# Patent

## Title
Methods for identifying and monitoring drug side effects

## Abstract

The present invention relates generally to methods for identifying drug side effects by detecting perturbations in organ-specific molecular blood fingerprints. The invention further relates to methods for identifying drug-specific organ-specific molecular blood fingerprints. As such, the present invention provides compositions comprising organ-specific proteins, detection reagents for detecting such proteins, and panels and arrays for determining organ-specific molecular blood fingerprints.

## Background
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation of copending application Ser. No. 13/023,366, filed Feb. 8, 2011, which is a continuation of application Ser. No. 12/468,834, filed May 19, 2009, which is a continuation of application Ser. No. 11/342,367, filed Jan. 27, 2006, which claims benefit of U.S. Provisional Application No. 60/683,004, filed May 20, 2005, and U.S. Provisional Application No. 60/647,792, filed Jan. 27, 2005. application Ser. No. 13/023,366, filed Feb. 8, 2011, application Ser. No. 12/468,834, filed May 19, 2009, application Ser. No. 11/342,367, filed Jan. 27, 2006, Application No. 60/683,004, filed May 20, 2005, and Application No. 60/647,792, filed Jan. 27, 2005 are hereby incorporated herein by reference in their entirety.


STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH
This invention was made with government support under Grant Nos. P50 CA097186 and P01 CA085857 awarded by the National Cancer Institute. The government has certain rights in this invention.


STATEMENT REGARDING SEQUENCE LISTING
The Sequence Listing submitted Apr. 25, 2014 as a text file named “ISB—101_CON—3_ST25.txt,” created on Apr. 9 , 2014, and having a size of 3,734,802 bytes is hereby incorporated by reference pursuant to 37 C.F.R. §1.52(e)(5).
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to organ-specific molecular blood fingerprints and methods for using the same in identifying and/or monitoring drug side effects.
2. Description of the Related Art
Side effects, particularly adverse side effects, are monitored and tested throughout the developmental path of all drugs. A variety of in vitro and in vivo toxicity assays are available in the art for testing on and off-target effects of drugs during development including metabolic effects on liver P450 enzymes. Clearly, however, as is evidenced by the recent controversy surrounding COX-2 inhibitors, off-target side effects can be subtle and difficult to detect. Therefore, monitoring and identifying drug side effects remains an important issue with regard to drug safety of developing drugs and approved drugs already on the market. The COX-2 story highlights the need in the art for improved methods to detect sometimes subtle adverse off-target effects of drugs.
The present invention provides methods that satisfy this and other needs.
BRIEF SUMMARY OF THE INVENTION
One aspect of the present invention provides a method for detecting a drug side effect comprising measuring in the blood of a subject taking the drug the level of a plurality of organ-specific proteins secreted from an organ wherein the levels of the plurality of organ-specific proteins together provide an organ-specific molecular blood fingerprint that indicates a drug side effect on the organ in the subject. In one embodiment, the level of the plurality of organ-specific proteins is measured with any of a variety of methods, including but not limited to mass spectrometry, such as tandem mass spectrometry, an immunoassay, such as an ELISA, Western blot, microfluidics/nanotechnology sensors, and aptamer capture assay. In this regard, an aptamer may be used in a similar manner to an antibody in a variety of appropriate binding assays known to the skilled artisan and described herein. In a further embodiment, the plurality of organ-specific proteins is measured using tandem mass spectrometry. In one embodiment, the level of one or more organ-specific proteins is measured. In yet an additional embodiment, the plurality of organ-specific proteins comprises from at least about 2 organ-specific proteins to about 100, 150, 160, 170, 180, 190, 200 or more organ-specific proteins. In this regard, the plurality of organ-specific proteins may comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, or more organ-specific proteins. In one embodiment, the plurality of organ-specific proteins comprises about 5, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20 organ-specific proteins. In another embodiment, the organ-specific proteins comprise proteins from any of a number of organs, such as, but not limited to the liver or kidney. In a further embodiment the organ-specific proteins comprise cardiac-specific proteins. In yet another embodiment, the organ-specific proteins are from an organ other than the expected therapeutic target of the drug.
Another aspect of the present invention provides a method for detecting a drug side effect comprising measuring in the blood of a subject taking the drug the level of one or more organ-specific proteins secreted from an organ wherein the level of the one or more organ-specific proteins together provide an organ-specific molecular blood fingerprint that indicates a drug side effect on the organ in the subject.
Another aspect of the invention provides a method for determining the presence or absence of a drug side effect in a subject taking the drug comprising, detecting a level of each of a plurality of organ-specific proteins in a blood sample from the subject, wherein the plurality of organ-specific proteins are secreted from the same organ; comparing said level of each of the plurality of organ-specific proteins in the blood sample from the subject to a level of each of the plurality of organ-specific proteins in a control sample of drug-free blood; wherein a statistically significant altered level of one or more of the plurality of organ-specific proteins in the blood is indicative of the presence or absence of a drug side effect. As would be readily appreciated by the skilled artisan, an altered level can mean an increase in the level or a decrease in the level. In this regard, the skilled artisan would readily appreciate that a variety of statistical tests can be used to determine if an altered level is significant. The Z-test (Man, M. Z., et al., Bioinformatics, 16: 953-959, 2000) or other appropriate statistical tests can be used to calculate P values for comparison of protein expression levels. In certain embodiments, the level of each of the plurality of organ-specific proteins in the blood sample from the subject is compared to a previously determined normal control level of each of the plurality of organ-specific proteins taking into account standard deviation (see e.g., U.S. Patent Application No. 20020095259). In one embodiment, the level of each of the plurality of organ-specific proteins is detected using any one or more methods, such as, but not limited to mass spectrometry (e.g., tandem mass spectrometry or other spectrometry-based techniques), and immunoassays (e.g., ELISA, Western blot, or other immunoaffinity-based assays). In an additional embodiment, the method level of each of the plurality of organ-specific proteins is measured using an antibody array. In yet an additional embodiment, the method provides for determining the presence or absence of a drug side effect wherein the organ-specific proteins comprise liver-specific proteins or kidney-specific proteins. In certain embodiments, the organ-specific proteins are from an organ other than the expected therapeutic target of the drug.
A further aspect of the present invention provides a method for detecting perturbation of a normal biological state induced by a drug, contacting a blood sample from a subject taking the drug with a plurality of detection reagents each specific for an organ-specific protein secreted into blood, wherein each organ-specific protein is secreted from the same organ; measuring the amount of the organ-specific protein detected in the blood sample by each detection reagent, comparing the amount of the organ-specific protein detected in the blood sample by each detection reagent to a predetermined control amount for each organ-specific protein; wherein an altered level in one or more of the organ-specific proteins indicates a perturbation in the normal biological state induced by the drug. In this regard, the plurality of detection reagents may comprise from at least about 2 detection reagents to about 100, 150, 160, 170, 180, 190, 200 or more detection reagents. In on embodiment, the plurality of detection reagents comprises about 5, 10, or 20 detection reagents. In yet another embodiment, the organ-specific proteins comprise kidney-specific proteins, liver-specific proteins, or cardiac-specific proteins. As would be recognized by the skilled artisan upon reading the present disclosure, the organ-specific proteins can be derived from any organ in the body as described further herein.
In yet a further aspect, the invention provides a diagnostic panel for determining a drug side effect in a subject taking the drug comprising, a plurality of detection reagents each specific for detecting one of a plurality of organ-specific proteins present in a blood/serum/plasma sample; wherein the organ-specific proteins are secreted from the same organ and wherein detection of the plurality of organ-specific proteins with the plurality of detection reagents results in an organ-specific molecular blood fingerprint indicative of the drug side effect in the subject. In certain embodiments, the fingerprint (e.g., the pattern of interaction of the detection reagents with each of the plurality of organ-specific proteins) is the combination of, a snapshot of sorts, of the different quantitative levels of the organ-specific proteins detected. Thus, in other words, the fingerprint is a set of numbers, each number corresponding to a level of a particular organ-specific protein. This set of numbers and the specific organ-specific proteins that they correspond to together make up the unique fingerprint that defines a biological condition. In this regard, the detection reagents may comprise antibodies or antigen-binding fragments thereof or monoclonal antibodies, or antigen-binding fragments thereof. The panels of the present invention may comprise from at least about 2 detection reagents to about 100, 150, 160, 170, 180, 190, 200 or more detection reagents. In one embodiment, the panel comprises about 5, 10, or 20 detection reagents. In a further embodiment, the plurality of detection reagents are specific for kidney-specific proteins, liver-specific proteins, cardiac-specific proteins, or indeed specific for proteins derived from any organ as described herein.
BRIEF DESCRIPTION OF THE SEQUENCE IDENTIFIERS
SEQ ID NO:1 is the cDNA sequence that encodes the WDR19 prostate specific secreted protein.
SEQ ID NO:2. is the amino acid sequence of the WDR19 prostate specific secreted protein.
SEQ ID NOs:3-72 are MPSS signature sequences that correspond to differentially expressed genes in LNCaP cells (early prostate cancer phenotype) to androgen-independent CL1 cells (late prostate cancer phenotype) (see Table 1).
SEQ ID NOs:73-593 are MPSS signature sequences that correspond to differentially expressed genes in prostate cancer cell lines LNCaP and CL1 that encode secreted proteins (see Table 3).
SEQ ID NOs:594-1511 are the GENBANK sequences of differentially expressed genes that encode predicted secreted proteins as referred to in Table 3. Both polynucleotide and amino acid sequences are provided for each GENBANK accession number.
SEQ ID NOs:1512-1573 are the amino acid sequences from GENBANK of prostate-specific proteins potentially secreted into blood as described in Table 4.
SEQ ID NOs:1574-1687 are the GENBANK sequences of examples of differentially expressed genes as described in Table 1. Both polynucleotide and amino acid sequences are provided where available for each GENBANK accession number.
SEQ ID NOs:1688-1796 are MPSS signature sequences that correspond to prostate-specific/enriched genes as described in Table 5.
SEQ ID NOs:1797-1947 are the GENBANK sequences of prostate-specific genes as described in Table 5. Both polynucleotide and amino acid sequences are provided where available for each GENBANK accession number.


