# Patent

## Title
Identification, monitoring and treatment of disease and characterization of biological condition using gene expression profiles

## Abstract

Gene expression data, in particular gene expression profiles, are created and used in the identification, monitoring and treatment of disease and characterization of biological conditions. Profile data sets are derived from subject samples and include quantitative substantially repeatable measures of a distinct amount of RNA or protein constituent in a panel selected to enable evaluation of a biological condition. Such profile data sets may be used to provide an index indicative of the biological state of a subject, which may be compared to a normative value of the index determined with respect to a relevant population of subjects.

## Background
CROSS REFERENCE TO RELATED APPLICATIONS
The present application is a continuation of U.S. application Ser. No. 10/291,225 filed Nov. 8, 2002, incorporated by reference herein, which application is a continuation-in-part application of U.S. application Ser. No. 09/821,850, filed Mar. 29, 2001 which in turn is a continuation-in-part of U.S. application Ser. No. 09/605,581, filed Jun. 28, 2000, which claims priority from U.S. Application Ser. Nos. 60/195,522, filed Apr. 7, 2000 and U.S. Application Ser. No. 60/141,542, filed Jun. 28, 1999, each of which is incorporated by reference herein. U.S. application Ser. No. 10/291,225 also claims priority from a series of provisional patent applications as follows: U.S. Application Ser. No. 60/348,213, filed Nov. 9, 2001; U.S. Application Ser. No. 60/340,881, filed Dec. 7, 2001; U.S. Application Ser. No. 60/369,633, filed Apr. 3, 2002; and U.S. Application Ser. No. 60/376,997, filed Apr. 30, 2002.


TECHNICAL FIELD AND BACKGROUND ART
The present invention relates to use of gene expression data, and in particular to use of gene expression data in identification, monitoring and treatment of disease and in characterization of biological condition of a subject.
The prior art has utilized gene expression data to determine the presence or absence of particular markers as diagnostic of a particular condition, and in some circumstances have described the cumulative addition of scores for over expression of particular disease markers to achieve increased accuracy or sensitivity of diagnosis. Information on any condition of a particular patient and a patient's response to types and dosages of therapeutic or nutritional agents has become an important issue in clinical medicine today not only from the aspect of efficiency of medical practice for the health care industry but for improved outcomes and benefits for the patients.
SUMMARY OF THE INVENTION
In a first embodiment, there is provided a method, for evaluating a biological condition of a subject, based on a sample from the subject. The method includes:

    
    
        deriving from the sample a profile data set, the profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents selected so that measurement of the constituents enables evaluation of the biological condition; and
        in deriving the profile data set, achieving such measure for each constituent under measurement conditions that are substantially repeatable.
    
    


There is a related embodiment for providing an index that is indicative of the state of a subject, as to a biological condition, based on a sample from the subject. This embodiment includes:

    
    
        deriving from the sample a profile data set, the profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents selected so that measurement of the constituents enables evaluation of the biological condition; and
        in deriving the profile data set, achieving such measure for each constituent under measurement conditions that are substantially repeatable; and
        applying values from the profile data set to an index function that provides a mapping from an instance of a profile data set into a single-valued measure of biological condition, so as to produce an index pertinent to the biological condition of the subject. In further embodiments related to the foregoing, there is also included, in deriving the profile data set, achieving such measure for each constituent under measurement conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar. Similarly further embodiments include alternatively or in addition, in deriving the profile data set, achieving such measure for each constituent under measurement conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar.
    
    


In embodiments relating to providing the index a further embodiment also includes providing with the index a normative value of the index function, determined with respect to a relevant population, so that the index may be interpreted in relation to the normative value. Optionally providing the normative value includes constructing the index function so that the normative value is approximately 1. Also optionally, the relevant population has in common a property that is at least one of age group, gender, ethnicity, geographic location, diet, medical disorder, clinical indicator, medication, physical activity, body mass, and environmental exposure.
In another related embodiment, efficiencies of amplification, expressed as a percent, for all constituents lie within a range of approximately 2 percent, and optionally, approximately 1 percent.
In another related embodiment, measurement conditions are repeatable so that such measure for each constituent has a coefficient of variation, on repeated derivation of such measure from the sample, that is less than approximately 3 percent.
In further embodiments, the panel includes at least three constituents and optionally fewer than approximately 500 constituents.
In another embodiment, the biological condition being evaluated is with respect to a localized tissue of the subject and the sample is derived from tissue or fluid of a type distinct from that of the localized tissue.
In related embodiments, the biological condition may be any of the conditions identified in Tables 1 through 12 herein, in which case there are measurements conducted corresponding to constituents of the corresponding Gene Expression Panel. The panel in each case includes at least two, and optionally at least three, four, five, six, seven, eight, nine or ten, of the constituents of the corresponding Gene Expression Panel.
In another embodiment, there is provided a method of providing an index that is indicative of the inflammatory state of a subject based on a sample from the subject that includes: deriving from the sample a first profile data set, the first profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents, the panel including at least two of the constituents of the Inflammation Gene Expression Panel of Table 1; (although in other embodiments, at least three, four, five, six or ten constituents of the panel of Table 1 may be used in a panel) wherein, in deriving the first profile data set, such measure is performed for each constituent both under conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar and under substantially repeatable conditions; and applying values from the first profile data set to an index function that provides a mapping from an instance of a profile data set into a single-valued measure of biological condition (in an embodiment, this may be an inflammatory condition), so as to produce an index pertinent to the biological condition of the sample or the subject. The biological condition may be any condition that is assessable using an appropriate Gene Expression Panel; the measurement of the extent of inflammation using the Inflammation Gene Expression Panel is merely an example.
In additional embodiments, the mapping by the index function may be further based on an instance of a relevant baseline profile data set and values may be applied from a corresponding baseline profile data set from the same subject or from a population of subjects or samples with a similar or different biological condition. Additionally, the index function may be constructed to deviate from a normative value generally upwardly in an instance of an increase in expression of a constituent whose increase is associated with an increase of inflammation and also in an instance of a decrease in expression of a constituent whose decrease is associated with an increase of inflammation. The index function alternatively be constructed to weigh the expression value of a constituent in the panel generally in accordance with the extent to which its expression level is determined to be correlated with extent of inflammation. The index function may be alternatively constructed to take into account clinical insight into inflammation biology or to take into account experimentally derived data or to take into account relationships derived from computer analysis of profile data sets in a data base associating profile data sets with clinical and demographic data. In this connection, the construction of the index function may be achieved using statistical methods, which evaluate such data, to establish a model of constituent expression values that is an optimized predictor of extent of inflammation.
In another embodiment, the panel includes at least one constituent that is associated with a specific inflammatory disease.
The methods described above may further utilize the step wherein (i) the mapping by the index function is also based on an instance of at least one of demographic data and clinical data and (ii) values are applied from the first profile data set including applying a set of values associated with at least one of demographic data and clinical data.
In another embodiment of the above methods, a portion of deriving the first profile data set is performed at a first location and applying the values from the first profile data set is performed at a second location, and data associated with performing the portion of deriving the first profile data set are communicated to the second location over a network to enable, at the second location, applying the values from the first profile data set.
In an embodiment of the methods, the index function is a linear sum of terms, each term being a contribution function of a member of the profile data set. Moreover, the contribution function may be a weighted sum of powers of one of the member or its reciprocal, and the powers may be integral, so that the contribution function is a polynomial of one of the member or its reciprocal. Optionally, the polynomial is a linear polynomial. The profile data set may include at least three, four or all members corresponding to constituents selected from the group consisting of IL1A, IL1B, TNF, IFNG and IL10. The index function may be proportional to 1⁄4{IL1A)+1⁄4IL1B}+1⁄4{TNF}+1⁄4{INFG}−1/{IL10} and braces around a constituent designate measurement of such constituent.
In an additional embodiment, a method is provided of analyzing complex data associated with a sample from a subject for information pertinent to inflammation, the method that includes: deriving a Gene Expression Profile for the sample, the Gene Expression Profile being based on a Signature Panel for Inflammation; and using the Gene Expression Profile to determine a Gene Expression Profile Inflammatory Index for the sample.
In an additional embodiment, a method is provided of monitoring the biological condition of a subject, that includes deriving a Gene Expression Profile for each of a series of samples over time from the subject, the Gene Expression Profile being based on a Signature Panel for Inflammation; and for each of the series of samples, using the corresponding Gene Expression Profile to determine a Gene Expression Profile Inflammatory Index.
In an additional embodiment, there is provided a method of determining at least one of (i) an effective dose of an agent to be administered to a subject and (ii) a schedule for administration of an agent to a subject, the method including: deriving a Gene Expression Profile for a sample from the subject, the Gene Expression Profile being based on a Signature Panel for Inflammation; using the Gene Expression Profile to determine a Gene Expression Profile Inflammatory Index for the sample; and using the Gene Expression Profile Inflammatory Index as an indicator in establishing at least one of the effective dose and the schedule.
In an additional embodiment, a method of guiding a decision to continue or modify therapy for a biological condition of a subject, is provided that includes: deriving a Gene Expression Profile for a sample from the subject, the Gene Expression Profile being based on a Signature Panel for Inflammation; and using the Gene Expression Profile to determine a Gene Expression Profile Inflammatory Index for the sample.
A method of predicting change in biological condition of a subject as a result of exposure to an agent, is provided that includes: deriving a first Gene Expression Profile for a first sample from the subject in the absence of the agent, the first Gene Expression Profile being based on a Signature Panel for Inflammation; deriving a second Gene Expression Profile for a second sample from the subject in the presence of the agent, the second Gene Expression Profile being based on the same Signature Panel; and using the first and second Gene Expression Profiles to determine correspondingly a first Gene Expression Profile Inflammatory Index and a second Gene Expression Profile Inflammatory Index. Accordingly, the agent may be a compound and the compound may be therapeutic.
In an additional embodiment, a method of evaluating a property of an agent is provided where the property is at least one of purity, potency, quality, efficacy or safety, the method including: deriving a first Gene Expression Profile from a sample reflecting exposure to the agent of (i) the sample, or (ii) a population of cells from which the sample is derived, or (iii) a subject from which the sample is derived; using the Gene Expression Profile to determine a Gene Expression Profile Inflammatory Index; and using the Gene Expression Profile Inflammatory Index in determining the property.
In accordance with another embodiment there is provided a method of providing an index that is indicative of the biological state of a subject based on a sample from the subject. The method of this embodiment includes:

    
    
        deriving from the sample a first profile data set, the first profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents, the panel including at least two of the constituents of the Inflammation Gene Expression Panel of Table 1; and
        applying values from the first profile data set to an index function that provides a mapping from an instance of a profile data set into a single-valued measure of biological condition, so as to produce an index pertinent to the biological condition of the sample or the subject.
    
    


In carrying out this method the index function also uses data from a baseline profile data set for the panel. Each member of the baseline data set is a normative measure, determined with respect to a relevant population of subjects, of the amount of one of the constituents in the panel. In addition, in deriving the first profile data set and the baseline data set, such measure is performed for each constituent both under conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar and under substantially repeatable conditions.
In another type of embodiment, there is provided a method, for evaluating a biological condition of a subject, based on a sample from the subject. In this embodiment, the method includes:

    
    
        deriving from the sample a first profile data set, the first profile dataset including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents selected so that measurement of the constituents enables measurement of the biological condition; and
        producing a calibrated profile data set for the panel, wherein each member of the calibrated profile data set is a function of a corresponding member of the first profile data set and a corresponding member of a baseline profile data set for the panel.
    
    


In this embodiment, each member of the baseline data set is a normative measure, determined with respect to a relevant population of subjects, of the amount of one of the constituents in the panel, and the calibrated profile data set provides a measure of the biological condition of the subject.
In a similar type of embodiment, there is provided a method, for evaluating a biological condition of a subject, based on a sample from the subject, and the method of this embodiment includes:

    
    
        applying the first sample or a portion thereof to a defined population of indicator cells;
        obtaining from the indicator cells a second sample containing at least one of RNAs or proteins;
        deriving from the second sample a first profile data set, the first profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents selected so that measurement of the constituents enables measurement of the biological condition; and
        producing a calibrated profile data set for the panel, wherein each member of the calibrated profile data set is a function of a corresponding member of the first profile data set and a corresponding member of a baseline profile data set for the panel, wherein each member of the baseline data set is a normative measure, determined with respect to a relevant population of subjects, of the amount of one of the constituents in the panel, the calibrated profile data set providing a measure of the biological condition of the subject.
    
    


Furthermore, another and similar, type of embodiment provides a method, for evaluating a biological condition affected by an agent. The method of this embodiment includes:

    
    
        obtaining, from a target population of cells to which the agent has been administered, a sample having at least one of RNAs and proteins;
        deriving from the sample a first profile data set, the first profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents selected so that measurement of the constituents enables measurement of the biological condition; and
        producing a calibrated profile data set for the panel, wherein each member of the calibrated profile data set is a function of a corresponding member of the first profile data set and a corresponding member of a baseline profile data set for the panel, wherein each member of the baseline data set is a normative measure, determined with respect to a relevant population of subjects, of the amount of one of the constituents in the panel, the calibrated profile data set providing a measure of the biological condition as affected by the agent.
    
    


In further embodiments based on these last three embodiments, the relevant population may be a population of healthy subjects. Alternatively, or in addition, the relevant population is has in common a property that is at least one of age group, gender, ethnicity, geographic location, diet, medical disorder, clinical indicator, medication, physical activity, body mass, and environmental exposure.
Alternatively or in addition, the panel includes at least two of the constituents of the Inflammation Gene Expression Panel of Table 1. (Other embodiments employ at least three, four, five, six, or ten of such constituents.) Also alternatively or in addition, in deriving the first profile data set, such measure is performed for each constituent both under conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar and under substantially repeatable conditions. Also alternatively, when such measure is performed for each constituent both under conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar and under substantially repeatable conditions, optionally one need not produce a calibrated profile data set, but may instead work directly with the first data set.
In another embodiment, there is provided a method, for evaluating the effect on a biological condition by a first agent in relation to the effect by a second agent. The method of this embodiment includes:

    
    
        obtaining, from first and second target populations of cells to which the first and second agents have been respectively administered, first and second samples respectively, each sample having at least one of RNAs and proteins;
        deriving from the first sample a first profile data set and from the second sample a second profile data set, the profile data sets each including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents selected so that measurement of the constituents enables measurement of the biological condition; and
        producing for the panel a first calibrated profile data set and a second profile data set, wherein (i) each member of the first calibrated profile data set is a function of a corresponding member of the first profile data set and a corresponding member of a baseline profile data set for the panel, wherein each member of the baseline data set is a normative measure, determined with respect to a relevant population of subjects, of the amount of one of the constituents in the panel, and (ii) each member of the second calibrated profile data set is a function of a corresponding member of the second profile data set and a corresponding member of the baseline profile data set, the calibrated profile data sets providing a measure of the effect by the first agent on the biological condition in relation to the effect by the second agent.
    
    


In this embodiment, in deriving the first and second profile data sets, such measure is performed for each constituent both under conditions wherein specificity and efficiencies of amplification for all constituents are substantially similar and under substantially repeatable conditions. In a further related embodiment, the first agent is a first drug and the second agent is a second drug. In another related embodiment, the first agent is a drug and the second agent is a complex mixture. In yet another related embodiment, the first agent is a drug and the second agent is a nutriceutical.



BRIEF DESCRIPTION OF THE DRAWINGS
The foregoing features of the invention will be more readily understood by reference to the following detailed description, taken with reference to the accompanying drawings, in which:
FIG. 1A shows the results of assaying 24 genes from the Source Inflammation Gene Panel (shown in Table 1) on eight separate days during the course of optic neuritis in a single male subject.
FIG. 1B illustrates use of an inflammation index in relation to the data of FIG. 1A, in accordance with an embodiment of the present invention.
FIG. 2 is a graphical illustration of the same inflammation index calculated at 9 different, significant clinical milestones.
FIG. 3 shows the effects of single dose treatment with 800 mg of ibuprofen in a single donor as characterized by the index.
FIG. 4 shows the calculated acute inflammation index displayed graphically for five different conditions.
FIG. 5 shows a Viral Response Index for monitoring the progress of an upper respiratory infection (UR1).
FIGS. 6 and 7 compare two different populations using Gene Expression Profiles (with respect to the 48 loci of the Inflammation Gene Expression Panel of Table 1).
FIG. 8 compares a normal population with a rheumatoid arthritis population derived from a longitudinal study.
FIG. 9 compares two normal populations, one longitudinal and the other cross sectional.
FIG. 10 shows the shows gene expression values for various individuals of a normal population.
FIG. 11 shows the expression levels for each of four genes (of the Inflammation Gene Expression Panel of Table 1), of a single subject, assayed monthly over a period of eight months.
FIGS. 12 and 13 similarly show in each case the expression levels for each of 48 genes (of the Inflammation Gene Expression Panel of Table 1), of distinct single subjects (selected in each case on the basis of feeling well and not taking drugs), assayed, in the case of FIG. 12 weekly over a period of four weeks, and in the case of FIG. 13 monthly over a period of six months.
FIG. 14 shows the effect over time, on inflammatory gene expression in a single human subject, of the administration of an anti-inflammatory steroid, as assayed using the Inflammation Gene Expression Panel of Table 1.
FIG. 15, in a manner analogous to FIG. 14, shows the effect over time, via whole blood samples obtained from a human subject, administered a single dose of prednisone, on expression of 5 genes (of the Inflammation Gene Expression Panel of Table 1).
FIG. 16 also shows the effect over time, on inflammatory gene expression in a single human subject suffering from rheumatoid arthritis, of the administration of a TNF-inhibiting compound, but here the expression is shown in comparison to the cognate locus average previously determined (in connection with FIGS. 6 and 7) for the normal (i.e., undiagnosed, healthy) population.
FIG. 17A further illustrates the consistency of inflammatory gene expression in a population.
FIG. 17B shows the normal distribution of index values obtained from an undiagnosed population.
FIG. 17C illustrates the use of the same index as FIG. 17B, where the inflammation median for a normal population has been set to zero and both normal and diseased subjects are plotted in standard deviation units relative to that median.
FIG. 18 plots, in a fashion similar to that of FIG. 17A, Gene Expression Profiles, for the same 7 loci as in FIG. 17A, two different (responder v. non-responder) 6-subject populations of rheumatoid arthritis patients.
FIG. 19 thus illustrates use of the inflammation index for assessment of a single subject suffering from rheumatoid arthritis, who has not responded well to traditional therapy with methotrexate.
FIG. 20 similarly illustrates use of the inflammation index for assessment of three subjects suffering from rheumatoid arthritis, who have not responded well to traditional therapy with methotrexate.
Each of FIGS. 21-23 shows the inflammation index for an international group of subjects, suffering from rheumatoid arthritis, undergoing three separate treatment regimens.
FIG. 24 illustrates use of the inflammation index for assessment of a single subject suffering from inflammatory bowel disease.
FIG. 25 shows Gene Expression Profiles with respect to 24 loci (of the Inflammation Gene Expression Panel of Table 1) for whole blood treated with Ibuprofen in vitro in relation to other non-steroidal anti-inflammatory drugs (NSAIDs).
FIG. 26 illustrates how the effects of two competing anti-inflammatory compounds can be compared objectively, quantitatively, precisely, and reproducibly.
FIGS. 27 through 41 illustrate the use of gene expression panels in early identification and monitoring of infectious disease.
FIG. 27 uses a novel bacterial Gene Expression Panel of 24 genes, developed to discriminate various bacterial conditions in a host biological system.
FIG. 28 shows differential expression for a single locus, IFNG, to LTA derived from three distinct sources: S. pyogenes, B. subtilis, and S. aureus. 
FIGS. 29 and 30 show the response after two hours of the Inflammation 48A and 48B loci respectively (discussed above in connection with FIGS. 6 and 7 respectively) in whole blood to administration of a Gram-positive and a Gram-negative organism.
FIGS. 31 and 32 correspond to FIGS. 29 and 30 respectively and are similar to them, with the exception that the monitoring here occurs 6 hours after administration.
FIG. 33 compares the gene expression response induced by E. coli and by an organism-free E. coli filtrate.
FIG. 34 is similar to FIG. 33, but here the compared responses are to stimuli from E. coli filtrate alone and from E. coli filtrate to which has been added polymyxin B.
FIG. 35 illustrates the gene expression responses induced by S. aureus at 2, 6, and 24 hours after administration.
FIGS. 36 through 41 compare the gene expression induced by E. coli and S. aureus under various concentrations and times.



DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS
Definitions
The following terms shall have the meanings indicated unless the context otherwise requires:
“Algorithm” is a set of rules for describing a biological condition. The rule set may be defined exclusively algebraically but may also include alternative or multiple decision points requiring domain-specific knowledge, expert interpretation or other clinical indicators.
An “agent” is a “composition” or a “stimulus”, as those terms are defined herein, or a combination of a composition and a stimulus.
“Amplification” in the context of a quantitative RT-PCR assay is a function of the number of DNA replications that are tracked to provide a quantitative determination of its concentration. “Amplification” here refers to a degree of sensitivity and specificity of a quantitative assay technique. Accordingly, amplification provides a measurement of concentrations of constituents that is evaluated under conditions wherein the efficiency of amplification and therefore the degree of sensitivity and reproducibility for measuring all constituents is substantially similar.
A “baseline profile data set” is a set of values associated with constituents of a Gene Expression Panel resulting from evaluation of a biological sample (or population of samples) under a desired biological condition that is used for mathematically normative purposes. The desired biological condition may be, for example, the condition of a subject (or population of subjects) before exposure to an agent or in the presence of an untreated disease or in the absence of a disease. Alternatively, or in addition, the desired biological condition may be health of a subject or a population of subjects. Alternatively, or in addition, the desired biological condition may be that associated with a population subjects selected on the basis of at least one of age group, gender, ethnicity, geographic location, diet, medical disorder, clinical indicator, medication, physical activity, body mass, and environmental exposure.
A “biological condition” of a subject is the condition of the subject in a pertinent realm that is under observation, and such realm may include any aspect of the subject capable of being monitored for change in condition, such as health, disease including cancer; trauma; aging; infection; tissue degeneration; developmental steps; physical fitness; obesity, and mood. As can be seen, a condition in this context may be chronic or acute or simply transient. Moreover, a targeted biological condition may be manifest throughout the organism or population of cells or may be restricted to a specific organ (such as skin, heart, eye or blood), but in either case, the condition may be monitored directly by a sample of the affected population of cells or indirectly by a sample derived elsewhere from the subject. The term “biological condition” includes a “physiological condition”.
“Body fluid” of a subject includes blood, urine, spinal fluid, lymph, mucosal secretions, prostatic fluid, semen, haemolymph or any other body fluid known in the art for a subject.
“Calibrated profile data set” is a function of a member of a first profile data set and a corresponding member of a baseline profile data set for a given constituent in a panel.
A “clinical indicator” is any physiological datum used alone or in conjunction with other data in evaluating the physiological condition of a collection of cells or of an organism. This term includes pre-clinical indicators.
A “composition” includes a chemical compound, a nutriceutical, a pharmaceutical, a homeopathic formulation, an allopathic formulation, a naturopathic formulation, a combination of compounds, a toxin, a food, a food supplement, a mineral, and a complex mixture of substances, in any physical state or in a combination of physical states.
To “derive” a profile data set from a sample includes determining a set of values associated with constituents of a Gene Expression Panel either (i) by direct measurement of such constituents in a biological sample or (ii) by measurement of such constituents in a second biological sample that has been exposed to the original sample or to matter derived from the original sample.
“Distinct RNA or protein constituent” in a panel of constituents is a distinct expressed product of a gene, whether RNA or protein. An “expression” product of a gene includes the gene product whether RNA or protein resulting from translation of the messenger RNA.
A “Gene Expression Panel” is an experimentally verified set of constituents, each constituent being a distinct expressed product of a gene, whether RNA or protein, wherein constituents of the set are selected so that their measurement provides a measurement of a targeted biological condition.
A “Gene Expression Profile” is a set of values associated with constituents of a Gene Expression Panel resulting from evaluation of a biological sample (or population of samples).
A “Gene Expression Profile Inflammatory Index” is the value of an index function that provides a mapping from an instance of a Gene Expression Profile into a single-valued measure of inflammatory condition.
The “health” of a subject includes mental, emotional, physical, spiritual, allopathic, naturopathic and homeopathic condition of the subject.
“Index” is an arithmetically or mathematically derived numerical characteristic developed for aid in simplifying or disclosing or informing the analysis of more complex quantitative information. A disease or population index may be determined by the application of a specific algorithm to a plurality of subjects or samples with a common biological condition.
“Inflammation” is used herein in the general medical sense of the word and may be an acute or chronic; simple or supporative; localized or disseminated; cellular and tissue response, initiated or sustained by any number of chemical, physical or biological agents or combination of agents.
“Inflammatory state” is used to indicate the relative biological condition of a subject resulting from inflammation, or characterizing the degree of inflammation
A “large number” of data sets based on a common panel of genes is a number of data sets sufficiently large to permit a statistically significant conclusion to be drawn with respect to an instance of a data set based on the same panel.
A “normative” condition of a subject to whom a composition is to be administered means the condition of a subject before administration, even if the subject happens to be suffering from a disease.
A “panel” of genes is a set of genes including at least two constituents.
A “sample” from a subject may include a single cell or multiple cells or fragments of cells or an aliquot of body fluid, taken from the subject, by means including venipuncture, excretion, ejaculation, massage, biopsy, needle aspirate, lavage sample, scraping, surgical incision or intervention or other means known in the art.
A “Signature Profile” is an experimentally verified subset of a Gene Expression Profile selected to discriminate a biological condition, agent or physiological mechanism of action.
A “Signature Panel” is a subset of a Gene Expression Panel, the constituents of which are selected to permit discrimination of a biological condition, agent or physiological mechanism of action.
A “subject” is a cell, tissue, or organism, human or non-human, whether in vivo, ex vivo or in vitro, under observation. When we refer to evaluating the biological condition of a subject based on a sample from the subject, we include using blood or other tissue sample from a human subject to evaluate the human subject's condition; but we also include, for example, using a blood sample itself as the subject to evaluate, for example, the effect of therapy or an agent upon the sample.
A “stimulus” includes (i) a monitored physical interaction with a subject, for example ultraviolet A or B, or light therapy for seasonal affective disorder, or treatment of psoriasis with psoralen or treatment of melanoma with embedded radioactive seeds, other radiation exposure, and (ii) any monitored physical, mental, emotional, or spiritual activity or inactivity of a subject.
“Therapy” includes all interventions whether biological, chemical, physical, metaphysical, or combination of the foregoing, intended to sustain or alter the monitored biological condition of a subject.
The PCT patent application publication number WO 01/25473, published Apr. 12, 2001, entitled “Systems and Methods for Characterizing a Biological Condition or Agent Using Calibrated Gene Expression Profiles,” filed for an invention by inventors herein, and which is herein incorporated by reference, discloses the use of Gene Expression Panels for the evaluation of (i) biological condition (including with respect to health and disease) and (ii) the effect of one or more agents on biological condition (including with respect to health, toxicity, therapeutic treatment and drug interaction).
In particular, Gene Expression Panels may be used for measurement of therapeutic efficacy of natural or synthetic compositions or stimuli that may be formulated individually or in combinations or mixtures for a range of targeted physiological conditions; prediction of toxicological effects and dose effectiveness of a composition or mixture of compositions for an individual or in a population; determination of how two or more different agents administered in a single treatment might interact so as to detect any of synergistic, additive, negative, neutral or toxic activity; performing pre-clinical and clinical trials by providing new criteria for pre-selecting subjects according to informative profile data sets for revealing disease status; and conducting preliminary dosage studies for these patients prior to conducting phase 1 or 2 trials. These Gene Expression Panels may be employed with respect to samples derived from subjects in order to evaluate their biological condition.
A Gene Expression Panel is selected in a manner so that quantitative measurement of RNA or protein constituents in the Panel constitutes a measurement of a biological condition of a subject. In one kind of arrangement, a calibrated profile data set is employed. Each member of the calibrated profile data set is a function of (i) a measure of a distinct constituent of a Gene Expression Panel and (ii) a baseline quantity.
We have found that valuable and unexpected results may be achieved when the quantitative measurement of constituents is performed under repeatable conditions (within a degree of repeatability of measurement of better than twenty percent, and preferably five percent or better, and more preferably three percent or better). For the purposes of this description and the following claims, we regard a degree of repeatability of measurement of better than twenty percent as providing measurement conditions that are “substantially repeatable”. In particular, it is desirable that, each time a measurement is obtained corresponding to the level of expression of a constituent in a particular sample, substantially the same measurement should result for the substantially the same level of expression. In this manner, expression levels for a constituent in a Gene Expression Panel may be meaningfully compared from sample to sample. Even if the expression level measurements for a particular constituent are inaccurate (for example, say, 30% too low), the criterion of repeatability means that all measurements for this constituent, if skewed, will nevertheless be skewed systematically, and therefore measurements of expression level of the constituent may be compared meaningfully. In this fashion valuable information may be obtained and compared concerning expression of the constituent under varied circumstances.
In addition to the criterion of repeatability, it is desirable that a second criterion also be satisfied, namely that quantitative measurement of constituents is performed under conditions wherein efficiencies of amplification for all constituents are substantially similar (within one to two percent and typically one percent or less). When both of these criteria are satisfied, then measurement of the expression level of one constituent may be meaningfully compared with measurement of the expression level of another constituent in a given sample and from sample to sample.
Present embodiments relate to the use of an index or algorithm resulting from quantitative measurement of constituents, and optionally in addition, derived from either expert analysis or computational biology (a) in the analysis of complex data sets; (b) to control or normalize the influence of uninformative or otherwise minor variances in gene expression values between samples or subjects; (c) to simplify the characterization of a complex data set for comparison to other complex data sets, databases or indices or algorithms derived from complex data sets; (d) to monitor a biological condition of a subject; (e) for measurement of therapeutic efficacy of natural or synthetic compositions or stimuli that may be formulated individually or in combinations or mixtures for a range of targeted physiological conditions; (f) for predictions of toxicological effects and dose effectiveness of a composition or mixture of compositions for an individual or in a population; (g) for determination of how two or more different agents administered in a single treatment might interact so as to detect any of synergistic, additive, negative, neutral of toxic activity (h) for performing pre-clinical and clinical trials by providing new criteria for pre-selecting subjects according to informative profile data sets for revealing disease status and conducting preliminary dosage studies for these patients prior to conducting phase 1 or 2 trials.
Gene expression profiling and the use of index characterization for a particular condition or agent or both may be used to reduce the cost of phase 3 clinical trials and may be used beyond phase 3 trials; labeling for approved drugs; selection of suitable medication in a class of medications for a particular patient that is directed to their unique physiology; diagnosing or determining a prognosis of a medical condition or an infection which may precede onset of symptoms or alternatively diagnosing adverse side effects associated with administration of a therapeutic agent; managing the health care of a patient; and quality control for different batches of an agent or a mixture of agents.
The Subject
The methods disclosed here may be applied to cells of humans, mammals or other organisms without the need for undue experimentation by one of ordinary skill in the art because all cells transcribe RNA and it is known in the art how to extract RNA from all types of cells.
Selecting Constituents of a Gene Expression Panel
The general approach to selecting constituents of a Gene Expression Panel has been described in PCT application publication number WO 01/25473. We have designed and experimentally verified a wide range of Gene Expression Panels, each panel providing a quantitative measure, of biological condition, that is derived from a sample of blood or other tissue. For each panel, experiments have verified that a Gene Expression Profile using the panel's constituents is informative of a biological condition. (We show elsewhere that in being informative of biological condition, the Gene Expression Profile can be used to used, among other things, to measure the effectiveness of therapy, as well as to provide a target for therapeutic intervention.) Examples of Gene Expression Panels, along with a brief description of each panel constituent, are provided in tables attached hereto as follows:

    
    
        Table 1. Inflammation Gene Expression Panel
        Table 2. Diabetes Gene Expression Panel
        Table 3. Prostate Gene Expression Panel
        Table 4. Skin Response Gene Expression Panel
        Table 5. Liver Metabolism and Disease Gene Expression Panel
        Table 6. Endothelial Gene Expression Panel
        Table 7. Cell Health and Apoptosis Gene Expression Panel
        Table 8. Cytokine Gene Expression Panel
        Table 9. TNF/IL1 Inhibition Gene Expression Panel
        Table 10. Chemokine Gene Expression Panel
        Table 11. Breast Cancer Gene Expression Panel
        Table 12. Infectious Disease Gene Expression Panel
    
    


Other panels may be constructed and experimentally verified by one of ordinary skill in the art in accordance with the principles articulated in the present application.
Design of Assays
We commonly run a sample through a panel in quadruplicate; that is, a sample is divided into aliquots and for each aliquot we measure concentrations of each constituent in a Gene Expression Panel. Over a total of 900 constituent assays, with each assay conducted in quadruplicate, we found an average coefficient of variation, (standard deviation/average)*100, of less than 2 percent, typically less than 1 percent, among results for each assay. This figure is a measure of what we call “intra-assay variability”. We have also conducted assays on different occasions using the same sample material. With 72 assays, resulting from concentration measurements of constituents in a panel of 24 members, and such concentration measurements determined on three different occasions over time, we found an average coefficient of variation of less than 5 percent, typically less than 2 percent. We regard this as a measure of what we call “inter-assay variability”.
We have found it valuable in using the quadruplicate test results to identify and eliminate data points that are statistical “outliers”; such data points are those that differ by a percentage greater, for example, than 3% of the average of all four values and that do not result from any systematic skew that is greater, for example, than 1%. Moreover, if more than one data point in a set of four is excluded by this procedure, then all data for the relevant constituent is discarded.
Measurement of Gene Expression for a Constituent in the Panel
For measuring the amount of a particular RNA in a sample, we have used methods known to one of ordinary skill in the art to extract and quantify transcribed RNA from a sample with respect to a constituent of a Gene Expression Panel. (See detailed protocols below. Also see PCT application publication number WO 98/24935 herein incorporated by reference for RNA analysis protocols). Briefly, RNA is extracted from a sample such as a tissue, body fluid, or culture medium in which a population of a subject might be growing. For example, cells may be lysed and RNA eluted in a suitable solution in which to conduct a DNAse reaction. First strand synthesis may be performed using a reverse transcriptase. Gene amplification, more specifically quantitative PCR assays, can then conducted and the gene of interest size calibrated against a marker such as 18S rRNA (Hirayama et al., Blood 92, 1998: 46-52). Samples are measured in multiple duplicates, for example, 4 replicates. Relative quantitation of the mRNA is determined by the difference in threshhold cycles between the internal control and the gene of interest. In an embodiment of the invention, quantitative PCR is performed using amplification, reporting agents and instruments such as those supplied commercially by Applied Biosystems (Foster City, Calif.). Given a defined efficiency of amplification of target transcripts, the point (e.g., cycle number) that signal from amplified target template is detectable may be directly related to the amount of specific message transcript in the measured sample. Similarly, other quantifiable signals such as fluorescence, enzyme activity, disintegrations per minute, absorbance, etc., when correlated to a known concentration of target templates (e.g., a reference standard curve) or normalized to a standard with limited variability can be used to quantify the number of target templates in an unknown sample.
Although not limited to amplification methods, quantitative gene expression techniques may utilize amplification of the target transcript. Alternatively or in combination with amplification of the target transcript, amplification of the reporter signal may also be used. Amplification of the target template may be accomplished by isothermic gene amplification strategies, or by gene amplification by thermal cycling such as PCR.
It is desirable to obtain a definable and reproducible correlation between the amplified target or reporter and the concentration of starting templates. We have discovered that this objective can be achieved by careful attention to, for example, consistent primer-template ratios and a strict adherence to a narrow permissible level of experimental amplification efficiencies (for example 99.0 to 100% relative efficiency, typically 99.8 to 100% relative efficiency). For example, in determining gene expression levels with regard to a single Gene Expression Profile, it is necessary that all constituents of the panels maintain a similar and limited range of primer template ratios (for example, within a 10-fold range) and amplification efficiencies (within, for example, less than 1%) to permit accurate and precise relative measurements for each constituent. We regard amplification efficiencies as being “substantially similar”, for the purposes of this description and the following claims, if they differ by no more than approximately 10%. Preferably they should differ by less than approximately 2% and more preferably by less than approximately 1%. These constraints should be observed over the entire range of concentration levels to be measured associated with the relevant biological condition. While it is thus necessary for various embodiments herein to satisfy criteria that measurements are achieved under measurement conditions that are substantially repeatable and wherein specificity and efficiencies of amplification for all constituents are substantially similar, nevertheless, it is within the scope of the present invention as claimed herein to achieve such measurement conditions by adjusting assay results that do not satisfy these criteria directly, in such a manner as to compensate for errors, so that the criteria are satisfied after suitable adjustment of assay results.
In practice, we run tests to assure that these conditions are satisfied. For example, we typically design and manufacture a number of primer-probe sets, and determine experimentally which set gives the best performance. Even though primer-probe design and manufacture can be enhanced using computer techniques known in the art, and notwithstanding common practice, we still find that experimental validation is useful. Moreover, in the course of experimental validation, we associate with the selected primer-probe combination a set of features:
The reverse primer should be complementary to the coding DNA strand. In one embodiment, the primer should be located across an intron-exon junction, with not more than three bases of the three-prime end of the reverse primer complementary to the proximal exon. (If more than three bases are complementary, then it would tend to competitively amplify genomic DNA.)
In an embodiment of the invention, the primer probe should amplify cDNA of less than 110 bases in length and should not amplify genomic DNA or transcripts or cDNA from related but biologically irrelevant loci.
A suitable target of the selected primer probe is first strand cDNA, which may be prepared, in one embodiment, is described as follows:
(a) Use of Whole Blood for Ex Vivo Assessment of a Biological Condition Affected by an Agent.
Human blood is obtained by venipuncture and prepared for assay by separating samples for baseline, no stimulus, and stimulus with sufficient volume for at least three time points. Typical stimuli include lipopolysaccharide (LPS), phytohemagglutinin (PHA) and heat-killed staphylococci (HKS) or carrageean and may be used individually (typically) or in combination. The aliquots of heparinized, whole blood are mixed without stimulus and held at 37° C. in an atmosphere of 5% CO2 for 30 minutes. Stimulus is added at varying concentrations, mixed and held loosely capped at 37° C. for 30 min. Additional test compounds may be added at this point and held for varying times depending on the expected pharmacokinetics of the test compound. At defined times, cells are collected by centrifugation, the plasma removed and RNA extracted by various standard means.
Nucleic acids, RNA and or DNA are purified from cells, tissues or fluids of the test population or indicator cell lines. RNA is preferentially obtained from the nucleic acid mix using a variety of standard procedures (or RNA Isolation Strategies, pp. 55-104, in RNA Methodologies, A laboratory guide for isolation and characterization, 2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press), in the present using a filter-based RNA isolation system from Ambion (RNAqueousTM, Phenol-free Total RNA Isolation Kit, Catalog #1912, version 9908; Austin, Tex.).
In accordance with one procedure, the whole blood assay for Gene Expression Profiles determination was carried out as follows: Human whole blood was drawn into 10 mL Vacutainer tubes with Sodium Heparin. Blood samples were mixed by gently inverting tubes 4-5 times. The blood was used within 10-15 minutes of draw. In the experiments, blood was diluted 2-fold, i.e. per sample per time point, 0.6 mL whole blood+0.6 mL stimulus. The assay medium was prepared and the stimulus added as appropriate.
A quantity (0.6 mL) of whole blood was then added into each 12×75 mm polypropylene tube. 0.6 mL of 2×LPS (from E. coli serotye 0127:B8, Sigma#L3880 or serotype 055, Sigma #L4005, 10 ng/ml, subject to change in different lots) into LPS tubes was added. Next, 0.6 mL assay medium was added to the “control” tubes with duplicate tubes for each condition. The caps were closed tightly. The tubes were inverted 2-3 times to mix samples. Caps were loosened to first stop and the tubes incubated @ 37° C., 5% CO2 for 6 hours. At 6 hours, samples were gently mixed to resuspend blood cells, and 1 mL was removed from each tube (using a micropipettor with barrier tip), and transfered to a 2 mL “dolphin” microfuge tube (Costar #3213).
The samples were then centrifuged for 5 min at 500×g, ambient temperature (IEC centrifuge or equivalent, in microfuge tube adapters in swinging bucket), and as much serum from each tube was removed as possible and discarded. Cell pellets were placed on ice; and RNA extracted as soon as possible using an Ambion RNAqueous kit.
(b) Amplification Strategies.
Specific RNAs are amplified using message specific primers or random primers. The specific primers are synthesized from data obtained from public databases (e.g., Unigene, National Center for Biotechnology Information, National Library of Medicine, Bethesda, Md.), including information from genomic and cDNA libraries obtained from humans and other animals. Primers are chosen to preferentially amplify from specific RNAs obtained from the test or indicator samples, see, for example, RT PCR, Chapter 15 in RNA Methodologies, A laboratory guide for isolation and characterization, 2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press; or Chapter 22 pp. 143-151, RNA isolation and characterization protocols, Methods in molecular biology, Volume 86, 1998, R. Rapley and D. L. Manning Eds., Human Press, or 14 in Statistical refinement of primer design parameters, Chapter 5, pp. 55-72, PCR applications: protocols for functional genomics, M. A. Innis, D. H. Gelfand and J. J. Sninsky, Eds., 1999, Academic Press). Amplifications are carried out in either isothermic conditions or using a thermal cycler (for example, a ABI 9600 or 9700 or 7700 obtained from Applied Biosystems, Foster City, Calif.; see Nucleic acid detection methods, pp. 1-24, in Molecular methods for virus detection, D. L. Wiedbrauk and D. H., Farkas, Eds., 1995, Academic Press). Amplified nucleic acids are detected using fluorescent-tagged detection primers (see, for example, TaqmanTM PCR Reagent Kit, Protocol, part number 402823 revision A, 1996, Applied Biosystems, Foster City Calif.) that are identified and synthesized from publicly known databases as described for the amplification primers. In the present case, amplified DNA is detected and quantified using the ABI Prism 7700 Sequence Detection System obtained from Applied Biosystems (Foster City, Calif.). Amounts of specific RNAs contained in the test sample or obtained from the indicator cell lines can be related to the relative quantity of fluorescence observed (see for example, Advances in quantitative PCR technology: 5′ nuclease assays, Y. S. Lie and C. J. Petropolus, Current Opinion in Biotechnology, 1998, 9:4348, or Rapid thermal cycling and PCR kinetics, pp. 211-229, chapter 14 in PCR applications: protocols for functional genomics, M. A. Innis, D. H. Gelfand and J. J. Sninsky, Eds., 1999, Academic Press).
As a particular implementation of the approach described here, we describe in detail a procedure for synthesis of first strand cDNA for use in PCR. This procedure can be used for both whole blood RNA and RNA extracted from cultured cells (i.e. THP-1 cells).
Materials
1. Applied Biosystems TAQMAN Reverse Transcription Reagents Kit (P/N 808-0234). Kit Components: 10× TaqMan RT Buffer, 25 mM Magnesium chloride, deoxyNTPs mixture, Random Hexamers, RNase Inhibitor, MultiScribe Reverse Transcriptase (50 U/mL) (2) RNase/DNase free water (DEPC Treated Water from Ambion (P/N 9915G), or equivalent)
Methods
1. Place RNase Inhibitor and MultiScribe Reverse Transcriptase on ice immediately. All other reagents can be thawed at room temperature and then placed on ice.
2. Remove RNA samples from −80° C. freezer and thaw at room temperature and then place immediately on ice.
3. Prepare the following cocktail of Reverse Transcriptase Reagents for each 100 mL RT reaction (for multiple samples, prepare extra cocktail to allow for pipetting error):













