4, TIFA, INSL3,


SOCS3, IFNGR1, ANKRD22, GADD45A,


IRAK4, CRTAP, CSF1R, TNFSF13B, PRV1,


PSTPIP2


IFNGR1, VNN1, ANKRD22, FCGR1A, JAK2,
0.83
0.78
0.87


MAP2K6, SOD2, TNFSF13B, IRAK4,


CEACAM1, LY96, MAPK14, INSL3, NCR1,


Gene_MMP9, CCL5, HLA-DRA, LDLR,


TNFRSF6, PFKFB3, ANXA3, SOCS3, ARG2,


ITGAM, CSF1R


LDLR, GADD45A, IFNGR1, ARG2,
0.83
0.83
0.82


MAPK14, HLA-DRA, CRTAP, OSM,


TDRD9, CSF1R, FCGR1A, Gene_MMP9


NCR1, PRV1, IRAK4, TGFBI, TLR4, LY96,


IL1RN, FAD104, SOD2, CCL5, TNFRSF6,


MAP2K6, TNFSF13B


IL18R1, IL1RN, IRAK4, CEACAM1,
0.83
0.83
0.83


ITGAM, LY96, ANKRD22, ARG2, TDRD9,


LDLR, NCR1, IL10alpha, ANXA3, CD86,


MAPK14, TNFRSF6, SOD2, MKNK1,


GADD45B, CRTAP, PFKFB3, CSF1R,


INSL3, PSTPIP2, CCL5


PSTPIP2, NCR1, MKNK1, SOCS3, IL1RN,
0.83
0.81
0.84


IFNGR1, IL18R1, CSF1R, ITGAM, LDLR,


TIFA, CRTAP, OSM, TLR4, CEACAM1,


Gene_MMP9, INSL3, MAP2K6, CCL5,


FAD104, HLA-DRA, PRV1, VNN1, PFKFB3,


JAK2









In some embodiments, the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use any one of the biomarker sets listed in Table M. The biomarker sets listed in Table M were identified in the computational experiments described in Section 6.14.2, below, in which 1600 random subcombinations of the biomarkers listed in Table K were tested. Table M lists some of the biomarker sets that provided high accuracy scores against the validation population described in Section 6.14.2. Each row of Table M lists a single biomarker set that can be used in the methods and kits respectively referenced in Sections 5.2 and 5.3. In other words, each row of Table M describes a biomarker set that can be used to discriminate between sepsis and SIRS subjects (e.g., to determine whether a subject is likely to acquire SEPSIS). In some embodiments, nucleic acid forms of the biomarkers listed in Table M are used in the methods and kits respectively referenced in Sections 5.2 and 5.3. In some embodiments, protein forms of the biomarkers listed in Table M are used. In some hybrid embodiments, some of the biomarkers in a biomarker set from Table M are in protein form and some of the biomarkers in the same biomarker set from Table M are in nucleic acid form in the methods and kits respectively referenced in Sections 5.2 and 5.3.
In some embodiments, a given biomarker set listed in Table M is used with the addition of one, two, three, four, five, six, seven, eight, or nine or more additional biomarkers from Table I that are not within the given set of biomarkers from Table M. In some embodiments, a given set of biomarkers from Table M is used with the addition of one, two, three, four, five, six, seven, eight, or nine or more additional biomarkers from any one of Table I, 30, 31, 32, 33, 34, or 36 that are not within the given biomarker set from Table M. In Table M, accuracy, specificity, and senstitivity are described with reference to T−12 time point data described in Section 6.14.2, below.







TABLE M







Exemplary sets of biomarkers used in the methods or kits referenced in


Sections 5.2 and 5.3










BIOMARKER SET
ACCURACY
SPECIFICITY
SENSISTIVITY













ALPHAFETOPROTEIN, IL6, IL8
0.78
0.76
0.8


CREACTIVEPROTEIN, TIMP1, IL6
0.78
0.75
0.8


PROTEIN_MMP9, IL8, IL6
0.77
0.8
0.74


IL8, IL6, IL10
0.77
0.72
0.81


CREACTIVEPROTEIN, IL6,
0.77
0.77
0.76


PROTEIN_MMP9


APOLIPOPROTEINCIII, IL8, IL6
0.76
0.74
0.78


IL6, IL8, CREACTIVEPROTEIN
0.76
0.74
0.79


ALPHAFETOPROTEIN, MCP1, IL10, IL6
0.8
0.8
0.8


ALPHAFETOPROTEIN, IL10, IL6,
0.79
0.7
0.86


PROTEIN_MMP9


ALPHAFETOPROTEIN,
0.78
0.74
0.81


PROTEIN_MMP9, IL6,


APOLIPOPROTEINCIII


APOLIPOPROTEINCIII, IL6,
0.78
0.73
0.81


BETA2MICROGLOBULIN, TIMP1


IL6, BETA2MICROGLOBULIN, IL10,
0.77
0.73
0.81


APOLIPOPROTEINCIII


IL6, PROTEIN_MMP9, IL10, MCP1
0.77
0.81
0.73


APOLIPOPROTEINCIII,
0.77
0.78
0.75


ALPHAFETOPROTEIN,


PROTEIN_MMP9, IL6


IL10, TIMP1, IL6, ALPHAFETOPROTEIN
0.77
0.71
0.83


TIMP1, IL6, CREACTIVEPROTEIN,
0.76
0.8
0.73


BETA2MICROGLOBULIN


PROTEIN_MMP9, CREACTIVEPROTEIN,
0.8
0.78
0.81


MCP1, IL10, IL6


APOLIPOPROTEINCIII,
0.79
0.81
0.78


CREACTIVEPROTEIN, IL10,


ALPHAFETOPROTEIN, IL6


CREACTIVEPROTEIN,
0.79
0.77
0.81


ALPHAFETOPROTEIN, IL6,


PROTEIN_MMP9, IL8


IL6, TIMP1, MCP1,
0.78
0.75
0.82


APOLIPOPROTEINCIII,


CREACTIVEPROTEIN


CREACTIVEPROTEIN,
0.78
0.79
0.76


APOLIPOPROTEINCIII, TIMP1, IL8,


PROTEIN_MMP9


CREACTIVEPROTEIN, IL10, MCP1, IL6,
0.77
0.78
0.77


TIMP1


IL10, IL8, APOLIPOPROTEINCIII, IL6,
0.77
0.73
0.8


TIMP1


IL10, CREACTIVEPROTEIN, MCP1, IL6,
0.77
0.72
0.82


APOLIPOPROTEINCIII


IL6, ALPHAFETOPROTEIN, IL8,
0.77
0.75
0.78


CREACTIVEPROTEIN, TIMP1


TIMP1, MCP1, PROTEIN_MMP9, IL6,
0.8
0.81
0.79


APOLIPOPROTEINCIII,


CREACTIVEPROTEIN


TIMP1, IL6, IL10, CREACTIVEPROTEIN,
0.79
0.77
0.8


APOLIPOPROTEINCIII, PROTEIN_MMP9


MCP1, PROTEIN_MMP9,
0.79
0.75
0.82


APOLIPOPROTEINCIII, IL6, TIMP1, IL10


IL10, CREACTIVEPROTEIN, IL6,
0.78
0.78
0.79


ALPHAFETOPROTEIN, TIMP1,


PROTEIN_MMP9


PROTEIN_MMP9, CREACTIVEPROTEIN,
0.78
0.77
0.79


ALPHAFETOPROTEIN, IL10, IL6, MCP1


IL6, MCP1, IL10, TIMP1,
0.78
0.76
0.79


APOLIPOPROTEINCIII, IL8


TIMP1, IL6, IL10,
0.77
0.72
0.83


BETA2MICROGLOBULIN,


PROTEIN_MMP9, APOLIPOPROTEINCIII


IL10, MCP1, ALPHAFETOPROTEIN,
0.77
0.76
0.78


APOLIPOPROTEINCIII, IL6,


PROTEIN_MMP9


BETA2MICROGLOBULIN, IL6, TIMP1,
0.77
0.74
0.79


ALPHAFETOPROTEIN,


CREACTIVEPROTEIN, PROTEIN_MMP9


MCP1, IL10, IL8, IL6, TIMP1,
0.79
0.77
0.81


PROTEIN_MMP9, CREACTIVEPROTEIN


PROTEIN_MMP9,
0.79
0.77
0.8


BETA2MICROGLOBULIN,


APOLIPOPROTEINCIII, IL8, IL6,


ALPHAFETOPROTEIN,


CREACTIVEPROTEIN


IL8, MCP1, CREACTIVEPROTEIN,
0.79
0.71
0.85


APOLIPOPROTEINCIII,


ALPHAFETOPROTEIN,


PROTEIN_MMP9, IL6


TIMP1, IL6, CREACTIVEPROTEIN,
0.78
0.76
0.8


APOLIPOPROTEINCIII,


PROTEIN_MMP9, IL8,


ALPHAFETOPROTEIN


IL10, IL6, BETA2MICROGLOBULIN,
0.78
0.7
0.85


CREACTIVEPROTEIN,


APOLIPOPROTEINCIII, MCP1, IL8


APOLIPOPROTEINCIII,
0.78
0.8
0.76


PROTEIN_MMP9, MCP1, IL6,


ALPHAFETOPROTEIN, IL10, TIMP1


IL10, CREACTIVEPROTEIN,
0.78
0.74
0.82


ALPHAFETOPROTEIN,


BETA2MICROGLOBULIN, IL8,


PROTEIN_MMP9, APOLIPOPROTEINCIII


TIMP1, IL10, CREACTIVEPROTEIN,
0.78
0.81
0.74


APOLIPOPROTEINCIII, IL6, IL8, MCP1


IL8, TIMP1, CREACTIVEPROTEIN, IL6,
0.78
0.8
0.76


IL10, BETA2MICROGLOBULIN,


APOLIPOPROTEINCIII


APOLIPOPROTEINCIII,
0.78
0.78
0.77


CREACTIVEPROTEIN, IL8, IL10,


PROTEIN_MMP9, IL6,


BETA2MICROGLOBULIN


TIMP1, MCP1, IL10,
0.8
0.74
0.86


BETA2MICROGLOBULIN,


PROTEIN_MMP9, IL6,


ALPHAFETOPROTEIN,


APOLIPOPROTEINCIII


TIMP1, PROTEIN_MMP9, IL6,
0.79
0.77
0.82


ALPHAFETOPROTEIN, IL10,


APOLIPOPROTEINCIII, MCP1, IL8


IL10, IL6, MCP1, CREACTIVEPROTEIN,
0.79
0.79
0.79


APOLIPOPROTEINCIII,


PROTEIN_MMP9,


BETA2MICROGLOBULIN,


ALPHAFETOPROTEIN


TIMP1, MCP1, IL10,
0.79
0.76
0.81


CREACTIVEPROTEIN,


ALPHAFETOPROTEIN, IL6,


PROTEIN_MMP9, IL8


APOLIPOPROTEINCIII,
0.79
0.73
0.83


ALPHAFETOPROTEIN, TIMP1,


BETA2MICROGLOBULIN, MCP1, IL10,


IL6, IL8


CREACTIVEPROTEIN, TIMP1,
0.78
0.78
0.79


APOLIPOPROTEINCIII, MCP1, IL6,


ALPHAFETOPROTEIN,


BETA2MICROGLOBULIN,


PROTEIN_MMP9


BETA2MICROGLOBULIN, IL10, IL8,
0.78
0.73
0.83


APOLIPOPROTEINCIII,


PROTEIN_MMP9, IL6, TIMP1,


CREACTIVEPROTEIN


APOLIPOPROTEINCIII, IL8,
0.78
0.78
0.77


ALPHAFETOPROTEIN, IL6,


PROTEIN_MMP9, IL10, TIMP1, MCP1


APOLIPOPROTEINCIII, IL6, IL8,
0.78
0.71
0.83


PROTEIN_MMP9, TIMP1,


BETA2MICROGLOBULIN, IL10,


CREACTIVEPROTEIN


APOLIPOPROTEINCIII, MCP1, IL10,
0.77
0.76
0.78


PROTEIN_MMP9, TIMP1,


ALPHAFETOPROTEIN,


CREACTIVEPROTEIN, IL6


PROTEIN_MMP9, CREACTIVEPROTEIN,
0.79
0.78
0.81


IL6, TIMP1, BETA2MICROGLOBULIN,


IL10, APOLIPOPROTEINCIII, MCP1,


ALPHAFETOPROTEIN


APOLIPOPROTEINCIII,
0.79
0.77
0.81


PROTEIN_MMP9,


ALPHAFETOPROTEIN,


CREACTIVEPROTEIN, IL6, IL10, IL8,


TIMP1, BETA2MICROGLOBULIN


ALPHAFETOPROTEIN, TIMP1,
0.79
0.79
0.79


PROTEIN_MMP9, MCP1, IL6,


APOLIPOPROTEINCIII,


BETA2MICROGLOBULIN, IL10,


CREACTIVEPROTEIN


APOLIPOPROTEINCIII,
0.79
0.78
0.79


PROTEIN_MMP9, MCP1,


BETA2MICROGLOBULIN, IL8, IL6, IL10,


CREACTIVEPROTEIN, TIMP1


TIMP1, APOLIPOPROTEINCIII, IL6,
0.79
0.72
0.84


CREACTIVEPROTEIN, MCP1,


PROTEIN_MMP9, IL8,


BETA2MICROGLOBULIN,


ALPHAFETOPROTEIN


BETA2MICROGLOBULIN, IL8,
0.78
0.77
0.79


CREACTIVEPROTEIN, TIMP1, IL6,


ALPHAFETOPROTEIN,


APOLIPOPROTEINCIII,


PROTEIN_MMP9, IL10


IL6, IL8, TIMP1, PROTEIN_MMP9, IL10,
0.78
0.73
0.83


BETA2MICROGLOBULIN,


APOLIPOPROTEINCIII,


CREACTIVEPROTEIN,


ALPHAFETOPROTEIN


PROTEIN_MMP9, IL10, MCP1,
0.78
0.8
0.75


CREACTIVEPROTEIN,


ALPHAFETOPROTEIN, IL6, TIMP1,


APOLIPOPROTEINCIII,


BETA2MICROGLOBULIN


IL10, IL8, ALPHAFETOPROTEIN, IL6,
0.78
0.79
0.76


TIMP1, PROTEIN_MMP9, MCP1,


BETA2MICROGLOBULIN,


CREACTIVEPROTEIN


ALPHAFETOPROTEIN, MCP1, IL6,
0.78
0.78
0.78


BETA2MICROGLOBULIN,


PROTEIN_MMP9, CREACTIVEPROTEIN,


TIMP1, APOLIPOPROTEINCIII, IL10


TIMP1, IL6, CREACTIVEPROTEIN,
0.79
0.78
0.81


ALPHAFETOPROTEIN, IL10,


BETA2MICROGLOBULIN, MCP1,


APOLIPOPROTEINCIII, IL8,


PROTEIN_MMP9


IL8, CREACTIVEPROTEIN, TIMP1, IL10,
0.79
0.78
0.8


MCP1, IL6, ALPHAFETOPROTEIN,


PROTEIN_MMP9,


APOLIPOPROTEINCIII,


BETA2MICROGLOBULIN


MCP1, TIMP1, APOLIPOPROTEINCIII,
0.78
0.8
0.77


ALPHAFETOPROTEIN,


PROTEIN_MMP9, IL10,


CREACTIVEPROTEIN,


BETA2MICROGLOBULIN, IL8, IL6


BETA2MICROGLOBULIN, MCP1, IL6,
0.78
0.78
0.79


CREACTIVEPROTEIN, IL10, IL8,


ALPHAFETOPROTEIN,


APOLIPOPROTEINCIII, TIMP1,


PROTEIN_MMP9


CREACTIVEPROTEIN, TIMP1, IL10, IL6,
0.78
0.76
0.8


ALPHAFETOPROTEIN,


APOLIPOPROTEINCIII, IL8,


BETA2MICROGLOBULIN, MCP1,


PROTEIN_MMP9


MCP1, TIMP1, IL6,
0.78
0.78
0.78


ALPHAFETOPROTEIN,


PROTEIN_MMP9,


BETA2MICROGLOBULIN,


APOLIPOPROTEINCIII,


CREACTIVEPROTEIN, IL8, IL10


ALPHAFETOPROTEIN,
0.78
0.8
0.75


APOLIPOPROTEINCIII,


PROTEIN_MMP9,


BETA2MICROGLOBULIN, IL10, TIMP1,


MCP1, IL6, IL8, CREACTIVEPROTEIN


TIMP1, IL10, BETA2MICROGLOBULIN,
0.78
0.76
0.8


IL8, APOLIPOPROTEINCIII, IL6, MCP1,


CREACTIVEPROTEIN,


ALPHAFETOPROTEIN, PROTEIN_MMP9


BETA2MICROGLOBULIN,
0.77
0.74
0.8


ALPHAFETOPROTEIN, MCP1, IL10,


APOLIPOPROTEINCIII, TIMP1,


CREACTIVEPROTEIN, IL8,


PROTEIN_MMP9, IL6


IL8, MCP1, BETA2MICROGLOBULIN,
0.77
0.79
0.75


PROTEIN_MMP9, IL10, TIMP1, IL6,


CREACTIVEPROTEIN,


ALPHAFETOPROTEIN,


APOLIPOPROTEINCIII









In some embodiments, the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use any one of the subsets of biomarkers listed in Table N. The subsets of biomarkers listed in Table N were identified in the computational experiments described in Section 6.14.5, below, in which 4600 random subcombinations of the biomarkers listed in Table I were tested. Table N lists some of the biomarker sets that provided high accuracy scores against the validation population described in Section 6.14.5. Each row of Table N lists a single set of biomarkers that can be used in the methods and kits respectively referenced in Sections 5.2 and 5.3. In other words, each row of Table N describes a set of biomarkers that can be used to discriminate between sepsis and SIRS subjects. In some embodiments, nucleic acid forms of the biomarkers listed in Table N are used in the methods and kits respectively referenced in Sections 5.2 and 5.3. In some embodiments, protein forms of the biomarkers listed in Table N are used. In some embodiments, some of the biomarkers in a biomarker set from Table N are in protein form and some of the biomarkers in the same biomarker set from Table N are in nucleic acid form in the methods and kits respectively referenced in Sections 5.2 and 5.3.
In some embodiments, a given set of biomarkers from Table N is used with the addition of one, two, three, four, five, six, seven, eight, or nine or more additional biomarkers from from any one of Table 30, 31, 32, 33, 34, or 36 that are not within the given set of biomarkers from Table N. In Table N, accuracy, specificity, and senstitivity are described with reference to T−12 time point data described in Section 6.14.5, below.