DETAILED DESCRIPTION OF THE INVENTION
A powerful new systems approach to disease is revealing powerful new blood diagnostics/monitoring approaches. Particularly, in specific cells there are protein and gene regulatory networks that mediate the normal functions of the cell. The disease process causes one or more of these networks to be perturbed, either genetically or environmentally (e.g. infections). The disease-altered networks result in altered patterns of protein expression—and some of the transcripts with altered expression levels are organ (cell)-specific and some of these organ-specific transcripts encode secreted proteins. Hence disease leads to altered expression patterns of organ-specific, secreted proteins in the blood. Drugs also cause altered expression of organ-specific secreted proteins in the blood. In particular, the liver and kidney are organs that often reflect the side effects of drugs.
Hence the blood may be viewed as a window into the health and disease of an individual. The levels of organ-specific secreted proteins present in the blood taken together represent molecular fingerprints in the blood that reflect the operation of normal organs. Each organ has a specific quantitative molecular fingerprint. When a drug has a side effect on a particular organ, that blood fingerprint changes, for example, in the levels of these proteins expressed in the blood and the change in the fingerprint correlates with the specific effect the drug has on the organ. The changes in the fingerprints occur as a consequence of virtually any disease or organ perturbation (e.g., drug effect) with each disease or drug effect resulting in a unique fingerprint. The changes in the fingerprints are sufficiently informative to visualize side effects of drugs, be they adverse or positive. Thus, as used herein, side effect refers to any unintended effect of a drug, either positive (e.g., a previously unrecognized positive indication for a drug) or negative (e.g., toxicity or other adverse effect). The drug-altered fingerprints are determined by comparing the blood from normal individuals against that from patients on a particular drug regimen. Not only will the absolute levels of the changes in the proteins constituting individual fingerprints be determined, but all the protein changes (e.g. N changed proteins) will be compared against one another to generate an N-dimensional shape space that will correlate even more powerfully with the stratifications of drug-induced alterations as described herein (see e.g., U.S. Patent Application No. 20020095259).
The studies described herein use prostate cancer as a model for studying perturbation of organ-specific molecular blood fingerprints. The same principles apply in the setting of determining perturbations that result from drugs. In the studies described herein, the transcriptomes of two prostate cancer cell lines were analyzed: LNCaP, an androgen sensitive cell line, and hence a model for early stage of prostate cancer; and a variant of this cell, CL1, an androgen unresponsive cell line, thus, a model for late stage of prostate cancer. Analyses of the transcriptomes of these two cell lines revealed changes in cellular states that occur with the progression of prostate cancer. These transcriptomes were also compared to normal prostate tissue, prostate cancer tissues and prostate cancer metastases. These prostate transcriptomes were compared against their counterparts from 29 other tissues to identify those transcripts that are primarily expressed in the prostate. Computational approaches were used to predict which of these transcripts encode secreted proteins. Further, a prostate protein, referred to as WDR19, that was previously shown by microarray and northern analysis to be prostate-specific, was used in a multiparameter analysis of prostate cancer samples.
Thus, the present invention is generally directed to methods for identifying organ-specific secreted proteins present in the blood. The present invention is also directed to methods for defining organ-specific molecular blood fingerprints and further provides defined examples of predicted organ-specific molecular blood fingerprints. Additionally, the present invention is directed to panels of reagents or proteomic techniques employing mass spectrometry that detect organ-specific secreted proteins in the blood for use in identifying side effects of drugs, evaluating drug toxicity, and other related applications.
By predefining the components of a given molecular blood fingerprint using the methods described herein, the present invention alleviates the need to blindly search for protein patterns using blood proteomics. Thus, the present invention enables the skilled artisan to 1) identify blood proteins which collectively constitute unique organ-specific molecular blood fingerprints for healthy, diseased individuals and individuals affected (either adversely or positively) by one or more drugs; 2) identify unique organ-specific molecular blood fingerprints associated with the direct (e.g., intended) effects of drugs or the side effects of different drugs; 3) identify fingerprints that can uniquely distinguish the different types of side effects. Importantly, the organ-specific, secreted blood fingerprints can be predicted from a combination of quantitative comparative transcriptome studies and computational methods to predict which transcripts encode secreted proteins. The methods for determining the organ-specific, blood fingerprints for all organs described herein allow drug effects (either adverse or positive) on any organ to be easily identified. Further, the present invention can be used to determine distinct, normal organ-specific molecular blood fingerprints, such as in different populations of people. In this regard, there may be differences in normal organ-specific molecular blood fingerprints between populations of individuals that permit the stratification of patients into classes of individuals who would respond positively to a particular drug and those who would not. Thus, the present invention provides the ability to determine those individuals who may have adverse reactions to drugs.
A drug, as used herein, refers to any substance (synthetic or natural) which when administered to or otherwise absorbed into a living organism or system derived therefrom, may modify one or more of its functions.
Methods for Identifying Organ-specific Proteins Secreted into the Blood.
The invention provides methods for identifying organ-specific secreted proteins. In this regard, as used herein, the term “organ” is defined as would be understood in the art. Thus, the term, “organ-specific” as used herein refers to proteins (or transcripts) that are primarily expressed in a single organ. It should be noted that the skilled artisan would readily appreciate upon reading the instant specification that cell-specific transcripts and proteins and tissue-specific transcripts and proteins are also contemplated in the present invention. As such, and as discussed further herein, in certain embodiments, organ-specific protein is defined as a protein encoded by a transcript that is expressed at a level of at least 3 copies/million (as measured, for example, by massively parallel signature sequencing (MPSS) in the cell/tissue/organ of interest but is expressed at less than 3 copies/million in other cells/tissues/organs. In a further embodiment, an organ-specific protein is one that is encoded by a transcript that is expressed 95% in one organ and the remaining 5% in one or more other organs. (In this context, total expression across all organs examined is taken as 100%).
In certain embodiments, an organ-specific protein is one that is encoded by a transcript that is expressed at about 50%, 55%, 60%, 65%, 70%, 75%, 80% to about 90% in one organ and wherein the remaining 10%-50% As would be readily recognized by the skilled artisan upon reading the present disclosure, in certain embodiments, an organ-specific molecular blood fingerprint can readily be discerned even if some expression of an “organ-specific” protein from a particular organ is detected at some level in another organ, or even more than one organ. For example, the organ-specific molecular blood fingerprint from prostate can conclusively identify a particular prostate disease (and stage of disease) despite expression of one or more protein members of the fingerprint in one or more other organs. Thus, an organ-specific protein as described herein may be predominantly or differentially expressed in an organ of interest rather than uniquely or specifically expressed in the organ. In this regard, in certain embodiments, differentially expressed means at least 1.5 fold expression in the organ of interest as compared to other organs. In another embodiment, differentially expressed means at least 2 fold expression in the organ of interest as compared to expression in other organs. In yet a further embodiment, differentially expressed means at least 2.5, 3, 3.5, 4, 4.5, 5 fold or higher expression in the organ of interest as compared to expression of the protein in other organs. As described elsewhere herein, “protein” expression can be determined by analysis of transcript expression using a variety of methods.
In one embodiment, the organ-specific proteins are identified by preparing a cDNA library from an organ of interest. Any organ of a mammalian body is contemplated herein. Illustrative organs include, but are not limited to, heart, kidney, ureter, bladder, urethra, liver, prostate, heart, blood vessels, bone marrow, skeletal muscle, smooth muscle, brain (amygdala, caudate nucleus, cerebellum, corpuscallosum, fetal, hypothalamus, thalamus), spinal cord, peripheral nerves, retina, nose, trachea, lungs, mouth, salivary gland, esophagus, stomach, small intestines, large intestines, hypothalamus, pituitary, thyroid, pancreas, adrenal glands, ovaries, oviducts, uterus, placenta, vagina, mammary glands, testes, seminal vesicles, penis, lymph nodes, PBMC, thymus, and spleen. As noted above, upon reading the present disclosure, the skilled artisan would recognize that cell-specific and tissue-specific proteins are contemplated herein and thus, proteins specifically expressed in cells or tissues that make up such organs are also contemplated herein. In certain embodiments, in each of these organs, transcriptomes are obtained for the cell types in which the disease of interest arises. For example, in the prostate there are two dominant types of cells—epithelial cells and stromal cells. About 98% of prostate cancers arise in epithelial cells. As such, in certain embodiments, “organ-specific” means the transcripts that are expressed in particular cell types of the organ of interest (e.g., prostate epithelial cells). In this regard, any cell type that makes up any of the organs described herein is contemplated herein. Illustrative cell types include, but are not limited to, epithelial cells, stromal cells, endothelial cells, endodermal cells, ectodermal cells, mesodermal cells, lymphocytes (e.g., B cells and T cells including CD4+ T helper 1 or T helper 2 type cells, CD8+ cytotoxic T cells), erythrocytes, keratinocytes, and fibroblasts. Particular cell types within organs or tissues may be obtained by histological dissection, by the use of specific cell lines (e.g., prostate epithelial cell lines), by cell sorting or by a variety of other techniques known in the art.
It should be noted that in certain embodiments, fingerprints can be determined from “organ-specific” proteins from multiple organs, such as from organs that share a common function or make up a system (e.g., digestive system, circulatory system, respiratory system, the immune system (including the different cells of the immune system, such as, but not limited to, B cells, T cells including CD4+ T helper 1 or T helper 2 type cells, regulatory T cells, CD8+ cytotoxic T cells, NK cells, dendritic cells, macrophages, monocytes, neutrophils, granulocytes, mast cells, etc.), cardiovascular system, the sensory system, the skin, brain and the nervous system, and the like).
Complementary DNA (cDNA) libraries can be generated using techniques known in the art, such as those described in Ausubel et al. (2001 Current Protocols in Molecular Biology, Greene Publ. Assoc. Inc. & John Wiley & Sons, Inc., NY, N.Y.); Sambrook et al. (1989 Molecular Cloning, Second Ed., Cold Spring Harbor Laboratory, Plainview, N.Y.); Maniatis et al. (1982 Molecular Cloning, Cold Spring Harbor Laboratory, Plainview, N.Y.) and elsewhere. Further, a variety of commercially available kits for constructing cDNA libraries are useful for making the cDNA libraries of the present invention. Libraries are constructed from organs/tissues/cells procured from normal subjects.
All or substantially all of the transcripts of the cDNA library, e.g., representing virtually or substantially all genes functioning in the organ of interest, are cloned and sequenced using any of a variety of techniques known in the art. In this regard, in certain embodiments, substantially all refers to a sample representing at least 80% of all genes functioning in the organ of interest. In a further embodiment, substantially all refers to a sample representing at least 85%, 90%, 95%, 96%, 97%, 98% 99% or higher of all genes functioning in the organ of interest. In one embodiment, substantially all the transcripts from a cDNA library are amplified, sorted and signature sequences generated therefrom according to the methods described in U.S. Pat. Nos. 6,013,445; 6,172,218; 6,172,214; 6,140,489 and Brenner, P., et al., Nat Biotechnol, 18:630-634 2000. Briefly, polynucleotide templates from a cDNA library of interest are cloned into a vector system that contains a vast set of minimally cross-hybridizing oligonucleotide tags (see U.S. Pat. No. 5,863,722). The number of tags is usually at least 100 times greater than the number of cDNA templates (see e.g., U.S. Pat. No. 6,013,445 and Brenner, P., et al., supra). Thus, the set of tags is such that a 1% sample taken of template-tag conjugates ensures that essentially every template in the sample is conjugated to a unique tag and that at least one of each of the different template cDNAs is represented in the sample with >99% probability (U.S. Pat. No. 6,013,445 and Brenner, P., et al., supra). The conjugates are then amplified and hybridized under stringent conditions to microbeads each of which has attached thereto a unique complementary, minimally cross-hybridizing oligonucleotide tag. The transcripts are then directly sequenced simultaneously in a flow cell using a ligation-based sequencing method (see e.g., U.S. Pat. No. 6,013,445). A short signature sequence of about 17-20 base pairs is generated simultaneously from each of the hundreds of thousands of beads (or more) in the flow cell, each having attached thereto copies of a unique transcript from the sample. This technique is termed massively parallel signature sequencing (MPSS).
In certain embodiments, other techniques may be used to evaluate the transcripts from a particular cDNA library, including microarray analysis (Han, M., et al., Nat Biotechnol, 19: 631-635, 2001; Bao, P., et al., Anal Chem, 74: 1792-1797, 2002; Schena et al., Proc. Natl. Acad. Sci. USA 93:10614-19, 1996; and Heller et al., Proc. Natl. Acad. Sci. USA 94:2150-55, 1997) and SAGE (serial analysis of gene expression). Like MPSS, SAGE is digital and can generate a large number of signature sequences. (see e.g., Velculescu, V. E., et al., Trends Genet, 16: 423-425., 2000; Tuteja R. and Tuteja N. Bioessays. 2004 August; 26(8):916-22) although the coverage is not nearly as deep as with MPSS.
The resulting sequences, (e.g., MPSS signature sequences), are generally about 20 bases in length. However, in certain embodiments, the sequences can be about 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 or more bases in length. The sequences are annotated using annotated human genome sequence (such as human genome release hg16, released in November, 2003, or other public databases) and the human Unigene (Unigene build #184) using methods known in the art, such as the method described by Meyers, B. C., et al., Genome Res, 14: 1641-1653, 2004. Other databases useful in this regard include Genbank, EMBL, or other publicly available databases. In certain embodiments, transcripts are considered only for those with 100% matches between an MPSS or other type of signature and a genome signature. As would be readily appreciated by the skilled artisan upon reading the present disclosure, this is a stringent match criterion and in certain embodiments, it may be desirable to use less stringent match criteria. Indeed, polymorphisms could lead to variations in transcripts that would be missed if only exact matches were used. For example, it may be desirable to consider signature sequences that match a genome signature with 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% identity. In one embodiment, signatures that are expressed at less than 3 transcripts per million in libraries of interest are disregarded, as they might not be reliably detected since this, in effect, represents less than one transcript per cell (see for example, Jongeneel, C. V., et al., Proc Natl Acad Sci USA, 2003). cDNA signatures are classified by their positions relative to polyadenylation signals and poly (A) tails and by their orientation relative to the 5′→3′ orientation of source mRNA. Full-length sequences corresponding to the signature sequences can be thus identified.
In order to identify organ-specific transcripts, the resulting annotated transcripts are compared against public and/or private sequence databases, such as a variety of annotated human genome sequence databases (e.g., Genebank, the EMBL and Japanese databases and databases generated and compiled from other normal tissues, to identify those transcripts that are expressed primarily in the organ of interest but are not expressed in other organs. As noted elsewhere herein, some expression in organs other than the organ of interest does not necessarily preclude the use of a particular transcript in a blood molecular signature panel of the present invention.
Comparisons of the transcripts between databases can be made using a variety of computer analysis algorithms known in the art. As such, alignment of sequences for comparison may be conducted by the local identity algorithm of Smith and Waterman (1981) Add. APL. Math 2:482, by the identity alignment algorithm of Needleman and Wunsch (1970) J. Mol. Biol. 48:443, by the search for similarity methods of Pearson and Lipman (1988) Proc. Natl. Acad. Sci. USA 85: 2444, by computerized implementations of these algorithms (GAP, BESTFIT, BLAST, FASTA, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group (GCG), 575 Science Dr., Madison, Wis.), or by inspection. As would be understood by the skilled artisan, many algorithms are available and are continually being developed. Appropriate algorithms can be chosen based on the specific needs for the comparisons being made (See also, e.g., J. A. Cuff, et al., Bioinformatics, 16(2):111-116, 2000; S. F Altschul and B. W. Erickson. Bulletin of Mathematical Biology, 48(5/6):603-616, 1986; S. F. Altschul and B. W. Erickson. Bulletin of Mathematical Biology, 48(5/6):633-660, 1986; S. F. Altschul, et al., J. Mol. Bio., 215:403-410, 1990; K. Bucka-Lassen, et al., BIOINFORMATICS, 15(2):122-130, 1999; K.-M. Chao, et al., Bulletin of Mathematical Biology, 55(3):503-524, 1993; W. M. Fitch and T. F. Smith. Proceedings of the National Academy of Sciences, 80:1382-1386, 1983; A. D. Gordon. Biometrika, 60:197-200, 1973; O. Gotoh. J Mol Biol, 162:705-708, 1982; O. Gotoh. Bulletin of Mathematical Biology, 52(3):359-373, 1990; X. Huang, et al., CABIOS, 6:373-381, 1990; X. Huang and W. Miller. Advances in Applied Mathematics, 12:337-357, 1991; J. D. Thompson, et al., Nucleic Acids Research, 27(13):2682-2690, 1999).
In certain embodiments, a particular transcript is considered to be organ-specific when the number of transcripts/million as determined by MPSS is 3 or greater in the organ of interest but is less than 3 in all other organs. In another embodiment, a transcript is considered organ-specific if it is expressed in the organ of interest at a detectable level using a standard measurement (e.g., microarray analysis, quantitative real-time RT-PCR, MPSS, etc.) in the organ of interest but is not detectably expressed in other organs, using appropriate negative and positive controls as would be familiar to the skilled artisan. In a further embodiment, an organ-specific transcript is one that is expressed 95% in one organ and the remaining 5% in one or more other organs. (In this context, total expression across all organs examined is taken as 100%). In certain embodiments, an organ-specific transcript is one that is expressed at about 50%, 55%, 60%, 65%, 70%, 75%, 80% to about 90% in one organ and wherein the remaining 10%-50% is expressed in one or more other organs.
In another embodiment, organ-specific transcripts are identified by determining the ratio of expression of a transcript in the organ of interest as compared to other organs. In this regard, expression levels in the organ of interest of at least 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0 fold or higher as compared to expression in all other organs is considered to be organ-specific expression.