1 reaction (mL)
11X, e.g. 10 samples (mL)


















10X RT Buffer
10.0
110.0



25 mM MgCl2
22.0
242.0



dNTPs
20.0
220.0



Random Hexamers
5.0
55.0



RNAse Inhibitor
2.0
22.0



Reverse Transcriptase
2.5
27.5



Water
18.5
203.5



Total:
80.0
880.0
(80 mL per sample)









4. Bring each RNA sample to a total volume of 20 mL in a 1.5 mL microcentrifuge tube (for example, for THP-1 RNA, remove 10 mL RNA and dilute to 20 mL with RNase/DNase free water, for whole blood RNA use 20 mL total RNA) and add 80 mL RT reaction mix from step 5,2,3. Mix by pipetting up and down.
5. Incubate sample at room temperature for 10 minutes.
6. Incubate sample at 37° C. for 1 hour.
7. Incubate sample at 90° C. for 10 minutes.
8. Quick spin samples in microcentrifuge.
9. Place sample on ice if doing PCR immediately, otherwise store sample at −20° C. for future use.
10. PCR QC should be run on all RT samples using 18S and b-actin (see SOP 200-020).
The use of the primer probe with the first strand cDNA as described above to permit measurement of constituents of a Gene Expression Panel is as follows:
Set up of a 24-gene Human Gene Expression Panel for Inflammation.
Materials
1. 20× Primer/Probe Mix for each gene of interest.
2. 20× Primer/Probe Mix for 18S endogenous control.
3. 2× Taqman Universal PCR Master Mix.
4. cDNA transcribed from RNA extracted from cells.
5. Applied Biosystems 96-Well Optical Reaction Plates.
6. Applied Biosystems Optical Caps, or optical-clear film.
7. Applied Biosystem Prism 7700 Sequence Detector.
Methods
1. Make stocks of each Primer/Probe mix containing the Primer/Probe for the gene of interest, Primer/Probe for 18S endogenous control, and 2×PCR Master Mix as follows. Make sufficient excess to allow for pipetting error e.g. approximately 10% excess. The following example illustrates a typical set up for one gene with quadruplicate samples testing two conditions (2 plates).














9X (2 plates



1X(1 well)
worth)

















 2X MasterMix
12.50
112.50


20X 18S Primer/Probe Mix
1.25
11.25


20X Gene of interest Primer/Probe Mix
1.25
11.25


Total
15.00
135.00









2. Make stocks of cDNA targets by diluting 95 μl of cDNA into 2000 μl of water. The amount of cDNA is adjusted to give Ct values between 10 and 18, typically between 12 and 13.
3. Pipette 15 μl of Primer/Probe mix into the appropriate wells of an Applied Biosystems 96-Well Optical Reaction Plate.
4. Pipette 10 μl of cDNA stock solution into each well of the Applied Biosystems 96-Well Optical Reaction Plate.
5. Seal the plate with Applied Biosystems Optical Caps, or optical-clear film.
6. Analyze the plate on the AB Prism 7700 Sequence Detector.
Methods herein may also be applied using proteins where sensitive quantitative techniques, such as an Enzyme Linked ImmunoSorbent Assay (ELISA) or mass spectroscopy, are available and well-known in the art for measuring the amount of a protein constituent. (see WO 98/24935 herein incorporated by reference).
Baseline Profile Data Sets
The analyses of samples from single individuals and from large groups of individuals provide a library of profile data sets relating to a particular panel or series of panels. These profile data sets may be stored as records in a library for use as baseline profile data sets. As the term “baseline” suggests, the stored baseline profile data sets serve as comparators for providing a calibrated profile data set that is informative about a biological condition or agent. Baseline profile data sets may be stored in libraries and classified in a number of cross-referential ways. One form of classification may rely on the characteristics of the panels from which the data sets are derived. Another form of classification may be by particular biological condition. The concept of biological condition encompasses any state in which a cell or population of cells may be found at any one time. This state may reflect geography of samples, sex of subjects or any other discriminator. Some of the discriminators may overlap. The libraries may also be accessed for records associated with a single subject or particular clinical trial. The classification of baseline profile data sets may further be annotated with medical information about a particular subject, a medical condition, a particular agent etc.
The choice of a baseline profile data set for creating a calibrated profile data set is related to the biological condition to be evaluated, monitored, or predicted, as well as, the intended use of the calibrated panel, e.g., as to monitor drug development, quality control or other uses. It may be desirable to access baseline profile data sets from the same subject for whom a first profile data set is obtained or from different subject at varying times, exposures to stimuli, drugs or complex compounds; or may be derived from like or dissimilar populations.
The profile data set may arise from the same subject for which the first data set is obtained, where the sample is taken at a separate or similar time, a different or similar site or in a different or similar physiological condition. For example, FIG. 5 provides a protocol in which the sample is taken before stimulation or after stimulation. The profile data set obtained from the unstimulated sample may serve as a baseline profile data set for the sample taken after stimulation. The baseline data set may also be derived from a library containing profile data sets of a population of subjects having some defining characteristic or biological condition. The baseline profile data set may also correspond to some ex vivo or in vitro properties associated with an in vitro cell culture. The resultant calibrated profile data sets may then be stored as a record in a database or library (FIG. 6) along with or separate from the baseline profile data base and optionally the first profile data set although the first profile data set would normally become incorporated into a baseline profile data set under suitable classification criteria. The remarkable consistency of Gene Expression Profiles associated with a given biological condition makes it valuable to store profile data, which can be used, among other things for normative reference purposes. The normative reference can serve to indicate the degree to which a subject conforms to a given biological condition (healthy or diseased) and, alternatively or in addition, to provide a target for clinical intervention.
Selected baseline profile data sets may be also be used as a standard by which to judge manufacturing lots in terms of efficacy, toxicity, etc. Where the effect of a therapeutic agent is being measured, the baseline data set may correspond to Gene Expression Profiles taken before administration of the agent. Where quality control for a newly manufactured product is being determined, the baseline data set may correspond with a gold standard for that product. However, any suitable normalization techniques may be employed. For example, an average baseline profile data set is obtained from authentic material of a naturally grown herbal nutriceutical and compared over time and over different lots in order to demonstrate consistency, or lack of consistency, in lots of compounds prepared for release.
Calibrated Data
Given the repeatability we have achieved in measurement of gene expression, described above in connection with “Gene Expression Panels” and “gene amplification”, we conclude that where differences occur in measurement under such conditions, the differences are attributable to differences in biological condition. Thus we have found that calibrated profile data sets are highly reproducible in samples taken from the same individual under the same conditions. We have similarly found that calibrated profile data sets are reproducible in samples that are repeatedly tested. We have also found repeated instances wherein calibrated profile data sets obtained when samples from a subject are exposed ex vivo to a compound are comparable to calibrated profile data from a sample that has been exposed to a sample in vivo. We have also found, importantly, that an indicator cell line treated with an agent can in many cases provide calibrated profile data sets comparable to those obtained from in vivo or ex vivo populations of cells. Moreover, we have found that administering a sample from a subject onto indicator cells can provide informative calibrated profile data sets with respect to the biological condition of the subject including the health, disease states, therapeutic interventions, aging or exposure to environmental stimuli or toxins of the subject.
Calculation of Calibrated Profile Data Sets and Computational Aids
The calibrated profile data set may be expressed in a spreadsheet or represented graphically for example, in a bar chart or tabular form but may also be expressed in a three dimensional representation. The function relating the baseline and profile data may be a ratio expressed as a logarithm. The constituent may be itemized on the x-axis and the logarithmic scale may be on the y-axis. Members of a calibrated data set may be expressed as a positive value representing a relative enhancement of gene expression or as a negative value representing a relative reduction in gene expression with respect to the baseline.
Each member of the calibrated profile data set should be reproducible within a range with respect to similar samples taken from the subject under similar conditions. For example, the calibrated profile data sets may be reproducible within one order of magnitude with respect to similar samples taken from the subject under similar conditions. More particularly, the members may be reproducible within 50%, more particularly reproducible within 20%, and typically within 10%. In accordance with embodiments of the invention, a pattern of increasing, decreasing and no change in relative gene expression from each of a plurality of gene loci examined in the Gene Expression Panel may be used to prepare a calibrated profile set that is informative with regards to a biological condition, biological efficacy of an agent treatment conditions or for comparison to populations. Patterns of this nature may be used to identify likely candidates for a drug trial, used alone or in combination with other clinical indicators to be diagnostic or prognostic with respect to a biological condition or may be used to guide the development of a pharmaceutical or nutriceutical through manufacture, testing and marketing.
The numerical data obtained from quantitative gene expression and numerical data from calibrated gene expression relative to a baseline profile data set may be stored in databases or digital storage mediums and may retrieved for purposes including managing patient health care or for conducting clinical trials or for characterizing a drug. The data may be transferred in physical or wireless networks via the World Wide Web, email, or internet access site for example or by hard copy so as to be collected and pooled from distant geographic sites (FIG. 8).
In an embodiment of the present invention, a descriptive record is stored in a single database or multiple databases where the stored data includes the raw gene expression data (first profile data set) prior to transformation by use of a baseline profile data set, as well as a record of the baseline profile data set used to generate the calibrated profile data set including for example, annotations regarding whether the baseline profile data set is derived from a particular Signature Panel and any other annotation that facilitates interpretation and use of the data.
Because the data is in a universal format, data handling may readily be done with a computer. The data is organized so as to provide an output optionally corresponding to a graphical representation of a calibrated data set.
For example, a distinct sample derived from a subject being at least one of RNA or protein may be denoted as PI. The first profile data set derived from sample PI is denoted Mj, where Mj is a quantitative measure of a distinct RNA or protein constituent of PI. The record Ri is a ratio of M and P and may be annotated with additional data on the subject relating to, for example, age, diet, ethnicity, gender, geographic location, medical disorder, mental disorder, medication, physical activity, body mass and environmental exposure. Moreover, data handling may further include accessing data from a second condition database which may contain additional medical data not presently held with the calibrated profile data sets. In this context, data access may be via a computer network.
The above described data storage on a computer may provide the information in a form that can be accessed by a user. Accordingly, the user may load the information onto a second access site including downloading the information. However, access may be restricted to users having a password or other security device so as to protect the medical records contained within. A feature of this embodiment of the invention is the ability of a user to add new or annotated records to the data set so the records become part of the biological information.
The graphical representation of calibrated profile data sets pertaining to a product such as a drug provides an opportunity for standardizing a product by means of the calibrated profile, more particularly a signature profile. The profile may be used as a feature with which to demonstrate relative efficacy, differences in mechanisms of actions, etc. compared to other drugs approved for similar or different uses.
The various embodiments of the invention may be also implemented as a computer program product for use with a computer system. The product may include program code for deriving a first profile data set and for producing calibrated profiles. Such implementation may include a series of computer instructions fixed either on a tangible medium, such as a computer readable medium (for example, a diskette, CD-ROM, ROM, or fixed disk), or transmittable to a computer system via a modem or other interface device, such as a communications adapter coupled to a network. The network coupling may be for example, over optical or wired communications lines or via wireless techniques (for example, microwave, infrared or other transmission techniques) or some combination of these. The series of computer instructions preferably embodies all or part of the functionality previously described herein with respect to the system. Those skilled in the art should appreciate that such computer instructions can be written in a number of programming languages for use with many computer architectures or operating systems. Furthermore, such instructions may be stored in any memory device, such as semiconductor, magnetic, optical or other memory devices, and may be transmitted using any communications technology, such as optical, infrared, microwave, or other transmission technologies. It is expected that such a computer program product may be distributed as a removable medium with accompanying printed or electronic documentation (for example, shrink wrapped software), preloaded with acomputer system (for example, on system ROM or fixed disk), or distributed from a server or electronic bulletin board over a network (for example, the Internet or World Wide Web). In addition, a computer system is further provided including derivative modules for deriving a first data set and a calibration profile data set.
The calibration profile data sets in graphical or tabular form, the associated databases, and the calculated index or derived algorithm, together with information extracted from the panels, the databases, the data sets or the indices or algorithms are commodities that can be sold together or separately for a variety of purposes as described in WO 01/25473.
Index Construction
In combination, (i) the remarkable consistency of Gene Expression Profiles with respect to a biological condition across a population and (ii) the use of procedures that provide substantially reproducible measurement of constituents in a Gene Expression Panel giving rise to a Gene Expression Profile, under measurement conditions wherein specificity and efficiencies of amplification for all constituents of the panel are substantially similar, make possible the use of an index that characterizes a Gene Expression Profile, and which therefore provides a measurement of a biological condition.
An index may be constructed using an index function that maps values in a Gene Expression Profile into a single value that is pertinent to the biological condition at hand. The values in a Gene Expression Profile are the amounts of each constituent of the Gene Expression Panel that corresponds to the Gene Expression Profile. These constituent amounts form a profile data set, and the index function generates a single value—the index—from the members of the profile data set.
The index function may conveniently be constructed as a linear sum of terms, each term being what we call a “contribution function” of a member of the profile data set. For example, the contribution function may be a constant times a power of a member of the profile data set. So the index function would have the form

I=ΣCiMiP(i),

where I is the index, M; is the value of the member i of the profile data set, Ci is a constant, and P(i) is a power to which Mi is raised, the sum being formed for all integral values of i up to the number of members in the data set. We thus have a linear polynomial expression.

The values Ci and P(i) may be determined in a number of ways, so that the index I is informative of the pertinent biological condition. One way is to apply statistical techniques, such as latent class modeling, to the profile data sets to correlate clinical data or experimentally derived data, or other data pertinent to the biological condition. In this connection, for example, may be employed the software from Statistical Innovations, Belmont, Mass., called Latent Gold®. See the web pages at www.statisticalinnovations.com/lg/, which are hereby incorporated herein by reference.
Alternatively, other simpler modeling techniques may be employed in a manner known in the art. The index function for inflammation may be constructed, for example, in a manner that a greater degree of inflammation (as determined by the a profile data set for the Inflammation Gene Expression Profile) correlates with a large value of the index function. In a simple embodiment, therefore, each P(i) may be +1 or −1, depending on whether the constituent increases or decreases with increasing inflammation. As discussed in further detail below, we have constructed a meaningful inflammation index that is proportional to the expression

1⁄4{IL1A}+1⁄4{IL1B}+1⁄4{TNF}+1⁄4{INFG}−1/{IL10},

where the braces around a constituent designate measurement of such constituent and the constituents are a subset of the Inflammation Gene Expression Panel of Table 1.