TABLE N







Exemplary sets of biomarkers used in the methods or kits referenced in


Sections 5.2 and 5.3










BIOMARKER SET
ACCURACY
SPECIFICITY
SENSISTIVITY













TLR4, ARG2, OSM
0.85
0.83
0.88


IRAK4, OSM, TNFSF10
0.83
0.79
0.87


PSTPIP2, SOCS3, TIMP1
0.82
0.81
0.83


FCGR1A, IL6, MAP2K6
0.81
0.84
0.79


SOCS3, TNFSF10, NCR1
0.81
0.73
0.87


IL8, IL18R1, Beta2Microglobulin
0.81
0.79
0.82


OSM, NCR1, IL8
0.81
0.77
0.83


PFKFB3, MKNK1, FCGR1A
0.8
0.79
0.81


TIMP1, IL18R1, ARG2
0.8
0.78
0.83


FCGR1A, MAP2K6, IRAK4
0.8
0.75
0.86


Gene_MMP9, IL8, GADD45B
0.8
0.75
0.84


INSL3, ANKRD22, MAP2K6, LDLR
0.87
0.83
0.9


PSTPIP2, ARG2, CRTAP, GADD45A
0.83
0.81
0.85


CEACAM1, GADD45B, GADD45A, OSM
0.83
0.75
0.91


OSM, CSF1R, IL10, ANKRD22
0.83
0.88
0.78


TIMP1, ARG2, GADD45B, VNN1
0.83
0.83
0.82


HLA-DRA, PSTPIP2, INSL3, MKNK1
0.83
0.79
0.86


CD86, TGFBI, ANKRD22, SOCS3
0.82
0.82
0.83


GADD45A, PSTPIP2, GADD45B, IL18R1
0.82
0.76
0.86


ANKRD22, MAP2K6, Protein_MMP9,
0.81
0.8
0.82


FAD104


IFNGR1, FAD104, CSF1R, IL8
0.81
0.78
0.84


OSM, TDRD9, ARG2, HLA-DRA
0.81
0.77
0.85


ANKRD22, CReactiveProtein, OSM, IL10
0.81
0.76
0.85


TDRD9, TNFSF13B, CReactiveProtein,
0.81
0.76
0.85


MAP2K6


TNFSF10, Gene_MMP9, IL8, FCGR1A
0.8
0.79
0.81


IL10, NCR1, IL6, INSL3
0.8
0.79
0.81


CD86, FCGR1A, BCL2A1, LY96
0.8
0.79
0.81


IL8, VNN1, IL6, GADD45B
0.8
0.79
0.82


HLA-DRA, TNFSF10, OSM, MKNK1
0.8
0.76
0.84


PFKFB3, INSL3, IL10alpha, FCGR1A
0.8
0.76
0.84


TNFSF10, IRAK4, OSM, ARG2, MAPK14
0.85
0.84
0.86


CD86, CEACAM1, IL18R1, GADD45B,
0.83
0.85
0.81


CCL5


MCP1, CSF1R, GADD45B, Protein_MMP9,
0.83
0.83
0.82


Beta2Microglobulin


IL8, CD86, IRAK2, IL1RN, TIFA
0.82
0.84
0.81


IRAK4, OSM, INSL3, CEACAM1,
0.82
0.82
0.82


TNFSF13B


CReactiveProtein, SOCS3, HLA-DRA,
0.82
0.76
0.88


GADD45B, OSM


CD86, NCR1, PRV1, IL1RN, GADD45B
0.82
0.77
0.86


TNFRSF6, ITGAM, PSTPIP2, ARG2,
0.82
0.77
0.87


BCL2A1


IRAK4, LDLR, OSM, PSTPIP2, GADD45A
0.81
0.81
0.82


Gene_MMP9, SOD2, PFKFB3, ARG2,
0.81
0.78
0.84


CD86


CReactiveProtein, IL18R1, NCR1, CD86,
0.81
0.78
0.84


GADD45A


IL8, IL18R1, LDLR, SOD2, PSTPIP2
0.81
0.77
0.84


Gene_MMP9, CSF1R, TGFBI, MAP2K6,
0.81
0.8
0.81


ANKRD22


CReactiveProtein, LDLR, IRAK2, OSM,
0.81
0.8
0.82


PSTPIP2


ITGAM, SOCS3, IL8, ARG2, JAK2
0.81
0.79
0.83


TNFSF10, LY96, IL10alpha, IL10, OSM
0.8
0.83
0.78


GADD45B, IL6, INSL3, ANKRD22, IL8
0.8
0.81
0.8


CSF1R, IL6, IL1RN, TLR4, JAK2
0.8
0.79
0.81


TDRD9, OSM, ITGAM, ANKRD22,
0.8
0.73
0.87


Gene_MMP9


IL8, TNFRSF6, CReactiveProtein, IRAK4,
0.8
0.79
0.81


PRV1


OSM, IL1RN, JAK2, GADD45B, CSF1R
0.8
0.78
0.82


CD86, Beta2Microglobulin, PFKFB3,
0.8
0.78
0.82


TNFSF13B, TNFRSF6


MAPK14, TGFBI, GADD45A, ANKRD22,
0.8
0.75
0.85


CReactiveProtein


MKNK1, CD86, OSM, TIFA, HLA-DRA,
0.85
0.79
0.89


SOCS3


CD86, CEACAM1, LDLR, NCR1,
0.83
0.81
0.84


AlphaFetoprotein, IRAK2


INSL3, PRV1, LY96, Protein_MMP9, IL8,
0.82
0.82
0.82


OSM


FAD104, ARG2, FCGR1A, SOCS3, HLA-
0.82
0.8
0.84


DRA, ANXA3


CCL5, TIMP1, ARG2, IL6, IFNGR1, SOD2
0.82
0.77
0.87


CRTAP, OSM, GADD45B, TNFSF10,
0.82
0.75
0.88


MKNK1, TGFBI


LDLR, OSM, IL6, JAK2, INSL3, FCGR1A
0.82
0.82
0.81


Beta2Microglobulin, FAD104, TGFBI,
0.82
0.82
0.82


NCR1, ARG2, GADD45B


CSF1R, VNN1, MAP2K6, LY96, OSM,
0.82
0.81
0.82


Beta2Microglobulin


ApolipoproteinCIII, HLA-DRA, GADD45A,
0.82
0.8
0.83


ITGAM, TNFRSF6, MAP2K6


PRV1, TGFBI, VNN1, GADD45B, IL1RN,
0.81
0.8
0.82


CSF1R


IRAK4, TIMP1, ANKRD22, GADD45B,
0.81
0.78
0.83


OSM, TLR4


SOD2, MKNK1, MCP1, OSM, TIFA,
0.81
0.77
0.84


SOCS3


FAD104, TGFBI, ANXA3, IL18R1, PRV1,
0.81
0.77
0.85


IL10alpha


FCGR1A, IL8, Beta2Microglobulin,
0.81
0.83
0.79


GADD45B, ANKRD22, TNFSF10


TIFA, Beta2Microglobulin, IL18R1,
0.81
0.8
0.81


CRTAP, IL6, TGFBI


CD86, IL10, MCP1, TIMP1, OSM, ANXA3
0.81
0.79
0.82


INSL3, FAD104, TGFBI, CEACAM1,
0.81
0.77
0.85


CSF1R, PFKFB3


PRV1, IL8, TNFSF10, FCGR1A, IFNGR1,
0.81
0.77
0.84


CReactiveProtein


ANKRD22, BCL2A1, CRTAP, NCR1,
0.81
0.72
0.88


SOCS3, IL18R1


INSL3, IRAK2, CD86, JAK2, IL10,
0.8
0.84
0.77


FAD104


MCP1, PSTPIP2, AlphaFetoprotein,
0.8
0.81
0.8


CReactiveProtein, IL6, ApolipoproteinCIII


CSF1R, OSM, IFNGR1, TDRD9,
0.8
0.8
0.8


Gene_MMP9, FCGR1A


TIMP1, IFNGR1, TNFSF10, GADD45A,
0.8
0.8
0.81


BCL2A1, SOD2


FCGR1A, MKNK1, CRTAP, LDLR,
0.8
0.79
0.81


Gene_MMP9, Beta2Microglobulin


ITGAM, AlphaFetoprotein, FCGR1A,
0.8
0.78
0.82


MCP1, MKNK1, GADD45A


MCP1, FCGR1A, OSM, PFKFB3, FAD104,
0.8
0.77
0.82


TDRD9


TNFSF10, Gene_MMP9, FCGR1A,
0.86
0.85
0.86


AlphaFetoprotein, INSL3, CSF1R, IL8


OSM, Beta2Microglobulin, ANKRD22,
0.84
0.85
0.83


CSF1R, GADD45B, TNFRSF6,


ApolipoproteinCIII


BCL2A1, TDRD9, OSM, PRV1, IRAK2,
0.84
0.83
0.85


TLR4, MAPK14


LDLR, OSM, ApolipoproteinCIII, IL6,
0.83
0.83
0.83


TIMP1, ARG2, TNFRSF6


IL1RN, TNFSF13B, AlphaFetoprotein,
0.83
0.8
0.85


MCP1, ANKRD22, ARG2, OSM


NCR1, ARG2, PSTPIP2, GADD45A, LY96,
0.83
0.81
0.84


OSM, BCL2A1


FCGR1A, TNFSF13B, INSL3, TIFA,
0.83
0.79
0.87


ApolipoproteinCIII, ITGAM, CD86


LY96, CReactiveProtein, FCGR1A,
0.82
0.85
0.8


Beta2Microglobulin, IL8, OSM, VNN1


PSTPIP2, ARG2, IRAK2, TNFSF13B,
0.82
0.85
0.8


GADD45A, IL8, CRTAP


MCP1, SOCS3, HLA-DRA,
0.82
0.84
0.81


ApolipoproteinCIII, IL10alpha, GADD45A,


MAP2K6


IL18R1, MAPK14, Gene_MMP9, TIFA,
0.82
0.75
0.89


FCGR1A, SOCS3, MKNK1


Beta2Microglobulin, CRTAP, ARG2,
0.82
0.82
0.82


ANKRD22, TNFRSF6, IRAK4, OSM


PFKFB3, IRAK2, IRAK4, OSM, JAK2,
0.82
0.82
0.82


Beta2Microglobulin, CEACAM1


TIFA, CRTAP, PFKFB3, JAK2, IL6,
0.82
0.82
0.82


TGFBI, CD86


GADD45B, Gene_MMP9, TNFSF13B,
0.82
0.76
0.88


IRAK2, VNN1, TIFA, SOCS3


INSL3, IL6, CD86, IL10alpha,
0.82
0.86
0.78


CReactiveProtein, TGFBI, ITGAM


GADD45B, MCP1, INSL3,
0.82
0.81
0.82


CReactiveProtein, ARG2, CCL5, SOCS3


TNFSF10, IL8, ApolipoproteinCIII, TGFBI,
0.82
0.8
0.83


CSF1R, OSM, SOD2


PRV1, PSTPIP2, ARG2, TIMP1,
0.82
0.79
0.84


Protein_MMP9, IL6, SOD2


CD86, LY96, MAP2K6, IL6, IL10, IRAK2,
0.81
0.78
0.84


TNFSF10


BCL2A1, MCP1, ARG2, SOCS3, NCR1,
0.81
0.8
0.81


IL10, LY96


SOCS3, ApolipoproteinCIII, NCR1,
0.81
0.8
0.82


CEACAM1, ANKRD22, FCGR1A, IL6


INSL3, TNFSF10, SOD2, FCGR1A,
0.81
0.77
0.85


PSTPIP2, IL10, IL8


FCGR1A, OSM, Protein_MMP9,
0.81
0.76
0.84


GADD45A, PSTPIP2, ARG2, Gene_MMP9


TIMP1, SOCS3, LY96, CSF1R,
0.81
0.84
0.77


CReactiveProtein, CCL5, TNFSF13B


ANKRD22, CEACAM1, TLR4,
0.81
0.82
0.8


ApolipoproteinCIII, SOCS3, ITGAM, IL10


INSL3, IRAK2, FCGR1A, MAP2K6,
0.81
0.81
0.8


CRTAP, ITGAM, CSF1R


VNN1, SOCS3, Beta2Microglobulin,
0.81
0.81
0.81


MAP2K6, IL6, ANKRD22, IL10


TNFSF10, TGFBI, CReactiveProtein,
0.81
0.8
0.82


Beta2Microglobulin, TNFRSF6, ARG2,


PRV1


IL18R1, IL6, CRTAP, IRAK4, GADD45A,
0.8
0.81
0.79


Protein_MMP9, TNFSF13B


AlphaFetoprotein, ARG2, NCR1, PSTPIP2,
0.8
0.8
0.81


ApolipoproteinCIII, CD86, GADD45B


ANKRD22, TIFA, JAK2, IL10, IL6, CCL5,
0.8
0.79
0.82


CSF1R


PRV1, TNFSF13B, TLR4, OSM, ARG2,
0.8
0.78
0.82


AlphaFetoprotein, HLA-DRA


CSF1R, TLR4, SOD2, FCGR1A, CRTAP,
0.8
0.78
0.83


TNFSF13B, GADD45A


JAK2, IRAK2, ITGAM, IL6, MKNK1,
0.8
0.77
0.83


Gene_MMP9, FCGR1A


GADD45B, PRV1, CSF1R, NCR1, CD86,
0.8
0.76
0.83


MKNK1, JAK2


Beta2Microglobulin, TNFSF10, IL18R1,
0.8
0.75
0.85


GADD45B, Protein_MMP9, FAD104,


PSTPIP2


MAPK14, TIFA, ITGAM, MKNK1, CSF1R,
0.8
0.73
0.86


IRAK4, Protein_MMP9


TIFA, TNFSF13B, LY96, GADD45B, IL6,
0.8
0.8
0.8


INSL3, OSM


IL6, GADD45B, CEACAM1, IRAK4,
0.8
0.8
0.8


TGFBI, INSL3, TNFSF13B


GADD45B, ARG2, IL18R1, ANKRD22,
0.8
0.8
0.8


AlphaFetoprotein, IL10, PSTPIP2


TNFSF13B, IFNGR1, OSM, FAD104,
0.8
0.8
0.8


CSF1R, PSTPIP2, TIFA


TDRD9, ITGAM, TNFSF10, ANXA3,
0.8
0.8
0.8


ApolipoproteinCIII, MCP1, INSL3


SOCS3, Protein_MMP9, SOD2, LY96,
0.8
0.78
0.82


ARG2, IRAK2, OSM


CEACAM1, IL10, TNFRSF6, IL18R1,
0.8
0.78
0.82


ARG2, FCGR1A, CReactiveProtein


CCL5, FCGR1A, CReactiveProtein,
0.8
0.74
0.86


ApolipoproteinCIII, IL18R1,


Protein_MMP9, ITGAM


NCR1, SOD2, IRAK2, IL8, OSM, HLA-
0.86
0.89
0.84


DRA, ARG2, GADD45A


PFKFB3, PSTPIP2, GADD45B, INSL3,
0.85
0.86
0.84


FAD104, TNFRSF6, ARG2, IL10alpha


CSF1R, CEACAM1, GADD45B, OSM,
0.84
0.86
0.82


LDLR, MCP1, ARG2, AlphaFetoprotein


TIFA, NCR1, BCL2A1, OSM, CCL5, TLR4,
0.82
0.82
0.82


CD86, CEACAM1


FAD104, LDLR, INSL3, IRAK4, LY96,
0.82
0.81
0.83


TLR4, GADD45B, TIMP1


FAD104, JAK2, TNFSF13B, ARG2,
0.82
0.79
0.85


CReactiveProtein, IL10alpha, TLR4, PRV1


CRTAP, LY96, TDRD9, Gene_MMP9,
0.82
0.79
0.85


HLA-DRA, SOCS3, IL8, Protein_MMP9


CRTAP, GADD45B, TIFA,
0.81
0.83
0.8


ApolipoproteinCIII, LY96, IL8, GADD45A,


MKNK1


IL8, CSF1R, ARG2, TGFBI, PRV1,
0.81
0.82
0.81


TNFRSF6, CEACAM1, JAK2


ARG2, Beta2Microglobulin, GADD45A,
0.81
0.79
0.83


IL6, INSL3, IL8, JAK2, TIMP1


SOD2, IL10, IL8, ARG2, PSTPIP2, INSL3,
0.81
0.77
0.86


CSF1R, ANXA3


CD86, IL6, BCL2A1, CCL5, GADD45B,
0.81
0.86
0.76


IRAK4, LDLR, ARG2


ANXA3, MAP2K6, VNN1, GADD45A,
0.81
0.81
0.81


CSF1R, FAD104, IL6, IRAK2


IL8, GADD45A, TDRD9,
0.81
0.8
0.82


Beta2Microglobulin, ANKRD22,


GADD45B, PRV1, CSF1R


IRAK4, PRV1, GADD45A, IL8,
0.81
0.79
0.82


TNFSF13B, CD86, FCGR1A, TIMP1


IRAK4, MAPK14, GADD45B, HLA-DRA,
0.81
0.77
0.84


JAK2, PRV1, SOD2, IL6


IL18R1, IL6, GADD45B, MAPK14, IL10,
0.81
0.76
0.86


JAK2, IL8, ANKRD22


TLR4, IRAK2, TNFRSF6, TGFBI, IL8,
0.81
0.88
0.74


ARG2, GADD45B, GADD45A


ANXA3, IFNGR1, SOCS3, VNN1, TIFA,
0.81
0.8
0.81


CReactiveProtein, IRAK4, AlphaFetoprotein


ANKRD22, CCL5, TGFBI, CEACAM1,
0.81
0.8
0.82


CD86, Gene_MMP9, IFNGR1, GADD45B


NCR1, ARG2, TIMP1, Beta2Microglobulin,
0.81
0.77
0.84


ANXA3, TIFA, BCL2A1, MAP2K6


OSM, TIMP1, IL1RN, IL8, BCL2A1,
0.81
0.76
0.84


IFNGR1, CSF1R, CD86


IL10, NCR1, IRAK2, IL18R1, ARG2,
0.81
0.76
0.84


PSTPIP2, Gene_MMP9, LY96


IL18R1, TNFSF10, SOCS3,
0.81
0.74
0.88


ApolipoproteinCIII, HLA-DRA, GADD45A,


Beta2Microglobulin, ARG2


IL10alpha, IL10, MKNK1, LY96, OSM,
0.8
0.8
0.81


JAK2, IFNGR1, CEACAM1


HLA-DRA, TIMP1, OSM, CD86, NCR1,
0.8
0.78
0.82


IL1RN, TNFSF10, FAD104


CSF1R, TNFSF13B, ANKRD22, IFNGR1,
0.8
0.77
0.82


Protein_MMP9, PFKFB3, NCR1, TGFBI


ANXA3, IL10alpha, PSTPIP2, CSF1R,
0.8
0.77
0.83


IL1RN, FAD104, CD86, CReactiveProtein


CSF1R, ANKRD22, TGFBI, IRAK4,
0.8
0.77
0.83


Protein_MMP9, TIMP1, HLA-DRA,


PFKFB3


TNFSF13B, JAK2, ARG2, CCL5, IL18R1,
0.8
0.76
0.85


GADD45B, CD86, GADD45A


GADD45B, BCL2A1, IL1RN, FCGR1A,
0.8
0.76
0.84


MAPK14, SOCS3, ITGAM, PRV1


IL6, JAK2, CReactiveProtein, MCP1,
0.8
0.85
0.76


TIMP1, BCL2A1, GADD45B, LY96


AlphaFetoprotein, ApolipoproteinCIII,
0.8
0.84
0.77


CEACAM1, CRTAP, IL18R1, NCR1,


TIMP1, TGFBI


ANXA3, FAD104, MKNK1,
0.8
0.82
0.78


CReactiveProtein, AlphaFetoprotein, CSF1R,


IRAK4, IL6


TIMP1, VNN1, TIFA, CCL5, TDRD9,
0.8
0.82
0.78


FCGR1A, ApolipoproteinCIII, IL1RN


TNFSF10, TLR4, JAK2, OSM,
0.8
0.8
0.81


Beta2Microglobulin, ITGAM, IL1RN, HLA-


DRA


ApolipoproteinCIII, MAPK14, IRAK2,
0.8
0.79
0.81


TNFSF13B, GADD45B, SOCS3,


CEACAM1, TNFSF10


IL1RN, ANKRD22, FCGR1A, GADD45A,
0.8
0.79
0.81


TGFBI, CSF1R, MCP1, MAPK14


SOCS3, HLA-DRA, IRAK2,
0.8
0.79
0.81


Protein_MMP9, MAP2K6, INSL3,


CReactiveProtein, Gene_MMP9


INSL3, BCL2A1, ARG2, GADD45B,
0.8
0.78
0.82


MAPK14, ITGAM, IRAK2, LDLR


GADD45B, Beta2Microglobulin,
0.8
0.77
0.82


Protein_MMP9, IFNGR1, IRAK2, PSTPIP2,


IL8, FCGR1A


CSF1R, HLA-DRA, IRAK4, FAD104,
0.84
0.88
0.82


CRTAP, MCP1, GADD45B, CCL5, IL6


HLA-DRA, Gene_MMP9, FAD104, IRAK2,
0.84
0.85
0.83


TNFRSF6, LY96, CReactiveProtein,


FCGR1A, CD86


GADD45A, GADD45B, OSM, ARG2,
0.84
0.81
0.86


FAD104, MAPK14, IRAK2, ITGAM,


MKNK1


IL6, IL18R1, IL1RN, HLA-DRA, CD86,
0.83
0.88
0.8


IRAK2, NCR1, TNFSF10, CCL5


CD86, IRAK2, ARG2, PFKFB3, MAPK14,
0.83
0.85
0.82


PRV1, VNN1, HLA-DRA, FAD104


TDRD9, FCGR1A, ARG2,
0.83
0.8
0.86


AlphaFetoprotein, JAK2,


ApolipoproteinCIII, TIMP1, MAP2K6,


CCL5


PFKFB3, CReactiveProtein, TDRD9, OSM,
0.83
0.85
0.81


IFNGR1, CCL5, TIMP1, ARG2, ITGAM


NCR1, CSF1R, MAP2K6, INSL3, IFNGR1,
0.83
0.85
0.81


FAD104, IL6, ARG2, IL18R1


PSTPIP2, OSM, LDLR Protein_MMP9,
0.83
0.82
0.84


LY96, TNFSF13B, ANXA3, IL1RN,


Beta2Microglobulin


FAD104, NCR1, VNN1, IRAK2,
0.83
0.81
0.84


ApolipoproteinCIII, IL10alpha, LDLR,


FCGR1A, IRAK4


IL10alpha, HLA-DRA, TGFBI, FCGR1A,
0.82
0.84
0.81


CSF1R, IRAK2, GADD45A, PFKFB3,


SOCS3


IL8, IRAK4, CSF1R, IL18R1,
0.82
0.83
0.81


AlphaFetoprotein, IL1RN, BCL2A1,


TNFSF13B, INSL3


SOCS3, MAP2K6, PSTPIP2, OSM,
0.82
0.82
0.82


MAPK14, MKNK1, ApolipoproteinCIII,


IL18R1, TLR4


TIMP1, IL8, PFKFB3, CD86, SOCS3,
0.82
0.81
0.83


JAK2, IRAK2, IL10alpha, Protein_MMP9


TIMP1, INSL3, TNFRSF6, PFKFB3, CD86,
0.82
0.8
0.85


JAK2, IL8, CRTAP, Protein_MMP9


IRAK4, MAPK14, ApolipoproteinCIII, IL6,
0.82
0.88
0.76


MAP2K6, MCP1, TIFA, ARG2, CD86


TLR4, IL10alpha, IL8, GADD45A, IRAK2,
0.82
0.82
0.81


MAP2K6, MCP1, HLA-DRA, MAPK14


LY96, MCP1, CD86, VNN1, OSM, ARG2,
0.81
0.83
0.8


TDRD9, CCL5, INSL3


CCL5, CRTAP, ApolipoproteinCIII,
0.81
0.81
0.81


Gene_MMP9, IFNGR1, TNFSF13B,


ANKRD22, GADD45A, OSM


MCP1, IL6, FCGR1A, PSTPIP2, VNN1,
0.81
0.79
0.83


TNFSF10, TIMP1, Protein_MMP9,


CReactiveProtein


PFKFB3, IL6, NCR1, MAP2K6, FAD104,
0.81
0.79
0.84


CD86, TLR4, TDRD9, OSM


ApolipoproteinCIII, CReactiveProtein,
0.81
0.77
0.86


TGFBI, MKNK1, PRV1, FAD104, HLA-


DRA, ARG2, TIMP1


IL10alpha, IL10, ANXA3, IL6, CSF1R,
0.81
0.82
0.8


TGFBI, PSTPIP2, IL8, INSL3


IL6, IRAK2, CReactiveProtein, CCL5,
0.81
0.8
0.82


ANKRD22, MCP1, GADD45A, PFKFB3,


IL10alpha


TIFA, IL1RN, IL6, ITGAM,
0.81
0.79
0.82


CReactiveProtein, CCL5, TGFBI, IL10,


NCR1


CEACAM1, IFNGR1, TNFSF10, INSL3,
0.81
0.78
0.84


BCL2A1, Beta2Microglobulin, IL10, ARG2,


SOCS3


SOCS3, LDLR, SOD2, FAD104, MAP2K6,
0.81
0.75
0.86


PSTPIP2, GADD45B, IRAK4, GADD45A


MKNK1, IL8, TNFSF13B, FAD104,
0.81
0.83
0.78


ITGAM, GADD45B, NCR1, IL18R1,


ApolipoproteinCIII


IL8, Gene_MMP9, TNFSF10, MKNK1,
0.81
0.82
0.79


MCP1, IL6, CCL5, ApolipoproteinCIII,


SOD2


NCR1, PFKFB3, ApolipoproteinCIII,
0.81
0.78
0.83


INSL3, OSM, VNN1, AlphaFetoprotein,


TNFSF10, CRTAP


FCGR1A, CReactiveProtein, PRV1, NCR1,
0.81
0.75
0.86


ARG2, INSL3, IL10, TGFBI, MAPK14


IL8, IRAK2, PFKFB3, CEACAM1, TIFA,
0.8
0.8
0.81


Protein_MMP9, IRAK4, CRTAP, TDRD9


ARG2, INSL3, CSF1R, TNFSF13B,
0.8
0.79
0.82


Beta2Microglobulin, PRV1, FCGR1A,


GADD45B, CRTAP


GADD45A, IL8, TIMP1, CReactiveProtein,
0.8
0.77
0.84


MAP2K6, TGFBI, CRTAP, TNFRSF6,


BCL2A1


HLA-DRA, ApolipoproteinCIII, INSL3,
0.8
0.77
0.84


FAD104, TIMP1, IRAK4, FCGR1A, IL6,


GADD45A


ARG2, JAK2, IL1RN, VNN1, IRAK4,
0.8
0.84
0.77


CSF1R, ANKRD22, BCL2A1, TDRD9


CReactiveProtein, PFKFB3, CD86, IL1RN,
0.8
0.8
0.8


TLR4, Beta2Microglobulin, IRAK2,


TNFSF10, TNFRSF6


GADD45B, MAP2K6, JAK2, MAPK14,
0.8
0.79
0.81


TIMP1, IRAK4, CReactiveProtein, TLR4,


TGFBI


JAK2, TLR4, CCL5, IL6, CReactiveProtein,
0.8
0.73
0.87


IFNGR1, ApolipoproteinCIII, GADD45B,


NCR1


CSF1R, TNFRSF6, INSL3, MKNK1, IL8,
0.86
0.85
0.86


MAP2K6, FAD104, NCR1, IL1RN, MCP1


IL8, PRV1, SOCS3, IRAK2, ARG2,
0.85
0.86
0.83


IL10alpha, NCR1, CCL5, CReactiveProtein,


MKNK1


TNFSF13B, TLR4, ARG2, IL6, SOCS3,
0.84
0.8
0.88


Beta2Microglobulin, FAD104, MCP1, HLA-


DRA, PSTPIP2


IL6, MCP1, Beta2Microglobulin, IL1RN,
0.84
0.82
0.86


TDRD9, IFNGR1, ApolipoproteinCIII,


FCGR1A, OSM, IL8


FCGR1A, IL6, LY96, LDLR, IL18R1,
0.84
0.81
0.87


CSF1R, CCL5, NCR1, TNFRSF6, IRAK4


IL6, TGFBI, IL18R1, ANXA3, IL1RN,
0.83
0.84
0.82


GADD45B, ANKRD22, LDLR, TLR4,


CEACAM1


MAPK14, IL6, CSF1R, IL1RN, ITGAM,
0.83
0.86
0.79


Beta2Microglobulin, MAP2K6, IL10,


PSTPIP2, FAD104


CReactiveProtein, FCGR1A, CCL5,
0.83
0.86
0.8


ApolipoproteinCIII, OSM, IRAK2,


GADD45A, CRTAP, PFKFB3, ITGAM


TNFSF10, AlphaFetoprotein, CCL5, IL8,
0.82
0.79
0.85


IRAK4, OSM, IL10alpha, ARG2,


CReactiveProtein, TIFA


TDRD9, TNFSF10, GADD45B,
0.82
0.85
0.78


CReactiveProtein, IL8, ARG2, ANXA3,


TGFBI, IL1RN, CCL5


IL10alpba, ANXA3, TNFSF10, IL1RN,
0.82
0.85
0.78


TGFBI, FAD104, INSL3, MAP2K6,


MAPK14, ApolipoproteinCIII


TIMP1, Beta2Microglobulin, ITGAM,
0.82
0.82
0.81


LDLR, MCP1, IL8, FCGR1A, TIFA,


IL10alpha, MAP2K6


TNFRSF6, TGFBI, JAK2, SOD2, ANXA3,
0.82
0.82
0.81


VNN1, CCL5, INSL3, CSF1R, IL10


TDRD9, IL10alpha, MAPK14, NCR1,
0.82
0.8
0.83


LY96, GADD45B, IRAK2,


CReactiveProtein, INSL3, ITGAM


LDLR, JAK2, IFNGR1, IRAK2, SOCS3,
0.82
0.79
0.84


ITGAM, Protein_MMP9, INSL3,


ApolipoproteinCIII, CEACAM1


CSF1R, Beta2Microglobulin, IRAK4,
0.82
0.78
0.85


MKNK1, PRV1, TNFRSF6, PSTPIP2,


IL18R1, HLA-DRA, CCL5


BCL2A1, TLR4, IL8, TIMP1, SOD2,
0.82
0.77
0.86


CReactiveProtein, CRTAP,


ApolipoproteinCIII, GADD45B, FAD104


ARG2, OSM, TNFSF13B, CReactiveProtein,
0.81
0.86
0.77


AlphaFetoprotein, IL6, CRTAP,


Beta2Microglobulin, MCP1, TDRD9


FAD104, TNFSF13B, IL1RN, GADD45B,
0.81
0.82
0.8


IFNGR1, IL18R1, TNFRSF6, MCP1, PRV1,


IL8


IL8, ITGAM, CSF1R, TNFRSF6, INSL3,
0.81
0.82
0.81


IL10alpha, IFNGR1, IL10, IL1RN, SOD2


MCP1, IFNGR1, TNFRSF6, MAPK14,
0.81
0.78
0.84


FAD104, IL18R1, IRAK4, INSL3,


IL10alpha, Beta2Microglobulin


NCR1, PRV1 Protein_MMP9, TIMP1,
0.81
0.74
0.88


ANKRD22, INSL3, CD86, CCL5, MKNK1,


Gene_MMP9


NCR1, INSL3, CEACAM1, FAD104,
0.81
0.86
0.76


IL10alpha, TIFA, TNFSF13B, IL6, CCL5,


CReactiveProtein


CRTAP, IL1RN, IL18R1, FAD104, NCR1,
0.81
0.8
0.82


HLA-DRA, TGFBI, LY96, IL6, IRAK4


OSM, NCR1, IL8, GADD45B,
0.81
0.8
0.82


Protein_MMP9, TNFRSF6, TNFSF13B,


Beta2Microglobulin, IL1RN, IRAK2


CD86, IL10alpha, CSF1R, IRAK2,
0.81
0.79
0.83


ANKRD22, OSM, AlphaFetoprotein,


Gene_MMP9, IL10, IRAK4


ARG2, IRAK4, GADD45A, VNN1, IL18R1,
0.81
0.79
0.83


JAK2, ANXA3, CSF1R, HLA-DRA,


PFKFB3


LY96, TDRD9, NCR1, TNFRSF6, CSF1R,
0.81
0.76
0.86


PRV1, IL18R1, ARG2, Beta2Microglobulin,


IL10alpha


INSL3, TDRD9, CRTAP, TNFRSF6,
0.81
0.75
0.88


IRAK4, SOD2, LDLR, ANKRD22, OSM,


CSF1R


FAD104, PFKFB3, IL18R1, IL10, MAPK14,
0.81
0.83
0.79


ARG2, CD86, IL1RN, CCL5, GADD45A


TNFSF10, CSF1R, TNFSF13B, MKNK1,
0.81
0.81
0.8


ITGAM, PFKFB3, AlphaFetoprotein,


SOCS3, TNFRSF6, FAD104


CSF1R, PFKFB3, ApolipoproteinCIII,
0.81
0.81
0.81


TLR4, ARG2, PRV1, ANKRD22, ITGAM,


TIFA, TNFRSF6


TGFBI, IL10, TDRD9, PFKFB3, INSL3,
0.81
0.8
0.81


CSF1R, PSTPIP2, MKNK1, NCR1, HLA-


DRA


ANKRD22, TIMP1, CRTAP, HLA-DRA,
0.81
0.78
0.83


ApolipoproteinCIII, CD86, TNFRSF6,


Gene_MMP9, VNN1, IL10


ARG2, NCR1, IRAK4, FCGR1A, FAD104,
0.81
0.77
0.84


TNFRSF6, PFKFB3, MAP2K6, TGFBI,


MKNK1


TLR4, ANKRD22, IL10alpha, VNN1,
0.81
0.76
0.84


Protein_MMP9, TNFRSF6, ARG2,


TNFSF10, OSM, FCGR1A


FAD104, PRV1, Protein_MMP9 IL10alpha,
0.81
0.75
0.86


ARG2, TNFSF13B, FCGR1A, CEACAM1,


CCL5, IL1RN


TNFRSF6, IL6, TGFBI, PSTPIP2, ANXA3,
0.8
0.83
0.78


ANKRD22, ApolipoproteinCIII, OSM,


SOCS3, MAPK14


IL8, OSM, IRAK4, TDRD9, LDLR,
0.8
0.81
0.8


TNFSF13B, IL10, IFNGR1, ARG2, SOD2


PSTPIP2, BCL2A1, CD86, ANXA3,
0.8
0.84
0.77


IL10alpha, SOD2, OSM, INSL3,


TNFSF13B, GADD45B


IL6, ANXA3, SOCS3, MAP2K6, TGFBI,
0.8
0.83
0.77


ANKRD22, CRTAP, BCL2A1, CCL5, TLR4


HLA-DRA, CSF1R, TGFBI, MAP2K6,
0.8
0.82
0.78


BCL2A1, CD86, TLR4, IL1RN, IL6,


ApolipoproteinCIII


ApolipoproteinCIII, CCL5, SOCS3, TIMP1,
0.8
0.81
0.79


Gene_MMP9, AlphaFetoprotein, ITGAM,


INSL3, CEACAM1, LDLR


IL8, TNFRSF6, IL6, IL1RN, PSTPIP2,
0.8
0.79
0.81


ApolipoproteinCIII, CD86, JAK2, TLR4,


Protein_MMP9


IL10alpha, JAK2, MCP1, CEACAM1,
0.8
0.78
0.82


ApolipoproteinCIII, BCL2A1, PRV1,


Protein_MMP9, MAP2K6, IFNGR1


FCGR1A, LY96, JAK2, GADD45B, LDLR,
0.8
0.77
0.82


IL6, VNN1, MCP1, Gene_MMP9, SOD2


CSF1R, TNFRSF6, INSL3, MKNK1, IL8,
0.86
0.85
0.86


MAP2K6, FAD104, NCR1, IL1RN, MCP1


IL8, PRV1, SOCS3, IRAK2, ARG2,
0.85
0.86
0.83


IL10alpha, NCR1, CCL5, CReactiveProtein,


MKNK1


LDLR, CD86, NCR1, IRAK4, IL18R1,
0.85
0.84
0.87


Protein_MMP9, PRV1, GADD45B, ARG2,


LY96, AlphaFetoprotein


MAP2K6, CD86, INSL3,
0.85
0.81
0.88


ApolipoproteinCIII, IL8, OSM, TNFSF13B,


IL1RN, BCL2A1, FAD104, GADD45A


NCR1, GADD45B, TNFSF10, IL10alpha,
0.84
0.87
0.82


FAD104, LY96, IL6, IL10, ARG2,


CReactiveProtein, TGFBI


CD86, CEACAM1, INSL3, PFKFB3,
0.83
0.86
0.81


IL10alpha, FAD104, SOD2, Gene_MMP9,


SOCS3, ApolipoproteinCIII, FCGR1A


SOCS3, ARG2, ApolipoproteinCIII, IRAK4,
0.83
0.84
0.82


PFKFB3, IFNGR1, NCR1, IL8,


CReactiveProtein, VNN1, TDRD9


ARG2, OSM, CReactiveProtein, SOD2,
0.83
0.85
0.81


CEACAM1, FCGR1A, TIMP1, IL10,


IL18R1, ANKRD22, IRAK2


TGFBI, SOD2, IL10, CD86, CEACAM1,
0.83
0.83
0.83


TDRD9, IRAK4, ANXA3, LDLR, OSM,


ARG2


CReactiveProtein, IL10alpha, TIMP1, LY96,
0.83
0.83
0.83


IL8, SOD2, MAP2K6, MAPK14, TLR4,


PSTPIP2, INSL3


ARG2, PSTPIP2, SOD2, INSL3, FAD104,
0.83
0.8
0.86


JAK2, TIFA, PFKFB3, IRAK2, IL6,


ANXA3


PSTPIP2, CEACAM1, GADD45A,
0.83
0.84
0.82


ApolipoproteinCIII, ITGAM, PRV1, TLR4,


IL10alpha, ARG2, SOCS3, NCR1


OSM, SOCS3, CSF1R, IRAK2, VNN1, IL6,
0.83
0.82
0.83


SOD2, LDLR, BCL2A1, ANKRD22, CD86


CRTAP, LDLR, TGFBI, INSL3, TIFA,
0.83
0.82
0.83


FAD104, AlphaFetoprotein, IL8, JAK2,


IRAK4, BCL2A1


CD86, ITGAM, PSTPIP2, IL18R1, IL6,
0.83
0.77
0.87


IFNGR1, GADD45B, IL10,


Beta2Microglobulin, FCGR1A, FAD104


PRV1, Beta2Microglobulin, IL1RN, NCR1,
0.82
0.84
0.81


CSF1R, IFNGR1, TIMP1, SOCS3, LDLR,


TIFA, ARG2


IL10alpha, GADD45A, LDLR, SOCS3,
0.82
0.83
0.81


MAP2K6, LY96, CSF1R, Protein_MMP9,


MCP1, TDRD9, IL8


CSF1R, TDRD9, TIMP1, SOD2, FCGR1A,
0.82
0.83
0.82


IFNGR1, INSL3, CD86, TNFRSF6, HLA-


DRA, MAP2K6


IL8, IL18R1, BCL2A1, MKNK1,
0.82
0.82
0.83


CReactiveProtein, CCL5, IL6, SOCS3,


FCGR1A, PSTPIP2, ApolipoproteinCIII


ANXA3, IL6, CD86, SOD2, CEACAM1,
0.82
0.8
0.85


FCGR1A, ANKRD22, NCR1, PSTPIP2, IL8,


MAPK14


Protein_MMP9, TNFRSF6, ITGAM,
0.82
0.79
0.85


CSF1R, INSL3, TIFA, BCL2A1, IL1RN,


TGFBI, FCGR1A, ApolipoproteinCIII


ANKRD22, IL10alpha, SOCS3, IRAK4,
0.82
0.76
0.88


OSM, INSL3, TGFBI, MCP1, IL8,


TNFSF13B, PRV1


ANKRD22, LDLR, VNN1, TIMP1, IRAK2,
0.82
0.85
0.8


IL10alpha, GADD45B, ARG2, MAPK14,


CSF1R, TNFRSF6


TIFA, ARG2, TNFSF10, INSL3, CD86, IL8,
0.82
0.82
0.82


IRAK2, OSM, CSF1R, HLA-DRA, ITGAM


ANKRD22, TIFA, PSTPIP2, CCL5,
0.82
0.79
0.84


Gene_MMP9, Beta2Microglobulin, NCR1,


FCGR1A, INSL3, SOCS3, IL10alpha


ApolipoproteinCIII, AlphaFetoprotein,
0.82
0.82
0.81


NCR1, CCL5, GADD45A, IL18R1, JAK2,


TDRD9, OSM, TLR4, Gene_MMP9


CReactiveProtein, IL18R1, TGFBI,
0.82
0.8
0.83


TNFSF10, MAP2K6, LDLR, FAD104,


ARG2, HLA-DRA, GADD45B, ANXA3


IL18R1, IRAK4, LY96, INSL3, TNFRSF6,
0.82
0.8
0.83


CReactiveProtein, CD86, GADD45B,


CRTAP, IL8, MAPK14


IL8, FCGR1A, CSF1R, VNN1, IL10alpha,
0.81
0.85
0.78


PSTPIP2, IL6, IL1RN, TLR4, GADD45B,


LY96


HLA-DRA, IL6, FAD104, GADD45A,
0.81
0.79
0.84


INSL3, ITGAM, CSF1R, IFNGR1,


Protein_MMP9, SOCS3, NCR1


IL10, LDLR, AlphaFetoprotein, IL1RN,
0.81
0.79
0.84


INSL3, ApolipoproteinCIII, PSTPIP2,


CCL5, SOD2, TGFBI, VNN1


Protein_MMP9, IL10, TGFBI, INSL3,
0.81
0.84
0.78


IRAK2, TNFRSF6, IL8, PSTPIP2, OSM,


AlphaFetoprotein, NCR1


IL8, TLR4, MCP1, ApolipoproteinCIII,
0.81
0.83
0.79


Beta2Microglobulin, IL6, IL10, VNN1,


CD86, PSTPIP2, ITGAM


FAD104, GADD45A, SOCS3, PSTPIP2,
0.81
0.81
0.81


IL6, TGFBI, TIMP1, HLA-DRA, TNFSF10,


IL10alpha, MKNK1


PRV1, IL8, FCGR1A, GADD45A, IRAK2,
0.81
0.81
0.81


VNN1, CD86, IL18R1, Protein_MMP9,


MAP2K6, ITGAM


CRTAP, JAK2, IRAK2, CEACAM1, PRV1,
0.81
0.79
0.82


CCL5, SOD2, BCL2A1, SOCS3, IL1RN,


ApolipoproteinCIII


MCP1, CCL5, HLA-DRA, IRAK4, OSM,
0.81
0.76
0.85


LDLR, PFKFB3, CReactiveProtein,


MKNK1, GADD45A, LY96


ARG2, INSL3, IL6, ITGAM, TGFBI, LDLR,
0.81
0.82
0.79


IL10, CD86, IL8, TNFSF13B, IL10alpha


Protein_MMP9, CD86, GADD45B, LY96,
0.81
0.82
0.8


SOD2, FCGR1A, IL8, AlphaFetoprotein,


CSF1R, FAD104, CRTAP


TNFSF13B, ApolipoproteinCIII, LDLR,
0.81
0.77
0.84


TDRD9, CEACAM1, AlphaFetoprotein,


IRAK4, INSL3, GADD45A, CRTAP,


IFNGR1


MKNK1, PSTPIP2, Beta2Microglobulin,
0.8
0.82
0.79


ANKRD22, TIFA, IL10alpha, TGFBI,


AlphaFetoprotein, NCR1, PRV1, SOCS3


TIFA, MKNK1, IL6, ANXA3, FAD104,
0.8
0.82
0.79


PSTPIP2, TNFSF13B, LDLR, INSL3,


SOD2, JAK2


TNFSF13B, IFNGR1, IL18R1, CD86,
0.8
0.81
0.8


Beta2Microglobulin, TGFBI, CSF1R,


CReactiveProtein, CRTAP, MCP1, JAK2


TNFSF13B, Beta2Microglobulin, CSF1R,
0.8
0.79
0.82


JAK2, CRTAP, IL1RN, IL10, SOCS3,


ANKRD22, PFKFB3, LDLR


TNFRSF6, OSM, PRV1, INSL3, TLR4,
0.8
0.77
0.83


MKNK1, IRAK4, HLA-DRA, VNN1,


IL10alpha, FCGR1A


IL8, ApolipoproteinCIII, GADD45B,
0.8
0.77
0.83


SOCS3, ARG2, TNFSF13B, IL1RN, CCL5,


ANXA3, CReactiveProtein, TIFA


TNFSF13B, SOCS3, Protein_MMP9, SOD2,
0.8
0.76
0.86


TNFRSF6, NCR1, FAD104, IL6, OSM,


CCL5, TDRD9


CD86, INSL3, ANXA3, GADD45B, VNN1,
0.8
0.84
0.77


IFNGR1, IL6, PFKFB3, PSTPIP2,


Beta2Microglobulin, IRAK2


IL6, Gene_MMP9, FAD104, TIFA, TGFBI,
0.8
0.82
0.78


Beta2Microglobulin, IL10alpha, ANXA3,


IL18R1, NCR1, INSL3


CEACAM1, JAK2, MCP1, OSM, IL18R1,
0.8
0.82
0.78


MKNK1, ANKRD22, TLR4, CSF1R,


PSTPIP2, IL1RN


CSF1R, AlphaFetoprotein, HLA-DRA,
0.8
0.8
0.8


TDRD9, ITGAM, SOCS3, FCGR1A,


IRAK2, TIFA, TNFSF10, Protein_MMP9


CSF1R, IL10alpha, TNFRSF6, TNFSF13B,
0.8
0.79
0.81


LDLR, INSL3, AlphaFetoprotein, IL10,


TIFA, VNN1, HLA-DRA


IL18R1, MCP1, ANKRD22, TGFBI, ARG2,
0.8
0.77
0.83


ANXA3, GADD45A, IL1RN, TNFRSF6,


PSTPIP2, IRAK2


IL10alpha, IFNGR1, MAPK14, FCGR1A,
0.8
0.76
0.83


Gene_MMP9, GADD45A, VNN1,


ANKRD22, TNFSF13B, CCL5, IRAK2


IL8, CReactiveProtein, CSF1R, TLR4,
0.85
0.85
0.85


TNFRSF6, Gene_MMP9, TDRD9, OSM,


PFKFB3, IFNGR1, ApolipoproteinCIII,


PSTPIP2


JAK2, OSM, GADD45B, MCP1, IL1RN,
0.85
0.81
0.88


ANKRD22, IL18R1, Gene_MMP9, ITGAM,


NCR1, ApolipoproteinCIII, PFKFB3


TNFSF10, MKNK1, PFKFB3, ANXA3,
0.84
0.84
0.84


CRTAP, CD86, MAPK14, IL8, OSM,


GADD45B, HLA-DRA, INSL3


IL1RN, AlphaFetoprotein, ARG2, MAP2K6,
0.83
0.86
0.82


CEACAM1, GADD45B, CRTAP, ANXA3,


INSL3, ApolipoproteinCIII, NCR1, FAD104


IL6, LDLR, TDRD9, TNFRSF6, NCR1,
0.83
0.84
0.83


ITGAM, AlphaFetoprotein, FCGR1A,


ARG2, TNFSF10, OSM, BCL2A1


IL10, SOD2, GADD45A, TNFSF13B,
0.83
0.86
0.81


IRAK4, LY96, HLA-DRA, PSTPIP2, IL6,


IFNGR1, ARG2, LDLR


CCL5, CSF1R, LDLR, GADD45A, INSL3,
0.83
0.8
0.86


JAK2, AlphaFetoprotein, OSM,


Beta2Microglobulin, PRV1, HLA-DRA,


MKNK1


ANKRD22, TNFSF13B, TIMP1, VNN1,
0.83
0.83
0.83


IRAK4, FCGR1A, CEACAM1, IRAK2,


ARG2, ANXA3, CD86, IL1RN


JAK2, AlphaFetoprotein, IL1RN, SOCS3,
0.82
0.83
0.82


ANKRD22, IL10alpha, IL8, TGFBI, CD86,


IL10, CSF1R, CReactiveProtein


VNN1, GADD45B, MAP2K6, TNFSF13B,
0.82
0.79
0.85


IRAK2, TLR4, CReactiveProtein, PSTPIP2,


MCP1, CSF1R, IL8, TDRD9


SOD2, IL10, CReactiveProtein,
0.82
0.77
0.86


ApolipoproteinCIII, Beta2Microglobulin,


IFNGR1, OSM, TNFSF13B, VNN1,


GADD45B, CD86, PFKFB3


LDLR, CRTAP, PSTPIP2, GADD45B, IL8,
0.82
0.82
0.82


TNFRSF6, MAP2K6, IL10, ARG2, LY96,


MAPK14, IL18R1


IFNGR1, NCR1, ApolipoproteinCIII,
0.82
0.82
0.82


ANXA3, CSF1R, CCL5, FCGR1A, TIFA,


TLR4, INSL3, IL8, ARG2


LY96, Beta2Microglobulin, CCL5, LDLR,
0.82
0.78
0.86


IRAK4, TIMP1, MKNK1,


ApolipoproteinCIII, IL8, SOCS3,


ANKRD22, PRV1


Protein_MMP9, MAPK14, IL1RN, SOCS3,
0.82
0.77
0.87


MKNK1, ApolipoproteinCIII, IL10, OSM,


MAP2K6, TNFSF13B, NCR1, IL18R1


TNFSF13B, FAD104, OSM, TNFRSF6,
0.82
0.81
0.83


TDRD9, TIFA, IL10alpha, INSL3,


Protein_MMP9, HLA-DRA,


Beta2Microglobulin, ApolipoproteinCIII


TLR4, Protein_MMP9, VNN1, IFNGR1,
0.82
0.78
0.85


ITGAM, MCP1, LY96, IRAK2, OSM,


TDRD9, IL8, ApolipoproteinCIII


MAP2K6, OSM, GADD45B, IL1RN,
0.81
0.81
0.81


MAPK14, ARG2, LY96, VNN1, TNFRSF6,


TGFBI, CD86, Beta2Microglobulin


MKNK1, ARG2, CEACAM1, GADD45A,
0.81
0.81
0.81


AlphaFetoprotein, GADD45B, HLA-DRA,


CReactiveProtein, SOD2, TLR4, LDLR,


TNFRSF6


Protein_MMP9, TGFBI, PRV1,
0.81
0.81
0.82


Beta2Microglobulin, TNFSF13B, TLR4,


INSL3, Gene_MMP9, ARG2,


ApolipoproteinCIII, MKNK1, IL10alpha


TLR4, TGFBI, FCGR1A, NCR1, LY96,
0.81
0.81
0.82


IL10, CCL5, IRAK2, INSL3, TDRD9, OSM,


BCL2A1


Gene_MMP9, FCGR1A, PSTPIP2, TIFA,
0.81
0.81
0.82


CSF1R, SOD2, ITGAM, PFKFB3, JAK2,


IL8, LY96, OSM


CRTAP, MKNK1, TDRD9, LY96, TLR4,
0.81
0.8
0.83


TNFSF10, SOD2, JAK2,


Beta2Microglobulin, CD86, PSTPIP2,


MAP2K6


TGFBI, TDRD9, ARG2, OSM, TNFSF13B,
0.81
0.78
0.85


CEACAM1, CCL5, CReactiveProtein,


TLR4, IL10alpha, LY96, SOCS3


IRAK2, LDLR, ARG2, SOD2, IL10alpha,
0.81
0.78
0.84


ANKRD22, FCGR1A, Beta2Microglobulin,


FAD104, ITGAM, PRV1, OSM


FAD104, IL10alpha, INSL3, IL18R1,
0.81
0.83
0.79


IL1RN, MKNK1, MAP2K6, Gene_MMP9,


IRAK2, PSTPIP2, CEACAM1, IL6


VNN1, ApolipoproteinCIII, IL10, JAK2,
0.81
0.82
0.8


Protein_MMP9, INSL3, Beta2Microglobulin,


OSM, IRAK4, MAP2K6, IL1RN,


AlphaFetoprotein


BCL2A1, GADD45A, JAK2, FCGR1A,
0.81
0.81
0.81


FAD104, Gene_MMP9, CRTAP, TDRD9,


MAP2K6, CSF1R, PRV1, Protein_MMP9


INSL3, TIFA, LY96, FAD104, PSTPIP2,
0.81
0.8
0.82


PFKFB3, Beta2Microglobulin, TIMP1,


IL18R1, GADD45A, IL6, AlphaFetoprotein


TDRD9, PFKFB3, CSF1R, ITGAM, MCP1,
0.81
0.79
0.83


ARG2, TNFSF13B, PSTPIP2, MAP2K6,


ANXA3, OSM, TGFBI


TNFSF10, TNFRSF6, Beta2Microglobulin,
0.81
0.77
0.84


PRV1, SOCS3, IL8, VNN1, TDRD9,


CReactiveProtein, GADD45B, TNFSF13B,


CD86


MAP2K6, IFNGR1, LY96,
0.81
0.76
0.85


Beta2Microglobulin, GADD45B,


ANKRD22, SOCS3, ANXA3, INSL3,


Protein_MMP9, CD86, HLA-DRA


SOD2, TIMP1, ApolipoproteinCIII,
0.81
0.76
0.85


Protein_MMP9, FAD104, ANXA3, TLR4,


CCL5, ITGAM, IRAK4, SOCS3, HLA-DRA


ITGAM, INSL3, FCGR1A, ARG2, IRAK2,
0.85
0.82
0.88


FAD104, IRAK4, MAPK14, LY96, TIMP1,


PRV1, TLR4, CD86


PRV1, MKNK1, IL8, FAD104, VNN1,
0.85
0.84
0.86


SOCS3, ARG2, MAP2K6, IL1RN, SOD2,


IL18R1, NCR1, BCL2A1


NCR1, CEACAM1, IRAK4, ARG2,
0.85
0.79
0.89


TNFSF13B, PFKFB3, OSM, TNFRSF6,


SOCS3, HLA-DRA, TNFSF10, JAK2,


SOD2


MCP1, Protein_MMP9, IL10alpha, FAD104,
0.84
0.81
0.87


FCGR1A, ITGAM, TGFBI,


ApolipoproteinCIII, ARG2, PRV1, CRTAP,


TIFA, LDLR


ARG2, ANKRD22, GADD45B, IRAK2,
0.84
0.83
0.85


OSM, MKNK1, ANXA3, IL18R1,


TNFRSF6, MAP2K6, AlphaFetoprotein,


MCP1, ApolipoproteinCIII


HLA-DRA, NCR1, CEACAM1,
0.83
0.82
0.85


Beta2Microglobulin, VNN1,


AlphaFetoprotein, MCP1, IL6, FCGR1A,


OSM, CSF1R, IRAK2, CRTAP


CReactiveProtein, SOD2, GADD45A,
0.83
0.86
0.81


ARG2, IRAK4, FCGR1A, IL18R1, TLR4,


JAK2, BCL2A1, IL10alpha, TGFBI,


AlphaFetoprotein


ApolipoproteinCIII, HLA-DRA, TNFSF10,
0.83
0.83
0.83


TLR4, IL10, GADD45B, BCL2A1, IL6,


CCL5, INSL3, MAP2K6, LDLR, IFNGR1


TIFA, JAK2, HLA-DRA, SOCS3, ARG2,
0.83
0.79
0.87


OSM, AlphaFetoprotein, MAPK14, IRAK2,


IFNGR1, FCGR1A, MAP2K6, PRV1


Beta2Microglobulin, IRAK2, MKNK1,
0.83
0.83
0.83


ANKRD22, CD86, OSM, CSF1R, TNFSF10,


IFNGR1, TLR4, MCP1, FAD104, TGFBI


VNN1, FCGR1A, ANKRD22, CRTAP,
0.83
0.82
0.84


ANXA3, IL8, PFKFB3, NCR1, TLR4,


AlphaFetoprotein, TIFA, IRAK4, CD86


Gene_MMP9, INSL3, FCGR1A, LDLR,
0.83
0.81
0.84


OSM, PFKFB3, ANKRD22, IL1RN, IL8,


IFNGR1, TDRD9, BCL2A1, TNFSF13B


PFKFB3, AlphaFetoprotein, IRAK4, NCR1,
0.82
0.81
0.84


TNFSF10, TDRD9, JAK2, FAD104,


IL10alpha, PRV1, CReactiveProtein, TGFBI,


Protein_MMP9


Gene_MMP9, MAP2K6, MAPK14,
0.82
0.76
0.88


CReactiveProtein, PFKFB3, CCL5, CSF1R,


INSL3, MKNK1, ARG2, FAD104, SOD2,


Protein_MMP9


IL8, TNFSF13B, ARG2, TIFA, CRTAP,
0.82
0.85
0.79


OSM, IL18R1, MCP1, IRAK4, LY96,


AlphaFetoprotein, TDRD9,


CReactiveProtein


PSTPIP2, CEACAM1, GADD45B,
0.82
0.83
0.81


MAPK14, ARG2, FCGR1A, ITGAM,


TGFBI, IL10alpha, OSM, PRV1, IL8, TLR4


OSM, CReactiveProtein, CD86, LY96,
0.82
0.82
0.82


IL10alpha, FAD104, TDRD9, IL6,


ApolipoproteinCIII, LDLR, CSF1R, IL18R1,


MCP1


JAK2, IL8, ARG2, OSM, BCL2A1, TIFA,
0.82
0.83
0.8


IL6, Gene_MMP9, PRV1, TLR4, IL1RN,


LY96, IRAK2


IL6, INSL3, BCL2A1, TLR4, HLA-DRA,
0.82
0.81
0.82


IL10alpha, MKNK1, TDRD9, GADD45A,


OSM, SOCS3, CCL5, MAPK14


LDLR, FCGR1A, SOD2, LY96, MKNK1,
0.82
0.77
0.86


PRV1, MAP2K6, NCR1, Protein_MMP9,


SOCS3, AlphaFetoprotein, IFNGR1, INSL3


TNFRSF6, ARG2, INSL3, ANXA3, IL10,
0.82
0.77
0.87


TIFA, ITGAM, VNN1, SOD2, TIMP1,


CSF1R, Protein_MMP9, SOCS3


IL10alpha, TNFRSF6, ARG2, TIMP1, IL8,
0.81
0.85
0.78


CSF1R, MAP2K6, IRAK4, PFKFB3,


FCGR1A, AlphaFetoprotein, OSM, HLA-


DRA


Protein_MMP9, CD86, IFNGR1, TIMP1,
0.81
0.81
0.81


IL1RN, FCGR1A, ARG2, TIFA, IL8,


CRTAP, CSF1R, IL6, ITGAM


CEACAM1, ANKRD22, CCL5, TLR4,
0.81
0.81
0.82


IRAK4, Beta2Microglobulin, MAP2K6,


PRV1, TGFBI, FAD104, SOD2, JAK2,


MCP1


CD86, VNN1, PSTPIP2, PFKFB3,
0.81
0.86
0.77


CReactiveProtein, IL6, TLR4, CCL5,


FCGR1A, TDRD9, TNFRSF6, CSF1R,


CRTAP


LDLR, OSM, MCP1, CD86, IL1RN,
0.81
0.83
0.78


Protein_MMP9, MAP2K6, FCGR1A, IL8,


CEACAM1, PFKFB3, IRAK4, LY96


CReactiveProtein, TNFSF13B,
0.81
0.79
0.82


ApolipoproteinCIII, IRAK2, VNN1,


FCGR1A, PFKFB3, HLA-DRA, ANKRD22,


SOD2, CD86, TGFBI, Beta2Microglobulin


LY96, TNFSF10, PRV1, PSTPIP2, SOCS3,
0.81
0.79
0.83


TIMP1, IFNGR1, ARG2, CEACAM1,


CCL5, TNFSF13B, LDLR,


ApolipoproteinCIII


PRV1, JAK2, FCGR1A, VNN1, SOCS3,
0.81
0.79
0.83


TIFA, CRTAP, INSL3, IFNGR1, TDRD9,


CEACAM1, Protein_MMP9, IL8


GADD45A, SOCS3, OSM, CD86, ITGAM,
0.81
0.77
0.84


ApolipoproteinCIII, FAD104, INSL3,


PSTPIP2, IL18R1, AlphaFetoprotein,


TDRD9, MAP2K6


PSTPIP2, VNN1, IL1RN, CSF1R, CD86,
0.81
0.77
0.85


TLR4, IRAK4, IFNGR1, CRTAP, TNFSF10,


SOD2, TIFA, TDRD9


TNFRSF6, IFNGR1, TNFSF13B, MAP2K6,
0.85
0.88
0.83


MKNK1, ANXA3, TGFBI, OSM, ARG2,


Beta2Microglobulin, CReactiveProtein,


LY96, ApolipoproteinCIII, TIFA


CSF1R, TLR4, IL6, TNFSF13B,
0.84
0.85
0.83


Beta2Microglobulin, IRAK4, FCGR1A,


CCL5, ITGAM, VNN1, TIFA, CRTAP,


PFKFB3, TDRD9


CReactiveProtein, IL6, MAP2K6, OSM,
0.84
0.82
0.85


ARG2, ANKRD22, JAK2, HLA-DRA,


ApolipoproteinCIII, MAPK14, TLR4,


TNFSF13B, IFNGR1, IL10alpha


VNN1, GADD45B, IRAK2, TGFBI, NCR1,
0.84
0.77
0.89


IL6, CEACAM1, CRTAP, Gene_MMP9,


TNFRSF6, CD86, TDRD9,


CReactiveProtein, IL10


CRTAP, IL18R1, Beta2Microglobulin,
0.83
0.81
0.85


ANXA3, TDRD9, MKNK1, Protein_MMP9,


IL6, TNFSF10, OSM, MCP1, PFKFB3,


ApolipoproteinCIII, VNN1


PSTPIP2, IL8, IL18R1, CEACAM1, HLA-
0.83
0.79
0.86


DRA, OSM, NCR1, MCP1, FCGR1A,


TNFRSF6, TLR4, IRAK2, Protein_MMP9,


CReactiveProtein


PRV1, IRAK4, FAD104, TGFBI,
0.83
0.83
0.82


Protein_MMP9, INSL3, AlphaFetoprotein,


CD86, VNN1, CSF1R, Beta2Microglobulin,


GADD45B, BCL2A1, IL10


CD86, MAP2K6, PSTPIP2, TNFSF10,
0.83
0.81
0.84


OSM, GADD45B, TLR4, HLA-DRA, LY96,


TNFSF13B, ARG2, SOD2, PRV1,


Beta2Microglobulin


TIFA, CSF1R, IL10alpha, IFNGR1,
0.83
0.77
0.87


CEACAM1, CRTAP, ANKRD22, FCGR1A,


MAP2K6, FAD104, PSTPIP2, MAPK14,


ARG2, IRAK2


CReactiveProtein, TDRD9, IL8, ITGAM,
0.82
0.86
0.78


IL10alpha, TNFRSF6, SOD2, MCP1,


SOCS3, MKNK1, FAD104, MAP2K6,


IFNGR1, AlphaFetoprotein


TLR4, ANKRD22, IL10alpha,
0.82
0.86
0.78


CReactiveProtein, ApolipoproteinCIII,


BCL2A1, FCGR1A, SOD2, OSM, IFNGR1,


TGFBI, TIFA, VNN1, CEACAM1


FCGR1A, IRAK4, MAP2K6, ANXA3,
0.82
0.86
0.79


MAPK14, INSL3, AlphaFetoprotein, IL8,


MKNK1, ARG2, VNN1, TIMP1, CSF1R,


GADD45A


ANKRD22, HLA-DRA, IFNGR1,
0.82
0.81
0.84


GADD45A, TNFSF13B, FAD104, LDLR,


IL10alpha, IL6, MAPK14,


ApolipoproteinCIII, PRV1,


CReactiveProtein, TIMP1


IL10, PSTPIP2, INSL3, LY96, NCR1,
0.82
0.76
0.87


MAPK14, VNN1, MCP1, PRV1,


ApolipoproteinCIII, TIMP1, Protein_MMP9,


TDRD9, PFKFB3


TNFRSF6, TGFBI, LY96, TDRD9, CRTAP,
0.82
0.85
0.79


AlphaFetoprotein, TNFSF10, CCL5, JAK2,


IL6, IRAK2, HLA-DRA, OSM,


ApolipoproteinCIII


TIFA, Gene_MMP9, IL18R1, TDRD9,
0.82
0.84
0.8


SOCS3, TIMP1, IL6, CCL5, ARG2, CSF1R,


OSM, IL10alpha, IL8, TNFSF13B


PSTPIP2, PRV1, MAPK14, OSM, CRTAP,
0.82
0.82
0.82


IFNGR1, IL6, FAD104, IL18R1, JAK2,


GADD45B, LY96, BCL2A1, TLR4


GADD45A, IL6, TGFBI, BCL2A1, CRTAP,
0.82
0.8
0.84


CCL5, TIFA, TLR4, CD86, PRV1, FAD104,


TDRD9, TNFSF10, SOCS3


Beta2Microglobulin, JAK2, TDRD9,
0.82
0.79
0.85


PSTPIP2, HLA-DRA, IL1RN, TGFBI,


INSL3, ARG2, LDLR, AlphaFetoprotein,


IRAK2, SOD2, MAPK14


CD86, FAD104, AlphaFetoprotein,
0.82
0.77
0.86


Gene_MMP9, MCP1, HLA-DRA, INSL3,


PSTPIP2, IL1RN, ITGAM, TIMP1,


Protein_MMP9, IL6, IRAK4


ANKRD22, MAPK14, GADD45A, TDRD9,
0.82
0.88
0.75


IL10alpha, Protein_MMP9, ARG2, CD86,


TIMP1, IRAK2, TIFA, VNN1, OSM,


ITGAM


SOCS3, IL1RN, CEACAM1, FCGR1A,
0.82
0.85
0.79


LDLR, CCL5, CReactiveProtein,


AlphaFetoprotein, ARG2, IL6, CD86,


MCP1, INSL3, IL18R1


ANKRD22, FAD104, ApolipoproteinCIII,
0.82
0.84
0.79


IRAK2, TNFSF13B, TGFBI, TLR4,


CRTAP, MCP1, LDLR, JAK2, SOD2,


PSTPIP2, Protein_MMP9


BCL2A1, IL1RN, FCGR1A, GADD45A,
0.82
0.84
0.79


JAK2, NCR1, TDRD9, TIFA, TNFSF10,


Protein_MMP9, CRTAP, CSF1R, IL6,


INSL3


SOD2, ITGAM, ApolipoproteinCIII,
0.82
0.8
0.83


ANXA3, FAD104, IL6, ARG2, CD86,


TGFBI, SOCS3, OSM, TDRD9, IL18R1,


LY96


VNN1, IRAK2, ApolipoproteinCIII, IL10,
0.82
0.79
0.84


TDRD9, FCGR1A, IL8, TIMP1, MCP1,


JAK2, TIFA, TGFBI, OSM, MAPK14


ApolipoproteinCIII, IL10, TDRD9, ARG2,
0.82
0.78
0.85


IRAK4, ANXA3, TNFRSF6,


CReactiveProtein, INSL3, JAK2, IL1RN,


IL6, NCR1, Gene_MMP9


CD86, CSF1R, TNFSF13B, FCGR1A,
0.81
0.83
0.79


MCP1, GADD45A, LDLR, IRAK2, CCL5,


Beta2Microglobulin, SOCS3, MAP2K6,


LY96, INSL3


MCP1, NCR1, TGFBI, TDRD9, MAP2K6,
0.81
0.83
0.8


ApolipoproteinCIII, INSL3, LY96, IFNGR1,


JAK2, Protein_MMP9, GADD45B, IRAK4,


CCL5


Beta2Microglobulin, FCGR1A, TNFSF13B,
0.81
0.82
0.8


OSM, IRAK4, IRAK2, IL8, MAPK14,


PSTPIP2, TIFA, TIMP1, ApolipoproteinCIII,


MAP2K6, TLR4


TNFSF13B, LY96, OSM, MAP2K6, IRAK2,
0.81
0.82
0.81


CRTAP, JAK2, PFKFB3, BCL2A1,


CReactiveProtein, INSL3, GADD45A,


TIFA, IL10alpha


OSM, JAK2, GADD45A, CEACAM1,
0.81
0.82
0.81


ARG2, NCR1, TLR4, PRV1, PFKFB3, IL8,


Beta2Microglobulin, GADD45B, HLA-


DRA, INSL3


LY96, TIFA, CSF1R, IL10, SOCS3, ARG2,
0.81
0.79
0.83


IRAK4, CD86, IL10alpha, Protein_MMP9,


TNFSF10, ITGAM, Gene_MMP9, LDLR


AlphaFetoprotein, ApolipoproteinCIII,
0.81
0.78
0.84


SOD2, PSTPIP2, CSF1R,


Beta2Microglobulin, NCR1, GADD45B,


FCGR1A, CReactiveProtein, CEACAM1,


CD86, Protein_MMP9, HLA-DRA


MAPK14, ARG2, TNFSF10, TNFSF13B,
0.81
0.74
0.88


FAD104, ANKRD22, GADD45A, ANXA3,


CReactiveProtein, NCR1, IFNGR1, OSM,


Protein_MMP9, IL18R1


VNN1, NCR1, IL10alpha, ARG2, IL6,
0.81
0.84
0.79


LY96, CReactiveProtein, JAK2, TGFBI,


SOCS3, CRTAP, ITGAM, IRAK4, PRV1


TNFSF13B, CReactiveProtein, INSL3,
0.81
0.78
0.84


CEACAM1, Beta2Microglobulin, CD86,


IL6, JAK2, ApolipoproteinCIII, IL18R1,


ANXA3, PSTPIP2, SOD2, IL1RN


IRAK2, FCGR1A, Gene_MMP9, BCL2A1,
0.81
0.78
0.84


TGFBI, PSTPIP2, CEACAM1, GADD45A,


CCL5, TNFSF13B, ARG2, IL8, TIFA,


IL18R1









In some embodiments, the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use any one of the subsets of biomarkers listed in Table O. The subsets of biomarkers listed in Table O were identified in the computational experiments described in Section 6.14.5, below, in which 4600 random subcombinations of the biomarkers listed in Table I were tested. Table O lists some of the biomarker sets that provided high accuracy scores against the validation population described in Section 6.14.5. Each row of Table O lists a single set of biomarkers that can be used in the methods and kits respectively referenced in Sections 5.2 and 5.3. In other words, each row of Table O describes a set of biomarkers that can be used to discriminate between sepsis and SIRS subjects. In some embodiments, nucleic acid forms of the biomarkers listed in Table O are used in the methods and kits respectively referenced in Sections 5.2 and 5.3. In some embodiments, protein forms of the biomarkers listed in Table O are used. In some embodiments, some of the biomarkers in a biomarker set from Table O are in protein form and some of the biomarkers in the same biomarker set from Table O are in nucleic acid form in the methods and kits respectively referenced in Sections 5.2 and 5.3.
In some embodiments, a given set of biomarkers from Table O is used with the addition of one, two, three, four, five, six, seven, eight, or nine or more additional biomarkers from any one of Table 30, 31, 32, 33, 34, or 36 that are not within the given set of biomarkers from Table O. In Table O, accuracy, specificity, and senstitivity are described with reference to T−36 time point data described in Section 6.14.6, below.







TABLE O







Exemplary sets of biomarkers used in the methods or kits referenced in


Sections 5.2 and 5.3










BIOMARKER SET
ACCURACY
SPECIFICITY
SENSISTIVITY













SOCS3, ApolipoproteinCIII, NCR1
0.81
0.75
0.85


IL8, PRV1, CEACAM1
0.8
0.79
0.8


PSTPIP2, TLR4, GADD45B
0.8
0.72
0.87


ARG2, PRV1, MKNK1
0.79
0.71
0.85


CD86, SOCS3, TLR4
0.79
0.74
0.82


PRV1, GADD45B, TNFSF13B, ITGAM
0.83
0.73
0.91


PRV1, ApolipoproteinCIII, FCGR1A,
0.81
0.78
0.84


LDLR


TNFRSF6, MAP2K6, PRV1, ANKRD22
0.81
0.77
0.85


PRV1, ARG2, CD86, CEACAM1
0.81
0.8
0.82


GADD45B, CReactiveProtein, PRV1, CD86
0.81
0.73
0.88


GADD45B, TNFSF13B, FAD104, PFKFB3
0.81
0.73
0.86


PRV1, FAD104, IL18R1, MCP1
0.8
0.69
0.88


PRV1, IRAK2, PSTPIP2, ANXA3
0.8
0.68
0.87


FCGR1A, JAK2, MKNK1, PRV1
0.8
0.65
0.91


IL10, TNFSF13B, GADD45B, CEACAM1
0.79
0.73
0.85


Beta2Microglobulin, GADD45B, ARG2,
0.81
0.73
0.88


TNFSF13B, OSM


CD86, BCL2A1, PSTPIP2, PRV1, JAK2
0.8
0.71
0.89


GADD45A, GADD45B, CSF1R, MAP2K6,
0.8
0.69
0.88


PSTPIP2


AlphaFetoprotein, CReactiveProtein,
0.8
0.76
0.82


GADD45B, MAPK14, ANXA3


PRV1, FCGR1A, NCR1, CReactiveProtein,
0.8
0.74
0.84


TNFRSF6


MAPK14, CSF1R, OSM, IL1RN, TLR4
0.8
0.74
0.84


IRAK4, MAPK14, GADD45B, TNFSF13B,
0.8
0.71
0.86


CSF1R


ITGAM, ANXA3, Beta2Microglobulin,
0.79
0.76
0.82


PRV1, IRAK2


NCR1, MCP1, PRV1, CD86, FCGR1A
0.79
0.72
0.86


CRTAP, Beta2Microglobulin, TDRD9,
0.79
0.65
0.91


GADD45A, PRV1


PRV1, PFKFB3, FCGR1A, TIFA,
0.79
0.73
0.84


ANKRD22


PRV1, ApolipoproteinCIII, FCGR1A,
0.79
0.72
0.85


Protein_MMP9, TIMP1


FCGR1A, IRAK2, TNFSF13B, OSM,
0.84
0.79
0.89


CRTAP, PFKFB3


ANXA3, CEACAM1, PRV1, OSM, MCP1,
0.81
0.77
0.84


CCL5


IRAK4, TNFSF10, MCP1, PRV1, MKNK1,
0.81
0.75
0.84


SOCS3


TGFBI, CEACAM1, CD86, MAPK14,
0.8
0.76
0.83


LDLR, PRV1


MCP1, GADD45B, CEACAM1, TIMP1,
0.8
0.76
0.83


MAP2K6, IFNGR1


LY96, PRV1, MCP1, IRAK2, CD86,
0.8
0.76
0.83


TNFSF10


BCL2A1, PRV1, LDLR, TNFSF10,
0.8
0.73
0.85


IL18R1, SOCS3


SOCS3, ApolipoproteinCIII, FCGR1A,
0.79
0.7
0.87


TNFSF13B, IFNGR1, Beta2Microglobulin


ARG2, PSTPIP2, TNFRSF6, GADD45B,
0.79
0.82
0.77


MAPK14, TIMP1


NCR1, IL8, FCGR1A, IL1RN,
0.79
0.73
0.84


ApolipoproteinCIII, IFNGR1


LDLR, MAP2K6, PRV1, TIMP1, HLA-
0.79
0.72
0.85


DRA, CCL5


TIFA, GADD45B, HLA-DRA, CEACAM1,
0.79
0.74
0.83


OSM, ARG2


TIMP1, GADD45A, MKNK1, SOCS3,
0.79
0.73
0.83


LDLR, TNFSF10


SOD2, LY96, PRV1, FAD104, BCL2A1,
0.79
0.72
0.83


GADD45A


CEACAM1, BCL2A1, IRAK4, LDLR,
0.79
0.69
0.85


TIFA, IL10alpha


TNFSF10, TIFA, GADD45B, ANXA3,
0.78
0.65
0.88


BCL2A1, TNFRSF6


Beta2Microglobulin, TIMP1, GADD45A,
0.78
0.79
0.77


CRTAP, FAD104, GADD45B


ApolipoproteinCIII, IL18R1, CSF1R,
0.78
0.72
0.83


LDLR, FCGR1A, MCP1


MKNK1, GADD45B, IL1RN, NCR1, IL10,
0.78
0.71
0.83


LDLR


CD86, IL10, IFNGR1, SOCS3, TDRD9,
0.78
0.7
0.85


MCP1


PRV1, SOD2, INSL3, TIFA, IRAK2, MCP1
0.78
0.7
0.84


AlphaFetoprotein, Protein_MMP9,
0.83
0.79
0.87


ANKRD22, HLA-DRA, MAP2K6,


GADD45B, CEACAM1


TNFSF13B, OSM, PRV1, CSF1R, IFNGR1,
0.83
0.79
0.85


TNFRSF6, FCGR1A


FCGR1A, CCL5, TNFSF13B,
0.81
0.83
0.8


Gene_MMP9, IL6, MAP2K6, OSM


GADD45B, IL1RN, Beta2Microglobulin,
0.81
0.68
0.91


VNN1, PRV1, CD86, IL10


IL8, TIFA, IL18R1, SOD2, CSF1R,
0.8
0.81
0.8


FAD104, PRV1


MAP2K6, SOD2, IL18R1, LDLR, ANXA3,
0.8
0.78
0.82


CD86, GADD45B


ANKRD22, PRV1, TIMP1, NCR1,
0.8
0.75
0.84


GADD45A, FCGR1A, TNFSF13B


IL10alpha, CRTAP, IL10, TIMP1, TIFA,
0.8
0.73
0.87


PRV1, ARG2


TNFRSF6, TLR4, LY96, CSF1R,
0.8
0.7
0.88


GADD45B, CCL5, INSL3


TDRD9, ANXA3, TNFSF10, TNFRSF6,
0.8
0.68
0.9


PRV1, CCL5, IFNGR1


CD86, GADD45B, CReactiveProtein,
0.8
0.82
0.78


LDLR, CCL5, FAD104, IL8


IRAK4, TGFBI, PRV1, CEACAM1,
0.8
0.75
0.83


IFNGR1, PSTPIP2, TLR4


OSM, Gene_MMP9, TLR4, TDRD9, CCL5,
0.8
0.72
0.84


CRTAP, HLA-DRA


CRTAP, CEACAM1, FAD104, GADD45A,
0.79
0.72
0.84


PRV1, MAP2K6, TNFSF10


TNFRSF6, MKNK1, SOD2, TGFBI, MCP1,
0.79
0.72
0.86


GADD45B, ANKRD22


TIMP1, BCL2A1, TNFSF10, PRV1, HLA-
0.79
0.7
0.86


DRA, CRTAP, PFKFB3


INSL3, ANXA3, Beta2Microglobulin,
0.79
0.7
0.86


GADD45B, TNFRSF6, ANKRD22, LDLR


TIFA, GADD45B, HLA-DRA, CD86, IL10,
0.79
0.72
0.83


IL10alpha, MCP1


FCGR1A, CReactiveProtein, BCL2A1,
0.79
0.71
0.83


GADD45B, PRV1, PFKFB3, MAP2K6


IL8, INSL3, ANKRD22, TNFSF10, HLA-
0.79
0.7
0.85


DRA, PFKFB3, CSF1R


IL10alpha, MCP1, SOD2, TNFSF13B,
0.78
0.75
0.81


CRTAP, MAP2K6, PRV1


FAD104, SOD2, LY96, IL8, IRAK4, PRV1,
0.78
0.73
0.83


Protein_MMP9


MAPK14, OSM, PRV1, CRTAP,
0.78
0.7
0.85


IL10alpha, MKNK1, IFNGR1


OSM, AlphaFetoprotein, IFNGR1, SOD2,
0.78
0.73
0.82


GADD45A, CEACAM1, MKNK1


IL18R1, TDRD9, INSL3, JAK2,
0.78
0.7
0.85


Protein_MMP9, TNFRSF6, NCR1


IFNGR1, CEACAM1, JAK2, SOD2, HLA-
0.83
0.82
0.84


DRA, MAPK14, PRV1, VNN1


NCR1, IRAK2, MAP2K6,
0.83
0.83
0.82


CReactiveProtein, FCGR1A, ARG2, CD86,


SOCS3


GADD45B, ARG2, GADD45A, IL10alpha,
0.81
0.75
0.84


TDRD9, PFKFB3, CReactiveProtein, OSM


PRV1, ITGAM, IL1RN, MAPK14,
0.81
0.74
0.85


TNFSF10, SOD2, ARG2, PFKFB3


TNFRSF6, Beta2Microglobulin, PSTPIP2,
0.81
0.73
0.87


IL8, SOCS3, GADD45B, CRTAP, IFNGR1


CReactiveProtein, LY96, MAP2K6,
0.8
0.78
0.82


IL18R1, INSL3, OSM, CSF1R, IL6


ITGAM, PRV1, MAP2K6, IL8, OSM,
0.8
0.74
0.86


SOD2, IRAK4, CCL5


CReactiveProtein, OSM, PSTPIP2,
0.8
0.73
0.85


TNFSF10, ANKRD22, TDRD9, INSL3,


CD86


ANKRD22, CD86, PRV1, ANXA3, IL10,
0.79
0.81
0.78


TNFSF13B, TIFA, AlphaFetoprotein


ApolipoproteinCIII, MKNK1, FCGR1A,
0.79
0.75
0.82


PSTPIP2, VNN1, TNFRSF6,


AlphaFetoprotein, OSM


PRV1, CCL5, PFKFB3, TNFSF13B,
0.79
0.74
0.83


TIMP1, LDLR, ANKRD22, MAP2K6


ARG2, VNN1, ANKRD22, IFNGR1,
0.79
0.74
0.85


IL1RN, CD86, FAD104, GADD45B


IL10, PFKFB3, NCR1, TNFSF13B, MCP1,
0.79
0.7
0.86


MAPK14, PRV1, TIMP1


ApolipoproteinCIII, INSL3, IL10alpha,
0.79
0.74
0.83


FCGR1A, IL1RN, IL6, TNFRSF6, IL8


IL10, FAD104, CCL5, SOCS3, CD86,
0.79
0.79
0.78


HLA-DRA, LDLR, GADD45A


PFKFB3, CReactiveProtein, MAPK14,
0.79
0.74
0.81


TNFSF10, BCL2A1, ITGAM, IL10alpha,


TDRD9


Beta2Microglobulin, TNFSF13B,
0.78
0.73
0.82


ANKRD22, MCP1, TDRD9, IRAK4,


TIMP1, OSM


PSTPIP2, MAP2K6, AlphaFetoprotein,
0.78
0.69
0.84


TDRD9, PFKFB3, IL8, ANXA3, PRV1


TIFA, AlphaFetoprotein, PRV1, IL18R1,
0.78
0.68
0.87


Gene_MMP9, VNN1, TDRD9, TNFRSF6


IRAK2, FAD104, PRV1, GADD45A, TIFA,
0.78
0.72
0.83


MCP1, TIMP1, SOD2


IL6, CSF1R, MAP2K6, ANXA3, MCP1,
0.78
0.72
0.82


PRV1, ITGAM, AlphaFetoprotein


CCL5, IL10alpha, GADD45B, LDLR,
0.78
0.69
0.84


PSTPIP2, CD86, HLA-DRA, TLR4


LDLR, CRTAP, NCR1, TNFRSF6,
0.78
0.69
0.84


ApolipoproteinCIII, MAPK14, FCGR1A,


IRAK2


TGFBI, ANXA3, IL18R1, MAP2K6,
0.78
0.67
0.87


FCGR1A, IL10, OSM, PRV1


NCR1, JAK2, ANKRD22, IL1RN, ANXA3,
0.82
0.78
0.86


LDLR, CD86, IFNGR1, OSM


CSF1R, TDRD9, FAD104, TNFSF10,
0.82
0.79
0.84


OSM, LDLR, MAPK14, TIFA, BCL2A1


TNFSF10, IFNGR1, TNFRSF6, GADD45B,
0.82
0.75
0.87


CCL5, TNFSF13B, ANXA3, JAK2, PRV1


TNFSF13B, CD86, TIFA, SOCS3,
0.81
0.68
0.91


GADD45B, ARG2, TNFSF10, IRAK4,


IL10


FCGR1A, PSTPIP2, CEACAM1, IL1RN,
0.81
0.79
0.83


FAD104, IL6, INSL3, CSF1R, PRV1


IL1RN, SOD2, TGFBI, ApolipoproteinCIII,
0.8
0.83
0.78


JAK2, CEACAM1, IRAK2, IFNGR1, OSM


TDRD9, CD86, Protein_MMP9 TNFRSF6,
0.8
0.78
0.82


SOCS3, MCP1, AlphaFetoprotein, TIFA,


INSL3


BCL2A1, TGFBI, TLR4, IL8, LDLR,
0.8
0.77
0.83


ANKRD22, TNFSF13B, IL10, GADD45B


TNFSF13B, AlphaFetoprotein, TDRD9,
0.8
0.73
0.86


MAPK14, SOCS3, ANXA3, IL1RN,


CRTAP, TNFRSF6


IL6, TNFRSF6, MCP1, JAK2, GADD45A,
0.8
0.75
0.84


TIFA, ARG2, FCGR1A, ANKRD22


PSTPIP2, ANXA3, MCP1, FAD104, PRV1,
0.8
0.73
0.86


ANKRD22, NCR1, HLA-DRA, FCGR1A


IL8, PRV1, TDRD9, Beta2Microglobulin,
0.8
0.73
0.86


IL10alpha, VNN1, INSL3, TIFA, CSF1R


GADD45B, TNFRSF6, OSM, IRAK4,
0.8
0.71
0.88


AlphaFetoprotein, IL1RN, TNFSF13B,


MCP1, FAD104


ANKRD22, OSM, INSL3, IFNGR1,
0.8
0.69
0.89


MKNK1, GADD45B, TDRD9, MAP2K6,


IRAK4


NCR1, JAK2, ANKRD22, IL1RN, ANXA3,
0.82
0.78
0.86


LDLR, CD86, IFNGR1, OSM


ApolipoproteinCIII, ANXA3, IL18R1,
0.84
0.73
0.93


PRV1, CD86, LDLR, TDRD9,


CReactiveProtein, MAP2K6, CSF1R,


CRTAP


CCL5, Protein_MMP9 NCR1, PRV1,
0.82
0.74
0.88


TNFRSF6, TGFBI, HLA-DRA, FCGR1A,


IFNGR1, CSF1R, MCP1


GADD45B, CSF1R, IL1RN, PSTPIP2,
0.81
0.81
0.81


PRV1, ApolipoproteinCIII, ARG2, SOCS3,


FAD104, ITGAM, TIMP1


JAK2, MKNK1, CRTAP, GADD45B,
0.81
0.79
0.83


OSM, INSL3, TIMP1, TIFA, TNFRSF6,


AlphaFetoprotein, CD86


ApolipoproteinCIII, CD86, FCGR1A,
0.81
0.73
0.88


ARG2, GADD45B, IL8, CRTAP, IFNGR1,


TIMP1, ANXA3, HLA-DRA


MCP1, IL8, TNFSF13B, AlphaFetoprotein,
0.81
0.78
0.83


LDLR, Protein_MMP9, JAK2, FAD104,


IRAK2, TNFRSF6, GADD45B


TLR4, NCR1, CCL5, IL6,
0.81
0.76
0.85


CReactiveProtein, IRAK4,


AlphaFetoprotein, FCGR1A,


ApolipoproteinCIII, GADD45B, PRV1


ANKRD22, OSM, VNN1, LDLR,
0.8
0.81
0.8


ApolipoproteinCIII, IL1RN, SOCS3,


MAPK14, GADD45B, JAK2, ITGAM


NCR1, ARG2, GADD45B, GADD45A,
0.8
0.76
0.84


CD86, TNFSF10, CCL5, PSTPIP2,


Beta2Microglobulin, CRTAP, LDLR


SOCS3, JAK2, IL1RN, IFNGR1, CRTAP,
0.8
0.76
0.84


TIMP1, Protein_MMP9, VNN1, TNFRSF6,


CD86, ANKRD22


OSM, PSTPIP2, IL1RN, AlphaFetoprotein,
0.8
0.86
0.75


PRV1, IL6, LY96, IL18R1, CSF1R,


TNFSF13B, LDLR


IL10alpha, CReactiveProtein, TIFA, NCR1,
0.8
0.75
0.84


CRTAP, TGFBI, PFKFB3, LDLR, IRAK4,


GADD45B, TDRD9


ApolipoproteinCIII, ANXA3, IL18R1,
0.84
0.73
0.93


PRV1, CD86, LDLR, TDRD9,


CReactiveProtein, MAP2K6, CSF1R,


CRTAP









In some embodiments, the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use at least two different biomarkers that each contain a probeset listed in any one of FIG. 6, 14, 17, or 26. In a particular embodiment, a biomarker profile comprises at least two different biomarkers that each contain one of the probesets listed in any one of FIG. 6, 14, 17, or 26, biomarkers that each contain the complement of one of the probesets of any one of FIG. 6, 14, 17, or 26, or biomarkers that each contain an amino acid sequence encoded by a gene that either contains one of the probesets of any one of FIG. 6, 14, 17, or 26, or the complement of one of the probesets of any one of FIG. 6, 14, 17, or 26. Such biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example, amplified nucleic acid, or proteins. The biomarker profile further comprises a respective corresponding feature for the at least two biomarkers. Generally, the at least two biomarkers are derived from at least two different genes. In the case where a biomarker is based upon a gene that includes the sequence of a probeset listed in any one of FIG. 6, 14, 17, or 26, the biomarker can be, for example, a transcript made by the gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein encoded by a gene that includes a probeset sequence described in any one of FIG. 6, 14, 17, or 26, or a discriminating fragment of the protein, or an indication of any of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein. In some embodiments, the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers that each contains a probeset listed in any one of FIG. 6, 14, 17, or 26.
In some embodiments, the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use at least two different biomarkers listed in any one of FIG. 39, 43, 52, 53, or 56. In a particular embodiment, the biomarker profile comprises at least two different biomarkers listed in any one of FIG. 39, 43, 52, 53, or 56. The biomarker profile further comprises a respective corresponding feature for the at least two biomarkers. Generally, the at least two biomarkers are derived from at least two different genes. In the case where a biomarker in the at least two different biomarkers is listed in any one of FIG. 39, 43, 52, 53, or 56, the biomarker can be, for example, a transcript made by the listed gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein encoded by a gene listed in any one of FIG. 39, 43, 52, 53, or 56, or a discriminating fragment of the protein, or an indication of any of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein. In accordance with this embodiment, the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker. Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in Table 30). In one embodiment, such an assay utilizes a nucleic acid microarray. In some embodiments, the biomarker profile comprises at least two different biomarkers from any one of FIG. 39, 43, 52, 53, or 56. In some embodiments, the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 different biomarkers from any one of FIG. 39, 43, 52, 53, or 56.
In some embodiments, the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use specific biomarkers containing probes from any one of the probeset collections listed in Table P. In a particular embodiment, a biomarker profile comprises at least two different biomarkers that each contain one of the probesets listed in any one of the probeset collections of Table P, biomarkers that each contain the complement of one of the probesets from any one of the probeset collections of Table P, or biomarkers that each contain an amino acid sequence encoded by a gene that either contains one of the probesets from any one of the probeset collections of Table P, or the complement of one of the probesets of any one of the probeset collections of Table P. Such biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example, amplified nucleic acid, or proteins. The biomarker profile further comprises a respective corresponding feature for the at least two biomarkers. Generally, the at least two biomarkers are derived from at least two different genes. In the case where a biomarker is based upon a gene that includes the sequence of a probeset listed in any one of the probeset collections of Table P, the biomarker can be, for example, a transcript made by the gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein encoded by a gene that includes a probeset sequence from any one of the probeset collections listed in Table P, or a discriminating fragment of the protein, or an indication of any of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein. In some embodiments, the biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers that each contains a probeset from any one of probeset collections listed in Table P.