As would be readily recognized by the skilled artisan upon reading the present disclosure, in certain embodiments, an organ-specific molecular blood fingerprint can readily be discerned even if some expression of an “organ-specific” protein from a particular organ is detected at some level in another organ, or even more than one organ. This is because the fingerprint (e.g., the combination of the levels of multiple proteins; the pattern of the expression levels of multiple markers) itself is unique despite that the expression levels of one or more individual members of the fingerprint may not be unique to a particular organ. For example, the organ-specific molecular blood fingerprint from prostate can conclusively identify a particular prostate disease (and stage of disease) despite some expression of one or more members of the fingerprint in one or more other organs. Thus the present invention relates to determining the presence or absence of a disease or condition or stage of disease based on a pattern (e.g., fingerprint) of markers measured concurrently using any one or more of a variety of methods described herein (e.g., antibody binding, mass spectrometry, and the like), rather than the measure of individual markers.
In further embodiments, specificity can be confirmed at the protein level using immunohistochemistry (IHC) and/or other protein measurement techniques known in the art (e.g., isotope-coded affinity tags and mass spectrometry, such as described by Han, D. K., et al., Nat Biotechnol, 19: 946-951, 2001). The Z-test (Man, M. Z., et al., Bioinformatics, 16: 953-959, 2000) or other appropriate statistical tests can be used to calculate P values for comparison of gene and protein expression levels between libraries from organs of interest.
Organ-specific sequences identified as described herein are further analyzed to determine which of the sequences encode secreted proteins. Proteins with signal peptides (classical secretory proteins) can be predicted using computation analysis known in the art. Illustrative methods include, but are not limited to the criteria described by Chen et al., Mamm Genome, 14: 859-865, 2003. In certain embodiments, such analyses are carried out using prediction servers, for example SignalP 3.0 server developed by The Center for Biological Sequence Analysis, Lyngby, Denmark (http colon double slash www dot cbs dot dtu dot dk/services/SignalP-3.0; see also, J. D. Bendtsen, et al., J. Mol. Biol., 340:783-795, 2004.) and the TMHMM2.0 server (see for example A. Krogh, et al., Journal of Molecular Biology, 305(3):567-580, January 2001; E. L. L. Sonnhammer, et al., In J. Glasgow, T. Littlejohn, F. Major, R. Lathrop, D. Sankoff, and C. Sensen, editors, Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology, pages 175-182, Menlo Park, Calif., 1998. AAAI Press). Other prediction methods that can be used in the context of the present invention include those described for example, in S. Moller, M. D. R. et al., Bioinformatics, 17(7):646-653, July 2001. Nonclassical secretory secreted proteins (without signal peptides) can be predicted using, for example, the SecretomeP 1.0 server, (http colon double slash www dot cbs dot dtu dot dk/services/SecretomeP-1.0/) with an odds ratio score>3.0. Updated versions of these analysis programs are also contemplated for use in the present methods as are other methods known in the art (e.g., PSORT (http colon double slash psort dot nibb dot ac dot jp/) and Sigfind (httpcolon double slash 139.91.72.10/sigfind/sigfind dot html).
Confirmation that the identified secreted proteins are present in blood can be carried out using a variety of methods known in the art. For example, the proteins can be expressed, purified, and specific antibodies can be made against them. The specific antibodies can then be used to test the presence of the protein in blood/serum/plasma by a variety of immunoaffinity based techniques (e.g., immunoblot, Western analysis, immunoprecipitation, ELISA, etc.). Antibodies specific for the organ-specific protein identified herein can also be used to study expression patterns of the identified proteins. It should be noted that in certain circumstances, the secreted protein may not be detectable in normal blood samples but will be detected in the blood as a result of perturbation due to disease or other environmental factors. Accordingly, both normal and disease samples are tested for the presence of the secreted protein and particularly for changes in levels of expression in the two states. As an alternative, aptamers (short DNA or RNA fragments with binding complementarity to the proteins of interest) may be used in assays similar to those described for antibodies (see for example, Biotechniques. 2001 February; 30(2):290-2, 294-5; Clinical Chemistry. 1999; 45:1628-1650). In addition, antibodies or aptamers may be used in connection with nanowires to create highly sensitive detections systems (see e.g., J. Heath et al., Science. 2004 Dec. 17; 306(5704):2055-6). In further embodiments, mass spectrometry-based methods can be used to confirm the presence of a particular protein in the blood.
As would be recognized by the skilled artisan, while the organ-specific secreted proteins, the levels of which make up a given fingerprint, need not be isolated, in certain embodiments, it may be desirable to isolate such proteins (e.g., for antibody production). As such, the present invention provides for isolated organ-specific secreted proteins or fragments or portions thereof and polynucleotides that encode such proteins. As used herein, the terms protein and polypeptide are used interchangeably. The terms “polypeptide” and “protein” encompass amino acid chains of any length, including full-length endogenous (i.e., native) proteins and variants of endogenous polypeptides described herein. Illustrative polypeptides of the present invention are described in Table 1 and Tables 3-5, the section entitled “Brief Description of the Sequence Identifiers” and are set forth in the sequence listing. “Variants” are polypeptides that differ in sequence from the polypeptides of the present invention only in substitutions, deletions and/or other modifications, such that either the variants' disease-specific expression patterns are not significantly altered or the polypeptides remain useful for diagnostics/detection of organ-specific blood fingerprints as described herein. For example, modifications to the polypeptides of the present invention may be made in the laboratory to facilitate expression and/or purification and/or to improve immunogenicity for the generation of appropriate antibodies and other binding agents, etc. Modified variants (e.g., chemically modified) of the polypeptides of organ-specific, secreted proteins may be useful herein, (e.g., as standards in mass spectrometry analyses of the corresponding proteins in the blood, and the like). As such, in certain embodiments, the biological function of a variant protein is not relevant for utility in the methods for detection and/or diagnostics described herein. Polypeptide variants generally encompassed by the present invention will typically exhibit at least about 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% or more identity along its length, to a polypeptide sequence set forth herein. Within a polypeptide variant, amino acid substitutions are usually made at no more than 50% of the amino acid residues in the native polypeptide, and in certain embodiments, at no more than 25% of the amino acid residues. In certain embodiments, such substitutions are conservative. A conservative substitution is one in which an amino acid is substituted for another amino acid that has similar properties, such that one skilled in the art of peptide chemistry would expect the secondary structure and hydropathic nature of the polypeptide to be substantially unchanged. In general, the following amino acids represent conservative changes: (1) ala, pro, gly, glu, asp, gln, asn, ser, thr; (2) cys, ser, tyr, thr; (3) val, ile, leu, met, ala, phe; (4) lys, arg, his; and (5) phe, tyr, trp, his. Thus, a variant may comprise only a portion of a native polypeptide sequence as provided herein. In addition, or alternatively, variants may contain additional amino acid sequences (such as, for example, linkers, tags and/or ligands), usually at the amino and/or carboxy termini. Such sequences may be used, for example, to facilitate purification, detection or cellular uptake of the polypeptide.
When comparing polypeptide sequences, two sequences are said to be “identical” if the sequence of amino acids in the two sequences is the same when aligned for maximum correspondence, as described below. Comparisons between two sequences are typically performed by comparing the sequences over a comparison window to identify and compare local regions of sequence similarity. A “comparison window” as used herein, refers to a segment of at least about 20 contiguous positions, usually 30 to about 75, 40 to about 50, in which a sequence may be compared to a reference sequence of the same number of contiguous positions after the two sequences are optimally aligned.
Optimal alignment of sequences for comparison may be conducted using the Megalign program in the Lasergene suite of bioinformatics software (DNASTAR, Inc., Madison, Wis.), using default parameters. This program embodies several alignment schemes described in the following references: Dayhoff, M. O. (1978) A model of evolutionary change in proteins—Matrices for detecting distant relationships. In Dayhoff, M. O. (ed.) Atlas of Protein Sequence and Structure, National Biomedical Research Foundation, Washington D.C. Vol. 5, Suppl. 3, pp. 345-358; Hein J. (1990) Unified Approach to Alignment and Phylogenes pp. 626-645 Methods in Enzymology vol. 183, Academic Press, Inc., San Diego, Calif.; Higgins, D. G. and Sharp, P. M. (1989) CABIOS 5:151-153; Myers, E. W. and Muller W. (1988) CABIOS 4:11-17; Robinson, E. D. (1971) Comb. Theon 11:105; Saitou, N. Nei, M. (1987) Mol. Biol. Evol. 4:406-425; Sneath, P. H. A. and Sokal, R. R. (1973) Numerical Taxonomy—the Principles and Practice of Numerical Taxonomy, Freeman Press, San Francisco, Calif.; Wilbur, W. J. and Lipman, D. J. (1983) Proc. Natl. Acad., Sci. USA 80:726-730.
Alternatively, optimal alignment of sequences for comparison may be conducted by the local identity algorithm of Smith and Waterman (1981) Add. APL. Math 2:482, by the identity alignment algorithm of Needleman and Wunsch (1970) J. Mol. Biol. 48:443, by the search for similarity methods of Pearson and Lipman (1988) Proc. Natl. Acad. Sci. USA 85: 2444, by computerized implementations of these algorithms (GAP, BESTFIT, BLAST, FASTA, and TFASTA in the Wisconsin Genetics Software Package, Genetics Computer Group (GCG), 575 Science Dr., Madison, Wis.), or by inspection.
Illustrative examples of algorithms that are suitable for determining percent sequence identity and sequence similarity include the BLAST and BLAST 2.0 algorithms, which are described in Altschul et al. (1977) Nucl. Acids Res. 25:3389-3402 and Altschul et al. (1990) J. Mol. Biol. 215:403-410, respectively. BLAST and BLAST 2.0 can be used, for example, to determine percent sequence identity for the polynucleotides and polypeptides of the invention. Software for performing BLAST analyses is publicly available through the National Center for Biotechnology Information.
An isolated polypeptide is one that is removed from its original environment. For example, a naturally occurring protein or polypeptide is isolated if it is separated from some or all of the coexisting materials in the natural system. In certain embodiments, such polypeptides are also purified, e.g., are at least about 90% pure, in some embodiments, at least about 95% pure and in further embodiments, at least about 99% pure.
In one embodiment of the present invention, a polypeptide comprises a fusion protein comprising an organ-specific secreted polypeptide. The present invention further provides, in other aspects, fusion proteins that comprise at least one polypeptide as described herein, as well as polynucleotides encoding such fusion proteins. The fusion proteins may comprise multiple polypeptides or portions/variants thereof, as described herein, and may further comprise one or more polypeptide segments for facilitating the expression, purification, detection, and/or activity of the polypeptide(s).
In certain embodiments, the proteins and/or polynucleotides, and/or fusion proteins are provided in the form of compositions, e.g., pharmaceutical compositions, vaccine compositions, compositions comprising a physiologically acceptable carrier or excipient. Such compositions may comprise buffers such as neutral buffered saline, phosphate buffered saline and the like; carbohydrates such as glucose, mannose, sucrose or dextrans, mannitol; proteins; polypeptides or amino acids such as glycine; antioxidants; chelating agents such as EDTA or glutathione; adjuvants (e.g., aluminum hydroxide); and preservatives.
In general, organ-specific secreted polypeptides and polynucleotides encoding such polypeptides as described herein, may be prepared using any of a variety of techniques that are well known in the art. For example, a DNA sequence encoding an organ-specific secreted protein may be prepared by amplification from a suitable cDNA or genomic library using, for example, polymerase chain reaction (PCR) or hybridization techniques. Libraries may generally be prepared and screened using methods well known to those of ordinary skill in the art, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratories, Cold Spring Harbor, N.Y., 1989. cDNA libraries may be prepared from any of a variety of organs, tissues, cells, as described herein. Other libraries that may be employed will be apparent to those of ordinary skill in the art upon reading the present disclosure. Primers for use in amplification may be readily designed based on the polynucleotide sequences encoding organ-specific polypeptides as provided herein, for example, using programs such as the PRIMER3 program (httpcolon double slash www-genome dot wi dot mit dot edu/cgi-bin/primer/primer3 www dot cgi).
Polynucleotides encoding the organ-specific secreted polypeptides as described herein are also provided by the present invention. A polynucleotide as used herein may be single-stranded (coding or antisense) or double-stranded, and may be DNA (genomic, cDNA or synthetic) or RNA molecules. Thus, within the context of the present invention, a polynucleotide encoding a polypeptide may also be a gene. A gene is a segment of DNA involved in producing a polypeptide chain; it includes regions preceding and following the coding region (leader and trailer) as well as intervening sequences (introns) between individual coding segments (exons). Additional coding or non-coding sequences may, but need not, be present within a polynucleotide of the present invention, and a polynucleotide may, but need not, be linked to other molecules and/or support materials. An isolated polynucleotide, as used herein, means that a polynucleotide is substantially away from other coding sequences, and that the DNA molecule does not contain large portions of unrelated coding DNA, such as large chromosomal fragments or other functional genes or polypeptide coding regions. Of course, this refers to the DNA molecule as originally isolated, and does not exclude genes or coding regions later added to the segment by the hand of man.
Polynucleotides of the present invention may comprise a native sequence (i.e., an endogenous polynucleotide, for instance, a native or non-artificially engineered or naturally occurring gene as provided herein) encoding an organ-specific secreted protein, an alternate form of such a sequence, or a portion or splice variant thereof or may comprise a variant of such a sequence. Polynucleotide variants may contain one or more substitutions, additions, deletions and/or insertions such that the polynucleotide encodes a polypeptide useful in the methods described herein, such as for the detection of organ-specific proteins (e.g., wherein said polynucleotide variants encode polypeptides that can be used to generate detection reagents as described herein that are specific for an organ-specific secreted protein). In certain embodiments, variants exhibit at least about 70% identity, and in other embodiments, exhibit at least about 80%, 85%, 86%, 87%, 88%, 89%, identity and in yet further embodiments, at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identity to a polynucleotide sequence that encodes a native organ-specific secreted polypeptide or an alternate form or a portion thereof. Illustrative polynucleotides of the present invention are described in Table 1 and Tables 3-5, the section entitled “Brief Description of the Sequence Identifiers” and are set forth in the sequence listing. The percent identity may be readily determined by comparing sequences using computer algorithms well known to those having ordinary skill in the art and described herein.
Polynucleotides that are complementary to the polynucleotides described herein, or that have substantial identity to a sequence complementary to a polynucleotide as described herein are also within the scope of the present invention. “Substantial identity”, as used herein refers to polynucleotides that exhibit at least about 70% identity, and in certain embodiments, at least about 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identity to a polynucleotide sequence that encodes a native organ-specific secreted polypeptide as described herein. Substantial identity can also refer to polynucleotides that are capable of hybridizing under stringent conditions to a polynucleotide complementary to a polynucleotide encoding an organ-specific secreted protein. Suitable hybridization conditions include prewashing in a solution of 5×SSC, 0.5% SDS, 1.0 mM EDTA (pH 8.0); hybridizing at 50-65° C., 5×SSC, overnight; followed by washing twice at 65° C. for 20 minutes with each of 2×, 0.5× and 0.2×SSC containing 0.1% SDS. Nucleotide sequences that, because of code degeneracy, encode a polypeptide encoded by any of the above sequences are also encompassed by the present invention.
Oligonucleotide primers for amplification of the polynucleotides encoding organ-specific secreted proteins are also within the scope of the present invention. Many amplification methods are known in the art such as PCR, RT-PCR, quantitative real-time PCR, and the like. The PCR conditions used can be optimized in terms of temperature, annealing times, extension times and number of cycles depending on the oligonucleotide and the polynucleotide to be amplified. Such techniques are well known in the art and are described in, for example, Mullis et al., Cold Spring Harbor Symp. Quant. Biol., 51:263, 1987; Erlich ed., PCR Technology, Stockton Press, NY, 1989. Oligonucleotide primers can be anywhere from 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 nucleotides in length. In certain embodiments, the oligonucleotide primers of the present invention are typically 35, 40, 45, 50, 55, 60, or more nucleotides in length.
Organ-Specific Molecular Blood Fingerprints
The present invention also provides methods for defining organ-specific molecular blood fingerprints. Additionally, the present invention provides defined examples of organ-specific molecular blood fingerprints as described further herein.
Each normal organ controls the expression of a variety of genes, some of which are expressed at major levels at other organs or tissues in the body and some of which are expressed only in the organ of interest or at significantly increased levels in the organ of interest as compared to expression in other organs/tissues (e.g., at least 2 fold, at least 2.5 fold, at least 3.0 fold, at least 3.5 fold, at least 4.0 fold, at least 4.5 fold, or higher fold expression in the organ of interest as compared to other tissues. Some of the organ-specific transcripts encode proteins which can be secreted into the blood. Hence these secreted proteins constitute an organ-specific molecular fingerprint for that organ in the blood. Analysis of levels of these proteins in the blood provides organ-specific molecular blood fingerprints that are indicative of biological states. A biological state may be a normal, healthy state or a disease state (e.g., perturbation from normal). Thus, there are molecular fingerprints in the blood that reflect the operation of normal organs and each organ has a specific molecular fingerprint. These organ-specific blood fingerprints are perturbed when disease, or other agents such as drugs, affects the organ. Different diseases will alter the organ-specific blood fingerprints in different ways (e.g. alter the expression levels of the corresponding secreted proteins). Likewise, different drugs will alter the organ-specific blood fingerprints in different ways. Thus, a unique perturbed blood molecular fingerprint is associated with each type of distinct disease and with each drug or combination of drugs. In effect, each drug or combination of drugs will create a unique organ-specific molecular blood fingerprint for each organ that it affects. As would be readily appreciated by the skilled artisan, each disease or stage of a disease or drug or combination of drugs can affect multiple organs. For example, in kidney cancer, a primary perturbation in the kidney-specific molecular blood fingerprint would occur. However, a secondary or indirect effect may also be observed in the bladder-specific molecular blood fingerprint. As another example, in liver cancer, perturbation of a liver-specific blood fingerprint as a primary indicator of disease would occur. However, secondary or indirect effects at other sites, for example in a lymphocyte-specific blood fingerprint, would also be observed. As described elsewhere herein, each disease type and stage results in a unique, identifiable fingerprint for each organ that it affects, for primary and secondary organs affected. Likewise, each drug or combination of drugs results in a unique, identifiable fingerprint for each organ that it effects, both primary and secondary organs. Thus, multiple organ-specific molecular blood fingerprints can be used in combination to determine a particular drug side effect and the fingerprints may include those for the primary organ affected and/or for a secondary or indirect organ that is affected by a particular drug or combination of drugs.