Just as a baseline profile data set, discussed above, can be used to provide an appropriate normative reference, and can even be used to create a Calibrated profile data set, as discussed above, based on the normative reference, an index that characterizes a Gene Expression Profile can also be provided with a normative value of the index function used to create the index. This normative value can be determined with respect to a relevant population, so that the index may be interpreted in relation to the normative value. The relevant population may have in common a property that is at least one of age group, gender, ethnicity, geographic location, diet, medical disorder, clinical indicator, medication, physical activity, body mass, and environmental exposure.
As an example, the index can be constructed, in relation to a normative Gene Expression Profile for a population of healthy subjects, in such a way that a reading of approximately 1 characterizes normative Gene Expression Profiles of healthy subjects. Let us further assume that the biological condition that is the subject of the index is inflammation; a reading of 1 in this example thus corresponds to a Gene Expression Profile that matches the norm for healthy subjects. A substantially higher reading then may identify a subject experiencing an inflammatory condition. The use of 1 as identifying a normative value, however, is only one possible choice; another logical choice is to use 0 as identifying the normative value. With this choice, deviations in the index from zero can be indicated in standard deviation units (so that values lying between −1 and +1 encompass 90% of a normally distributed reference population. Since we have found that Gene Expression Profile values (and accordingly constructed indices based on them) tend to be normally distributed, the 0-centered index constructed in this manner is highly informative. It therefore facilitates use of the index in diagnosis of disease and setting objectives for treatment. The choice of 0 for the normative value, and the use of standard deviation units, for example, are illustrated in FIG. 17B, discussed below.
EXAMPLES
Example 1
Acute Inflammatory Index to Assist in Analysis of Large, Complex Data Sets. In one embodiment of the invention the index value or algorithm can be used to reduce a complex data set to a single index value that is informative with respect to the inflammatory state of a subject. This is illustrated in FIGS. 1A and 1B.
FIG. 1A is entitled Source Precision Inflammation Profile Tracking of A Subject Results in a Large, Complex Data Set. The figure shows the results of assaying 24 genes from the Inflammation Gene Expression Panel (shown in Table 1) on eight separate days during the course of optic neuritis in a single male subject.
FIG. 1B shows use of an Acute Inflammation Index. The data displayed in FIG. 1A above is shown in this figure after calculation using an index function proportional to the following mathematical expression: (1⁄4{IL1A}+1⁄4{IL1B}+1⁄4{TNF}+1⁄4{INFG}−1/{IL10}).
Example 2
Use of acute inflammation index or algorithm to monitor a biological condition of a sample or a subject. The inflammatory state of a subject reveals information about the past progress of the biological condition, future progress, response to treatment, etc. The Acute Inflammation Index may be used to reveal such information about the biological condition of a subject. This is illustrated in FIG. 2.
The results of the assay for inflammatory gene expression for each day (shown for 24 genes in each row of FIG. 1A) is displayed as an individual histogram after calculation. The index reveals clear trends in inflammatory status that may correlated with therapeutic intervention (FIG. 2).
FIG. 2 is a graphical illustration of the acute inflammation index calculated at 9 different, significant clinical milestones from blood obtained from a single patient treated medically with for optic neuritis. Changes in the index values for the Acute Inflammation Index correlate strongly with the expected effects of therapeutic intervention. Four clinical milestones have been identified on top of the Acute Inflammation Index in this figure including (1) prior to treatment with steroids, (2) treatment with IV solumedrol at 1 gram per day, (3) post-treatment with oral prednisone at 60 mg per day tapered to 10 mg per day and (4) post treatment. The data set is the same as for FIG. 1. The index is proportional to 1⁄4{IL1A}+1⁄4{IL1B}+1⁄4{TNF}+1⁄4{INFG}−1/{IL10}. As expected, the acute inflammation index falls rapidly with treatment with IV steroid, goes up during less efficacious treatment with oral prednisone and returns to the pre-treatment level after the steroids have been discontinued and metabolized completely.
Example 3
Use of the acute inflammatory index to set dose, including concentrations and timing, for compounds in development or for compounds to be tested in human and non-human subjects as shown in FIG. 3. The acute inflammation index may be used as a common reference value for therapeutic compounds or interventions without common mechanisms of action. The compound that induces a gene response to a compound as indicated by the index, but fails to ameliorate a known biological conditions may be compared to a different compounds with varying effectiveness in treating the biological condition.
FIG. 3 shows the effects of single dose treatment with 800 mg of ibuprofen in a single donor as characterized by the Acute Inflammation Index. 800 mg of over-the-counter ibuprofen were taken by a single subject at Time=0 and Time=48 hr. Gene expression values for the indicated five inflammation-related gene loci were determined as described below at times=2, 4, 6, 48, 50, 56 and 96 hours. As expected the acute inflammation index falls immediately after taking the non-steroidal anti-inflammatory ibuprofen and returns to baseline after 48 hours. A second dose at T=48 follows the same kinetics at the first dose and returns to baseline at the end of the experiment at T=96.
Example 4
Use of the acute inflammation index to characterize efficacy, safety, and mode of physiological action for an agent, which may be in development and/or may be complex in nature. This is illustrated in FIG. 4.
FIG. 4 shows that the calculated acute inflammation index displayed graphically for five different conditions including (A) untreated whole blood; (B) whole blood treated in vitro with DMSO, an non-active carrier compound; (C) otherwise unstimulated whole blood treated in vitro with dexamethasone (0.08 ug/ml); (D) whole blood stimulated in vitro with lipopolysaccharide, a known pro-inflammatory compound, (LPS, 1 ng/ml) and (E) whole blood treated in vitro with LPS (1 ng/ml) and dexamethasone (0.08 ug/ml). Dexamethasone is used as a prescription compound that is commonly used medically as an anti-inflammatory steroid compound. The acute inflammation index is calculated from the experimentally determined gene expression levels of inflammation-related genes expressed in human whole blood obtained from a single patient. Results of mRNA expression are expressed as Ct's in this example, but may be expressed as, e.g., relative fluorescence units, copy number or any other quantifiable, precise and calibrated form, for the genes IL1A, IL1B, TNF, IFNG and IL10. From the gene expression values, the acute inflammation values were determined algebraically according in proportion to the expression 1⁄4 {IL1A}+1⁄4 {IL1B}+1⁄4 {TNF}+1⁄4{INFG}−1/{IL10}.
Example 5
Development and use of population normative values for Gene Expression Profiles. FIGS. 6 and 7 show the arithmetic mean values for gene expression profiles (using the 48 loci of the Inflammation Gene Expression Panel of Table 1) obtained from whole blood of two distinct patient populations. These populations are both normal or undiagnosed. The first population, which is identified as Bonfils (the plot points for which are represented by diamonds), is composed of 17 subjects accepted as blood donors at the Bonfils Blood Center in Denver, Colo. The second population is 9 donors, for which Gene Expression Profiles were obtained from assays conducted four times over a four-week period. Subjects in this second population (plot points for which are represented by squares) were recruited from employees of Source Precision Medicine, Inc., the assignee herein. Gene expression averages for each population were calculated for each of 48 gene loci of the Gene Expression Inflammation Panel. The results for loci 1-24 (sometimes referred to below as the Inflammation 48A loci) are shown in FIG. 6 and for loci 25-48 (sometimes referred to below as the Inflammation 48B loci) are shown in FIG. 7.
The consistency between gene expression levels of the two distinct populations is dramatic. Both populations show gene expressions for each of the 48 loci that are not significantly different from each other. This observation suggests that there is a “normal” expression pattern for human inflammatory genes, that a Gene Expression Profile, using the Inflammation Gene Expression Panel of Table 1 (or a subset thereof) characterizes that expression pattern, and that a population-normal expression pattern can be used, for example, to guide medical intervention for any biological condition that results in a change from the normal expression pattern.
In a similar vein, FIG. 8 shows arithmetic mean values for gene expression profiles (again using the 48 loci of the Inflammation Gene Expression Panel of Table 1) also obtained from whole blood of two distinct patient populations. One population, expression values for which are represented by triangular data points, is 24 normal, undiagnosed subjects (who therefore have no known inflammatory disease). The other population, the expression values for which are represented by diamond-shaped data points, is four patients with rheumatoid arthritis and who have failed therapy (who therefore have unstable rheumatoid arthritis).
As remarkable as the consistency of data from the two distinct normal populations shown in FIGS. 6 and 7 is the systematic divergence of data from the normal and diseased populations shown in FIG. 8. In 45 of the shown 48 inflammatory gene loci, subjects with unstable rheumatoid arthritis showed, on average, increased inflammatory gene expression (lower cycle threshold values; Ct), than subjects without disease. The data thus further demonstrate that is possible to identify groups with specific biological conditions using gene expression if the precision and calibration of the underlying assay are carefully designed and controlled according to the teachings herein.
FIG. 9, in a manner analogous to FIG. 8, shows the shows arithmetic mean values for gene expression profiles using 24 loci of the Inflammation Gene Expression Panel of Table 1) also obtained from whole blood of two distinct patient populations. One population, expression values for which are represented by diamond-shaped data points, is 17 normal, undiagnosed subjects (who therefore have no known inflammatory disease) who are blood donors. The other population, the expression values for which are represented by square-shaped data points, is 16 subjects, also normal and undiagnosed, who have been monitored over six months, and the averages of these expression values are represented by the square-shaped data points. Thus the cross-sectional gene expression-value averages of a first healthy population match closely the longitudinal gene expression-value averages of a second healthy population, with approximately 7% or less variation in measured expression value on a gene-to-gene basis.
FIG. 10 shows the shows gene expression values (using 14 loci of the Inflammation Gene Expression Panel of Table 1) obtained from whole blood of 44 normal undiagnosed blood donors (data for 10 subjects of which is shown). Again, the gene expression values for each member of the population are closely matched to those for the population, represented visually by the consistent peak heights for each of the gene loci. Other subjects of the population and other gene loci than those depicted here display results that are consistent with those shown here.
In consequence of these principles, and in various embodiments of the present invention, population normative values for a Gene Expression Profile can be used in comparative assessment of individual subjects as to biological condition, including both for purposes of health and/or disease. In one embodiment the normative values for a Gene Expression Profile may be used as a baseline in computing a “calibrated profile data set” (as defined at the beginning of this section) for a subject that reveals the deviation of such subject's gene expression from population normative values. Population normative values for a Gene Expression Profile can also be used as baseline values in constructing index functions in accordance with embodiments of the present invention. As a result, for example, an index function can be constructed to reveal not only the extent of an individual's inflammation expression generally but also in relation to normative values.
Example 6
Consistency of expression values, of constituents in Gene Expression Panels, over time as reliable indicators of biological condition. FIG. 11 shows the expression levels for each of four genes (of the Inflammation Gene Expression Panel of Table 1), of a single subject, assayed monthly over a period of eight months. It can be seen that the expression levels are remarkably consistent over time.
FIGS. 12 and 13 similarly show in each case the expression levels for each of 48 genes (of the Inflammation Gene Expression Panel of Table 1), of distinct single subjects (selected in each case on the basis of feeling well and not taking drugs), assayed, in the case of FIG. 12 weekly over a period of four weeks, and in the case of FIG. 13 monthly over a period of six months. In each case, again the expression levels are remarkably consistent over time, and also similar across individuals.
FIG. 14 also shows the effect over time, on inflammatory gene expression in a single human subject, of the administration of an anti-inflammatory steroid, as assayed using the Inflammation Gene Expression Panel of Table 1. In this case, 24 of 48 loci are displayed. The subject had a baseline blood sample drawn in a PAX RNA isolation tube and then took a single 60 mg dose of prednisone, an anti-inflammatory, prescription steroid. Additional blood samples were drawn at 2 hr and 24 hr post the single oral dose. Results for gene expression are displayed for all three time points, wherein values for the baseline sample are shown as unity on the x-axis. As expected, oral treatment with prednisone resulted in the decreased expression of most of inflammation-related gene loci, as shown by the 2-hour post-administration bar graphs. However, the 24-hour post-administration bar graphs show that, for most of the gene loci having reduced gene expression at 2 hours, there were elevated gene expression levels at 24 hr.
Although the baseline in FIG. 14 is based on the gene expression values before drug intervention associated with the single individual tested, we know from the previous example, that healthy individuals tend toward population normative values in a Gene Expression Profile using the Inflammation Gene Expression Panel of Table 1 (or a subset of it). We conclude from FIG. 14 that in an attempt to return the inflammatory gene expression levels to those demonstrated in FIGS. 6 and 7 (normal or set levels), interference with the normal expression induced a compensatory gene expression response that over-compensated for the drug-induced response, perhaps because the prednisone had been significantly metabolized to inactive forms or eliminated from the subject.
FIG. 15, in a manner analogous to FIG. 14, shows the effect over time, via whole blood samples obtained from a human subject, administered a single dose of prednisone, on expression of 5 genes (of the Inflammation Gene Expression Panel of Table 1). The samples were taken at the time of administration (t=0) of the prednisone, then at two and 24 hours after such administration. Each whole blood sample was challenged by the addition of 0.1 ng/ml of lipopolysaccharide (a Gram-negative endotoxin) and a gene expression profile of the sample, post-challenge, was determined. It can seen that the two-hour sample shows dramatically reduced gene expression of the 5 loci of the Inflammation Gene Expression Panel, in relation to the expression levels at the time of administration (t=0). At 24 hours post administration, the inhibitory effect of the prednisone is no longer apparent, and at 3 of the 5 loci, gene expression is in fact higher than at t=0, illustrating quantitatively at the molecular level the well-known rebound effect.
FIG. 16 also shows the effect over time, on inflammatory gene expression in a single human subject suffering from rheumatoid arthritis, of the administration of a TNF-inhibiting compound, but here the expression is shown in comparison to the cognate locus average previously determined (in connection with FIGS. 6 and 7) for the normal (i.e., undiagnosed, healthy) population. As part of a larger international study involving patients with rheumatoid arthritis, the subject was followed over a twelve-week period. The subject was enrolled in the study because of a failure to respond to conservative drug therapy for rheumatoid arthritis and a plan to change therapy and begin immediate treatment with a TNF-inhibiting compound. Blood was drawn from the subject prior to initiation of new therapy (visit 1). After initiation of new therapy, blood was drawn at 4 weeks post change in therapy (visit 2), 8 weeks (visit 3), and 12 weeks (visit 4) following the start of new therapy. Blood was collected in PAX RNA isolation tubes, held at room temperature for two hours and then frozen at −30° C.
Frozen samples were shipped to the central laboratory at Source Precision Medicine, the assignee herein, in Boulder, Colo. for determination of expression levels of genes in the 48-gene Inflammation Gene Expression Panel of Table 1. The blood samples were thawed and RNA extracted according to the manufacturer's recommended procedure. RNA was converted to cDNA and the level of expression of the 48 inflammatory genes was determined. Expression results are shown for 11 of the 48 loci in FIG. 16. When the expression results for the 11 loci are compared from visit one to a population average of normal blood donors from the United States, the subject shows considerable difference. Similarly, gene expression levels at each of the subsequent physician visits for each locus are compared to the same normal average value. Data from visits 2, 3 and 4 document the effect of the change in therapy. In each visit following the change in the therapy, the level of inflammatory gene expression for 10 of the 11 loci is closer to the cognate locus average previously determined for the normal (i.e., undiagnosed, healthy) population.
FIG. 17A further illustrates the consistency of inflammatory gene expression, illustrated here with respect to 7 loci of (of the Inflammation Gene Expression Panel of Table 1), in a population of 44 normal, undiagnosed blood donors. For each individual locus is shown the range of values lying within ±2 standard deviations of the mean expression value, which corresponds to 95% of a normally distributed population. Notwithstanding the great width of the confidence interval (95%), the measured gene expression value (ΔCT)—remarkably—still lies within 10% of the mean, regardless of the expression level involved. As described in further detail below, for a given biological condition an index can be constructed to provide a measurement of the condition. This is possible as a result of the conjunction of two circumstances: (i) there is a remarkable consistency of Gene Expression Profiles with respect to a biological condition across a population and (ii) there can be employed procedures that provide substantially reproducible measurement of constituents in a Gene Expression Panel giving rise to a Gene Expression Profile, under measurement conditions wherein specificity and efficiencies of amplification for all constituents of the panel are substantially similar and which therefore provides a measurement of a biological condition. Accordingly, a function of the expression values of representative constituent loci of FIG. 17A is here used to generate an inflammation index value, which is normalized so that a reading of 1 corresponds to constituent expression values of healthy subjects, as shown in the right-hand portion of FIG. 17A.
In FIG. 17B, an inflammation index value was determined for each member of a population of 42 normal undiagnosed blood donors, and the resulting distribution of index values, shown in the figure, can be seen to approximate closely a normal distribution, notwithstanding the relatively small population size. The values of the index are shown relative to a 0-based median, with deviations from the median calibrated in standard deviation units. Thus 90% of the population lies within +1 and −1 of a 0 value. We have constructed various indices, which exhibit similar behavior.
FIG. 17C illustrates the use of the same index as FIG. 17B, where the inflammation median for a normal population has been set to zero and both normal and diseased subjects are plotted in standard deviation units relative to that median. An inflammation index value was determined for each member of a normal, undiagnosed population of 70 individuals (black bars). The resulting distribution of index values, shown in FIG. 17C, can be seen to approximate closely a normal distribution. Similarly, index values were calculated for individuals from two diseased population groups, (1) rheumatoid arthritis patients treated with methotrexate (MTX) who are about to change therapy to more efficacious drugs (e.g., TNF inhibitors)(hatched bars), and (2) rheumatoid arthritis patients treated with disease modifying anti-rheumatoid drugs (DMARDS) other than MTX, who are about to change therapy to more efficacious drugs (e.g., MTX). Both populations present index values that are skewed upward (demonstrating increased inflammation) in comparison to the normal distribution. This figure thus illustrates the utility of an index to derived from Gene Expression Profile data to evaluate disease status and to provide an objective and quantifiable treatment objective. When these two populations were treated appropriately, index values from both populations returned to a more normal distribution (data not shown here).
FIG. 18 plots, in a fashion similar to that of FIG. 17A, Gene Expression Profiles, for the same 7 loci as in FIG. 17A, two different 6-subject populations of rheumatoid arthritis patients. One population (called “stable” in the figure) is of patients who have responded well to treatment and the other population (called “unstable” in the figure) is of patients who have not responded well to treatment and whose therapy is scheduled for change. It can be seen that the expression values for the stable population, lie within the range of the 95% confidence interval, whereas the expression values for the unstable population for 5 of the 7 loci are outside and above this range. The right-hand portion of the figure shows an average inflammation index of 9.3 for the unstable population and an average inflammation index of 1.8 for the stable population, compared to I for a normal undiagnosed population. The index thus provides a measure of the extent of the underlying inflammatory condition, in this case, rheumatoid arthritis. Hence the index, besides providing a measure of biological condition, can be used to measure the effectiveness of therapy as well as to provide a target for therapeutic intervention.
FIG. 19 thus illustrates use of the inflammation index for assessment of a single subject suffering from rheumatoid arthritis, who has not responded well to traditional therapy with methotrexate. The inflammation index for this subject is shown on the far right at start of a new therapy (a TNF inhibitor), and then, moving leftward, successively, 2 weeks, 6 weeks, and 12 weeks thereafter. The index can be seen moving towards normal, consistent with physician observation of the patient as responding to the new treatment.
FIG. 20 similarly illustrates use of the inflammation index for assessment of three subjects suffering from rheumatoid arthritis, who have not responded well to traditional therapy with methotrexate, at the beginning of new treatment (also with a TNF inhibitor), and 2 weeks and 6 weeks thereafter. The index in each case can again be seen moving generally towards normal, consistent with physician observation of the patients as responding to the new treatment.
Each of FIGS. 21-23 shows the inflammation index for an international group of subjects, suffering from rheumatoid arthritis, each of whom has been characterized as stable (that is, not anticipated to be subjected to a change in therapy) by the subject's treating physician. FIG. 21 shows the index for each of 10 patients in the group being treated with methotrexate, which known to alleviate symptoms without addressing the underlying disease. FIG. 22 shows the index for each of 10 patients in the group being treated with Enbrel (an TNF inhibitor), and FIG. 23 shows the index for each 10 patients being treated with Remicade (another TNF inhibitor). It can be seen that the inflammation index for each of the patients in FIG. 21 is elevated compared to normal, whereas in FIG. 22, the patients being treated with Enbrel as a class have an inflammation index that comes much closer to normal (80% in the normal range). In FIG. 23, it can be seen that, while all but one of the patients being treated with Remicade have an inflammation index at or below normal, two of the patients have an abnormally low inflammation index, suggesting an immunosuppressive response to this drug. (Indeed, studies have shown that Remicade has been associated with serious infections in some subjects, and here the immunosuppressive effect is quantified.) Also in FIG. 23, one subject has an inflammation index that is significantly above the normal range. This subject in fact was also on a regimen of an anti-inflammation steroid (prednisone) that was being tapered; within approximately one week after the inflammation index was sampled, the subject experienced a significant flare of clinical symptoms.
Remarkably, these examples show a measurement, derived from the assay of blood taken from a subject, pertinent to the subject's arthritic condition. Given that the measurement pertains to the extent of inflammation, it can be expected that other inflammation-based conditions, including, for example, cardiovascular disease, may be monitored in a similar fashion.
FIG. 24 illustrates use of the inflammation index for assessment of a single subject suffering from inflammatory bowel disease, for whom treatment with Remicade was initiated in three doses. The graphs show the inflammation index just prior to first treatment, and then 24 hours after the first treatment; the index has returned to the normal range. The index was elevated just prior to the second dose, but in the normal range prior to the third dose. Again, the index, besides providing a measure of biological condition, is here used to measure the effectiveness of therapy (Remicade), as well as to provide a target for therapeutic intervention in terms of both dose and schedule.
FIG. 25 shows Gene Expression Profiles with respect to 24 loci (of the Inflammation Gene Expression Panel of Table 1) for whole blood treated with Ibuprofen in vitro in relation to other non-steroidal anti-inflammatory drugs (NSAIDs). The profile for Ibuprofen is in front. It can be seen that all of the NSAIDs, including Ibuprofen share a substantially similar profile, in that the patterns of gene expression across the loci are similar. Notwithstanding these similarities, each individual drug has its own distinctive signature.
FIG. 26 illustrates how the effects of two competing anti-inflammatory compounds can be compared objectively, quantitatively, precisely, and reproducibly. In this example, expression of each of a panel of two genes (of the Inflammation Gene Expression Panel of Table 1) is measured for varying doses (0.08-250 μg/ml) of each drug in vitro in whole blood. The market leader drug shows a complex relationship between dose and inflammatory gene response. Paradoxically, as the dose is increased, gene expression for both loci initially drops and then increases in the case the case of the market leader. For the other compound, a more consistent response results, so that as the dose is increased, the gene expression for both loci decreases more consistently.
FIGS. 27 through 41 illustrate the use of gene expression panels in early identification and monitoring of infectious disease. These figures plot the response, in expression products of the genes indicated, in whole blood, to the administration of various infectious agents or products associated with infectious agents. In each figure, the gene expression levels are “calibrated”, as that term is defined herein, in relation to baseline expression levels determined with respect to the whole blood prior to administration of the relevant infectious agent. In this respect the figures are similar in nature to various figures of our below-referenced patent application WO 01/25473 (for example, FIG. 15 therein). The concentration change is shown ratiometrically, and the baseline level of 1 for a particular gene locus corresponds to an expression level for such locus that is the same, monitored at the relevant time after addition of the infectious agent or other stimulus, as the expression level before addition of the stimulus. Ratiometric changes in concentration are plotted on a logarithmic scale. Bars below the unity line represent decreases in concentration and bars above the unity line represent increases in concentration, the magnitude of each bar indicating the magnitude of the ratio of the change. We have shown in WO 01/25473 and other experiments that, under appropriate conditions, Gene Expression Profiles derived in vitro by exposing whole blood to a stimulus can be representative of Gene Expression Profiles derived in vivo with exposure to a corresponding stimulus.
FIG. 27 uses a novel bacterial Gene Expression Panel of 24 genes, developed to discriminate various bacterial conditions in a host biological system. Two different stimuli are employed: lipotechoic acid (LTA), a gram positive cell wall constituent, and lipopolysaccharide (LPS), a gram negative cell wall constituent. The final concentration immediately after administration of the stimulus was 100 ng/mL, and the ratiometric changes in expression, in relation to pre-administration levels, were monitored for each stimulus 2 and 6 hours after administration. It can be seen that differential expression can be observed as early as two hours after administration, for example, in the IFNA2 locus, as well as others, permitting discrimination in response between gram positive and gram negative bacteria.
FIG. 28 shows differential expression for a single locus, IFNG, to LTA derived from three distinct sources: S. pyogenes, B. subtilis, and S. aureus. Each stimulus was administered to achieve a concentration of 100 ng/mL, and the response was monitored at 1, 2, 4, 6, and 24 hours after administration. The results suggest that Gene Expression Profiles can be used to distinguish among different infectious agents, here different species of gram positive bacteria.
FIGS. 29 and 30 show the response of the Inflammation 48A and 48B loci respectively (discussed above in connection with FIGS. 6 and 7 respectively) in whole blood to administration of a stimulus of S. aureus and of a stimulus of E. coli (in the indicated concentrations, just after administration, of 107 and 106 CFU/mL respectively), monitored 2 hours after administration in relation to the pre-administration baseline. The figures show that many of the loci respond to the presence of the bacterial infection within two hours after infection.
FIGS. 31 and 32 correspond to FIGS. 29 and 30 respectively and are similar to them, with the exception that the monitoring here occurs 6 hours after administration. More of the loci are responsive to the presence of infection. Various loci, such as IL2, show expression levels that discriminate between the two infectious agents.
FIG. 33 shows the response of the Inflammation 48A loci to the administration of a stimulus of E. coli (again in the concentration just after administration of 106 CFU/mL) and to the administration of a stimulus of an E. coli filtrate containing E. coli bacteria by products but lacking E. coli bacteria. The responses were monitored at 2, 6, and 24 hours after administration. It can be seen, for example, that the responses over time of loci IL1B, IL18 and CSF3 to E. coli and to E. coli filtrate are different.
FIG. 34 is similar to FIG. 33, but here the compared responses are to stimuli from E. coli filtrate alone and from E. coli filtrate to which has been added polymyxin B, an antibiotic known to bind to lipopolysaccharide (LPS). An examination of the response of IL1B, for example, shows that presence of polymyxin B did not affect the response of the locus to E. coli filtrate, thereby indicating that LPS does not appear to be a factor in the response of IL1B to E. coli filtrate.
FIG. 35 illustrates the responses of the Inflammation 48A loci over time of whole blood to a stimulus of S. aureus (with a concentration just after administration of 107 CFU/mL) monitored at 2, 6, and 24 hours after administration. It can be seen that response over time can involve both direction and magnitude of change in expression. (See for example, IL5 and IL18.)
FIGS. 36 and 37 show the responses, of the Inflammation 48A and 48B loci respectively, monitored at 6 hours to stimuli from E. coli (at concentrations of 106 and 102 CFU/mL immediately after administration) and from S. aureus (at concentrations of 107 and 102 CFU/mL immediately after administration). It can be seen, among other things, that in various loci, such as B7 (FIG. 36), TACI, PLA2G7, and CIQA (FIG. 37), E. coli produces a much more pronounced response than S. aureus. The data suggest strongly that Gene Expression Profiles can be used to identify with high sensitivity the presence of gram negative bacteria and to discriminate against gram positive bacteria.
FIGS. 38 and 39 show the responses, of the Inflammation 48B and 48A loci respectively, monitored 2, 6, and 24 hours after administration, to stimuli of high concentrations of S. aureus and E. coli respectively (at respective concentrations of 107 and 106 CFU/mL immediately after administration). The responses over time at many loci involve changes in magnitude and direction. FIG. 40 is similar to FIG. 39, but shows the responses of the Inflammation 48B loci.
FIG. 41 similarly shows the responses of the Inflammation 48A loci monitored at 24 hours after administration to stimuli high concentrations of S. aureus and E. coli respectively (at respective concentrations of 107 and 106 CFU/mL immediately after administration). As in the case of FIGS. 20 and 21, responses at some loci, such as GRO1 and GRO2, discriminate between type of infection.
These data support our conclusion that Gene Expression Profiles with sufficient precision and calibration as described herein (1) can determine subpopulations of individuals with a known biological condition; (2) may be used to monitor the response of patients to therapy; (3) may be used to assess the efficacy and safety of therapy; and (4) may used to guide the medical management of a patient by adjusting therapy to bring one or more relevant Gene Expression Profiles closer to a target set of values, which may be normative values or other desired or achievable values. We have shown that Gene Expression Profiles may provide meaningful information even when derived from ex vivo treatment of blood or other tissue. We have also shown that Gene Expression Profiles derived from peripheral whole blood are informative of a wide range of conditions neither directly nor typically associated with blood.
Furthermore, in embodiments of the present invention, Gene Expression Profiles can also be used for characterization and early identification (including pre-symptomatic states) of infectious disease, such as sepsis. This characterization includes discriminating between infected and uninfected individuals, bacterial and viral infections, specific subtypes of pathogenic agents, stages of the natural history of infection (e.g., early or late), and prognosis. Use of the algorithmic and statistical approaches discussed above to achieve such identification and to discriminate in such fashion is within the scope of various embodiments herein.