TABLE P







Exemplary probesets








PROBESET



COLLECTION
IDENTITY OF PROBE IN PROBESET COLLECTION





1
X206513_at, X214681_at, X235359_at, X221850_x_at, X213524_s_at,



X225656_a, X200881_s_at, X229743_at, X215178_x_at, X215178_x_at,



X216841_s_at, X216841_at, X244158_at, X238858_at, X205287_s_at,



X233651_s_at, X229572_at, X214765_s_at.


2
X206513_at, X213524_s_at, X200881_s_at, X218992_at, X238858_at,



X221123_x_at, X228402_at, X230585_at, X209304_x_at, X214681_at.


3
X204102_s_at, X236013_at, X213668_s_at, X1556639_at, X218220_at,



X207860_at, X232422_at, X218578_at, X205875_s_at, X226043_at,



X225879_at, X224618_at, X216316_x_at, X243159_x_at,



X202200_s_at, X201936_s_at, X242492_at, X216609_at, X214328_s_at,



X228648_at, X223797_at, X225622_at, X205988_at, X201978_s_at,



X200874_s_at, X210105_s_at, X203913_s_at, X204225_at, X227587_at,



X220865_s_at, X206682_at, X222664_at, X212264_s_at, X219669_at,



X221971_x_at, X1554464_a_at, X242590_at, X227925_at,



X221926_s_at, X202101_s_at, X211078_s_at, X44563_at, X206513_at,



X215178_x_at, X235359_at, X225656_at, X244158_at, X214765_s_at,



X229743_at, X214681.









In some embodiments, the methods or kits respectively described or referenced in Section 5.2 and Section 5.3 use at least two different biomarkers listed in any one of the biomarker sets in Table Q. In a particular embodiment, the biomarker profile comprises at least two different biomarkers listed in any one of the biomarker sets in Table Q. The biomarker profile further comprises a respective corresponding feature for the at least two biomarkers listed in any of the biomarker sets in Table Q. Generally, the at least two biomarkers are derived from at least two different genes. In the case where a biomarker in the at least two different biomarkers is listed in any one of biomarker sets of Table Q, the biomarker can be, for example, a transcript made by the listed gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein encoded by a gene listed in any one of the biomarker sets in Table Q, or a discriminating fragment of the protein, or an indication of any of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein. In accordance with this embodiment, the biomarker profiles of the present invention can be obtained using any standard assay known to those skilled in the art, or in an assay described herein, to detect a biomarker. Such assays are capable, for example, of detecting the products of expression (e.g., nucleic acids and/or proteins) of a particular gene or allele of a gene of interest (e.g., a gene disclosed in any on of the biomarker sets of Table Q). In one embodiment, such an assay utilizes a nucleic acid microarray. In some embodiments, a biomarker profile comprising at least 2 or 3 different biomarkers from any one of the biomarker sets of Table Q is used. Exemplary biomarker sets







TABLE Q







Exemplary biomarker sets








BIOMARKER



SET NUMBER
IDENTITY OF BIOMARKERS





1
IL18R1, ARG2, FCGR1A


2
ITGAM, TGFB1, TLR4, TNFSF, FCGR1A, IL18R1, ARG2


3
ARG2, TGFB1, MMP9, TLR4, ITGAM, IL18R1, TNFSF, IL1RN, FCGR1A


4
OSM, GADD45B, ARG2, IL18R1, TDRD9, PFKFB3, MAPK14,



PRV1, MAP2K6, TNFRSF6, FCGR1A, INSL3, LY96, PSTPIP2,



ANKRD22, TNFSF10, HLA-DRA, FNDC3B, TIFA, GADD45A,



VNN1, ITGAM, BCL2A1, TLR4, TNFSF13B, SOCS3, IL1RN,



CEACAM1, SOD2


5
ARG2, GADD45B, OSM, LY96, INSL3, ANKRD22, MAP2K6,



PSTPIP2, TGFB1, GADD45B, TDRD9, MAP2K6, OSM, TNFSF10,



ANKRD22









6. EXAMPLES
The following examples are representative of the embodiments encompassed by the present invention and in no way limit the subject embraced by the present invention. In the following examples, data was collected at twenty-four hour time intervals from each subject in a population of subjects. The population included two subject types. The first subject type was those that initially had SIRS and developed sepsis at a terminal time point in the analysis. The second subject type was those that initially had SIRS and did not develop sepsis at the terminal time point in the analysis. For subjects that initially had SIRS and developed sepsis, a T−12 time point was defined as the time frame immediate prior to the onset of clinically-diagnosed sepsis. In practice, the T−12 time point for each respective sepsis subject was the day the last blood sample was collected from the respective subject prior to being diagnosed with sepsis.
For each time point, two types of analyses were performed, a static and a baseline analysis. In the static analysis, only data from a single time point was considered. In particular, univariate and/or multivariate techniques were used to identify biomarkers whose abundance on corresponding microarray probesets on the U133 plus 2.0 (Affymetrix, Santa Clara, Calif.) discriminate between those subjects that develop sepsis from those subjects that do not develop sepsis during the study. To illustrate, consider the case in which there are two subjects in the population, subject A, who develops sepsis shortly after time period T−12, and subject B, who does not develop sepsis in any of the observed time points. In the static analysis, microarray biomarker abundance data from the two subjects that was collected at a particular single time point is evaluated in order to identify those biomarkers that have different abundance levels in the two subjects, as determined by a U133 plus 2.0 microarray experiment. In fact, in the present examples, a whole population of subjects of type A and type B are evaluated and parametric and/or nonparametric statistical techniques are used to identify those biomarkers whose abundance levels discriminate between subjects that develop sepsis at some point during the observation period and subjects that do not develop sepsis during the observation period. Here, an observation period refers to a time period that was a matter of hours, days, or weeks.
In addition to static analyses, baseline analyses were performed in the examples below. In a baseline analysis, rather than identifying biomarkers whose corresponding features (e.g. abundance value) at a single time point discriminate between sepsis subjects (subjects that develop sepsis at some point during the observation time period) and subjects that do not develop sepsis during the observed time frame, biomarkers whose change in abundance value across two or more time points discriminates between the two populations types were identified. For example, again consider subject A, who develops sepsis shortly after time period T−12, and subject B, who does not develop sepsis in any of the observed time points. In the basesline analysis, what were needed are biomarker abundance values for each subject from two different time points (e.g., time point 1 and time point 2). For each respective biomarker considered, the difference in the abundance of the biomarker at the two different time points was computed. These differential abundances from each of the subjects is then used to determine which corresponding biomarkers, expressed as a differential between two different time points, discriminate between subjects that develop sepsis during the observation period and subjects that do not develop sepsis during the observation time period.
6.1 Data Collection
SIRS positive subjects admitted to an ICU were recruited for the study. Subjects were eighteen years of age or older and gave informed consent to comply with the study protocol. Subjects were excluded from the study if they were (i) pregnant, (ii) taking antibiotics to treat a suspected infection, (iii) were taking systemic corticosteroids (total dosage greater than 100 mg hydrocortisone or equivalent in the past 48 hours prior to study entry), (iv) had a spinal cord injury or other illness requiring high-dose corticosteroid therapy, (v) pharmacologically immunosuppressed (e.g., azathioprine, methotrexate, cyclosporin, tacrolimus, cyclophosphamide, etanercept, anakinra, infliximab, leuflonamide, mycophenolic acid, OKT3, pentoxyphylin, etc.), (vi) were an organ transplant recipient, (vii) had active or metastatic cancer, (viii) had received chemotherapy or radiation therapy within 8 weeks prior to enrollment, and/or (ix) had taken investigational use drugs within thirty days prior to enrollment.
In the study SIRS criteria were evaluated daily. APACHE II and SOFA scoring was performed following ICU admission. APACHE II is a system for rating the severity of medical illness. APACHE stands for “Acute Physiology And Chronic Health Evaluation,” and is most frequently used to predict in-hospital death for patients in an intensive care unit. See, for example, Gupta et al., 2004, Indian Journal of Medical Research 119, 273-282, which is hereby incorporated herein by reference in its entirety. SOFA is a test to measure the severity of sepsis. See, for example, Vincent et al., 1996, Intensive Care Med. 22, 707-710, which is hereby incorporated herein by reference in its entirety. Patients were monitored daily for up to two weeks for clinical suspicion of sepsis including, but not limited to, any of the following signs and symptoms:

    
    
        pneumonia: temperature>38.3° C. or <36° C.+white blood cell count (WBC)>12,000/mm3 or <4,000/mm3+new-onset of purulent sputum+new or progressive infiltrate on chest radiograph (3 out of 4 findings);
        wound infection: temperature>38.3° C. or <36° C.+pain+erythema+purulent discharge (3 out of 4 findings);
        urinary tract infection: temperature>38.3° C. or WBC>12,000/mm3 or <4,000/mm3+bacteruria and pyuria (>10 WBC/hpf or positive leukocyte esterase) (all findings);
        line sepsis: temperature>38.3° C. or <36° C.+erythema/pain/purulence at catheter exit site (3 out of 4 findings, including fever);
        intra-abdominal abscess: temperature>38.3° C. or <36° C.+WBC>12,000/mm3 or <4,000/mm3+radiographic evidence of fluid collection (2 out of 3 criteria);
        CNS Infection: temperature>38.3° C. or <36° C.+WBC>12,000/mm3 or <4,000/mm3+CSF pleocytosis via LP or Ventricular drainage.
    
    


Blood was drawn daily for a minimum of four consecutive days beginning within 24 hours following study entry. Patients were followed and blood samples were drawn daily for a maximum of fourteen consecutive days unless clinical suspicion of infection occurred. The maximum volume of blood drawn from any one subject did not exceed 210 mL over the course of a 14 day study maximum. Blood draws for the study were discontinued if the loss of blood posed risk to the patient as defined by physician's judgment. Each patient had two Paxgene (RNA) tubes drawn on each day. One tube was used for the microarray analysis described in Section 6.2. The other tube was used for the RT-PCR analysis described in Section 6.10.
6.2 Microarray Analysis
RNA was extracted from each blood sample described in Section 6.1, labeled, reversed transcribed to generate cDNA which was labeled, and the labeled cDNA was hybridized to Affymetrix (Santa Clara, Calif.) U133 plus 2.0 human genome chips containing 54,675 probesets. To enhance detection sensitivity of the microarray, globin mRNA molecules were removed from the total RNA extracted from the blood samples using the methods described in, for example, U.S. Patent Publication 20050221310, filed Aug. 9, 2004, and Ser. No. 10/948,635, filed Sep. 24, 2004, both entitled “Methods of Enhancing Gene Expression Analysis,” each of which is incorporated by reference herein in its entirety. The U133 plus 2.0 has 62 probesets designed for special functions, such as measuring supplementally added transcripts. This leaves 54,613 probesets designed specifically for the detection of human genes. The Affymetrix human genome U133 (HG-U133) set, consisting of two microarrays, contains almost 45,000 probesets representing more than 39,000 transcripts derived from approximately 33,000 human genes. This set design uses sequences selected from GenBank, dbEST, and RefSeq. As used herein, the abundance value measured for each of the biomarkers that bind to these probesets is referred to as a feature. The examples below discuss abundance values of biomarkers that bind to particular probesets in the U133 plus 2.0 human genome chip.
6.3 Static T−36 Data Analysis
In one experiment, a T−36 static analysis was performed. In the T−36 static analysis, biomarkers features are determined using a specific blood sample, designated the T−36 blood sample, from each subject in a training population. The identity of this specific blood sample from each respective subject in the training population is dependent upon whether the subject was a SIRS subject (did not develop sepsis during the observation period) or was a sepsis subject (did develop sepsis during the observation period). In the case of a sepsis subject, the T−36 sample is defined as the second to last blood sample taken from the subject before the subject acquired sepsis. Identification of T−36 samples in the SIRS subjects in the training population was more discretionary than for the sepsis counterpart subjects because there was no significant event in which the SIRS subjects became septic. Because of this, the identity of the T−36 samples for the sepsis subjects in the training population was used to identify the T−36 samples in the SIRS subjects in the training population. Specifically, T−36 time points (blood samples) for SIRS subjects in the training population were identified by “time-matching” a septic subject and a SIRS subject. For example, consider the case in which a subject that entered the study became clinically-defined as septic on their sixth day of enrollment. For this subject, T−36 is day four of the study, and the T−36 blood sample is the blood sample that was obtained on day four of the study. Likewise, T−36 for the SIRS subject that was matched to this sepsis subject is deemed to be day four of the study on this paired SIRS subject.
Although SIRS subjects did not progress on to develop sepsis, they did have changes in their expressed genes (and proteins, etc.) over time. Thus, a one-to-one time matching of sepsis subjects to SIRS subjects for the purpose of obtaining a relevant set of T−36 blood samples from the SIRS subjects was sought in the manner described above. Just as subjects who progressed to become septic did so at varying rates, this time matching was done to mimic feature variability in SIRS subjects. While time matching between arbitrary pairs of SIRS and sepsis subjects was done to identify T−36 blood samples for as many of the SIRS subjects in the training population as possible, in some instances, T−36 samples from SIRS subjects had to be selected from time points based on sample availability.
For the T−36 static analysis there were 54,613 biomarkers measured on 84 samples for a total of 84 corresponding microarray experiments from 84 different subjects. Each sample was collected from a different subject in the population of 84 subject. Of the 54,613 probesets measured in each microarray experiment, 30,464 were transformed by log transformations. The log transformation is described in Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman & Hall/CRC, Boca Raton, pp. 309-311, which is hereby incorporated by reference in its entirety. Further, of the 54,613 probesets in each microarray experiment, 2317 were transformed by a square root transformation. The square root transformation is described in Ramdas, 2001, Genome Biology 2, 47.1-47.7, which is hereby incorporated by reference in its entirety. The remaining 21,832 probesets in each microarray experiment were not transformed.
The 84 member population was initially split into a training set (n=64) and a validation set (n=20). The training set was used to estimate the appropriate classification algorithm parameters while the trained algorithm was applied to the validation set to independently assess performance. Of the 64 training samples, 35 were Sepsis, meaning that the subjects developed sepsis at some point during the observation time period, and 29 were SIRS, meaning that they did not develop sepsis during the observation time period. Table 1 provides distributions of the race, gender and age for these samples.







TABLE 1





Distributions of the race, gender, and age for the training data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
10 
13
1




Female
0
10
1



SIRS
Male
5
17
0




Female
0
 7
0

















Group
Minimum
Mean
Median
Maximum







Sepsis
18
42
41
80



SIRS
18
43
40
90










For the 20 validation samples, 9 were Sepsis and 11 were SIRS. Table 2 provides distributions of the race, gender and age for these samples.







TABLE 2





Distributions of the race, gender, and age for the validation data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
1
7
0




Female
0
3
0



SIRS
Male
0
6
0




Female
0
3
0

















Group
Minimum
Mean
Median
Maximum







Sepsis
18
41.8
43
81



SIRS
19
47.7
51
77










Each sample in the training data was randomly assigned to one of ten groups used for cross-validation. The number of training samples in these groups ranged from 6 to 7. The samples were assigned in way that attempted to balance the number of sepsis and SIRS samples across folds. As described in more detail below, several different methods were used to judge whether select biomarkers, which bind to particular probesets in the microarray, discriminate between the Sepsis and SIRS groups.
Wilcoxon and Q-value tests. The first method used to identify discriminating biomarkers was a Wilcoxon test (unadjusted). The Wilcoxon test is a distribution-free test is resistant to extreme values. The Wilcoxon test is described in Agresti, 1996, An Introduction to Categorical Data Analysis, John Wiley & Sons, Inc, New York, Chapter 2, which is hereby incorporated by reference in its entirety. The Wilcoxon test produces a p value. The abundance value for a given biomarker from all samples in the training data is subjected to the Wilcoxon test. The Wilcoxon test considers both group classification (sepsis versus SIRS) and abundance value in order to compute a p value for the given biomarker. The p value provides an indication of how well the abundance value for the given biomarker across the samples collected in the training set discriminates between the sepsis and SIRS state. When the p value is less than a specific confidence level, such as 0.05, an inference is made that the biomarker discriminates between Sepsis and SIRS. There were 9520 significant biomarkers using this method (see Table 3).
The second method used to identify discriminating biomarkers was the Wilcoxon Test (adjusted). Due to the large number of biomarkers, 54613, and the relatively small number of samples, 84, there was a high risk of finding falsely significant biomarkers. An adjusted p-value was used to counter this risk. In particular, the method of Benjamini and Hochberg, 1995, J. R. Statist. Soc. B 57, pp 289-300, which is hereby incorporated by reference in its entirety, was used to control the false discovery rate. Here, the false discovery rate is defined as the number of biomarkers truly significant divided by the number of biomarkers declared significant. For example, if the adjusted p-value is less than 0.05, there is a 5% chance that the biomarker is a false discovery. Results using this test are reported in Table 3. There were 1618 significant biomarkers using this method (see Table 3). As used, herein, a biomarker is considered significant if the feature values corresponding to the biomarker have a p-value of less than 0.05 as determined by the Wilcoxon test (adjusted).
The third method used to identify discriminating biomarkers was the use of Q values. Q-values are described in Storey, 2002, J.R. Statist. Soc. B 64, Part 3, pp. 479-498, which is hereby incorporated by reference in its entirety. The biomarkers are ordered by their q-values and if a biomarker has a q-value of X, then this biomarker and all others more significant have a combined false discovery rate of X. However, the false discovery rate for any one biomarker may be much larger. There were 2431 significant markers using this method (see Table 3).







TABLE 3







Cumulative number of significant calls for the three methods.


Note that all 84 samples (training and validation) were used to compare converters


and nonconverters. Missing biomarker values were not included in the analyses.















≦1e−04
≦0.001
≦0.01
≦0.025
≦0.05
≦0.1
≦1


















p-value
0
1362
4210
6637
9520
13945
54613


(unadjusted)


p-value
0
0
0
584
1618
3315
54613


(adjusted)


q-value
0
0
0
1055
2431
4785
54613









CART. In addition to analyzing the microarray data using Wilcoxon test and Q-value tests in order to identify biomarkers that discriminate between the sepsis and SIRS subpopulations in the training set, classification and regression tree (CART) analysis was used. CART is described in Section 5.5.1, above. Specifically, the data summarized above was used to predict the disease state by iteratively partitioning the data based on the best single-variable (feature of biomarker across training set) split of the data. In other words, at each stage of the tree building process, the biomarker whose abundance value across the training population best discriminates between the sepsis and SIRS population was invoked as a decision branch. Cross-validation was carried out, with the optimal number of splits estimated independently in each of the 10 iterations. The final tree is depicted in FIG. 1. In FIG. 1, decision 102 makes a decision based on the abundance of the biomarker that binds to X204319_s_at. If the biomarker that binds to X204319_s_at has an abundance that is greater than 2.331 units in a biological sample from a subject to be diagnosed (test biological sample), then control passes to decision 104. If, on the other hand, the biomarker that binds to probeset X204319_s_at has abundance that is less than 2.331 units in the test biological sample, decision control passes to decision 106. Decisions are made in this manner until a terminal leaf of the decision tree is reached, at which point diagnoses of sepsis or SIRS is made. The decision tree in FIG. 1 makes use of the biomarkers that bind to the following five probesets: X204319_s_at, X1562290_at, X1552501_a_at, X1552283_s_at, and X117_at.
FIG. 2 shows the distribution of the biomarkers that bind to the five probesets used in the decision tree between the sepsis and SIRS groups in the training data set. In FIG. 2, the top of each box denotes the 75th percentile of the data across the training set and the bottom of each box denotes the 25th percentile, and the median value for each biomarker across the training set is drawn as a line within each box. The confusion matrix for the training data where the predicted classifications were made from the cross-validated model is given in Table 4. From this confusion matrix, the overall accuracy was estimated to be 70.3% with a 95% confidence interval of 57.6% to 81.1%. The estimated sensitivity was 60% and the estimated specificity was 82.8%.







TABLE 4







Confusion matrix for training samples using the


cross-validated CART algorithm of FIG. 1.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
21
5



SIRS
14
24










For the 20 validation samples held back from training data set, the overall accuracy was estimated to be 70% with a 95% confidence interval of 45.7% to 88.1%, sensitivity 88.9% and specificity 54.5%. Table 5 shows the confusion matrix for the validation samples.







TABLE 5







Confusion matrix for validation samples using the


cross-validated CART algorithm of FIG. 1.










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
8
5



SIRS
1
6










Random Forests. Another decision rule that can be developed using biomarkers of the present invention is a Random Forests decision tree. Random Forests is a tree based method that uses bootstrapping instead of cross-validation. For each iteration, a random sample (with replacement) is drawn and the largest tree possible is grown. Each tree receives a vote in the final class prediction. To fit a random forest, the number of trees (e.g. bootstrap iterations) is specified. No more than 500 were used in this example, but at least 50 are needed for a burn-in period. The number of trees was chosen based on the accuracy of the training data. For this data, 500 trees were used to train the algorithm (see FIG. 3). In FIG. 3, curve 302 is a smoothed estimate of overall accuracy as a function of tree number. Curve 304 is a smoothed curve of tree sensitivity as a function of tree number. Curve 306 is a smoothed curve of tree specificity as a function of tree number. Using this algorithm, 901 biomarkers had non-zero importance and were used in the model. The random forest algorithm gauges biomarker importance by the average reduction in the training accuracy. The biomarkers were ranked by this method and are shown in FIG. 4. In FIG. 4, the biomarkers are labeled by the name of the U133 plus 2.0 probeset to which they bind. The figure only reflects the 50 most important biomarkers found by using Random Forest analysis. However, 901 biomarkers were actually found to have discriminating significance. The random forest method uses a number of different decision trees. A biomarker is considered to have discriminating significance if it served as a decision branch of a decision tree from a significant random forest analysis. As used herein, a significant random forest analysis is one where the lower 95% confidence interval on accuracy by cross validation on a training data set is greater than 50% and the point estimate for accuracy on a validation set is greater than 65%.
The predicted confusion matrix for the training dataset using the decision tree developed using the Random Forest method is given in Table 6. From this confusion matrix, the overall accuracy was estimated to be 68.8% (confidence intervals cannot be computed when using the bootstrap accuracy estimate). The estimated sensitivity was 74.3% and the estimated specificity was 62.1%.







TABLE 6







Confusion matrix for training samples against the decision


tree developed using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
18
9



SIRS
11
26










For the 20 validation samples held back from training, the overall accuracy was estimated to be 65% with a 95% confidence interval of 40.8% to 84.6%, sensitivity 66.7% and specificity 63.6%. Table 7 shows the confusion matrix for the validation samples.







TABLE 7







Confusion matrix for the 20 validation samples against the


decision tree developed using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
6
4



SIRS
3
7










PAM. Yet another decision rule developed using the biomarkers of the present invention is predictive analysis of microarrays (PAM), which is described in Section 5.5.2, above. In this method, a shrinkage parameter that determines the number of biomarkers used to classify samples is specified. This parameter was chosen via cross-validation. There were no biomarkers with missing values. Based on cross-validation, the optimal threshold value was 2.07, corresponding to 258 biomarkers. FIG. 5 shows the accuracy across different thresholds. In FIG. 5, curve 502 is the overall accuracy (with 95% confidence interval bars). Curve 504 shows decision rule sensitivity as a function of threshold value. Curve 506 shows decision rule specificity as a function of threshold value. Using the threshold of 2.07, the overall accuracy for the training samples was estimated to be 73.4% with 95% a confidence interval of 61.4% to 82.8%. The estimated sensitivity was 79.3% and the estimated specificity was 68.6%.







TABLE 8







Confusion matrix for training samples using


cross-validated PAM algorithm










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
23
11



SIRS
6
24










For the twenty validation samples held back from training, the overall accuracy was estimated to be 70% with a 95% confidence interval of 45.7% to 88.1%, sensitivity 66.7% and specificity 72.7%. Table 9 shows the confusion matrix for the validation samples.







TABLE 9







Confusion matrix for training samples using


cross-validated PAM algorithm










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
6
3



SIRS
3
8










FIG. 6 shows the selected biomarkers, ranked by their relative discriminatory power, and their relative importance in the model. FIG. 6 only shows the fifty most important biomarkers found using the PAM analysis. However, 258 important biomarkers were found. The biomarkers in FIG. 6 are labeled based upon the U133 plus 2.0 probeset to which they bind.
FIG. 7 provides a summary of the CART, PAM, and random forests classification algorithm (decision rule) performance and associated 95% confidence intervals. Fifty distinct biomarkers were selected from across all the algorithms illustrated in FIG. 7. FIG. 8 illustrates the number of times that common biomarkers were selected across the techniques of Wilcoxon (adjusted), CART, PAM, and RF. FIG. 9 illustrates an overall ranking of biomarkers for the T-36 data set. For the selected biomarkers, the x-axis depicts the percentage of times that it was selected. Within the percentage of times that biomarkers were selected, the biomarkers are ranked. The biomarkers in FIG. 7 are labeled based upon the probeset (oligonucleotide identity) to which they bind.
6.4 Static T−12 Data Analysis
In another experiment, a T−12 static analysis was performed. In the T−12 static analysis, biomarkers features were measured using a specific blood sample, designated the T−12 blood sample, obtained from each subject in the training population. The identity of this specific blood sample from a given subject in the training population was dependent upon whether the subject was a SIRS subject (did not develop sepsis during the observation period) or a sepsis subject (did develop sepsis during the observation period). In the case of a sepsis subject, the T−12 sample was defined as the last blood sample taken from the subject before the subject acquired sepsis. Identification of T−12 samples in the SIRS subjects in the training population was more discretionary than for the sepsis counterpart subjects because there was no significant event in which the SIRS subjects became septic. Because of this, the identity of the T−12 samples for the sepsis subjects in the training population was used to identify the T−12 samples in the SIRS subjects in the training population. Specifically, T−12 time points (blood samples) for SIRS subjects in the training population were identified by “time-matching” a septic subject and a SIRS subject. For example, consider the case in which a subject that entered the study became clinically-defined as septic on their sixth day of enrollment. For this subject, T−12 was day five of the study (1-24 hours prior to sepsis), and the T−12 blood sample was the blood sample that was obtained on day five of the study. Likewise, T−12 for the SIRS subject that was matched to this sepsis subject was deemed to be day five of study on this paired SIRS subject. While time matching between arbitrary pairs of SIRS and sepsis subjects was done to identify T−12 blood samples for as many of the SIRS subjects in the training population as possible, in some instances, T−12 samples from SIRS subjects had to be selected from the time points based on sample availability.
For the T−12 static analysis, there were 54,613 biomarkers measured on 90 samples for a total of 90 corresponding microarray experiments from 90 different subjects. Each sample was collected from a different member the population. Of the 54,613 probesets in each microarray experiment, 31,047 were transformed by log transformations. Further, of the 54,613 probesets in each microarray experiment, 2518 were transformed by a square root transformation. The remaining 21,048 probesets in each microarray experiment were not transformed.
The 90 member population was initially split into a training set (n=69) and a validation set (n=21). The training set was used to estimate the appropriate classification algorithm parameters while the trained algorithm was applied to the validation set to independently assess performance. Of the 69 training samples, 34 were labeled Sepsis, meaning that the subjects developed sepsis at some point during the observation time period, and 35 were SIRS, meaning that they did not develop sepsis during the observation time period. Table 10 provides distributions of the race, gender and age for these samples.







TABLE 10





Distributions of the race, gender, and age for the training data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
9
13
1




Female
0
10
1



SIRS
Male
5
20
0




Female
0
10
0

















Group
Minimum
Mean
Median
Maximum







Sepsis
18
42.1
39
80



SIRS
18
44.1
40
90










For the 21 validation samples, 11 were labeled Sepsis and 10 were labeled SIRS. Table 11 provides distributions of the race, gender and age for these samples.







TABLE 11





Distributions of the race, gender, and age for the validation data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
0
7
0




Female
0
3
0



SIRS
Male
2
6
0




Female
0
3
0

















Group
Minimum
Mean
Median
Maximum







Sepsis
18
43.3
40
81



SIRS
19
53
52
85










Each sample in the training data was randomly assigned to one of ten groups used for cross-validation. The number of training samples in these groups ranged from 6 to 8. The samples were assigned in way that attempted to balance the number of sepsis and SIRS samples across folds. As described in more detail below, several different methods were used to judge whether select biomarkers discriminate between the Sepsis and SIRS groups.
Wilcoxon and Q-value tests. The first method used to identify discriminating biomarkers was a Wilcoxon test (unadjusted). The abundance value for a given biomarker across the samples in the training data was subjected to the Wilcoxon test. The Wilcoxon test considers both group classification (sepsis versus SIRS) and abundance value in order to compute a p value for the given biomarker. The p value provides an indication of how well the abundance value for the given biomarker across the samples collected in the training set discriminates between the sepsis and SIRS state. The lower the p value, the better the discrimination. When the p value is less than a specific confidence level, such as 0.05, an inference is made that the biomarker discriminates between the sepsis and SIRS phenotype. There were 19,791 significant biomarkers using this method (see Table 12).
The second method used to identify discriminating biomarkers was the Wilcoxon Test (adjusted). Due to the large number of biomarkers, 54613, and the relatively small number of samples, 90, there was a high risk of finding falsely significant biomarkers. An adjusted p-value was used to counter this risk. In particular, the method of Benjamini and Hochberg, 1995, J. R. Statist. Soc. B 57, pp 289-300, which is hereby incorporated by reference in its entirety, was used to control the false discovery rate. Here, the false discovery rate is defined as the number of biomarkers truly significant divided by the number of biomarkers declared significant. For example, if the adjusted p-value is less than 0.05, there is a 5% chance that the biomarker is a false discovery. Results using this test are reported in Table 12. There were 11851 significant biomarkers using this method (see Table 12). As used, herein, a biomarker is considered significant if it has a p-value of less than 0.05 as determined by the Wilcoxon test (adjusted).
The third method used to identify discriminating biomarkers was the use of Q values. In such an approach, the biomarkers are ordered by their q-values and if a respective biomarker has a q-value of X, then respective biomarker and all others more significant have a combined false discovery rate of X. However, the false discovery rate for any one biomarker may be much larger. There were 11851 significant biomarkers using this method (see Table 12).







TABLE 12







Cumulative number of significant calls for the three methods.


Note that all 90 samples (training and validation) were used to compare Sepsis and


SIRS groups. Missing biomarker feature values were not included in the analyses.















≦1e−04
≦0.001
≦0.01
≦0.025
≦0.05
≦0.1
≦1


















p-value
0
5417
11537
15769
19791
24809
54613


(unadjusted)


p-value
0
0
5043
8374
11851
16973
54613


(adjusted)


q-value
0
0
7734
12478
17820
24890
54613









CART. In addition to analyzing the microarray data using Wilcoxon test and Q-value tests in order to identify biomarkers that discriminate between the sepsis and SIRS subpopulations in the training set, classification and regression tree (CART) analysis was used. CART is described in Section 5.5.1, above. Specifically, the data summarized above was used to predict the disease state by iteratively partitioning the data based on the best single-variable split of the data. In other words, at each stage of the tree building process, the biomarker whose expression values across the training population best discriminate between the sepsis and SIRS population was invoked as a decision branch. Cross-validation was carried out, with the optimal number of splits estimated independently in each of the 10 iterations. The final tree is depicted in FIG. 10. In FIG. 10, decision 1002 makes a decision based on the abundance of the biomarker that binds to probeset X214681_at. If biomarker X214681_at has an abundance that is greater than 7.862 units in a biological sample from a subject to be diagnosed (test biological sample), than control passes to decision 1004. If, on the other hand, if the biomarker that binds to probeset (U133 plus 2.0 oligonucleotide) X214681_at has an abundance that is less than 7.862 units in the test biological sample, decision control passes to decision 1006. Decisions are made in this manner until a terminal leaf of the decision tree is reached, at which point diagnoses of sepsis or SIRS is made. The decision tree in FIG. 10 makes use of the biomarkers that bind to the following four probesets: X214681_at, X1560432_at, X230281_at, and X1007_s_at.
FIG. 11 shows the distribution of the four biomarkers used in the decision tree between the sepsis and SIRS groups in the training data set. In FIG. 11, the top of each box denotes the 75th percentile of the data across the training set and the bottom of each box denotes the 25th percentile, and the median value for each biomarker across the training set is drawn as a line within each box. The biomarkers are labeled in FIG. 11 based on the identity of the U133 plus 2.0 probes to which they bind). The confusion matrix for the training data where the predicted classifications were made from the cross-validated model is given in Table 13. From this confusion matrix, the overall accuracy was estimated to be 65.2% with a 95% confidence interval of 52.8% to 76.3%. The estimated sensitivity was 61.8% and the estimated specificity was 68.6%.







TABLE 13







Confusion matrix for training samples using the


cross-validated CART algorithm of FIG. 10










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
21
11



SIRS
13
24










For the 21 validation samples held back from training data set, the overall accuracy was estimated to be 71.4% with a 95% confidence interval of 47.8% to 88.7%, sensitivity 90.9% and specificity 50%. Table 14 shows the confusion matrix for the validation samples.







TABLE 14







Confusion matrix for validation samples using the


cross-validated CART algorithm of FIG. 10










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
10
5



SIRS
1
5










Random Forests. Another decision rule that can be developed using biomarkers of the present invention is a Random Forests decision tree. Random Forests is a tree based method that uses bootstrapping instead of cross-validation. For each iteration, a random sample (with replacement) is drawn and the largest tree possible is grown. Each tree receives a vote in the final class prediction. To fit a random forest, the number of trees (e.g. bootstrap iterations) is specified. No more than 500 were used in this example, but at least 50 are needed for a burn-in period. The number of trees was chosen based on the accuracy of the training data. For this data, 439 trees were used to train the algorithm (see FIG. 12). In FIG. 12, curve 1202 is a smoothed estimate of overall accuracy as a function of tree number. Curve 1204 is a smoothed curve of tree sensitivity as a function of tree number. Curve 1206 is a smoothed curve of tree specificity as a function of tree number. Using this algorithm, 845 biomarkers had non-zero importance and were used in the model. The random forest algorithm gauges biomarker importance by the average reduction in the training accuracy. The biomarkers were ranked by this method and are shown in FIG. 13. The figure only reflects the 50 most important biomarkers found by using Random Forest analysis. However, 845 biomarkers were actually found to have discriminating significance. The random forest method uses a number of different decision trees. A biomarker is considered to have discriminating significance if it served as a decision branch of a decision tree from a significant random forest analysis. As used herein, a significant random forest analysis is one where the lower 95% confidence interval on accuracy by cross validation on a training data set is greater than 50% and the point estimate for accuracy on a validation set is greater than 65%.
The predicted confusion matrix for the training dataset using the decision tree developed using the Random Forest method is given in Table 15. From this confusion matrix, the overall accuracy was estimated to be 75.4% (confidence intervals cannot be computed when using the bootstrap accuracy estimate). The estimated sensitivity was 73.5% and the estimated specificity was 77.1%.







TABLE 15







Confusion matrix for training samples against the decision


tree developed using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
27
9



SIRS
8
25










For the 21 validation samples held back from training, the overall accuracy was estimated to be 95.2% with a 95% confidence interval of 76.2% to 99.9%, sensitivity 100% and specificity 90%. Table 16 shows the confusion matrix for the validation samples.







TABLE 16







Confusion matrix for the 20 validation samples against the


decision tree developed using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
11
1



SIRS
0
9










MART. Multiple Additive Regression Trees (MART), also known as “gradient boosting machines,” was used to simultaneously assess the importance of biomarkers and classify the subject samples. Several fitting parameters are specified in this approach including (i) number of trees, (ii) step size (commonly referred to as “shrinkage”), and (iii) degree of interaction (related to the number of splits for each tree). More information on MART is described in Section 5.5.4 above. The degree of interaction was set to 1 to enforce an additive model (e.g. each tree has one split and only uses one biomarker).
Estimating interactions may require more data to function well. The step size was set to 0.05 so that the model complexity was dictated by the number of trees. The optimal number of trees was estimated by leaving out a random subset of cases at each fitting iteration, then assessing quality of prediction on that subset. After fitting more trees than were warranted, the point at which prediction performance stopped improving was estimated as the optimal point.
The estimated model used 28 trees and 17 biomarkers across all trees. The MART algorithm also provides a calculation of biomarker importance (summing to 100%), which are given in FIG. 14. Biomarkers with zero importance were excluded. In FIG. 14, biomarkers are labeled by the U133 plus 2.0 oligonucleotide to which they bind. FIG. 15 shows the distribution of the selected biomarkers between the Sepsis and SIRS groups. In FIG. 15, biomarkers are labeled by the U133 plus 2.0 oligonucleotide to which they bind.
Cross-validation was carried out, with the optimal number of trees estimated independently in each of the 10 iterations. The confusion matrix for the training data where the predicted classifications were made from the cross-validated model is given in Table 17. From this confusion matrix, the overall accuracy was estimated to be 76.8% with a 95% confidence interval of 65.1% to 86.1%. The estimated sensitivity was 76.5% and the estimated specificity was 77.1%.







TABLE 17







Confusion matrix for the training samples using the


cross-validated MART algorithm.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
26
8



SIRS
8
27










For the 21 validation samples held back from training, the overall accuracy was estimated to be 85.7% with a 95% confidence interval of 63.7% to 97%, sensitivity 80% and specificity 90.9%. Table 18 shows the confusion matrix for the validation samples.







TABLE 18







Confusion matrix for the validation samples using the MART algorithm.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
8
1



SIRS
2
10










PAM. Yet another decision rule developed using biomarkers of the present invention is predictive analysis of microarrays (PAM), which is described in Section 5.5.2, above. In this method, a shrinkage parameter that determines the number of biomarkers used to classify samples is specified. This parameter was chosen via cross-validation. There were no biomarkers with missing values. Based on cross-validation, the optimal threshold value was 2.1, corresponding to 820 biomarkers. FIG. 16 shows the accuracy across different thresholds. In FIG. 16, curve 1602 is the overall accuracy (with 95% confidence interval bars). Curve 1604 shows decision rule sensitivity as a function of threshold value. Curve 1606 shows decision rule specificity as a function of threshold value. Using the threshold of 2.1, the overall accuracy for the training samples was estimated to be 80.9% with a 95% confidence interval of 73.4% to 86.7%. The estimated sensitivity was 85.7% and the estimated specificity was 76.5%. Table 19 shows the confusion matrix for the training data where the predicted classifications were made from the cross-validated models.







TABLE 19







Confusion matrix for training samples using


cross-validated PAM algorithm










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
30
8



SIRS
5
26










For the 21 validation samples held back from training, the overall accuracy was estimated to be 95.2% with a 95% confidence interval of 76.2% to 99.9%, sensitivity 100% and specificity 90%. Table 20 shows the confusion matrix for the validation samples.







TABLE 20







Confusion matrix for validation samples using cross-validated PAM


algorithm










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
11
1



SIRS
0
9










FIG. 17 shows the selected biomarkers, ranked by their relative discriminatory power, and their relative importance in the model. FIG. 17 only shows the fifty most important biomarkers found using the PAM analysis. However, 820 important biomarkers were found. In FIG. 17, biomarkers are labeled by the U133 plus 2.0 oligonucleotide to which they bind.
FIG. 18 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals. Fifty distinct biomarkers were selected from across all the algorithms illustrated in FIG. 18. The identity of these fifty selected features is shown in FIG. 20. FIG. 19 illustrates the number of times that common biomarkers were selected across the techniques of CART, MART, PAM, RF, and Wilcoxon (adjusted). FIG. 20 illustrates an overall ranking of biomarkers for the T−12 data set. For the selected biomarkers, the x-axis depicts the percentage of times that it was selected. Within the percentage of times that biomarkers were selected, the biomarkers are ranked. In FIG. 20, biomarkers are labeled by the U133 plus 2.0 oligonucleotide to which they bind.
6.5 Baseline T−12 Data Analysis
In another example, a baseline T−12 analysis was performed. Feature values for biomarkers in this example were computed as the differential between two time points. The two time points for each respective subject in a training population were (i) the T−12 time point and (ii) the first measurement, Tfirst, taken of the respective subject. It will be appreciated that Tfirst could differ across the training population. For example, in some subjects, Tfirst was two days before T−12, in some subjects Tfirst was three days before T−12, and so forth. To illustrate the computation of a feature value in accordance with the T−12 baseline analysis, consider the case in which biomarker A was evaluated. To compute a feature value for biomarker A for the purposes of the baseline T−12 analysis, the abundance of biomarker A in the T−12 blood sample for a respective subject in the training population [A]T−12, was obtained. Further, the abundance of biomarker A from the first blood sample taken for the respective subject, [A]first, was obtained. The feature value for A for this respective subject was then computed as ΔA=[A]T−12−[A]first. This calculation was repeated for each subject in the training population and for each biomarker under consideration.
For the baseline T−12 analysis, there were 54,613 probesets measured on 89 samples for a total of 89 corresponding microarray experiments from 89 different subjects. Each sample was collected from a different member of the population. Of the 54,613 probesets in each microarray experiment, 31,047 were transformed by log transformations. Further, of the 54,613 probesets in each microarray experiment, 2518 were transformed by a square root transformation. The remaining 21,048 probesets in each microarray experiment were not transformed.
The 89 member population was initially split into a training set (n=68) and a validation set (n=21). The training set was used to estimate the appropriate classification algorithm parameters while the trained algorithm was applied to the validation set to independently assess performance. Of the 68 training samples, 33 were Sepsis, meaning that the subjects developed sepsis at some point during the observation time period, and 35 were SIRS, meaning that they did not develop sepsis during the observation time period. Table 21 provides distributions of the race, gender and age for these samples.







TABLE 21





Distributions of the race, gender, and age for the training data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
9
12
1




Female
0
10
1



SIRS
Male
5
20
0




Female
0
10
0

















Group
Minimum
Mean
Median
Maximum







Sepsis
18
42.7
39
80



SIRS
18
44.1
40
90










For the 21 validation samples, 11 were Sepsis and 10 were SIRS. Table 22 provides distributions of the race, gender and age for these samples.







TABLE 22





Distributions of the race, gender, and age for the validation data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
0
7
0




Female
0
3
0



SIRS
Male
2
6
0




Female
0
3
0

















Group
Minimum
Mean
Median
Maximum







Sepsis
18
43.3
40
81



SIRS
19
53
52
85










Each sample in the training data was randomly assigned to one of ten groups used for cross-validation. The number of training samples in these groups ranged from 6 to 8. The samples were assigned in way that attempted to balance the number of sepsis and SIRS samples across folds. As described in more detail below, several different methods were used to judge whether select biomarkers discriminate between the Sepsis and SIRS groups.
Wilcoxon and Q-value tests. The first method used to identify discriminating biomarkers was a Wilcoxon test (unadjusted). The abundance value for a given biomarker from all samples in the training data was subjected to the Wilcoxon test. The Wilcoxon test considers both group classification (sepsis versus SIRS) and abundance value in order to compute a p value for the given biomarker. The p value provides an indication of how well the abundance value for the given biomarker across the samples collected in the training set discriminates between the sepsis and SIRS state. The lower the p value, the better the discrimination. When the p value is less than a specific confidence level, such as 0.05, an inference is made that the biomarker discriminates between the sepsis and SIRS phenotype. There were 6427 significant biomarkers using this method (see Table 23).
The second method used to identify discriminating biomarkers was the Wilcoxon Test (adjusted). Due to the large number of biomarkers, 54613, and the relatively small number of samples, 89, there was a high risk of finding falsely significant biomarkers. An adjusted p-value was used to counter this risk. In particular, the method of Benjamini and Hochberg, 1995, J. R. Statist. Soc. B 57, pp 289-300, which is hereby incorporated by reference in its entirety, was used to control the false discovery rate. Here, the false discovery rate is defined as the number of biomarkers truly significant divided by the number of biomarkers declared significant. For example, if the adjusted p-value is less than 0.05, there is a 5% chance that the biomarker is a false discovery. Results using this test are reported in Table 12. There were 482 significant biomarkers using this method (see Table 23). As used, herein, a biomarker is considered significant if it has a p-value of less than 0.05 as determined by the Wilcoxon test (adjusted).
The third method used to identify discriminating biomarkers was the use of Q values. The biomarkers are ordered by their q-values and if a biomarker has a q-value of X, then this biomarker and all others more biomarkers have a combined false discovery rate of X. However, the false discovery rate for any one biomarker may be much larger. There were 482 significant biomarkers using this method (see Table 23).







TABLE 23







Cumulative number of significant calls for the three methods. Note that all


89 samples (training and validation) were used to compare Sepsis and SIRS groups.


Missing biomarker values were not included in the analyses.















≦1e−04
≦0.001
≦0.01
≦0.025
≦0.05
≦0.1
≦1


















p-value
0
808
2486
4230
6427
10051
54613


(unadjusted)


p-value
0
0
0
0
482
1035
54613


(adjusted)


q-value
0
0
0
0
606
1283
54613









CART. In addition to analyzing the microarray data using Wilcoxon test and Q-value tests in order to identify biomarkers that discriminate between the sepsis and SIRS subpopulations in the training set, classification and regression tree (CART) analysis was used. CART is described in Section 5.5.1, above. Specifically, the data summarized above was used to predict the disease state by iteratively partitioning the data based on the best single-variable (biomarker) split of the data. In other words, at each stage of the tree building process, the biomarker whose abundance value across the training population best discriminates between the sepsis and SIRS population was invoked as a decision branch. Cross-validation was carried out, with the optimal number of splits estimated independently in each of the 10 iterations. The final tree is depicted in FIG. 21. In FIG. 21, decision 2102 makes a decision based on the abundance of the biomarker that bind to U133 plus 2.0 probe X210119_at. If this biomarker that binds to X210119_at has an abundance that is less than −0.03669 units in a biological sample from a subject to be diagnosed (test biological sample), then control passes to decision 2104. If, on the other hand, the biomarker that binds to probeset X210119_at has an abundance that is greater than −0.03669 units in the test biological sample, decision control passes to decision 2106. Decisions are made in this manner until a terminal leaf of the decision tree is reached, at which point diagnoses of sepsis or SIRS is made. The decision tree in FIG. 21 makes use of the biomarkers that bind to the following five U133 plus 2.0 oligonucleotides: X210119_at, X1552554_a_at, X1554390_s_at, X1552301_a_at, and X1555868_at.
FIG. 22 shows the distribution of the five biomarkers used in the decision tree between the sepsis and SIRS groups in the training data set. In FIG. 22, the top of each box denotes the 75th percentile of the data across the training set and the bottom of each box denotes the 25th percentile, and the median value for each biomarker across the training set is drawn as a line within each box. In FIG. 22, biomarkers are labeled by the U133 plus 2.0 oligonucleotides to which they bind. The confusion matrix for the training data where the predicted classifications were made from the cross-validated model is given in Table 24. From this confusion matrix, the overall accuracy was estimated to be 80.9% with a 95% confidence interval of 69.5% to 89.4%. The estimated sensitivity was 93.9% and the estimated specificity was 68.6%.







TABLE 24







Confusion matrix for training samples using the cross-validated CART


algorithm of FIG. 21.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
31
11



SIRS
2
24










For the 21 validation samples held back from training data set, the overall accuracy was estimated to be 71.4% with a 95% confidence interval of 47.8% to 88.7%, sensitivity 72.7% and specificity 70%. Table 25 shows the confusion matrix for the validation samples.







TABLE 25







Confusion matrix for validation samples using the cross-validated CART


algorithm of FIG. 21.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
10
5



SIRS
1
5










Random Forests. Another decision rule that can be developed using biomarkers is a Random Forests decision tree. To fit a random forest, the number of trees (e.g. bootstrap iterations) is specified. No more than 500 were used in this example, but at least 50 are needed for a burn-in period. The number of trees was chosen based on the accuracy of the training data. For this data, 482 trees were used to train the algorithm (see FIG. 23). In FIG. 23, curve 2302 is a smoothed estimate of overall accuracy as a function of tree number. Curve 2304 is a smoothed curve of tree sensitivity as a function of tree number. Curve 2306 is a smoothed curve of tree specificity as a function of tree number. Using this algorithm, 482 biomarkers had non-zero importance and were used in the model. The random forest algorithm gauges biomarker importance by the average reduction in the training accuracy. The biomarkers were ranked by this method and are shown in FIG. 24. The figure only reflects the 50 most important biomarkers found by using Random Forest analysis. However, 893 biomarkers were actually found to have discriminating significance. The random forest method uses a number of different decision trees. A biomarker is considered to have discriminating significance if it served as a decision branch of a decision tree from a significant random forest analysis. As used herein, a significant random forest analysis is one where the lower 95% confidence interval on accuracy by cross validation on a training data set is greater than 50% and the point estimate for accuracy on a validation set is greater than 65%.
The predicted confusion matrix for the training dataset using the decision tree developed using the Random Forest method is given in Table 26. From this confusion matrix, the overall accuracy was estimated to be 61.8% (confidence intervals cannot be computed when using the bootstrap accuracy estimate). The estimated sensitivity was 57.6% and the estimated specificity was 65.7%.







TABLE 26







Confusion matrix for training samples against the decision tree developed


using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
23
14



SIRS
12
19










For the 21 validation samples held back from training, the overall accuracy was estimated to be 72.6% with a 95% confidence interval of 52.8% to 91.8%, sensitivity 63.9% and specificity 90%. Table 27 shows the confusion matrix for the validation samples.







TABLE 27







Confusion matrix for the 20 validation samples against the decision tree


developed using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
7
1



SIRS
4
9










PAM. Yet another decision rule developed using biomarkers is predictive analysis of microarrays (PAM), which is described in Section 5.5.2, above. In this method, a shrinkage parameter that determines the number of biomarkers used to classify samples is specified. This parameter was chosen via cross-validation. There were no biomarkers with missing values. Based on cross-validation, the optimal threshold value was 1.62, corresponding to 269 biomarkers. FIG. 25 shows the accuracy across different thresholds. In FIG. 25, curve 2502 is the overall accuracy (with 95% confidence interval bars). Curve 2504 shows decision rule sensitivity as a function of threshold value. Curve 2506 shows decision rule specificity as a function of threshold value. Using the threshold of 1.62, the overall accuracy for the training samples was estimated to be 67.7% with a 95% confidence interval of 55.9% to 77.6%. The estimated sensitivity was 68.6% and the estimated specificity was 66.7%. Table 28 shows the confusion matrix for the training data where the predicted classifications were made from the cross-validated models.







TABLE 28







Confusion matrix for training samples using cross-validated PAM


algorithm










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
24
11



SIRS
11
22










For the 21 validation samples held back from training, the overall accuracy was estimated to be 81% with a 95% confidence interval of 58.1% to 94.6%, sensitivity 72.7% and specificity 100%. Table 26 shows the confusion matrix for the validation samples.







TABLE 29







Confusion matrix for validation samples using cross-validated PAM


algorithm










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
8
1



SIRS
3
9










FIG. 26 shows the selected biomarkers, ranked by their relative discriminatory power, and their relative importance in the model. FIG. 26 only shows the fifty most important biomarkers found using the PAM analysis. However, 269 biomarker were found. In FIG. 26, biomarkers are labeled by the U133 plus 2.0 oligonucleotides to which they bind.
FIG. 27 provides a summary of the CART, PAM and random forests classification algorithm (decision rule) performance and associated 95% confidence intervals. Fifty distinct biomarkers were selected from across all the algorithms illustrated in FIG. 27. FIG. 28 illustrates the number of times that common biomarkers were selected across the techniques of CART, PAM, RF, and Wilcoxon (adjusted). In FIG. 28, biomarkers are labeled by the U133 plus 2.0 oligonucleotide to which they bind. FIG. 29 illustrates an overall ranking of biomarkers for the T0 base data set. In FIG. 29, biomarkers are labeled by the U133 plus 2.0 oligonucleotide to which they bind. For the selected biomarkers, the x-axis depicts the percentage of times that it was selected. Within the percentage of times that biomarkers were selected, the biomarkers are ranked.
6.6 Select Biomarkers
Sections 6.3 through 6.5 describe experiments in which blood samples from SIRS positive subjects have been tested using Affymetrix U133 plus 2.0 human genome chips containing 54,613 probesets. This section describes the criteria applied to the data described in Sections 6.3 through 6.5 in order to identify a list of biomarkers that discriminate between subjects likely to develop sepsis in a defined time period (sepsis subjects) and subjects not likely to develop sepsis in a defined time period (SIRS subjects). FIG. 30 illustrates the filters applied to identify this list of biomarkers.
A first criterion that was imposed was a requirement that a biomarker discriminate between SIRS and sepsis with a p value of 0.05 or less, as determined by the Wilcoxon test after correction for multiple comparisons, at any time point measured or the biomarker was used in a multivariate analysis with significant classification performance where significant classification performance is defined by having a lower 95th percentile for accuracy on a training data set that is greater than 50% and a point estimate for accuracy on the validation set greater than 65% at any time point measured. At T−36 (Section 6.3), 1,618 biomarkers met this criterion. At T−12 (Section 6.4), 12,728 biomarkers met this criterion. Some biomarkers met this criterion at both T−12 and T−36 time points. In total, there were 14,346 biomarkers (including duplicates from T−12 and T−36 time points) that discriminated between the sepsis and SIRS states. Thus, the first filter criterion reduced the number of eligible biomarkers from 54,613 to 14,346.
The second criterion that was imposed was a requirement that each respective biomarker under consideration exhibit at least a 1.2× fold change between the median value for the respective biomarker among the subjects that acquired sepsis during a defined time period (sepsis subjects) and the median value for the respective biomarker among subjects that do not acquire sepsis during the defined time period (SIRS subjects) at the T−36 time or the T−12 time point period. Furthermore, to satisfy the second criterion, the biomarker must have been used in at least one multivariate analysis with significant classification performance where significant classification performance is defined by having a lower 95th percentile for accuracy on a training data set that is greater than 50% and a point estimate for accuracy on the validation set that is greater then 65% at any time point measured. As noted in FIG. 30, application of the third filter criterion reduced the number of eligible biomarkers from 14,346 to 626.
In column one of Table 30, each biomarker is listed by a gene name, such as, for example, a Human Gene Nomenclature Database (HUGO) symbol set forth by the Gene Nomenclature Committee, Department of Biology, University College London. As is known in the art, some human genome genes are represented by more than one probeset in the U133 plus 2.0 array. Furthermore, some of the oligonucleotides in the U133 plus 2.0 array represent expressed sequence tags (ESTs) that do not correspond to a known gene (see column two of Table 30). Where known, the names of the different human genes are listed in column three of Table 30.
In the case where a biomarker is based upon a gene that includes the sequence of a probeset listed in Table 30 or a complement thereof, the biomarker can be, for example, a transcript made by the gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein encoded by a gene that includes a probeset sequence described in Table 30 or a discriminating fragment of the protein, or an indication of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein. In one embodiment, a biomarker profile of the present invention comprises a plurality of biomarkers that contain at least five, at least ten at least fifteen, at least twenty, at least thirty, between 2 and 5, between 3 and 7, or less than 15 of the sequences of the probesets of Table 30, or complements thereof, or genes including one of at least five of the sequences or complements thereof, or a discriminating fragment thereof, or an amino acid sequence encoded by any of the foregoing nucleic acid sequences, or any discriminating fragment of such an amino acid sequence. Such biomarkers can be mRNA transcripts, cDNA or some other form of amplified nucleic acid or proteins. In some embodiments a biomarker is any gene that includes the sequence in an Affymetrix probeset given in Table 30, or any gene that includes a complement of the sequence in an Affymetrix probeset given in Table 30, or any mRNA, cDNA or other form of amplified nucleic acid of the foregoing, for any discriminating fragment of the foregoing, or any amino acid sequence coded by the foregoing, or any discriminating fragment of such a protein.