Most common diseases such as prostate cancer actually represent multiple distinct diseases that initially appear similar (e.g., benign and very slowly growing prostate cancer, slowly invasive prostate cancer and rapidly metastatic prostate cancer represent three different types of prostate cancer—the process of dividing individual prostate cancers into one of these three types is called stratification). The blood molecular fingerprints will be distinct for each of these disease types, thus allowing for the stratification of similar diseases and rapid intervention where necessary. The blood fingerprints will also be perturbed in unique ways as each type of disease progresses—hence the blood fingerprints will also permit the progression of disease to be followed. The blood fingerprints also change with therapy, and hence will permit the effectiveness of therapy to be followed, thereby allowing a physician to alter treatment accordingly. Importantly, the blood fingerprints change with exposure to a variety of environmental factors, such as drugs, and can be used to assess toxic or off target damage by the drug and will even permit following the subsequent recovery from such adverse drug exposure.
One of the advantages of the organ-specific, secreted blood fingerprints is the possibility that very subtle side effects of drugs can be detected, either adverse effects, or previously unrecognized positive effects.
Thus, an organ-specific molecular blood fingerprint for a given setting (e.g., one or more particular drug side effects) is defined by the levels in the blood of the organ-specific proteins that make up the fingerprint. As such, an organ-specific molecular blood fingerprint for a given organ at any given time and in any given setting (e.g., drug-induced perturbation) is determined by measuring the levels of each of a plurality of organ-specific proteins in the blood. It is the combination of the different levels in the blood of the organ-specific proteins that reveals a unique pattern that defines the fingerprint. Equally important, each of the levels of the proteins can be compared against one another to create an N-dimensional measure of the fingerprint space, a very powerful correlate to health, disease, and drug-induced changes (see e.g., U.S. Patent Application No 20020095259). It should be noted that, in certain embodiments, an organ-specific molecular blood fingerprint may be comprised of the determined level in the blood of one or more organ-specific secreted proteins. In one embodiment, an organ-specific molecular blood fingerprint may comprise the determined level in the blood of anywhere from about 2 to more than about 100, 200 or more organ-specific secreted proteins from a particular organ of interest. In one embodiment, the organ-specific molecular blood fingerprint comprises the quantitatively measured level in the blood of at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 organ-specific secreted proteins. In another embodiment, the organ-specific molecular blood fingerprint comprises the determined level in the blood of at least, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, or 30 organ-specific secreted proteins. In a further embodiment, the organ-specific molecular blood fingerprint comprises the determined level in the blood of at least, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 organ-specific secreted proteins. In yet a further embodiment, the organ-specific molecular blood fingerprint comprises the determined level in the blood of at least, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 organ-specific secreted proteins. In an additional embodiment, the organ-specific molecular blood fingerprint comprises the determined level in the blood of 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 organ-specific secreted proteins. In another embodiment, the organ-specific molecular blood fingerprint comprises the determined level in the blood of 61, 62, 63, 64, 65, 66, 67, 68, 69, or 70 organ-specific secreted proteins. In further embodiments, the organ-specific molecular blood fingerprint comprises the determined level in the blood of 75, 80, 85, 90, 100, or more organ-specific secreted proteins.
It should be noted that in certain circumstances, an organ-specific molecular blood fingerprint can be defined (in part or entirely) merely by the presence or absence of one or a plurality of organ-specific proteins, and determining the exact level of each of a plurality of organ-specific proteins in the blood may not necessary.
In a further embodiment, the fingerprints associated with a particular drug and side effects thereof are determined by comparing the blood from normal individuals against that from subjects taking a particular drug of interest. As such, a statistically significant change in the levels (e.g., an increase or a decrease) of one or more of the organ-specific proteins that comprise the fingerprint as compared to normal is indicative of a perturbation of the fingerprint and is useful in identifying direct effects or side effects of the drug of interest. The skilled artisan would readily appreciate that a variety of statistical tests can be used to determine if an altered level of a given protein is significant. The Z-test (Man, M. Z., et al., Bioinformatics, 16: 953-959, 2000) or other appropriate statistical tests can be used to calculate P values for comparison of protein expression levels. In certain embodiments, the level of each of the plurality of organ-specific proteins in the blood sample from the subject is compared to a previously determined normal control level of each of the plurality of organ-specific proteins taking into account standard deviation. Thus, the present invention provides determined normal control levels of each of a plurality of organ-specific proteins that make up a particular molecular blood fingerprint.
In an additional embodiment, the present invention can be used to determine distinct, normal organ-specific molecular blood fingerprints, such as in different populations of people. In this regard, differences in normal organ-specific molecular blood fingerprints between populations of individuals can be defined and these differences permit the stratification of patients into classes of individuals who would respond positively to a particular drug and those who would not. Thus, the present invention provides the ability to determine those individuals who may have adverse reactions to drugs.
Organ-specific molecular blood fingerprints can be determined using any of a variety of detection reagents in the context of a variety of methods for measuring protein levels. Any detection reagent that can specifically bind to or otherwise detect an organ-specific secreted protein as described herein is contemplated as a suitable detection reagent. Illustrative detection reagents include, but are not limited to antibodies, or antigen-binding fragments thereof, yeast ScFv, DNA or RNA aptamers, isotope labeled peptides, microfluidic/nanotechnology measurement devices and the like.
In one illustrative embodiment, a detection reagent is an antibody or an antigen-binding fragment thereof. Antibodies may be prepared by any of a variety of techniques known to those of ordinary skill in the art. See, e.g., Harlow and Lane, Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, 1988. In general, antibodies can be produced by cell culture techniques, including the generation of monoclonal antibodies as described herein, or via transfection of antibody genes into suitable bacterial or mammalian cell hosts, in order to allow for the production of recombinant antibodies. In one technique, an immunogen comprising the polypeptide is initially injected into any of a wide variety of mammals (e.g., mice, rats, rabbits, sheep or goats). In this step, the polypeptides of this invention may serve as the immunogen without modification. Alternatively, particularly for relatively short polypeptides, a superior immune response may be elicited if the polypeptide is joined to a carrier protein, such as bovine serum albumin or keyhole limpet hemocyanin. The immunogen is injected into the animal host, usually according to a predetermined schedule incorporating one or more booster immunizations, and the animals are bled periodically. Polyclonal antibodies specific for the polypeptide may then be purified from such antisera by, for example, affinity chromatography using the polypeptide coupled to a suitable solid support.
In one embodiment, multiple target proteins or peptides are used in a single immune response to generate multiple useful detection reagents simultaneously. In one embodiment, the individual specificities are later separated out.
In certain embodiments, antibody can be generated by phage display methods (such as described by Vaughan, T. J., et al., Nat Biotechnol, 14: 309-314, 1996; and Knappik, A., et al., Mol Biol, 296: 57-86, 2000); ribosomal display (such as described in Hanes, J., et al., Nat Biotechnol, 18: 1287-1292, 2000), or periplasmic expression in E. coli (see e.g., Chen, G., et al., Nat Biotechnol, 19: 537-542, 2001.). In further embodiments, antibodies can be isolated using a yeast surface display library. See e.g., nonimmune library of 109 human antibody scFv fragments as constructed by Feldhaus, M. J., et al., Nat Biotechnol, 21: 163-170, 2003. There are several advantages of this yeast surface display compared to more traditional large nonimmune human antibody repertoires such as phage display, ribosomal display, and periplasmic expression in E. coli 1). The yeast library can be amplified 1010-fold without measurable loss of clonal diversity and repertoire bias as the expression is under control of the tightly GAL1/10 promoter and expansion can be done under non induction conditions; 2) nanomolar-affinity scFvs can be routinely obtained by magnetic bead screening and flow-cytometric sorting, thus greatly simplified the protocol and capacity of antibody screening; 3) with equilibrium screening, a minimal affinity threshold of the antibodies desired can be set; 4) the binding properties of the antibodies can be quantified directly on the yeast surface; 5) multiplex library screening against multiple antigens simultaneously is possible; and 6) for applications demanding picomolar affinity (e.g. in early diagnosis), subsequent rapid affinity maturation (Kieke, M. C., et al., J Mol Biol, 307: 1305-1315, 2001.) can be carried out directly on yeast clones without further re-cloning and manipulations.
Monoclonal antibodies specific for an organ-specific secreted polypeptide of interest may be prepared, for example, using the technique of Kohler and Milstein, Eur. J. Immunol. 6:511-519, 1976, and improvements thereto. Briefly, these methods involve the preparation of immortal cell lines capable of producing antibodies having the desired specificity (i.e., reactivity with the polypeptide of interest). Such cell lines may be produced, for example, from spleen cells obtained from an animal immunized as described above. The spleen cells are then immortalized by, for example, fusion with a myeloma cell fusion partner, in certain embodiments, one that is syngeneic with the immunized animal. A variety of fusion techniques may be employed. For example, the spleen cells and myeloma cells may be combined with a nonionic detergent for a few minutes and then plated at low density on a selective medium that supports the growth of hybrid cells, but not myeloma cells. An illustrative selection technique uses HAT (hypoxanthine, aminopterin, thymidine) selection. After a sufficient time, usually about 1 to 2 weeks, colonies of hybrids are observed. Single colonies are selected and their culture supernatants tested for binding activity against the polypeptide. Hybridomas having high reactivity and specificity are preferred.
Monoclonal antibodies may be isolated from the supernatants of growing hybridoma colonies. In addition, various techniques may be employed to enhance the yield, such as injection of the hybridoma cell line into the peritoneal cavity of a suitable vertebrate host, such as a mouse. Monoclonal antibodies may then be harvested from the ascites fluid or the blood. Contaminants may be removed from the antibodies by conventional techniques, such as chromatography, gel filtration, precipitation, and extraction. The polypeptides of this invention may be used in the purification process in, for example, an affinity chromatography step.
A number of therapeutically useful molecules are known in the art which comprise antigen-binding sites that are capable of exhibiting immunological binding properties of an antibody molecule. The proteolytic enzyme papain preferentially cleaves
IgG molecules to yield several fragments, two of which (the “F(ab)” fragments) each comprise a covalent heterodimer that includes an intact antigen-binding site. The enzyme pepsin is able to cleave IgG molecules to provide several fragments, including the “F(ab′)2” fragment which comprises both antigen-binding sites. An “Fv” fragment can be produced by preferential proteolytic cleavage of an IgM, and on rare occasions IgG or IgA immunoglobulin molecule. Fv fragments are, however, more commonly derived using recombinant techniques known in the art. The Fv fragment includes a non-covalent VH::VL heterodimer including an antigen-binding site which retains much of the antigen recognition and binding capabilities of the native antibody molecule. Inbar et al. (1972) Proc. Nat. Acad. Sci. USA 69:2659-2662; Hochman et al. (1976) Biochem 15:2706-2710; and Ehrlich et al. (1980) Biochem 19:4091-4096.
A single chain Fv (“sFv”) polypeptide is a covalently linked VH::VL heterodimer which is expressed from a gene fusion including VH- and VL-encoding genes linked by a peptide-encoding linker. Huston et al. (1988) Proc. Nat. Acad. Sci. USA 85(16):5879-5883. A number of methods have been described to discern chemical structures for converting the naturally aggregated—but chemically separated—light and heavy polypeptide chains from an antibody V region into an sFv molecule which will fold into a three dimensional structure substantially similar to the structure of an antigen-binding site. See, e.g., U.S. Pat. Nos. 5,091,513 and 5,132,405, to Huston et al.; and U.S. Pat. No. 4,946,778, to Ladner et al.
Each of the above-described molecules includes a heavy chain and a light chain CDR set, respectively interposed between a heavy chain and a light chain FR set which provide support to the CDRS and define the spatial relationship of the CDRs relative to each other. As used herein, the term “CDR set” refers to the three hypervariable regions of a heavy or light chain V region. Proceeding from the N-terminus of a heavy or light chain, these regions are denoted as “CDR1,” “CDR2,” and “CDR3” respectively. An antigen-binding site, therefore, includes six CDRs, comprising the CDR set from each of a heavy and a light chain V region. A polypeptide comprising a single CDR, (e.g., a CDR1, CDR2 or CDR3) is referred to herein as a “molecular recognition unit.” Crystallographic analysis of a number of antigen-antibody complexes has demonstrated that the amino acid residues of CDRs form extensive contact with bound antigen, wherein the most extensive antigen contact is with the heavy chain CDR3. Thus, the molecular recognition units are primarily responsible for the specificity of an antigen-binding site.
As used herein, the term “FR set” refers to the four flanking amino acid sequences which frame the CDRs of a CDR set of a heavy or light chain V region. Some FR residues may contact bound antigen; however, FRs are primarily responsible for folding the V region into the antigen-binding site, particularly the FR residues directly adjacent to the CDRS. Within FRs, certain amino residues and certain structural features are very highly conserved. In this regard, all V region sequences contain an internal disulfide loop of around 90 amino acid residues. When the V regions fold into a binding-site, the CDRs are displayed as projecting loop motifs which form an antigen-binding surface. It is generally recognized that there are conserved structural regions of FRs which influence the folded shape of the CDR loops into certain “canonical” structures—regardless of the precise CDR amino acid sequence. Further, certain FR residues are known to participate in non-covalent interdomain contacts which stabilize the interaction of the antibody heavy and light chains.
The detection reagents of the present invention may comprise any of a variety of detectable labels. The invention contemplates the use of any type of detectable label, including, e.g., visually detectable labels, fluorophores, and radioactive labels. The detectable label may be incorporated within or attached, either covalently or non-covalently, to the detection reagent.
Methods for measuring organ-specific protein levels from blood/serum/plasma include, but are not limited to, immunoaffinity based assays such as ELISAs, Western blots, and radioimmunoassays, and mass spectrometry based methods (matrix-assisted laser desorption ionization (MALDI), MALDI-Time-of-Flight (TOF), Tandem MS (MS/MS), electrospray ionization (ESI), Surface Enhanced Laser Desorption Ionization (SELDI)-TOF MS, liquid chromatography (LC)-MS/MS, etc). Other methods useful in this context include isotope-coded affinity tag (ICAT) followed by multidimensional chromatography and MS/MS. The procedures described herein for analysis of blood organ-specific protein fingerprints can be modified and adapted to make use of microfluidics and nanotechnology in order to miniaturize, parallelize, integrate and automate diagnostic procedures (see e.g., L. Hood, et al., Science 306:640-643; R. H. Carlson, et al., Phys. Rev. Lett. 79:2149 (1997); A. Y. Fu, et al., Anal. Chem. 74:2451 (2002); J. W. Hong, et al., Nature Biotechnol. 22:435 (2004); A. G. Hadd, et al., Anal. Chem. 69:3407 (1997); I. Karube, et al., Ann. N.Y. Acad. Sci. 750:101 (1995); L. C. Waters et al., Anal. Chem. 70:158 (1998); J. Fritz et al., Science 288, 316 (2000)).
It should be noted that when the term “blood” is used herein, any part of the blood is intended. Accordingly, for determining molecular blood fingerprints, whole blood may be used directly where appropriate, or plasma or serum may be used.
Panels/Arrays for Detecting Organ-Specific Molecular Blood Fingerprints
The present invention also provides panels for detecting the organ-specific blood fingerprints at any given time in a subject. The term “subject” is intended to include any mammal or indeed any vertebrate that may be used as a model system for human disease. Examples of subjects include humans, monkeys, apes, dogs, cats, mice, rats, fish, zebra fish, birds, horses, pigs, cows, sheep, goats, chickens, ducks, donkeys, turkeys, peacocks, chinchillas, ferrets, gerbils, rabbits, guinea pigs, hamsters and transgenic species thereof. Further subjects contemplated herein include, but are not limited to, reptiles and amphibians, e.g., lizards, snakes, turtles, frogs, toads, salamanders, and newts. In one embodiment, the panel/array of the present invention comprises one detection reagent that specifically detects an organ-specific secreted protein. In another embodiment, the panel/arrays are comprised of a plurality of detection reagents that each specifically detects an organ-specific secreted protein, wherein the levels of organ-specific secreted proteins taken together form a unique pattern that defines the fingerprint. In certain embodiments, detection reagents can be bispecific such that the panel/array is comprised of a plurality of bispecific detection reagents that may specifically detect more than one organ-specific secreted protein. The term “specifically” is a term of art that would be readily understood by the skilled artisan to mean, in this context, that the protein of interest is detected by the particular detection reagent but other proteins are not detected in a statistically significant manner under the same conditions. Specificity can be determined using appropriate positive and negative controls and by routinely optimizing conditions.
The panels/arrays may be comprised of a solid phase surface having attached thereto a plurality of detection reagents each attached at a distinct location. As would be recognized by the skilled artisan, the number of detection reagents on a given panel would be determined from the number of organ-specific secreted proteins in the fingerprint to be measured. In one embodiment, the panel/array comprises one or more detection reagents. In a further embodiment, the panel/array comprises a plurality of detection reagents, wherein, the plurality of detection reagents may be anywhere from about 2 to about 100, 150, 160, 170, 180, 190, 200, or more detection reagents each specific for an organ-specific secreted protein. In one embodiment, the panel/array comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint. In another embodiment, the panel/array comprises at least 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint. In a further embodiment, the panel/array comprises at least 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint. In an additional embodiment, the panel/array comprises at least 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint. In yet a further embodiment, the panel/array comprises at least 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint. In an additional embodiment, the panel/array comprises at least 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint. In one embodiment, the panel/array comprises at least 61, 62, 63, 64, 65, 66, 67, 68, 69, or 70 detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint. In one embodiment, the panel/array comprises at least 75, 80, 85, 90, 100, 150, 160, 170, 180, 190, 200, or more, detection reagents each specific for one of the plurality of organ-specific secreted proteins that make up a given fingerprint.