TABLE 1







Inflammation Gene Expression Panel










Symbol
Name
Classification
Description





IL1A
Interleukin 1,
cytokines-
Proinflammatory; constitutively and



alpha
chemokines-growth
inducibly expressed in variety of cells.




factors
Generally cytosolic and released only





during severe inflammatory disease


IL1B
Interleukin 1,
cytokines-
Proinflammatory; constitutively and



beta
chemokines-growth
inducibly expressed by many cell types,




factors
secreted


TNFA
Tumor necrosis
cytokines-
Proinflammatory, TH1, mediates host



factor, alpha
chemokines-growth
response to bacterial stimulus, regulates




factors
cell growth & differentiation


IL6
Interleukin 6
cytokines-
Pro- and antiinflammatory activity, TH2



(interferon, beta
chemokines-growth
cytokine, regulates hemotopoietic



2)
factors
system and activation of innate response


IL8
Interleukin 8
cytokines-
Proinflammatory, major secondary




chemokines-growth
inflammatory mediator, cell adhesion,




factors
signal transduction, cell—cell signaling,





angiogenesis, synthesized by a wide





variety of cell types


IFNG
Interferon
cytokines-
Pro- and antiinflammatory activity, TH1



gamma
chemokines-growth
cytokine, nonspecific inflammatory




factors
mediator, produced by activated T-cells


IL2
Interleukin 2
cytokines-
T-cell growth factor, expressed by




chemokines-growth
activated T-cells, regulates lymphocyte




factors
activation and differentiation; inhibits





apoptosis, TH1 cytokine


IL12B
Interleukin 12
cytokines-
Proinflammatory; mediator of innate



p40
chemokines-growth
immunity, TH1 cytokine, requires co-




factors
stimulation with IL-18 to induce ILFN-g


IL15
Interleukin 15
cytokines-
Proinflammatory; mediates T-cell




chemokines-growth
activation, inhibits apoptosis, synergizes




factors
with IL-2 to induce IFN-g and TNF-a


IL18
Interleukin 18
cytokines-
Proinflammatory, TH1, innate and




chemokines-growth
aquired immunity, promotes apoptosis,




factors
requires co-stimulation with IL-1 or IL-





2 to induce TH1 cytokines in T- and





NK-cells


IL4
Interleukin 4
cytokines-
Antiinflammatory; TH2; suppresses




chemokines-growth
proinflammatory cytokines, increases




factors
expression of IL-1RN, regulates





lymphocyte activation


IL5
Interleukin 5
cytokines-
Eosinophil stimulatory factor;




chemokines-growth
stimulates late B cell differentiation to




factors
secretion of Ig


IL10
Interleukin 10
cytokines-
Antiinflammatory; TH2; suppresses




chemokines-growth
production of proinflammatory




factors
cytokines


IL13
Interleukin 13
cytokines-
Inhibits inflammatory cytokine




chemokines-growth
production




factors



IL1RN
Interleukin 1
cytokines-
IL1 receptor antagonist;



receptor
chemokines-growth
Antiinflammatory; inhibits binding of



antagonist
factors
IL-1 to IL-1 receptor by binding to





receptor without stimulating IL-1-like





activity


IL18BP
IL-18 Binding
cytokines-
Implicated in inhibition of early TH1



Protein
chemokines-growth
cytokine responses




factors



TGFB1
Transforming
cytokines-
Pro- and antiinflammatory activity, anti-



growth factor,
chemokines-growth
apoptotic; cell—cell signaling, can either



beta 1
factors
inhibit or stimulate cell growth


IFNA2
Interferon, alpha 2
cytokines-
interferon produced by macrophages




chemokines-growth
with antiviral effects




factors



GRO1
GRO1 oncogene
cytokines-
AKA SCYB1; chemotactic for



(melanoma
chemokines-growth
neutrophils



growth
factors




stimulating





activity, alpha)




GRO2
GRO2 oncogene
cytokines-
AKA MIP2, SCYB2; Macrophage




chemokines-growth
inflammatory protein produced by




factors
moncytes and neutrophils


TNFSF5
Tumor necrosis
cytokines-
ligand for CD40; expressed on the



factor (ligand)
chemokines-growth
surface of T cells. It regulates B cell



superfamily,
factors
function by engaging CD40 on the B



member 5

cell surface


TNFSF6
Tumor necrosis
cytokines-
AKA FasL; Ligand for FAS antigen;



factor (ligand)
chemokines-growth
transduces apoptotic signals into cells



superfamily,
factors




member 6




CSF3
Colony
cytokines-
AKA GCSF; cytokine that stimulates



stimulating
chemokines-growth
granulocyte development



factor 3
factors




(granulocyte)




B7
B7 protein
cell signaling and
Regulatory protein that may be




activation
associated with lupus


CSF2
Granulocyte-
cytokines-
AKA GM-CSF; Hematopoietic growth



monocyte
chemokines-growth
factor; stimulates growth and



colony
factors
differentiation of hematopoietic



stimulating

precursor cells from various lineages,



factor

including granulocytes, macrophages,





eosinophils, and erythrocytes


TNFSF13B
Tumor necrosis
cytokines-
B cell activating factor, TNF family



factor (ligand)
chemokines-growth




superfamily,
factors




member 13b




TACI
Transmembrane
cytokines-
T cell activating factor and calcium



activator and
chemokines-growth
cyclophilin modulator



CAML
factors




interactor




VEGF
vascular
cytokines-
Producted by monocytes



endothelial
chemokines-growth




growth factor
factors



ICAM1
Intercellular
Cell Adhesion/
Endothelial cell surface molecule;



adhesion
Matrix Protein
regulates cell adhesion and trafficking,



molecule 1

upregulated during cytokine stimulation


PTGS2
Prostaglandin-
Enzyme/Redox
AKA COX2; Proinflammatory, member



endoperoxide

of arachidonic acid to prostanoid



synthase 2

conversion pathway; induced by





proinflammatory cytokines


NOS2A
Nitric oxide
Enzyme/Redox
AKA iNOS; produces NO which is



synthase 2A

bacteriocidal/tumoricidal


PLA2G7
Phospholipase
Enzyme/Redox
Platelet activating factor



A2, group VII





(platelet





activating factor





acetylhydrolase,





plasma)




HMOX1
Heme oxygenase
Enzyme/Redox
Endotoxin inducible



(decycling) 1




F3
F3
Enzyme/Redox
AKA thromboplastin, Coagulation





Factor 3; cell surface glycoprotein





responsible for coagulation catalysis


CD3Z
CD3 antigen,
Cell Marker
T-cell surface glycoprotein



zeta polypeptide




PTPRC
protein tyrosine
Cell Marker
AKA CD45; mediates T-cell activation



phosphatase,





receptor type, C




CD14
CD14 antigen
Cell Marker
LPS receptor used as marker for





monocytes


CD4
CD4 antigen
Cell Marker
Helper T-cell marker



(p55)




CD8A
CD8 antigen,
Cell Marker
Suppressor T cell marker



alpha





polypeptide




CD19
CD19 antigen
Cell Marker
AKA Leu 12; B cell growth factor


HSPA1A
Heat shock
Cell Signaling and
heat shock protein 70 kDa



protein 70
activation



MMP3
Matrix
Proteinase/Proteinase
AKA stromelysin; degrades fibronectin,



metalloproteinase 3
Inhibitor
laminin and gelatin


MMP9
Matrix
Proteinase/Proteinase
AKA gelatinase B; degrades



metalloproteinase 9
Inhibitor
extracellular matrix molecules, secreted





by IL-8-stimulated neutrophils


PLAU
Plasminogen
Proteinase/Proteinase
AKA uPA; cleaves plasminogen to



activator,
Inhibitor
plasmin (a protease responsible for



urokinase

nonspecific extracellular matrix





degradation)


SERPINE1
Serine (or
Proteinase/Proteinase
Plasminogen activator inhibitor-1/PAI-1



cysteine)
Inhibitor




protease





inhibitor, clade





B (ovalbumin),





member 1




TIMP1
tissue inhibitor
Proteinase/Proteinase
Irreversibly binds and inhibits



of
Inhibitor
metalloproteinases, such as collagenase



metalloproteinase 1




C1QA
Complement
Proteinase/Proteinase
Serum complement system; forms C1



component 1, q
Inhibitor
complex with the proenzymes c1r and



subcomponent,

c1s



alpha





polypeptide




HLA-
Major
Histocompatibility
Binds antigen for presentation to CD4+


DRB1
histocompatibility

cells



complex, class





II, DR beta 1
















TABLE 2







Diabetes Gene Expression Panel










Symbol
Name
Classification
Description





G6PC
glucose-6-
Glucose-6-
Catalyzes the final step in the



phosphatase,
phosphatase/Glycogen
gluconeogenic and



catalytic
metabolism
glycogenolytic pathways.





Stimulated by glucocorticoids





and strongly inhibited by





insulin. Overexpression (in





conjunction with PCK1





overexpression) leads to





increased hepatic glucose





production.


GCG
glucagon
pancreatic/peptide hormone
Pancreatic hormone which





counteracts the glucose-





lowering action of insulin by





stimulating glycogenolysis





and gluconeogenesis.





Underexpression of glucagon





is preferred. Glucagon-like





peptide (GLP-1) proposed for





type 2 diabetes treatment





inhibits glucag


GCGR
glucagon receptor
glucagon receptor
Expression of GCGR is





strongly upregulated by





glucose. Deficiency or





imbalance could play a role in





NIDDM. Has been looked as





a potential for gene therapy.


GFPT1
glutamine-fructose-
Glutamine amidotransferase
The rate limiting enzyme for



6-phosphate

glucose entry into the



transaminase 1

hexosamine biosynthetic





pathway (HBP).





Overexpression of GFA in





muscle and adipose tissue





increases products of the HBP





which are thought to cause





insulin resistance (possibly





through defects to glucose


GYS1
glycogen synthase 1
Transferase/Glycogen
A key enzyme in the



(muscle)
metabolism
regulation of glycogen





synthesis in the skeletal





muscles of humans. Typically





stimulated by insulin, but in





NIDDM individuals GS is





shown to be completely





resistant to insulin stimulation





(decreased activity and





activation in muscle)


HK2
hexokinase 2
hexokinase
Phosphorylates glucose into





glucose-6-phosphate.





NIDDM patients have lower





HK2 activity which may





contribute to insulin





resistance. Similar action to





GCK.


INS
insulin
Insulin receptor ligand
Decreases blood glucose





concentration and accelerates





glycogen synthesis in the





liver. Not as critical in





NIDDM as in IDDM.


IRS1
insulin receptor
signal
Positive regultion of insulin



substrate 1
transduction/transmembrane
action. This protein is




receptor protein
activated when insulin binds





to insulin receptor - binds 85-





kDa subunit of PI 3-K.





decreased in skeletal muscle





of obese humans.


PCK1
phosphoenolpyruvate
rate-limiting gluconeogenic
Rate limiting enzyme for



carboxykinase 1
enzyme
gluconeogenesis - plays a key





role in the regulation of





hepatic glucose output by





insulin and glucagon.





Overexpression in the liver





results in increased hepatic





glucose production and





hepatic insulin resistance to





glycogen synthe


PIK3R1
phosphoinositide-3-
regulatory enzyme
Positive regulation of insulin



kinase, regulatory

action. Docks in IRS proteins



subunit, polypeptide

and Gab1 - activity is required



1 (p85 alpha)

for insulin stimulated





translocation of glucose





transporters to the plasma





membrane and activation of





glucose uptake.


PPARG
peroxisome
transcription factor/Ligand-
The primary pharmacological



proliferator-activated
dependent nuclear receptor
target for the treatment of



receptor, gamma

insulin resistance in NIDDM.





Involved in glucose and lipid





metabolism in skeletal muscle.


PRKCB1
protein kinase C,
protein kinase C/protein
Negative regulation of insulin



beta 1
phosphorylation
action. Activated by





hyperglycemia - increases





phosphorylation of IRS-1 and





reduces insulin receptor kinase





activity. Increased PKC





activation may lead to





oxidative stress causing





overexpression of TGF-beta





and fibronectin


SLC2A2
solute carrier family
glucose transporter
Glucose transporters



2 (facilitated glucose

expressed uniquely in b-cells



transporter), member 2

and liver. Transport glucose





into the b-cell. Typically





underexpressed in pancreatic





islet cells of individuals with





NIDDM.


SLC2A4
solute carrier family
glucose transporter
Glucose transporter protein



2 (facilitated glucose

that is final mediator in



transporter), member 4

insulin-stimulated glucose





uptake (rate limiting for





glucose uptake).





Underexpression not





important, but overexpression





in muscle and adipose tissue





consistently shown to increase





glucose transport.


TGFB1
transforming growth
Transforming growth factor
Regulated by glucose - in



factor, beta 1
beta receptor ligand
NIDDM individuals,





overexpression (due to





oxidative stress - see PKC)





promotes renal cell





hypertrophy leading to





diabetic nephropathy.


TNF
tumor necrosis factor
cytokine/tumor necrosis
Negative regulation of insulin




factor receptor ligand
action. Produced in excess by





adipose tissue of obese





individuals - increases IRS-1





phosphorylation and decreases





insulin receptor kinase





activity.
















TABLE 3







Prostate Gene Expression Panel










Symbol
Name
Classification
Description





ABCC1
ATP-binding
membrane transporter
AKA MRP1, ABC29:



cassette, sub-family

Multispecific organic anion



C, member 1

membrane transporter;





overexpression confers





tissue protection against a





wide variety of xenobiotics





due to their removal from





the cell.


ACPP
Acid phosphatase,
phosphatase
AKA PAP: Major



prostate

phosphatase of the prostate;





synthesized under androgen





regulation; secreted by the





epithelial cells of the





prostrate


BCL2
B-cell CLL/
apoptosis Inhibitor - cell
Blocks apoptosis by



lymphoma 2
cycle control -
interfering with the




oncogenesis
activation of caspases


BIRC5
Baculoviral IAP
apoptosis Inhibitor
AKA Survivin; API4: May



repeat-containing 5

counteract a default





induction of apoptosis in





G2/M phase of cell cycle;





associates with





microtubules of the mitotic





spindle during apoptosis


CDH1
Cadherin 1, type 1,
cell—cell adhesion/
AKA ECAD, UVO:



E-cadherin
interaction
Calcium ion-dependent cell





adhesion molecule that





mediates cell to cell





interactions in epithelial





cells


CDH2
Cadherin 2, type 1,
cell—cell adhesion/
AKA NCAD, CDHN:



N-cadherin
interaction
Calcium-dependent





glycoprotein that mediates





cell—cell interactions; may





be involved in neuronal





recognition mechanism


CDKN2A
Cyclin-dependent
cell cycle control -
AKA p16, MTS1, INK4:



kinase inhibitor 2A
tumor suppressor
Tumor suppressor gene





involved in a variety of





malignancies; arrests





normal diploid cells in late





G1


CTNNA1
Catenin, alpha 1
cell adhesion
Binds cadherins and links





them with the actin





cytoskeleton


FOLH1
Folate Hydrolase
hydrolase
AKA PSMA, GCP2:





Expressed in normal and





neoplastic prostate cells;





membrane bound





glycoprotein; hydrolyzes





folate and is an N-acetylated





a-linked acidic dipeptidase


GSTT1
Glutathione-S-
metabolism
Catalyzes the conjugation of



Transferase, theta 1

reduced glutathione to a





wide number of exogenous





and endogenous





hydrophobic electrophiles;





has an important role in





human carcinogenesis


HMGIY
High mobility group
DNA binding -
Potential oncogene with



protein, isoforms I
transcriptional
MYC binding site at



and Y
regulation - oncogene
promoter region; involved





in the transcription





regulation of genes





containing, or in close





proximity to a + t-rich





regions


HSPA1A
Heat shock 70 kD
cell signalling and
AKA HSP-70, HSP70-1:



protein 1A
activation
Molecular chaperone,





stabilizes AU rich mRNA


IGF1R
Insulin-like growth
cytokines - chemokines-
Mediates insulin stimulated



factor 1 receptor
growth factors
DNA synthesis; mediates





IGF1 stimulated cell





proliferation and





differentiation


IL6
Interleukin 6
cytokines - chemokines -
Pro- and anti-inflammatory




growth factors
activity, TH2 cytokine,





regulates hematopoiesis,





activation of innate





response, osteoclast





development; elevated in





sera of patients with





metastatic cancer


IL8
Interleukin 8
cytokines - chemokines -
AKA SCYB8, MDNCF:




growth factors
Proinflammatory





chemokine; major





secondary inflammatory





mediator resulting in cell





adhesion, signal





transduction, cell—cell





signaling; regulates





angiogenesis in prostate





cancer


KAI1
Kangai 1
tumor suppressor
AKA SAR2, CD82, ST6:





suppressor of metastatic





ability of prostate cancer





cells


KLK2
Kallikrein 2,
protease - kallikrein
AKA hGK-1: Glandular



prostatic

kallikrein; expression





restricted mainly to the





prostate.


KLK3
Kallikrein 3
protease - kallikrein
AKA PSA: Kallikrein-like





protease which functions





normally in liquefaction of





seminal fluid. Elevated in





prostate cancer.


KRT19
Keratin 19
structural protein -
AKA K19: Type I




differentiation
epidermal keratin; may





form intermediate filaments


KRT5
Keratin 5
structural protein -
AKA EBS2: 58 kD Type II




differentiation
keratin co-expressed with





keratin 14, a 50 kD Type I





keratin, in stratified





epithelium. KRT5





expression is a hallmark of





mitotically active





keratinocytes and is the





primary structural





component of the 10 nm





intermediate filaments of





the mitotic epidermal basal





cells.


KRT8
Keratin 8
structural protein -
AKA K8, CK8: Type II




differentiation
keratin; coexpressed with





Keratin 18; involved in





intermediate filament





formation


LGALS8
Lectin, Galactoside-
cell adhesion - growth
AKA PCTA-1: binds to beta



binding, soluble 8
and differentiation
galactoside; involved in





biological processes such as





cell adhesion, cell growth





regulation, inflammation,





immunomodulation,





apoptosis and metastasis


MYC
V-myc avian
transcription factor -
Transcription factor that



myelocytomatosis
oncogene
promotes cell proliferation



viral oncogene

and transformation by



homolog

activating growth-





promoting genes; may also





repress gene expression


NRP1
Neuropilin 1
cell adhesion
AKA NRP, VEGF165R: A





novel VEGF receptor that





modulates VEGF binding to





KDR (VEGF receptor) and





subsequent bioactivity and





therefore may regulate





VEGF-induced





angiogenesis; calcium-





independent cell adhesion





molecule that function





during the formation of





certain neuronal circuits


PART1
Prostate androgen-

Exhibits increased



regulated transcript 1

expression in LNCaP cells





upon exposure to androgens


PCA3
Prostate cancer

AKA DD3: prostate



antigen 3

specific; highly expressed in





prostate tumors


PCANAP7
Prostate cancer

AKA IPCA7: unknown



associated protein 7

function; co-expressed with





known prostate cancer





genes


PDEF
Prostate epithelium
transcription factor
Acts as an androgen-



specific Ets

independent transcriptional



transcription factor

activator of the PSA





promoter; directly interacts





with the DNA binding





domain of androgen





receptor and enhances





androgen-mediated





activation of the PSA





promoter


PLAU
Urokinase-type
proteinase
AKA UPA, URK: cleaves



plasminogen

plasminogen to plasmin



activator




POV1
Prostate cancer

RNA expressed selectively



overexpressed gene 1

in prostate tumor samples


PSCA
Prostate stem cell
antigen
Prostate-specific cell



antigen

surface antigen expressed





strongly by both androgen-





dependent and -independent





tumors


PTGS2
Prostaglandin-
cytokines - chemokines -
AKA COX-2:



endoperoxide
growth factors
Proinflammatory; member



synthase 2

of arachidonic acid to





prostanoid conversion





pathway


SERPINB5
Serine proteinase
proteinase inhibitor -
AKA Maspin, PI5: Protease



inhibitor, clade B,
tumor suppressor
Inhibitor; Tumor



member 5

suppressor, especially for





metastasis.