TABLE 30







Exemplary biomarkers that discriminate between responders and


nonresponders














Gene
Protein



Affymetrix

Accession
Accession


Gene Symbol
Probeset name
Gene Name
Number
Number


Column
Column
Column
Column
Column


1
2
3
4
5





FLJ20445
218582_at
HYPOTHETICAL PROTEIN
NM_017824





FLJ20445


3′HEXO
231852_at
HISTONE MRNA 3′ END
NM_153332
NP_699163




EXORIBONUCLEASE


3′HEXO
226416_at
HISTONE MRNA 3′ END
NM_153332
NP_699163




EXORIBONUCLEASE


ABCA2
212772_s_at
ATP-BINDING CASSETTE,
NM_001606
NP_001597




SUB-FAMILY A (ABC1),
NM_212533
NP_997698




MEMBER 2


ABHD2
228490_at
ABHYDROLASE DOMAIN
NM_007011
NP_008942




CONTAINING 2
NM_152924
NP_690888


ACN9
218981_at
ACN9 HOMOLOG (S. CEREVISIAE)
NM_020186
NP_064571


ACSL1
201963_at
ACYL-COA SYNTHETASE
NM_001995
NP_001986




LONG-CHAIN FAMILY




MEMBER 1


ACSL3
201660_at
ACYL-COA SYNTHETASE
NM_004457
NP_004448




LONG-CHAIN FAMILY
NM_203372
NP_976251




MEMBER 3


ACSL4
202422_s_at
ACYL-COA SYNTHETASE
NM_004458
NP_004449




LONG-CHAIN FAMILY
NM_022977
NP_075266




MEMBER 4


ACTR3
213101_s_at
ARP3 ACTIN-RELATED
NM_005721
NP_005712




PROTEIN 3 HOMOLOG




(YEAST)


ADM
202912_at
ADRENOMEDULLIN
NM_001124
NP_001115


ADORA2A
205013_s_at
ADENOSINE A2
NM_000675
NP_000666




RECEPTOR


AIM2
206513_at
ABSENT IN MELANOMA 2
NM_004833
NP_004824


ALOX5AP
204174_at
ARACHIDONATE 5-
NM_001629
NP_001620




LIPOXYGENASE-




ACTIVATING PROTEIN


AMPD2
212360_at
ADENOSINE
NM_004037
NP_004028




MONOPHOSPHATE
NM_139156
NP_631895




DEAMINASE 2 (ISOFORM
NM_203404
NP_981949




L)


ANKRD22
238439_at
ANKYRIN REPEAT
NM_144590
NP_653191




DOMAIN 22


ANKRD22
239196_at
ANKYRIN REPEAT
NM_144590
NP_653191




DOMAIN 22


ANXA3
209369_at
ANNEXIN A3
NM_005139
NP_005130


APG3L
220237_at
APG3 AUTOPHAGY 3-LIKE
NM_022488
NP_071933




(S. CEREVISIAE)


ARHGAP8
47069_at
RHO GTPASE
NM_015366
NP_056181




ACTIVATING PROTEIN 8
NM_017701
NP_060171





NM_181333
NP_851850





NM_181334
NP_851851





NM_181335
NP_851852


ARID5B
212614_at
AT RICH INTERACTIVE
XM_084482
XP_084482




DOMAIN 5B (MRF1-LIKE)


ASAHL
214765_s_at
N-ACYLSPHINGOSINE
NM_014435
NP_055250




AMIDOHYDROLASE-LIKE




PROTEIN


ASAHL
232072_at
N-ACYLSPHINGOSINE
NM_014435
NP_055250




AMIDOHYDROLASE-LIKE




PROTEIN


ASAHL
227135_at
N-ACYLSPHINGOSINE
NM_014435
NP_055250




AMIDOHYDROLASE-LIKE




PROTEIN


ASPH
242037_at
ASPARTATE BETA-
NM_004318
NP_004309




HYDROXYLASE
NM_020164
NP_064549





NM_032466
NP_115855





NM_032467
NP_115856





NM_032468
NP_115857


ATP11B
1554557_at
ATPASE, CLASS VI, TYPE
XM_087254
XP_087254




11B


ATP11B
1564064_a_at
ATPASE, CLASS VI, TYPE
XM_087254
XP_087254




11B


ATP11B
1554556_a_at
ATPASE, CLASS VI, TYPE
XM_087254
XP_087254




11B


ATP11B
212536_at
ATPASE, CLASS VI, TYPE
XM_087254
XP_087254




11B


ATP11B
1564063_a_at
ATPASE, CLASS VI, TYPE
XM_087254
XP_087254




11B


ATP6V1C1
202872_at
ATPASE, H+
NM_001007254
NP_001007255




TRANSPORTING,
NM_001695
NP_001686




LYSOSOMAL, 42-KD, V1




SUBUNIT C, ISOFORM 1


ATP6V1C1
202874_s_at
ATPASE, H+
NM_001007254
NP_001007255




TRANSPORTING,
NM_001695
NP_001686




LYSOSOMAL, 42-KD, V1




SUBUNIT C, ISOFORM 1


ATP6V1C1
226463_at
ATPASE, H+
NM_001007254
NP_001007255




TRANSPORTING,
NM_001695
NP_001686




LYSOSOMAL, 42-KD, V1




SUBUNIT C, ISOFORM 1


ATP9A
212062_at
ATPASE, CLASS II, TYPE
XM_030577
XP_030577




9A


B4GALT5
221485_at
BETA-1,4-
NM_004776
NP_004767




GALACTOSYLTRANSFERASE


BASP1
202391_at
BRAIN-ABUNDANT
NM_006317
NP_006308




SIGNAL PROTEIN


BAT5
224756_s_at
HLA-B ASSOCIATED
NM_021160
NP_066983




TRANSCRIPT 5


BATF
205965_at
BASIC LEUCINE ZIPPER
NM_006399
NP_006390




TRANSCRIPTION FACTOR,




ATF-LIKE


BAZ1A
217986_s_at
BROMODOMAIN
NM_013448
NP_038476




ADJACENT TO ZINC
NM_182648
NP_872589




FINGER DOMAIN, 1A


BAZ1A
217985_s_at
BROMODOMAIN
NM_013448
NP_038476




ADJACENT TO ZINC
NM_182648
NP_872589




FINGER DOMAIN, 1A


BCL2A1
205681_at
BCL2-RELATED PROTEIN
NM_004049
NP_004040




A1


BCL3
204908_s_at
B-CELL CLL/LYMPHOMA 3
NM_005178
NP_005169


BCL3
204907_s_at
B-CELL CLL/LYMPHOMA 3
NM_005178
NP_005169


BCL6
203140_at
B-CELL LYMPHOMA 6
NM_001706
NP_001697





NM_138931
NP_620309


BCL6
215990_s_at
B-CELL LYMPHOMA 6
NM_001706
NP_001697





NM_138931
NP_620309


BIK
205780_at
BCL2-INTERACTING
NM_001197
NP_001188




KILLER (APOPTOSIS-




INDUCING)


BMX
206464_at
BONE MARROW KINASE,
NM_001721
NP_001712




X-LINKED
NM_203281
NP_975010


C13orf12
217769_s_at
CHROMOSOME 13 OPEN
NM_015932
NP_057016




READING FRAME 12


C14orf101
225675_at
CHROMOSOME 14 OPEN
NM_017799
NP_060269




READING FRAME 101


C14orf101
219757_s_at
CHROMOSOME 14 OPEN
NM_017799
NP_060269




READING FRAME 101


C14orf147
213508_at
CHROMOSOME 14 OPEN
NM_138288
NP_612145




READING FRAME 147


C16orf30
219315_s_at
CHROMOSOME 16 OPEN
NM_024600
NP_078876




READING FRAME 30


C16orf7
205781_at
CHROMOSOME 16 OPEN-
NM_004913
NP_004904




READING FRAME 7


C1GALT1
219439_at
CORE 1 SYNTHASE,
NM_020156
NP_064541




GLYCOPROTEIN-N-




ACETYLGALACTOSAMINE




3-BETA-




GALACTOSYLTRANSFERASE


C1GALT1C1
219283_at
C1GALT1-SPECIFIC
NM_001011551
NP_001011551




CHAPERONE 1
NM_152692
NP_689905


C1GALT1C1
238989_at
C1GALT1-SPECIFIC
NM_001011551
NP_001011551




CHAPERONE 1
NM_152692
NP_689905


C1orf8
200620_at
CHROMOSOME 1 OPEN
NM_004872
NP_004863




READING FRAME 8


C1RL
218983_at
COMPLEMENT
NM_016546
NP_057630




COMPONENT 1, R




SUBCOMPONENT-LIKE


C20orf24
217835_x_at
CHROMOSOME 20 OPEN
NM_018840
NP_061328




READING FRAME 24
NM_199483
NP_955777





NM_199484
NP_955778





NM_199485
NP_955779


C20orf24
223880_x_at
CHROMOSOME 20 OPEN
NM_018840
NP_061328




READING FRAME 24
NM_199483
NP_955777





NM_199484
NP_955778





NM_199485
NP_955779


C20orf32
1554786_at
CHROMOSOME 20 OPEN-
NM_020356
NP_065089




READING FRAME 32


C21orf91
220941_s_at
CHROMOSOME 21 OPEN
NM_017447
NP_059143




READING FRAME 91


C2orf25
217883_at
CHROMOSOME 2 OPEN
NM_015702
NP_056517




READING FRAME 25


C2orf33
223354_x_at
CHROMOSOME 2 OPEN
NM_020194
NP_064579




READING FRAME 33


C6orf83
225850_at
CHROMOSOME 6 OPEN
NM_145169
NP_660152




READING FRAME 83


C9orf19
225604_s_at
CHROMOSOME 9 OPEN
NM_022343
NP_071738




READING FRAME 19


C9orf46
218992_at
CHROMOSOME 9 OPEN
NM_018465
NP_060935




READING FRAME 46


C9orf84
1553920_at
CHROMOSOME 9 OPEN
NM_173521
NP_775792




READING FRAME 84


CA4
206208_at
CARBONIC ANHYDRASE
NM_000717
NP_000708




IV


CA4
206209_s_at
CARBONIC ANHYDRASE
NM_000717
NP_000708




IV


CAB39
217873_at
CALCIUM BINDING
NM_016289
NP_057373




PROTEIN 39


CACNA1E
236013_at
CALCIUM CHANNEL,
NM_000721
NP_000712




VOLTAGE-DEPENDENT,




ALPHA 1E SUBUNIT


CACNA2D3
219714_s_at
CALCIUM CHANNEL,
NM_018398
NP_060868




VOLTAGE-DEPENDENT,




ALPHA 2/DELTA 3




SUBUNIT


CAPZA2
1569450_at
CAPPING PROTEIN (ACTIN
NM_006136
NP_006127




FILAMENT) MUSCLE Z-




LINE, ALPHA 2


CARD12
1552553_a_at
CASPASE RECRUITMENT
NM_021209
NP_067032




DOMAIN FAMILY,




MEMBER 12


CASP4
209310_s_at
CASPASE 4, APOPTOSIS-
NM_001225
NP_001216




RELATED CYSTEINE
NM_033306
NP_150649




PROTEASE
NM_033307
NP_150650


CCL5
1555759_a_at
CHEMOKINE (C—C
NM_002985
NP_002976




MOTIF) LIGAND 5


CCPG1
221511_x_at
CELL CYCLE
NM_004748
NP_004739




PROGRESSION 1
NM_020739
NP_065790


CD4
203547_at
CD4 ANTIGEN (P55)
NM_000616
NP_000607


CD48
237759_at
CD48 ANTIGEN (B-CELL
NM_001778
NP_001769




MEMBRANE PROTEIN)


CD58
211744_s_at
CD58 ANTIGEN,
NM_001779
NP_001770




(LYMPHOCYTE




FUNCTION-ASSOCIATED




ANTIGEN 3)


CD58
205173_x_at
CD58 ANTIGEN,
NM_001779
NP_001770




(LYMPHOCYTE




FUNCTION-ASSOCIATED




ANTIGEN 3)


CD58
216942_s_at
CD58 ANTIGEN,
NM_001779
NP_001770




(LYMPHOCYTE




FUNCTION-ASSOCIATED




ANTIGEN 3)


CD59
228748_at
CD59 ANTIGEN P18–20
NM_000611
NP_000602




(ANTIGEN IDENTIFIED BY
NM_203329
NP_976074




MONOCLONAL
NM_203330
NP_976075




ANTIBODIES 16.3A5, EJ16,
NM_203331
NP_976076




EJ30, EL32 AND G344)


CD59
200985_s_at
CD59 ANTIGEN P18–20
NM_000611
NP_000602




(ANTIGEN IDENTIFIED BY
NM_203329
NP_976074




MONOCLONAL
NM_203330
NP_976075




ANTIBODIES 16.3A5, EJ16,
NM_203331
NP_976076




EJ30, EL32 AND G344)


CD59
200984_s_at
CD59 ANTIGEN P18–20
NM_000611
NP_000602




(ANTIGEN IDENTIFIED BY
NM_203329
NP_976074




MONOCLONAL
NM_203330
NP_976075




ANTIBODIES 16.3A5, EJ16,
NM_203331
NP_976076




EJ30, EL32 AND G344)


CD59
212463_at
CD59 ANTIGEN P18–20
NM_000611
NP_000602




(ANTIGEN IDENTIFIED BY
NM_203329
NP_976074




MONOCLONAL
NM_203330
NP_976075




ANTIBODIES 16.3A5, EJ16,
NM_203331
NP_976076




EJ30, EL32 AND G344)


CD74
209619_at
CD74 ANTIGEN
NM_004355
NP_004346




(INVARIANT




POLYPEPTIDE OF MAJOR




HISTOCOMPATIBILITY




COMPLEX, CLASS II




ANTIGEN-ASSOCIATED)


CD74
1567628_at
CD74 ANTIGEN
NM_004355
NP_004346




(INVARIANT




POLYPEPTIDE OF MAJOR




HISTOCOMPATIBILITY




COMPLEX, CLASS II




ANTIGEN-ASSOCIATED)


CD86
210895_s_at
CD86 ANTIGEN (CD28
NM_006889
NP_008820




ANTIGEN LIGAND 2, B7-2
NM_175862
NP_787058




ANTIGEN)


CDKN3
209714_s_at
CYCLIN-DEPENDENT
NM_005192
NP_005183




KINASE INHIBITOR 3




(CDK2-ASSOCIATED




DUAL SPECIFICITY




PHOSPHATASE)


CEACAM1
209498_at
CARCINOEMBRYONIC
NM_001712
NP_001703




ANTIGEN-RELATED CELL




ADHESION MOLECULE 1


CEACAM1
206576_s_at
CARCINOEMBRYONIC
NM_001712
NP_001703




ANTIGEN-RELATED CELL




ADHESION MOLECULE 1


CEACAM1
211889_x_at
CARCINOEMBRYONIC
NM_001712
NP_001703




ANTIGEN-RELATED CELL




ADHESION MOLECULE 1


CEACAM1
211883_x_at
CARCINOEMBRYONIC
NM_001712
NP_001703




ANTIGEN-RELATED CELL




ADHESION MOLECULE 1


CEACAM3
208052_x_at
CARCINOEMBRYONIC
NM_001815
NP_001806




ANTIGEN-RELATED CELL




ADHESION MOLECULE 3


CECR1
219505_at
CAT EYE SYNDROME
NM_017424
NP_059120




CHROMOSOME REGION,
NM_177405
NP_803124




CANDIDATE 1


CHCHD7
222701_s_at
COILED-COIL-HELIX
NM_001011667
NP_001011667




DOMAIN-CONTAINING
NM_001011668
NP_001011668




PROTEIN 7
NM_001011669
NP_001011669





NM_001011670
NP_001011670





NM_001011671
NP_001011671





NM_024300
NP_077276


CHSY1
203044_at
CARBOHYDRATE
NM_014918
NP_055733




SYNTHASE 1


CIR
209571_at
CBF1 INTERACTING
NM_004882
NP_004873




COREPRESSOR
NM_199075
NP_951057


CKLF
223451_s_at
CHEMOKINE-LIKE
NM_016326
NP_057410




FACTOR
NM_016951
NP_058647





NM_181640
NP_857591





NM_181641
NP_857592


CKLF
219161_s_at
CHEMOKINE-LIKE
NM_016326
NP_057410




FACTOR
NM_016951
NP_058647





NM_181640
NP_857591





NM_181641
NP_857592


CKLF
221058_s_at
CHEMOKINE-LIKE
NM_016326
NP_057410




FACTOR
NM_016951
NP_058647





NM_181640
NP_857591





NM_181641
NP_857592


CKLFSF1
235286_at
CHEMOKINE-LIKE
NM_052999
NP_443725




FACTOR SUPER FAMILY 1
NM_181268
NP_851785





NM_181269
NP_851786





NM_181270
NP_851787





NM_181271
NP_851788





NM_181272
NP_851789





NM_181283
NP_851800





NM_181285
NP_851802





NM_181286
NP_851803





NM_181287
NP_851804





NM_181288
NP_851805





NM_181289
NP_851806





NM_181290
NP_851807





NM_181292
NP_851809





NM_181293
NP_851810





NM_181294
NP_851811





NM_181295
NP_851812





NM_181296
NP_851813





NM_181297
NP_851814





NM_181298
NP_851815





NM_181299
NP_851816





NM_181300
NP_851817





NM_181301
NP_851818


CLEC10A
206682_at
C-TYPE LECTIN DOMAIN
NM_006344
NP_006335




FAMILY 10, MEMBER A
NM_182906
NP_878910


CLEC4D
1552772_at
C-TYPE LECTIN DOMAIN
NM_080387
NP_525126




FAMILY 4, MEMBER D


CLEC4E
219859_at
C-TYPE LECTIN DOMAIN
NM_014358
NP_055173




FAMILY 4, MEMBER E


CLEC5A
219890_at
C-TYPE LECTIN DOMAIN
NM_013252
NP_037384




FAMILY 5, MEMBER A


COL4A3BP
223465_at
COLLAGEN, TYPE IV,
NM_005713
NP_005704




ALPHA 3 (GOODPASTURE
NM_031361
NP_112729




ANTIGEN) BINDING




PROTEIN


COP1
1552701_a_at
CARD ONLY PROTEIN
NM_052889
NP_443121


COX15
235204_at
COX15 HOMOLOG,
NM_004376
NP_004367




CYTOCHROME C
NM_078470
NP_510870




OXIDASE ASSEMBLY




PROTEIN (YEAST)


CPD
201941_at
CARBOXYPEPTIDASE D
NM_001304
NP_001295


CPD
201940_at
CARBOXYPEPTIDASE D
NM_001304
NP_001295


CPEB4
242384_at
CYTOPLASMIC
NM_030627
NP_085130




POLYADENYLATION




ELEMENT BINDING




PROTEIN 4


CPEB4
224829_at
CYTOPLASMIC
NM_030627
NP_085130




POLYADENYLATION




ELEMENT BINDING




PROTEIN 4


CPVL
208146_s_at
CARBOXYPEPTIDASE,
NM_019029
NP_061902




VITELLOGENIC-LIKE
NM_031311
NP_112601


CR1
206244_at
COMPLEMENT
NM_000573
NP_000564




COMPONENT (3B/4B)
NM_000651
NP_000642




RECEPTOR 1, INCLUDING




KNOPS BLOOD GROUP




SYSTEM


CRTAP
1554464_a_at
CARTILAGE-ASSOCIATED
NM_006371
NP_006362




PROTEIN


CRTAP
1555889_a_at
CARTILAGE-ASSOCIATED
NM_006371
NP_006362




PROTEIN


CSF1R
203104_at
COLONY STIMULATING
NM_005211
NP_005202




FACTOR 1 RECEPTOR,




FORMERLY MCDONOUGH




FELINE SARCOMA VIRAL




(V-FMS) ONCOGENE




HOMOLOG


CTGLF1
221850_x_at
CENTAURIN, GAMMA-
NM_133446
NP_597703




LIKE FAMILY, MEMBER 1


CYP4F2
210452_x_at
CYTOCHROME P450,
NM_001082
NP_001073




FAMILY 4, SUBFAMILY F,




POLYPEPTIDE 2


DCP2
235258_at
DCP2 DECAPPING
NM_152624
NP_689837




ENZYME HOMOLOG (S. CEREVISIAE)


DDAH2
202262_x_at
DIMETHYLARGININE
NM_013974
NP_039268




DIMETHYLAMINOHYDROLASE 2


DDAH2
215537_x_at
DIMETHYLARGININE
NM_013974
NP_039268




DIMETHYLAMINOHYDROLASE 2


DDAH2
214909_s_at
DIMETHYLARGININE
NM_013974
NP_039268




DIMETHYLAMINOHYDROLASE 2


DDX26
222239_s_at
DEAD/H (Asp-Glu-Ala-
NM_012141
NP_036273




Asp/His) BOX




POLYPEPTIDE 26


DHRS9
223952_x_at
MEMBRANE PROTEIN,
NM_005771
NP_005762




PALMITOYLATED 3; MPP3
NM_199204
NP_954674


DHRS9
224009_x_at
MEMBRANE PROTEIN,
NM_005771
NP_005762




PALMITOYLATED 3; MPP3
NM_199204
NP_954674


DHRS9
219799_s_at
MEMBRANE PROTEIN,
NM_005771
NP_005762




PALMITOYLATED 3; MPP3
NM_199204
NP_954674


DKFZP564B1023
228385_at
KINESIN FAMILY
XM_375825
XP_375825




MEMBER 14 (KIF14)


DKFZP566M1046
223637_s_at
HYPOTHETICAL PROTEIN
NM_032127
NP_115503




DKFZP566M1046


DKFZp667F0711
1559756_at
HYPOTHETICAL PROTEIN
XM_374767
XP_374767




DKFZp667F0711


DLGAP1
239421_at
DISCS, LARGE
NM_001003809
NP_001003809




(DROSOPHILA)
NM_004746
NP_004737




HOMOLOG-ASSOCIATED




PROTEIN 1


DNAJA1
200881_s_at
DNAJ (HSP40) HOMOLOG,
NM_001539
NP_001530




SUBFAMILY A, MEMBER 1


DR1
216652_s_at
DOWN-REGULATOR OF
NM_001938
NP_001929




TRANSCRIPTION 1, TBP-




BINDING (NEGATIVE




COFACTOR 2)


E2F3
203693_s_at
E2F TRANSCRIPTION
NM_001949
NP_001940




FACTOR 3


EFHC1
225656_at
EF-HAND DOMAIN (C-
NM_018100
NP_060570




TERMINAL) CONTAINING 1


EGFL5
212831_at
EGF-LIKE-DOMAIN,
XM_376905
XP_376905




MULTIPLE 5


EIF4E
201435_s_at
EUKARYOTIC
NM_001968
NP_001959




TRANSLATION




INITIATION FACTOR 4E


EIF4E3
225941_at
EUKARYOTIC
NM_173359
NP_775495




TRANSLATION




INITIATION FACTOR 4E




MEMBER 3


EIF4E3
225940_at
EUKARYOTIC
NM_173359
NP_775495




TRANSLATION




INITIATION FACTOR 4E




MEMBER 3


EIF4E3
238461_at
EUKARYOTIC
NM_173359
NP_775495




TRANSLATION




INITIATION FACTOR 4E




MEMBER 3


EIF4G3
201935_s_at
EUKARYOTIC
NM_003760
NP_003751




TRANSLATION




INITIATION FACTOR 4-




GAMMA, 3


EIF4G3
201936_s_at
EUKARYOTIC
NM_003760
NP_003751




TRANSLATION




INITIATION FACTOR 4-




GAMMA, 3


EIF4G3
243149_at
EUKARYOTIC
NM_003760
NP_003751




TRANSLATION




INITIATION FACTOR 4-




GAMMA, 3


EMILIN2
242288_s_at
ELASTIN MICROFIBRIL
NM_032048
NP_114437




INTERFACER 2


ETS2
201328_at
V-ETS
NM_005239
NP_005230




ERYTHROBLASTOSIS




VIRUS E26 ONCOGENE




HOMOLOG 2 (AVIAN)


EXOSC4
91684_g_at
EXOSOME COMPONENT 4
NM_019037
NP_061910


EXOSC4
218695_at
EXOSOME COMPONENT 4
NM_019037
NP_061910


EXOSC4
58696_at
EXOSOME COMPONENT 4
NM_019037
NP_061910


FAD104
244022_at
FIBRONECTIN TYPE III
NM_022763
NP_073600




DOMAIN CONTAINING 3B




(FNDC3B)


FAM53C
218023_s_at
FAMILY WITH SEQUENCE
NM_016605
NP_057689




SIMILARITY 53, MEMBER C


FAS
204781_s_at
FAS (TNF RECEPTOR
NM_000043
NP_000034




SUPERFAMILY, MEMBER
NM_152871
NP_690610




6)
NM_152872
NP_690611





NM_152873
NP_690612





NM_152874
NP_690613





NM_152875
NP_690614





NM_152876
NP_690615





NM_152877
NP_690616


FAS
204780_s_at
FAS (TNF RECEPTOR
NM_000043
NP_000034




SUPERFAMILY, MEMBER
NM_152871
NP_690610




6)
NM_152872
NP_690611





NM_152873
NP_690612





NM_152874
NP_690613





NM_152875
NP_690614





NM_152876
NP_690615





NM_152877
NP_690616


FBXL13
1553798_a_at
F-BOX AND LEUCINE-
NM_145032
NP_659469




RICH REPEAT PROTEIN 13


FBXO9
1559094_at
F-BOX PROTEIN 9
NM_012347
NP_036479





NM_033480
NP_258441





NM_033481
NP_258442


FBXO9
1559096_x_at
F-BOX PROTEIN 9
NM_012347
NP_036479





NM_033480
NP_258441





NM_033481
NP_258442


FCAR
211307_s_at
FC FRAGMENT OF IGA,
NM_002000
NP_001991




RECEPTOR FOR
NM_133269
NP_579803





NM_133271
NP_579805





NM_133272
NP_579806





NM_133273
NP_579807





NM_133274
NP_579808





NM_133277
NP_579811





NM_133278
NP_579812





NM_133279
NP_579813





NM_133280
NP_579814


FCAR
211306_s_at
FC FRAGMENT OF IGA,
NM_002000
NP_001991




RECEPTOR FOR
NM_133269
NP_579803





NM_133271
NP_579805





NM_133272
NP_579806





NM_133273
NP_579807





NM_133274
NP_579808





NM_133277
NP_579811





NM_133278
NP_579812





NM_133279
NP_579813





NM_133280
NP_579814


FCAR
207674_at
FC FRAGMENT OF IGA,
NM_002000
NP_001991




RECEPTOR FOR
NM_133269
NP_579803





NM_133271
NP_579805





NM_133272
NP_579806





NM_133273
NP_579807





NM_133274
NP_579808





NM_133277
NP_579811





NM_133278
NP_579812





NM_133279
NP_579813





NM_133280
NP_579814


FCAR
211305_x_at
FC FRAGMENT OF IGA,
NM_002000
NP_001991




RECEPTOR FOR
NM_133269
NP_579803





NM_133271
NP_579805





NM_133272
NP_579806





NM_133273
NP_579807





NM_133274
NP_579808





NM_133277
NP_579811





NM_133278
NP_579812





NM_133279
NP_579813





NM_133280
NP_579814


FCGR1A
216950_s_at
FC FRAGMENT OF IGG,
NM_000566
NP_000557




HIGH AFFINITY IA


FCGR1A
216951_at
FC FRAGMENT OF IGG,
NM_000566
NP_000557




HIGH AFFINITY IA


FCGR1A
214511_x_at


LOC440607


FEM1C
213341_at
FEM-1 HOMOLOG C
NM_020177
NP_064562




(C. ELEGANS)


FLJ10213
219906_at
HYPOTHETICAL PROTEIN
NM_018029
NP_060499




FLJ10213


FLJ10521
1570511_at
HYPOTHETICAL PROTEIN
NM_001011722
NP_001011722




FLJ10521
NM_018125
NP_060595


FLJ11011
222657_s_at
HYPOTHETICAL PROTEIN
NM_001001481
NP_001001481




FLJ11011
NM_001001482
NP_001001482





NM_018299
NP_060769


FLJ11259
218627_at
HYPOTHETICAL PROTEIN
NM_018370
NP_060840




FLJ11259


FLJ11795
220112_at
HYPOTHETICAL PROTEIN
NM_024669
NP_078945




FLJ11795


FLJ12770
226059_at
HYPOTHETICAL PROTEIN
NM_032174
NP_115550




FLJ12770


FLJ13154
218060_s_at
HYPOTHETICAL PROTEIN
NM_024598
NP_078874




FLJ13154


FLJ13448
219397_at
HYPOTHETICAL PROTEIN
NM_025147
NP_079423




FLJ13448


FLJ14001
238983_at
HYPOTHETICAL PROTEIN
NM_024677
NP_078953




FLJ14001


FLJ20273
218035_s_at
RNA-BINDING PROTEIN
NM_019027
NP_061900


FLJ20273
222496_s_at
RNA-BINDING PROTEIN
NM_019027
NP_061900


FLJ20481
227889_at
HYPOTHETICAL PROTEIN
NM_017839
NP_060309




FLJ20481


FLJ20481
222833_at
HYPOTHETICAL PROTEIN
NM_017839
NP_060309




FLJ20481


FLJ20701
219093_at
HYPOTHETICAL PROTEIN
NM_017933
NP_060403




FLJ20701


FLJ22833
219334_s_at
HYPOTHETICAL PROTEIN
NM_022837
NP_073748




FLJ22833


FLJ22833
233085_s_at
HYPOTHETICAL PROTEIN
NM_022837
NP_073748




FLJ22833


FLJ22833
222872_x_at
HYPOTHETICAL PROTEIN
NM_022837
NP_073748




FLJ22833


FLJ23231
218810_at
MCP-1 TREATMENT-
NM_025079
NP_079355




INDUCED PROTEIN




(MCPIP)


FLJ25416
228281_at
HYPOTHETICAL PROTEIN
NM_145018
NP_659455




FLJ25416


FLJ31033
228152_s_at
HYPOTHETICAL PROTEIN
XM_037817
XP_037817




FLJ31033
XM_376353
XP_376353


FLJ36031
226756_at
HYPOTHETICAL PROTEIN
NM_175884
NP_787080




FLJ36031


FLJ37858
227354_at
FLJ37858 PROTEIN
NM_001007549
NP_001007550


FLOT1
210142_x_at
FLOTILLIN 1
NM_005803
NP_005794


FNDC3B
225032_at
FIBRONECTIN TYPE III
NM_022763
NP_073600




DOMAIN CONTAINING 3B


FNDC3B
222692_s_at
FIBRONECTIN TYPE III
NM_022763
NP_073600




DOMAIN CONTAINING 3B


FNDC3B
222693_at
FIBRONECTIN TYPE III
NM_022763
NP_073600




DOMAIN CONTAINING 3B


FNDC3B
229865_at
FIBRONECTIN TYPE III
NM_022763
NP_073600




DOMAIN CONTAINING 3B


FNDC3B
218618_s_at
FIBRONECTIN TYPE III
NM_022763
NP_073600




DOMAIN CONTAINING 3B


FTS
218373_at
FUSED TOES HOMOLOG
NM_001012398
NP_001012398




(MOUSE)
NM_022476
NP_071921


FYB
227266_s_at
FYN BINDING PROTEIN
NM_001465
NP_001456




(FYB-120/130)
NM_199335
NP_955367


FYB
211795_s_at
FYN BINDING PROTEIN
NM_001465
NP_001456




(FYB-120/130)
NM_199335
NP_955367


G0S2
213524_s_at
PUTATIVE LYMPHOCYTE
NM_015714
NP_056529




G0/G1 SWITCH GENE


GAB2
238405_at
GRB2-ASSOCIATED
NM_012296
NP_036428




BINDING PROTEIN 2
NM_080491
NP_536739


GADD45A
203725_at
GROWTH ARREST AND
NM_001924
NP_001915




DNA-DAMAGE-




INDUCIBLE, ALPHA


GADD45B
209304_x_at
GROWTH ARREST- AND
NM_015675
NP_056490




DNA DAMAGE-




INDUCIBLE GENE




GADD45


GADD45B
207574_s_at
GROWTH ARREST- AND
NM_015675
NP_056490




DNA DAMAGE-




INDUCIBLE GENE




GADD45


GALNT3
203397_s_at
UDP-N-ACETYL-ALPHA-D-
NM_004482
NP_004473




GALACTOSAMINE:POLYPEPTIDE




N-




ACETYLGALACTOSAMINYL




TRANSFERASE 3




(GALNAC-T3)


GBA
210589_s_at
GLUCOSIDASE, BETA;
NM_000157
NP_000148




ACID (INCLUDES
NM_001005741
NP_001005741




GLUCOSYLCERAMIDASE)
NM_001005742
NP_001005742





NM_001005749
NP_001005749





NM_001005750
NP_001005750


GBA
209093_s_at
GLUCOSIDASE, BETA;
NM_000157
NP_000148




ACID (INCLUDES
NM_001005741
NP_001005741




GLUCOSYLCERAMIDASE)
NM_001005742
NP_001005742





NM_001005749
NP_001005749





NM_001005750
NP_001005750


GBP2
242907_at
GUANYLATE BINDING
NM_004120
NP_004111




PROTEIN 2, INTERFERON-




INDUCIBLE


GCA
203765_at
GRANCALCIN, EF-HAND
NM_012198
NP_036330




CALCIUM BINDING




PROTEIN


GCLM
203925_at
GLUTAMATE-CYSTEINE
NM_002061
NP_002052




LIGASE, MODIFIER




SUBUNIT


GK
214681_at
GLYCEROL KINASE
NM_000167
NP_000158





NM_203391
NP_976325


GK
207387_s_at
GLYCEROL KINASE
NM_000167
NP_000158





NM_203391
NP_976325


GK
217167_x_at
GLYCEROL KINASE
NM_000167
NP_000158





NM_203391
NP_976325


GK
216316_x_at
GLYCEROL KINASE
NM_000167
NP_000158





NM_203391
NP_976325


GK
215977_x_at
GLYCEROL KINASE
NM_000167
NP_000158





NM_203391
NP_976325


GNAI3
201180_s_at
GUANINE NUCLEOTIDE
NM_006496
NP_006487




BINDING PROTEIN (G




PROTEIN), ALPHA




INHIBITING ACTIVITY




POLYPEPTIDE 3


GNG5
207157_s_at
GUANINE NUCLEOTIDE
NM_005274
NP_005265




BINDING PROTEIN (G




PROTEIN), GAMMA 5


GNS
212335_at
GLUCOSAMINE (N-
NM_002076
NP_002067




ACETYL)-6-SULFATASE




(SANFILIPPO DISEASE




IIID)


GPR160
223423_at
G PROTEIN-COUPLED
NM_014373
NP_055188




RECEPTOR 160


GPR43
221345_at
G PROTEIN-COUPLED
NM_005306
NP_005297




RECEPTOR 43


GPR84
223767_at
G PROTEIN-COUPLED
NM_020370
NP_065103




RECEPTOR 84


GPR97
220404_at
G PROTEIN-COUPLED
NM_170776
NP_740746




RECEPTOR 97


GTDC1
219770_at
GLYCOSYLTRANSFERASE-
NM_001006636
NP_001006637




LIKE DOMAIN
NM_024659
NP_078935




CONTAINING 1


GTF2B
208066_s_at
GENERAL
NM_001514
NP_001505




TRANSCRIPTION FACTOR




IIB


GYG
201554_x_at
GLYCOGENIN
NM_004130
NP_004121


GYG
211275_s_at
GLYCOGENIN
NM_004130
NP_004121


HAGH
205012_s_at
HYDROXYACYLGLUTATHIONE
NM_005326
NP_005317




HYDROLASE


HDAC4
204225_at
HISTONE DEACETYLASE 4
NM_006037
NP_006028


HGF
209960_at
HEPATOCYTE GROWTH
NM_000601
NP_000592




FACTOR (HEPAPOIETIN A;
NM_001010931
NP_001010931




SCATTER FACTOR)
NM_001010932
NP_001010932





NM_001010933
NP_001010933





NM_001010934
NP_001010934


HIP1
226364_at
HUNTINGTIN
NM_005338
NP_005329




INTERACTING PROTEIN 1


HIP1
205425_at
HUNTINGTIN
NM_005338
NP_005329




INTERACTING PROTEIN 1


HIP1
205426_s_at
HUNTINGTIN
NM_005338
NP_005329




INTERACTING PROTEIN 1


HIST1H2BD
209911_x_at
HISTONE 1, H2BD
NM_021063
NP_066407





NM_138720
NP_619790


HIST2H2AA
214290_s_at
HISTONE 2, H2AA
NM_003516
NP_003507


HLA-DMA
217478_s_at
HLA-D
NM_006120
NP_006111




HISTOCOMPATIBILITY




TYPE


HLA-DMB
203932_at
MAJOR
NM_002118
NP_002109




HISTOCOMPATIBILITY




COMPLEX, CLASS II, DM




BETA


HLA-DPA1
211990_at
MAJOR
NM_033554
NP_291032




HISTOCOMPATIBILITY




COMPLEX, CLASS II, DP




ALPHA 1


HLA-DPA1
211991_s_at
MAJOR
NM_033554
NP_291032




HISTOCOMPATIBILITY




COMPLEX, CLASS II, DP




ALPHA 1


HLA-DPB1
201137_s_at
MAJOR
NM_002121
NP_002112




HISTOCOMPATIBILITY




COMPLEX, CLASS II, DP




BETA 1


HLA-DQB1
209823_x_at
MAJOR
NM_002123
NP_002114




HISTOCOMPATIBILITY




COMPLEX, CLASS II, DQ




BETA 1


HLA-DQB1
211656_x_at
MAJOR
NM_002123
NP_002114




HISTOCOMPATIBILITY




COMPLEX, CLASS II, DQ




BETA 1


HLA-DRA
208894_at
MAJOR
NM_002123
NP_002114




HISTOCOMPATIBILITY




COMPLEX, CLASS II, DR




ALPHA


HLA-DRA
210982_s_at
MAJOR
NM_002123
NP_002114




HISTOCOMPATIBILITY




COMPLEX, CLASS II, DR




ALPHA


HLA-DRB1
208306_x_at
MAJOR
NM_002124
NP_002115




HISTOCOMPATIBILITY




COMPLEX, CLASS II, DR




BETA 1


HLA-DRB1
204670_x_at
MAJOR
NM_002124
NP_002115




HISTOCOMPATIBILITY




COMPLEX, CLASS II, DR




BETA 1


HLA-DRB1
209312_x_at
MAJOR
NM_002124
NP_002115




HISTOCOMPATIBILITY




COMPLEX, CLASS II, DR




BETA 1


HLA-DRB1
215193_x_at
MAJOR
NM_002124
NP_002115




HISTOCOMPATIBILITY




COMPLEX, CLASS II, DR




BETA 1


HNRPLL
241692_at
HETEROGENEOUS
NM_138394
NP_612403




NUCLEAR




RIBONUCLEOPROTEIN L-




LIKE


HPGD
203913_s_at
HYDROXYPROSTAGLAND
NM_000860
NP_000851




IN DEHYDROGENASE 15-




(NAD)


HRPT2
218578_at
HYPERPARATHYROIDISM
NM_024529
NP_078805




2 (WITH JAW TUMOR)


HSPC163
228306_at
HSPC163 PROTEIN
NM_014184
NP_054903


HSPC163
218728_s_at
HSPC163 PROTEIN
NM_014184
NP_054903


HSPC163
228437_at
HSPC163 PROTEIN
NM_014184
NP_054903


HSPC163
223993_s_at
HSPC163 PROTEIN
NM_014184
NP_054903


HSPC163
243051_at
HSPC163 PROTEIN
NM_014184
NP_054903


HSPCA
214328_s_at
HEAT SHOCK 90 KDA
NM_005348
NP_005339




PROTEIN 1, ALPHA


HTATIP2
209448_at
HIV-1 TAT INTERACTIVE
NM_006410
NP_006401




PROTEIN 2, 30 KDA


HTATIP2
210253_at
HIV-1 TAT INTERACTIVE
NM_006410
NP_006401




PROTEIN 2, 30 KDA


IDI1
204615_x_at
ISOPENTENYL-
NM_004508
NP_004499




DIPHOSPHATE DELTA




ISOMERASE


IDI1
208881_x_at
ISOPENTENYL-
NM_004508
NP_004499




DIPHOSPHATE DELTA




ISOMERASE


IFNAR1
225669_at
INTERFERON (ALPHA,
NM_000629
NP_000620




BETA AND OMEGA)




RECEPTOR 1


IFNAR1
225661_at
INTERFERON (ALPHA,
NM_000629
NP_000620




BETA AND OMEGA)




RECEPTOR 1


IFNAR2
204786_s_at
INTERFERON (ALPHA,
NM_000874
NP_000865




BETA AND OMEGA)
NM_207584
NP_997467




RECEPTOR 2
NM_207585
NP_997468


IFNGR1
202727_s_at
INTERFERON GAMMA
NM_000416
NP_000407




RECEPTOR 1


IGSF2
207167_at
IMMUNOGLOBULIN
NM_004258
NP_004249




SUPERFAMILY, MEMBER 2


IL10RA
204912_at
INTERLEUKIN 10
NM_001558
NP_001549




RECEPTOR, ALPHA


IL18R1
206618_at
INTERLEUKIN 18
NM_003855
NP_003846




RECEPTOR 1


IL1R1
202948_at
INTERLEUKIN 1
NM_000877
NP_000868




RECEPTOR, TYPE I


IL1R2
211372_s_at
INTERLEUKIN 1
NM_004633
NP_004624




RECEPTOR, TYPE II
NM_173343
NP_775465


IL1R2
205403_at
INTERLEUKIN 1
NM_004633
NP_004624




RECEPTOR, TYPE II
NM_173343
NP_775465


IL1RAP
205227_at
INTERLEUKIN 1
NM_002182
NP_002173




RECEPTOR ACCESSORY
NM_134470
NP_608273




PROTEIN


INSL3
214572_s_at
INSULIN-LIKE 3 (LEYDIG
NM_005543
NP_005534




CELL)


IRAK2
231779_at
INTERLEUKIN-1
NM_001570
NP_001561




RECEPTOR-ASSOCIATED




KINASE 2


IRAK3
220034_at
INTERLEUKIN-1
NM_007199
NP_009130




RECEPTOR-ASSOCIATED




KINASE 3


IRAK4
219618_at
INTERLEUKIN-1
NM_016123
NP_057207




RECEPTOR-ASSOCIATED




KINASE 4


ITGAM
205786_s_at
INTEGRIN, ALPHA M
NM_000632
NP_000623




(COMPLEMENT




COMPONENT RECEPTOR




3, ALPHA; ALSO KNOWN




AS CD11B (P170),




MACROPHAGE ANTIGEN




ALPHA POLYPEPTIDE)


ITGB3
216261_at
INTEGRIN, BETA 3
NM_000212
NP_000203




(PLATELET




GLYCOPROTEIN IIIA,




ANTIGEN CD61)


IVNS1ABP
201362_at
INFLUENZA VIRUS NS1A
NM_006469
NP_006460




BINDING PROTEIN
NM_016389
NP_057473


JAK2
205842_s_at
JANUS KINASE 2 (A
NM_004972
NP_004963




PROTEIN TYROSINE




KINASE)


JAK2
205841_at
JANUS KINASE 2 (A
NM_004972
NP_004963




PROTEIN TYROSINE




KINASE)


JAK3
211108_s_at
JANUS KINASE 3 (A
NM_000215
NP_000206




PROTEIN TYROSINE




KINASE, LEUKOCYTE)


JAK3
227677_at
JANUS KINASE 3 (A
NM_000215
NP_000206




PROTEIN TYROSINE




KINASE, LEUKOCYTE)


JUNB
201473_at
JUN B PROTO-ONCOGENE
NM_002229
NP_002220


KCNE1
236407_at
POTASSIUM VOLTAGE-
NM_000219
NP_000210




GATED CHANNEL, ISK-




RELATED FAMILY,




MEMBER 1


KCNJ15
238428_at
POTASSIUM INWARDLY-
NM_002243
NP_002234




RECTIFYING CHANNEL,
NM_170736
NP_733932




SUBFAMILY J, MEMBER
NM_170737
NP_733933




15


KCNJ15
210119_at
POTASSIUM INWARDLY-
NM_002243
NP_002234




RECTIFYING CHANNEL,
NM_170736
NP_733932




SUBFAMILY J, MEMBER
NM_170737
NP_733933




15


KIAA0040
203144_s_at
KIAA0040
NM_014656
NP_055471


KIAA0103
203584_at
KIAA0103
NM_014673
NP_055488


KIAA0182
212057_at
KIAA0182 PROTEIN
NM_014615
NP_055430


KIAA0261
212264_s_at
KIAA0261
NM_015045
NP_055860


KIAA0635
206003_at
CENTROSOMAL PROTEIN 4
NM_025009
NP_079285


KIAA0746
212314_at
KIAA0746 PROTEIN
NM_015187
NP_056002


KIAA1160
223831_x_at
KIAA1160 PROTEIN
NM_020701
NP_065752


KIAA1533
244808_at
KIAA1533
NM_020895
NP_065946


KIAA1533
224807_at
KIAA1533
NM_020895
NP_065946


KIAA1600
226155_at
KIAA1600
NM_020940
NP_065991


KIAA1632
227638_at
KIAA1632
NM_020964
NP_066015


KIAA1991
242808_at
HYPOTHETICAL PROTEIN
XM_495886
XP_495886




KIAA1991


KIF1B
225878_at
KINESIN FAMILY
NM_015074
NP_055889




MEMBER 1B
NM_183416
NP_904325


KIF1B
209234_at
KINESIN FAMILY
NM_015074
NP_055889




MEMBER 1B
NM_183416
NP_904325


KIF1B
241216_at
KINESIN FAMILY
NM_015074
NP_055889




MEMBER 1B
NM_183416
NP_904325


KLF11
218486_at
KRUPPEL-LIKE FACTOR
NM_003597
NP_003588




11


KLF7
204334_at
KRUPPEL-LIKE FACTOR 7
NM_003709
NP_003700




(UBIQUITOUS)


KLHL2
219157_at
KELCH-LIKE 2, MAYVEN
NM_007246
NP_009177




(DROSOPHILA)


KLHL6
1560396_at
KELCH-LIKE 6
NM_130446
NP_569713




(DROSOPHILA)


KPNA4
225267_at
KARYOPHERIN ALPHA 4
NM_002268
NP_002259




(IMPORTIN ALPHA 3)


KPNA4
209653_at
KARYOPHERIN ALPHA 4
NM_002268
NP_002259




(IMPORTIN ALPHA 3)


KREMEN1
227250_at
KRINGLE CONTAINING
NM_032045
NP_114434




TRANSMEMBRANE
NM_153379
NP_700358




PROTEIN 1


KREMEN1
235370_at
KRINGLE CONTAINING
NM_032045
NP_114434




TRANSMEMBRANE
NM_153379
NP_700358




PROTEIN 1


KREMEN1
224534_at
KRINGLE CONTAINING
NM_032045
NP_114434




TRANSMEMBRANE
NM_153379
NP_700358




PROTEIN 1


LDLR
202068_s_at
LOW DENSITY
NM_000527
NP_000518




LIPOPROTEIN RECEPTOR


LFNG
228762_at
LUNATIC FRINGE
NM_002304
NP_002295




HOMOLOG (DROSOPHILA)


LGALS8
208934_s_at
LECTIN, GALACTOSIDE-
NM_006499
NP_006490




BINDING, SOLUBLE, 8
NM_201543
NP_963837




(GALECTIN 8)
NM_201544
NP_963838





NM_201545
NP_963839


LIMK2
1561654_at
LIM DOMAIN KINASE 2
NM_005569
NP_005560





NM_016733
NP_057952


LIMK2
202193_at
LIM DOMAIN KINASE 2
NM_005569
NP_005560





NM_016733
NP_057952


LIMK2
210582_s_at
LIM DOMAIN KINASE 2
NM_005569
NP_005560





NM_016733
NP_057952


LIMK2
217475_s_at
LIM DOMAIN KINASE 2
NM_005569
NP_005560





NM_016733
NP_057952


LIN7A
240027_at
LIN-7 HOMOLOG A (C. ELEGANS)
NM_004664
NP_004655


LIN7A
206440_at
LIN-7 HOMOLOG A (C. ELEGANS)
NM_004664
NP_004655


LIR9
1555634_a_at
LEUKOCYTE
NM_021250
NP_067073




IMMUNOGLOBULIN-LIKE
NM_181879
NP_870994




RECEPTOR, SUBFAMILY B




(WITH TM AND ITIM
NM_181985
NP_871714




DOMAINS), MEMBER 7




(LILRB7)
NM_181986
NP_871715


LMNB1
203276_at
LAMIN B1
NM_005573
NP_005564


LMTK2
226375_at
LEMUR TYROSINE
NM_014916
NP_055731




KINASE 2


LOC145758
226513_at
HYPOTHETICAL PROTEIN




LOC145758


LOC153561
232889_at
HYPOTHETICAL PROTEIN
NM_207331
NP_997214




LOC153561


LOC199675
235568_at
HYPOTHETICAL PROTEIN
NM_174918
NP_777578




LOC199675


LOC220929
229743_at
HYPOTHETICAL PROTEIN
NM_182755
NP_877432




LOC220929


LOC285771
237870_at
HYPOTHETICAL PROTEIN




LOC285771


LOC286044
222662_at
HYPOTHETICAL PROTEIN




LOC286044


LOC338758
238893_at
HYPOTHETICAL PROTEIN




LOC338758


LOC401152
224602_at
HCV F-TRANSACTIVATED
NM_001001701
NP_001001701




PROTEIN 1


LOC440731
237563_s_at
LOC440731
XM_498838
XP_498838


LOC440823
227168_at


LOC57149
203897_at
HYPOTHETICAL PROTEIN
NM_020424
NP_065157




A-211C6.1


LOC88523
214748_at
CG016
NM_033111
NP_149102


LRG1
228648_at
LEUCINE-RICH ALPHA-2-
NM_052972
NP_443204




GLYCOPROTEIN 1


LRPAP1
201186_at
LOW DENSITY
NM_002337
NP_002328




LIPOPROTEIN RECEPTOR-




RELATED PROTEIN




ASSOCIATED PROTEIN 1


LRRC17
1560527_at
LEUCINE RICH REPEAT
NM_005824
NP_005815




CONTAINING 17


LTB4R
236172_at
LEUKOTRIENE B4
NM_181657
NP_858043




RECEPTOR


LTBP2
204682_at
LATENT TRANSFORMING
NM_000428
NP_000419




GROWTH FACTOR BETA




BINDING PROTEIN 2


LY86
205859_at
LYMPHOCYTE ANTIGEN
NM_004271
NP_004262




86


LY96
206584_at
LYMPHOCYTE ANTIGEN
NM_015364
NP_056179




96


MAP2K1IP1
217971_at
MITOGEN-ACTIVATED
NM_021970
NP_068805




PROTEIN KINASE KINASE




1 INTERACTING PROTEIN 1


MAP2K6
205698_s_at
MITOGEN-ACTIVATED
NM_002758
NP_002749




PROTEIN KINASE KINASE 6
NM_031988
NP_114365


MAPK14
210449_x_at
MAPK14 MITOGEN-
NM_001315
NP_001306




ACTIVATED PROTEIN
NM_139012
NP_620581




KINASE 14
NM_139013
NP_620582





NM_139014
NP_620583


MAPK14
211561_x_at
MAPK14 MITOGEN-
NM_001315
NP_001306




ACTIVATED PROTEIN
NM_139012
NP_620581




KINASE 14
NM_139013
NP_620582





NM_139014
NP_620583


MAPK14
211087_x_at
MAPK14 MITOGEN-
NM_001315
NP_001306




ACTIVATED PROTEIN
NM_139012
NP_620581




KINASE 14
NM_139013
NP_620582





NM_139014
NP_620583


MAPK14
202530_at
MAPK14 MITOGEN-
NM_001315
NP_001306




ACTIVATED PROTEIN
NM_139012
NP_620581




KINASE 14
NM_139013
NP_620582





NM_139014
NP_620583


MARCKSL1
200644_at
MARCKS-LIKE 1
NM_023009
NP_075385


MCTP2
220603_s_at
MULTIPLE C2-DOMAINS
NM_018349
NP_060819




WITH TWO




TRANSMEMBRANE




REGIONS 2


MCTP2
229005_at
MULTIPLE C2-DOMAINS
NM_018349
NP_060819




WITH TWO




TRANSMEMBRANE




REGIONS 2


MCTP2
239893_at
MULTIPLE C2-DOMAINS
NM_018349
NP_060819




WITH TWO




TRANSMEMBRANE




REGIONS 2


MEF2A
214684_at
MADS BOX
NM_005587
NP_005578




TRANSCRIPTION




ENHANCER FACTOR 2,




POLYPEPTIDE A




(MYOCYTE ENHANCER




FACTOR 2A)


MEF2C
236395_at
MADS BOX
NM_002397
NP_002388




TRANSCRIPTION




ENHANCER FACTOR 2,




POLYPEPTIDE C




(MYOCYTE ENHANCER




FACTOR 2C)


MGC11324
224480_s_at
HYPOTHETICAL PROTEIN
NM_032717
NP_116106




MGC11324


MGC15619
226879_at
HYPOTHETICAL PROTEIN
NM_032369
NP_115745




MGC15619


MGC15887
226448_at
HYPOTHETICAL GENE
NM_198552
NP_940954




SUPPORTED BY BC009447


MGC17301
227055_at
HYPOTHETICAL PROTEIN
NM_152637
NP_689850




MGC17301


MGC23280
226121_at
HYPOTHETICAL PROTEIN
NM_144683
NP_653284




MGC23280


MKNK1
209467_s_at
MAP KINASE
NM_003684
NP_003675




INTERACTING
NM_198973
NP_945324




SERINE/THREONINE




KINASE 1


MLKL
238025_at
MIXED LINEAGE KINASE
NM_152649
NP_689862




DOMAIN-LIKE


MLLT2
201924_at
MYELOID/LYMPHOID OR
NM_005935
NP_005926




MIXED-LINEAGE




LEUKEMIA (TRITHORAX




HOMOLOG,




DROSOPHILA);




TRANSLOCATED TO, 2


MMP9
203936_s_at
MATRIX
NM_004994
NP_004985




METALLOPROTEINASE 9




(GELATINASE B, 92 KDA




GELATINASE, 92 KDA




TYPE IV COLLAGENASE)


MOBK1B
201298_s_at
MOB1, MPS ONE BINDER
NM_018221
NP_060691




KINASE ACTIVATOR-LIKE




1B (YEAST)


MOBKL2C
227066_at
MOB1, MPS ONE BINDER
NM_145279
NP_660322




KINASE ACTIVATOR-LIKE
NM_201403
NP_958805




2C (YEAST)