Further in this regard, the solid phase surface may be of any material, including, but not limited to, plastic, polycarbonate, polystyrene, polypropylene, polyethlene, glass, nitrocellulose, dextran, nylon, metal, silicon and carbon nanowires, nanoparticles that can be made of a variety of materials and photolithographic materials. In certain embodiments, the solid phase surface is a chip. In another embodiment, the solid phase surface may comprise microtiter plates, beads, membranes, microparticles, the interior surface of a reaction vessel such as a test tube or other reaction vessel. In other embodiments the peptides will be fractionated by one or more one-dimensional columns using size separations, ion exchange or hydrophobicity properties and, for example, deposited in a MALDI 96 or 384 well plate and then injected into an appropriate mass spectrometer.
In one embodiment, the panel/array is an addressable array. As such, the addressable array may comprise a plurality of distinct detection reagents, such as antibodies or aptamers, attached to precise locations on a solid phase surface, such as a plastic chip. The position of each distinct detection reagent on the surface is known and therefore “addressable”. In one embodiment, the detection reagents are distinct antibodies that each have specific affinity for one of a plurality of organ-specific polypeptides.
In one embodiment, the detection reagents, such as antibodies, are covalently linked to the solid surface, such as a plastic chip, for example, through the Fc domains of antibodies. In another embodiment, antibodies are adsorbed onto the solid surface. In a further embodiment, the detection reagent, such as an antibody, is chemically conjugated to the solid surface. In a further embodiment, the detection reagents are attached to the solid surface via a linker. In certain embodiments, detection with multiple specific detection reagents is carried out in solution.
Methods of constructing protein arrays, including antibody arrays, are known in the art (see, e.g., U.S. Pat. Nos. 5,489,678; 5,252,743; Blawas and Reichert, 1998, Biomaterials 19:595-609; Firestone et al., 1996, J. Amer. Chem. Soc. 18, 9033-9041; Mooney et al., 1996, Proc. Natl. Acad. Sci. 93, 12287-12291; Pirrung et al, 1996, Bioconjugate Chem. 7, 317-321; Gao et al, 1995, Biosensors Bioelectron 10, 317-328; Schena et al, 1995, Science 270, 467-470; Lom et al., 1993, J. Neurosci. Methods, 385-397; Pope et al., 1993, Bioconjugate Chem. 4, 116-171; Schramm et al., 1992, Anal. Biochem. 205, 47-56; Gombotz et al., 1991, J. Biomed. Mater. Res. 25, 1547-1562; Alarie et al., 1990, Analy. Chim. Acta 229, 169-176; Owaku et al, 1993, Sensors Actuators B, 13-14, 723-724; Bhatia et al., 1989, Analy. Biochem. 178, 408-413; Lin et al., 1988, IEEE Trans. Biomed. Engng., 35(6), 466-471).
In one embodiment, the detection reagents, such as antibodies, are arrayed on a chip comprised of electronically activated copolymers of a conductive polymer and the detection reagent. Such arrays are known in the art (see e.g., U.S. Pat. No. 5,837,859 issued Nov. 17, 1998; PCT publication WO 94/22889 dated Oct. 13, 1994). The arrayed pattern may be computer generated and stored. The chips may be prepared in advance and stored appropriately. The antibody array chips can be regenerated and used repeatedly.
Using the methods described herein, a vast array of organ-specific molecular blood fingerprints can be defined for any of a variety of drugs as described further herein. As such, the present invention further provides information databases comprising data that make up molecular blood fingerprints as described herein. As such, the databases may comprise the defined differential expression levels as determined using any of a variety of methods such as those described herein, of each of the plurality of organ-specific secreted proteins that make up a given fingerprint in any of a variety of settings (e.g., normal or drug-associated fingerprints).
Methods of Use
The present invention provides methods for identifying organ-specific secreted proteins and methods for identifying organ-specific molecular blood fingerprints. The present invention further provides panels/arrays of detection reagents for detecting such fingerprints. The present invention also provides defined organ-specific molecular blood fingerprints for normal and disease settings and for fingerprints associated with/resulting from a particular drug or combination of drugs. As such, the present invention provides for methods for identifying and monitoring drug effects in any of a variety of settings. Further, the present invention provides methods for following responses to therapy in a variety of disease settings such that any adverse or other drug side effects can be monitored. The present invention also provides methods of detecting disease, stratifying disease, monitoring the progression of disease, and monitoring responses to therapy such as described in U.S. Provisional Application Nos. 60/647,685 and 60/683,071 filed Jan. 27, 2005 and May 20, 2005, respectively, and Copending U.S. application Ser. No. 11/342,366 entitled Methods for Identifying and Using Organ-Specific Proteins in Blood, filed concurrently on Jan. 27, 2006.
The present invention can be used as a standard screening test. In this regard, one or more of the detection panels described herein can be run on an individual taking a particular drug and any statistically significant deviation from a normal organ-specific molecular blood fingerprint would indicate that drug-related perturbation was present. Thus, the present invention provides a standard or “normal” blood fingerprint for any given organ. In certain embodiments, a normal blood fingerprint is determined by measuring the normal range of levels of the individual protein members of a fingerprint. Any deviation therefrom or perturbation of the normal fingerprint that is outside the standard deviation (normal range) has utility in determining drug side effects (see also U.S. Patent Application No. 0020095259). As would be recognized by the skilled artisan, the significance of any deviation in the levels of (e.g., a significantly altered level of one or more of) the individual protein members of a fingerprint can be determined using statistical methods known in the art and described herein. In certain embodiments, a normal standard can be generated from a blood sample taken from the individual prior to administration of the drug such that comparisons can be made thereto.
Further, the present invention provides methods for determining and evaluating not only the absolute levels of the changes in the proteins constituting individual fingerprints, but also for evaluating all the protein changes (e.g. N changed proteins) and comparing them against one another to generate an N-dimensional shape space that provides more powerful correlation with the stratifications of drug-induced alterations described above (see e.g., U.S. Patent Application No. 20020095259).
In a further embodiment, the present invention can be used to determine the risk of having one or more side effects from a drug or combination thereof. A statistically significant alteration (e.g., increase or decrease) in the levels of one or more members of a particular molecular blood fingerprint may signify a risk of developing a one or more side effects from a drug or combination thereof.
The organ-specific molecular blood fingerprints of the present invention can be used to detect side effects, either positive or negative (or lack thereof) from any of a variety of drugs. As would be recognized by the skilled artisan, the present invention can be used to detect the side effects (or lack thereof) from virtually any drug. In this regard, any drug either currently under development or already approved and on the market is contemplated in the context of this invention. In particular, the present invention provides methods for monitoring the organ-specific molecular blood fingerprint of organs/tissues/cells that fall outside the expected therapeutic targets (e.g., monitoring off-target effects of drugs). For example, the liver and kidney are organs that often reflect the side effects of drugs. Further, as demonstrated by the side-effects of COX-2 inhibitors, drugs can have off-target effects on the cardiovascular system, or any other cell/organ/tissue/system as described herein. Thus, the present invention also provides methods for monitoring non-target organs for any drug, including drugs under development and drugs currently on the market for which subtle side-effects may not have been detected.
In a further embodiment, the present invention can be used to determine side effects of combinations of drugs on any organ. In a further embodiment, the organ-specific molecular blood fingerprints can be monitored in subjects taking very low (non-toxic) doses of drugs to determine whether subtle side effects are occurring.
Thus, the organ-specific molecular blood fingerprints of the present invention can be used to detect direct effects or side effects of any drug on the heart, kidney, ureter, bladder, urethra, liver, prostate, heart, blood vessels, bone marrow, skeletal muscle, smooth muscle, various specific regions of the brain (including, but not limited to the amygdala, caudate nucleus, cerebellum, corpuscallosum, fetal, hypothalamus, thalamus), spinal cord, peripheral nerves, retina, nose, trachea, lungs, mouth, salivary gland, esophagus, stomach, small intestines, large intestines, hypothalamus, pituitary, thyroid, pancreas, adrenal glands, ovaries, oviducts, uterus, placenta, vagina, mammary glands, testes, seminal vesicles, penis, lymph nodes, thymus, and spleen. The present invention can be used to detect drug side effects on the cardiovascular system, neurological system, metabolic system, respiratory system, the immune system, etc. As would be recognized by the skilled artisan, the present invention can be used to detect any side effects wherein the side effects cause perturbation in organ-specific secreted proteins. In this regard, a side effect may be an adverse effect or may be a positive effect. Accordingly, the present invention can be used to identify potential new indications for a particular drug.
In an additional embodiment, the present invention can be used to determine distinct, normal organ-specific molecular blood fingerprints, such as in different populations of people. In this regard, there may be differences in normal organ-specific molecular blood fingerprints between populations of individuals that permit the stratification of patients into classes of individuals who would respond positively to a particular drug and those who would not. Thus, the present invention provides the ability to determine those individuals who may have adverse reactions to drugs. Additionally, in certain embodiments, the nature of the drug-induced changes in one or more organ-specific molecular blood fingerprints can be used to predict which patients might effectively respond to the drug. Thus, the organ-specific molecular blood fingerprints of the present invention provide the ability to stratify patients with regard to drug response and the ability to assess the toxicity of drugs.
Once a side effect is detected by identifying a perturbation in an organ-specific molecular blood fingerprint, the effect can be further mapped using systems approaches by mapping the gene and protein networks perturbed (see Example 1). In certain embodiments, a single organ-specific secreted protein may be perturbed (as indicated by detection of an increase or decrease in the level of the protein in the blood). In further embodiments, more than one organ-specific secreted protein may be perturbed.
To monitor the monitor responses to therapy or responses to any drug, one or more organ-specific molecular blood fingerprints are detected/measured as described herein using any of the methods as described herein at one time point and detected/measured again at subsequent time points, thereby monitoring responses to therapy or to any drug.
Organ-specific molecular blood fingerprints can also be defined and/or perturbations thereof tested in any of a variety of animal models. Animals that can be used in this context, include, for example, mice, rats, rabbits, pigs, monkeys, apes, zebra fish, etc, and transgenic species thereof.
Business Methods
A further embodiment of the present invention comprises a business method of identifying a particular drug side effect in a subject taking a drug that comprises detecting an organ-specific molecular blood fingerprint as described herein.
Thus, the present invention contemplates methods for (a) manufacturing one or more of the detection reagents, panels, arrays, (b) providing diagnostic services for determining organ-specific blood fingerprints, and identifying particular drug side effects (c) providing manufacturers of genomics devices the use of the detection reagents, panels, arrays, blood fingerprints or transcriptomes described herein to develop diagnostic devices, where the genomics device includes any device that may be used to define differences in a blood sample between the normal and disturbed state resulting from one or more drug side effects (d) providing manufacturers of proteomics devices the use of the detection reagents, panels, arrays, blood fingerprints or transcriptomes described herein to develop diagnostic devices, where the proteomics device includes any device that may be used to define differences in a blood sample between the normal and disturbed state resulting from a drug side effect and (e) providing manufacturers of imaging devices the use of the detection reagents, panels, arrays, blood fingerprints or transcriptomes described herein to develop diagnostic devices, where the proteomics device includes any device that may be used to define differences in a blood sample between the normal and disturbed state resulting from one or more drug side effects (f) providing manufacturers of molecular imaging devices the use of the detection reagents, panels, arrays, blood fingerprints or transcriptomes described herein to develop diagnostic devices, where the proteomics device includes any device that may be used to define differences in a blood sample between the normal and disturbed state resulting from one or more drug side effects and g) marketing to healthcare providers the benefits of using the detection reagents, panels, arrays, and diagnostic services of the present invention to enhance diagnostic capabilities and thus, to better treat patients.
Another aspect of the invention relates to a method for conducting a business, which includes: (a) manufacturing one or more of the detection reagents, panels, arrays, (b) providing services for determining organ-specific molecular blood fingerprints and (c) marketing to healthcare providers the benefits of using the detection reagents, panels, arrays, and services of the present invention to enhance capabilities to identify drug side effects and thus, to better treat patients.
Another aspect of the invention relates to a method for conducting a business, comprising: (a) providing a distribution network for selling the detection reagents, panels, arrays, diagnostic services, and access to organ-specific molecular blood fingerprint databases (b) providing instruction material to physicians or other skilled artisans for using the detection reagents, panels, arrays, and organ-specific molecular blood fingerprint databases to improve the ability to identify drug side effects for patients.
Yet another aspect of the invention relates to a method for conducting a business, comprising: (a) identifying organ-specific secreted proteins in the blood sera, etc. (b) determining the organ-specific molecular fingerprints as described herein and (c) providing a distribution network for selling access to the database of organ-specific molecular fingerprints identified in step (b).
For instance, the subject business method can include an additional step of providing a sales group for marketing the database, or panels, or arrays, to healthcare providers.
Another aspect of the invention relates to a method for conducting a business, comprising: (a) determining one or more organ-specific molecular blood fingerprints and (b) licensing, to a third party, the rights for further development and sale of panels, arrays, and information databases related to the organ-specific molecular blood fingerprints of (a).
The business methods of the present application relate to the commercial and other uses, of the methodologies, panels, arrays, organ-specific secreted proteins, organ-specific molecular blood fingerprints, and databases comprising identified fingerprints of the present invention. In one aspect, the business method includes the marketing, sale, or licensing of the present invention in the context of providing consumers, i.e., patients, medical practitioners, medical service providers, and pharmaceutical distributors and manufacturers, with all aspects of the invention described herein, (e.g., the methods for identifying organ-specific secreted proteins, detection reagents for such proteins, molecular blood fingerprints, etc., as provided by the present invention).
In a particular embodiment of the present invention, a business method relating to providing information related to molecular blood fingerprints (e.g., levels of the plurality of organ-specific secreted proteins that make up a given fingerprint), method for determining fingerprints and sale of panels for determining such molecular blood fingerprints. In a specific embodiment, that method may be implemented through the computer systems of the present invention. For example, a user (e.g. a health practitioner such as a physician or a diagnostic laboratory technician) may access the computer systems of the present invention via a computer terminal and through the Internet or other means. The connection between the user and the computer system is preferably secure.
In practice, the user may input, for example, information relating to a patient such as the patient's disease state and/or drugs that the patient is taking, e.g., levels determined for the proteins that make up a given molecular blood fingerprint using a panel or array of the present invention. The computer system may then, through the use of the resident computer programs, provide a diagnosis or determination of drug side effects that fits with the input information by matching the fingerprint parameters (e.g., levels of the proteins present in the blood as detected using a particular panel or array of the present invention) with a database of fingerprints.
A computer system in accordance with a preferred embodiment of the present invention may be, for example, an enhanced IBM AS/400 mid-range computer system. However, those skilled in the art will appreciate that the methods and apparatus of the present invention apply equally to any computer system, regardless of whether the computer system is a complicated multi-user computing apparatus or a single user device such as a personal computer or workstation. Computer systems suitably comprise a processor, main memory, a memory controller, an auxiliary storage interface, and a terminal interface, all of which are interconnected via a system bus. Note that various modifications, additions, or deletions may be made to the computer system within the scope of the present invention such as the addition of cache memory or other peripheral devices.
The processor performs computation and control functions of the computer system, and comprises a suitable central processing unit (CPU). The processor may comprise a single integrated circuit, such as a microprocessor, or may comprise any suitable number of integrated circuit devices and/or circuit boards working in cooperation to accomplish the functions of a processor.
In a preferred embodiment, the auxiliary storage interface allows the computer system to store and retrieve information from auxiliary storage devices, such as magnetic disk (e.g., hard disks or floppy diskettes) or optical storage devices (e.g., CD-ROM). One suitable storage device is a direct access storage device (DASD). A DASD may be a floppy disk drive that may read programs and data from a floppy disk. It is important to note that while the present invention has been (and will continue to be) described in the context of a fully functional computer system, those skilled in the art will appreciate that the mechanisms of the present invention are capable of being distributed as a program product in a variety of forms, and that the present invention applies equally regardless of the particular type of signal bearing media to actually carry out the distribution. Examples of signal bearing media include: recordable type media such as floppy disks and CD ROMS, and transmission type media such as digital and analog communication links, including wireless communication links.
The computer systems of the present invention may also comprise a memory controller, through use of a separate processor, which is responsible for moving requested information from the main memory and/or through the auxiliary storage interface to the main processor. While for the purposes of explanation, the memory controller is described as a separate entity, those skilled in the art understand that, in practice, portions of the function provided by the memory controller may actually reside in the circuitry associated with the main processor, main memory, and/or the auxiliary storage interface.