SERPINE1
Serine (or cystein)
proteinase inhibitor
AKA PAI1: regulates



proteinase inhibitor,

fibrinolysis; inhibits PLAU



clade E, member 1




STAT3
Signal transduction
transcription factor
AKA APRF: Transcription



and activator of

factor for acute phase



transcription 3

response genes; rapidly





activated in response to





certain cytokines and





growth factors; binds to IL6





response elements


TERT
Telomerase reverse

AKA TCS1, EST2:



transcriptase

Ribonucleoprotein which in





vitro recognizes a single-





stranded G-rich telomere





primer and adds multiple





telomeric repeats to its 3-





prime end by using an RNA





template


TGFB1
Transforming
cytokines - chemokines -
AKA DPD1, CED: Pro- and



growth factor, beta 1
growth factors
antiinflammatory activity;





anti-apoptotic; cell—cell





signaling, can either inhibit





or stimulate cell growth


TNF
Tumor necrosis
cytokines - chemokines -
AKA TNF alpha:



factor, member 2
growth factors
Proinflammatory cytokine





that is the primary mediator





of immune response and





regulation, associated with





TH1 responses, mediates





host response to bacterial





stimuli, regulates cell





growth & differentiation


TP53
Tumor protein 53
DNA binding protein -
AKA P53: Activates




cell cycle - tumor
expression of genes that




suppressor
inhibit tumor growth and/or





invasion; involved in cell





cycle regulation (required





for growth arrest at G1);





inhibits cell growth through





activation of cell-cycle





arrest and apoptosis


VEGF
Vascular
cytokines -
AKA VPF:



Endothelial
chemokines -
Induces vascular



Growth
growth
permeability,



Factors
factors
endothelial cell





proliferation,





angiogenesis
















TABLE 4







Skin Response Gene Expression Panel










Symbol
Name
Classification
Description





BAX
BCL2
apoptosis induction
Accelerates



associated X
germ cell development
programmed cell



protein

death by binding to





and antagonizing the





apoptosis repressor





BCL2; may induce





caspase activation


BCL2
B-cell
apoptosis inhibitor-
Integral



CLL/lumphoma 2
cell cycle control-
mitochondrial




oncogenesis
membrane protein





that blocks the





apoptotic death of





some cells such as





lymphocytes;





constitutive





expression of BCL2





thought to be cause of





follicular lymphoma


BSG
Basignin
signal transduction-
Member of Ig




peripheral plasma
superfamily; tumor




membrane protein
cell-derived





collagenase





stimulatory factor;





stimulates matrix





metalloproteinase





synthesis in





fibroblasts


COL7A1
Type VII
collagen-
alpha 1 subunit of



collagen, alpha 1
differentiation-
type VII collagen;




extracellular matrix
may link collagen





fibrils to the





basement membrane


CRABP2
Celluar
retinoid binding-
Low molecular



Retinoic Acid
signal transduction-
weight protein highly



Binding Protein
transcription
expressed in skin;




regulation
thought to be





important in RA-





mediated regulation





of skin growth &





differentiation


CTGF
Connective
insulin-like growth
Member of family of



Tissue Growth
factor-
peptides including



Factor
differentiation-
serum-induced




wounding response
immediate early gene





products expressed





after induction by





growth factors;





overexpressed in





fibrotic disorders


DUSP1
Dual Specificity
oxidative stress
Induced in human



Phosphatase
response-tyrosine
skin fibroblasts by




phosphatase
oxidative/heat stress





& growth factors; de-





phosphorylates MAP





kinase erk2; may play





a role in negative





regulation of cellular





proliferation


FGF7
Fibroblast
growth factor-
aka KGF; Potent



growth factor 7
differentiation-
mitogen for epithelial




wounding response-
cells; induced after




signal transduction
skin injury


FN1
Fibronectin
cell adhesion-
Major cell surface




motility-signal
glycoprotein of many




transduction
fibroblast cells;





thought to have a role





in cell adhesion,





morphology, wound





healing & cell





motility


FOS
v-fos FBJ
transcription factor-
Proto-oncoprotein



murine
inflammatory
acting with JUN,



osteosarcoma
response-cell
stimulates



virus oncogene
growth &
transcription of genes



homolog
maintanence
with AP-1 regulatory





sites; in some cases





FOS expression is





associated with





apototic cell death


GADD45A
Growth Arrest
cell cycle-DNA
Transcriptionally



and DNA-
repair-apoptosis
induced following



damage-

stressful growth arrest



inducible alpha

conditions &





treatment with DNA





damaging agents;





binds to PCNA





affecting it's





interaction with some





cell division protein





kinase


GRO1
GRO1
cytokines-
AKA SCYB1;



oncogene
chemokines-growth
chemotactic for



(melanoma
factors
neutrophils



growth





stimulating





activity, alpha)




HMOX1
Heme
metabolism-
Essential enzyme in



Oxygenase 1
endoplasmic
heme catabolism;




reticulum
HMOX1 induced by





its substrate heme &





other substances such





as oxidizing agents &





UVA


ICAM1
Intercellular
Cell Adhesion/
Endothelial cell



adhesion
Matrix Protein
surface molecule;



molecule 1

regulates cell





adhesion and





trafficking,





upregulated during





cytokine stimulation


IL1A
Interleukin 1,
cytokines-
Proinflammatory;



alpha
chemokines-growth
constitutively and




factors
inducibly expressed





in variety of cells.





Generally cytosolic





and released only





during severe





inflammatory disease


IL1B
Interleukin 1,
cytokines-
Proinflammatory; con



beta
chemokines-growth
stitutively and




factors
inducibly expressed





by many cell types,





secreted


IL8
Interleukin 8
cytokines-
Proinflammatory,




chemokines-growth
major secondary




factors
inflammatory





mediator, cell





adhesion, signal





transduction, cell—cell





signaling,





angiogenesis,





synthesized by a wide





variety of cell types


IVL
Involucrin
structural protein-
Component of the




peripheral plasma
keratinocyte




membrane protein
crosslinked envelope;





first appears in the





cytosol becoming





crosslinked to





membrane proteins





by transglutaminase


JUN
v-jun avian
transcription factor-
Proto-oncoprotein;



sarcoma virus
DNA binding
component of



17 oncogene

transcription factor



homolog

AP-1 that interacts





directly with target





DNA sequences to





regulate gene





expression


KRT14
Keratin 14
structural protein-
Type I keratin;




differentiation-cell
associates with




shape
keratin 5; component





of intermediate





filaments; several





autosomal dominant





blistering skin





disorders caused by





gene defects


KRT16
Keratin 16
structural protein-
Type I keratin;




differentiation-cell
component of




shape
intermediate





filaments; induced in





skin conditions





favoring enhanced





proliferation or





abnormal





differentiation


KRT5
Keratin 5
structural protein-
Type II intermediate




differentiation-cell
filament chain




shape
expessed largely in





stratified epithelium;





hallmark of





mitotically active





keratinocytes


MAPK8
Mitogen
kinase-stress
aka JNK1; mitogen



Activated
response-signal
activated protein



Protein Kinase 8
transduction
kinase regulates c-Jun





in response to cell





stress; UV irradiation





of skin activates





MAPK8


MMP1
Matrix
Proteinase/
aka Collagenase;



Metalloproteinase 1
Proteinase Inhibtor
cleaves collagens





types I-III; plays a





key role in





remodeling occuring





in both normal &





diseased conditions;





transcriptionally





regulated by growth





factors, hormones,





cytokines & cellular





transformation


MMP2
Matrix
Proteinase/
aka Gelatinase;



Metalloproteinase 2
Proteinase Inhibitor
cleaves collagens





types IV, V, VII and





gelatin type I;





produced by normal





skin fibroblasts; may





play a role in





regulation of





vascularization & the





inflammatory





response


MMP3
Matrix
Proteinase/
aka Stromelysin;



Metalloproteinase 3
Proteinase Inhibitor
degrades fibronectin,





laminin, collagens III,





IV, IX, X, cartilage





proteoglycans,





thought to be





involved in wound





repair; progression of





atherosclerosis &





tumor initiation;





produced





predominantly by





connective tissue





cells


MMP9
Matrix
Proteinase/
AKA gelatinase B;



metalloproteinase 9
Proteinase Inhibitor
degrades extracellular





matrix molecules,





secreted by IL-8-





stimulated





neutrophils


NR1I2
Nuclear
transcription
aka PAR2; Member



receptor
activation factor-
of nuclear hormone



subfamily 1
signal transduction-
receptor family of




xenobiotic
ligand-activated




metabolism
transcription factors;





activates transcription





of cytochrome P-450





genes


PCNA
Proliferating
DNA binding-DNA
Required for both



Cell Nuclear
replication-DNA
DNA replication &



Antigen
repair-cell
repair; processivity




proliferation
factor for DNA





polymerases delta and





epsilon


PI3
Proteinase
proteinase
aka SKALP;



inhibitor 3 skin
inhibitor-protein
Proteinase inhibitor



derived
binding-
found in epidermis of




extracellular matrix
several inflammatory





skin diseases; it's





expression can be





used as a marker of





skin irritancy


PLAU
Plasminogen
Proteinase/
AKA uPA; cleaves



activator,
Proteinase Inhibitor
plasminogen to



urokinase

plasmin (a protease





responsible for





nonspecific





extracellular matrix





degradation)


PTGS2
Prostaglandin-
Enzyme/Redox
aka COX2;



endoperoxide

Proinflammatory,



synthase 2

member of





arachidonic acid to





prostanoid conversion





pathway; induced by





proinflammatory





cytokines


S100A7
S100 calcium-
calcium binding-
Member of S100



binding protein 7
epidermal
family of calcium




differentiation
binding proteins;





localized in the





cytoplasm &/or





nucleus of a wide





range of cells;





involved in the





regulation of cell





cycle progression &





differentiation;





markedly





overexpressed in skin





lesions of psoriatic





patients


TGFB1
Transforming
cytokines-
Pro- and



growth factor,
chemokines-growth
antiinflammatory



beta
factors
activity, anti-





apoptotic; cell—cell





signaling, can either





inhibit or stimulate





cell growth


TIMP1
Tissue Inhibitor
Metalloproteinases;
Member of TIMP



of Matrix
inhibitor-ECM
family; natural



metalloproteinase 1
maintenance-
inhibitors of matrix




positive control cell
metalloproteinases;




proliferation
transcriptionally





induced by cytokines





& hormones;





mediates





erythropoeisis in vitro


TNF
Tumor necrosis
cytokines-
Proinflammatory,



factor, alpha
chemokines-growth
TH1, mediates host




factors
response to bacterial





stimulus, regulates





cell growth &





differentiation


TNFSF6
Tumor necrosis
ligand-apoptosis
aka FASL; Apoptosis



factor (ligand)
induction-signal
antigen ligand 1 is the



superfamily,
transduction
ligand for FAS;



member 6

interaction of FAS





with its ligand is





critical in triggering





apoptosis of some





types of cells such as





lymphocytes; defects





in protein may be





related to some cases





of SLE


TP53
tumor protein
transcription factor-
Tumor protein p53, a



p53
DNA binding-
nuclear protein, plays




tumor suppressor-
a role in regulation of




DNA
cell cycle; binds to




recombination/repair
DNA p53 binding site





and activates





expression of





downstream genes





that inhibit growth





and/or invasion of





tumor


VEGF
vascular
cytokines-
Producted by



endothelial
chemokines-growth
monocytes



growth factor
factors
















TABLE 5







Liver Metabolism and Disease Gene Expression Panel










Symbol
Name
Classification
Description





ABCC1
ATP-binding cassette,
Liver Health Indicator
AKA Multidrug resistance



sub-family C, member 1

protein 1; AKA CFTR/MRP;





multispecific organic anion





membrane transporter;





mediates drug resistance by





pumping xenobiotics out of





cell


AHR
Aryl hydrocarbon
Metabolism
Increases expression of



receptor
Receptor/Transcription
xenobiotic metabolizing




Factor
enzymes (ie P450) in





response to binding of





planar aromatic





hydrocarbons


ALB
Albumin
Liver Health Indicator
Carrier protein found in





blood serum, synthesized in





the liver, downregulation





linked to decreased liver





function/health


COL1A1
Collagen, type 1, alpha 1
Tissue Remodelling
AKA Procollagen;





extracellular matrix protein;





implicated in fibrotic





processes of damaged liver


CYP1A1
Cytochrome P450 1A1
Metabolism Enzyme
Polycyclic aromatic





hydrocarbon metabolism;





monooxygenase


CYP1A2
Cytochrome P450 1A2
Metabolism Enzyme
Polycyclic aromatic





hydrocarbon metabolism;





monooxygenase


CYP2C19
Cytochrome P450
Metabolism Enzyme
Xenobiotic metabolism;



2C19

monooxygenase


CYP2D6
Cytochrome P450 2D6
Metabolism Enzyme
Xenobiotic metabolism;





monooxygenase


CYP2E
Cytochrome P450 2E1
Metabolism Enzyme
Xenobiotic metabolism;





monooxygenase; catalyzes





formation of reactive





intermediates from small





organic molecules (i.e.





ethanol, acetaminophen,





carbon tetrachloride)


CYP3A4
Cytochrome P450 3A4
Metabolism Enzyme
Xenobiotic metabolism;





broad catalytic specificity,





most abundantly expressed





liver P450


EPHX1
Epoxide hydrolase 1,
Metabolism Enzyme
Catalyzes hydrolysis of



microsomal

reactive epoxides to water



(xenobiotic)

soluble dihydrodiols


FAP
Fibroblast activation
Liver Health Indicator
Expressed in cancer stroma



protein, □

and wound healing


GST
Glutathione S-
Metabolism Enzyme
Catalyzes glutathione



transferase

conjugation to metabolic





substrates to form more





water-soluble, excretable





compounds; primer-probe





set nonspecific for all





members of GST family


GSTA1 and
Glutathione S-
Metabolism Enzyme
Catalyzes glutathione


A2
transferase 1A1/2

conjugation to metabolic





substrates to form more





water-soluble, excretable





compounds


GSTM1
Glutathione S-
Metabolism Enzyme
Catalyzes glutathione



transferase M1

conjugation to metabolic





substrates to form more





water-soluble, excretable





compounds


KITLG
KIT ligand
Growth Factor
AKA Stem cell factor





(SCF); mast cell growth





factor, implicated in





fibrosis/cirrhosis due to





chronic liver inflammation


LGALS3
Lectin, galactoside-
Liver Health Indicator
AKA galectin 3; Cell



binding, soluble, 3

growth regulation


NR1I2
Nuclear receptor
Metabolism
AKA Pregnane X receptor



subfamily 1, group I,
Receptor/Transcription
(PXR); heterodimer with



family 2
Factor
retinoid X receptor forms





nuclear transcription factor





for CYP3A4


NR1I3
Nuclear receptor
Metabolism
AKA Constitutive



subfamily 1, group I,
Receptor/Transcription
androstane receptor beta



family 3
Factor
(CAR); heterodimer with





retinoid X receptor forms





nuclear transcription factor;





mediates P450 induction by





phenobarbital-like inducers.


ORM1
Orosomucoid 1
Liver Health Indicator
AKA alpha 1 acid





glycoprotein (AGP), acute





phase inflammation protein


PPARA
Peroxisome proliferator
Metabolism Receptor
Binds peroxisomal



activated receptor □

proliferators (ie fatty acids,





hypolipidemic drugs) &





controls pathway for beta-





oxidation of fatty acids


SCYA2
Small inducible
Cytokine/Chemokine
AKA Monocyte chemotactic



cytokine A2

protein 1 (MCP1); recruits





monocytes to areas of injury





and infection, upregulated in





liver inflammation


UCP2
Uncoupling protein 2
Liver Health Indicator
Decouples oxidative





phosphorylation from ATP





synthesis, linked to diabetes,





obesity


UGT
UDP-
Metabolism Enzyme
Catalyzes glucuronide



Glucuronosyltransferase

conjugation to metabolic





substrates, primer-probe set





nonspecific for all members





of UGT1 family
















TABLE 6







Endothelial Gene Expression Panel










Symbol
Name
Classification
Description





ADAMTS1
Disintegrin-like and
Protease
AKA METH1; Inhibits



metalloprotease

endothelial cell proliferation;



(reprolysin type) with

may inhibit angiogenesis;



thrombospondin type 1

expression may be associated



motif, 1

with development of cancer





cachexia.


CLDN14
Claudin 14

AKA DFNB29; Component of





tight junction strands


ECE1
Endothelin converting
Metalloprotease
Cleaves big endothelin 1 to



enzyme 1

endothelin 1


EDN1
Endothelin 1
Peptide hormone
AKA ET1; Endothelium-





derived peptides; potent





vasoconstrictor


EGR1
Early growth response 1
Transcription factor
AKA NGF1A; Regulates the





transcription of genes involved





in mitogenesis and





differentiation


FLT1
Fms-related tyrosine

AKA VEGFR1; FRT;



kinase 1 (vascular

Receptor for VEGF; involved



endothelial growth

in vascular development and



factor/vascular

regulation of vascular



permeability factor

permeability



receptor)




GJA1
gap junction protein,

AKA CX43; Protein



alpha 1, 43 kD

component of gap junctions;





major component of gap





junctions in the heart; may be





important in synchronizing





heart contractions and in





embryonic development


GSR
Glutathione reductase 1
Oxidoreductase
AKA GR; GRASE; Maintains





high levels of reduced





glutathione in the cytosol


HIF1A
Hypoxia-inducible factor
Transcription factor
AKA MOP1; ARNT



1, alpha subunit

interacting protein; mediates





the transcription of oxygen





regulated genes; induced by





hypoxia


HMOX1
Heme oxygenase
Redox Enzyme
AKA HO1; Essential for heme



(decycling) 1

catabolism, cleaves heme to





form biliverdin and CO;





endotoxin inducible


ICAM1
Intercellular adhesion
Cell Adhesion/
Endothelial cell surface



molecule 1
Matrix Protein
molecule; regulates cell





adhesion and trafficking,





upregulated during cytokine





stimulation


IGFBP3
Insulin-like growth

AKA IBP3; Expressed by



factor binding protein 3

vascular endothelial cells; may





influence insulin-like growth





factor activity


IL15
Interleukin 15
cytokines-
Proinflammatory; mediates T-




chemokines-growth
cell activation, inhibits




factors
apoptosis, synergizes with IL-2





to induce IFN-g and TNF-a


IL1B
Interleukin 1, beta
cytokines-
Proinflammatory; constitutively




chemokines-growth
and inducibly expressed by




factors
many cell types, secreted


IL8
Interleukin 8
cytokines-
Proinflammatory, major




chemokines-growth
secondary inflammatory




factors
mediator, cell adhesion, signal





transduction, cell-cell





signaling, angiogenesis,





synthesized by a wide variety





of cell types


MAPK1
mitogen-activated
Transferase
AKA ERK2; May promote



protein kinase 1

entry into the cell cycle,





growth factor responsive


NFKB1
Nuclear Factor kappa B
Transcription Factor
AKA KBF1, EBP1;





Transcription factor that





regulates the expression of





infolammatory and immune





genes; central role in Cytokine





induced expression of E-





selectin


NOS2A
Nitric oxide synthase 2A
Enzyme/Redox
AKA iNOS; produces NO





which is





bacteriocidal/tumoricidal


NOS3
EndothelialNitric Oxide

AKA ENOS, CNOS;



Synthase

Synthesizes nitric oxide from





oxygen and arginine; nitric





oxide is implicated in vascular





smooth muscle relaxation,





vascular endothelial growth





factor induced angiogenesis,





and blood clotting through the





activation of platelets


PLAT
Plasminogen activator,
Protease
AKA TPA; Converts



tissue

plasminogin to plasmin;





involved in fibrinolysis and





cell migration


PTGIS
Prostaglandin I2
Isomerase
AKA PGIS; PTGI; CYP8;



(prostacyclin) synthase

CYP8A1; Converts





prostaglandin h2 to





prostacyclin (vasodilator);





cytochrome P450 family;





imbalance of prostacyclin may





contribute to myocardial





infarction, stroke,





atherosclerosis


PTGS2
Prostaglandin-
Enzyme/Redox
AKA COX2;



endoperoxide synthase 2

Proinflammatory, member of





arachidonic acid to prostanoid





conversion pathway; induced





by proinflammatory cytokines


PTX3
pentaxin-related gene,

AKA TSG-14; Pentaxin 3;



rapidly induced by IL-1

Similar to the pentaxin



beta

subclass of inflammatory





acute-phase proteins; novel





marker of inflammatory





reactions


SELE
selectin E (endothelial
Cell Adhesion
AKA ELAM; Expressed by



adhesion molecule 1)

cytokine-stimulated





endothelial cells; mediates





adhesion of neutrophils to the





vascular lining


SERPINE1
Serine (or cysteine)
Proteinase Inhibitor
AKA PAI1; Plasminogen



protease inhibitor, clade

activator inhibitor type 1;



B (ovalbumin), member 1

interacts with tissue





plasminogen activator to





regulate fibrinolysis


TEK
tyrosine kinase,
Transferase Receptor
AKA TIE2, VMCM; Receptor



endothelial

for angiopoietin-1; may





regulate endothelial cell





proliferation and





differentiation; involved in





vascular morphogenesis; TEK





defects are associated with





venous malformations


VCAM1
vascular cell adhesion
Cell Adhesion/
AKA L1CAM; CD106;



molecule 1
Matrix Protein
INCAM-100; Cell surface





adhesion molecule specific for





blood leukocytes and some





tumor cells; mediates signal





transduction; may be linked to





the development of





atherosclerosis, and





rheumatoid arthritis


VEGF
Vascular Endothelial
Growth factor
AKA VPF; Induces vascular



Growth Factor

permeability and endothelial





cell growth; associated with





angiogenesis
















TABLE 7







Cell Health and Apoptosis Gene Expression Panel










Symbol
Name
Classification
Description





ABL1
V-abl Abelson murine leukemia
oncogene
Cytoplasmic and nuclear



viral oncogene homolog 1

protein tyrosine kinase





implicated in cell





differentiation, division,





adhesion and stress response.