MPEG1
226818_at
MACROPHAGE
XM_166227
XP_166227




EXPRESSED GENE 1


MPEG1
226841_at
MACROPHAGE
XM_166227
XP_166227




EXPRESSED GENE 1


MSL3L1
207551_s_at
MALE-SPECIFIC LETHAL
NM_006800
NP_006791




3-LIKE 1 (DROSOPHILA)
NM_078628
NP_523352





NM_078629
NP_523353





NM_078630
NP_523354


MSL3L1
214009_at
MALE-SPECIFIC LETHAL
NM_006800
NP_006791




3-LIKE 1 (DROSOPHILA)
NM_078628
NP_523352





NM_078629
NP_523353





NM_078630
NP_523354


MSRB2
218773_s_at
METHIONINE SULFOXIDE
NM_012228
NP_036360




REDUCTASE B2


MTF1
227150_at
METAL-REGULATORY
NM_005955
NP_005946




TRANSCRIPTION FACTOR 1


MTMR6
214429_at
MYOTUBULARIN
NM_004685
NP_004676




RELATED PROTEIN 6


MYO10
201976_s_at
MYOSIN X
NM_012334
NP_036466


NALP1
210113_s_at
NACHT, LEUCINE RICH
NM_014922
NP_055737




REPEAT AND PYD (PYRIN
NM_033004
NP_127497




DOMAIN) CONTAINING 1
NM_033006
NP_127499





NM_033007
NP_127500


NALP1
211824_x_at
NACHT, LEUCINE RICH
NM_014922
NP_055737




REPEAT AND PYD (PYRIN
NM_033004
NP_127497




DOMAIN) CONTAINING 1
NM_033006
NP_127499





NM_033007
NP_127500


NARF
219862_s_at
NUCLEAR PRELAMIN A
NM_012336
NP_036468




RECOGNITION FACTOR
NM_031968
NP_114174


NBS1
202906_s_at
NIJMEGEN BREAKAGE
NM_002485
NP_002476




SYNDROME 1 (NIBRIN)


NBS1
217299_s_at
NIJMEGEN BREAKAGE
NM_002485
NP_002476




SYNDROME 1 (NIBRIN)


NCR1
207860_at
NATURAL
NM_004829
NP_004820




CYTOTOXICITY




TRIGGERING RECEPTOR 1


NDST2
203916_at
N-DEACETYLASE/N-
NM_003635
NP_003626




SULFOTRANSFERASE




(HEPARAN




GLUCOSAMINYL) 2


NDUFA1
202298_at
NADH DEHYDROGENASE
NM_004541
NP_004532




(UBIQUINONE) 1 ALPHA




SUBCOMPLEX, 1, 7.5 KDA


NDUFB3
203371_s_at
NADH DEHYDROGENASE
NM_002491
NP_002482




(UBIQUINONE) 1 BETA




SUBCOMPLEX, 3, 12 KDA


NFKBIZ
223218_s_at
NUCLEAR FACTOR OF
NM_001005474
NP_001005474




KAPPA LIGHT
NM_031419
NP_113607




POLYPEPTIDE GENE




ENHANCER IN B-CELLS




INHIBITOR, ZETA


NMI
203964_at
N-MYC (AND STAT)
NM_004688
NP_004679




INTERACTOR


NT5C2
209155_s_at
5′-NUCLEOTIDASE,
NM_012229
NP_036361




CYTOSOLIC II


NTNG2
233072_at
NETRIN G2
NM_032536
NP_115925


NUPL1
204435_at
NUCLEOPORIN LIKE 1
NM_001008564
NP_001008564





NM_001008565
NP_001008565





NM_014089
NP_054808


OACT2
226726_at
O-ACYLTRANSFERASE
NM_138799
NP_620154




(MEMBRANE BOUND)




DOMAIN CONTAINING 2


OAT
201599_at
ORNITHINE
NM_000274
NP_000265




AMINOTRANSFERASE




(GYRATE ATROPHY)


OMG
207093_s_at
OLIGODENDROCYTE
NM_002544
NP_002535




MYELIN GLYCOPROTEIN


OPLAH
222025_s_at
5-OXOPROLINASE (ATP-
NM_017570
NP_060040




HYDROLYSING)


ORF1-FL49
224707_at
PUTATIVE NUCLEAR
NM_032412
NP_115788




PROTEIN ORF1-FL49


OSM
230170_at
ONCOSTATIN M
NM_020530
NP_065391


OSTalpha
229230_at
ORGANIC SOLUTE
NM_152672
NP_689885




TRANSPORTER ALPHA


OTUD1
226140_s_at
OTU DOMAIN
XM_166659
XP_166659




CONTAINING 1


P2RX1
210401_at
PURINERGIC RECEPTOR
NM_002558
NP_002549




P2X, LIGAND-GATED ION




CHANNEL, 1


PAG
225622_at
PHOSPHOPROTEIN
NM_018440
NP_060910




ASSOCIATED WITH




GLYCOSPHINGOLIPID-




ENRICHED




MICRODOMAINS


PAM
202336_s_at
PEPTIDYLGLYCINE
NM_000919
NP_000910




ALPHA-AMIDATING
NM_138766
NP_620121




MONOOXYGENASE
NM_138821
NP_620176





NM_138822
NP_620177


PAPSS1
209043_at
3′-PHOSPHOADENOSINE
NM_005443
NP_005434




5′-PHOSPHOSULFATE




SYNTHASE 1


PBEF1
217738_at
PRE-B-CELL COLONY
NM_005746
NP_005737




ENHANCING FACTOR 1
NM_182790
NP_877591


PBEF1
217739_s_at
PRE-B-CELL COLONY
NM_005746
NP_005737




ENHANCING FACTOR 1
NM_182790
NP_877591


PBEF1
1555167_s_at
PRE-B-CELL COLONY
NM_005746
NP_005737




ENHANCING FACTOR 1
NM_182790
NP_877591


PBEF1
243296_at
PRE-B-CELL COLONY
NM_005746
NP_005737




ENHANCING FACTOR 1
NM_182790
NP_877591


PCGF5
227935_s_at
POLYCOMB GROUP RING
NM_032373
NP_115749




FINGER 5


PCMT1
208857_s_at
PROTEIN-L-
NM_005389
NP_005380




ISOASPARTATE (D-




ASPARTATE) O-




METHYLTRANSFERASE


PCMT1
210156_s_at
PROTEIN-L-
NM_005389
NP_005380




ISOASPARTATE (D-




ASPARTATE) O-




METHYLTRANSFERASE


PCMT1
205202_at
PROTEIN-L-
NM_005389
NP_005380




ISOASPARTATE (D-




ASPARTATE) O-




METHYLTRANSFERASE


PDCD1LG1
227458_at
CD274 ANTIGEN (CD274)
NM_014143
NP_054862


PDCD1LG1
223834_at
CD274 ANTIGEN (CD274)
NM_014143
NP_054862


PDE5A
239556_at
PHOSPHODIESTERASE 5A,
NM_001083
NP_001074




CGMP-SPECIFIC
NM_033430
NP_236914





NM_033437
NP_246273





NM_033431
NP_237223


PDK1
226452_at
PYRUVATE
NM_002610
NP_002601




DEHYDROGENASE




KINASE, ISOENZYME 1


PEA15
200788_s_at
PHOSPHOPROTEIN
NM_003768
NP_003759




ENRICHED IN




ASTROCYTES 15


PFKFB2
209992_at
6-PHOSPHOFRUCTO-2-
NM_006212
NP_006203




KINASE/FRUCTOSE-2,6-




BIPHOSPHATASE 2


PFKFB2
226733_at
6-PHOSPHOFRUCTO-2-
NM_006212
NP_006203




KINASE/FRUCTOSE-2,6-




BIPHOSPHATASE 2


PFKFB3
202464_s_at
6-PHOSPHOFRUCTO-2-
NM_004566
NP_004557




KINASE/FRUCTOSE-2,6-




BISPHOSPHATASE 3


PFTK1
204604_at
PFTAIRE PROTEIN
NM_012395
NP_036527




KINASE 1


PGLYRP1
207384_at
PEPTIDOGLYCAN
NM_005091
NP_005082




RECOGNITION PROTEIN 1


PGM2
225366_at
PHOSPHOGLUCOMUTASE 2
NM_018290
NP_060760


PGS1
219394_at
PHOSPHATIDYLGLYCEROPHOSPHATE
NM_024419
NP_077733




SYNTHASE


PHTF1
210191_s_at
PUTATIVE
NM_006608
NP_006599




HOMEODOMAIN




TRANSCRIPTION FACTOR 1


PHTF1
215285_s_at
PUTATIVE
NM_006608
NP_006599




HOMEODOMAIN




TRANSCRIPTION FACTOR 1


PHTF1
205702_at
PUTATIVE
NM_006608
NP_006599




HOMEODOMAIN




TRANSCRIPTION FACTOR 1


PIK3AP1
226459_at
PHOSPHOINOSITIDE-3-
NM_152309
NP_689522




KINASE ADAPTOR




PROTEIN 1


PIK3CB
212688_at
PHOSPHOINOSITIDE-3-
NM_006219
NP_006210




KINASE, CATALYTIC,




BETA POLYPEPTIDE


PIM3
224739_at
PIM-3 ONCOGENE
NM_001001852
NP_001001852


PIP5K1A
235646_at
PHOSPHATIDYLINOSITOL-
NM_003557
NP_003548




4-PHOSPHATE 5-KINASE,




TYPE I, ALPHA


PLSCR1
244315_at
PHOSPHOLIPID
NM_021105
NP_066928




SCRAMBLASE 1


PLSCR1
241916_at
PHOSPHOLIPID
NM_021105
NP_066928




SCRAMBLASE 1


POR
208928_at
P450 (CYTOCHROME)
NM_000941
NP_000932




OXIDOREDUCTASE


PPP1R12A
201602_s_at
PROTEIN PHOSPHATASE
NM_002480
NP_002471




1, REGULATORY




(INHIBITOR) SUBUNIT




12A


PPP4R2
225519_at
PROTEIN PHOSPHATASE
NM_174907
NP_777567




4, REGULATORY SUBUNIT 2


PPP4R2
226317_at
PROTEIN PHOSPHATASE
NM_174907
NP_777567




4, REGULATORY SUBUNIT 2


PRO0149
225183_at
PRO0149 PROTEIN
NM_014117
NP_054836


PRV1
219669_at
NEUTROPHIL-SPECIFIC
NM_020406
NP_065139




ANTIGEN 1




(POLYCYTHEMIA RUBRA




VERA 1)


PSTPIP2
219938_s_at
PROLINE/SERINE/THREONINE
NM_024430
NP_077748




PHOSPHATASE-




INTERACTING PROTEIN 1




(PROLINE-SERINE-




THREONINE




PHOSPHATASE




INTERACTING PROTEIN 2)


PTDSR
212723_at
CHROMOSOME 17
NM_015167
NP_055982




GENOMIC CONTIG,




ALTERNATE ASSEMBLY




(PHOSPHATIDYLSERINE




RECEPTOR)


PTDSR
212722_s_at
CHROMOSOME 17
NM_015167
NP_055982




GENOMIC CONTIG,




ALTERNATE ASSEMBLY




(PHOSPHATIDYLSERINE




RECEPTOR)


PTGFR
207177_at
CHROMOSOME 17
NM_015167
NP_055982




GENOMIC CONTIG,




ALTERNATE ASSEMBLY




(PHOSPHATIDYLSERINE




RECEPTOR)


PTPN1
202716_at
PROTEIN TYROSINE
NM_002827
NP_002818




PHOSPHATASE, NON-




RECEPTOR TYPE 1


PTX1
226422_at
PTX1 PROTEIN
NM_016570
NP_057654


QSCN6
201482_at
QUIESCIN Q6
NM_001004128
NP_001004128





NM_002826
NP_002817


RAB10
222981_s_at
RAB10, MEMBER RAS
NM_016131
NP_057215




ONCOGENE FAMILY


RAB20
219622_at
RAB20, MEMBER RAS
NM_017817
NP_060287




ONCOGENE FAMILY


RAB24
225251_at
RAB24, MEMBER RAS
NM_130781
NP_570137




ONCOGENE FAMILY


RAB27A
209514_s_at
RAB27A, MEMBER RAS
NM_004580
NP_004571




ONCOGENE FAMILY
NM_183234
NP_899057





NM_183235
NP_899058





NM_183236
NP_899059


RAB27A
210951_x_at
RAB27A, MEMBER RAS
NM_004580
NP_004571




ONCOGENE FAMILY
NM_183234
NP_899057





NM_183235
NP_899058





NM_183236
NP_899059


RAB43
225632_s_at
RAB43, MEMBER RAS
NM_198490
NP_940892




ONCOGENE FAMILY


RAB8B
219210_s_at
RAB8B, MEMBER RAS
NM_016530
NP_057614




ONCOGENE FAMILY


RABGEF1
218310_at
RAB GUANINE
NM_014504
NP_055319




NUCLEOTIDE EXCHANGE




FACTOR (RAB GUANINE




NUCLEOTIDE EXCHANGE




FACTOR (GEF) 1)


RAD23B
201222_s_at
RAD23 HOMOLOG B (S. CEREVISIAE)
NM_002874
NP_002865


RALB
202101_s_at
V-RAL SIMIAN LEUKEMIA
NM_002881
NP_002872




VIRAL ONCOGENE




HOMOLOG B (RAS




RELATED; GTP BINDING




PROTEIN)


RAPGEFL1
218657_at
RAP GUANINE
NM_014504
NP_055319




NUCLEOTIDE EXCHANGE




FACTOR (GEF)-LIKE 1


RARA
228037_at
RETINOIC ACID
NM_000964
NP_000955




RECEPTOR, ALPHA


RASSF4
226436_at
RAS ASSOCIATION
NM_032023
NP_114412




(RALGDS/AF-6) DOMAIN
NM_178145
NP_835281




FAMILY 4


RB1CC1
202033_s_at
RB1-INDUCIBLE COILED-
NM_014781
NP_055596




COIL 1


RBMS1
225265_at
RNA BINDING MOTIF,
NM_002897
NP_002888




SINGLE STRANDED
NM_016836
NP_058520




INTERACTING PROTEIN 1
NM_016839
NP_058523


RBMS1
238317_x_at
RNA BINDING MOTIF,
NM_002897
NP_002888




SINGLE STRANDED
NM_016836
NP_058520




INTERACTING PROTEIN 1
NM_016839
NP_058523


RFWD2
234950_s_at
RING FINGER AND WD
NM_001001740
NP_001001740




REPEAT DOMAIN 2
NM_022457
NP_071902


Rgr
235816_s_at
RAL-GDS RELATED
NM_153615
NP_705843




PROTEIN RGR


RGS10
204319_s_at
REGULATOR OF G-
NM_001005339
NP_001005339




PROTEIN SIGNALLING 10
NM_002925
NP_002916


RHOT1
222148_s_at
RAS HOMOLOG GENE
NM_018307
NP_060777




FAMILY, MEMBER T1


RIT1
209882_at
RAS-LIKE WITHOUT
NM_006912
NP_008843




CAAX 1


RNASE6
213566_at
RIBONUCLEASE, RNASE A
NM_005615
NP_005606




FAMILY, K6


RNASEL
229285_at
RIBONUCLEASE L (2′,5′-
NM_021133
NP_066956




OLIGOISOADENYLATE




SYNTHETASE-




DEPENDENT)


RNF13
201779_s_at
RING FINGER PROTEIN 13
NM_007282
NP_009213





NM_183381
NP_899237





NM_183382
NP_899238





NM_183383
NP_899239





NM_183384
NP_899240


RNF13
201780_s_at
RING FINGER PROTEIN 13
NM_007282
NP_009213





NM_183381
NP_899237





NM_183382
NP_899238





NM_183383
NP_899239





NM_183384
NP_899240


ROD1
224618_at
ROD1 REGULATOR OF
NM_005156
NP_005147




DIFFERENTIATION 1 (S. POMBE)


ROD1
214697_s_at
ROD1 REGULATOR OF
NM_005156
NP_005147




DIFFERENTIATION 1 (S. POMBE)


RRM2
209773_s_at
RIBONUCLEOTIDE
NM_001034
NP_001025




REDUCTASE M2




POLYPEPTIDE


RSBN1
213694_at
ROUND SPERMATID
NM_018364
NP_060834




BASIC PROTEIN 1


RTN1
203485_at
RETICULON 1
NM_021136
NP_066959





NM_206852
NP_996734





NM_206857
NP_996739


RTN4
214629_x_at
RETICULON 4
NM_007008
NP_008939





NM_020532
NP_065393





NM_153828
NP_722550





NM_207520
NP_997403





NM_207521
NP_997404


RY1
212438_at
PUTATIVE NUCLEIC ACID
NM_006857
NP_006848




BINDING PROTEIN RY-1


S100A12
205863_at
S100 CALCIUM BINDING
NM_005621
NP_005612




PROTEIN A12




(CALGRANULIN C)


SAMSN1
1555638_a_at
SAM DOMAIN, SH3
NM_022136
NP_071419




DOMAIN, AND NUCLEAR




LOCALIZATION SIGNALS, 1


SAMSN1
220330_s_at
SAM DOMAIN, SH3
NM_022136
NP_071419




DOMAIN, AND NUCLEAR




LOCALIZATION SIGNALS, 1


SAP30L
219129_s_at
SIN3A ASSOCIATED
NM_024632
NP_078908




PROTEIN P30-LIKE


SART2
218854_at
SQUAMOUS CELL
NM_013352
NP_037484




CARCINOMA ANTIGEN




RECOGNIZED BY T CELLS 2


SBNO1
218737_at
SNO, STRAWBERRY
NM_018183
NP_060653




NOTCH HOMOLOG 1




(DROSOPHILA)


SDF2
203090_at
STROMAL CELL-DERIVED
NM_006923
NP_008854




FACTOR 2


SDHC
238056_at
SUCCINATE
NM_003001
NP_002992




DEHYDROGENASE




COMPLEX, SUBUNIT C,




INTEGRAL MEMBRANE




PROTEIN, 15 KDA


SEC15L1
226259_at
SEC15-LIKE 1 (S. CEREVISIAE)
NM_019053
NP_061926





NM_001013848
NP_001013870


SEC15L1
233924_s_at
SEC15-LIKE 1 (S. CEREVISIAE)
NM_019053
NP_061926





NM_001013848
NP_001013870


SEC24A
212902_at
SEC24 RELATED GENE
XM_094581
XP_094581




FAMILY, MEMBER A (S. CEREVISIAE)


SEL1L
202064_s_at
SEL-1 SUPPRESSOR OF
NM_005065
NP_005056




LIN-12-LIKE (C. ELEGANS)


SERPINB1
228726_at
SERINE (OR CYSTEINE)
NM_030666
NP_109591




PROTEINASE INHIBITOR,




CLADE B (OVALBUMIN),




MEMBER 1


SERPINB1
212268_at
SERINE (OR CYSTEINE)
NM_030666
NP_109591




PROTEINASE INHIBITOR,




CLADE B (OVALBUMIN),




MEMBER 1


SF3B14
223416_at
SPLICING FACTOR 3B, 14 KDA
NM_016047
NP_057131




SUBUNIT


SH3GLB1
209091_s_at
SH3-DOMAIN GRB2-LIKE
NM_016009
NP_057093




ENDOPHILIN B1


SH3GLB1
210101_x_at
SH3-DOMAIN GRB2-LIKE
NM_016009
NP_057093




ENDOPHILIN B1


SIPA1L2
225056_at
SIGNAL-INDUCED
NM_020808
NP_065859




PROLIFERATION-




ASSOCIATED GENE 1




(SIGNAL-INDUCED




PROLIFERATION-




ASSOCIATED 1 LIKE 2)


SLA
203761_at
SRC-LIKE-ADAPTOR
NM_006748
NP_006739


SLA
244492_at
SRC-LIKE-ADAPTOR
NM_006748
NP_006739


SLC22A4
205896_at
SOLUTE CARRIER
NM_003059
NP_003050




FAMILY 22 (ORGANIC




CATION TRANSPORTER),




MEMBER 4


SLC25A28
221432_s_at
SOLUTE CARRIER
NM_031212
NP_112489




FAMILY 25, MEMBER 28


SLC26A8
237340_at
SOLUTE CARRIER
NM_052961
NP_443193




FAMILY 26, MEMBER 8
NM_138718
NP_619732


SLC2A3
202497_x_at
SOLUTE CARRIER
NM_006931
NP_008862




FAMILY 2 (FACILITATED




GLUCOSE




TRANSPORTER), MEMBER 3


SLC2A3
202498_s_at
SOLUTE CARRIER
NM_006931
NP_008862




FAMILY 2 (FACILITATED




GLUCOSE




TRANSPORTER), MEMBER 3


SLC2A3
216236_s_at
SOLUTE CARRIER
NM_006931
NP_008862




FAMILY 2 (FACILITATED




GLUCOSE




TRANSPORTER), MEMBER 3


SLC2A3
202499_s_at
SOLUTE CARRIER
NM_006931
NP_008862




FAMILY 2 (FACILITATED




GLUCOSE




TRANSPORTER), MEMBER 3


SLC2A3
222088_s_at
SOLUTE CARRIER
NM_006931
NP_008862




FAMILY 2 (FACILITATED




GLUCOSE




TRANSPORTER), MEMBER 3


SLC2A3
236571_at
SOLUTE CARRIER
NM_006931
NP_008862




FAMILY 2 (FACILITATED




GLUCOSE




TRANSPORTER), MEMBER 3


SLC37A3
223304_at
SOLUTE CARRIER
NM_032295
NP_115671




FAMILY 37 (GLYCEROL-3-
NM_207113
NP_996996




PHOSPHATE




TRANSPORTER), MEMBER 3


SLC38A2
220924_s_at
SOLUTE CARRIER
NM_018976
NP_061849




FAMILY 38, MEMBER 2


SMPDL3A
213624_at
SPHINGOMYELIN
NM_006714
NP_006705




PHOSPHODIESTERASE,




ACID-LIKE 3A


SOCS3
227697_at
SUPPRESSOR OF
NM_003955
NP_003946




CYTOKINE SIGNALING 3


SOCS3
206359_at
SUPPRESSOR OF
NM_003955
NP_003946




CYTOKINE SIGNALING 3


SOD2
216841_s_at
SUPEROXIDE DISMUTASE
NM_000636
NP_000627




2, MITOCHONDRIAL


SP100
202863_at
NUCLEAR ANTIGEN SP100
NM_003113
NP_003104


SPPL2A
226353_at
SIGNAL PEPTIDE
NM_032802
NP_116191




PEPTIDASE-LIKE 2A


SQRDL
217995_at
SULFIDE QUINONE
NM_021199
NP_067022




REDUCTASE-LIKE




(YEAST)


SRPK1
202200_s_at
PROTEIN KINASE,
NM_003137
NP_003128




SERINE/ARGININE-




SPECIFIC, 1 (SFRS




PROTEIN KINASE 1)


ST3GAL4
203759_at
ST3 BETA-GALACTOSIDE
NM_006278
NP_006269




ALPHA-2,3-




SIALYLTRANSFERASE 4


ST6GALNAC4
223285_s_at
ST6 (ALPHA-N-ACETYL-
NM_014403
NP_055218




NEURAMINYL-2,3-BETA-
NM_175039
NP_778204




GALACTOSYL-1,3)-N-
NM_175040
NP_778205




ACETYLGALACTOSAMINIDE




ALPHA-2,6-




SIALYLTRANSFERASE 4


STAT5B
1555086_at
SIGNAL TRANSDUCER
NM_012448
NP_036580




AND ACTIVATOR OF




TRANSCRIPTION 5B


STK17B
205214_at
SERINE/THREONINE
NM_004226
NP_004217




KINASE 17B (APOPTOSIS-




INDUCING)


STK3
204068_at
SERINE/THREONINE
NM_006281
NP_006272




PROTEIN KINASE 3




(SERINE/THREONINE




KINASE 3 (STE20




HOMOLOG, YEAST))


STOM
201060_x_at
STOMATIN
NM_004099
NP_004090





NM_198194
NP_937837


STX3A
209238_at
SYNTAXIN 3A
NM_004177
NP_004168


SULF2
233555_s_at
SULFATASE 2
NM_018837
NP_061325





NM_198596
NP_940998


SULF2
224724_at
SULFATASE 2
NM_018837
NP_061325





NM_198596
NP_940998


SULT1A1
203615_x_at
SULFOTRANSFERASE
NM_001055
NP_001046




FAMILY, CYTOSOLIC, 1A,
NM_177529
NP_803565




PHENOL-PREFERRING,
NM_177530
NP_803566




MEMBER 1
NM_177534
NP_803878





NM_177536
NP_803880


SULT1B1
207601_at
SULFOTRANSFERASE
NM_014465
NP_055280




FAMILY, CYTOSOLIC, 1B,




MEMBER 1


TBC1D15
218268_at
TBC1 DOMAIN FAMILY,
NM_022771
NP_073608




MEMBER 15


TBC1D8
204526_s_at
TBC1 DOMAIN FAMILY,
NM_007063
NP_008994




MEMBER 8 (WITH GRAM




DOMAIN)


TCTEL1
201999_s_at
T-COMPLEX-
NM_006519
NP_006510




ASSOCIATED-TESTIS-




EXPRESSED 1 (T-




COMPLEX-ASSOCIATED-




TESTIS-EXPRESSED 1-




LIKE 1


TCTEL1
242109_at
T-COMPLEX-
NM_006519
NP_006510




ASSOCIATED-TESTIS-




EXPRESSED 1 (T-




COMPLEX-ASSOCIATED-




TESTIS-EXPRESSED 1-




LIKE 1


TDRD9
228285_at
TUDOR DOMAIN
NM_153046
NP_694591




CONTAINING 9


TGFBI
201506_at
TRANSFORMING
NM_000358
NP_000349




GROWTH FACTOR, BETA-




1 (TRANSFORMING




GROWTH FACTOR, BETA-




INDUCED, 68 KDA)


TIAM2
222942_s_at
T-CELL LYMPHOMA
NM_001010927
NP_001010927




INVASION AND
NM_012454
NP_036586




METASTASIS 2


TIFA
238858_at
TRAF-INTERACTING
NM_052864
NP_443096




PROTEIN WITH A




FORKHEAD-ASSOCIATED




DOMAIN


TIFA
226117_at
TRAF-INTERACTING
NM_052864
NP_443096




PROTEIN WITH A




FORKHEAD-ASSOCIATED




DOMAIN


TIFA
235971_at
TRAF-INTERACTING
NM_052864
NP_443096




PROTEIN WITH A




FORKHEAD-ASSOCIATED




DOMAIN


TLR5
210166_at
TOLL-LIKE RECEPTOR 5
NM_003268
NP_003259


TMEM2
218113_at
TRANSMEMBRANE
NM_013390
NP_037522




PROTEIN 2


TMEM33
235907_at
TRANSMEMBRANE
NM_018126
NP_060596




PROTEIN 33


TMOD3
223078_s_at
TROPOMODULIN 3
NM_014547
NP_055362




(UBIQUITOUS)


TncRNA
214657_s_at
TROPHOBLAST-DERIVED




NONCODING RNA


TNFAIP6
206026_s_at
TUMOR NECROSIS
NM_007115
NP_009046




FACTOR, ALPHA-




INDUCED PROTEIN 6


TNFAIP6
206025_s_at
TUMOR NECROSIS
NM_007115
NP_009046




FACTOR, ALPHA-




INDUCED PROTEIN 6


TNFAIP9
225987_at
TUMOR NECROSIS
NM_024636
NP_078912




FACTOR, ALPHA-




INDUCED PROTEIN 9


TNFSF10
202687_s_at
TUMOR NECROSIS
NM_003810
NP_003801




FACTOR (LIGAND)




SUPERFAMILY, MEMBER




10


TNFSF13B
223501_at
TUMOR NECROSIS
NM_006573
NP_006564




FACTOR (LIGAND)




SUPERFAMILY, MEMBER




13B


TOP1
208901_s_at
TOPOISOMERASE (DNA) I
NM_003286
NP_003277


TOSO
221602_s_at
FAS APOPTOTIC
NM_005449
NP_005440




INHIBITORY MOLECULE




(FAIM3)


TPARL
218095_s_at
TPA REGULATED LOCUS
NM_018475
NP_060945


TPCN1
242108_at
TWO PORE SEGMENT
NM_017901
NP_060371




CHANNEL 1


TPRT
220865_s_at
TRANS-
NM_014317
NP_055132




PRENYLTRANSFERASE


TRA@
234013_at
T CELL RECEPTOR ALPHA




LOCUS


TRA@
211902_x_at
T CELL RECEPTOR ALPHA




LOCUS


TRBC1
211796_s_at
T CELL RECEPTOR BETA




CONSTANT 1


TRIB1
202241_at
TRIBBLES HOMOLOG 1
NM_025195
NP_079471




(DROSOPHILA)


TRPM6
224412_s_at
TRANSIENT RECEPTOR
NM_017662
NP_060132




POTENTIAL CATION




CHANNEL, SUBFAMILY




M, MEMBER 6


TTN
240793_at
TITIN
NM_003319
NP_003310





NM_133378
NP_596869





NM_133379
NP_596870





NM_133432
NP_597676





NM_133437
NP_597681


TTYH2
223741_s_at
TWEETY, DROSOPHILA,
NM_032646
NP_116035




HOMOLOG OF, 2
NM_052869
NP_443101


TXN
208864_s_at
THIOREDOXIN
NM_003329
NP_003320


UBE2H
222421_at
UBIQUITIN-
NM_003344
NP_003335




CONJUGATING ENZYME
NM_182697
NP_874356




E2H (UBC8 HOMOLOG,




YEAST)


UBE2J1
217826_s_at
UBIQUITIN-
NM_016021
NP_057105




CONJUGATING ENZYME




E2, J1 (UBC6 HOMOLOG,




YEAST)


UBQLN2
215884_s_at
UBIQUILIN 2
NM_013444
NP_038472


UNC84B
229548_at
UNC-84 HOMOLOG B (C. ELEGANS)
NM_015374
NP_056189


USP38
223289_s_at
UBIQUITIN SPECIFIC
NM_032557
NP_115946




PROTEASE 38


USP9X
201099_at
UBIQUITIN SPECIFIC
NM_004652
NP_004643




PROTEASE 9, X-LINKED
NM_021906
NP_068706




(FAT FACETS-LIKE,




DROSOPHILA)


VAV3
218807_at
VAV 3 ONCOGENE
NM_006113
NP_006104


WBP4
203598_s_at
WW DOMAIN BINDING
NM_007187
NP_009118




PROTEIN 4 (FORMIN




BINDING PROTEIN 21)


WDFY3
212606_at
WD REPEAT AND FYVE
NM_014991
NP_055806




DOMAIN CONTAINING 3
NM_178583
NP_848698





NM_178585
NP_848700


WDFY3
212602_at
WD REPEAT AND FYVE
NM_014991
NP_055806




DOMAIN CONTAINING 3
NM_178583
NP_848698





NM_178585
NP_848700


WDFY3
212598_at
WD REPEAT AND FYVE
NM_014991
NP_055806




DOMAIN CONTAINING 3
NM_178583
NP_848698





NM_178585
NP_848700


WSB1
227501_at
WD REPEAT AND SOCS
NM_015626
NP_056441




BOX-CONTAINING 1
NM_134264
NP_599026





NM_134265
NP_599027


WSB1
210561_s_at
WD REPEAT AND SOCS
NM_015626
NP_056441




BOX-CONTAINING 1
NM_134264
NP_599026





NM_134265
NP_599027


WSB1
201296_s_at
WD REPEAT AND SOCS
NM_015626
NP_056441




BOX-CONTAINING 1
NM_134264
NP_599026





NM_134265
NP_599027


XRN1
1555785_a_at
5′-3′ EXORIBONUCLEASE 1
NM_019001
NP_061874


XRN1
233632_s_at
5′-3′ EXORIBONUCLEASE 1
NM_019001
NP_061874


ZC3HAV1
225634_at
ZINC FINGER CCCH TYPE,
NM_020119
NP_064504




ANTIVIRAL 1
NM_024625
NP_078901


ZCSL2
225195_at
ZINC FINGER, CSL
NM_206831
NP_996662




DOMAIN CONTAINING 2


ZDHHC19
231122_x_at
ZINC FINGER, DHHC
NM_144637
NP_653238




DOMAIN CONTAINING 19


ZDHHC19
1553952_at
ZINC FINGER, DHHC
NM_144637
NP_653238




DOMAIN CONTAINING 19


ZFP276
213778_x_at
ZINC FINGER PROTEIN 276
NM_152287
NP_689500




HOMOLOG (MOUSE)


ZFP36L2
201367_s_at
ZINC FINGER PROTEIN 36,
NM_006887
NP_008818




C3H TYPE-LIKE 2; ZFP36L2


ZFP36L2
201369_s_at
ZINC FINGER PROTEIN 36,
NM_006887
NP_008818




C3H TYPE-LIKE 2; ZFP36L2


ZNF167
206314_at
ZINC FINGER PROTEIN 167
NM_018651
NP_061121





NM_025169
NP_079445



230585_at
EST



200880_at
EST



230267_at
EST



215966_x_at
EST



237071_at
EST



230683_at
EST



241388_at
EST



204166_at
EST



241652_x_at
EST



229968_at
EST



223596_at
EST



240310_at
EST



216609_at
EST



224604_at
EST



223797_at
EST



238973_s_at
EST



230632_at
EST



230575_at
EST



159777_at
EST



244313_at
EST



242582_at
EST



233264_at
EST



219253_at
EST



235427_at
EST



1555311_at
EST



229934_at
EST



231035_s_at
EST



230999_at
EST



224261_at
EST



239780_at
EST



239669_at
EST



213002_at
EST



227925_at
EST



235456_at
EST



233312_at
EST



239167_at
EST



1569263_at
EST



216198_at
EST



232876_at
EST



237387_at
EST



216621_at
EST



235352_at
EST



1564933_at
EST



222376_at
EST



205922_at
EST



1557626_at
EST



228758_at
EST



1557733_a_at
EST



236898_at
EST









Each of the sequences, genes, proteins, and probesets identified in Table 30 is hereby incorporated by reference.
6.7 Exemplary Biomarker Combinations
In one embodiment of the present invention, an additional criterion was applied to the set of biomarkers identified in Section 6.6. Specifically, the additional criterion that was imposed was a requirement that each respective biomarker under consideration exhibit at least a 1.2× fold change between the median value for the respective biomarker among the subjects that acquired sepsis during a defined time period (sepsis subjects) and the median value for the respective biomarker among subjects that do not acquire sepsis during the defined time period (SIRS subjects) at the T−12 static time and at the T−36 static time periods. Furthermore, to satisfy the third criterion, the biomarker must have been used in at least one multivariate analysis with significant classification performance where significant classification performance is defined by having a lower 95th percentile for accuracy on a training data set that is grater than 50% and a point estimate for accuracy on the validation set that is greater than 65% at any time point measured. As noted in FIG. 30, application of this third filter criterion reduced the number of eligible biomarkers from 626 to 130. These biomarkers are listed column two of Table 31. In column two of Table 31, the biomarkers are indicated by the U133 plus 2.0 probeset to which they bind. However, in some embodiments, each such biomarker is, in fact, an mRNA, cDNA, or other such nucleic acid molecule corresponding to the identified U133 plus 2.0 oligonucleotide probe listed in column two of Table 31.
In column one of Table 31, each biomarker is listed by a gene name, such as, for example, a Human Gene Nomenclature Database (HUGO) symbol set forth by the Gene Nomenclature Committee, Department of Biology, University College London. As is known in the art, some human genome genes are represented by more than one probeset in the U133 plus 2.0 array. Furthermore, some of the oligonucleotides in the U133 plus 2.0 array represent expression sequence tags (ESTs) that do not correspond to a known gene. As a result, the 130 biomarkers listed in Table 31, in fact, represent 95 different known genes (see FIG. 30). Where known, the names of the 95 different human genes are listed in column three of Table 31.
In column four of Table 31, the median fold change between the mean value of the biomarker measured from T−12 samples of those subjects in the training population that develop sepsis (sepsis subjects) versus the mean value of the biomarker measured from T−12 samples of those subjects in the training population that do not develop sepsis (SIRS subjects) is given. In column five of Table 31, the direction of the fold change, where “+” indicates that the mean value in the sepsis subjects is greater than in the SIRS subjects, is given.
In column six of Table 31, the median fold change between the mean value of the biomarker measured from T−36 samples of those subjects in the training population that develop sepsis (sepsis subjects) versus the mean value of the biomarker measured from T−36 samples of those subjects in the training population that do not develop sepsis (SIRS subjects) is given. In column seven of Table 31, the direction of the fold change, where “+” indicates that the mean value in the sepsis subjects is greater than in the SIRS subjects, is given.
In a particular embodiment, the biomarker profile comprises at least two different biomarkers that each contain one of the probesets of Table 32, biomarkers that contain the complement of one of the probesets of Table 32, or biomarkers that contain an amino acid sequence encoded by a gene that contains one of the probesets of Table 32. Such biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example amplified nucleic acid, or proteins. The biomarker profile further comprises a respective corresponding feature for the at least two biomarkers. Generally, the at least two biomarkers are derived from at least two different genes. In the case where a biomarker is based upon a gene that includes the sequence of a probeset listed in Table 32 or a complement thereof, the biomarker can be, for example, a transcript made by the gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein encoded by a gene that includes a probeset sequence described in Table 32 or a discriminating fragment of the protein, or an indication of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein. In one embodiment, a biomarker profile of the present invention comprises a plurality of biomarkers that contain at least five, at least ten at least fifteen, at least twenty, at least thirty, between 2 and 5, between 3 and 7, or less than 15 of the sequences of the probesets of Table 32, or complements thereof, or genes including one of at least five of the sequences or complements thereof, or a discriminating fragment thereof, or an amino acid sequence encoded by any of the foregoing nucleic acid sequences, or any discriminating fragment of such an amino acid sequence. Such biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example amplified nucleic acid, or proteins. In some embodiments a biomarker is any gene that includes the sequence in an Affymetrix probeset given in Table 31, or any gene that includes a complement of the sequence in an Affymetrix probeset given in Table 32, or any mRNA, cDNA or other form of amplified nucleic acid of the foregoing, for any discriminating fragment of the foregoing, or any amino acid sequence coded by the foregoing, or any discriminating fragment of such a protein.







TABLE 31







Exemplary biomarkers that discriminate between converters and non-converters










T−12 Values
T−36 Values













Gene
Affymetrix

Median

Median



Symbol
Probeset name
Gene Name
FC
Direction
FC
Direction


Column 1
Column 2
Column 3
Column 3
Column 4
Column 5
Column 6

















1555785_a_at
EST
1.34
Up
1.31
up



227150_at
EST
1.45
Up
1.34
up



238973_s_at
EST
1.33
Up
1.25
up



239893_at
EST
1.89
Up
1.46
up



237563_s_at
EST
1.84
Up
1.60
up



244313_at
EST
1.87
Up
1.80
up



237071_at
EST
1.73
up
1.37
up



229934_at
EST
1.67
up
1.41
up



1555311_at
EST
1.31
up
1.21
up



233264_at
EST
1.52
up
1.36
up



239780_at
EST
1.93
up
1.42
up



238405_at
EST
3.02
up
2.23
up


3′HEXO
226416_at
HISTONE
1.70
up
1.41
up




MRNA 3′ END




EXORIBONUCLEASE


3′HEXO
231852_at

1.45
up
1.22
up


ADORA2A
205013_s_at
ADENOSINE
1.32
up
1.33
up




A2 RECEPTOR


ANXA3
209369_at
ANNEXIN A3
2.82
up
2.23
up


ASAHL
214765_s_at
N-
1.28
down
1.29
down




ACYLSPHINGOSINE




AMIDOHYDROLASE-




LIKE




PROTEIN


ASAHL
232072_at

1.23
down
1.31
down


ASAHL
227135_at

1.24
down
1.26
down


ATP11B
1554557_at
ATPASE,
1.70
up
1.49
up




CLASS VI,




TYPE 11B


ATP6V1C1
202872_at
ATPASE, H+
1.85
up
1.49
up




TRANSPORTING,




LYSOSOMAL,




42-KD, V1




SUBUNIT C,




ISOFORM 1


B4GALT5
221485_at
BETA-1,4-
1.67
up
1.43
up




GALACTOSYL




TRANSFERASE


BASP1
202391_at
BRAIN-
1.42
up
1.23
up




ABUNDANT




SIGNAL




PROTEIN


BAZ1A
217986_s_at
BROMODOMAIN
1.89
up
1.57
up




IN ADJACENT




TO ZINC




FINGER




DOMAIN, 1A


BCL6
203140_at
B-CELL
1.46
up
1.33
up




LYMPHOMA 6


BMX
206464_at
BONE
1.87
up
1.52
up




MARROW




KINASE, X-




LINKED


C16orf7
205781_at
CHROMOSOME
1.96
up
1.49
up




16 OPEN-




READING




FRAME 7


C20orf32
1554786_at
CHROMOSOME
1.27
down
1.24
down




20 OPEN-




READING




FRAME 32


C3F
203547_at
COMPLEMENT
1.28
down
1.26
down




COMPONENT 3


C8FW
202241_at
C8FW GENE;
1.57
up
1.34
up




PHOSPHOPROTEIN.


CEACAM1
209498_at
CARCINOEMBRYONIC
2.78
up
2.20
up




ANTIGEN-




RELATED




CELL




ADHESION




MOLECULE 1


CEACAM1
211883_x_at

1.87
up
1.61
up


CECR1
219505_at
CAT EYE
1.25
down
1.26
down




SYNDROME




CHROMOSOME




REGION,




CANDIDATE 1


CHCHD7
222701_s_at
COILED-COIL-
1.58
up
1.26
up




HELIX




DOMAIN-




CONTAINING




PROTEIN 7


CHSY1
203044_at
CARBOHYDRATE
1.78
up
1.24
up




SYNTHASE 1


CKLF
223451_s_at
CHEMOKINE-
1.59
up
1.40
up




LIKE FACTOR


CKLF
219161_s_at

1.38
up
1.31
up


CL25022
217883_at

1.36
up
1.31
up


CPD
201940_at
CARBOXYPEPTIDASE D
1.61
up
1.38
up


CPD
201941_at

1.61
up
1.35
up


CRTAP
1554464_a_at
CARTILAGE-
1.23
down
1.21
down




ASSOCIATED




PROTEIN


DHRS9
219799_s_at
MEMBRANE
1.94
up
1.52
up




PROTEIN,




PALMITOYLATED




3; MPP3


EIF4G3
201936_s_at
EUKARYOTIC
1.58
up
1.28
up




TRANSLATION




INITIATION




FACTOR 4-




GAMMA, 3


FAD104
218618_s_at
FIBRONECTIN
1.61
up
1.38
up




TYPE III




DOMAIN




CONTAINING




3B (FNDC3B


FAD104
225032_at

1.63
up
1.41
up


FAD104
222692_s_at

1.74
up
1.59
up


FAD104
222693_at

1.88
up
1.92
up


FCGR1A
214511_x_at
FC
2.37
up
1.71
up




FRAGMENT




OF IGG, HIGH




AFFINITY IA


FCGR1A
216950_s_at

2.56
up
1.60
up


FLJ11175
229005_at

1.77
up
1.50
up


FLJ11175
220603_s_at

2.12
up
1.77
up


FLJ11259
218627_at

1.54
up
1.25
up


FLJ11795
220112_at

1.80
up
1.62
up


FLJ22833
219334_s_at

1.49
up
1.28
up


G0S2
213524_s_at

1.69
up
1.30
up


GADD45B
207574_s_at
GROWTH
1.55
up
1.37
up




ARREST- AND




DNA




DAMAGE-




INDUCIBLE




GENE GADD45


GADD45B
209304_x_at

1.42
up
1.25
up


GK
214681_at
GLYCEROL
1.76
up
1.39
up




KINASE


GPR160
223423_at
G PROTEIN-
1.83
up
1.54
up




COUPLED




RECEPTOR 160


HLA-DMA
217478_s_at
HLA-D
1.32
down
1.27
down




HISTOCOMPATIBILITY




TYPE


HLA-DMB
203932_at

1.29
down
1.27
down


HLA-
211991_s_at

1.34
down
1.27
down


DPA1


HLA-
209823_x_at

1.31
down
1.20
down


DQB1


HLA-DRA
210982_s_at

1.25
down
1.24
down


HLA-DRA
208894_at

1.29
down
1.23
down


HLA-
215193_x_at

1.30
down
1.30
down


DRB1


HLA-
209312_x_at

1.27
down
1.30
down


DRB1


HLA-
204670_x_at

1.25
down
1.27
down


DRB4


HLA-
208306_x_at

1.27
down
1.27
down


DRB4


HPGD
203913_s_at
15-
2.01
up
1.57
up




HYDROXYPROSTAGLANDIN




DEHYDROGENASE


HRPT2
218578_at
HYPERPARATHYROIDISM 2
1.43
up
1.27
up


HSPC163
228437_at
HSPC163
1.64
up
1.46
up




PROTEIN


HSPC163
218728_s_at

2.00
up
1.52
up


IDI1
204615_x_at
ISOPENTENYL-
1.51
up
1.31
up




DIPHOSPHATE




DELTA-




ISOMERASE


IL18R1
206618_at
INTERLEUKIN
3.18
up
2.37
up




18 RECEPTOR 1


KCNE1
236407_at
POTASSIUM
1.68
up
1.45
up




CHANNEL,




VOLTAGE-




GATED, ISK-




RELATED




SUBFAMILY


KIF1B
225878_at
KINESIN
2.04
up
1.62
up




FAMILY




MEMBER 1B;


KREMEN1
227250_at
KRINGLE
2.11
up
1.32
up




CONTAINING




TRANSMEMBRANE




PROTEIN 1


LDLR
202068_s_at
LOW DENSITY
1.55
up
1.52
up




LIPOPROTEIN




RECEPTOR


LIMK2
202193_at
LIM DOMAIN
2.05
up
1.51
up




KINASE 2


LOC199675
235568_at

2.91
up
2.03
up


LOC284829
225669_at

1.48
up
1.30
up


LOC285771
237870_at

1.40
up
1.34
up


LRG1
228648_at
LEUCINE-
2.08
up
1.62
up




RICH ALPHA-




2-




GLYCOPROTEIN 1


MGC22805
239196_at
NOVEL GENE
2.30
up
1.86
up




(MGC22805),


MGC22805
238439_at

3.38
up
2.24
up


MPEG1
226841_at
MACROPHAGE
1.23
down
1.21
down




EXPRESSED




GENE 1


OAT
201599_at
ORNITHINE
1.53
up
1.44
up




AMINOTRANSFERASE




DEFICIENCY


ORF1-
224707_at

1.75
up
1.65
up


FL49


PDCD1LG1
227458_at
PROGRAMMED
2.22
up
1.86
up




CELL




DEATH 1




LIGAND 1


PFKFB2
226733_at
6-
1.70
up
1.28
up




PHOSPHOFRUCTO-




2-KINASE


PFKFB2
209992_at

1.65
up
1.43
up


PFKFB3
202464_s_at
6-
3.02
up
1.95
up




PHOSPHOFRUCTO-




2-




KINASE/FRUCTOSE-




2,6-




BISPHOSPHATASE 3


PGS1
219394_at
PHOSPHATIDYLGLYCEROPHOSPHATE
2.32
up
1.70
up




SYNTHASE


PHTF1
205702_at
PUTATIVE
1.37
up
1.20
up




HOMEODOMAIN




TRANSCRIPTION




FACTOR 1


PIK3AP1
226459_at
PHOSPHOINOSITIDE
1.64
up
1.39
up




3-




KINASE




ADAPTOR




PROTEIN 1


PLSCR1
241916_at
PHOSPHOLIPID
2.01
up
1.57
up




SCRAMBLASE 1


PRO2852
223797_at

1.62
up
1.42
up


PRV1
219669_at
NEUTROPHIL-
7.08
up
4.72
up




SPECIFIC




ANTIGEN 1


PSTPIP2
219938_s_at
PROLINE/SERINE/
2.54
up
1.82
up




THREONINE




PHOSPHATASE-




INTERACTING




PROTEIN 1


PTDSR
212723_at
CHROMOSOME
1.45
up
1.34
up




17 GENOMIC




CONTIG,




ALTERNATE




ASSEMBLY


RABGEF1
218310_at
RAB GUANINE
2.03
up
1.59
up




NUCLEOTIDE




EXCHANGE




FACTOR


RARA
228037_at
RETINOIC
1.54
up
1.20
up




ACID




RECEPTOR,




ALPHA


RNASEL
229285_at
RIBONUCLEASE L
1.68
up
1.36
up


SAMSN1
1555638_a_at
SAM DOMAIN,
2.22
up
1.42
up




SH3 DOMAIN,




AND




NUCLEAR




LOCALIZATION




SIGNALS 1


SAMSN1
220330_s_at

2.29
up
2.10
up


SEC15L1
226259_at
SEC15-LIKE 1
1.64
up
1.43
up




(S. CEREVISIAE)




(SEC15L1),




MRNA


SIPA1L2
225056_at
SIGNAL-
2.02
up
1.52
up




INDUCED




PROLIFERATION-




ASSOCIATED




GENE 1


SLC26A8
237340_at

2.01
up
1.48
up


SLC2A3
202499_s_at
SOLUTE
1.87
up
1.71
up




CARRIER




FAMILY 2




(FACILITATED




GLUCOSE




TRANSPORTER),




MEMBER 3


SOCS3
227697_at
SUPPRESSOR
2.84
up
2.15
up




OF CYTOKINE




SIGNALING 3


SOD2
216841_s_at
SUPEROXIDE
1.56
up
1.55
up




DISMUTASE 2


SPPL2A
226353_at
SIGNAL
1.48
up
1.51
up




PEPTIDE




PEPTIDASE-




LIKE 2A


SRPK1
202200_s_at
PROTEIN
1.80
up
1.46
up




KINASE,




SERINE/ARGININE-




SPECIFIC, 1


STK3
204068_at
SERINE/THREONINE
1.74
up
1.33
up




PROTEIN




KINASE 3


SULF2
224724_at
SULFATASE 2
1.36
down
1.35
down




(SULF2),




TRANSCRIPT




VARIANT 2


SULF2
233555_s_at

1.37
down
1.30
down


T2BP
226117_at
TRAF-2
2.90
up
1.90
up




BINDING




PROTEIN


T2BP
235971_at

1.47
up
1.27
up


T2BP
238858_at

1.32
up
1.21
up


TBC1D8
204526_s_at
TBC1 DOMAIN
1.55
up
1.40
up




FAMILY,




MEMBER 8


TCTEL1
201999_s_at
T-COMPLEX-
1.46
up
1.22
up




ASSOCIATED-




TESTIS-




EXPRESSED 1


TGFBI
201506_at
TRANSFORMING
1.38
down
1.41
down




GROWTH




FACTOR,




BETA-1


TTYH2
223741_s_at
TWEETY,
1.28
down
1.24
down




DROSOPHILA,




HOMOLOG OF, 2


WDFY3
212598_at
WD REPEAT
1.77
up
1.35
up




AND FYVE




DOMAIN




CONTAINING 3


WDFY3
212606_at

1.56
up
1.35
up


WSB1
201296_s_at
WD REPEAT
1.74
up
1.52
up




AND SOCS




BOX-




CONTAINING 1


ZDHHC19
231122_x_at
ZINC FINGER,
2.08
up
2.13
up




DHHC




DOMAIN




CONTAINING




19 (ZDHHC19


ZDHHC19
1553952_at

1.37
up
1.38
up


ZFP36L2
201367_s_at
ZINC FINGER
1.23
down
1.28
down




PROTEIN 36,




C3H TYPE-




LIKE 2;




ZFP36L2









Each of the sequences, genes, proteins, and probesets identified in Table 31 is hereby incorporated by reference herein in its entirety.
Table 31, above, provides a list of select biomarkers of the present invention. Where known, gene names are provided. Column two of Table 32, below, provides the GenBank® database accession numbers for the human nucleotide sequences of the biomarkers listed in Table 31, where known. Column three of Table 32 further provides the GenBank® database accession numbers for the corresponding amino acid sequences of the biomarkers of Table 31, where known. The biomarkers of the present invention include, but are not limited to, the genes and proteins identified by the accession numbers of Table 32, splicing variants thereof, discriminating fragments of mRNA, cDNA or other nucleic acids and/or peptides corresponding to all or a discriminating portion of such genes and proteins, etc.
These gene and protein accession numbers are provided in order to identify some of the biomarkers of the present invention. GenBank® is the publicly available genetic sequence database of the National Institutes of Health (NIH), and is an annotated collection of all publicly available DNA sequences (see, e.g., Nucleic Acids Research 2004 Jan. 1; 32(1):23-26, which is incorporated by reference herein in its entirety). GenBank® is part of the International Nucleotide Sequence Database Collaboration, which comprises the DNA DataBank of Japan (DDBJ), the European Molecular Biology Laboratory (EMBL), and GenBank at the National Center for Biotechnology Information (NCBI).







TABLE 32







Gene and protein accession numbers for exemplary biomarkers that


discriminate between converters and non-converters














Gene
Protein



Affymetrix

Accession
Accession


Gene Symbol
Probeset name
Gene Name
Number
Number


Column 1
Column 2
Column 3
Column 4
Column 5






1555785_a_at
EST




<NA>
227150_at
EST


<NA>
238973_s_at
EST


<NA>
239893_at
EST


<NA>
237563_s_at
EST


<NA>
244313_at
EST


<NA>
237071_at
EST


<NA>
229934_at
EST


<NA>
1555311_at
EST


<NA>
233264_at
EST


<NA>
239780_at
EST


<NA>
238405_at
EST


3′HEXO
226416_at
HISTONE MRNA 3′ END
NM_153332
NP_699163




EXORIBONUCLEASE


3′HEXO
231852_at


ADORA2A
205013_s_at
ADENOSINE A2 RECEPTOR
NM_000675
NP_000666


ANXA3
209369_at
ANNEXIN A3
NM_005139
NP_005130


ASAHL
214765_s_at
N-ACYLSPHINGOSINE
NM_014435
NP_055250




AMIDOHYDROLASE-LIKE




PROTEIN


ASAHL
232072_at


ASAHL
227135_at


ATP11B
1554557_at
ATPASE, CLASS VI, TYPE 11B
XM_087254
XP_087254


ATP6V1C1
202872_at
ATPASE, H+ TRANSPORTING,
NM_001695
NP_001686




LYSOSOMAL, 42-KD, V1
NM_001007254
NP_001007255




SUBUNIT C, ISOFORM 1


B4GALT5
221485_at
BETA-1,4-
NM_004776
NP_004767




GALACTOSYLTRANSFERASE


BASP1
202391_at
BRAIN-ABUNDANT SIGNAL
NM_006317
NP_006308




PROTEIN


BAZ1A
217986_s_at
BROMODOMAIN ADJACENT
NM_013448
NP_038476




TO ZINC FINGER DOMAIN,
NM_182648
NP_872589




1A


BCL6
203140_at
B-CELL LYMPHOMA 6
NM_001706
NP_001697





NM_138931
NP_620309


BMX
206464_at
BONE MARROW KINASE, X-
NM_001721
NP_001712




LINKED
NM_203281
NP_975010


C16orf7
205781_at
CHROMOSOME 16 OPEN-
NM_004913
NP_004904




READING FRAME 7


C20orf32
1554786_at
CHROMOSOME 20 OPEN-
NM_020356
NP_065089




READING FRAME 32


C3F
203547_at
COMPLEMENT COMPONENT 3
NM_005768
NP_005759


C8FW
202241_at
C8FW GENE;
NM_025195
NP_079471




PHOSPHOPROTEIN.