Furthermore, the computer systems of the present invention may comprise a terminal interface that allows system administrators and computer programmers to communicate with the computer system, normally through programmable workstations. It should be understood that the present invention applies equally to computer systems having multiple processors and multiple system buses. Similarly, although the system bus of the preferred embodiment is a typical hardwired, multidrop bus, any connection means that supports bidirectional communication in a computer-related environment could be used.
The main memory of the computer systems of the present invention suitably contains one or more computer programs relating to the organ-specific molecular blood fingerprints and an operating system. Computer program is used in its broadest sense, and includes any and all forms of computer programs, including source code, intermediate code, machine code, and any other representation of a computer program. The term “memory” as used herein refers to any storage location in the virtual memory space of the system. It should be understood that portions of the computer program and operating system may be loaded into an instruction cache for the main processor to execute, while other files may well be stored on magnetic or optical disk storage devices. In addition, it is to be understood that the main memory may comprise disparate memory locations.
All of the U.S. patents, U.S. patent application publications, U.S. patent applications, foreign patents, foreign patent applications and non-patent publications referred to in this specification and/or listed in the Application Data Sheet, are incorporated herein by reference, in their entirety. Moreover, all numerical ranges utilized herein explicitly include all integer values within the range and selection of specific numerical values within the range is contemplated depending on the particular use. Further, the following examples are offered by way of illustration, and not by way of limitation.
EXAMPLES
Example 1
Evidence for the Presence of Disease-Perturbed Networks in Prostate Cancer Cells by Genomic and Proteomic Analyses: a Systems Approach to Disease
The following example demonstrates the presence of disease-perturbed networks in prostate. This provides a model for studying perturbation of organ-specific molecular blood fingerprints. The same principles apply in the setting of determining perturbations that result from drugs.
Prostate cancer is the most common nondermatological cancer in the United States (Greenlee, R. T., et al., CA Cancer J Clin, 50: 7-33., 2000). Initially, its growth is androgen-dependent (AD); early-stage therapies, including chemical and surgical castration, kill cancerous cells by androgen deprivation. Although such therapies produce tumor regression, they eventually fail because most prostate carcinomas become androgen-independent (AI) (Isaacs, J. T. Urol Clin North Am, 26: 263-273., 1999). To improve the efficacy of prostate cancer therapy, it is necessary to understand the molecular mechanisms underlying the transition from androgen dependence to androgen independence.
The transition from AD to AI status likely results from multiple processes, including activation of oncogenes, inactivation of tumor suppressor genes, and changes in key components of signal transduction pathways and gene regulatory networks. Systems approaches to biology and disease are predicated on the identification of the elements of the systems, the delineation of their interactions and their changes in distinct disease states. Biological information is of two types: the digital information of the genome (e.g. genes and cis-control elements) and environmental cues. Proteins rarely act in isolation; rather, they form parts of molecular machines or participate in network interactions mediating cellular functions such as signal transduction and developmental or physiological response patterns. Gene regulatory networks, whose architecture and linkages are established by cis-control elements, integrate information from signal transduction networks and output it to developmental or physiological batteries or networks of effector proteins. Normal protein and gene regulatory networks may be perturbed by disease—through genetic and/or environmental perturbations and understanding these differences lies at the heart of systems approaches to disease. Disease-perturbed networks initiate altered responses that bring about pathologic phenotypes such as the invasiveness of cancer cells.
To map network perturbations in cancer initiation and progression, changes in expression levels of virtually all transcripts must be measured. Certain low-abundance transcripts, such as those encoding transcription factors and signal transducers, wield significant regulatory influences in spite of the fact they may be present in the cell at very low copy numbers. Differential display (Bussemakers, M. J., et al., Cancer Res, 59: 5975-5979, 1999) or cDNA microarrays (Vaarala, M. H., et al., Lab Invest, 80: 1259-1268, 2000; Chang, G. T., et al., Cancer Res, 57: 4075-4081, 1997) have been used to profile changes in gene expression during the AD to AI transition; however, those technologies can identify only a limited number of more abundant mRNAs, and they miss many low-abundance mRNAs due to their low detection sensitivities. Massively parallel signature sequencing (MPSS), allows 20-nucleotide signature sequences to be determined in parallel for more than 1,000,000 DNA sequences (Brenner, et al., 2000, supra). MPSS technology allows identification and cataloging of almost all mRNAs that are changed between two cell states, even those with one or a few transcripts per cell, or between different organs or tissues. Differentially expressed genes thus identified can be mapped onto cellular networks to provide a systemic understanding of changes in cellular state.
Although transcriptome (mRNA levels) differences are easier to study than proteome (protein levels) differences and provide extremely valuable information, cellular functions are usually performed by proteins. RNA expression profiling studies do not address how the encoded proteins function biologically, and transcript abundance levels do not always correlate with protein abundance levels (Chen, G., et al., Mol Cell Proteomics, 1: 304-313, 2002). Therefore, the mRNA expression profiling described herein was complemented with a more limited protein profiling by using isotope-coded affinity tags (ICAT) coupled with tandem mass spectrometry (MS/MS) (Gygi, S. P., et al., Nat Biotechnol, 17: 994-999., 1999).
The LNCaP cell line is a widely used androgen-sensitive model for early-stage prostate cancer from which androgen-independent sublines have been generated (Vaarala, M. H., et al., 2000, supra; Chang, G. T., et al., 1997, supra; Patel, B. J., et al., J Urol, 164: 1420-1425., 2000). The cells of one such variant, CL-1, in contrast to their LNCaP progenitors, are highly tumorigenic, and exhibit invasive and metastatic characteristics in intact and castrated mice (Patel, G. J., et al., 2000, supra; Tso, C. L., et al., Cancer J Sci Am, 6: 220-233., 2000). Thus CL-1 cells model late-stage prostate cancer. MPSS and ICAT data extracted from these model cell lines can be validated by real-time RT-PCR or western blot analysis in more relevant biological models (tumor xenografts) and in tumor biopsies.
An MPSS analysis of about 5 million signatures was conducted for the androgen-dependent LNCaP cell line and its androgen-independent derivative CL1. The resulting database offers the first comprehensive view of the digital transcriptomes of prostate cancer cells and allows exploration of the cellular pathways perturbed during the transition from AD to AI growth. Additionally, protein expression profiles between LNCaP and CL1 cells were compared using ICAT/MS/MS technology. Further, computational analysis was used to identify those proteins that are secreted. Once such protein was further investigated and shown to be a diagnostic marker for prostate cancer used either alone, or in combination with the known PSA prostate cancer marker.
MPSS Analysis: LNCaP and CL1 cells were grown using methods known in the art, for example, as described by Tso et al. 2000, supra). RNAs were isolated using Trizol (Life Technologies) according to the manufacturer's protocols (see, e.g., as described by Nelson et al. Proc Natl Acad Sci USA, 99: 11890-11895, 2002). MPSS cDNA libraries were constructed, individual cDNA sequences were amplified and attached to individual beads and sequenced as described by Brenner, et al., 2000, supra. The resulting signatures, generally 20 bases in length, were annotated using the then most recently annotated human genome sequence (human genome release hg16, released in November, 2003) and the human Unigene (Unigene build #184) according to a previously published method (Meyers, B. C., et al., Genome Res, 14: 1641-1653, 2004). Only 100% matches between an MPSS signature and a genome signature were considered. Those signatures that expressed at less than 3 tpm in both LNCaP and CL1 libraries were also excluded, as they might not be reliably detected (this represents less than one transcript per cell) (Jongeneel, C. V., et al., Proc Natl Acad Sci USA, 2003). Additionally, cDNA signatures were classified by their positions relative to polyadenylation signals and poly (A) tails and by their orientation relative to the 5′→3′ orientation of source mRNA. The Z-test (Man, M. Z., et al., Bioinformatics, 16: 953-959, 2000) was used to calculate P values for comparison of gene expression levels between the cell lines.
Isotope-Coded Affinity Tag (ICAT) Analysis: ICAT reagents were purchased from Applied Biosystems Inc. Fractionation of cells into cytosolic, microsomal and nuclear fractions, as well as ICAT labeling, MS/MS, and data analyses were performed as described by Han et al. Nat Biotechnol, 19: 946-951, 2001. In addition, probability score analysis (Keller, A., et al., Anal Chem, 74: 5383-5392, 2002) and ASAPRatio (Automated Statistical Analysis on Protein Ratio) (Li, X. J., et al., Anal Chem, 75: 6648-6657, 2003) were used to assess the quality of MS spectra and to calculate protein ratios from multiple peptide ratios. (Briefly, and as described at http://regis.systemsbiology.net/software, Automated Statistical Analysis on Protein Ratio (ASAPRatio) accurately calculates the relative abundances of proteins and the corresponding confidence intervals from ICAT-type ESI-LC/MS data. The software first uses a Savitzky-Golay smoothing filter to reconstruct LC spectra of a peptide and its partner in a single charge state, subtracts background noise from each spectrum, and calculates light:heavy ratio of the peptide in that charge state. The ratios of the same peptide in different charge states are averaged and weighted by the corresponding spectrum intensity to obtain the peptide light:heavy ratio and its error. Subsequently, all unique peptides identified for a given protein are collected, their ratios and errors calculated, outliers are checked for using Dixon's tests, and the relative abundance and confidence interval for the protein are calculated by applying statistics for weighed samples. The software quickly generates a list of interesting proteins based on their relative abundance. A byproduct of the software is to identify outlier peptides which may be misidentified or, more interestingly, post-translationally modified.) To compare protein and mRNA expression levels, the Unigene numbers of the differentially expressed proteins were used to find MPSS signatures and their expression levels in transcripts per million (tpm). If one Unigene had more than one MPSS signature, likely due to alternative terminations, the average tpm of all signatures was taken.
Real-Time RT-PCR: All primers were designed with the PRIMER3 program (http colon double slash www-genome dot wi dot mit dot edu/cgi-bin/primer/primer3_www dot cgi) and BLAST-searched against the human cDNA and EST database for uniqueness. Real-time PCR was performed on an ABI 7700 machine (PE Biosystems) and the SYBR Green dye (Molecular Probe Inc.) was used as a reporter. PCR conditions were designed to give bands of the expected size with minimal primer dimer bands.
Identification of Perturbed Networks: Genes in the 328 Biocarta and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways or networks (http colon double slash cgap dot nci dot nih dot gov/Pathways/) were downloaded and compared with the MPSS data, using Unigene IDs as identifiers. If a Unigene ID or an E.C. number corresponded to multiple signatures, potentially due to multiple alternatively terminated isoforms, the tpm counts of the isoforms were combined and then subjected to the Z-test (Man, M. Z., et al., 2000, supra). Genes with P values of 0.001 or less were considered to be significantly differentially expressed. The following criteria were used to identify perturbed networks: a perturbed network must have more than 3 genes represented our differentially expressed gene list (p<0.001) and at least 50% of those genes must be up regulated, it was considered an up-regulated pathway (vice versa for the down-regulated pathways).
Display of KEGG Networks by Cytoscape: Cytoscape software was used (www dot cytoscape dot org) (Shannon, P., et al., Genome Res, 13: 2498-2504, 2003), to map the data onto the web of intracellular molecular interactions. We imported metabolic network maps and related information such as enzymes, substrates, and reactions from the recently developed KEGG (http colon double slash www dot genome dot ad dot jp/) API 2.0 web server into the Cytoscape program. Expression data were thus automatically mapped to the KEGG and Biocarta pathways/networks and visualized by Cytoscape.
MPSS Analyses of the Androgen-Dependent LNCaP Cell Line and its Androgen-Independent Variant CL1: Using MPSS technology, 2.22 million signature sequences were sequenced for LNCaP cells and 2.96 million for CL1 cells.
A total of 19,595 unique transcript signatures expressed at levels>3 tpm in at least one of the samples were identified. The signatures were classified into three major categories: 1093 signatures matched repeat sequences; 15,541 signatures matched unique cDNAs or ESTs, and 2961 signatures had no matches to any cDNA or EST sequences (but did match genomic sequences). The last category included sequences falling into one of three different categories: signatures representing new transcripts yet to be defined, signatures representing polymorphisms in cDNA sequences (a match of an MPSS sequence to cDNA or EST sequences requires 100% sequence identity), or errors in the MPSS reads. Transcript tags with matches to a cDNA or EST sequence were further classified based on the signatures' relative orientation to transcription direction and their position relative to a polyadenylation site and/or poly(A) tail. A searchable MySQL database (www dot mysql dot com) was also built containing the expression levels (tpm), the genomic locations of the MPSS sequences, the cDNAs or EST matches, and the classification of each signature.
The first analysis was restricted to those MPSS signatures corresponding to cDNAs with poly(A) tails and/or polyadenylation sites, so that corresponding genes could be conclusively identified. The Z-test was used to compare differential gene expression between LNCaP cells and CL1 cells (Mann, et al., 2000, supra). Using very stringent P values (less than 0.001), 2088 MPSS signatures were identified (corresponding to 1987 unique genes, as some genes have two or more MPSS signatures, due to alternative usages of polyadenylation sites) with significant differential expression. Of these, 1011 signatures (965 genes) were overexpressed in CL1 cells, and 1077 signatures (1022 genes) were overexpressed in LNCaP cells. The significance score of Z-test was dependent on the expression level. If a cut off P value of less than 0.001 was taken in the dataset, the expression level in tpm changed from 0 to 26 tpm for the most lowly expressed transcript (>26 fold); and changed from 7591 and 11206 tpm for the most highly expressed transcript (1.48 fold).
The expression levels of nine randomly chosen genes were identified using the MPSS and quantitative real-time RT-PCR techniques and showed that both RNA data sets were concordant. The MPSS expression profiling data were consistent with the available published data. For example, using RT-PCR, Patel et al. (Patel, B. J., et al., J Urol, 164: 1420-1425, 2000) showed that CL1 tumors express barely detectable prostate-specific antigen (PSA) and androgen receptor (AR) mRNAs as compared with LNCaP cells. The present MPSS results indicated that LNCaP cells expressed 584 tpm of androgen receptor (AR) and 841 tpm of PSA; CL1 cells did not express either AR or PSA (0 tpm in both cases). Freedland et al. found that CD10 expression was lost in CL1 cells compared with LNCaP cells (Freedland, S. J., et al., Prostate, 55: 71-80, 2003); the present study found that CD10 was expressed at 0 tpm in CL1 cells but at 56 tpm in LNCaP cells. Using cDNA microarrays, Vaarala et al. (Vaarala, M. H., et al., Lab Invest, 80: 1259-1268, 2000) compared LNCaP cells and another androgen-independent variant, non-PSA-producing LNCaP line, which is similar to CL1, and identified a total of 56 differentially expressed genes. We found completely concordant expression changes in these 56 genes between LNCaP and CL1 (in contrast to 1987 found by MPSS), and between LNCaP and non-PSA-producing LNCaP cells. This underscores the striking differences in sensitivity between the MPSS and cDNA microarray techniques.
CL1 cells do not express AR and thus lack the AR-mediated response program. To distinguish androgen response from other programs contributing to prostate cancer progression, the list of genes differentially expressed between LNCaP and CL1 cells were compared with a complementary list derived from MPSS analysis of LNCaP cells grown in the presence or absence of androgens (LNCaP R+/R−). From the 1987 differentially expressed gene between LNCaP and CL1, 525 genes were identified that were also differentially expressed in the LNCaP R+/R− dataset. Differential expression of these genes between LNCaP and CL1 cells probably reflects the fact that LNCaP cells express AR but CL1 does not, and the fact that normal medium contains some androgen. The remaining 1462 differentially expressed genes were not directly related to cellular AR status.
To compare the sensitivity of the MPSS and cDNA microarray procedures, cDNA microarrays containing 40,000 human cDNAs were hybridized to the same LNCaP and CL1 RNAs that were used for MPSS. Three replicate array hybridizations were performed. MPSS signatures and array clone IDs were mapped to Unigene IDs for data extraction and comparisons. The results showed that only those genes expressed at >40 tpm by MPSS could be reliably detected as changing levels by cDNA microarray hybridizations [judged by an expression level twice the standard deviation of the background, a standard cutoff value for microarray data analysis]. This observation is consistent with the 33-60 tpm sensitivity of microarrays estimated from the experiment performed by Hill et al. Science, 290: 809-812, 2000, in which known concentrations of synthetic transcripts were added. In LNCaP and CL1 cells, about 68.75% (13,471 of 19,595) of MPSS signatures (>3 tpm) were expressed at a level below 40 tpm; changes in the levels of these genes will be missed by microarray methods. Many attempts have been made to increase the sensitivity of DNA array technology (Han, M., et al., Nat Biotechnol, 19: 631-635, 2001; Bao, P., et al., Anal Chem, 74: 1792-1797, 2002.), however, the present study has not compared these new improvements against MPSS but it is clear that there will still be significant differences in the levels of change that can be detected.
SAGE (serial analysis of gene expression) (Velculescu, V. E., et al., Trends Genet, 16: 423-425., 2000) is another technology for gene expression profiling; like MPSS, it is digital and can generate a large number of signature sequences. However, MPSS, which can sequence  ̃1 million signatures per sample, can achieve a much deeper coverage than SAGE (typical  ̃10,000-100,000 signatures sequenced/sample) at reasonable cost. The MPSS data on LNCaP cells was compared against publicly available SAGE data on LNCaP cells (NCBI SAGE database) through common Unigene IDs. The SAGE library GSM724 (total SAGE tags sequenced: 22,721) (Lal, A., et al., Cancer Res, 59: 5403-5407, 1999) was derived from LNCaP cells with an inactivated PTEN gene; it is the SAGE library most similar to the LNCaP cells. Only 400 (about 20%) of the 1987 significantly differentially expressed genes (P<0.001) had any SAGE tag entry in GSM724. These data illustrate the importance of deep sequence coverage in identifying state changes in transcripts expressed at low abundance levels.
Functional Classifications of Genes Differentially Expressed Between LNCaP and CL1 Cells: Examination of the GO (Gene Ontology) classification of the 1987 genes revealed that multiple cellular processes change during the transition from LNCaP cells to CL1 cells. The most interesting groups, categorized by function, are shown in Table 1.
Nineteen differentially expressed proteins are related to apoptosis. Twelve of these are up regulated in CL1 cells, including the apoptosis inhibitors Tax 1 (human T-cell leukemia virus type I) binding protein 1 (TAX1BP1) and CASP8 and FADD-like apoptosis regulator. Seven are down regulated in CL1, including programmed cell death 8 and 5 (apoptosis-inducing factors), and BCL2-like 13 (an apoptosis facilitator). Since CL1 cells have increased expression of apoptosis inhibitors and decreased expression of apoptosis inducers, net inhibition of apoptosis may contribute to their greater tumorigenicity.