Alterations of ABL1 lead to





malignant transformations.


APAF1
Apoptotic Protease Activating
protease
Cytochrome c binds to



Factor 1
activator
APAF1, triggering activation





of CASP3, leading to





apoptosis. May also facilitate





procaspase 9 autoactivation.


BAD
BCL2 Agonist of Cell Death
membrane
Heterodimerizes with BCLX




protein
and counters its death





repressor activity. This





displaces BAX and restores its





apoptosis-inducing activity.


BAK1
BCL2-antagonist/killer 1
membrane
In the presence of an




protein
apropriate stimulus BAK 1





accelerates programed cell





death by binding to, and





antagonizing the repressor





BCL2 or its adenovirus





homolog e1b 19k protein.


BAX
BCL2-associated X protein
membrane
Accelerates apoptosis by




protein
binding to, and antagonizing





BCL2 or its adenovirus





homolog e1b 19k protein. It





induces the release of





cytochrome c and activation of





CASP3


BCL2
B-cell CLL/lymphoma 2
membrane
Interferes with the activation




protein
of caspases by preventing the





release of cytochrome c, thus





blocking apoptosis.


BCL2L1
BCL2-like 1 (long form)
membrane
Dominant regulator of




protein
apoptotic cell death. The long





form displays cell death





repressor activity, whereas the





short isoform promotes





apoptosis. BCL2L1 promotes





cell survival by regulating the





electrical and osmotic





homeostasis of mitochondria.


BID
BH3-Interacting Death Domain

Induces ice-like proteases and



Agonist

apoptosis. counters the





protective effect of bcl-2 (by





similarity). Encodes a novel





death agonist that





heterodimerizes with either





agonists (BAX) or antagonists





(BCL2).


BIK
BCL2-Interacting Killer

Accelerates apoptosis.





Binding to the apoptosis





repressors BCL2L1, bhrf1,





BCL2 or its adenovirus





homolog e1b 19 k protein





suppresses this death-





promoting activity.


BIRC2
Baculoviral IAP Repeat-
apoptosis
May inhibit apoptosis by



Containing 2
suppressor
regulating signals required for





activation of ICE-like





proteases. Interacts with





TRAF1 and TRAF2.





Cytoplasmic


BIRC3
Baculoviral IAP Repeat-
apoptosis
Apoptotic suppressor.



Containing 3
suppressor
Interacts with TRAF1 and





TRAF2.Cytoplasmic


BIRC5
Survivin
apoptosis
Inhibits apoptosis. Inhibitor of




suppressor
CASP3 and CASP7.





Cytoplasmic


CASP1
Caspase 1
proteinase
Activates IL1B; stimulates





apoptosis


CASP3
Caspase 3
proteinase
Involved in activation cascade





of caspases responsible for





apoptosis - cleaves CASP6,





CASP7, CASP9


CASP9
Caspase 9
proteinase
Binds with APAF1 to become





activated; cleaves and activates





CASP3


CCNA2
Cyclin A2
cyclin
Drives cell cycle at G1/S and





G2/M phase; interacts with





cdk2 and cdc2


CCNB1
Cyclin B1
cyclin
Drives cell cycle at G2/M





phase; complexes with cdc2 to





form mitosis promoting factor


CCND1
Cyclin D1
cyclin
Controls cell cycle at G1/S





(start) phase; interacts with





cdk4 and cdk6; has oncogene





function


CCND3
Cyclin D3
cyclin
Drives cell cycle at G1/S





phase; expression rises later in





Gi and remains elevated in S





phase; interacts with cdk4 and





cdk6


CCNE1
Cyclin E1
cyclin
Drives cell cycle at G1/S





transition; major downstream





target of CCND1; cdk2-





CCNE1 activity required for





centrosome duplication during





S phase; interacts with RB


cdk2
Cyclin-dependent kinase 2
kinase
Associated with cyclins A, D





and E; activity maximal during





S phase and G2; CDK2





activation, through caspase-





mediated cleavage of CDK





inhibitors, may be instrumental





in the execution of apoptosis





following caspase activation


cdk4
Cyclin-dependent kinase 4
kinase
cdk4 and cyclin-D type





complexes are responsible for





cell proliferation during G1;





inhibited by CDKN2A (p16)


CDKN1A
Cyclin-Dependent Kinase
tumor
May bind to and inhibit cyclin-



Inhibitor 1A (p21)
suppressor
dependent kinase activity,





preventing phosphorylation of





critical cyclin-dependent





kinase substrates and blocking





cell cycle progression;





activated by p53; tumor





suppressor function


CDKN2B
Cyclin-Dependent Kinase
tumor
Interacts strongly with cdk4



Inhibitor 2B (p15)
suppressor
and cdk6; role in growth





regulation but limited role as





tumor suppressor


CHEK1
Checkpoint, S. pombe

Involved in cell cycle arrest





when DNA damage has





occurred, or unligated DNA is





present; prevents activation of





the cdc2-cyclin b complex


DAD1
Defender Against Cell Death
membrane
Loss of DAD1 protein triggers




protein
apoptosis


DFFB
DNA Fragmentation Factor, 40-
nuclease
Induces DNA fragmentation



KD, Beta Subunit

and chromatin condensation





during apoptosis; can be





activated by CASP3


FADD
Fas (TNFRSF6)-associated via
co-receptor
Apoptotic adaptor molecule



death domain

that recruits caspase-8 or





caspase-10 to the activated fas





(cd95) or tnfr-1 receptors; this





death-inducing signalling





complex performs CASP8





proteolytic activation


GADD45A
Growth arrest and DNA damage
regulator of
Stimulates DNA excision



inducible, alpha
DNA repair
repair in vitro and inhibits





entry of cells into S phase;





binds PCNA


K-ALPHA-1
Alpha Tubulin, ubiquitous
microtubule
Major constituent of




peptide
microtubules; binds 2





molecules of GTP


MADD
MAP-kinase activating death
co-receptor
Associates with TNFR1



domain

through a death domain-death





domain interaction;





Overexpression of MADD





activates the MAP kinase





ERK2, and expression of the





MADD death domain





stimulates both the ERK2 and





JNK1 MAP kinases and





induces the phosphorylation of





cytosolic phospholipase A2


MAP3K14
Mitogen-activated protein
kinase
Activator of NFKB1



kinase kinase kinase 14




MRE11A
Meiotic recombination (S. cerevisiae)
nuclease
Exonuclease involved in DNA



11 homolog A

double-strand breaks repair


NFKB1
Nuclear factor of kappa light
nuclear
p105 is the precursor of the



polypeptide gene enhancer in B-
translational
p50 submit of the nuclear



cells 1 (p105)
regulator
factor NFKB, which binds to





the kappa-b consensus





sequence located in the





enhancer region of genes





involved in immune response





and acute phase reactions; the





precursor does not bind DNA





itself


PDCD8
Programmed Cell Death 8
enzyme,
The principal mitochondrial



(apoptosis-inducing factor)
reductase
factor causing nuclear





apoptosis. Independent of





caspase apoptosis.


PNKP
Polynucleotide kinase 3′-
phosphatase
Catalyzes the 5-prime



phosphatase

phosphorylation of nucleic





acids and can have associated





3-prime phosphatase activity,





predictive of an important





function in DNA repair





following ionizing radiation or





oxidative damage


PTEN
Phosphatase and tensin homolog
tumor
Tumor suppressor that



(mutated in multiple advanced
suppressor
modulates G1 cell cycle



cancers 1)

progression through negatively





regulating the PI3-kinase/Akt





signaling pathway; one critical





target of this signaling process





is the cyclin-dependent kinase





inhibitor p27 (CDKN1B).


RAD52
RAD52 (S. cerevisiae) homolog
DNA binding
Involved in DNA double-




proteinsor
stranded break repair and





meiotic/mitotic





recombination


RB1
Retinoblastoma 1 (including
tumor
Regulator of cell growth;



osteosarcoma)
suppressor
interacts with E2F-like





transcription factor; a nuclear





phosphoprotein with DNA





binding activity; interacts with





histone deacetylase to repress





transcription


SMAC
Second mitochondria-derived
mitochondrial
Promotes caspase activation in



activator of caspase
peptide
cytochrome c/APAF-1/





caspase 9 pathway of apoptosis


TERT
Telomerase reverse transcriptase
transcriptase
Ribonucleoprotein which in





vitro recognizes a single-





stranded G-rich telomere





primer and adds multiple





telomeric repeats to its 3-prime





end by using an RNA template


TNF
Tumor necrosis factor
cytokines-
Proinflammatory, TH1,




chemokines-
mediates host response to




growth factors
bacterial stimulus, regulates





cell growth & differentiation


TNFRSF11A
Tumor necrosis factor receptor
receptor
Activates NFKB1; Important



superfamily, member 11a,

regulator of interactions



activator of NFKB

between T cells and dendritic





cells


TNFRSF12
Tumor necrosis factor receptor
receptor
Induces apoptosis and activates



superfamily, member 12

NF-kappaB; contains a



(translocating chain-association

cytoplasmic death domain and



membrane protein)

transmembrane domains


TOSO
Regulator of Fas-induced
receptor
Potent inhibitor of Fas induced



apoptosis

apoptosis; expression of





TOSO, like that of FAS and





FASL, increases after T-cell





activation, followed by a





decline and susceptibility to





apoptosis; hematopoietic cells





expressing TOSO resist anti-





FAS-, FADD-, and TNF-





induced apoptosis without





increasing expression of the





inhibitors of apoptosis BCL2





and BCLXL; cells expressing





TOSO and activated by FAS





have reduced CASP8 and





increased CFLAR expression,





which inhibits CASP8





processing


TP53
Tumor Protein 53
DNA binding
Activates expression of genes




protein - cell
that inhibit tumor growth




cycle - tumor
and/or invasion; involved in




suppressor
cell cycle regulation (required





for growth arrest at G1);





inhibits cell growth through





activation of cell-cycle arrest





and apoptosis


TRADD
TNFRSF1A-associated via
co-receptor
Overexpression of TRADD



death domain

leads to 2 major TNF-induced





responses, apoptosis and





activation of NF-kappa-B


TRAF1
TNF receptor-associated factor 1
co-receptor
Interact with cytoplasmic





domain of TNFR2


TRAF2
TNF receptor-associated factor 2
co-receptor
Interact with cytoplasmic





domain of TNFR2


VDAC1
Voltage-dependent anion
membrane
Functions as a voltage-gated



channel 1
protein
pore of the outer mitochondrial





membrane; proapoptotic





proteins BAX and BAK





accelerate the opening of





VDAC allowing cytochrome c





to enter, whereas the





antiapoptotic protein BCL2L1





closes VDAC by binding





directly to it


XRCC5
X-ray repair complementing
helicase
Functions together with the



defective repair in Chinese

DNA ligase IV-XRCC4



hamster cells 5

complex in the repair of DNA





double-strand breaks
















TABLE 8







Cytokine Gene Expression Panel










Symbol
Name
Classification
Description





CSF3
Colony Stimulating
Cytokines/Cytokines
AKA G-CSF; Cytokine that



Factor 3 (Granulocyte)
Growth Factors
stimulates granulocyte





development


IFNG
Interferon, Gamma
Cytokines/
Pro- and anti-inflammatory




Chemokines/Growth
activity; TH1 cytokine;




factors
nonspecific inflammatory





mediator; produced by





activated T-cells.





Antiproliferative effects on





transformed cells.


IL1A
Interleukin 1, Alpha
Cytokines/
Proinflammatory;




Chemokines/Growth
constitutively and inducibly




Factors
expressed in variety of cells.





Generally cytosolic and





released only during severe





inflammatory disease


IL1B
Interleukin 1, Beta
Cytokines/
Proinflammatory; 





constituitively




Chemokines/Growth
and inducibly expressed




factors
many cell types, secreted


IL1RN
Interleukin 1 Receptor
Cytokines/
IL1 receptor antagonist;



Antagonist
Chemokines/Growth
Antiinflammatory; inhibits




factors
binding of IL-1 to IL-1





receptor by binding to 





receptor without





stimulating IL-1-like





activity


IL2
Interleukin 2
Cytokines/
T-cell growth factor, 




Chemokines/Growth
expressed by




factors
activated T-cells, regulates





lymphocyte activation and





differentiation; inhibits





apoptosis, TH1 cytokine


IL4
Interleukin 4
Cytokines/
Antiinflammatory; TH2;




Chemokines/Growth
suppresses 




factors
proinflammatory





cytokines, increases 





expression





of IL-1RN, regulates





lymphocyte activation


IL5
Interleukin 5
Cytokines/
Eosinophil stimulatory 




Chemokines/Growth
factor; stimulates late 




factors
B cell differentiation to 





secretion of Ig


IL6
Interleukin 6
Cytokines/
AKA interferon, Beta 2; Pro




Chemokines/Growth
and anti-inflammatory 




factors
activity,





TH2 cytokine, regulates





hematopoiesis, activation





innate response, osteoclast





development; elevated in





sera of patients with 





metastatic cancer


IL10
Interleukin 10
Cytokines/
Antiinflammatory; TH2;




Chemokines/Growth
suppresses production of




factors
proinflammatory cytokines



Interleukin 12 (p40)
Cytokines/
Proinflammatory; 




Chemokines/Growth
mediator of




factors
innate immunity, TH1





cytokine, requires co-





stimulation with IL-18 to





induce IFN-y


IL13
Interleukin 13
Cytokines/
Inhibits inflammatory 




Chemokines/Growth
cytokine production




factors



IL15
Interleukin 15
Cytokines/
Proinflammatory; mediates




Chemokines/Growth
T-cell activation, inhibits




factors
apoptosis, synergizes with





IL-2 to induce





IFN-γ and TNF-α


IL18
Interleukin 18
Cytokines/
Proinflammatory, TH1, 





innate and aquired




Chemokines/Growth
immunity, promotes




factors
apoptosis, requires





co-stimulation with IL-1 or





IL-2 to induce TH1 





cytokines in T- and NK-cells


IL18BP
IL-18 Binding Protein
Cytokines/
Implicated in inhibition of




Chemokines/Growth
early TH1 cytokine 




factors
responses


TGFA
Transforming Growth
Transferase/Signal
Proinflammatory cytokine



Factor, Alpha
Transduction
that is the primary mediator 





of immune response and





regulation, Associated with





TH1 responses, mediates





host response to





bacterial stimuli,





regulates cell growth &





differentiation; Negative





regulation of insulin action


TGFB1
Transforming Growth
Cytokines/
AKA DPD1, CED; Pro- and



Factor, Beta 1
Chemokines/Growth
antiinflammatory activity;




factors
Anti-apoptotic; cell-cell





signaling, Can either inhibit





or stimulate cell growth;





Regulated by glucose in





NIDDM individuals,





overexpression (due to





oxidative stress promotes





renal cell hypertrophy leading to diabetic





nephropathy


TNFSF5
Tumor Necrosis Factor
Cytokines/
Ligand for CD40;



(Ligand) Superfamily,
Chemokines/Growth
Expressed



Member 5
factors
on the surface of T-cells;





Regulates B-cell function by





engaging CD40 on the B-cell





surface


TNFSF6
Tumor Necrosis Factor
Cytokines/
AKA FASL; Apoptosis



(Ligand) Superfamily,
Chemokines/Growth
antigen ligand 1 is the



Member 6
factors
ligand for FAS antigen;





Critical in triggering





apoptosis of some





types of cells such as





lymphocytes; Defects in





protein may be related to





some cases of SLE


TNFSF13B
Tumor Necrosis Factor
Cytokines/
B-cell activating factor,



(Ligand) Superfamily,
Chemokines/Growth
TNF family



Member 13
factors
















TABLE 9







TNF/IL1 Inhibition Gene Expression Panel










HUGO Symbol
Name
Classification
Description





CD14
CD14
Cell Marker
LPS receptor used as marker for



Antigen

monocytes


GRO1
GRO1
Cytokines/Chemokines/
AKA SCYB1, Melanoma



Oncogene
Growth factors
growth stimulating activity,





Alpha; Chemotactic for





neutrophils


HMOX1
Heme
Enzyme: Redox
Enzyme that cleaves heme to



Oxygenase

form biliverdin and CO;



(Decycling) 1

Endotoxin inducible


ICAM1
Intercellular
Cell Adhesion: Matrix
Endothelial cell surface



Adhesion
Protein
molecule; Regulates cell



Molecule 1

adhesion and trafficking; Up-





regulated during cytokine





stimulation


IL1B
Interleukin 1,
Cytokines/Chemokines/
Pro-inflammatory;



Beta
Growth factors
Constitutively and inducibly





expressed by many cell types;





Secreted


IL1RN
Interleukin 1
Cytokines/Chemokines/
Anti-inflammatory; Inhibits



Receptor
Growth factors
binding of IL-1 to IL-1 receptor



Antagonist

by binding to receptor without





stimulating IL-1-like activity


IL10
Interleukin 10
Cytokines/Chemokines/
Anti-inflammatory; TH2




Growth factors
cytokine; Suppresses production





of pro-inflammatory cytokines


MMP9
Matrix
Proteinase/Proteinase
AKA Gelatinase B; Degrades



Metalloproteinase 9
Inhibitor
extracellular matrix molecules;





Secreted by IL-8 stimulated





neutrophils


SERPINE1
Serine (or
Proteinase/Proteinase
AKA Plasminogen activator



Cysteine)
Inhibitor
inhibitor-1, PAI-1; Regulator of



Protease

fibrinolysis



Inhibitor,





Clade E





(Ovalbumin),





Member 1




TGFB1
Transforming
Cytokines/Chemokines/
Pro- and anti-inflammatory



Growth
Growth factors
activity; Anti-apoptotic; Cell-cell



Factor, Beta 1

signaling; Can either inhibit or





stimulate cell growth


TIMP1
Tissue
Proteinase/Proteinase
Irreversibly binds and inhibits



Inhibitor of
Inhibitor
metalloproteinases such as



Metalloproteinase 1

collagenase


TNFA
Tumor
Cytokines/Chemokines/
Pro-inflammatory; TH1 cytokine;



Necrosis
Growth factors
Mediates host response to



Factor, Alpha

bacterial stimulus; Regulates cell





growth & differentiation
















TABLE 10







Chemokine Gene Expression Panel










Symbol
Name
Classification
Description





CCR1
chemokine (C—C
chemokine receptor
A member of the beta



motif) receptor 1

chemokine receptor family





(seven transmembrane





protein). Binds SCYA3/MIP-





1a, SCYA5/RANTES, MCP-3,





HCC-1, 2, and 4, and MPIF-1.





Plays role in dendritic cell





migration to inflammation sites





and recruitment of monocytes.


CCR3
chemokine (C—C
Chemokine receptor
C—C type chemokine receptor



motif) receptor 3

(Eotaxin receptor) binds to





Eotaxin, Eotaxin-3, MCP-3,





MCP-4, SCYA5/RANTES and





mip-1 delta thereby mediating





intracellular calcium flux.





Alternative co-receptor with





CD4 for HIV-1 infection.





Involved in recruitment of





eosinophils. Primarily a Th2





cell chemokine receptor.


CCR5
chemokine (C—C
Chemokine receptor
Member of the beta chemokine



motif) receptor 5

receptor family (seven





transmembrane protein).





Binds to SCYA3/MIP-1a and





SCYA5/RANTES. Expressed





by T cells and macrophages,





and is an important co-receptor





for macrophage-tropic virus,





including HIV, to enter host





cells. Plays a role in Th1 cell





migration. Defective alleles of





this gene have been associated





with the HIV infection





resistance.