CEACAM1
209498_at
CARCINOEMBRYONIC
NM_001712
NP_001703




ANTIGEN-RELATED CELL




ADHESION MOLECULE 1


CEACAM1
211883_x_at


CECR1
219505_at
CAT EYE SYNDROME
NM_017424
NP_059120




CHROMOSOME REGION,
NM_177405
NP_803124




CANDIDATE 1


CHCHD7
222701_s_at
COILED-COIL-HELIX
NM_001011667
NP_001011667




DOMAIN-CONTAINING
NM_001011668
NP_001011668




PROTEIN 7
NM_001011669
NP_001011669





NM_001011670
NP_001011670





NM_001011671
NP_001011671





NM_024300
NP_077276


CHSY1
203044_at
CARBOHYDRATE
NM_014918
NP_055733




SYNTHASE 1


CKLF
223451_s_at
CHEMOKINE-LIKE FACTOR
NM_016326
NP_057410





NM_016951
NP_058647





NM_181640
NP_857591





NM_181641
NP_857592


CKLF
219161_s_at


CL25022
217883_at
C2ORF25
NM_015702
NP_056517


CPD
201940_at
CARBOXYPEPTIDASE D
NM_001304
NP_001295


CPD
201941_at


CRTAP
1554464_a_at
CARTILAGE-ASSOCIATED
NM_006371
NP_006362




PROTEIN


DHRS9
219799_s_at
MEMBRANE PROTEIN,
NM_005771
NP_005762




PALMITOYLATED 3; MPP3
NM_199204
NP_954674


EIF4G3
201936_s_at
EUKARYOTIC
NM_003760
NP_003751




TRANSLATION INITIATION




FACTOR 4-GAMMA, 3


FAD104
218618_s_at
FIBRONECTIN TYPE III
NM_022763
NP_073600




DOMAIN CONTAINING 3B




(FNDC3B)


FAD104
225032_at


FAD104
222692_s_at


FAD104
222693_at


FCGR1A
214511_x_at
FC FRAGMENT OF IGG, HIGH
NM_000566
NP_000557




AFFINITY IA


FCGR1A
216950_s_at


FLJ11175
229005_at

NM_018349
NP_060819


FLJ11175
220603_s_at


FLJ11259
218627_at

NM_018370
NP_060840


FLJ11795
220112_at

NM_024669
NP_078945


FLJ22833
219334_s_at
HYPOTHETICAL PROTEIN
NM_022837




FLJ22833


G0S2
213524_s_at

NM_015714
NP_056529


GADD45B
207574_s_at
GROWTH ARREST- AND DNA
NM_015675
NP_056490




DAMAGE-INDUCIBLE GENE




GADD45


GADD45B
209304_x_at


GK
214681_at
GLYCEROL KINASE
NM_000167
NP_000158





NM_203391
NP_976325


GPR160
223423_at
G PROTEIN-COUPLED
NM_014373
NP_055188




RECEPTOR 160


HLA-DMA
217478_s_at
HLA-D
NM_006120
NP_006111




HISTOCOMPATIBILITY TYPE


HLA-DMB
203932_at

NM_002118
NP_002109


HLA-DPA1
211991_s_at

NM_033554
NP_291032


HLA-DQB1
209823_x_at

NM_002123
NP_002114


HLA-DRA
210982_s_at

NM_002123
NP_002114


HLA-DRA
208894_at


HLA-DRB1
215193_x_at

NM_002124
NP_002115


HLA-DRB1
209312_x_at


HLA-DRB4
204670_x_at

NM_021983
NP_068818


HLA-DRB4
208306_x_at


HPGD
203913_s_at
15-
NM_000860
NP_000851




HYDROXYPROSTAGLANDIN




DEHYDROGENASE




HYDROXYPROSTAGLANDIN




DEHYDROGENASE 15-(NAD)


HRPT2
218578_at
HYPERPARATHYROIDISM 2
NM_024529
NP_078805


HSPC163
228437_at
HSPC163 PROTEIN
NM_014184
NP_054903


HSPC163
218728_s_at


IDI1
204615_x_at
ISOPENTENYL-
NM_004508
NP_004499




DIPHOSPHATE DELTA-




ISOMERASE


IL18R1
206618_at
INTERLEUKIN 18 RECEPTOR 1
NM_003855
NP_003846


KCNE1
236407_at
POTASSIUM CHANNEL,
NM_000219
NP_000210




VOLTAGE-GATED, ISK-




RELATED SUBFAMILY


KIF1B
225878_at
KINESIN FAMILY MEMBER
NM_015074
NP_055889




1B
NM_183416
NP_904325


KREMEN1
227250_at
KRINGLE CONTAINING
NM_032045
NP_114434




TRANSMEMBRANE PROTEIN 1
NM_153379
NP_700358


LDLR
202068_s_at
LOW DENSITY LIPOPROTEIN
NM_000527
NP_000518




RECEPTOR


LIMK2
202193_at
LIM DOMAIN KINASE 2
NM_005569
NP_005560





NM_016733
NP_057952


LOC199675
235568_at
HYPOTHETICAL PROTEIN
NM_174918
NP_777578




LOC199675


LOC284829
225669_at


LOC285771
237870_at
HYPOTHETICAL PROTEIN




LOC285771)


LRG1
228648_at
LEUCINE-RICH ALPHA-2-
NM_052972
NP_443204




GLYCOPROTEIN 1


MGC22805
239196_at
NOVEL GENE (MGC22805),


MGC22805
238439_at


MPEG1
226841_at
MACROPHAGE EXPRESSED
XM_166227
XP_166227




GENE 1


OAT
201599_at
ORNITHINE
NM_000274
NP_000265




AMINOTRANSFERASE




DEFICIENCY


ORF1-FL49
224707_at
PUTATIVE NUCLEAR
NM_032412
NP_115788




PROTEIN ORF1-FL49


PDCD1LG1
227458_at
PROGRAMMED CELL DEATH
NM_014143
NP_054862




1 LIGAND 1


PFKFB2
226733_at
6-PHOSPHOFRUCTO-2-
NM_006212
NP_006203




KINASE


PFKFB2
209992_at


PFKFB3
202464_s_at
6-PHOSPHOFRUCTO-2-
NM_004566
NP_004557




KINASE/FRUCTOSE-2,6-




BISPHOSPHATASE 3


PGS1
219394_at
PHOSPHATIDYLGLYCEROPHOSPHATE
NM_024419
NP_077733




SYNTHASE


PHTF1
205702_at
PUTATIVE HOMEODOMAIN
NM_006608
NP_006599




TRANSCRIPTION FACTOR 1


PIK3AP1
226459_at
PHOSPHOINOSITIDE 3-
NM_152309
NP_689522




KINASE ADAPTOR PROTEIN 1


PLSCR1
241916_at
PHOSPHOLIPID
NM_021105
NP_066928




SCRAMBLASE 1


PRO2852
223797_at
HYPOTHETICAL PROTEIN




PRO2852


PRV1
219669_at
NEUTROPHIL-SPECIFIC
NM_020406
NP_065139




ANTIGEN 1 (POLYCYTHEMIA




RUBRA VERA 1)


PSTPIP2
219938_s_at
PROLINE/SERINE/THREONINE
NM_024430
NP_077748




PHOSPHATASE-




INTERACTING PROTEIN 1




(PROLINE-SERINE-




THREONINE PHOSPHATASE




INTERACTING PROTEIN 2)


PTDSR
212723_at
CHROMOSOME 17 GENOMIC
NM_015167
NP_055982




CONTIG, ALTERNATE




ASSEMBLY




(PHOSPHATIDYLSERINE




RECEPTOR)


RABGEF1
218310_at
RAB GUANINE NUCLEOTIDE
NM_014504
NP_055319




EXCHANGE FACTOR (RAB




GUANINE NUCLEOTIDE




EXCHANGE FACTOR (GEF)




1)


RARA
228037_at
RETINOIC ACID RECEPTOR,
NM_000964
NP_000955




ALPHA


RNASEL
229285_at
RIBONUCLEASE L
NM_021133
NP_066956


SAMSN1
1555638_a_at
SAM DOMAIN, SH3 DOMAIN,
NM_022136
NP_071419




AND NUCLEAR




LOCALIZATION SIGNALS 1


SAMSN1
220330_s_at


SEC15L1
226259_at
SEC15-LIKE 1 (S. CEREVISIAE)
NM_019053
NP_061926




(SEC15L1),




MRNA


SIPA1L2
225056_at
SIGNAL-INDUCED
NM_020808
NP_065859




PROLIFERATION-




ASSOCIATED GENE 1




(SIGNAL-INDUCED




PROLIFERATION-




ASSOCIATED 1 LIKE 2)


SLC26A8
237340_at
SOLUTE CARRIER FAMILY
NM_052961
NP_443193




26, MEMBER 8
NM_138718
NP_619732


SLC2A3
202499_s_at
SOLUTE CARRIER FAMILY 2
NM_006931
NP_008862




(FACILITATED GLUCOSE




TRANSPORTER), MEMBER 3


SOCS3
227697_at
SUPPRESSOR OF CYTOKINE
NM_003955
NP_003946




SIGNALING 3


SOD2
216841_s_at
SUPEROXIDE DISMUTASE 2
NM_000636
NP_000627


SPPL2A
226353_at
SIGNAL PEPTIDE
NM_032802
NP_116191




PEPTIDASE-LIKE 2A


SRPK1
202200_s_at
PROTEIN KINASE,
NM_003137
NP_003128




SERINE/ARGININE-SPECIFIC,




1 (SFRS PROTEIN KINASE 1)


STK3
204068_at
SERINE/THREONINE
NM_006281
NP_006272




PROTEIN KINASE 3


SULF2
224724_at
SULFATASE 2 (SULF2),
NM_198596
NP_940998




TRANSCRIPT VARIANT 2


SULF2
233555_s_at


T2BP
226117_at
TRAF-2 BINDING PROTEIN
NM_052864
NP_443096




(TRAF-INTERACTING




PROTEIN WITH A




FORKHEAD-ASSOCIATED




DOMAIN)


T2BP
235971_at


T2BP
238858_at


TBC1D8
204526_s_at
TBC1 DOMAIN FAMILY,
NM_007063
NP_008994




MEMBER 8


TCTEL1
201999_s_at
T-COMPLEX-ASSOCIATED-
NM_006519
NP_006510




TESTIS-EXPRESSED 1


TGFBI
201506_at
TRANSFORMING GROWTH
NM_000358
NP_000349




FACTOR, BETA-1


TTYH2
223741_s_at
TWEETY, DROSOPHILA,
NM_032646
NP_116035




HOMOLOG OF, 2
NM_052869
NP_443101


WDFY3
212598_at
WD REPEAT AND FYVE
NM_014991
NP_055806




DOMAIN CONTAINING 3
NM_178583
NP_848698





NM_178585
NP_848700


WDFY3
212606_at


WSB1
201296_s_at
WD REPEAT AND SOCS BOX-
NM_015626
NP_056441




CONTAINING 1
NM_134264
NP_599026





NM_134265
NP_599027


ZDHHC19
231122_x_at
ZINC FINGER, DHHC
NM_144637
NP_653238




DOMAIN CONTAINING 19




(ZDHHC19)


ZDHHC19
1553952_at


ZFP36L2
201367_s_at
ZINC FINGER PROTEIN 36,
NM_006887
NP_008818




C3H TYPE-LIKE 2; ZFP36L2









Each of the sequences, genes, proteins, and probesets identified in Table 32 is hereby incorporated by reference herein in its entirety.
6.8 Biomarker Combinations Based on Additional Filtering Criteria
Section 6.6 describes exemplary biomarkers that discriminate between converters and nonconverters. Section 6.7 describes one exemplary combination of the biomarkers of Section 6.6. The biomarkers of Section 6.7 were identified by the application of an additional filtering criterion to the biomarkers of Section 6.6. This section describes additional combinations of the biomarkers identified in Section 6.7. The subsections identified in this section discriminate between converters and nonconverters.
Table 33 lists biomarkers in one exemplary combination. The combination detailed in Table 33 was identified by taking the list of biomarkers in Table 31 and imposing additional filtering criteria. These additional criteria include a requirement that each respective biomarker under consideration exhibit at least a 1.2× fold change between the median feature value for the respective biomarker among the subjects that acquired sepsis during a defined time period (sepsis subjects) and the median value for the respective biomarker among subjects that do not acquire sepsis during the defined time period (SIRS subjects) in the T−12 baseline data described in Section 6.5. Furthermore, the summation of the PAM score, CART score, and RF score for the biomarker in the T−12 baseline data time period had to exceed unity. Application of these additional filtering criteria reduced the biomarkers from the 130 found in Table 31, to ten biomarkers.







TABLE 33







Exemplary combination of biomarkers that discriminate between converters and


non-converters










T−12 Values
T−36 Values













Gene
Affymetrix

Median

Median



Symbol
Probeset name
Gene Name
FC
Direction
FC
Direction


Column 1
Column 2
Column 3
Column 4
Column 5
Column 6
Column 7

















1555785_a_at
EST
1.34
up
1.31
up


CL25022
217883_at

1.36
up
1.31
up


IDI1
204615_x_at
ISOPENTENYL-
1.51
up
1.31
up




DIPHOSPHATE




DELTA-ISOMERASE


MGC22805
239196_at
NOVEL GENE
2.30
up
1.86
up




(MGC22805)


ORF1-
224707_at

1.75
up
1.65
up


FL49


ZDHHC19
1553952_at

1.37
up
1.38
up


CHSY1
203044_at
CARBOHYDRATE
1.78
up
1.24
up




SYNTHASE 1


SLC2A3
202499_s_at
SOLUTE CARRIER
1.87
up
1.71
up




FAMILY 2




(FACILITATED




GLUCOSE




TRANSPORTER),




MEMBER 3


FAD104
225032_at

1.63
up
1.41
up


T2BP
235971_at

1.47
up
1.27
up









Each of the sequences, genes, proteins, and probesets identified in Table 33 is hereby incorporated by reference.
Table 34 lists biomarkers in yet another exemplary combination of biomarkers. The combination detailed in Table 34 was identified by taking the list of biomarkers in Table 31 and imposing the additional requirement that each biomarker is annotated with a corresponding known gene and that such a gene has a known biological function. Methods, tables, software and other resources for addressing this latter question are available from the Gene Ontology Consortium, (www.geneontology.org), which is hereby incorporated by reference in its entirety. Application of these additional filtering criteria reduced the biomarkers from the 130 found in the set of Table 31, to 52 biomarkers, representing 42 unique gene sequences (see FIG. 30).







TABLE 34







Exemplary combination of biomarkers that discriminate between


converters and non-converters








Gene Symbol
Corresponding Gene Name





(BCL6 na)
(B-cell CLL/lymphoma 6 (zinc finger protein 51)



LOC389185)


(HLA-
(major histocompatibility complex class II DR


DRB1,3,4,5)
beta 1 3,4,5)


(RABGEF1 na)
(LOC401368 LOC402538 RAB guanine nucleotide



exchange factor (GEF) 1)


3HEXO
3 exoribonuclease


ADORA2A
adenosine A2a receptor


ANKRD22
ankyrin repeat domain 22


ANXA3
annexin A3


ATP11B
ATPase Class VI type 11B


ATP6V1C1
ATPase H+ transporting lysosomal 42 kDa V1 subunit



C isoform 1


BASP1
brain abundant membrane attached signal protein 1


BAZ1A
bromodomain adjacent to zinc finger domain 1A


C16orf7
chromosome 16 open reading frame 7


CD4
CD4 antigen (p55)


CEACAM1
carcinoembryonic antigen-related cell adhesion



molecule 1 (biliary glycoprotein)


CECR1
cat eye syndrome chromosome region candidate 1


CKLF
chemokine-like factor


CPD
carboxypeptidase D


EIF4G3
eukaryotic translation initiation factor 4 gamma 3


FCGR1A
Fc fragment of IgG high affinity Ia receptor for (CD64)


G0S2
putative lymphocyte G0/G1 switch gene


GADD45B
growth arrest and DNA-damage-inducible beta


HLA-
major histocompatibility complex class II DM beta


DMB


HLA-DPA1
major histocompatibility complex class II DP alpha 1


HLA-DQB1
major histocompatibility complex class II DQ beta 1


HLA-DRA
major histocompatibility complex class II DR alpha


HPGD
hydroxyprostaglandin dehydrogenase 15-(NAD)


IL18R1
interleukin 18 receptor 1


KCNE1
potassium voltage-gated channel Isk-related



family member 1


LDLR
low density lipoprotein receptor (familial



hypercholesterolemia)


PDCD1LG1
programmed cell death 1 ligand 1


PHTF1
putative homeodomain transcription factor 1


PRV1
polycythemia rubra vera 1


PTDSR
phosphatidylserine receptor


RARA
retinoic acid receptor alpha


RNASEL
ribonuclease L (25-oligoisoadenylate synthetase-



dependent)


SEC15L1
SEC15-like 1 (S. cerevisiae)


SLC26A8
solute carrier family 26 member 8


SLC2A3
solute carrier family 2 (facilitated glucose transporter)



member 3


STK3
serine/threonine kinase 3 (STE20 homolog yeast)


TGFBI
transforming growth factor beta-induced 68 kDa


XRN1
5–3 exoribonuclease 1


ZFP36L2
zinc finger protein 36 C3H type-like 2









Each of the sequences, genes, proteins, and probesets identified in Table 34 is hereby incorporated by reference.
In one embodiment, the biomarker profile comprises a plurality of biomarkers that collectively contain at least five, at least ten at least fifteen, at least twenty, at least thirty, between 2 and 5, between 3 and 7, or less than 15 of the sequences of the probesets of Table 32, or complements thereof, or genes including one of at least five of the sequences or complements thereof, or a discriminating fragment thereof, or an amino acid sequence encoded by any of the foregoing nucleic acid sequences, or any discriminating fragment of such an amino acid sequence. Such biomarkers can be mRNA transcripts, cDNA or some other form of amplified nucleic acid or proteins.
In one embodiment, the biomarker profile comprises a plurality of biomarkers that collectively contain at least five, at least ten at least fifteen, at least twenty, at least thirty, between 2 and 5, between 3 and 7, or less than 15 of the sequences of the probesets of Table 33, or complements thereof, or genes including one of at least five of the sequences or complements thereof, or a discriminating fragment thereof, or an amino acid sequence encoded by any of the foregoing nucleic acid sequences, or any discriminating fragment of such an amino acid sequence. Such biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example amplified nucleic acid, or proteins.
In one embodiment, the biomarker profile comprises a plurality of biomarkers that collectively contain at least five, at least ten at least fifteen, at least twenty, at least thirty, between 2 and 5, between 3 and 7, or less than 15 of the sequences of the probesets of Table 34, or complements thereof, or genes including one of at least five of the sequences or complements thereof, or a discriminating fragment thereof, or an amino acid sequence encoded by any of the foregoing nucleic acid sequences, or any discriminating fragment of such an amino acid sequence. Such biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example amplified nucleic acid, or proteins.
In one embodiment, the biomarker profile comprises a plurality of biomarkers that collectively contain at least five, at least ten at least fifteen, at least twenty, at least thirty, between 2 and 5, between 3 and 7, or less than 15 of the sequences of the probesets of Table 33 or Table 34, or complements thereof, or genes including one of at least five of the sequences or complements thereof, or a discriminating fragment thereof, or an amino acid sequence encoded by any of the foregoing nucleic acid sequences, or any discriminating fragment of such an amino acid sequence. Such biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example amplified nucleic acid, or proteins.
In one embodiment, the biomarker profile comprises a biomarker that has the sequence of U133 plus 2.0 probeset SLC2A3 or a complement thereof, or a gene including the sequence of the probeset SLC2A3 or a complement thereof, or a discriminating fragment thereof, or an amino acid sequence encoded by any of the foregoing nucleic acid sequences, or any discriminating fragment of such an amino acid sequence. Such biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example amplified nucleic acid, or proteins.
In the case where a biomarker is based upon a gene that includes the sequence of a probeset listed in Table 30, 31, 32, 33, or 34 or a complement thereof, the biomarker can be, for example, a transcript made by the gene, a complement thereof, or a discriminating fragment or complement thereof, or a cDNA thereof, or a discriminating fragment of the cDNA, or a discriminating amplified nucleic acid molecule corresponding to all or a portion of the transcript or its complement, or a protein encoded by the gene, or a discriminating fragment of the protein, or an indication of any of the above. Further still, the biomarker can be, for example, a protein encoded by a gene that includes a probeset sequence described in Table 30, 31, 32, 33 or 34, or a discriminating fragment of the protein, or an indication of the above. Here, a discriminating molecule or fragment is a molecule or fragment that, when detected, indicates presence or abundance of the above-identified transcript, cDNA, amplified nucleic acid, or protein.
6.9 Differential Gene Expression in the TH1/TH2 Pathway in SIRS and SEPSIS Patients
This section describes methods used to identify a set of biomarkers that discriminate between converters and nonconverters, using the methods described, e.g., in Section 5.10, supra. Briefly, 97 SIRS subject were admitted to critical care units of a major university trauma center were evaluated using the methods described in Section 6.1. Comparisons were made using T−36 and T−12 static data described in Sections 6.3 and 6.4, respectively. The subjects were divided into two classes: converters (47) and nonconverters (50). Blood samples drawn from converters were time matched to samples from nonconverters in order to perform comparisons, as described in Sections 6.3 and 6.4, respectively. The blood samples were collected and analyzed as described in Section 6.2.
Biomarkers that discriminated between converters and nonconverters with (i) a Wilcoxon (adjusted) p value of 0.05 or less and (ii) an exhibited a mean fold differential expression value between converters and nonconverters of 1.2 or greater in either the T−36 or the T−12 static test were selected as the set of discriminating biomarkers. This set of discriminating biomarkers was then filtered using an annotation data based filtering rule imposed by DAVID 2.0, which is available from the National Institutes of Health (see, http://apps1.niaid.nih.gov/david/, the contents of which are incorporated by reference herein in their entirety). Specifically, that annotation data based filtering rule imposed by David 2.0 had the form of annotation rule 4 in Section 5.10, reproduce below
Annotation Rule 4.


    Select all biomarkers that are in biological pathway X.

The specific form of this annotation data based filtering rule in this example was

    Select all biomarkers that are in the Th1/Th2 biological pathway (cell differentiation pathway).

Table 35 below lists the Affymetrix U133 plus 2.0 probesets that are in genes known to be involved in this Th1/Th2 cell differentiation pathway.










TABLE 35







U133 plus 2.0 Probesets in genes involved in the Th1/Th2 cell


differentiation pathway.












U133 + 2.0
P value



Symbol
probeset
(adjusted)















CD28
211856_x_at
0.018



CD28
211861_x_at



CD28
206545_at



CD86
205685_at
0.0034



CD86
205685_at
0.0187



CD86
210895_s_at
0.0034



CD86
210895_s_at
0.0187



CD86
205686_s_at
0.0114



HLA-DRA
210982_s_at
0.0034



HLA-DRA
210982_s_at
0.0187



HLA-DRA
208894_at
0.0034



HLA-DRA
208894_at
0.0187



HLA-DRB1
221491_x_at



HLA-DRB1
217323_at



HLA-DRB1
217362_x_at
0.0034



HLA-DRB1
217362_x_at
0.0472



HLA-DRB1
215193_x_at
0.0034



HLA-DRB1
215193_x_at
0.0187



HLA-DRB1
209728_at



HLA-DRB1
204670_x_at
0.0034



HLA-DRB1
204670_x_at
0.0187



HLA-DRB1
208306_x_at
0.0034



HLA-DRB1
208306_x_at
0.0187



HLA-DRB1
209312_x_at
0.0034



HLA-DRB1
209312_x_at
0.0187



HLA-DRB1
215666_at



HLA-DRB1
215669_at



HLA-DRB3
221491_x_at



HLA-DRB3
217323_at



HLA-DRB3
217362_x_at
0.0034



HLA-DRB3
217362_x_at
0.0472



HLA-DRB3
215193_x_at
0.0034



HLA-DRB3
215193_x_at
0.0187



HLA-DRB3
209728_at



HLA-DRB3
204670_x_at
0.0034



HLA-DRB3
204670_x_at
0.0187



HLA-DRB3
208306_x_at
0.0034



HLA-DRB3
208306_x_at
0.0187



HLA-DRB3
209312_x_at
0.0034



HLA-DRB3
209312_x_at
0.0187



HLA-DRB3
215666_at



HLA-DRB3
215669_at



HLA-DRB4
221491_x_at



HLA-DRB4
217323_at



HLA-DRB4
217362_x_at
0.0034



HLA-DRB4
217362_x_at
0.0472



HLA-DRB4
215193_x_at
0.0034



HLA-DRB4
215193_x_at
0.0187



HLA-DRB4
209728_at



HLA-DRB4
204670_x_at
0.0034



HLA-DRB4
204670_x_at
0.0187



HLA-DRB4
208306_x_at
0.0034



HLA-DRB4
208306_x_at
0.0187



HLA-DRB4
209312_x_at
0.0034



HLA-DRB4
209312_x_at
0.0187



HLA-DRB4
215666_at



HLA-DRB4
215669_at



HLA-DRB5
221491_x_at



HLA-DRB5
217323_at



HLA-DRB5
217362_x_at
0.0034



HLA-DRB5
217362_x_at
0.0472



HLA-DRB5
215193_x_at
0.0034



HLA-DRB5
215193_x_at
0.0187



HLA-DRB5
209728_at



HLA-DRB5
204670_x_at
0.0034



HLA-DRB5
204670_x_at
0.0187



HLA-DRB5
208306_x_at
0.0034



HLA-DRB5
208306_x_at
0.0187



HLA-DRB5
209312_x_at
0.0034



HLA-DRB5
209312_x_at
0.0187



HLA-DRB5
215666_at



HLA-DRB5
215669_at



IFNG
210354_at



IFNGR1
211676_s_at
0.00342



IFNGR1
242903_at
0.0034



IFNGR1
202727_s_at
0.0054



IFNGR2
231696_x_at



IFNGR2
201642_at
0.0034



IL12A
207160_at



IL12B
207901_at



IL12RB1
239522_at



IL12RB1
206890_at



IL12RB2
206999_at



IL18
206295_at



IL18R1
206618_at
0.0034



IL18R1
206618_at
0.0187



IL2
207849_at



IL2RA
211269_s_at



IL2RA
206341_at
0.0247



IL4
207538_at



IL4
207539_s_at



IL4R
203233_at
0.0034



IL4R
203233_at
0.0187



TNFRSF5
222292_at
0.0126



TNFRSF5
215346_at



TNFRSF5
205153_s_at



TNFRSF5
35150_at
0.0086



TNFSF5
207892_at
0.0034










Table 36 below identifies the genes that contain the probesets that remained in the set of discriminating biomarkers upon application of the annotation data based filtering rule.







TABLE 36







Identified genes.












Data

Fold-change




source
Adjusted
(Median sepsis vs.
Relative


Gene name
(static)
p value
Median SIRS)
regulation





CD86
T−12
0.003
1.56
Down



T−36
0.019
1.23
Down


HLA-DRA
T−12
0.003
1.29
Down



T−36
0.019
1.23
Down


HLA-DRB1,3,4,5
T−12
0.003
1.25
Down



T−36
0.019
1.27
Down


IFNGR1
T−12
0.003
1.39
Up


IFNGR2
T−12
0.003
1.25
Up


IL18R1
T−12
0.003
3.17
Up



T−36
0.019
2.37
Up


IL4R
T−12
0.003
1.61
Up



T−36
0.019
1.39
Up









The genes in Table 36 represent biomarkers that discriminate between converters and converters. Further, these genes are in the Th1/Th2 cell differentiation pathway. The results in the table show that, although clinically similar, SIRS patients who subsequently developed sepsis expressed genes related to Th1/Th2 Cells differently than SIRS patients who remained uninfected. These differences occurred prior to the onset of clinical sepsis. For a discussion of Th1/Th2 cell differentiation pathway genes and related genes, see, e.g., Abbas et al., 1996, Functional diversity of helper T lymphocytes, Nature 383:787-793; Fearon and Locksley, 1996, Science 272:50-53; and Mossman and Sad, 1996, Immunol. Today 17:138-146; each of which is hereby incorporated by reference in its entirety.
6.10 RT-PCR
In Section 6.1, it was noted that two Paxgene (RNA) tubes were drawn from each subject in the study on each day of the study. One tube was used for microarray analysis as described in Section 6.2. The other tube was used for RT-PCR analysis. In this section, the correlation between the gene expression values obtained by RT-PCR and the gene expression values obtained by microarray is presented for three of the genes listed in Table 30, IL18R1, FCGR1A, and MMP9. In this comparison, static expression data from both assays (RT-PCR and microarray) for all time points measured in the subject were correlated to obtain a correlation coefficient. The correlations were computed within ‘R’, a public domain statistical computing language (http://www.r-project.org/, which is hereby incorporated by reference), using the following code:















corCalc <- function(x,y){



  n <- length(x)



  ## if there are any missing values in the data



  if(any(is.na(x)) || any(is.na(y))){



    ## index where missing values occur



    rm.idx <- which(is.na(x))



    rm.idx <- c(rm.idx,which(is.na(y)))



    ## remove missing values



    x <- x[−rm.idx]



    y <- y[−rm.idx]



    ## update length



    n <- length(x)



  }



  R <- (n*sum(x*y)−sum(x)*sum(y))/(sqrt((n*sum(x{circumflex over ( )}2) −



(sum(x)){circumflex over ( )}2)*(n*sum(y{circumflex over ( )}2) − (sum(y)){circumflex over ( )}2)))



  return(R)



}










FIG. 31 shows the correlation between IL18R1 expression, as determined by RT-PCR, and the intensity of the X206618_at probeset, as determined using the techniques described in Section 6.2, using all available time points across the training population. Each point in FIG. 31 is the gene expression value for a given subject in the training population from the RT-PCR data and the microarray data. Substantial correlation between the RT-PCR and the microarray data was found. In particular, the overall correlation between expression of IL18R1 as determined by RT-PCR and microarray data for X206618_at was 0.85.
FIG. 32 shows the correlation between FCGR1A expression, as determined by RT-PCR, and the intensity of the X214511_x_at, X216950_s_at and X216951_at probesets, as determined using the techniques described in Section 6.2, using all available time points in the training population. Each point in FIG. 32 is the gene expression value for a given subject in the training population from the RT-PCR data and the microarray data. As is evident in FIG. 32, the overall correlation between expression of FCGR1A and each of the two FCGR1A probesets that are found in Table 30, X214511_x_at and X216950_s_at, was significant. In particular the correlation coefficient between FCGR1A and X214511_x_at was 0.88. Likewise, the correlation coefficient between and FCGR1A and X216950_s_at was 0.88. The overall correlation between expression of FCGR1A and the FCGR1A probeset not found in Table 30, X216951_at, was 0.53, which was not as significant as the other two probesets.
FIG. 33 shows the correlation between MMP9 expression, as determined by RT-PCR, and the intensity of the X203936—s_at probeset, as determined using the techniques described in Section 6.2, using all available time points in the training population. Each point in FIG. 32 is the gene expression value for a given subject in the study from the RT-PCR data and the microarray data. Substantial correlation between the RT-PCR and the microarray data was found. In particular, the overall correlation between expression of MMP9 as determined by RT-PCR and microarray data for X203936_s_at was 0.87.
FIG. 34 shows the correlation between CD86 expression, as determined by RT-PCR, and the intensity of the X205685_at, X205686_s_at, and X210895_s_at probesets, as determined using the techniques described in Section 6.2, using all available time points. Each point in FIG. 34 is the gene expression value for a given subject in the study from the RT-PCR data and the microarray data. As is evident in FIG. 34, the overall correlation between expression of CD86 and CD86 probeset that is found in Table 30, 210895_s_at, was significant (correlation coefficient of 0.71). The overall correlation between expression of CD86 and the probesets not found in Table 30, X205685_at, X205686_s_at, was not as significant (correlation coefficient of 0.66 and 0.56, respectively).
In one embodiment, a biomarker profile of the present invention comprises a plurality of biomarkers selected from Table 30, including at least one sequence of a probeset in the set of:

{X206618_at, X214511_x_at, X216950_s_at, X203936_s_at, and 210895_s_at}

or complements thereof, or genes including the sequence or a complement of the sequence thereof, or a discriminating fragment thereof, or an amino acid sequence encoded by any of the foregoing nucleic acid sequences, or any discriminating fragment of such an amino acid sequence. Such biomarkers can be, for example, mRNA transcripts, cDNA or some other nucleic acid, for example, amplified nucleic acid or proteins. In one embodiment, a biomarker profile the of the present invention comprises a nucleic acid that codes for TGFB1, IL18R1, or FCGR1A, a discriminating portion of TGFB1, IL18R1, or FCGR1A, complements of such nucleic acids, proteins encoded by such nucleic acids, or antibodies that selectively bind to any of the foregoing.

6.11 Discovery of Select Nucleic Acid Biomarkers
The experiments described above identified a number of biomarkers that discriminate between sepsis and SIRS. In this example, a discovery process was performed in order to confirm which biomarkers differentiate between patients who subsequently develop sepsis (“sepsis patients”) and patients who do not (“SIRS patients”). In the discovery process, samples from SIRS patients and sepsis patients taken at: (i) date of entry, (ii) T−60, (iii) T−36, and (iv) T−12 data points were studied by RT-PCR, as described in Section 6.11.1 and by Affymetrix gene chip analysis, as described in Section 6.11.2.
6.11.1 RT-PCR Analysis
Biomarkers in multiple samples were measured by RT-PCR at multiple time points and analyzed in several different ways: static time of entry, static T−60, static T−36, baseline T−60, baseline T−36, and baseline T−12 data points. RT-PCR is described in Section 5.4.1.2, and 6.10, above. Representative of these analyses is the static T−12 data analysis which is described in detail below. In the T−12 static analysis, biomarkers features were measured using a specific blood sample, designated the T−12 blood sample, as defined in Section 6.4, above.
For the T−12 static analysis, there were 72 biomarkers measured on 96 samples. Each sample was collected from a different member the population. Of these features, 15 were transformed by log transformations, 5 by square root transformations and the remaining 52 were not transformed.
The 96 member population was initially split into a training set (n=73) and a validation set (n=23). The training set was used to estimate the appropriate classification algorithm parameters while the trained algorithm was applied to the validation set to independently assess performance. Of the 73 training samples, 36 were labeled Sepsis, meaning that the subjects developed sepsis at some point during the observation time period, and 37 were SIRS, meaning that they did not develop sepsis during the observation time period. Table 37 provides distributions of the race, gender and age for these samples.







TABLE 37





Distributions of the race, gender, and age for the training data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
10
14
1




Female
0
10
1



SIRS
Male
5
22
0




Female
0
10
0

















Group
Minimum
Mean
Median
Maximum







Sepsis
18
43.2
40
80



SIRS
18
44.6
40
90










For the 23 validation samples, 12 were labeled Sepsis and 11 were labeled SIRS. Table 38 provides distributions of the race, gender and age for these samples.







TABLE 38





Distributions of the race, gender, and age for the validation data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
0
7
0




Female
0
4
0



SIRS
Male
2
6
0




Female
0
4
0

















Group
Minimum
Mean
Median
Maximum







Sepsis
18
43.4
43
81



SIRS
19
51.9
51.5
85










Each sample in the training data was randomly assigned to one of ten groups used for cross-validation. The number of training samples in these groups ranged from 6 to 8. The samples were assigned in way that attempted to balance the number of sepsis and SIRS samples across folds. As described in more detail below, several different methods were used to judge whether select biomarkers discriminate between the Sepsis and SIRS groups.
Wilcoxon and Q-value tests. The first method used to identify discriminating biomarkers was a Wilcoxon test (unadjusted). The abundance value for a given biomarker across the samples in the training data was subjected to the Wilcoxon test. The Wilcoxon test considers both group classification (sepsis versus SIRS) and abundance value in order to compute a p value for the given biomarker. The p value provides an indication of how well the abundance value for the given biomarker across the samples collected in the training set discriminates between the sepsis and SIRS state. The lower the p value, the better the discrimination. When the p value is less than a specific confidence level, such as 0.05, an inference is made that the biomarker discriminates between the sepsis and SIRS phenotype. There were 33 significant biomarkers using this method (see Table 39).
The second method used to identify discriminating biomarkers was the Wilcoxon Test (adjusted). Due to the large number of biomarkers, 72, and the relatively small number of samples, 96, there was a high risk of finding falsely significant biomarkers. An adjusted p-value was used to counter this risk. In particular, the method of Benjamini and Hochberg, 1995, J.R. Statist. Soc. B 57, pp 289-300, which is hereby incorporated herein by reference in its entirety, was used to control the false discovery rate. Here, the false discovery rate is defined as the number of biomarkers truly significant divided by the number of biomarkers declared significant. For example, if the adjusted p-value is less than 0.05, there is a 5% chance that the biomarker is a false discovery. Results using this test are reported in Table 39. There were 11851 significant biomarkers using this method (see Table 39). As used, herein, a biomarker is considered significant if it has a p-value of less than 0.05 as determined by the Wilcoxon test (adjusted).
The third method used to identify discriminating biomarkers was the use of Q values. In such an approach, the biomarkers are ordered by their q-values and if a respective biomarker has a Q value of X, then respective biomarker and all others more significant have a combined false discovery rate of X. However, the false discovery rate for any one biomarker may be much larger. There were 27 significant biomarkers using this method (see Table 39).







TABLE 39







Cumulative number of significant calls for the three methods.


Note that all samples (training and validation) were used to


compare Sepsis and SIRS groups. Missing biomarker feature


values were not included in the analyses.















≦1e−04
≦0.001
≦0.01
≦0.025
≦0.05
≦0.1
≦1


















p-value
0
22
25
29
33
38
72


(unadjusted)


p-value
0
16
25
25
27
33
72


(adjusted)


q-value
0
0
28
38
47
59
72









CART. In addition to analyzing the microarray data using Wilcoxon and Q-value tests in order to identify biomarkers that discriminate between the sepsis and SIRS subpopulations in the training set, classification and regression tree (CART) analysis was used. CART is described in Section 5.5.1, above. Specifically, the data summarized above was used to predict the disease state by iteratively partitioning the data based on the best single-variable split of the data. In other words, at each stage of the tree building process, the biomarker whose expression values across the training population best discriminate between the sepsis and SIRS population was invoked as a decision branch. Cross-validation was carried out, with the optimal number of splits estimated independently in each of the 10 iterations. The final tree is depicted in FIG. 36, and uses seven biomarkers: TNFSF13B, FCGR1A, HMOX1, MMP9, APAF1, APAF1.1, and CCL3.
FIG. 37 shows the distribution of the seven biomarkers used in the decision tree between the sepsis and SIRS groups in the training data set. In FIG. 37, the top of each box denotes the 75th percentile of the data across the training set and the bottom of each box denotes the 25th percentile, and the median value for each biomarker across the training set is drawn as a line within each box. The confusion matrix for the training data where the predicted classifications were made from the cross-validated model is given in Table 40. From this confusion matrix, the overall accuracy was estimated to be 68.5% with a 95% confidence interval of 56.6% to 78.9%. The estimated sensitivity was 72.2% and the estimated specificity was 64.9%.







TABLE 40







Confusion matrix for training samples using the cross-validated CART


algorithm of FIG. 36










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
26
13



SIRS
10
24










For the 23 validation samples held back from training data set, the overall accuracy was estimated to be 78.3% with a 95% confidence interval of 56.3% to 92.5%, sensitivity 66.7% and specificity 90.9%. Table 41 shows the confusion matrix for the validation samples.







TABLE 41







Confusion matrix for validation samples using the cross-validated CART


algorithm










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
8
1



SIRS
4
10










Random Forests. Another decision rule that can be developed using biomarkers of the present invention is a Random Forests decision tree. Random Forests is a tree based method that uses bootstrapping instead of cross-validation. For each iteration, a random sample (with replacement) is drawn and the largest tree possible is grown. Each tree receives a vote in the final class prediction. To fit a random forest, the number of trees (e.g. bootstrap iterations) is specified. No more than 500 were used in this example, but at least 50 are needed for a burn-in period. The number of trees was chosen based on the accuracy of the training data. For this data, 462 trees were used to train the algorithm (see FIG. 38). In FIG. 38, curve 3202 is a smoothed estimate of overall accuracy as a function of tree number. Curve 3804 is a smoothed curve of tree sensitivity as a function of tree number. Curve 3806 is a smoothed curve of tree specificity as a function of tree number. Using this algorithm, 49 biomarkers had non-zero importance and were used in the model. The random forest algorithm gauges biomarker importance by the average reduction in the training accuracy. The biomarkers were ranked by this method and are shown in FIG. 39. The random forest method uses a number of different decision trees. A biomarker is considered to have discriminating significance if it served as a decision branch of a decision tree from a significant random forest analysis. As used herein, a significant random forest analysis is one where the lower 95% confidence interval on accuracy by cross validation on a training data set is greater than 50% and the point estimate for accuracy on a validation set is greater than 65%.
The predicted confusion matrix for the training dataset using the decision tree developed using the Random Forest method is given in Table 42. From this confusion matrix, the overall accuracy was estimated to be 76.7% (confidence intervals cannot be computed when using the bootstrap accuracy estimate). The estimated sensitivity was 77.8% and the estimated specificity was 75.7%.







TABLE 42







Confusion matrix for training samples against the decision tree developed


using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
28
8



SIRS
9
28










For the 23 validation samples held back from training, the overall accuracy was estimated to be 78.3% with a 95% confidence interval of 56.3% to 92.5%, sensitivity 75% and specificity 81.8%. Table 43 shows the confusion matrix for the validation samples.







TABLE 43







Confusion matrix for the validation samples against the decision tree


developed using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
9
2



SIRS
3
9










MART. Multiple Additive Regression Trees (MART), also known as “gradient boosting machines,” was used to simultaneously assess the importance of biomarkers and classify the subject samples. Several fitting parameters are specified in this approach including (i) number of trees, (ii) step size (commonly referred to as “shrinkage”), and (iii) degree of interaction (related to the number of splits for each tree). More information on MART is described in Section 5.5.4 above. The degree of interaction was set to 1 to enforce an additive model (e.g. each tree has one split and only uses one biomarker).
Estimating interactions may require more data to function well. The step size was set to 0.05 so that the model complexity was dictated by the number of trees. The optimal number of trees was estimated by leaving out a random subset of cases at each fitting iteration, then assessing quality of prediction on that subset. After fitting more trees than were warranted, the point at which prediction performance stopped improving was estimated as the optimal point.
The estimated model used 15 trees and 6 biomarkers across all trees. The MART algorithm also provides a calculation of biomarker importance (summing to 100%), which are given in FIG. 40. Biomarkers with zero importance were excluded. FIG. 41 shows the distribution of the selected biomarkers between the Sepsis and SIRS groups.
Cross-validation was carried out, with the optimal number of trees estimated independently in each of the 10 iterations. The confusion matrix for the training data where the predicted classifications were made from the cross-validated model is given in Table 44. From this confusion matrix, the overall accuracy was estimated to be 75.3% with a 95% confidence interval of 63.9% to 84.7%. The estimated sensitivity was 72.2% and the estimated specificity was 78.4%.







TABLE 44







Confusion matrix for the training samples using the cross-validated MART


algorithm.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
26
8



SIRS
10
29










For the 23 validation samples held back from training, the overall accuracy was estimated to be 78.3% with a 95% confidence interval of 56.3% to 92.5%, sensitivity 81.8% and specificity 75%. Table 45 shows the confusion matrix for the validation samples.







TABLE 45







Confusion matrix for the validation samples using the MART algorithm.










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
9
3



SIRS
2
9










PAM. Yet another decision rule developed using biomarkers of the present invention is predictive analysis of microarrays (PAM), which is described in Section 5.5.2, above. In this method, a shrinkage parameter that determines the number of biomarkers used to classify samples is specified. This parameter was chosen via cross-validation. There were no biomarkers with missing values. Based on cross-validation, the optimal threshold value was 2.12, corresponding to 5 biomarkers. FIG. 42 shows the accuracy across different thresholds. In FIG. 42, curve 4202 is the overall accuracy (with 95% confidence interval bars). Curve 4204 shows decision rule sensitivity as a function of threshold value. Curve 4206 shows decision rule specificity as a function of threshold value. Using the threshold of 2.12, the overall accuracy for the training samples was estimated to be 80.8% with a 95% confidence interval of 70.9% to 87.9%. The estimated sensitivity was 89.2% and the estimated specificity was 72.2%. Table 46 shows the confusion matrix for the training data where the predicted classifications were made from the cross-validated models.







TABLE 46







Confusion matrix for training samples using


cross-validated PAM algorithm










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
33
10



SIRS
4
26










For the 23 validation samples held back from training, the overall accuracy was estimated to be 82.6% with a 95% confidence interval of 61.2% to 95%, sensitivity 91.7% and specificity 72.7%. Table 47 shows the confusion matrix for the validation samples.







TABLE 47







Confusion matrix for validation samples using cross-validated PAM


algorithm










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
11
3



SIRS
1
8










FIG. 43 shows the selected biomarkers, ranked by their relative discriminatory power, and their relative importance in the model.
FIG. 44 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals. Fifty distinct biomarkers were selected from across all the algorithms illustrated in FIG. 44. The identity of these fifty selected features is found in FIG. 45, which further illustrates an overall ranking of these biomarkers for the T−12 data set. For the selected biomarkers, the x-axis depicts the percentage of times that it was selected. Within the percentage of times that biomarkers were selected, the biomarkers are ranked.
From the analysis of the T−12 data set and the other data sets, biomarkers were ranked according to how often they were included in the CART, MART, PAM, random forests. The results of this ranking is summarized in Table 48 below:







TABLE 48







Top ranked biomarkers as determined by RT-PCR














Gene
Protein




Gene

Accession
Accession
T−12


symbol
Gene Name
Number
Number
Hours
T−36 Hours















FCGR1A
FC FRAGMENT OF IGG, HIGH
NM_000566
NP_000557
1
1



AFFINITY IA


MMP9
MATRIX
NM_004994
NP_004985
2
5



METALLOPROTEINASE 9



(GELATINASE B, 92 KDA



GELATINASE, 92 KDA TYPE IV



COLLAGENASE)


IL18R1
INTERLEUKIN 18 RECEPTOR 1
NM_003855
NP_003846
3
2


ARG2
ARGINASE TYPE II
NM_001172
CAG38787
4
3


IL1RN
INTERLEUKIN-1 RECEPTOR
NM_000577,
AAN87150
4
4



ANTAGONIST GENE
NM_173841,




NM_173842,




NM_173843


TNFSF13B
TUMOR NECROSIS FACTOR
NM_006573
NP_006564
4
5



(LIGAND) SUPERFAMILY,



MEMBER 13B


ITGAM
INTEGRIN, ALPHA M
NM_000632
NP_000623
5
7



(COMPLEMENT COMPONENT



RECEPTOR 3, ALPHA; ALSO



KNOWN AS CD11B (P170),



MACROPHAGE ANTIGEN



ALPHA POLYPEPTIDE)


CD4
CD4 ANTIGEN (P55)
NM_000616
NP_000607
6
7


TGFBI
TRANSFORMING GROWTH
NM_000358
NP_000349
6
9



FACTOR, BETA-1



(TRANSFORMING GROWTH



FACTOR, BETA-INDUCED,



68 KDA)


CD86
CD86 ANTIGEN (CD28
NM_006889
NP_008820
6
6



ANTIGEN LIGAND 2, B7-2
NM_175862
NP_787058



ANTIGEN)


TLR4
TOLL-LIKE RECEPTOR 4
AH009665
AAF05316
6
6


IFI16
INTERFRON, GAMMA-
NM_005531
AAH17059
6
9



INDUCIBLE PROTEIN 16


ICAM1
INTERCELLULAR ADHESION
NM_010493
NP_000192,
6
10



MOLECULE 1

AAQ14902,





AAQ14901


TGFBR2
TRANSFORMING GROWTH
NM_003242
AAF27281
6
8



FACTOR, BETA RECEPTOR II


PLA2G7
PLATELET-ACTIVATING
NM_005084
CAH73907
7
6



FACTOR ACETYLHYDROLASE


IL-10
INTERLEUKIN 10
NM_000572
CAH73907
8
7









As Table 48 indicates, in general, important biomarkers at T−12 were also important biomarkers at earlier time points. The ten biomarkers that are italicized in Table 48 were carried forward to confirmation as described in Section 6.12.1, below. CD4 was excluded in this embodiment because it was found to be different on day of entry.
6.11.2 Discovery Affymetrix Gene Chip Analysis
The patients were also analyzed using Affymetrix gene chip analysis. Such an analysis is described in Section 6.2 Biomarkers in multiple samples were measured by Affymetrix gene chip analysis at multiple time points and analyzed in several different ways: static time of entry, static T−60, static T−36, baseline T−60, baseline T−36, and baseline T−12 data points. The Affymetrix gene chip assay is described in Section 6.2, above. Representative of these analyses is the static T−12 data analysis described in detail below. In the T−12 static analysis, biomarkers features were measured using a specific blood sample, designated the T−12 blood sample, as defined in Section 6.4, above.
For the T−12 static analysis, there were 54,613 biomarkers measured on 90 samples. Each sample was collected from a different member the population. Of these features, 31,047 were transformed by log transformations, 2518 by square root transformations and the remaining 21,048 were not transformed.
The 90 member population was initially split into a training set (n=69) and a validation set (n=21). The training set was used to estimate the appropriate classification algorithm parameters while the trained algorithm was applied to the validation set to independently assess performance. Of the 69 training samples, 34 were labeled Sepsis, meaning that the subjects developed sepsis at some point during the observation time period, and 35 were SIRS, meaning that they did not develop sepsis during the observation time period. Table 49 provides distributions of the race, gender and age for these samples.







TABLE 49





Distributions of the race, gender, and age for the training data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
9
13
1




Female
0
10
1



SIRS
Male
5
20
0




Female
0
10
0







Group
Minimum
Mean
Median
Maximum







Sepsis
18
44.1
39
80



SIRS
18
44.1
40
90










For the 21 validation samples, 11 were labeled Sepsis and 10 were labeled SIRS. Table 50 provides distributions of the race, gender and age for these samples.







TABLE 50





Distributions of the race, gender, and age for the validation data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
0
7
0




Female
0
3
0



SIRS
Male
2
6
0




Female
0
3
0







Group
Minimum
Mean
Median
Maximum







Sepsis
18
43.4
40
81



SIRS
19
53
52
85










Each sample in the training data was randomly assigned to one of ten groups used for cross-validation. The number of training samples in these groups ranged from 6 to 8. The samples were assigned in way that attempted to balance the number of sepsis and SIRS samples across folds. As described in more detail below, several different methods were used to judge whether select biomarkers discriminate between the Sepsis and SIRS groups.
Wilcoxon and Q-value tests. The first method used to identify discriminating biomarkers was a Wilcoxon test (unadjusted). The abundance value for a given biomarker across the samples in the training data was subjected to the Wilcoxon test. The Wilcoxon test considers both group classification (sepsis versus SIRS) and abundance value in order to compute a p value for the given biomarker. The p value provides an indication of how well the abundance value for the given biomarker across the samples collected in the training set discriminates between the sepsis and SIRS state. The lower the p value, the better the discrimination. When the p value is less than a specific confidence level, such as 0.05, an inference is made that the biomarker discriminates between the sepsis and SIRS phenotype. There were 19791 significant biomarkers using this method (see Table 51).
The second method used to identify discriminating biomarkers was the Wilcoxon Test (adjusted). Due to the large number of biomarkers, 54613, and the relatively small number of samples, 90, there was a high risk of finding falsely significant biomarkers. An adjusted p-value was used to counter this risk. In particular, the method of Benjamini and Hochberg, 1995, J.R. Statist. Soc. B 57, pp 289-300, which is hereby incorporated herein by reference in its entirety, was used to control the false discovery rate. Here, the false discovery rate is defined as the number of biomarkers truly significant divided by the number of biomarkers declared significant. For example, if the adjusted p value is less than 0.05, there is a 5% chance that the biomarker is a false discovery. Results using this test are reported in Table 51. There were 11851 significant biomarkers using this method (see Table 51). As used, herein, a biomarker is considered significant if it has a p-value of less than 0.05 as determined by the Wilcoxon test (adjusted).
The third method used to identify discriminating biomarkers was the use of Q values. In such an approach, the biomarkers are ordered by their q-values and if a respective biomarker has a Q value of X, then respective biomarker and all others more significant have a combined false discovery rate of X. However, the false discovery rate for any one biomarker may be much larger. There were 11581 significant biomarkers using this method (see Table 51).







TABLE 51







Cumulative number of significant calls for the three methods. Note that all


96 samples (training and validation) were used to compare Sepsis and SIRS groups.


Missing biomarker feature values were not included in the analyses.















≦1e−04
≦0.001
≦0.01
≦0.025
≦0.05
≦0.1
≦1


















p-value
0
5417
11537
15769
19791
24809
54613


(unadjusted)


p-value
0
0
5043
8374
11851
16973
54613


(adjusted)


q-value
0
0
7734
12478
17820
24890
54613









CART. In addition to analyzing the microarray data using Wilcoxon and Q-value tests in order to identify biomarkers that discriminate between the sepsis and SIRS subpopulations in the training set, classification and regression tree (CART) analysis was used. CART is described in Section 5.5.1, above. Specifically, the data summarized above was used to predict the disease state by iteratively partitioning the data based on the best single-variable split of the data. In other words, at each stage of the tree building process, the biomarker whose expression values across the training population best discriminate between the sepsis and SIRS population was invoked as a decision branch. Cross-validation was carried out, with the optimal number of splits estimated independently in each of the 10 iterations. The final tree uses four probesets: X214681_at, X230281_at, X1007_s_at, and X1560432_at, where each given probeset is the U133 plus 2.0 Affymetrix probe set name. The confusion matrix for the training data, based on the final tree from the cross-validated CART algorithm is given in Table 52. From this confusion matrix, the overall accuracy was estimated to be 65.2% with a 95% confidence interval of 52.8% to 76.3%. The estimated sensitivity was 61.8% and the estimated specificity was 68.6%.







TABLE 52







Confusion matrix for training samples using the cross-validated CART


algorithm










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
21
11



SIRS
13
24










For the 21 validation samples held back from training data set, the overall accuracy was estimated to be 71.4% with a 95% confidence interval of 47.8% to 88.7%, sensitivity 90.9% and specificity 50%. The confusion matrix for the validation samples was predicted Sepsis/true Sepsis 10, predicted SIRS/true Sepis 1, predicted Sepsis/true SIRS 5, predicted SIRS/true SIRS 5.
Random Forests. Another decision rule that can be developed using biomarkers of the present invention is a Random Forests decision tree. Random Forests is a tree based method that uses bootstrapping instead of cross-validation. For each iteration, a random sample (with replacement) is drawn and the largest tree possible is grown. Each tree receives a vote in the final class prediction. To fit a random forest, the number of trees (e.g. bootstrap iterations) is specified. No more than 500 were used in this example, but at least 50 are needed for a burn-in period. The number of trees was chosen based on the accuracy of the training data. For this data, 439 trees were used to train the algorithm. Using this algorithm, 845 biomarkers had non-zero importance and were used in the model. The random forest algorithm gauges biomarker importance by the average reduction in the training accuracy.
The random forest method uses a number of different decision trees. A biomarker is considered to have discriminating significance if it served as a decision branch of a decision tree from a significant random forest analysis. As used herein, a significant random forest analysis is one where the lower 95% confidence interval on accuracy by cross validation on a training data set is greater than 50% and the point estimate for accuracy on a validation set is greater than 65%.
The predicted confusion matrix for the training dataset using the decision tree developed using the Random Forest method is given in Table 53. From this confusion matrix, the overall accuracy was estimated to be 75.4% (confidence intervals cannot be computed when using the bootstrap accuracy estimate). The estimated sensitivity was 73.5% and the estimated specificity was 77.1%.