TABLE 1







EXAMPLES OF DIFFERENTIALLY EXPRESSED GENES


AND THEIR FUNCTIONAL CLASSIFICATIONS













LNCaP
CL1

GenBank
SEQ ID


Signatures
(tpm)
(tpm)
Description
ID
NOS:










Apoptosis related












GATCAAATGTGTGGCCT
   0
3609
lectin, 
BC001693
1574-1575


(SEQ ID NO: 3)


galactoside- 







binding,







soluble, 1







(galectin 1),




 


GATCATAATGTTAACTA
   0
  14
pleiomorphic
NM_002656
1576-1577


(SEQ ID NO: 4)


adenoma 







gene-like







1 (PLAGL1)




 


GATCATCCAGAGGAGCT
   0
  16
caspase 7,
U40281
1578-1579


(SEQ ID NO: 5)


apoptosis-related







cysteine protease




 


GATCGCGGTATTAAATC
   0
  15
tumor necrosis
U75380
1580-1581


(SEQ ID NO: 6)


factor receptor







superfamily,







member 12




 


GATCTCCTGTCCATCAG
   0
  24
interleukin 
M15330
1582-1583


(SEQ ID NO: 7)


1, beta




 


GATCCCCTTCAAGGACA
   1
  19
nudix (nucleoside
NM_006024
1584-1585


(SEQ ID NO: 8)


diphosphate linked







moiety X)-type







motif 1




 


GATCATTGCCATCACCA
  51
 278
EST, Highly
AL832733
1586


(SEQ ID NO: 9)


similar to







CUL2_HUMAN







CULLIN







HOMOLOG 2




 


GATCTGAAAATTCTTGG
  16
  56
CASP8 and
U97075
1587-1588


(SEQ ID NO: 10)


FADD-like







apoptosis 







regulator




 


GATCCACCTTGGCCTCC
  49
 149
tumor necrosis
NM_003842
1589-1590


(SEQ ID NO: 11)


factor receptor







superfamily,







member 10b




 


GATCATGAATGACTGAC
 118
 257
cytochrome c
BC009582
1591-1592


(SEQ ID NO: 12)







 


GATCAAGTCCTTTGTGA
 299
 102
programmed cell
H20713
1593


(SEQ ID NO: 13)


death 8 







(apoptosis-







inducing factor)




 


GATCACCAAAACCTGAT
  72
  24
BCL2-like 13
BM904887
1594


(SEQ ID NO: 14)


(apoptosis







facilitator)




 


GATCAATCTGAACTATC
 563
 146
apoptosis related
NM_016085
1595-1596


(SEQ ID NO: 15)


protein APR-3







(APR-3)




GATCCCTCTGTACAGGC
  83
  13
unc-13-like (C.
NM_006377
1597-1598


(SEQ ID NO: 16)


elegans) (UNC13),







mRNA.




 


GATCTGGTTGAAAATTG
1006
  49
CED-6 protein
NM_016315
1599-1600


(SEQ ID NO: 17)


(CED-6), mRNA.




 


GATCTCCCATGTTGGCT
  86
   4
CASP2 and RIPK1
BC017042
1601-1602


(SEQ ID NO: 18)


domain containing







adaptor with 







death domain




 


GATCAGAAAATCCCTCT
  27
   1
DEAD/H (Asp-
BC011556
1603-1604


(SEQ ID NO: 19)


Glu-Ala-Asp/His)







box polypeptide







20, 103 kDa




 


GATCAAGGATGAAAGCT
  50
   3
programmed cell
D20426
1605


(SEQ ID NO: 20)


death 2




 


GATCTGATTATTTACTT
1227
 321
programmed cell
NM_004708
1606-1607


(SEQ ID NO: 21)


death 5




 


GATCAAGTCCTTTGTGA
 299
 102
programmed cell
NM_004208
1608-1609


(SEQ ID NO: 22)


death 8 







(apoptosis-







inducing factor)




 







Cyclins












GATCCTGTCAAAATAGT
   2
  47
MCT-1 protein
NM_014060
1610-1611


(SEQ ID NO: 23)


(MCT-1), mRNA.




 


GATCATTATATCATTGG
   3
  39
cyclin-dependent
NM_078487
1612-1613


(SEQ ID NO: 24)


kinase inhibitor







2B(CDKN2B)




 


GATCATCAGTCACCGAA
  38
 396
cyclin-dependent
BM054921
1614


(SEQ ID NO: 25)


kinase inhibitor 







2A (p16)




 


GATCGGGGGCGTAGCAT
   5
  43
cyclin D1
NM_053056
1615-1616


(SEQ ID NO: 26)







 


GATCTACTCTGTATGGG
  40
 144
cyclin fold 
BG119256
1617


(SEQ ID NO: 27)


protein 1




 


GATCAGCACTCTACCAC
 530
 258
cyclin B1
BM973693
1618


(SEQ ID NO: 28)







 


GATCTGGTGTAGTATAT
 210
  77
cyclin G2
BM984551
1619


(SEQ ID NO: 29)







 


GATCAGTACACAATGAA
 642
 224
cyclin Gl,
BC000196
1620-1621


(SEQ ID NO: 30)







 


GATCTCAGTTCTGCGTT
 918
 308
CDK2-associated
NM_004642
1622-1623


(SEQ ID NO: 31)


protein 1







(CDK2AP1),







mRNA.




 


GATCCTGAGCTCCCTTT
2490
 650
cyclin I,
BC000420
1624-1625


(SEQ ID NO: 32)







 


GATCATGCAGTGACATA
  15
   1
KIAA1028 protein
AL122055
1626-1627


(SEQ ID NO: 33)







 


GATCTGTATGTGATTGG
  28
   1
cyclin M3
AA489077
1628


(SEQ ID NO: 34)







 







Kallikreins












GATCCACACTGAGAGAG
 841
   0
KLK3
AA523902
1629


(SEQ ID NO: 35)







 


GATCCAGAAATAAAGTC
 385
   0
KLK4
AA595489
1630


(SEQ ID NO: 36)







 


GATCCTCCTATGTTGTT
 314
   0
KLK2
S39329
1631-1633


(SEQ ID NO: 37)







 







CD markers












GATCAGAGAAGATGATA
   0
 810
CD213a2, inter-
U70981
1634-1635


(SEQ ID NO: 38)


leukin 13 re-







ceptor, alpha







2




 


GATCCCTAGGTCTTGGG
  23
 161
CD213a1, inter-
AW874023
1636


(SEQ ID NO: 39)


leuki n 13 re-







ceptor, alpha







1




 


GATCCACATCCTCTACA
   0
  63
CD33, CD33
BC028152
1637-1638


(SEQ ID NO: 40)


antigen (gp67)




 


GATCAATAATAATGAGG
   0
 151
CD44, CD44
AL832642
1639-1640


(SEQ ID NO: 41)


antigen




 


GATCCTTCAGCCTTCAG
   0
  35
CD73, 5′-
AI831695
1641


(SEQ ID NO: 42)


nucleotidase, 







ecto (CD73)




 


GATCTGGAACCTCAGCC
   1
  50
CD49e, integrin,
BC008786
1642-1643


(SEQ ID NO: 43)


alpha 5




 


GATCAGAGATGCACCAC
   8
 122
CD138, 
BM974052
1644


(SEQ ID NO: 44)


syndecan 1




 


GATCAAAGGTTTAAAGT
  38
 189
CD166, activated
AL833702
1645


(SEQ ID NO: 45)


leukocyte cell







adhesion molecule




 


GATCAGCTGTTTGTCAT
  53
 295
CD71, transferrin
BC001188
1646-1647


(SEQ ID NO: 46)


receptor (p90,







CD71)




 


GATCGGTGCGTTCTCCT
 287
 509
CD107a,
AI521424
1648


(SEQ ID NO: 47)


lysosomal-







associated







membrane protein







1




 


GATCTACAAAGGCCATG
 161
 681
CD29, integrin,
NM_002211
1649-1650


(SEQ ID NO: 48)


beta 1




 


GATCATTTATTTTAAGC
  56
   0
CD10 (neutral
BQ013520
1651


(SEQ ID NO: 49)


endopeptidase,







enkephalinase)




 


GATCAGTCTTTATTAAT
 150
  50
CD107b,
AI459107
1652


(SEQ ID NO: 50)


lysosomal-







associated







membrane protein







2




 


GATCTTGGCTGTATTTA
  84
1014
CD59 antigen p18-
NM_000611
1653-1654


(SEQ ID NO: 51)


20




 


GATCTTGTGCTGTGCTA
 408
 234
CD9 antigen (p24)
NM_001769
1655-1656


(SEQ ID NO: 52)







 







Transcription factors












GATCAAATAACAAGTCT
   0
  62
transcription 
BM854818
1657


(SEQ ID NO: 53)


factor BMAL2




 


GATCTCTATGTTTACTT
   0
  27
transcription 
BG163364
1658


(SEQ ID NO: 54)


factor BMAL2




 


GATCCTGACACATAAGA
  12
  74
transcription 
BF055294
1659


(SEQ ID NO: 55)


factor BMAL2




 


GATCATTTTGTATTAAT
  10
  61
transcription 
BC047878
1660-1661


(SEQ ID NO: 56)


factor NRF




 


GATCGTCTCATATTTGC
  52
   0
transcriptional
NM_025085
1662-1663


(SEQ ID NO: 57)


coactivator







tubedown-100




 


GATCCCCCTCTTCAATG
   0
  31
transcriptional 
AJ299431
1664-1665


(SEQ ID NO: 58)


co-activator







with PDZ-







binding motif




 


GATCAAATGCTATTGCA
   1
  55
transcriptional
AI126500
1666


(SEQ ID NO: 59)


regulator







interacting 







with the







PHS-bromodomain







2




 


GATCTGTGACAGCAGCA
 140
  35
transducer of
BC031406
1667-1668


(SEQ ID NO: 60)


ERBB2, 1




 


GATCAAATCTGTACAGT
 239
  23
transducer of
AA694240
1669


(SEQ ID NO: 61)


ERBB2, 2




 







Annexins and their ligands












GATCCTGTGCAACAAGA
   0
  69
annexin A10
BC007320
1670-1671


(SEQ ID NO: 62)







 


GATCTGTGGTGGCAATG
  41
 630
annexin A11
AL576782
1672


(SEQ ID NO: 63)







 


GATCAGAATCATGGTCT
   0
1079
annexin A2
BC001388
1673-1674


(SEQ ID NO: 64)







 


GATCTCTTTGACTGCTG
 210
 860
annexin A5
BC001429
1675-1676


(SEQ ID NO: 65)







 


GATCCAAAAACATCCTG
  83
 241
annexin A6
AI566871
1677


(SEQ ID NO: 66)







 


GATCAGAAGACTTTAAT
   0
 695
annexin A1
BC001275
1678-1679


(SEQ ID NO: 67)







 


GATCAGGACACTTAGCA
   0
2949
S100 calcium
BC015973
1680-1681


(SEQ ID NO: 68)


binding protein







A10 (annexin II







ligand)




 







Matrix metalloproteinase












GATCATCACAGTTTGAG
   0
  38
matrix
BC002591
1682-1683


(SEQ ID NO: 69)


metalloproteinase







10 







(stromelysin 2)




 


GATCCCAGAGAGCAGCT
   0
 108
matrix
BC013118
1684-1685


(SEQ ID NO: 70)


metalloproteinase 







1 (interstitial







collagenase)




 


GATCGGCCATCAAGGGA
   0
  25
matrix
AI370581
1686


(SEQ ID NO: 71)


metalloproteinase







13 







(collagenase 3)




 


GATCTGGACCAGAGACA
   0
  10
matrix
BG332150
1687


(SEQ ID NO: 72)


metalloproteinase 







2 (gelatinase A)









Matrix metalloproteinases (MMPs), which degrade extracellular matrix components that physically impede cell migration, are implicated in tumor cell growth, invasion, and metastasis. MMP1, 2, 10 and 13 were found to be significantly overexpressed in CL1 cells (Table 1), which may partially explain these cells' aggressive and metastatic behavior.
CD (cluster designation of monoclonal antibodies) markers are generally localized at the cell surface; some may be associated with prostate cancer (Liu, A. Y., et al., Prostate, 40: 192-199, 1999). All currently identified CD markers (CD1 to CD247) from the PROW CD index database (httpcolon double slash www dot ncbi Dot nlm dot nih dot gov/prow/guide/45277084 dot htm) were converted to UniGene numbers and the Unigene numbers used to identify their signatures and their expression levels. Fifteen CD markers were identified that were differentially expressed between LNCaP and CL1 cells (Z score<0.001) (Table 1). Eleven CD markers, including CD213a2 and CD213a1, which encode IL-13 receptors alpha 1 and 2, are up regulated in CL1 cells; three CD markers, CD9, CD10, and CD107, WERE downregulated in these cells (Table 1). Six CD markers went from 0 or 1 tpm to >35 tpm (Table 1), making them good digital or absolute markers or therapeutic targets. These data suggest that carefully selected CD markers may be useful in following the progression of prostate cancer, and indeed could serve as potential targets for antibody-mediated therapies (Liu, A. Y., et al., Prostate, 40: 192-199, 1999).
Delineation of Disease-perturbed Networks in Prostate Cancer Cells.
Genes and proteins rarely act alone but rather generally operate in networks of interactions. Identifying key nodes (proteins) in the disease-perturbed networks may provide insights into effective drug targets. Comparing the genes (proteins) currently available in the 314 BioCarta and 155 KEGG pathway or network (httpcolon double slash cgap dot nci dot nih dot gov/Pathways/) databases with the MPSS data through Unigene IDs, we identified 37 BioCarta and 14 KEGG pathways that are up regulated and 23 BioCarta and 22 KEGG pathways down regulated in LNCaP cells versus CL1 cells (Table 2). The number of genes whose expression patterns changed in each pathway is listed in Table 2. Each gene along with its expression level in LNCaP and CL1 cells is listed pathway by pathway in our database (ftp colon double slash ftp dot systemsbiology dot net/blin/mpss). Changes in these pathways reveal the underlying phenotypic differences between LNCaP and CL1 cells. For example, multiple networks involved in modulating cell mobility, adhesion and spreading are up regulated in CL1 cells, which are more metastatic and invasive than LNCaP cells (Table 2). In the uCalpain and Friends in Cell Spread pathway, calpains are calcium-dependent thiol proteases implicated in cytoskeletal rearrangements and cell migration. During cell migration, calpain cleaves target proteins such as talin, ezrin, and paxillin at the leading edge of the membrane, while at the same time cleaving the cytoplasmic tails of the integrins β1(a) and β3(b) to release adhesion attachments at the trailing membrane edge. Increased activity of calpains increases migration rates and facilitates cell invasiveness (Liu, A. et al., Prostate, 40: 192-199, 1999).