CX3CR1
chemokine (C—X3—C)
Chemokine receptor
CX3CR1 is an HIV coreceptor



receptor 1

as well as a leukocyte





chemotactic/adhesion receptor





for fractalkine. Natural killer





cells predominantly express





CX3CR1 and respond to





fractalkine in both migration





and adhesion.


CXCR4
chemokine (C—X—C
Chemokine receptor
Receptor for the CXC



motif), receptor 4

chemokine SDF1. Acts as a



(fusin)

co-receptor with CD4 for





lymphocyte-tropic HIV-1





viruses. Plays role in B cell,





Th2 cell and naive T cell





migration.


GPR9
G protein-coupled
Chemokine receptor
CXC chemokine receptor



receptor 9

binds to SCYB10/IP-10,





SCYB9/MIG, SCYB11/I-





TAC. Binding of chemokines





to GPR9 results in integrin





activation, cytoskeletal





changes and chemotactic





migration. Prominently





expressed in in vitro cultured





effector/memory T cells and





plays a role in Th1 cell





migration.


GRO1
GRO1 oncogene
Chemokine
AKA SCYB1; chemotactic for



(melanoma growth

neutrophils. GRO1 is also a



stimulating activity,

mitogenic polypeptide secreted



alpha)

by human melanoma cells.


GRO2
GRO2 oncogene
Chemokine
AKA MIP2, SCYB2;



(MIP-2)

Macrophage inflammatory





protein produced by moncytes





and neutrophils. Belongs to





intercrine family alpha (CXC





chemokine).


IL8
interleukin 8
Chemokine
Proinflammatory, major





secondary inflammatory





mediator, cell adhesion, signal





transduction, cell-cell





signaling, angiogenesis,





synthesized by a wide variety





of cell types


PF4
Platelet Factor 4
Chemokine
PF4 is released during platelet



(SCYB4)

aggregation and is chemotactic





for neutrophils and monocytes.





PF4's major physiologic role





appears to be neutralization of





heparin-like molecules on the





endothelial surface of blood





vessels, thereby inhibiting





local antithrombin III activity





and promoting coagulation.


SCYA2
small inducible
Chemokine
Recruits monocytes to areas of



cytokine A2 (MCP1)

injury and infection.





Stimulates IL-A production;





implicated in diseases





involving monocyte, basophil





infiltration of tissue (ie.g.,





psoriasis, rheumatoid arthritis,





atherosclerosis).


SCYA3
small inducible
Chemokine
A “monokine” involved in the



cytokine A3 (MIP1a)

acute inflammatory state





through the recruitment and





activation of





polymorphonuclear





leukocytes. A major HIV-





suppressive factor produced by





CD8-positive T cells.


SCYA5
small inducible
Chemokine
Binds to CCR1, CCR3, and



cytokine A5

CCR5 and is a chemoattractant



(RANTES)

for blood monocytes, memory





t helper cells and eosinophils.





A major HIV-suppressive





factor produced by CD8-





positive T cells.


SCYB10
small inducible
Chemokine
A CXC subfamily chemokine.



cytokine subfamily B

Binding of SCYB10 to



(Cys-X-Cys),

receptor CXCR3/GPR9 results



member 10

in stimulation of monocytes,





natural killer and T-cell





migration, and modulation of





adhesion molecule expression.





SCYB10 is Induced by IFNg





and may be a key mediator in





IFNg response.


SDF1
stromal cell-derived
Chemokine
Belongs to the CXC subfamily



factor 1

of the intercrine family, which





activate leukocytes. SDF1 is





the primary ligand for CXCR4,





a coreceptor with CD4 for





human immunodeficiency





virus type 1 (HIV-1). SDF1 is





a highly efficacious





lymphocyte chemoattractant.
















TABLE 11







Breast Cancer Gene Expression Panel










Symbol
Name
Classification
Description





ACTB
Actin, beta
Cell Structure
Actins are highly conserved





proteins that are involved in cell





motility, structure and integrity.





ACTB is one of two non-muscle





cytoskeletal actins. Site of action





for cytochalasin B effects on cell





motility.


BCL2
B-cell
membrane protein
Interferes with the activation of



CLL/lymphoma 2

caspases by preventing the release





of cytochrome c, thus blocking





apoptosis.


CD19
CD19 antigen
Cell Marker
AKA Leu 12; B cell growth factor


CD34
CD34 antigen
Cell Marker
AKA: hematopoietic progenitor





cell antigen. Cell surface antigen





selectively expressed on human





hematopoietic progenitor cells.





Endothelial marker.


CD44
CD44 antigen
Cell Marker
Cell surface receptor for





hyaluronate. Probably involved in





matrix adhesion, lymphocyte





activation and lymph node





homing.


DC13
DC13 protein

unknown function


DSG1
Desmoglein 1
membrane protein
Calcium-binding transmembrane





glycoprotein involved in the





interaction of plaque proteins and





intermediate filaments mediating





cell-cell adhesion. Interact with





cadherins.


EDR2
Early

The specific function in human



Development

cells has not yet been determined.



Regulator 2

May be part of a complex that may





regulate transcription during





embryonic development.


ERBB2
v-erb-b2
Oncogene
Oncogene. Overexpression of



erythroblastic

ERBB2 confers Taxol resistance



leukemia viral

in breast cancers. Belongs to the



oncogene

EGF tyrosine kinase receptor



homolog 2

family. Binds gp130 subunit of





the IL6 receptor in an IL6





dependent manner. An essential





component of IL-6 signalling





through the MAP kinase pathway.


ERBB3
v-erb-b2
Oncogene
Oncogene. Overexpressed in



Erythroblastic

mammary tumors. Belongs to the



Leukemia Viral

EGF tyrosine kinase receptor



Oncogene

family. Activated through



Homolog 3

neuregulin and ntak binding.


ESR1
Estrogen
Receptor/
ESR1 is a ligand-activated



Receptor 1
Transcription Factor
transcription factor composed of





several domains important for





hormone binding, DNA binding,





and activation of transcription.


FGF18
Fibroblast
Growth Factor
Involved in a variety of biological



Growth Factor 18

processes, including embryonic





development, cell growth,





morphogenesis, tissue repair,





tumor growth, and invasion.


FLT1
Fms-related
Receptor
Receptor for VEGF; involved in



tyrosine kinase 1

vascular development and





regulation of vascular





permeability.


FOS
V-fos FBJ murine
Oncogene/
Leucine zipper protein that forms



osteosarcoma
Transcriptional
the transcription factor AP-1 by



viral oncogene
Activator
dimerizing with JUN. Implicated



homolog

in the processes of cell





proliferation, differentiation,





transformation, and apoptosis.


GRO1
GRO1 oncogene
Chemokine/Growth
Proinflammatory; chemotactic for




Factor/Oncogene
neutrophils. Growth regulator





that modulates the expression of





metalloproteinase activity.


IFNG
Interferon,
Cytokine
Pro- and antiinflammatory



gamma

activity; TH1 cytokine;





nonspecific inflammatory





mediator; produced by activated





T-cells. Antiproliferative effects





on transformed cells.


IRF5
Interferon
Transcription Factor
Regulates transcription of



regulatory factor 5

interferon genes through DNA





sequence-specific binding.





Diverse roles, include virus-





mediated activation of interferon,





and modulation of cell growth,





differentiation, apoptosis, and





immune system activity.


KRT14
Keratin 14
Cytoskeleton
Type I keratin, intermediate





filament component; KRT14 is





detected in the basal layer, with





lower expression in more apical





layers, and is not present in the





stratum corneum. Together with





KRT5 forms the cytoskeleton of





epithelial cells.


KRT19
Keratin 19
Cytoskeleton
Type I epidermal keratin; may





form intermediate filaments.





Expressed often in epithelial cells





in culture and in some carcinomas


KRT5
Keratin 5
Cytoskeleton
Coexpressed with KRT14 to form





cytoskeleton of epithelial cells.





KRT5 expression is a hallmark of





mitotically active keratinocytes





and is the primary structural





component of the 10 nm





intermediate filaments of the





mitotic epidermal basal cells.


MDM2
Mdm2,
oncogene/
Inhibits p53- and p73-mediated



transformed 3T3
Transcription Factor
cell cycle arrest and apoptosis by



cell double

binding its transcriptional



minute 2, p53

activation domain, resulting in



binding protein

tumorigenesis. Permits the nuclear





export of p53 and targets it for





proteasome-mediated proteolysis.


MMP9
Matrix
Proteinase/
Degrades extracellular matrix by



metalloproteinase 9
Proteinase Inhibitor
cleaving types IV and V collagen.





Implicated in arthritis and





metastasis.


MP1
Metalloprotease 1
Proteinase/
Member of the pitrilysin family.




Proteinase Inhibitor
A metalloendoprotease. Could





play a broad role in general





cellular regulation.


N33
Putative prostate
Tumor Suppressor
Integral membrane protein.



cancer tumor

Associated with homozygous



suppressor

deletion in metastatic prostate





cancer.


OXCT
3-oxoacid CoA
Transferase
OXCT catalyzes the reversible



transferase

transfer of coenzyme A from





succinyl-CoA to acetoacetate as





the first step of ketolysis (ketone





body utilization) in extrahepatic





tissues.


PCTK1
PCTAIRE protein

Belongs to the SER/THR family of



kinase 1

protein kinases; CDC2/CDKX





subfamily. May play a role in





signal transduction cascades in





terminally differentiated cells.


SERPINB5
Serine proteinase
Proteinase/
Protease Inhibitor; Tumor



inhibitor, clade B,
Proteinase Inhibitor/
suppressor, especially for



member 5
Tumor Suppressor
metastasis. Inhibits tumor





invasion by inhibiting cell





motility.


SRP19
Signal

Responsible for signal-



recognition

recognition-particle assembly.



particle 19 kD

SRP mediates the targeting of





proteins to the endoplasmic





reticulum.


STAT1
Signal transducer
DNA-Binding
Binds to the IFN-Stimulated



and activator of
Protein
Response Element (ISRE) and to



transcription 1,

the GAS element; specifically



91 kD

required for interferon signaling.





STAT1 can be activated by IFN-





alpha, IFN-gamma, EGF, PDGF





and IL6. BRCA1-regulated genes





overexpressed in breast





tumorigenesis included STAT1





and JAK1.


TGFB3
Transforming
Cell Signalling
Transmits signals through



growth factor,

transmembrane serine/threonine



beta 3

kinases. Increased expression of





TGFB3 may contribute to the





growth of tumors.


TLX3
T-cell leukemia,
Transcription Factor
Member of the homeodomain



homeobox 3

family of DNA binding proteins.





May be activated in T-ALL





leukomogenesis.


VWF
Von Willebrand
Coagulation Factor
Multimeric plasma glycoprotein



factor

active in the blood coagulation





system as an antihemophilic factor





(VIIIC) carrier and platelet-vessel





wall mediator. Secreted by





endothelial cells.
















TABLE 12







Infectious Disease Gene Expression Panel










Symbol
Name
Classification
Description





C1QA
Complement
Proteinase/
Serum complement system; forms C1



component 1, q
Proteinase
complex with the proenzymes c1r and



subcomponent, alpha
Inhibitor
c1s



polypeptide




CASP1
Caspase 1
proteinase
Activates IL1B; stimulates apoptosis


CD14
CD14 antigen
Cell Marker
LPS receptor used as marker for





monocytes


CSF2
Granulocyte-
cytokines-
AKA GM-CSF; Hematopoietic



monocyte colony
chemokines-
growth factor; stimulates growth and



stimulating factor
growth factors
differentiation of hematopoietic





precursor cells from various lineages,





including granulocytes, macrophages,





eosinophils, and erythrocytes


EGR1
Early growth
cell signaling
master inflammatory switch for



response-1
and activation
ischemia-related responses including





chemokine sysntheis, adhesion





moelcules and macrophage





differentiation


F3
F3
Enzyme/
AKA thromboplastin, Coagulation




Redox
Factor 3; cell surface glycoprotein





responsible for coagulation catalysis


GRO2
GRO2 oncogene
cytokines-
AKA MIP2, SCYB2; Macrophage




chemokines-
inflammatory protein produced by




growth factors
moncytes and neutrophils


HMOX1
Heme oxygenase
Enzyme/
Endotoxin inducible



(decycling) 1
Redox



HSPA1A
Heat shock protein 70
Cell Signaling
heat shock protein 70 kDa




and activation



ICAM1
Intercellular adhesion
Cell Adhesion/
Endothelial cell surface molecule;



molecule 1
Matrix Protein
regulates cell adhesion and trafficking,





upregulated during cytokine





stimulation


IFI16
gamma interferon
cell signaling
Transcriptional repressor



inducible protein 16
and activation



IFNG
Interferon gamma
cytokines-
Pro- and antiinflammatory activity,




chemokines-
TH1 cytokine, nonspecific




growth factors
inflammatory mediator, produced by





activated T-cells


IL10
Interleukin 10
cytokines-
Antiinflammatory; TH2; suppresses




chemokines-
production of proinflammatory




growth factors
cytokines


IL12B
Interleukin 12 p40
cytokines-
Proinflammatory; mediator of innate




chemokines-
immunity, TH1 cytokine, requires co-




growth factors
stimulation with IL-18 to induce IFN-g


IL13
Interleukin 13
cytokines
Inhibits inflammatory cytokine




chemokines-
production




growth factors



IL18
Interleukin 18
cytokines-
Proinflammatory,TH1, innate and




chemokines-
aquired immunity, promotes




growth factors
apoptosis, requires co-stimulation with





IL-1 or IL-2 to induce TH1 cytokines





in T- and NK-cells


IL18BP
IL-18 Binding
cytokines-
Implicated in inhibition of early TH1



Protein
chemokines-
cytokine responses




growth factors



IL1A
Interleukin 1, alpha
cytokines-
Proinflammatory; constitutively and




chemokines-
inducibly expressed in variety of cells.




growth factors
Generally cytosolic and released only





during severe inflammatory disease


IL1B
Interleukin 1, beta
cytokines-
Proinflammatory; constitutively and




chemokines-
inducibly expressed by many cell




growth factors
types, secreted


IL1R1
interleukin 1
receptor
AKA: CD12 or IL1R1RA



receptor, type I




IL1RN
Interleukin 1 receptor
cytokines-
IL1 receptor antagonist;



antagonist
chemokines-
Antiinflammatory; inhibits binding of




growth factors
IL-1 to IL-1 receptor by binding to





receptor without stimulating IL-1-like





activity


IL2
Interleukin 2
cytokines-
T-cell growth factor, expressed by




chemokines-
activated T-cells, regulates




growth factors
lymphocyte activation and





differentiation; inhibits apoptosis,





TH1 cytokine


IL4
Interleukin 4
cytokines-
Antiinflammatory; TH2; suppresses




chemokines-
proinflammatory cytokines, increases




growth factors
expression of IL-1RN, regulates





lymphocyte activation


IL6
Interleukin 6
cytokines-
Pro- and antiinflammatory activity,



(interferon, beta 2)
chemokines-
TH2 cytokine, regulates




growth factors
hemotopoietic system and activation





of innate response


IL8
Interleukin 8
cytokines-
Proinflammatory, major secondary




chemokines-
inflammatory mediator, cell adhesion,




growth factors
signal transduction, cell-cell signaling,





angiogenesis, synthesized by a wide





variety of cell types


MMP3
Matrix
Proteinase/
AKA stromelysin; degrades



metalloproteinase 3
Proteinase
fibronectin, laminin and gelatin




Inhibitor



MMP9
Matrix
Proteinase/
AKA gelatinase B; degrades



metalloproteinase 9
Proteinase
extracellular matrix molecules,




Inhibitor
secreted by IL-8-stimulated





neutrophils


PLA2G7
Phospholipase A2,
Enzyme/
Platelet activating factor



group VII (platelet
Redox




activating factor





acetylhydrolase,





plasma)




PLAU
Plasminogen
Proteinase/
AKA uPA; cleaves plasminogen to



activator, urokinase
Proteinase
plasmin (a protease responsible for




Inhibitor
nonspecific extracellular matrix





degradation)


SERPINE1
Serine (or cysteine)
Proteinase/
Plasminogen activator inhibitor-1/



protease inhibitor,
Proteinase
PAI-1



clade B (ovalbumin),
Inhibitor




member 1




SOD2
superoxide dismutase
Oxidoreductase
Enzyme that scavenges and destroys



2, mitochondrial

free radicals within mitochondria


TACI
Tumor necrosis factor
cytokines-
T cell activating factor and calcium



receptor superfamily,
chemokines-
cyclophilin modulator



member 13b
growth factors



TIMP1
tissue inhibitor of
Proteinase/
Irreversibly binds and inhibits



metalloproteinase 1
Proteinase
metalloproteinases, such as




Inhibitor
collagenase


TLR2
toll-like receptor 2
cell signaling
mediator of petidoglycan and




and activation
lipotechoic acid induced signalling


TLR4
toll-like receptor 4
cell signaling
mediator of LPS induced signalling




and activation



TNF
Tumor necrosis
cytokines-
Proinflammatory, TH1, mediates host



factor, alpha
chemokines-
response to bacterial stimulus,




growth factors
regulates cell growth & differentiation


TNFSF13B
Tumor necrosis factor
cytokines-
B cell activating factor, TNF family



(ligand) superfamily,
chemokines-




member 13b
growth factors



TNFSF5
Tumor necrosis factor
cytokines-
ligand for CD40; expressed on the



(ligand) superfamily,
chemokines-
surface of T cells. It regulates B cell



member 5
growth factors
function by engaging CD40 on the B





cell surface


TNFSF6
Tumor necrosis factor
cytokines-
AKA FasL; Ligand for FAS antigen;



(ligand) superfamily,
chemokines-
transduces apoptotic signals into cells



member 6
growth factors



VEGF
vascular endothelial
cytokines-
Producted by monocytes



growth factor
chemokines-





growth factors



IL5
Interleukin 5
Cytokines-
Eosinophil stimulatory factor;




chemokines-
stimulates late B cell differentiation to




growth factors
secretion of Ig


IFNA2
Interferon alpha 2
Cytokines-
interferon produced by macrophages




chemokines-
with antiviral effects




growth factors



TREM1
TREM-1
Triggering
Receptor/Cell Signaling and




Receptor
Activation




Expressed on





Myeloid Cells 1



SCYB10
small inducible
Chemokine
A CXC subfamily chemokine.



cytokine subfamily B

Binding of SCYB10 to receptor



(Cys-X-Cys),

CXCR3/GPR9 results in stimulation



member 10

of monocytes, natural killer and T-cell





migration, and modulation of adhesion





molecule expression. SCYB10 is





Induced by IFNg and may be a key





mediator in IFNg response.


CCR1
Chemokine (C—C
Chemokine
A member of the beta chemokine



motif) receptor 1
receptor
receptor family (seven transmembrane





protein). Binds SCYA3/MIP-1a,





SCYA5/RANTES, MCP-3, HCC-1, 2,





and 4, and MPIF-1. Plays role in





dendritic cell migration to





inflammation sites and recruitment of





monocytes.


CCR3
Chemokine (C—C
Chemokine
C—C type chemokine receptor



motif) receptor 3
receptor
(Eotaxin receptor) binds to Eotaxin,





Eotaxin-3, MCP-3, MCP-4,





SCYA5/RANTES and mip-1 delta





thereby mediating intracellular





calcium flux. Alternative co-receptor





with CD4 for HIV-1 infection.





Involved in recruitment of eosinophils.





Primarily a Th2 cell chemokine





receptor.


SCYA3
Small inducile
Chemokine
A “monokine” involved in the acute



cytokine A3 (MIP1a)

inflammatory state through the





recruitment and activation of





polymorphonuclear leukocytes. A





major HIV-suppressive factor





produced by CD8-positive T cells.


CX3CR1
Chemokine (C—X3—C)
Chemokine
CX3CR1 is an HIV coreceptor as



receptor 1
receptor
well as a leukocyte





chemotactic/adhesion receptor for





fractalkine. Natural killer cells





predominantly express CX3CR1 and





respond to fractalkine in both





migration and adhesion.

## Claims

1. A method, for evaluating a biological condition of a target population of cells affected by an agent based on a sample from the population of cells, the sample providing a source of RNAs, the method comprising:
using amplification to determine a quantitative measure of the amount of RNA of at least two constituents from any one of Tables 1 through 12 from the sample of the target population of cells, where a panel of constituents is selected so that measurement of the constituents enables measurement of the biological condition and wherein the measures of all constituents in the panel form a first profile data set;
using amplification to determine a quantitative measure of the amount of RNA of all constituents in said panel wherein measures of all constituents in the panel are from a relevant population of target cells and form a normative baseline profile data set, wherein the measures for each constituent are performed under measurement conditions that are (i) within a degree of repeatability of better than ten percent and (ii) the efficiencies of amplification for all constituents differ by less than 10 percent; and
producing a calibrated profile data set for said panel, wherein each member of the calibrated profile data set is a function of a corresponding member of the first profile data set and the corresponding member of a baseline profile data set; and

wherein the calibrated profile data set provides a measure of the biological condition of the target population of cells as affected by the agent.

2. A method according to claim 1, wherein the relevant population of target cells is a population of cells from healthy subjects.

3. A method according to claim 1, wherein the relevant population of target cells has in common a property that is at least one of age group, gender, ethnicity, geographic location, diet, medical disorder, clinical indicator, medication, physical activity, body mass, and environmental exposure.

4. A method according to claim 1, wherein the panel includes at least two of the constituents of the Inflammation Gene Expression Panel of Table 1.