TABLE 53







Confusion matrix for training samples against the decision tree developed


using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
27
9



SIRS
8
25










For the 21 validation samples held back from training, the overall accuracy was estimated to be 76.2% with a 95% confidence interval of 76.2% to 99.9%, sensitivity 100% and specificity 90%. Table 54 shows the confusion matrix for the validation samples.







TABLE 54







Confusion matrix for the validation samples against the decision tree


developed using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
11
1



SIRS
0
9










MART. Multiple Additive Regression Trees (MART), also known as “gradient boosting machines,” was used to simultaneously assess the importance of biomarkers and classify the subject samples. Several fitting parameters are specified in this approach including (i) number of trees, (ii) step size (commonly referred to as “shrinkage”), and (iii) degree of interaction (related to the number of splits for each tree). More information on MART is described in Section 5.5.4 above. The degree of interaction was set to 1 to enforce an additive model (e.g. each tree has one split and only uses one biomarker).
Estimating interactions may require more data to function well. The step size was set to 0.05 so that the model complexity was dictated by the number of trees. The optimal number of trees was estimated by leaving out a random subset of cases at each fitting iteration, then assessing quality of prediction on that subset. After fitting more trees than were warranted, the point at which prediction performance stopped improving was estimated as the optimal point.
The estimated model used 28 trees and 17 biomarkers across all trees. The MART algorithm also provides a calculation of biomarker importance (summing to 100%). Biomarkers ranked in decreasing order of importance to the model, with the most important biomarker first were: X206513_at, X214681_at, X235359_at, X221850_x_at, X213524_s_at, X225656_a, X200881_s_at, X229743_at, X215178_x_at, X215178_x_at, X216841_s_at, X216841_at, X244158_at, X238858_at, X205287_s_at, X233651_s_at, X229572_at, X214765_s_at.
Cross-validation was carried out, with the optimal number of trees estimated independently in each of the 10 iterations. The confusion matrix for the training data where the predicted classifications were made from the cross-validated model is given in Table 55. From this confusion matrix, the overall accuracy was estimated to be 76.8% with a 95% confidence interval of 65.1% to 86.1%. The estimated sensitivity was 76.5% and the estimated specificity was 77.1%.







TABLE 55







Confusion matrix for the training samples using the cross-validated MART


algorithm.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
26
8



SIRS
8
27










For the 21 validation samples held back from training, the overall accuracy was estimated to be 85.7% with a 95% confidence interval of 63.7% to 97%, sensitivity 80% and specificity 90.9%. Table 56 shows the confusion matrix for the validation samples.







TABLE 56







Confusion matrix for the validation samples


using the MART algorithm.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
8
1



SIRS
2
10










PAM. Yet another decision rule developed using biomarkers of the present invention is predictive analysis of microarrays (PAM), which is described in Section 5.5.2, above. In this method, a shrinkage parameter that determines the number of biomarkers used to classify samples is specified. This parameter was chosen via cross-validation. There were no biomarkers with missing values. Based on cross-validation, the optimal threshold value was 2.1, corresponding to 820 biomarkers.
Using the threshold of 2.1, the overall accuracy for the training samples was estimated to be 80.9% with a 95% confidence interval of 73.4% to 86.7%. The estimated sensitivity was 85.7% and the estimated specificity was 76.5%. Table 57 shows the confusion matrix for the training data where the predicted classifications were made from the cross-validated models.







TABLE 57







Confusion matrix for training samples using


cross-validated PAM algorithm










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
11
1



SIRS
0
9










For the 21 validation samples held back from training, the overall accuracy was estimated to be 95.2% with a 95% confidence interval of 76.2% to 99.9%, sensitivity 100% and specificity 90%. Table 58 shows the confusion matrix for the validation samples.







TABLE 58







Confusion matrix for validation samples using


cross-validated PAM algorithm










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
11
1



SIRS
0
9










The top ten biomarkers identified by PAM, ranked from most important to least important were: X206513_at, X213524_s_at, X200881_s_at, X218992_at, X238858_at, X221123_x_at, X228402_at, X230585_at, X209304_x_at, X214681_at.
FIG. 46 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T−12 static data obtained from an Affymetrix gene chip discovery training population. Fifty distinct biomarkers were selected from across all the algorithms illustrated in FIG. 46. The identity of the top 50 biomarkers, ranked from most to least significant is: X204102_s_at, X236013_at, X213668_s_at, X1556639_at, X218220_at, X207860_at, X232422_at, X218578_at, X205875_s_at, X226043_at, X225879_at, X224618_at, X216316_x_at, X243159_x_at, X202200_s_at, X201936_s_at, X242492_at, X216609_at, X214328_s_at, X228648_at, X223797_at, X225622_at, X205988_at, X201978_s_at, X200874_s_at, X210105_s_at, X203913_s_at, X204225_at, X227587_at, X220865_s_at, X206682_at, X222664_at, X212264_s_at, X219669_at, X221971_x_at, X1554464_a_at, X242590_at, X227925_at, X221926_s_at, X202101_s_at, X211078_s_at, X44563_at, X206513_at, X215178_x_at, X235359_at, X225656_at, X244158_at, X214765_s_at, X229743_at, X214681.
From the analysis of the T−12 data set and the other data sets, the 34 biomarkers indicated in Table 59 below were selected for confirmation. As indicated in Table 59, biomarkers were selected based on the Affymetrix gene chip analysis for one of three criteria, biological relevance (BR), high fold change (HF), or statistical importance (SI) in the Affymetrix gene chip analysis.







TABLE 59







Nucleic acid based biomarkers selected for confirmation from Affymetrix Assay













Gene
Protein





Accession
Accession
Selection


Gene Symbol
Gene Name
Number
Number
Criterion





BCL2A1
BCL2-RELATED PROTEIN
NM_004049
NP_004040
BR



A1


CCL5
CHEMOKINE (C-C MOTIF)
NM_002985
NP_002976
BR



LIGAND 5


CSF1R
COLONY STIMULATING
NM_005211
NP_005202
BR



FACTOR 1 RECEPTOR,



FORMERLY MCDONOUGH



FELINE SARCOMA VIRAL



(V-FMS) ONCOGENE



HOMOLOG


GADD45A
GROWTH ARREST AND
NM_001924
NP_001915
BR



DNA-DAMAGE-



INDUCIBLE, ALPHA


GADD45B
GROWTH ARREST-AND
NM_015675
NP_056490
BR



DNA DAMAGE-



INDUCIBLE GENE



GADD45


IFNGR1
INTERFERON GAMMA
NM_000416
NP_000407
BR



RECEPTOR 1


IL10RA
INTERLEUKIN 10
NM_001558
NP_001549
BR



RECEPTOR, ALPHA


IRAK2
INTERLEUKIN-1
NM_001570
NP_001561
BR



RECEPTOR-ASSOCIATED



KINASE 2


IRAK4
INTERLEUKIN-1
NM_016123
NP_057207
BR



RECEPTOR-ASSOCIATED



KINASE 4


JAK2
JANUS KINASE 2 (A
NM_004972
NP_004963
BR



PROTEIN TYROSINE



KINASE)


LY96
LYMPHOCYTE ANTIGEN
NM_015364
NP_056179
BR



96


MAP2K6
MITOGEN-ACTIVATED
NM_002758
NP_002749
BR



PROTEIN KINASE KINASE 6
NM_031988
NP_114365


MAPK14
MAPK14 MITOGEN-
NM_001315
NP_001306
BR



ACTIVATED PROTEIN
NM_139012
NP_620581



KINASE 14
NM_139013
NP_620582




NM_139014
NP_620583


MKNK1
MAP KINASE
NM_003684
NP_003675
BR



INTERACTING
NM_198973
NP_945324



SERINE/THREONINE



KINASE 1


OSM
ONCOSTATIN M
NM_020530
NP_065391
BR


SOCS3
SUPPRESSOR OF
NM_003955
NP_003946
BR



CYTOKINE SIGNALING 3


TDRD9
TUDOR DOMAIN
NM_153046
NP_694591
BR



CONTAINING 9


TNFRSF6
TUMOR NECROSIS
NM_152877
NP_000034
BR



FACTOR RECEPTOR



SUPERFAMILY, MEMBER 6


TNFSF10
TUMOR NECROSIS
NM_003810
NP_003801
BR



FACTOR (LIGAND)



SUPERFAMILY, MEMBER



10


ANKRD22
ANKYRIN REPEAT
NM_144590
NP_653191
HF



DOMAIN 22


ANXA3
ANNEXIN A3
NM_005139
NP_005130
HF


CEACAM1
CARCINOEMBRYONIC
NM_001712
NP_001703
HF



ANTIGEN-RELATED CELL



ADHESION MOLECULE 1


LDLR
LOW DENSITY
NM_000527
NP_000518
HF



LIPOPROTEIN RECEPTOR


PFKFB3
6-PHOSPHOFRUCTO-2-
NM_004566
NP_004557
HF



KINASE/FRUCTOSE-2,6-



BISPHOSPHATASE 3


PRV1
NEUTROPHIL-SPECIFIC
NM_020406
NP_065139
HF



ANTIGEN 1



(POLYCYTHEMIA RUBRA



VERA 1)


PSTPIP2
PROLINE/SERINE/THREONINE
NM_024430
NP_077748
HF



PHOSPHATASE-



INTERACTING PROTEIN 1



(PROLINE-SERINE-



THREONINE



PHOSPHATASE



INTERACTING PROTEIN 2)


TIFA
TRAF-INTERACTING
NM_052864
NP_443096
HF



PROTEIN WITH A



FORKHEAD-ASSOCIATED



DOMAIN


VNN1
VANIN 1
NM_004666
NP004657
HF


NCR1
NATURAL
NM_004829
NP_004820
SI



CYTOTOXICITY



TRIGGERING RECEPTOR 1


FAD104
FIBRONECTIN TYPE III
NM_022763
NP_073600
SI



DOMAIN CONTAINING 3B



(FNDC3B)


INSL3
INSULIN-LIKE 3 (LEYDIG
NM_005543
NP_005534
SI



CELL)


CRTAP
CARTILAGE-ASSOCIATED
NM_006371
NP_006362
SI



PROTEIN


HLA-DRA
MAJOR
NM_002123
NP_002114
SI



HISTOCOMPATIBILITY



COMPLEX, CLASS II, DR



ALPHA


SOD2
SUPEROXIDE DISMUTASE
NM_000636
NP_000627
SI



2, MITOCHONDRIAL









6.12 Confirmation of Select Nucleic Acid Biomarkers
In this example, a confirmatory process was performed in order to confirm which biomarkers differentiate between patients who subsequently develop sepsis (“sepsis patients”) and patients who do not (“SIRS patients”).
6.12.1 Confirmatory Analysis of Biomarkers Identified by RT-PCR
The biomarkers identified by italicizes in Table 48 of Section 6.11.1, namely FCGR1A, MMP9, IL18R1, ARG2, IL1RN, TNFSF13B, ITGAM, TGFB1, CD86, and TLR4, were analyzed using RT-PCR at multiple time points and analyzed in several different ways: static time of entry, static T−60, static T−36, baseline T−60, baseline T−36, and baseline T−12 data points. RT-PCR is described in Section 5.4.1.2, above. Representative of these analyses is the static T−12 data analysis described in detail below. In the T−12 static analysis, biomarkers features were measured using a specific blood sample, designated the T−12 blood sample, as defined in Section 6.4, above.
For the T−12 static analysis, the biomarkers FCGR1A, MMP9, IL18R1, ARG2, IL1RN, TNFSF13B, ITGAM, TGFB1, CD86, and TLR4, were measured from 50 samples. Each sample was collected from a different member of the population. Of these biomarkers, seven were transformed by log transformations, and three by square root transformations.
The 50 member population was initially split into a training set (n=39) and a validation set (n=11). The training set was used to estimate the appropriate classification algorithm parameters while the trained algorithm was applied to the validation set to independently assess performance. Of the 50 training samples, 23 were labeled Sepsis, meaning that the subjects developed sepsis at some point during the observation time period, and 16 were SIRS, meaning that they did not develop sepsis during the observation time period. Table 60 provides distributions of the race, gender and age for these samples.







TABLE 60





Distributions of the race, gender, and age for the training data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
3
13
0




Female
0
7
0



SIRS
Male
5
7
1




Female
0
2
0

















Group
Minimum
Mean
Median
Maximum







Sepsis
20
52.3
56
80



SIRS
20
39.9
32.5
79










For the 11 validation samples, five were labeled Sepsis and six were labeled SIRS. Table 61 provides distributions of the race, gender and age for these samples.







TABLE 61





Distributions of the race, gender, and age for the validation data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
2
1
0




Female
0
3
0



SIRS
Male
0
3
0




Female
0
2
0

















Group
Minimum
Mean
Median
Maximum







Sepsis
18
51.7
59.5
76



SIRS
24
47.2
43
76










Each sample in the training data was randomly assigned to one of ten groups used for cross-validation. The number of training samples in these groups ranged from three to five. The samples were assigned in way that attempted to balance the number of sepsis and SIRS samples across folds. As described in more detail below, several different methods were used to judge whether select biomarkers discriminate between the Sepsis and SIRS groups.
Wilcoxon and Q-value tests. The first method used to identify discriminating biomarkers was a Wilcoxon test (unadjusted). The abundance value for a given biomarker across the samples in the training data was subjected to the Wilcoxon test. The Wilcoxon test considers both group classification (sepsis versus SIRS) and abundance value in order to compute a p value for the given biomarker. The p value provides an indication of how well the abundance value for the given biomarker across the samples collected in the training set discriminates between the sepsis and SIRS state. The lower the p value, the better the discrimination. When the p value is less than a specific confidence level, such as 0.05, an inference is made that the biomarker discriminates between the sepsis and SIRS phenotype. There were nine significant biomarkers using this method (see Table 62).
The second method used to identify discriminating biomarkers was the Wilcoxon Test (adjusted). Due to the large number of biomarkers, 10, and the relatively small number of samples, 50, there was a high risk of finding falsely significant biomarkers. An adjusted p value was used to counter this risk. In particular, the method of Benjamini and Hochberg, 1995, J.R. Statist. Soc. B 57, pp 289-300, which is hereby incorporated herein by reference in its entirety, was used to control the false discovery rate. Here, the false discovery rate is defined as the number of biomarkers truly significant divided by the number of biomarkers declared significant. For example, if the adjusted p value is less than 0.05, there is a 5% chance that the biomarker is a false discovery. Results using this test are reported in Table 62. There were nine significant biomarkers using this method (see Table 62). As used, herein, a biomarker is considered significant if it has a p value of less than 0.05 as determined by the Wilcoxon test (adjusted).
The third method used to identify discriminating biomarkers was the use of Q values. In such an approach, the biomarkers are ordered by their Q values and if a respective biomarker has a Q value of X, then respective biomarker and all others more significant have a combined false discovery rate of X. However, the false discovery rate for any one biomarker may be much larger. There were nine significant biomarkers using this method (see Table 62).







TABLE 62







Cumulative number of significant calls for the three methods.


Note that all samples (training and validation) were used to


compare Sepsis and SIRS groups. Missing biomarker feature


values were not included in the analyses.















≦1e−04
≦0.001
≦0.01
≦0.025
≦0.05
≦0.1
≦1


















p-value
0
7
9
9
9
9
10


(unadjusted)


p-value
0
7
9
9
9
9
10


(adjusted)


q-value
0
0
0
0
0
0
10









CART. In addition to analyzing the microarray data using Wilcoxon and Q-value tests in order to identify biomarkers that discriminate between the sepsis and SIRS subpopulations in the training set, classification and regression tree (CART) analysis was used. CART is described in Section 5.5.1, above. Specifically, the data summarized above was used to predict the disease state by iteratively partitioning the data based on the best single-variable split of the data. In other words, at each stage of the tree building process, the biomarker whose expression values across the training population best discriminate between the sepsis and SIRS population was invoked as a decision branch. Cross-validation was carried out, with the optimal number of splits estimated independently in each of the 10 iterations. The final tree uses three biomarkers which are listed in order of importance IL18R1, ARG2, and FCGR1A, where IL18R1 was the most important. The confusion matrix for the training data, based on the final tree from the cross-validated CART algorithm is given in Table 63. From this confusion matrix, the overall accuracy was estimated to be 82.1% with a 95% confidence interval of 66.5% to 92.5%. The estimated sensitivity was 82.6% and the estimated specificity was 81.2%.







TABLE 63







Confusion matrix for training samples using


the cross-validated CART algorithm










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
19
3



SIRS
4
13










For the 11 validation samples held back from training data set, the overall accuracy was estimated to be 100% with a 95% confidence interval of 71.5% to 100%, sensitivity 100% and specificity 100%. Table 64 shows the confusion matrix for the validation samples.







TABLE 64







Confusion matrix for validation samples


using the cross-validated CART algorithm










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
5
0



SIRS
0
6










Random Forests. Another decision rule that can be developed using biomarkers of the present invention is a Random Forests decision tree. Random Forests is a tree based method that uses bootstrapping instead of cross-validation. For each iteration, a random sample (with replacement) is drawn and the largest tree possible is grown. Each tree receives a vote in the final class prediction. To fit a random forest, the number of trees (e.g. bootstrap iterations) is specified. For this data, 1000 trees were used to train the algorithm. Using this algorithm, 9 of the 10 biomarkers had non-zero importance and were used in the model. Biomarker importance, from greatest to smallest, was: TGFB1, MMP9, TLR4, IL1RN, TNFSF, ARG2, FCGR1A, and IL18R1.
The random forest method uses a number of different decision trees. A biomarker is considered to have discriminating significance if it served as a decision branch of a decision tree from a significant random forest analysis. As used herein, a significant random forest analysis is one where the lower 95% confidence interval on accuracy by cross validation on a training data set is greater than 50% and the point estimate for accuracy on a validation set is greater than 65%.
The predicted confusion matrix for the training dataset using the decision tree developed using the Random Forest method is given in Table 65. From this confusion matrix, the overall accuracy was estimated to be 79.5% with a 95% confidence interval between 63.5% and 90.7%. The estimated sensitivity was 87% and the estimated specificity was 68.8%.







TABLE 65







Confusion matrix for training samples against the decision


tree developed using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
20
5



SIRS
3
11










For the 11 validation samples held back from training, the overall accuracy was estimated to be 81.8% with a 95% confidence interval of 48.2% to 97.7%, sensitivity 60% and specificity 100%. Table 66 shows the confusion matrix for the validation samples.







TABLE 66







Confusion matrix for the 11 validation samples against the decision


tree developed using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
6
0



SIRS
2
3










MART. Multiple Additive Regression Trees (MART), also known as “gradient boosting machines,” was used to simultaneously assess the importance of biomarkers and classify the subject samples. Several fitting parameters are specified in this approach including (i) number of trees, (ii) step size (commonly referred to as “shrinkage”), and (iii) degree of interaction (related to the number of splits for each tree). More information on MART is described in Section 5.5.4 above. The degree of interaction was set to 1 to enforce an additive model (e.g. each tree has one split and only uses one biomarker).
Estimating interactions may require more data to function well. The step size was set to 0.05 so that the model complexity was dictated by the number of trees. The optimal number of trees was estimated by leaving out a random subset of cases at each fitting iteration, then assessing quality of prediction on that subset. After fitting more trees than were warranted, the point at which prediction performance stopped improving was estimated as the optimal point.
The estimated model used 30 trees and 7 biomarkers across all trees. The MART algorithm also provides a calculation of biomarker importance (summing to 100%). Biomarkers ranked in decreasing order of importance to the model, with the most important biomarker first were: ITGAM, TGFB1, TLR4, TNFSF, FCGR1A, IL18R1, and ARG2.
Cross-validation was carried out, with the optimal number of trees estimated independently in each of the 10 iterations. The confusion matrix for the training data where the predicted classifications were made from the cross-validated model is given in Table 67. From this confusion matrix, the overall accuracy was estimated to be 74.4% with a 95% confidence interval of 57.9% to 87%. The estimated sensitivity was 73.8% and the estimated specificity was 68.8%.







TABLE 67







Confusion matrix for the training samples using


the cross-validated MART algorithm.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
18
5



SIRS
5
11










For the 11 validation samples held back from training, the overall accuracy was estimated to be 74.4% with a 95% confidence interval of 57.9% to 87%, sensitivity 78.3% and specificity 68.8%. Table 68 shows the confusion matrix for the validation samples.







TABLE 68







Confusion matrix for the validation samples


using the MART algorithm.










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
6
2



SIRS
0
3










PAM. Yet another decision rule developed using biomarkers of the present invention is predictive analysis of microarrays (PAM), which is described in Section 5.5.2, above. In this method, a shrinkage parameter that determines the number of biomarkers used to classify samples is specified. This parameter was chosen via cross-validation. There were no biomarkers with missing values. Based on cross-validation, the optimal threshold value was 0.55, corresponding to 9 biomarkers.
Using the threshold of 0.55, the overall accuracy for the training samples was estimated to be 82.3% with a 95% confidence interval of 68.8% to 90.7%. The estimated sensitivity was 68.8% and the estimated specificity was 91.3%. Table 69 shows the confusion matrix for the training data where the predicted classifications were made from the cross-validated models.







TABLE 69







Confusion matrix for training samples using


cross-validated PAM algorithm










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
11
2



SIRS
5
21










For the 11 validation samples held back from training, the overall accuracy was estimated to be 72.67% with a 95% confidence interval of 39% to 94%, sensitivity 40% and specificity 100%. Table 70 shows the confusion matrix for the validation samples.







TABLE 70







Confusion matrix for validation samples using


cross-validated PAM algorithm










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
2
0



SIRS
3
6










The top nine biomarkers identified by PAM, ranked from most important to least important were: ARG2, TGFB1, MMP9, TLR4, ITGAM, IL18R1, TNFSF, IL1RN, and FCGR1A. FIG. 47 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T−12 static data obtained from an Affymetrix gene chip confirmatory training population. Based on the results of the RT-PCR analysis summarized in FIG. 47 and at other time points, all ten biomarkers under study in this confirmation process were significant. Some of the biomarkers discriminated as early as T−60.
6.12.2 Confirmatory Analysis of Biomarkers Identified by Affymetrix Gene Chip Analysis
The biomarkers identified in Table 59 of Section 6.11.2 and the ten biomarkers identified in Table 48 of Section 6.11.1 (FCGR1A, MMP9, IL18R1, ARG2, IL1RN, TNFSF13B, ITGAM, TGFB1, CD86, and TLR4), a total of 44 biomarkers, were analyzed using RT-PCR at multiple time points and analyzed in several different ways: static time of entry, static T−60, static T−36, baseline T−60, baseline T−36, and baseline T−12 data points. RT-PCR is described in Section 5.4.1.2, above. Representative of these analyses is the static T−12 data analysis described in detail below. In the T−12 static analysis, biomarkers features were measured using a specific blood sample, designated the T−12 blood sample, as defined in Section 6.4, above.
For the T−12 static analysis, the 44 biomarkers were measured from 37 samples. Each sample was collected from a different member of the population. Of these biomarkers, 23 were transformed by log transformations, and 21 by square root transformations.
The 37 member population was initially split into a training set (n=28) and a validation set (n=9). The training set was used to estimate the appropriate classification algorithm parameters while the trained algorithm was applied to the validation set to independently assess performance. Of the 28 training samples, 14 were labeled Sepsis, meaning that the subjects developed sepsis at some point during the observation time period, and 14 were SIRS, meaning that they did not develop sepsis during the observation time period. Table 71 provides distributions of the race, gender and age for these samples.







TABLE 71





Distributions of the race, gender, and age for the training data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
1
7
0




Female
0
6
0



SIRS
Male
4
6
1




Female
0
2
0

















Group
Minimum
Mean
Median
Maximum







Sepsis
28
58
56
76



SIRS
20
42.5
39.5
79










For the 9 validation samples, five were labeled Sepsis and four were labeled SIRS. Table 72 provides distributions of the race, gender and age for these samples.







TABLE 72





Distributions of the race, gender, and age for the validation data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
2
0
0




Female
0
3
0



SIRS
Male
0
2
0




Female
0
2
0

















Group
Minimum
Mean
Median
Maximum







Sepsis
18
49.8
58
76



SIRS
24
45.8
41.5
76










Each sample in the training data was randomly assigned to one of ten groups used for cross-validation. The number of training samples in these groups ranged from two to four. The samples were assigned in way that attempted to balance the number of sepsis and SIRS samples across folds. As described in more detail below, several different methods were used to judge whether select biomarkers discriminate between the Sepsis and SIRS groups.
Wilcoxon and Q-value tests. The first method used to identify discriminating biomarkers was a Wilcoxon test (unadjusted). The abundance value for a given biomarker across the samples in the training data was subjected to the Wilcoxon test. The Wilcoxon test considers both group classification (sepsis versus SIRS) and abundance value in order to compute a p value for the given biomarker. The p value provides an indication of how well the abundance value for the given biomarker across the samples collected in the training set discriminates between the sepsis and SIRS state. The lower the p value, the better the discrimination. When the p value is less than a specific confidence level, such as 0.05, an inference is made that the biomarker discriminates between the sepsis and SIRS phenotype. There were 38 significant biomarkers using this method (see Table 73).
The second method used to identify discriminating biomarkers was the Wilcoxon Test (adjusted). Due to the large number of biomarkers, 44, and the relatively small number of samples, 37, there was a high risk of finding falsely significant biomarkers. An adjusted p value was used to counter this risk. In particular, the method of Benjamini and Hochberg, 1995, J.R. Statist. Soc. B 57, pp 289-300, which is hereby incorporated herein by reference in its entirety, was used to control the false discovery rate. Here, the false discovery rate is defined as the number of biomarkers truly significant divided by the number of biomarkers declared significant. For example, if the adjusted p value is less than 0.05, there is a five percent chance that the biomarker is a false discovery. Results using this test are reported in Table 73. There were 38 significant biomarkers using this method (see Table 73). As used, herein, a biomarker is considered significant if it has a p value of less than 0.05 as determined by the Wilcoxon test (adjusted).
The third method used to identify discriminating biomarkers was Q values. In this third approach, the biomarkers were ordered by their Q values and if a respective biomarker has a Q value of X, then respective biomarker and all others more significant have a combined false discovery rate of X. However, the false discovery rate for any one biomarker may be much larger. There were 38 significant biomarkers using this method (see Table 73).







TABLE 73







Cumulative number of significant calls for the three methods.


Note that all samples (training and validation) were used to


compare sepsis and SIRS groups. Missing biomarker


feature values were not included in the analyses.















≦1e−04
≦0.001
≦0.01
≦0.025
≦0.05
≦0.1
≦1


















p-value
0
27
38
38
38
38
44


(unadjusted)


p-value
0
23
38
38
38
38
44


(adjusted)


q-value
0
36
38
39
39
39
44









CART. In addition to analyzing the microarray data using Wilcoxon and Q-value tests in order to identify biomarkers that discriminate between the sepsis and SIRS subpopulations in the training set, classification and regression tree (CART) analysis was used. CART is described in Section 5.5.1, above. Specifically, the data summarized above was used to predict the disease state by iteratively partitioning the data based on the best single-variable split of the data. In other words, at each stage of the tree building process, the biomarker whose expression values across the training population best discriminate between the sepsis and SIRS population was invoked as a decision branch. Cross-validation was carried out with the optimal number of splits estimated independently in each of the 10 iterations. The final tree uses three biomarkers which are listed in order of importance OSM, HLA-DRA, and IL-18, where OSM was the most important. The confusion matrix for the training data, based on the final tree from the cross-validated CART algorithm is given in Table 74. From this confusion matrix, the overall accuracy was estimated to be 67.9% with a 95% confidence interval of 47.6% to 84.1%. The estimated sensitivity was 64.3% and the estimated specificity was 71.4%.







TABLE 74







Confusion matrix for training samples using the


cross-validated CART algorithm










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
9
4



SIRS
5
10










For the 9 validation samples held back from training data set, the overall accuracy was estimated to be 88.9% with a 95% confidence interval of 51.8% to 99.7%, sensitivity 75% and specificity 100%. Table 75 shows the confusion matrix for the validation samples.







TABLE 75







Confusion matrix for validation samples using


the cross-validated CART algorithm










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
3
0



SIRS
1
5










Random Forests. Another decision rule that can be developed using biomarkers of the present invention is a Random Forests decision tree. Random Forests is a tree based method that uses bootstrapping instead of cross-validation. For each iteration, a random sample (with replacement) is drawn and the largest tree possible is grown. Each tree receives a vote in the final class prediction. To fit a random forest, the number of trees (e.g. bootstrap iterations) is specified. For this data, 1000 trees were used to train the algorithm. Using this algorithm, 35 of the 44 biomarkers had non-zero importance and were used in the model. Biomarker importance, from greatest to smallest, was: OSM, GADD45B, ARG2, IL18R1, TDRD9, PFKFB3, MAPK14, PRV1, MAP2K6, TNFRSF6, FCGR1A, INSL3, LY96, PSTPIP2, ANKRD22, TNFSF10, HLA-DRA, FNDC3B, TIFA, GADD45A, VNN1, ITGAM, BCL2A1, TLR4, TNFSF13B, SOCS3, IL1RN, CEACAM1, and SOD2.
The random forest method uses a number of different decision trees. A biomarker is considered to have discriminating significance if it served as a decision branch of a decision tree from a significant random forest analysis. As used herein, a significant random forest analysis is one where the lower 95% confidence interval on accuracy by cross validation on a training data set is greater than 50% and the point estimate for accuracy on a validation set is greater than 65%.
The predicted confusion matrix for the training dataset using the decision tree developed using the Random Forest method is given in Table 76. From this confusion matrix, the overall accuracy was estimated to be 78.6%. The estimated sensitivity was 78.6% and the estimated specificity was also 78.6%.







TABLE 76







Confusion matrix for training samples against the decision tree


developed using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
11
3



SIRS
3
11










For the 9 validation samples held back from training, the overall accuracy was estimated to be 77.8% with a 95% confidence interval of 40.0% to 97.2%, sensitivity 50% and specificity 100%. Table 77 shows the confusion matrix for the validation samples.







TABLE 77







Confusion matrix for validation samples against the decision


tree developed using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
5
0



SIRS
2
2










MART. Multiple Additive Regression Trees (MART), also known as “gradient boosting machines,” was used to simultaneously assess the importance of biomarkers and classify the subject samples. Several fitting parameters are specified in this approach including (i) number of trees, (ii) step size (commonly referred to as “shrinkage”), and (iii) degree of interaction (related to the number of splits for each tree). More information on MART is described in Section 5.5.4 above. The degree of interaction was set to 1 to enforce an additive model (e.g. each tree has one split and only uses one biomarker).
Estimating interactions may require more data to function well. The step size was set to 0.05 so that the model complexity was dictated by the number of trees. The optimal number of trees was estimated by leaving out a random subset of cases at each fitting iteration, then assessing quality of prediction on that subset. After fitting more trees than were warranted, the point at which prediction performance stopped improving was estimated as the optimal point.
The estimated model used 21 trees and 9 biomarkers across all trees. The MART algorithm also provides a calculation of biomarker importance (summing to 100%). Biomarkers ranked in decreasing order of importance to the model, with the most important biomarker first were: ARG2, GADD45B, OSM, LY96, INSL3, ANKRD22, MAP2K6, PSTPIP2, and TGFB1.
Cross-validation was carried out, with the optimal number of trees estimated independently in each of the 10 iterations. The confusion matrix for the training data where the predicted classifications were made from the cross-validated model is given in Table 78. From this confusion matrix, the overall accuracy was estimated to be 75% with a 95% confidence interval of 55.1 to 89.3%. The estimated sensitivity was 71.4% and the estimated specificity was 78.6%.







TABLE 78







Confusion matrix for the training samples using the cross-validated MART


algorithm.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
10
3



SIRS
4
11










For the 9 validation samples held back from training, the overall accuracy was estimated to be 88.9% with a 95% confidence interval of 51.8% to 99.7%, sensitivity 100% and specificity 75%. Table 79 shows the confusion matrix for the validation samples.







TABLE 79







Confusion matrix for the validation samples using the MART algorithm.










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
5
1



SIRS
0
3










PAM. Yet another decision rule developed using biomarkers of the present invention is predictive analysis of microarrays (PAM), which is described in Section 5.5.2, above. In this method, a shrinkage parameter that determines the number of biomarkers used to classify samples is specified. This parameter was chosen via cross-validation. There were no biomarkers with missing values. Based on cross-validation, the optimal threshold value was 2.05, corresponding to 6 biomarkers.
Using the threshold of 2.05, the overall accuracy for the training samples was estimated to be 82.5% with a 95% confidence interval of 68.7% to 91%. The estimated sensitivity was 78.6% and the estimated specificity was 85.7%. Table 80 shows the confusion matrix for the training data where the predicted classifications were made from the cross-validated models.







TABLE 80







Confusion matrix for training samples using cross-validated PAM


algorithm










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
11
2



SIRS
3
12










For the 9 validation samples held back from training, the overall accuracy was estimated to be 77.8% with a 95% confidence interval of 40% to 97.2%, sensitivity 50% and specificity 100%. Table 81 shows the confusion matrix for the validation samples.







TABLE 81







Confusion matrix for validation samples using cross-validated PAM


algorithm










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
2
0



SIRS
2
5










The top six biomarkers identified by PAM, ranked from most important to least important were: GADD45B, TDRD9, MAP2K6, OSM, TNFSF10, and ANKRD22. FIG. 48 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals using T−12 static data for the 44 biomarkers analyzed in this Section. Based on the results of the RT-PCR analysis summarized in FIG. 48 and at other time points, all forty-four biomarkers under study in this confirmation process were significant. Some of the biomarkers discriminated as early as T−60.
6.13 Select Protein Biomarkers
In this example, experiments were performed in order to confirm which protein based biomarkers differentiate between patients who subsequently develop sepsis (“sepsis patients) and patients who do not (“SIRS patients). In the discovery process, samples were analyzed by a bead based protein immunoassay, as described in Section 6.13.1.
6.13.1 Discovery of Protein Biomarkers Using a Bead Based Protein Immunoassay
Multiplex Analysis. A set of biomarkers was analyzed simultaneously in real time, using a multiplex analysis method described in U.S. Pat. No. 5,981,180 (“the '180 patent”), herein incorporated by reference in its entirety, and in particular for its teachings of the general methodology, bead technology, system hardware and antibody detection. For this analysis, a matrix of microparticles was synthesized, where the matrix consisted of different sets of microparticles. Each set of microparticles had thousands of molecules of a distinct antibody capture reagent immobilized on the micro particle surface and was color-coded by incorporation of varying amounts of two fluorescent dyes. The ratio of the two fluorescent dyes provided a distinct emission spectrum for each set of microparticles, allowing the identification of a microparticle within a set following the pooling of the various sets of microparticles. U.S. Pat. Nos. 6,268,222 and 6,599,331 also are incorporated herein by reference in their entirety, and in particular for their teachings of various methods of labeling microparticles for multiplex analysis.
The sets of labeled beads were pooled and combined with a plasma sample from individuals. The labeled beads were identified by passing them single file through a flow device that interrogated each microparticle with a laser beam that excited the fluorophore labels. An optical detector then measured the emission spectrum of each bead to classify the beads into the appropriate set. Because the identity of each antibody capture reagent was known for each set of microparticles, each antibody specificity was matched with an individual microparticle that passes through the flow device. U.S. Pat. No. 6,592,822 is also incorporated herein by reference in its entirety, and in particular for its teachings of multi-analyte diagnostic system that can be used in this type of multiplex analysis.
To determine the amount of analyte that bound a given set of microparticles, a reporter molecule was added such that it formed a complex with the antibodies bound to their respective analyte. In the present example, the reporter molecule was a fluorophore-labeled secondary antibody. The fluorophore on the reporter was excited by a second laser having a different excitation wavelength, allowing the fluorophore label on the secondary antibody to be distinguished from the fluorophores used to label the microparticles. A second optical detector measured the emission from the fluorophore label on the secondary antibody to determine the amount of secondary antibody complexed with the analyte bound by the capture antibody. In this manner, the amount of multiple analytes captured to beads could be measured in a single reaction.
Data Analysis and Results. For each sample, the concentrations of analytes that bound several different antibodies were measured. Each analyte is a biomarker, and the concentration of each analyte in the sample can be a feature of that biomarker. The biomarkers were analyzed with select antibody reagents listed in Table 14 of United States Patent Publication Number U.S. 2004/0096917 A1, which is hereby incorporated herein by reference in its entirety. These antibody reagents are commercially available from Rules Based Medicine (Austin, Tex.). The antibody reagents are categorized as specifically binding either (1) circulating protein biomarker components of blood, (2) circulating antibodies that normally bind molecules associated with various pathogens (identified by the pathogen that each biomarker is associated with, where indicated), or (3) autoantibody biomarkers that are associated with various disease states. Various approaches may be used to identify features that can inform a decision rule to classify individuals into the SIRS or sepsis groups. The methods chosen were CART, MART, PAM and random forests.
Biomarkers in multiple samples were measured using the above described assay at multiple time points and analyzed in several different ways: static time of entry, static T−60, static T−36, baseline T−60, baseline T−36, and baseline T−12 data points. Representative of these analyses is the static T−12 data analysis which is described in detail below. In the T−12 static analysis, biomarkers features were measured using a specific blood sample, designated the T−12 blood sample, as defined in Section 6.4, above.
For the T−12 static analysis, there were 60 biomarkers measured on 97 samples. Each sample was collected from a different member the population. Of these features, 53 were transformed by log transformations, 11 by square root transformations and the remaining 2 were not transformed.
The 97 member population was initially split into a training set (n=74) and a validation set (n=23). The training set was used to estimate the appropriate classification algorithm parameters while the trained algorithm was applied to the validation set to independently assess performance. Of the 74 training samples, 36 were labeled Sepsis, meaning that the subjects developed sepsis at some point during the observation time period, and 38 were labeled SIRS, meaning that they did not develop sepsis during the observation time period. Table 82 provides distributions of the race, gender and age for these samples.







TABLE 82





Distributions of the race, gender, and age for the training data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
10
14
1




Female
0
10
1



SIRS
Male
5
24
0




Female
0
9
0

















Group
Minimum
Mean
Median
Maximum







Sepsis
18
43.2
40
80



SIRS
18
44.9
40
90










For the 23 validation samples, 12 were labeled Sepsis and 11 were labeled SIRS. Table 83 provides distributions of the race, gender and age for these samples.







TABLE 83





Distributions of the race, gender, and age for the validation data





















Group
Gender
Black
Caucasian
Other







Sepsis
Male
0
7
0




Female
0
4
0



SIRS
Male
2
6
0




Female
0
4
0

















Group
Minimum
Mean
Median
Maximum







Sepsis
18
43.4
43
81



SIRS
19
51.9
51.5
85










Each sample in the training data was randomly assigned to one of ten groups used for cross-validation. The number of training samples in these groups ranged from 6 to 8. The samples were assigned in way that attempted to balance the number of sepsis and SIRS samples across folds. As described in more detail below, several different methods were used to judge whether select biomarkers discriminate between the Sepsis and SIRS groups.
Wilcoxon and Q-value tests. The first method used to identify discriminating biomarkers was a Wilcoxon test (unadjusted). The abundance value for a given biomarker across the samples in the training data was subjected to the Wilcoxon test. The Wilcoxon test considers both group classification (sepsis versus SIRS) and abundance value in order to compute a p value for the given biomarker. The p value provides an indication of how well the abundance value for the given biomarker across the samples collected in the training set discriminates between the sepsis and SIRS state. The lower the p value, the better the discrimination. When the p value is less than a specific confidence level, such as 0.05, an inference is made that the biomarker discriminates between the sepsis and SIRS phenotype. There were 24 significant biomarkers using this method (see Table 84).
The second method used to identify discriminating biomarkers was the Wilcoxon Test (adjusted). Due to the large number of biomarkers, 60, in combination with the relatively small number of samples, 97, there was a high risk of finding falsely significant biomarkers. An adjusted p-value was used to counter this risk. In particular, the method of Benjamini and Hochberg, 1995, J.R. Statist. Soc. B 57, pp 289-300, which is hereby incorporated herein by reference in its entirety, was used to control the false discovery rate. Here, the false discovery rate is defined as the number of biomarkers truly significant divided by the number of biomarkers declared significant. For example, if the adjusted p-value is less than 0.05, there is a 5% chance that the biomarker is a false discovery. Results using this test are reported in Table 84. There were 16 significant biomarkers using this method (see Table 84). As used, herein, a biomarker is considered significant if it has a p-value of less than 0.05 as determined by the Wilcoxon test (adjusted).
The third method used to identify discriminating biomarkers was the use of Q values. In such an approach, the biomarkers are ordered by their q-values and if a respective biomarker has a q-value of X, then respective biomarker and all others more significant have a combined false discovery rate of X. However, the false discovery rate for any one biomarker may be much larger. There were 16 significant biomarkers using this method (see Table 84).







TABLE 84







Cumulative number of significant calls for the three methods.


Note that all samples (training and validation) were used to


compare Sepsis and SIRS groups. Missing biomarker


feature values were not included in the analyses.















≦1e−04
≦0.001
≦0.01
≦0.025
≦0.05
≦0.1
≦1


















p-value
0
6
14
20
24
25
60


(unadjusted)


p-value
0
0
 6
13
16
24
60


(adjusted)


q-value
0
0
13
20
25
31
60









CART. In addition to analyzing the microarray data using Wilcoxon and Q-value tests in order to identify biomarkers that discriminate between the sepsis and SIRS subpopulations in the training set, classification and regression tree (CART) analysis was used. CART is described in Section 5.5.1, above. Specifically, the data summarized above was used to predict the disease state by iteratively partitioning the data based on the best single-variable split of the data. In other words, at each stage of the tree building process, the biomarker whose expression values across the training population best discriminate between the sepsis and SIRS population was invoked as a decision branch. Cross-validation was carried out, with the optimal number of splits estimated independently in each of the 10 iterations. The final tree is depicted in FIG. 49, and uses ten biomarkers: MIP1beta, thrombopoietin, C reactive protein, IL-10, IL-16, beta-2 microglobulin, alpha fetoprotein, IL-6, adiponectin, and ICAM1.
FIG. 50 shows the distribution of the ten biomarkers used in the decision tree between the sepsis and SIRS groups in the training data set. In FIG. 50, the top of each box denotes the 75th percentile of the data across the training set and the bottom of each box denotes the 25th percentile, and the median value for each biomarker across the training set is drawn as a line within each box. The confusion matrix for the training data where the predicted classifications were made from the cross-validated model is given in Table 85. From this confusion matrix, the overall accuracy was estimated to be 63.5% with a 95% confidence interval of 51.5% to 74.4%. The estimated sensitivity was 66.7% and the estimated specificity was 60.5%.







TABLE 85







Confusion matrix for training samples using cross-validated CART










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
24
15



SIRS
12
23










For the 23 validation samples held back from training data set, the overall accuracy was estimated to be 65.2% with a 95% confidence interval of 42.7% to 83.6%, sensitivity 66.7% and specificity 63.6%. Table 86 shows the confusion matrix for the validation samples.







TABLE 86







Confusion matrix for validation samples using cross-validated CART










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
8
1



SIRS
4
10










Random Forests. Another decision rule that can be developed using biomarkers of the present invention is a Random Forests decision tree. Random Forests is a tree based method that uses bootstrapping instead of cross-validation. For each iteration, a random sample (with replacement) is drawn and the largest tree possible is grown. Each tree receives a vote in the final class prediction. To fit a random forest, the number of trees (e.g. bootstrap iterations) is specified. No more than 500 were used in this example, but at least 50 are needed for a burn-in period. The number of trees was chosen based on the accuracy of the training data. For this data, 64 trees were used to train the algorithm (see FIG. 51). In FIG. 51, curve 4802 is a smoothed estimate of overall accuracy as a function of tree number. Curve 4804 is a smoothed curve of tree sensitivity as a function of tree number. Curve 4806 is a smoothed curve of tree specificity as a function of tree number. Using this algorithm, 34 biomarkers had non-zero importance and were used in the model. The random forest algorithm gauges biomarker importance by the average reduction in the training accuracy. The biomarkers were ranked by this method and are shown in FIG. 52. The random forest method uses a number of different decision trees. A biomarker is considered to have discriminating significance if it served as a decision branch of a decision tree from a significant random forest analysis. As used herein, a significant random forest analysis is one where the lower 95% confidence interval on accuracy by cross validation on a training data set is greater than 50% and the point estimate for accuracy on a validation set is greater than 65%.
The predicted confusion matrix for the training dataset using the decision tree developed using the Random Forest method is given in Table 87. From this confusion matrix, the overall accuracy was estimated to be 70.3% (confidence intervals cannot be computed when using the bootstrap accuracy estimate). The estimated sensitivity was 69.4% and the estimated specificity was 71.1%.







TABLE 87







Confusion matrix for training samples against the decision tree developed


using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS







Sepsis
27
11



SIRS
11
25










For the 23 validation samples held back from training, the overall accuracy was estimated to be 60.9% with a 95% confidence interval of 38.5% to 80.3%, sensitivity 83.3% and specificity 36.4%. Table 88 shows the confusion matrix for the validation samples.







TABLE 88







Confusion matrix for the 23 validation samples against the decision tree


developed using the Random Forest method.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
10
7



SIRS
2
4










MART. MART was used to simultaneously assess the importance of biomarkers and classify the subject samples. Several fitting parameters are specified in this approach including (i) number of trees, (ii) step size (commonly referred to as “shrinkage”), and (iii) degree of interaction (related to the number of splits for each tree). More information on MART is described in Section 5.5.4 above. The degree of interaction was set to 1 to enforce an additive model (e.g. each tree has one split and only uses one biomarker).
The degree of interaction was set to 1 to enforce an additive model (e.g. each tree has one split and only uses one feature), because this often works well even when a weak interaction is present. Moreover, estimating interactions may require more data to function well. The step size was set to 0.05 so that the model complexity was dictated by the number of trees. The optimal number of trees was estimated by leaving out a random subset of cases at each fitting iteration, then assessing quality of prediction on that subset. After fitting more trees than were warranted, the point at which prediction performance stopped improving was estimated as the optimal point.
The estimated model used 11 trees and 4 biomarkers across all trees. The MART algorithm also provides a calculation of biomarker importance (summing to 100%), which are given in FIG. 53. Biomarkers with zero importance were excluded. FIG. 54 shows the distribution of the selected biomarkers between the Sepsis and SIRS groups.
Cross-validation was carried out, with the optimal number of trees estimated independently in each of the 10 iterations. The confusion matrix for the training data where the predicted classifications were made from the cross-validated model is given in Table 89. From this confusion matrix, the overall accuracy was estimated to be 70.3% with a 95% confidence interval of 58.5% to 80.3%. The estimated sensitivity was 63.9% and the estimated specificity was 76.3%.







TABLE 89







Confusion matrix for the training samples using the cross-validated MART


algorithm.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
23
9



SIRS
13
29










For the 23 validation samples held back from training, the overall accuracy was estimated to be 73.9% with a 95% confidence interval of 51.6% to 89.8%, sensitivity 63.6% and specificity 83.3%. Table 90 shows the confusion matrix for the validation samples.







TABLE 90







Confusion matrix for the validation samples using the MART algorithm.










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
7
2



SIRS
4
10










PAM. Yet another decision rule developed using biomarkers of the present invention is predictive analysis of microarrays (PAM), which is described in Section 5.5.2, above. In this method, a shrinkage parameter that determines the number of biomarkers used to classify samples is specified. This parameter was chosen via cross-validation. There were no biomarkers with missing values. Based on cross-validation, the optimal threshold value was 0.08, corresponding to 59 biomarkers. FIG. 55 shows the accuracy across different thresholds. In FIG. 55, curve 5202 is the overall accuracy (with 95% confidence interval bars). Curve 5204 shows decision rule sensitivity as a function of threshold value. Curve 5206 shows decision rule specificity as a function of threshold value. Using the threshold of 0.08, the overall accuracy for the training samples was estimated to be 74.9 with a 95% confidence interval of 65.3% to 82.5%. The estimated sensitivity was 78.9% and the estimated specificity was 69.4%. Table 91 shows the confusion matrix for the training data where the predicted classifications were made from the cross-validated models.







TABLE 91







Confusion matrix for training samples using


cross-validated PAM algorithm










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
30
11



SIRS
8
25










For the 23 validation samples held back from training, the overall accuracy was estimated to be 65.2% with a 95% confidence interval of 42.7% to 83.6%, sensitivity 91.7% and specificity 36.4%. Table 92 shows the confusion matrix for the validation samples.







TABLE 92







Confusion matrix for validation samples using cross-validated PAM


algorithm










True Diagnosis












Predicted
Sepsis
SIRS















Sepsis
11
7



SIRS
1
4










FIG. 56 shows the selected biomarkers, ranked by their relative discriminatory power, and their relative importance in the model.

FIG. 57 provides a summary of the CART, MART, PAM, and random forests (RF) classification algorithm (decision rule) performance and associated 95% confidence intervals. Fifty distinct biomarkers were selected from across all the algorithms illustrated in FIG. 58. The identity of these fifty selected features is found in FIG. 58. FIG. 58 illustrates an overall ranking of biomarkers for the T−12 data set. For the selected biomarkers, the x-axis depicts the percentage of times that it was selected. Within the percentage of times that biomarkers were selected, the biomarkers are ranked.
From the analysis of the T−12 data set and the other data sets, ten protein based biomarkers were selected for confirmation using the methodology described in Section 16.3.2. These biomarkers are listed in Table 93, below.







TABLE 93







Protein based biomarkers selected for confirmation from immunoassay












Gene
Protein


Gene

Accession
Accession


Symbol
Gene Name
Number
Number





IL-6
INTERLEUKIN 6
NM_000600
NP_000591


IL-8
INTERLEUKIN 8
M28130
AAA59158


CRP
C Reactive protein
CAA39671
NM_000567


IL-10
INTERLEUKIN 10
NM_000572
CAH73907


APOC3
APOLIPOPROTEIN CIII
NM_000040
CAA25648


MMP9
MATRIX
NM_004994
NP_004985



METALLOPROTEINASE 9



(GELATINASE B, 92 KDA



GELATINASE, 92 KDA



TYPE IV COLLAGENASE)


TIMP1
TISSUE INHIBITOR OF
NM_003254
AAA75558



METALLOPROTEINASE 1


MCP1
MONOCYTE
AF493698,
AAQ75526



CHEMOATTRACTANT
AF493697



PROTEIN 1


AFP
ALPHA-FETOPROTEIN
NM_001134
CAA79592


B2M
BETA-2 MICROGLOBULIN
NM_004048
AAA51811









Each of the sequences, genes, proteins, and probesets identified in Table 93 is hereby incorporated by reference.
6.13.2 Confirmation of Protein Biomarkers Using a Bead Based Protein Immunoassay
Confirmation of the biomarkers identified in Table 93 was performed using the same assay described in Section 6.13.1 at multiple time points and analyzed in several different ways: static time of entry, static T−60, static T−36, baseline T−60, baseline T−36, and baseline T−12 data points. Representative of these analyses is the static T−12 data analysis which is described in detail below. In the T−12 static analysis, biomarkers features were measured using a specific blood sample, designated the T−12 blood sample, as defined in Section 6.4, above. FIG. 59 illustrates the results of the analysis of static T−12 bead based protein assay, using CART, MART, PAM and random forests, where the static T−12 time point is as described in Section 6.4. The best decision tree in both the training and validation datasets for CART used six biomarkers. For both the training data and the validation data, the estimated model for MART used 4 biomarkers across all trees. A total of 7 biomarkers were of significance in both the training and the validation sets using PAM. Using random forest, 4 biomarkers under study were actually found to have discriminating significance in both the training and validation data sets. Based on the results of the analysis of the bead based protein immunoassay summarized in FIG. 59, each of the ten protein based biomarkers identified in Section 6.13.1 were confirmed by this experiment.
6.13.3 Confirmation of Protein Biomarkers Using BD Cytometric Bead Array Assay
IL-6, IL-8, and IL-10 proteins were confirmed using the BDTM Cytometric Bead Array (CBA) assay as embodied in the BDTM CBA Human Inflammation Kit. Flow cytometry is an analysis tool that allows for the discrimination of different particles on the basis of size and color. Multiplexing is the simultaneous assay of many analytes in a single sample. CBA employs a series of particles with discrete fluorescence intensities to simultaneously detect multiple soluble analytes. CBA is combined with flow cytometry to create a multiplexed assay. The BD CBA system uses the sensitivity of amplified fluorescence detection by flow cytometry to measure soluble analytes in a particle-based immunoassay. Each bead in a CBA provides a capture surface for a specific protein and is analogous to an individually coated well in an ELISA plate. The BD CBA capture bead mixture is in suspension to allow for the detection of multiple analytes in a small volume sample.
The combined advantages of the broad dynamic range of fluorescent detection via flow cytometry and the efficient capturing of analytes via suspended particles enable CBA to use fewer sample dilutions and to obtain the value of an unknown in substantially less time (compared to conventional ELISA). The BDTM CBA Human Inflammation Kit can be used to quantitatively measure Interleukin-8 (IL-8), Interleukin-1β (IL-1β), Interleukin-6 (IL-6), Interleukin-10 (IL-10), Tumor Necrosis Factor (TNF), and Interleukin-12p70 (IL-12p70) protein levels in a single sample. The kit performance has been optimized for analysis of specific proteins in tissue culture supernatants, EDTA plasma, and serum samples.
Six bead populations with distinct fluorescence intensities have been coated with capture antibodies specific for IL-8, IL-1β, IL-6, IL-10, TNF, and IL-12p70 proteins. The six bead populations are mixed together to form the BDTM CBA which is resolved in the FL3 channel of a flow cytometer such as the BD FACScanTM or BD FACSCaliburTM flow cytometer. The capture beads, PE-conjugated detection antibodies, and recombinant standards or test samples are incubated together to form sandwich complexes. Following acquisition of sample data using the flow cytometer, the sample results are generated in graphical and tabular format using the BDTM CBA Analysis Software. More details about the BDTM CBA Human Inflammation Kit are described in the BDTM CBA Human Inflammation Kit Instruction Manual, catalog number 551811, available from BD biosciences, which is hereby incorporated by reference herein in its entirety. Using the BDTM CBA Human Inflammation Kit, the biomarkers IL-6, IL-8, and IL-10 were confirmed as discriminating between sepsis and SIRS.
6.14 Assessing Subcombinations of the Biomarkers Identified in Table I
One embodiment of the present invention encompasses any 2 or more of the 53 biomarkers listed in Table I as predictors for classifying a subject as sepsis or SIRS. One embodiment of the present invention encompasses any 3 or more of the 53 biomarkers listed in Table I as predictors for classifying a subject as sepsis or SIRS. As such, the present invention further encompasses any subcombination of the 53 biomarkers listed in Table I as predictors for classifying a subject as sepsis or SIRS provided that there are at least 2 or 3 biomarkers in the subcombination. This section discloses experiments that demonstrate the predictive power of exemplary subcombinations of the 53 biomarkers listed in Table I. Several thousand subcombinations were tested and the vast majority of those subcombinations had an accuracy of at least seventy percent. This indicates that the vast majority of the possible subcombinations of the 53 biomarkers listed in Table I will discriminate between sepsis and SIRS subjects.
6.14.1 Subcombinations of Nucleic Acid Biomarkers at T−12 
There are a total of 44 biomarkers for which RT-PCR nucleic acid data is available as reported in Table J. A total of 4800 different subcombinations of this set of biomarkers were constructed using the T−12 time point data described in Section 6.12. Each different subcombination was then tested for its ability to discriminate between sepsis subjects and SIRS subjects. The 4800 subcombinations represent a random sampling of the total number of possible subcombinations possible for the 44 biomarkers of the present invention reported in Table J. Randomness of the 4800 subcombinations was ensured using the following algorithm:












CONSIDER 2 to 25 biomarkers from Table J


{


  LET the current number be k;


  DO the following 200 times


  {


    SELECT k biomarkers at random from Table J;


    LET the current set of biomarkers be S;


  }


  DO the following 10 times


  {


    FOR biomarker set S, randomly set aside 10% of patients as a


    validation population and 90% as a training population;


    FIT a model to the training population using Random Forest


    with T-12 time point data;


    PREDICT results for the validation population;


    CALCULATE agreement with the known status of the validation


    population;


  }


  AVERAGE the ten agreement rates and report;


  SET k = k+1;


  IF k > 10 then END; ELSE return to top;


}


END









There were a total of 152 patients for which T−12 data was available from a combination of discovery and confirmatory data described above. Of these 152 patients, 80 were sepsis and 72 were SIRs. The calculations described above test 200 subcombinations at each interval 2 through 25. In other words, 200 subcombinations each consisting of two biomarkers randomly selected from Table J were tested, 200 subcombinations each consisting of three biomarkers randomly selected from Table J were tested, and so forth, through 200 subcombinations each consisting of twenty-five biomarkers randomly selected from Table J for a total of 24 families of subcombinations, where each family of subcombinations consists of 200 subcombinations of biomarkers each having k biomarkers, where k is a number in the set 2 through 25.
The data set with assay results for all biomarkers under consideration was maintained in memory, as were a list of unique biomarker names. To evaluate subsets of a specific size (say, k=three), then that many (three) biomarker names were selected randomly from the set of unique biomarker names, using a pseudorandom number generators provided in the R software package. See Venables and Smith, An Introduction to R, ISBN 0-9541617-4-2, which is hereby incorporated by reference in its entirety. A matrix of assay results for the selected biomarker names was constructed. This matrix could have more columns than the number of selected biomarker names, since some biomarkers have more than one assay result. An estimate of true predictive accuracy, when using the modeling technique “Random Forest,” was then constructed for this matrix. The Random Forest algorithm was implemented as described in Breiman, 2001, “Random Forests,” Machine Learning 45(1), pp. 5-32, which is hereby incorporated by reference in its entirety.
For a given data matrix, the prediction of true predictive accuracy was calculated as follows: 10% of patients were randomly selected in a balanced manner, i.e., 10% of septic patients and 10% of SIRS patients were selected. Selected patients were set aside for the validation population, and a random forest model was fitted to the remaining data (the training population). If there were any missing values in the training data or the set-aside data, a recursive partitioning model was fitted to other assay results as well as the Sepsis/SIRS information in order to predict assay values. The recursive partitioning model is described in Breiman et al., 1984, Classification and Regression Trees, Wadsworth, which is hereby incorporated by reference in its entirety. Missing values were then replaced with their predicted values. Missing values in the set-aside data were replaced with predictions from the recursive-partition model fitted to the training data, so that knowledge of the SIRS/Sepsis status for the validation population was not used in any way to classify validation patients.
By comparing the true status of the 10% set aside (the validation population) with the predicted status according to the random forest model fitted to the other 90% (the training population), sensitivity, specificity, and agreement were calculated. This process was repeated 10 times, and the final sensitivity, specificity, and agreement estimates (also termed accuracy, also termed performance) for the given marker subset were those values averaged across the 10 iterations. This process was applied to every subset. For each size considered (k value, i.e., number of biomarkers), 200 random subsets were selected and evaluated. These 200 performance (accuracy) estimates form an estimate of the distribution of performances of all subsets of biomarkers of a given size (k value).
FIG. 60 plots the accuracy of each of these 24 families of subcombinations as bar graphs. FIG. 61 plots the accuracy (performance) of each individual subcombination in each of the 24 families of subcombinations. Thus, FIG. 61 plots the accuracy (performance) of a total of 4800 subcombinations of the set of biomarkers listed in Table J.
FIGS. 60 and 61 indicate that for k>5, the distributions are Gaussian, (bell-shaped), indicating that each respective family (k=5, . . . , 24) is an accurate depiction of the subcombination space represented by the family. For k<=5, a handful of subsets give lower accuracy (performance) estimates. However, the results available for k<=5 indicate that this class of biomarker subcombinations discriminate between sepsis and SIRS as well. The results reported in FIGS. 60 and 61 show that, with as few as two biomarkers randomly selected from Table J, an accuracy (performance) estimate above 50% was virtually always obtained. Table 94 contains the number of subcombinations in each family (k=2, 4, . . . , 25) that performed with a threshold accuracy of greater than 60% (column 2), greater than 70% (column 3), greater than 80% (column 4), greater than 90% (column 5), or an accuracy of less than 60% (column 6). The data summarized in FIGS. 60 and 61, as well as Table 94, demonstrates that, for time T−12 data, almost all subcombinations of biomarkers comprising between 2 and 25 biomarkers from Table J will discriminate between sepsis and SIRs subjects.







TABLE 94







Number of subcombinations from Table J that


performed with a given threshold accuracy using


T-12 nucleic acid data












Column 1
Column 2
Column 3
Column 4
Column 5
Column 6


Number of
Greater
Greater
Greater
Greater
Less than


Biomarkers
than 60%
than 70%
than 80%
than 90%
60%















2
197
145
17
0
3


3
199
160
25
0
1


4
199
174
38
0
1


5
200
186
51
0
0


6
200
194
50
0
0


7
200
195
48
0
0


8
200
194
64
0
0


9
200
200
61
1
0


10
200
196
64
0
0


11
200
196
70
0
0


12
200
199
70
0
0


13
200
199
73
0
0


14
200
198
67
0
0


15
200
198
79
0
0


16
200
198
70
0
0


17
200
198
64
0
0


18
200
199
84
0
0


19
200
197
83
0
0


20
200
199
82
0
0


21
200
199
85
0
0


22
200
198
80
0
0


23
200
200
83
0
0


24
200
198
83
0
0


25
200
198
81
0
0









6.14.2 Subcombinations of Protein Biomarkers at T−12 
There are a total of 10 biomarkers for which protein abundance data is available as reported in Table K. A total of 1600 different subcombinations of this set of biomarkers were constructed using the T−12 time point data described in Section 6.13. Each different subcombination was then tested for its ability to discriminate between sepsis and SIRS subjects. The 1600 subcombinations represent a random sampling of the total number of possible subcombinations possible for the 10 biomarkers of the present invention reported in Table K. Randomness of the 1600 subcombinations was ensured using the following algorithm:












CONSIDER 3 to 10 biomarkers from Table K


{


  LET the current number be k;


  DO the following 200 times


  {


    SELECT k biomarkers at random from Table K;


    LET the current set of biomarkers be S;


  }


  DO the following 10 times


  {


    FOR biomarker set S, randomly set aside 10% of patients as a


    validation population and 90% as a training population;


    FIT a model to the training population using Random Forest


    with T-12 time point data;


    PREDICT results for the validation population;


    CALCULATE agreement with the known status of the validation


    population;


  }


  AVERAGE the ten agreement rates and report;


  SET k = k+1;


  IF k > 10 then END; ELSE return to top;


}


END









Computations were performed as described in further detail in Section 6.14.1. There were a total of 152 patients for which T−12 data was available from a combination of discovery and confirmatory data described above. Of these 152 patients, 80 were sepsis and 72 were SIRs. For some biomarkers in Table K, there were multiple data sources. For instance, there is IL-6, IL-8, and IL-10 protein data from three different labs. Thus, there is a complex pattern of incidence among patients. Some patients may be tested by one lab, others by two, etc. This was determined by how the project evolved and what samples were available (some patient samples were exhausted before they could be tested with assays developed later). To handle this complex incidence, the following strategy was used. In any given iteration, if a protein that was tested in multiple labs was selected, all assay results for the protein were selected. A missing-value imputation scheme was then used to fill out missing values, making it look like all patients were tested with all assays. This data was then fed into the Random Forest model as correlated inputs that measure the same underlying compound. Thus, consider the case where k is equal to 3 and one of the randomly chosen proteins from Table K is IL-8, from 3 different laboratories, and the other 2 proteins are unique meaning that they are each from only one laboratory. The data from all three IL-8 sources are selected, plus the other two unique assays for the other two proteins, for a total of five assays. The missing-value imputation scheme is then used to fill out missing values, making it look like all patients had results from three different sources for a total of nine assays.
The calculations described above test 200 subcombinations at each interval 3 through 10. In other words, 200 subcombinations each consisting of three randomly selected biomarkers from Table K were tested, 200 subcombinations each consisting of four biomarkers randomly selected from Table K were tested, and so forth, through 200 subcombinations each consisting of ten biomarkers randomly selected from Table K for a total of 8 families of subcombinations, where each family of subcombinations consists of 200 subcombinations of biomarkers all having k biomarkers, where k is a number in the set 3 through 10. FIG. 62 plots the accuracy of each of these eight families of subcombinations as bar graphs. FIG. 63 plots the accuracy (performance) of each individual subcombination in each of the eight families of subcombinations. Thus, FIG. 63 plots the accuracy (performance) of a total of 1600 subcombinations of the set of biomarkers listed in Table K.
FIGS. 62 and 63 show that the distribution for each family of subcombinations is Gaussian (bell-shaped), indicating that each respective family (k=3, 4, . . . , 10) is an accurate depiction of the subcombination space represented by the family. The results reported in FIGS. 62 and 63 show that, with as few as three biomarkers randomly selected from Table K, an accuracy (performance) estimate above 50% was virtually always obtained. Table 95 contains the number of subcombinations in each family (k=3, 4, . . . , 10) that performed with a threshold accuracy of greater than 60% (column 2), greater than 70% (column 3), greater than 80% (column 4), greater than 90% (column 5), or an accuracy of less than 60% (column 6). The data summarized in FIGS. 62 and 63, as well as Table 95, demonstrates that, for time T−12 data, almost all subcombinations of biomarkers comprising between 3 and 10 biomarkers from Table K will discriminate between sepsis and SIRs subjects.







TABLE 95







Number of subcombinations from Table K that performed with


a given threshold accuracy using T-12 protein data












Column 1
Column 2
Column 3
Column 4
Column 5
Column 6


Number of
Greater
Greater
Greater
Greater
Less than


Biomarkers
than 60%
than 70%
than 80%
than 90%
60%















3
192
58
0
0
8


4
196
106
1
0
4


5
200
117
1
0
0


6
200
136
0
0
0


7
200
151
1
0
0


8
200
172
1
0
0


9
200
188
1
0
0


10
200
187
0
0
0









6.14.3 Subcombinations of Protein Biomarkers at T−36 
A total of 1600 different subcombinations of the set of biomarkers of Table K were constructed for the T−36 time point using the protein based data described in Section 6.13. Each different subcombination was then tested for its ability to discriminate between sepsis and SIRS subjects. There were a total of 142 patients for which T−36 data was available from a combination of discovery and confirmatory data described above. Of these 142 patients, 79 were sepsis and 63 were SIRs. The 1600 subcombinations represent a random sampling of the total number of possible subcombinations possible for the 10 biomarkers of the present invention reported in Table K. Randomness of the 1600 subcombinations was ensured using the algorithm identified in Section 6.14.2, the only difference being that T−36 data rather than T−12 data was used. Computations were performed as described in further detail in Section 6.14.1. FIG. 64 plots the accuracy of each of these eight families of subcombinations as bar graphs. FIG. 65 plots the accuracy (performance) of each individual subcombination in each of the eight families of subcombinations. Thus, FIG. 65 plots the accuracy (performance) of a total of 1600 subcombinations of the set of biomarkers listed in Table K.
The results reported in FIGS. 64 and 65 show that, with as few as three biomarkers randomly selected from Table K, an accuracy (performance) estimate above 60% was typically obtained. Table 95 contains the number of subcombinations in each family (k=3, 4, . . . , 10) that performed with a threshold accuracy of greater than 60% (column 2), greater than 70% (column 3), greater than 80% (column 4), greater than 90% (column 5), or an accuracy of less than 60% (column 6). The data summarized in FIGS. 64 and 65, as well as Table 95, demonstrates that, for time T−36 data, most subcombinations of biomarkers comprising between 3 and 10 biomarkers from Table K will discriminate between sepsis and SIRs subjects.







TABLE 96







Number of subcombinations from Table K that performed with


a given threshold accuracy using T-36 protein data












Column 1
Column 2
Column 3
Column 4
Column 5
Column 6


Number of
Greater
Greater
Greater
Greater
Less than


Biomarkers
than 60%
than 70%
than 80%
than 90%
60%















3
138
23
0
0
62


4
152
25
0
0
48


5
171
32
0
0
29


6
180
30
0
0
20


7
188
37
0
0
12


8
194
40
0
0
6


9
194
27
0
0
6


10
199
20
0
0
1









6.14.4 Subcombinations of Nucleic Acid Biomarkers at T−36 
A total of 4600 different subcombinations of the set of biomarkers of Table J were constructed for the T−36 time point using the nucleic acid based data described in Section 6.13. Each different subcombination was then tested for its ability to discriminate between sepsis and SIRS subjects. There were a total of 142 patients for which T−36 data was available from a combination of discovery and confirmatory data described above. Of these 142 patients, 79 were sepsis and 63 were SIRs. The 4600 subcombinations represent a random sampling of the total number of possible subcombinations possible for the 44 biomarkers of the present invention reported in Table J at the T−36 time point. Randomness of the 4600 subcombinations was ensured using the algorithm identified in Section 6.14.1, the only difference being that T−36 data rather than T−12 data was used and that the minimum k value was 3. Computations were performed as described in further detail in Section 6.14.1, FIG. 66 plots the accuracy of each of these 23 families of subcombinations as bar graphs. FIG. 67 plots the accuracy (performance) of each individual subcombination in each of the 23 families of subcombinations. Thus, FIG. 67 plots the accuracy (performance) of a total of 4600 subcombinations of the set of biomarkers listed in Table J.
The results reported in FIGS. 66 and 67 show that, with as few as three biomarkers randomly selected from Table J, an accuracy (performance) estimate above 60% was typically obtained. Table 97 contains the number of subcombinations in each family (k=3, 4, . . . , 25) that performed with a threshold accuracy of greater than 60% (column 2), greater than 70% (column 3), greater than 80% (column 4), greater than 90% (column 5), or an accuracy of less than 60% (column 6). The data summarized in FIGS. 66 and 67, as well as Table 97, demonstrate that, for time T−36 data, most subcombinations of biomarkers comprising between 3 and 25 biomarkers from Table J will discriminate between sepsis and SIRs subjects.







TABLE 97







Number of subcombinations from Table J that performed with a


given threshold accuracy using T-36 nucleic acid data












Column 1
Column 2
Column 3
Column 4
Column 5
Column 6


Number of
Greater
Greater
Greater
Greater
Less than


Biomarkers
than 60%
than 70%
than 80%
than 90%
60%















3
194
115
4
0
6


4
196
128
8
0
4


5
200
159
6
0
0


6
200
161
6
0
0


7
200
173
10
0
0


8
200
182
10
0
0


9
200
189
8
0
0


10
200
188
10
0
0


11
200
194
10
0
0


12
200
193
13
0
0


13
200
190
19
0
0


14
200
192
24
0
0


15
200
196
20
0
0


16
200
195
16
0
0


17
200
196
22
0
0


18
200
195
18
0
0


19
200
196
14
0
0


20
200
194
14
0
0


21
200
197
14
0
0


22
200
197
11
0
0


23
200
200
15
0
0


24
200
199
20
0
0


25
200
199
19
0
0









6.14.5 Subcombinations of Combined Nucleic Acid and Protein biomarkers Data at T−12 
There are a total of 53 biomarkers listed Table I. A total of 4600 different subcombinations of this set of biomarkers were constructed using all available T−12 time point data. For the subset of biomarkers in Table I that are listed in Table J, the T−12 time point data consisted of RT-PCR data described above. For the subset of biomarkers that are listed in Table K, the T−12 time point data consisted of bead based data described above. The one exception to this was “MMP9” for which both protein and gene-expression data was available. Therefore, MMP9 gene and protein abundance data was treated as separate biomarkers. To accomplish this, MMP9 nucleic data was termed “MMP9.GE” and MMP9 protein abundance data from the bead based assays was termed MMP9.Protein.
Each different subcombination was tested for its ability to discriminate between sepsis subjects and SIRS subjects. The 4600 subcombinations represent a random sampling of the total number of possible subcombinations possible for the 53 biomarkers of the present invention reported in Table I. Randomness of the 4600 subcombinations was ensured using the following algorithm:












CONSIDER 3 to 25 biomarkers from Table I


{


  LET the current number be k;


  DO the following 200 times


  {


    SELECT k biomarkers at random from Table I;


    LET the current set of biomarkers be S;


  }


  DO the following 10 times


  {


    FOR biomarker set S, randomly set aside 10% of patients as a


    validation population and 90% as a training population;


    FIT a model to the training population using Random Forest


    with T-12 time point data;


    PREDICT results for the validation population;


    CALCULATE agreement with the known status of the validation


    population;


  }


  AVERAGE the ten agreement rates and report;


  SET k = k+1;


  IF k > 10 then END; ELSE return to top;


}


END









There were a total of 152 patients for which T−12 data was available from a combination of discovery and confirmatory data described above. Of these 152 patients, 80 were sepsis and 72 were SIRs. Computations were performed as described in further detail in Section 6.14.1. The calculations described above test 200 subcombinations at each interval 2 through 25. In other words, 200 subcombinations each consisting of three biomarkers randomly selected from Table I were tested, 200 subcombinations each consisting of four biomarkers randomly selected from Table I were tested, and so forth, through 200 subcombinations each consisting of twenty-five biomarkers randomly selected from Table I for a total of 23 families of subcombinations, where each family of subcombinations consists of 200 subcombinations of biomarkers each having k biomarkers, where k is a number in the set 3 through 25. FIG. 68 plots the accuracy of each of these 23 families of subcombinations as bar graphs. FIG. 69 plots the accuracy (performance) of each individual subcombination in each of the 23 families of subcombinations. Thus, FIG. 69 plots the accuracy (performance) of a total of 4600 subcombinations of the set of biomarkers listed in Table I.
FIGS. 68 and 69 indicate that for k>5, the distributions are Gaussian, (bell-shaped), indicating that each respective family (k=5, . . . , 25) is an accurate depiction of the subcombination space represented by the family. For k<=5, a handful of subsets give lower accuracy (performance) estimates. The results reported in FIGS. 68 and 69 show that, with as few as three biomarkers randomly selected from Table I, an accuracy (performance) estimate above 50% was virtually always obtained. Table 98 contains the number of subcombinations in each family (k=3, 4, . . . , 25) that performed with a threshold accuracy of greater than 60% (column 2), greater than 70% (column 3), greater than 80% (column 4), greater than 90% (column 5), or an accuracy of less than 60% (column 6). The data summarized in FIGS. 68 and 69, as well as Table 98, demonstrate that, for time T−12 data, almost all subcombinations of biomarkers comprising between 3 and 25 biomarkers from Table I will discriminate between sepsis and SIRs subjects.







TABLE 98







Number of subcombinations from Table I that performed


with a given threshold accuracy using T-12 combined


nucleic acid and protein data












Column 1
Column 2
Column 3
Column 4
Column 5
Column 6


Number of
Greater
Greater
Greater
Greater
Less than


Biomarkers
than 60%
than 70%
than 80%
than 90%
60%















3
200
157
11
0
0


4
200
166
12
0
0


5
200
191
25
0
0


6
200
191
30
0
0


7
200
195
33
0
0


8
200
198
44
0
0


9
200
200
49
0
0


10
200
198
63
0
0


11
200
199
65
0
0


12
200
200
71
0
0


13
200
200
64
0
0


14
200
200
66
0
0


15
200
200
74
0
0


16
200
199
74
0
0


17
200
200
73
0
0


18
200
200
76
0
0


19
200
200
87
0
0


20
200
199
94
0
0


21
200
200
77
0
0


22
200
200
84
0
0


23
200
200
81
0
0


24
200
200
93
0
0


25
200
200
85
0
0









6.14.6 Subcombinations of Combined Nucleic Acid and Protein Biomarker Data at T−36 
Subcombinations of biomarkers were selected as described in Section 6.14.5, that only difference being that T−36 data rather than T−12 data was used. A total of 4600 different subcombinations of Table I were constructed using all available T−36 time point data. There were a total of 142 patients for which T−36 data was available from a combination of discovery and confirmatory data described above. Of these 142 patients, 79 were sepsis and 63 were SIRs. Computations were performed as described in further detail in Section 6.14.1. The calculations described above test 200 subcombinations at each interval 3 through 25. In other words, 200 subcombinations each consisting of three biomarkers randomly selected from Table I were tested, 200 subcombinations each consisting of four biomarkers randomly selected from Table I were tested, and so forth, through 200 subcombinations each consisting of twenty-five biomarkers randomly selected from Table I for a total of 23 families of subcombinations, where each family of subcombinations consists of 200 subcombinations of biomarkers each having k biomarkers, where k is a number in the set 3 through 25. FIG. 70 plots the accuracy of each of these 23 families of subcombinations as bar graphs. FIG. 71 plots the accuracy (performance) of each individual subcombination in each of the 23 families of subcombinations. Thus, FIG. 71 plots the accuracy (performance) of a total of 4600 subcombinations of the set of biomarkers listed in Table I.
FIGS. 70 and 71 indicate that for k>5, the distributions are Gaussian, (bell-shaped), indicating that each respective family (k=5, . . . , 25) is an accurate depiction of the subcombination space represented by the family. For k<=5, a handful of subsets give lower accuracy (performance) estimates. The results reported in FIGS. 70 and 71 show that, with as few as three biomarkers randomly selected from Table I, an accuracy (performance) estimate above 50% was virtually always obtained. Table 99 contains the number of subcombinations in each family (k=3, 4, . . . , 25) that performed with a threshold accuracy of greater than 60% (column 2), greater than 70% (column 3), greater than 80% (column 4), greater than 90% (column 5), or an accuracy of less than 60% (column 6). The data summarized in FIGS. 70 and 71, as well as Table 99, demonstrate that, for time T−36 data, almost all subcombinations of biomarkers comprising between 3 and 25 biomarkers from Table I will discriminate between sepsis and SIRs subjects.







TABLE 99







Number of subcombinations from Table I that performed with a given


threshold accuracy using T-36 combined nucleic acid and protein data












Column 1
Column 2
Column 3
Column 4
Column 5
Column 6


Number of
Greater
Greater
Greater
Greater
Less than


Biomarkers
than 60%
than 70%
than 80%
than 90%
60%















3
187
96
4
0
13


4
199
127
5
0
1


5
200
145
7
0
0


6
200
149
9
0
0


7
200
148
9
0
0


8
200
157
8
0
0


9
200
175
13
0
0


10
199
179
7
0
1


11
200
180
11
0
0


12
200
175
11
0
0


13
200
184
15
0
0


14
200
184
10
0
0


15
200
180
5
0
0


16
200
190
17
0
0


17
200
191
16
0
0


18
200
190
15
0
0


19
200
191
8
0
0


20
200
196
13
0
0


21
200
198
15
0
0


22
200
195
9
0
0


23
200
195
14
0
0


24
200
195
10
0
0


25
200
196
18
0
0









6.15 Mean Expression Value of Biomarkers in Sepsis and SIRS Patients Identified in Table I
The mean expression values of the biomarkers of Table I were determined for subjects that acquired sepsis (Sepis subjects) and subjects that did not acquire sepsis (SIRS subjects) in the populations described in Sections 6.11.2, 6.12.2 (Affymetrix data), 611.1, 6.12.1 (RT-PCR data), 6.13.3 (bead data), and 6.13.1 and 6.13.2 (bead data) at the T−12, T−36, and T−60 time points. This data is set forth in Table 100 below. In Table 100, a biomarker with the _Affy extension represents the combined data of Sections 6.11.2 and 6.11.2 (Affymetrix data), a biomarker with the 0.18S extension represents the combined data of Sections 6.11.1 and 6.12.1 (RT-PCR data), a biomarker with the BDB extension represents the data of Section 6.13.3, and a biomarker with the RBM extension represents the data of Sections 6.13.1 and 6.13.2.
For nucleic acid biomarkers in Table 100 that were identified by gene arrays (.Affy), the values in Table 100 represent mean relative fluorescence intensity units obtained for the specific probe sequences examined. As such, they are not actual units of measure, just relative quantity of one group to another. Additionally, as part of the data analysis, some of these values may have undergone a log transformation or other adjustment, prior to being reported. The expression values for the nucleic acid biomarkers (0.18S) found in Table 100 are defined by the relative “cycle-time to threshold.” As such, they do not cite actual units of measure, just relative quantity of one group to another. A sample with a higher amount of nucleic acid will become positive sooner (fewer cycles) than one with less nucleic acid, which will require more cycles before the resultant signal crosses a positivity threshold. For the protein biomarkers in Table 101, the units were as follows alphafetoprotein (AFP) μg/mL (micrograms per milliliter of plasma), Beta-2-Microglobulin B2M) μg/mL, Interleukin-6 (IL-6) pg/mL (picograms/milliliter), Interleukin-8 (IL-8) pg/mL, Interleukin-10 (IL-10) pg/mL, Monocyte Chemoatractant Protein 1 (MCP) pg/mL, Matrix Metalloproteinase 9 (MMP9) ng/mL (nanogram/milliliter), Tissue Inhibitor of Metalloproteinase 1 (TIMP 1) ng/mL (nanogram/mL), C Reactive protein (CRP) μg/mL, and Apoliprotein CIII μg/mL.







TABLE 100







Mean expression values for the biomarkers of Table I as measured in the


experimental Affymetrix, RT-PCR, and bead data of Section 6.














SIRS
Sepsis
SIRS
Sepsis
SIRS
Sepsis


Marker
T-12
T-12
T-36
T-36
T-60
T-60
















ANKRD22_Affy
8.33
9.72
8.28
9.27
8.51
9.17


ANXA3_Affy
10.06
11.28
10.19
11.197
10.49
11.14


BCL2A1_Affy
8.55
9.87
8.73
9.48
9.01
9.60


CCL5_Affy
11.69
11.30
11.73
11.22
11.49
11.29


CD86_Affy
8.59
8.10
8.68
8.13
8.57
8.20


CEACAM1_Affy
7.81
8.74
7.83
8.43
7.91
8.43


CRTAP_Affy
9.30
8.81
9.31
8.87
9.31
8.96


CSF1R_Affy
9.36
8.79
9.34
8.93
9.40
8.89


FAD104_Affy
7.89
8.63
8.04
8.39
8.18
8.36


FCGR1A_Affy
7.38
8.10
7.34
7.90
7.53
7.76


GADD45A_Affy
8.26
9.03
8.36
8.95
8.53
9.01


GADD45B_Affy
8.81
9.40
8.80
9.21
8.91
9.20


HLA.DRA_Affy
11.91
11.22
11.80
11.13
11.78
11.28


IFNGR1_Affy
11.20
11.51
11.25
11.53
11.38
11.52


IL18R1_Affy
6.65
8.23
6.81
7.90
7.00
7.95


INSL3_Affy
6.93
7.37
7.01
7.31
7.10
7.23


IRAK2_Affy
7.13
7.66
7.20
7.52
7.19
7.50


IRAK4_Affy
7.76
8.052
7.90
7.90
7.88
7.96


ITGAM_Affy
11.26
11.80
11.36
11.78
11.53
11.78


JAK2_Affy
6.85
7.523
7.02
7.35
7.08
7.38


LDLR_Affy
7.05
7.788
7.09
7.76
7.12
7.75


LY96_Affy
9.50
10.26
9.65
10.03
9.76
10.02


MAP2K6_Affy
8.261
9.17
8.49
9.02
8.59
9.05


MAPK14_Affy
8.75
9.75
8.99
9.51
9.16
9.48


MKNK1_Affy
10.02
10.59
10.07
10.50
10.30
10.60


Gene_MMP9_Affy
12.05
13.03
12.28
12.95
12.41
12.93


NCR1_Affy
5.64
5.94
5.645
5.90
5.77
5.91


OSM_Affy
6.70
7.51
6.79
7.25
7.00
7.32


PFKFB3_Affy
9.60
10.92
9.78
10.83
10.18
10.82


PRV1_Affy
9.79
11.99
9.89
11.81
10.58
11.72


PSTPIP2_Affy
8.63
9.64
8.68
9.45
8.90
9.44


SOCS3_Affy
7.26
8.43
7.31
8.10
7.69
8.12


SOD2_Affy
9.94
10.66
10.07
10.69
10.27
10.59


TDRD9_Affy
6.84
8.30
7.02
8.30
7.43
8.18


TGFBI_Affy
10.17
9.31
10.27
9.45
10.37
9.63


TIFA_Affy
5.99
6.51
5.94
6.31
6.01
6.27


TNFSF10_Affy
10.38
10.77
10.51
10.56
10.47
10.52


TNFSF13B_Affy
10.23
10.70
10.24
10.56
10.48
10.58


IL10alpha_Affy
9.91
9.53
9.87
9.56
9.91
9.63


ANKRD22.18S
20.35
18.33
20.31
18.65
20.01
18.70


ANXA3.18S
17.29
15.52
17.00
15.50
16.78
15.68


ARG2.18S
20.36
19.09
20.12
19.26
19.93
19.13


BCL2A1.18S
18.58
17.00
18.20
17.05
17.84
17.04


CD86.18S
19.81
20.19
19.70
20.15
19.68
20.10


CEACAM1.18S
19.98
18.34
19.84
18.39
19.73
18.36


FCGR1A.18S
16.78
15.03
16.51
15.32
16.329
15.59


GADD45A.18S
19.73
18.46
19.69
18.45
19.38
18.53


GADD45B.18S
16.72
15.52
16.61
15.65
16.50
15.86


IFNGR1.18S
16.00
15.23
15.77
15.20
15.68
15.23


IL1RN.18S
17.24
16.01
17.03
16.05
16.95
16.27


IL18R1.18S
20.56
18.71
20.15
18.72
20.04
18.72


INSL3.18S
21.41
20.03
21.22
20.01
21.00
20.22


IRAK2.18S
20.45
19.20
20.28
19.29
20.32
19.49


IRAK4.18S
18.25
17.74
18.06
17.65
17.98
17.71


ITGAM.18S
15.45
14.51
15.27
14.57
15.12
14.60


JAK2.18S
17.77
16.90
17.56
16.97
17.50
17.06


LDLR.18S
20.34
19.31
20.19
19.10
20.28
19.24


LY96.18S
19.24
18.20
18.95
18.27
18.75
18.35


MAP2K6.18S
18.11
16.78
17.86
16.62
17.73
16.74


MKNK1.18S
17.61
16.48
17.37
16.48
17.22
16.58


Gene_MMP9.18S
14.89
13.40
14.70
13.27
14.53
13.15


NCR1.18S
21.89
21.06
21.79
20.99
21.82
21.11


OSM.18S
19.98
18.41
19.71
18.51
19.49
18.57


PFKFB3.18S
17.83
15.86
17.53
15.85
17.18
16.00


PRV1.18S
16.87
13.49
16.28
13.08
15.42
13.34


PSTPIP2.18S
17.45
16.18
17.24
16.31
17.13
16.37


SOCS3.18S
16.83
15.09
16.57
15.12
16.20
15.30


SOD2.18S
13.62
12.83
13.50
12.87
13.41
13.12


TDRD9.18S
22.65
20.64
22.32
20.45
21.95
20.57


TIFA.18S
19.02
17.40
18.89
17.69
18.70
17.97


TLR4.18S
17.93
17.03
17.83
17.24
17.73
17.33


TNFRSF6.18S
17.31
16.51
17.02
16.74
17.05
16.88


TNFSF10.18S
16.21
15.32
15.98
15.49
16.00
15.68


TNFSF13B.18S
16.32
15.43
16.16
15.72
16.01
15.71


VNN1.18S
17.16
15.39
16.70
15.19
16.51
15.12


AlphaFetoprotein_RBM
3.77
4.22
3.70
4.34
3.34
4.01


Apolipoprotein_CIII_RBM
59.72
38.05
53.84
38.34
53.38
40.03


Beta2Microglobulin_RBM
2.83
3.63
2.57
3.02
2.43
2.89


CReactiveProtein_RBM
146.95
261.16
206.21
258.20
171.76
245.76


IL6_RBM
82.87
4858.72
99.21
308.50
98.28
341.38


IL8_RBM
31.03
121.58
36.78
78.93
30.15
69.30


IL10_RBM
21.92
64.45
33.16
38.60
21.12
37.43


MCP1_RBM
237.84
796.87
259.82
505.33
229.04
558.73


Protein_MMP9_RBM
1143.25
1809.30
992.84
1390.05
961.12
1364.40


TIMP1_RBM
226.68
406.90
237.22
351.29
235.05
344.12


ARG2_SPM
21.34
20.25
21.30
20.43
21.32
20.56


CD86_SPM
17.31
17.95
17.47
17.90
17.53
17.87


FCGR1A_SPM
16.16
14.48
15.98
14.85
15.80
15.13


IL1RN_SPM
15.75
14.81
15.59
14.95
15.55
15.10


IL6_SPM
23.58
23.55
23.71
23.67
23.70
23.58


IL8_SPM
21.97
21.99
22.46
22.48
22.68
22.41


IL10_SPM
21.15
20.02
21.10
20.26
20.96
20.24


IL18R1_SPM
18.33
16.54
18.06
16.75
18.04
16.83


ITGAM_SPM
14.66
13.82
14.37
13.84
14.37
13.96


Gene_MMP9_SPM
13.14
11.71
12.90
11.82
13.01
11.95


TIMP1_SPM
13.57
13.00
13.38
12.95
13.47
13.02


TLR4_SPM
14.86
14.22
14.80
14.35
14.71
14.41


TNFSF13B_SPM
14.63
13.91
14.56
14.13
14.52
14.21


CReactiveProtein_SPM
23.19
23.00
23.26
23.20
23.27
23.25


IL6_BDB
−0.15
0.59
−0.25
−0.02
−0.19
0.147


IL8_BDB
−0.24
0.74
−0.30
0.24
−0.32
0.20


IL10_BDB
−0.18
0.57
−0.27
0.35
−0.20
0.10


MCP1_BDB
−0.18
0.58
−0.18
0.01
−0.25
0.20









The range of expression values of the biomarkers of Table I were determined for subjects that acquired sepsis (Sepis subjects) and subjects that did not acquire sepsis (SIRS subjects) in the populations described in Sections 6.11.2, 6.12.2 (Affymetrix data), 611.1, 6.12.1 (RT-PCR data), 6.13.3 (bead data), and 6.13.1 and 6.13.2 (bead data) at the T−12, and T−36 time points. This data is set forth in Table 101 below. In Table 101, a biomarker with the _Affy extension represents the combined data of Sections 6.11.2 and 6.11.2 (Affymetrix data), a biomarker with the 0.18S extension represents the combined data of Sections 6.11.1 and 6.12.1 (RT-PCR data), a biomarker with the BDB extension represents the data of Section 6.13.3, and a biomarker with the RBM extension represents the data of Sections 6.13.1 and 6.13.2. Time points are given in column 6, where T-12 represents the T−12 time point, and T-36 represents the T−36 time point. Units in Table 101 are as described for Table 100.







TABLE 101







Expression value ranges for the biomarkers of Table I as measured in the


experimental Affymetrix, RT-PCR, and bead data of Section 6.














Maximum

Maximum





value in
Minimum
value in



Minimum value
sepsis
value in SIRS
SIRS


Biomarker
in sepsis subjects
subjects
subjects
subjects
Time


1
2
3
4
5
6















ANKRD22_Affy
7.953749641
12.12893617
7.152671629
11.15686619
T-12


ANXA3_Affy
8.694223626
12.60070358
7.888396707
11.77082747
T-12


BCL2A1_Affy
7.44669819
11.98497181
6.831314584
10.15766376
T-12


CCL5_Affy
8.898803629
12.84470384
10.18676932
12.76079187
T-12


CD86_Affy
7.053070944
9.279967603
7.868508261
9.428684419
T-12


CEACAM1_Affy
7.392836443
10.11429986
6.897178024
8.990696622
T-12


CRTAP_Affy
7.96569575
10.04792044
8.402744052
10.11557268
T-12


CSF1R_Affy
8.12973354
9.802040206
8.308888545
10.10429638
T-12


FAD104_Affy
7.670795141
10.63246961
5.857923276
9.290926038
T-12


FCGR1A_Affy
6.796282224
9.51719797
6.335911956
9.135545399
T-12


GADD45A_Affy
7.874842459
10.63426409
6.244572007
9.859106279
T-12


GADD45B_Affy
8.479965651
10.29001883
7.992247915
9.49903678
T-12


HLA.DRA_Affy
9.174921757
12.40164437
10.91664573
12.59721891
T-12


IFNGR1_Affy
9.881934243
12.20352664
10.20622485
12.04433802
T-12


IL18R1_Affy
5.617961135
10.58599306
5.401354816
8.680200674
T-12


INSL3_Affy
6.498476621
8.453102101
6.380834209
7.658960107
T-12


IRAK2_Affy
6.875497116
9.68130844
6.749722992
8.274075293
T-12


IRAK4_Affy
7.433778424
8.878566727
7.144224029
8.232236692
T-12


ITGAM_Affy
10.49945783
12.40537329
10.40124379
12.02696288
T-12


JAK2_Affy
5.855248527
8.799277378
5.832795685
7.719583127
T-12


LDLR_Affy
6.257275326
9.674292614
6.131286337
7.912581783
T-12


LY96_Affy
8.415332968
11.70570487
8.133946247
10.62910735
T-12


MAP2K6_Affy
7.596722579
10.56652274
7.340508485
9.871737402
T-12


MAPK14_Affy
8.070466208
10.92164826
7.114917424
10.02925699
T-12


MKNK1_Affy
9.279266935
11.72051891
8.987912062
11.32996284
T-12


Gene_MMP9_Affy
11.53180146
14.23119077
10.53501559
13.54148736
T-12


NCR1_Affy
5.317900783
6.518234278
5.112152822
6.108537679
T-12


OSM_Affy
6.711016689
8.50414686
5.914544406
7.870308905
T-12


PFKFB3_Affy
8.872516729
12.35520872
8.119052014
11.65471067
T-12


PRV1_Affy
8.247231438
14.06000214
7.787420639
12.82213193
T-12


PSTPIP2_Affy
7.917121337
10.93727651
7.633999161
10.56933315
T-12


SOCS3_Affy
6.800978821
10.18734767
6.095547925
8.999643638
T-12


SOD2_Affy
9.638778614
11.7873112
8.477270195
11.33496413
T-12


TDRD9_Affy
5.982800228
10.55761021
5.394035579
9.208195372
T-12


TGFBI_Affy
7.954609344
11.19226116
8.973848308
11.1560402
T-12


TIFA_Affy
5.620694754
8.03295421
5.430693745
7.140866404
T-12


TNFSF10_Affy
9.847323138
11.63713214
9.035999538
11.33035464
T-12


TNFSF13B_Affy
8.585414556
11.79818987
8.90534601
11.34212616
T-12


IL10alpha_Affy
8.690880784
10.54878771
9.024669305
10.52469989
T-12


ANKRD22.18S
14.554
20.607
18.026
22.621
T-12


ANXA3.18S
12.994
18.89
14.989
20.069
T-12


ARG2.18S
16.703
22.511
17.729
23.054
T-12


BCL2A1.18S
14.219
19.664
15.565
20.206
T-12


CD86.18S
18.391
22.429
18.245
21.426
T-12


CEACAM1.18S
15.174
20.37
16.808
21.929
T-12


FCGR1A.18S
12.273
17.502
14.289
19.097
T-12


GADD45A.18S
15.697
20.631
18.602
21.118
T-12


GADD45B.18S
14.214
17.599
15.52
18.143
T-12


IFNGR1.18S
13.746
17.418
14.233
17.389
T-12


IL1RN.18S
13.7
18.656
14.995
19.231
T-12


IL18R1.18S
15.6
21.649
16.63
23.144
T-12


INSL3.18S
18.077
22.515
19.735
23.106
T-12


IRAK2.18S
16.851
21.205
17.775
22.286
T-12


IRAK4.18S
16.637
19.164
17.001
19.648
T-12


ITGAM.18S
12.6
17.346
13.709
17.172
T-12


JAK2.18S
15.292
19.228
16.431
19.001
T-12


LDLR.18S
16.909
21.4
18.195
22.081
T-12


LY96.18S
16.811
20.726
17.543
20.956
T-12


MAP2K6.18S
14.062
18.942
16.2005
20.678
T-12


MKNK1.18S
14.677
18.261
15.589
19.195
T-12


Gene_MMP9.18S
10.128
16.281
12.644
17.265
T-12


NCR1.18S
18.175
23.462
19.544
23.756
T-12


OSM.18S
16.6
20.544
17.835
22.174
T-12


PFKFB3.18S
12.897
18.994
14.446
20.982
T-12


PRV1.18S
9.4745
19.6
11.749
23.823
T-12


PSTPIP2.18S
14.243
18.013
16.098
18.792
T-12


SOCS3.18S
13.312
18.094
14.603
18.51
T-12


SOD2.18S
10.822
14.875
11.836
15.269
T-12


TDRD9.18S
17.069
23.9
20.012
25.404
T-12


TIFA.18S
14.66
20.607
16.122
20.522
T-12


TLR4.18S
15.013
19.366
15.452
19.347
T-12


TNFRSF6.18S
14.771
18.42
16.046
18.633
T-12


TNFSF10.18S
14.175
16.71
14.913
18.005
T-12


TNFSF13B.18S
13.296
17.2755
14.113
18.23
T-12


VNN1.18S
12.363
19.695
14.788
20.209
T-12


AlphaFetoprotein_RBM
0.465
28.9
0.862
15
T-12


ApolipoproteinCIII_RBM
9.1
104
14
170
T-12


Beta2Microglobulin_RBM
0.912
17
0.815
14.2
T-12


CReactiveProtein_RBM
5.2
743
9.3
435
T-12


IL6_RBM
11.9
350000
5.85
1090
T-12


IL8_RBM
5.7
2430
4.6
136
T-12


IL10_RBM
9.4
1080
6.73
115
T-12


MCP1_RBM
68
20100
54
1860
T-12


Protein_MMP9_RBM
82.7
6100
37
5500
T-12


TIMP1_RBM
99.6
1670
91.7
777
T-12


ARG2_SPM
18.19
22.88
19.11
23.53
T-12


CD86_SPM
16.51
20.02
15.83
18.57
T-12


FCGR1A_SPM
11.78
16.69
13.448
18.43
T-12


IL1RN_SPM
12.82
17.055
12.92
17.13
T-12


IL6_SPM
22.71
24.493
22.61
24.22
T-12


IL8_SPM
17.728
23.645
19.218
24.27
T-12


IL10_SPM
16.905
22.09
17.41
23.38
T-12


IL18R1_SPM
13.69
19.36
13.628
20.7
T-12


ITGAM_SPM
12.33
16.26
12.21
16.24
T-12


Gene_MMP9_SPM
8.53
14.62
9.58
15.92
T-12


TIMP1_SPM
11.63
15.048
11.71
14.93
T-12


TLR4_SPM
12.5
16.64
13.61
16.06
T-12


TNFSF13B_SPM
12.29
15.86
12.37
15.998
T-12


CReactiveProtein_SPM
16.23
24.313
18.803
24.245
T-12


IL6_BDB
−0.158501071
12.45941145
−0.166932601
−0.076083727
T-12


IL8_BDB
−0.260734671
10.31273265
−0.284430649
−0.128681359
T-12


IL10_BDB
−0.317504403
4.487381808
−0.541755786
0.071431589
T-12


MCP1_BDB
−0.215354271
11.61107183
−0.231122309
−0.10084064
T-12


IL6_CBA
−0.674647421
5.170323126
−0.825867097
−0.022530263
T-12


IL8_CBA
−0.620553789
5.923519248
−0.821374574
2.902006709
T-12


IL10_CBA
−0.661588916
4.74196577
−0.730146126
6.047793474
T-12


ANKRD22_Affy
7.396771628
11.57062629
7.129021339
9.443944368
T-36


ANXA3_Affy
8.70013622
12.54023119
8.331913923
12.10925762
T-36


BCL2A1_Affy
7.183882918
11.76715511
6.405590222
10.37097944
T-36


CCL5_Affy
9.328902305
12.61675161
10.40637568
12.86027708
T-36


CD86_Affy
6.732706362
9.538835708
7.716006192
9.498278669
T-36


CEACAM1_Affy
7.13157116
9.873881135
7.032079139
9.080997559
T-36


CRTAP_Affy
7.558590076
9.700256103
8.59265066
10.03618173
T-36


CSF1R_Affy
7.837660302
9.949036275
8.464494614
10.30550413
T-36


FAD104_Affy
7.014972123
10.15391937
6.239710352
8.845757597
T-36


FCGR1A_Affy
6.911908197
9.049485977
6.738241369
8.397213121
T-36


GADD45A_Affy
7.56337487
10.85396197
6.589642416
9.749741171
T-36


GADD45B_Affy
8.51689696
10.06660743
8.254872004
9.532337644
T-36


HLA.DRA_Affy
9.633249373
12.27644605
10.46875169
12.67263889
T-36


IFNGR1_Affy
10.57959732
12.27228887
9.808984607
11.81736908
T-36


IL18R1_Affy
5.49992437
10.36284472
5.348365647
9.056683263
T-36


INSL3_Affy
6.751235769
8.470974618
6.354223568
7.671828775
T-36


IRAK2_Affy
6.667783945
8.746281062
6.803703241
7.823003239
T-36


IRAK4_Affy
7.263029682
8.81320843
7.431560803
8.530795242
T-36


ITGAM_Affy
10.78220173
12.65177246
10.81608481
11.95316198
T-36


JAK2_Affy
6.39933742
8.609398755
6.113876348
7.761009287
T-36


LDLR_Affy
6.592115082
9.576882573
6.429539125
7.875471828
T-36


LY96_Affy
8.653718448
11.10873698
7.823465941
10.99470657
T-36


MAP2K6_Affy
7.76590836
10.96462704
7.656234297
9.824692325
T-36


MAPK14_Affy
8.115337243
10.65189637
8.035017587
10.2553217
T-36


MKNK1_Affy
9.648189763
11.7078247
9.01371136
10.87910413
T-36


Gene_MMP9_Affy
11.11827617
14.10554558
11.20801397
13.49402871
T-36


NCR1_Affy
5.193987983
6.909045684
5.005834897
6.207479976
T-36


OSM_Affy
6.580322029
7.935265514
6.196973735
7.452713795
T-36


PFKFB3_Affy
9.424977102
12.34048574
8.499191685
11.18985748
T-36


PRV1_Affy
8.075175165
13.89885359
7.885024966
13.11270704
T-36


PSTPIP2_Affy
8.014852906
10.87208092
7.546671853
9.463070109
T-36


SOCS3_Affy
7.377913606
9.55384035
6.2535144
8.311473326
T-36


SOD2_Affy
9.246088148
11.68031375
9.036280605
11.01924042
T-36


TDRD9_Affy
6.248252454
11.18511333
5.639880173
9.344436192
T-36


TGFBI_Affy
7.994695342
11.02064264
9.079527674
11.18636937
T-36


TIFA_Affy
5.521978531
7.303421302
5.384514217
6.593248984
T-36


TNFSF10_Affy
9.60197872
11.18227009
9.614398352
11.21902779
T-36


TNFSF13B_Affy
9.376572388
11.47004606
8.423154647
11.14896209
T-36


IL10alpha_Affy
8.838180931
10.20810676
9.056040579
10.61188847
T-36


ANKRD22.18S
16.059
21.635
18.344
22.188
T-36


ANXA3.18S
13.508
18.565
14.637
19.196
T-36


ARG2.18S
16.7215
22.085
16.452
23.1025
T-36


BCL2A1.18S
14.892
20.169
15.558
21.208
T-36


CD86.18S
18.109
22.671
17.734
21.408
T-36


CEACAM1.18S
15.864
20.381
17.141
21.711
T-36


FCGR1A.18S
12.919
17.238
13.773
18.616
T-36


GADD45A.18S
16.021
20.468
17.567
21.221
T-36


GADD45B.18S
14.051
16.799
15.43
18.189
T-36


IFNGR1.18S
13.89
16.44
14.532
17.1035
T-36


IL1RN.18S
14.2965
18.584
14.569
18.818
T-36


IL18R1.18S
15.6475
21.683
17.068
21.9465
T-36


INSL3.18S
18.708
21.952
19.775
23.714
T-36


IRAK2.18S
17.563
20.591
18.878
21.765
T-36


IRAK4.18S
16.688
18.592
16.633
19.467
T-36


ITGAM.18S
12.974
16.862
12.696
16.597
T-36


JAK2.18S
15.659
18.42
16.185
18.67
T-36


LDLR.18S
16.925
20.765
18.764
21.351
T-36


LY96.18S
17.018
20.384
17.293
21.37
T-36


MAP2K6.18S
14.375
19.44
15.5485
20.507
T-36


MKNK1.18S
15.064
17.922
15.281
19.103
T-36


Gene_MMP9.18S
10.4315
16.075
12.673
16.9025
T-36


NCR1.18S
18.189
22.531
20.01
23.281
T-36


OSM.18S
16.79
20.311
18.219
21.612
T-36


PFKFB3.18S
13.095
18.678
14.042
19.647
T-36


PRV1.18S
9.732
18.921
10.63
20.57
T-36


PSTPIP2.18S
14.568
17.943
15.363
18.848
T-36


SOCS3.18S
13.309
16.962
14.629
18.503
T-36


SOD2.18S
11.004
15.117
12.246
14.872
T-36


TDRD9.18S
17.108
22.863
19.203
24.013
T-36


TIFA.18S
15.498
20.069
16.808
20.769
T-36


TLR4.18S
15.77
19.352
15.578
19.604
T-36


TNFRSF6.18S
14.782
17.867
15.952
18.014
T-36


TNFSF10.18S
14.049
16.332
14.388
17.196
T-36


TNFSF13B.18S
13.898
16.896
13.276
18.038
T-36


VNN1.18S
12.406
18.076
13.061
20.4895
T-36


AlphaFetoprotein_RBM
0.0878
45.2
0.399
14
T-36


ApolipoproteinCIII_RBM
8.1
105
17
143
T-36


Beta2Microglobulin_RBM
0.857
13.4
0.953
10.6
T-36


CReactiveProtein_RBM
9.3
548
45
735
T-36


IL6_RBM
8.93
6140
5.85
1480
T-36


IL8_RBM
2.4
977
2.5
429
T-36


IL10_RBM
2.68
397
4.4
760
T-36


MCP1_RBM
77
4280
60
2170
T-36


Protein_MMP9_RBM
59
9000
73
3840
T-36


TIMP1_RBM
89.9
1130
85.2
1050
T-36


ARG2_SPM
17.868
23.15
17.68
23.42
T-36


CD86_SPM
16.15
20.528
16.36
18.78
T-36


FCGR1A_SPM
12.53
17.43
13.633
17.868
T-36


IL1RN_SPM
13.375
17.043
13.98
17.695
T-36


IL6_SPM
23.12
24.378
23.17
24.46
T-36


IL8_SPM
20.33
23.78
19.38
24.24
T-36


IL10_SPM
16.785
23.34
18.648
23.63
T-36


IL18R1_SPM
14.16
19.13
15.98
19.68
T-36


ITGAM_SPM
12.55
15.84
12.683
15.79
T-36


Gene_MMP9_SPM
9.163
15
10.66
14.91
T-36


TIMP1_SPM
11.53
14.895
11.77
15.08
T-36


TLR4_SPM
13.2
15.91
13.52
16
T-36


TNFSF13B_SPM
12.56
15.415
13.19
16.725
T-36


CReactiveProtein_SPM
19.6
23.97
20.8
24
T-36


IL6_BDB
−0.684465464
2.880494108
−0.730942369
1.994658691
T-36


IL8_BDB
−0.825836521
3.74397758
−1.025991342
0.789180774
T-36


IL10_BDB
−1.371058252
8.324182809
−1.079489666
2.72772331
T-36


MCP1_BDB
−0.80163532
2.625526703
−0.828696306
2.820125749
T-36









7. REFERENCES CITED
The present invention can be implemented as a computer program product that comprises a computer program mechanism embedded in a computer readable storage medium. For instance, the computer program product could contain the program modules shown in FIG. 35. These program modules can be stored on a CD-ROM, DVD, magnetic disk storage product, or any other computer readable data or program storage product. The program modules can also be embedded in permanent storage, such as ROM, one or more programmable chip, or one or more application specific integrated circuits (ASICs). Such permanent storage can be localized in a server, 802.11 access point, 802.11 wireless bridge/station, repeater, router, mobile phone, or other electronic devices. The software modules in the computer program product can also be distributed electronically, via the Internet or otherwise, by transmission of a computer data signal (in which the software modules are embedded) either digitally or on a carrier wave.
Having now fully described the invention with reference to certain representative embodiments and details, it will be apparent to one of ordinary skill in the art that changes and modifications can be made thereto without departing from the spirit or scope of the invention as set forth herein. The specific embodiments described herein are offered by way of example only, and the invention is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled.

## Claims

1. A method of predicting an increased likelihood of developing sepsis in a human SIRS patient comprising:
obtaining a first blood sample from said patient, said sample comprising biomarker mRNAs;
measuring the abundances of said biomarker mRNAs in said first blood sample taken from said patient, wherein said biomarker mRNAs comprise TRAF-interacting protein with a forkhead-associated domain (TIFA) mRNA, growth arrest and DNA damage-inducible gene (GADD45B) mRNA, major histocompatibility complex, class II, DR alpha (HLA-DRA) mRNA, carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) mRNA, oncostatin M (OSM) mRNA, and arginase, type II (ARG2) mRNA; and
comparing said abundances of said biomarker mRNAs in said first blood sample to abundances of said biomarker mRNAs in (i) blood samples taken 0-36 hours prior to sepsis development in a SIRS-positive human patient population that progresses to sepsis, and (ii) blood samples taken from a SIRS-positive human patient population that does not progress to sepsis;
wherein an increased likelihood of developing sepsis is predicted in the SIRS patient when it is determined that the biomarker mRNA abundances in the first blood sample are statistically significantly similar to the biomarker mRNA abundances in the blood samples taken from the SIRS-positive human patient population that progresses to sepsis;
wherein a decreased likelihood of developing sepsis is predicted in the SIRS patient when it is determined that the mRNA abundances in the first blood sample are statistically significantly similar to the mRNA abundances in the blood samples taken from the SIRS-positive human patient population that does not progress to sepsis; and
wherein said SIRS-positive human patient population that progresses to sepsis and said SIRS-positive human patient human population that does not progress to sepsis each comprises at least 20 individuals.

2. The method of claim 1, wherein said first blood sample is a whole blood sample or a plasma sample.

3. The method of claim 1, wherein the abundances of said biomarker mRNAs are expressed in relative fluorescence intensity units.

4. The method of claim 1, wherein the measuring step comprises performing reverse-transcription-polymerase chain reaction to make cDNA complimentary to said biomarker RNAs.

5. The method of claim 1, wherein the measuring step comprises contacting said biomarker mRNAs, or cDNAs thereof, to a nucleic acid array.

6. The method of claim 1, wherein the comparing step comprises applying a decision rule to predict the increased or decreased likelihood of sepsis in the SIRS patient.

7. The method of claim 1, wherein the comparing step is performed on a computer.