TABLE 2







PATHWAYS THAT ARE UP OR DOWN REGULATED


COMPARING LNCAP TO CL1 CELLS.












# Genes hits
# p < 0.001 &
# p < 0.001 &
# no


Pathways
in a pathway
LNCA > CL1
LNCA < CL1
change














Up-regulated Pathways in






LNCAP cells


BioCarta Pathways


Mechanism of Gene Regulation
35
9
2
24


by Peroxisome Proliferators via


PPARa alpha


T Cell Receptor Signaling
21
6
2
13


Pathway


ATM Signaling Pathway
15
5
2
8


CARM1 and Regulation of the
18
5
2
11


Estrogen Receptor


HIV-I Nef negative effector of
33
5
2
26


Fas and TNF


EGF Signaling Pathway
17
5
1
11


Role of BRCA1 BRCA2 and
16
5
1
10


ATR in Cancer Susceptibility


TNFR1 Signaling Pathway
17
5
1
11


Toll-Like Receptor Pathway
17
5
1
11


FAS signaling pathway CD95
17
4
1
12


VEGF Hypoxia and
16
4
1
11


Angiogenesis


Bone Remodelling
9
3
1
5


ER associated degradation
11
3
1
7


ERAD Pathway


Estrogen-responsive protein
11
3
1
7


Efp controls cell cycle and


breast tumors growth


Influence of Ras and Rho
16
3
1
12


proteins on G1 to S Transition


Inhibition of Cellular
13
3
1
9


Proliferation by Gleevec


Map Kinase Inactivation of
9
3
1
5


SMRT Corepressor


NFkB activation by
16
3
1
12


Nontypeable Hemophilus


influenzae


RB Tumor Suppressor
10
3
1
6


Checkpoint Signaling in


response to DNA damage


Transcription Regulation by
10
3
1
6


Methyltransferase of CARM1


Ceramide Signaling Pathway
13
4
0
9


Cystic fibrosis transmembrane
7
4
0
3


conductance regulator and beta


2 adrenergic receptor pathway


Nerve growth factor pathway
11
4
0
7


NGF


PDGF Signaling Pathway
16
4
0
12


TNF Stress Related Signaling
14
4
0
10


Activation of Csk by cAMP-
9
3
0
6


dependent Protein Kinase


Inhibits Signaling through the T


Cell Receptor


AKAP95 role in mitosis and
11
3
0
8


chromosome dynamics


Attenuation of GPCR Signaling
7
3
0
4


Chaperones modulate
11
3
0
8


interferon Signaling Pathway


ChREBP regulation by
12
3
0
9


carbohydrates and cAMP


IGF-1 Signaling Pathway
11
3
0
8


Insulin Signaling Pathway
11
3
0
8


NF-kB Signaling Pathway
11
3
0
8


Protein Kinase A at the
12
3
0
9


Centrosome


Regulation of ck1 cdk5 by type
10
3
0
7


1 glutamate receptors


Role of Mitochondria in
10
3
0
7


Apoptotic Signaling


Signal transduction through
14
3
0
11


IL1R


KEGG Pathways


Aminosugars metabolism
24
9
4
11


Androgen and estrogen
37
13
5
19


metabolism


Benzoate degradation via
5
3
1
1


hydroxylation


C21-Steroid hormone
4
1
0
3


metabolism


C5-Branched dibasic acid
2
2
0
0


metabolism


Carbazole degradation
1
1
0
0


Terpenoid biosynthesis
6
4
1
1


Chondroitin_heparan sulfate
14
8
3
3


biosynthesis


Fatty acid biosynthesis (path 1)
3
2
0
1


Fluorene degradation
3
2
0
1


Pentose and glucuronate
19
9
1
9


interconversions


Phenylalanine, tyrosine and
10
5
2
3


tryptophan biosynthesis


Porphyrin and chlorophyll
28
13
3
12


metabolism


Streptomycin biosynthesis
6
4
1
1


Up-regulated Pathways in


CL1 cells


BioCarta Pathways


Rho cell motility signaling
18
2
6
10


pathway


Trefoil Factors Initiate
14
1
6
7


Mucosal Healing


Integrin Signaling Pathway
14
1
5
8


Ca Calmodulin-dependent
7
1
4
2


Protein Kinase Activation


Effects of calcineurin in
9
1
4
4


Keratinocyte Differentiation


Angiotensin II mediated
12
1
3
8


activation of JNK Pathway via


Pyk2 dependent signaling


Bioactive Peptide Induced
16
1
3
12


Signaling Pathway


CBL mediated ligand-induced
6
1
3
2


downregulation of EGF


receptors


Control of skeletal myogenesis
12
1
3
8


by HDAC calcium


calmodulin-dependent kinase


CaMK


How does salmonella hijack a
8
1
3
4


cell


Melanocyte Development and
4
1
3
0


Pigmentation Pathway


Overview of telomerase protein
7
1
3
3


component gene hTert


Transcriptional Regulation


Regulation of PGC-1a
9
0
4
5


ADP-Ribosylation Factor
9
0
3
6


Downregulated of MTA-3 in
7
0
3
4


ER-negative Breast Tumors


Endocytotic role of NDK
7
0
3
4


Phosphins and Dynamin


Mechanism of Protein Import
7
0
3
4


into the Nucleus


Nuclear Receptors in Lipid
7
0
3
4


Metabolism and Toxicity


Pertussis toxin-insensitive
9
0
3
6


CCR5 Signaling in


Macrophage


Platelet Amyloid Precursor
5
0
3
2


Protein Pathway


Role of Ran in mitotic spindle
8
0
3
5


regulation


Sumoylation by RanBP2
8
0
3
5


Regulates Transcriptional


Repression


uCalpain and friends in Cell
5
0
3
2


spread


KEGG Pathways


Arginine and proline
45
7
16
22


metabolism


ATP synthesis
31
7
15
9


Biotin metabolism
5
1
3
1


Blood group glycolipid
12
1
6
5


biosynthesis - lactoseries


Cyanoamino acid metabolism
5
0
3
2


Ethylbenzene degradation
9
1
3
5


Ganglioside biosynthesis
16
2
6
8


Globoside metabolism
17
3
8
6


Glutathione metabolism
26
4
10
12


Glycine, serine and threonine
32
6
14
12


metabolism


Glycosphingolipid metabolism
35
6
18
11


Glycosylphosphatidylinositol(GPI)-
26
5
12
9


anchor biosynthesis


Glyoxylate and dicarboxylate
9
1
6
2


metabolism


Huntington's disease
25
4
10
11


Methane metabolism
9
1
3
5


O-Glycans biosynthesis
19
3
8
8


One carbon pool by folate
12
2
8
2


Oxidative phosphorylation
93
21
45
27


Parkinson's disease
30
5
14
11


Phospholipid degradation
21
4
12
5


Synthesis and degradation of
7
1
3
3


ketone bodies


Urea cycle and metabolism of
18
2
8
8


amino groups









Many pathways we identified as perturbed in the LNCaP and CL1 comparison are interconnected to form networks (in fact there are probably no discrete pathways, only networks). For example, the insulin signaling pathway, the signal transduction through IL1R pathway, NF-kB signaling pathway are interconnected through c-Jun, IL1R and NF-kB. The mapping of genes onto networks/pathways will be an ongoing objective as more networks/pathways become available. Our transcriptome data will be an invaluable resource in delineating these relationships.
As gene regulatory networks controlled by transcription factors form the top layer of the hierarchy that controls the physiological network, we sought to identify differentially expressed transcription factors. Of 554 transcription factors expressed in LNCaP and CL1 cells, 112 showed significantly different levels between the cell lines (P<0.001) This clearly demonstrated significant difference in the functioning of the corresponding gene regulatory networks during the progression of prostate cancer from the early to late stages.
Quantitative Proteomics Analysis of Prostate Cancer Cells. We quantitatively profiled the protein expression changes between LNCaP and CL1 cells using the ICAT-MS/MS protocol described by Han et al. Nat Biotechnol, 19: 946-951, 2001. To increase proteome coverage, cells were separated into nuclear, cytosolic and microsomal fractions prior to ICAT analysis as described in Han et al., 2001, supra. We generated a total of 142,849 tandem mass spectra, 7282 of which corresponded to peptides with a mass spectrum quality score P value (Keller, A., et al., Anal Chem. 2002 Oct. 15; 74(20):5383-92) greater than 0.9 (allowing unambiguous identification of peptides). These 7282 peptides represented 971 proteins (Keller, A., et al., 2002, supra). We obtained quantitative peptide ratios for 4583 peptides corresponding to 941 proteins. The number of peptides is greater than the number of proteins because 1) mass spectrometry identified multiple peptides from the same protein and 2) the ionization step of mass spectrometry created different charge states for the same peptide. The protein ratios were calculated from multiple peptide ratios using an algorithm for the automated statistical analysis of protein abundance ratios (ASAPRatio) (Li, X. J., et al., Anal Chem, 75: 6648-6657, 2003). In the end, we identified 82 proteins that are down regulated and 108 proteins that are up regulated by at least 1.8-fold in LNCaP cells compared with CL1 cells. For example, five proteins belong to annexins that were markers for prostate and other cancers (Hayes, M. J. and Moss, S. E. Biochem Biophys Res Commun, 322: 1166-1170, 2004), seven are involved in fatty acids and lipid metabolism that are involved in the carcinogenesis and progression of prostate cancer (Pandian, S. S., et al., J R Coll Surg Edinb, 44: 352-361, 1999), five are related to apoptosis, 11 are cancer related, and five proteins are putative transcription factors. As we only identified a limited number of proteins that are significantly differentially expressed due to low sensitivity of ICAT technology, we were only able to identify a few pathways that are perturbed based on ICAT data alone (using the stringent criteria discussed above). This also illustrated the importance of MPSS analysis described earlier.
103 of 190 (54%) differentially expressed proteins identified have enzymatic activity and hence many are involved in metabolism. Notably, many of the proteins identified are involved in fatty acid and lipid metabolism, including fatty acid synthase, carnitine palmitoyltransferase II and propionyl Coenzyme A carboxylase alpha polypeptide. Fatty acid and lipid metabolism is known to be perturbed in prostate cancer (Fleshner, N., et al., J Urol, 171: S19-24, 2004). Additionally, many genes involved in lipid transport were altered, including the annexins, prosaposin, and fatty acid binding protein 5. Annexin A1 has previously been shown to be overexpressed in non-PSA-producing LNCaP cells as compared with PSA-producing LNCaP cells (Vaarala, M. H., et al., 2000, supra) Annexin A7 is postulated to be a prostate tumor suppressor gene (Cardo-Vila, M., et al., Pharmacogenomics J, 1: 92-94, 2001). Annexin A2 expression is reduced or lost in prostate cancer cells, and its re-expression inhibits prostate cancer cell migration (Liu, J. W., et al., Oncogene, 22: 1475-1485, 2003).
Other genes identified here have been implicated in carcinogenesis, including tumor suppressor p16 and insulin-like growth factor 2 receptor (Chi, S. G., et al., Clin Cancer Res, 3: 1889-1897, 1997; Kiess, W., et al., Horm Res, 41 Suppl 2: 66-73, 1994). Some genes have previously been implicated in prostate cancer, such as prostate cancer over expressed gene 1 POV1, which is over expressed in prostate cancer (Cole, K. A., et al., Genomics, 51: 282-287, 1998), and delta 1 and alpha 1 catenin (cadherin-associated protein) and junction plakoglobin, which are down regulated in prostate cancer cells (Kallakury, B. V., et al., Cancer, 92: 2786-2795, 2001). However, the potential relationships of most of the proteins identified here to prostate cancer require further elucidation. For example, transmembrane protein 4 (TMEM4), a gene predicted to encode a 182-amino acid type II transmembrane protein, is downregulated about twofold in CL1 cells compared with LNCaP cells. MPSS data also indicated that TMEM4 is down regulated about twofold in CL1 cells. Many type II transmembrane proteins, such as TMPRSS2, are overexpressed in prostate cancer patients (Vaarala, M. H., et al., Int J Cancer, 94: 705-710, 2001). It will be interesting to see whether TMEM4 overexpression plays a primary role in prostate carcinogenesis. We also identified 12 proteins that have not been annotated or functionally characterized.
The mRNA expression level of eight proteins change from 0 tpm in LNCaP cells to greater than 50 tpm (we called them ‘digital changes’ because they go from zero to some expression) in CL1 cells, and that of one protein changed from 0 tpm in CL1 cells to greater than 50 in LNCaP cells. These genes can be used as digital diagnostic signals. Twenty-two of the differentially expressed proteins were predicted to be secreted proteins (See Table 3) and can be further evaluated as serum marker (see also Example 2 below).
Additionally, we sought to compare the expression at the protein level with that at the mRNA level. We converted the protein IDs and MPSS signatures to Unigene IDs to compare the MPSS data with the ICAT-MS/MS data. We limited this comparison to those with common Unigene IDs and with reliable ICAT ratios (standard deviation less than 0.5) and ended up with a subset of 79 proteins. Of these, 66 genes (83.5%) were concordant in their changes in mRNA and protein levels of expression and 13 genes (16.5%) were discordant, i.e. having higher protein expression but lower mRNA expression or vice versa. There are no functional similarities among the discordant genes. As these mRNAs and proteins are expressed at relatively high levels, discordance due to measurement errors is unlikely. Clearly posttranscriptional mechanism(s) of protein expression are functioning, although the elucidation of the specific mechanism(s) awaits further studies.
This system provides a model for studying perturbation of organ-specific molecular blood fingerprints. These results, and those described in the Examples below, indicate a systems approach will offer powerful tools for disease diagnostics, drug side effects diagnostics, and therapeutics.







TABLE 3







DIFFERENTIALLY EXPRESSED GENES THAT ENCODE PREDICTED


SECRETED PROTEINS.












SEQ ID
Accession
SEQ ID



Signature
NO:
Number
NOS:
Description





GATCAGCATGGGCCACG
 73
NM_001928
 594-595
D component of






complement (adipsin)


 


GATCTACTACTTGGCCT
 74
NM_006280
 596-597
signal sequence






receptor, delta






(translocon-






associated protein






delta)


 


GATCCTGTTGGGAAAGA
 75
NM_203329
 598-599
CD59 antigen p18-20






(antigen identified by






monoclonal






antibodies 16.3A5,






EJ16, EJ30, EL32






and G344)


 


GATCCTGTTGGGAAAGA
 76
NM_203331
 600-601
CD59 antigen p18-20






(antigen identified by






monoclonal






antibodies 16.3A5,






EJ16, EJ30, EL32






and G344)


 


GATCCCTGAAGTTGCCC
 77
NM_203331
 600-601
CD59 antigen p18-20






(antigen identified by






monoclonal






antibodies 16.3A5,






EJ16, EJ30, EL32






and G344)


 


GATCTTGGCTGTATTTA
 78
NM_203331
 600-601
CD59 antigen p18-20






(antigen identified by






monoclonal






antibodies 16.3A5,






EJ16, EJ30, EL32






and G344)


 


GATCCCTGAAGTTGCCC
 79
NM_203330
 602-603
CD59 antigen p18-20






(antigen identified by






monoclonal






antibodies 16.3A5,






EJ16, EJ30, EL32






and G344)


 


GATCCTGTTGGGAAAGA
 80
NM_203330
 602-603
CD59 antigen p18-20






(antigen identified by






monoclonal






antibodies 16.3A5,






EJ16, EJ30, EL32






and G344)


 


GATCTTGGCTGTATTTA
 81
NM_203330
 602-603
CD59 antigen p18-20






(antigen identified by






monoclonal






antibodies 16.3A5,






EJ16, EJ30, EL32






and G344)


 


GATCCCTGAAGTTGCCC
 82
NM_203329
 598-599
CD59 antigen p18-20






(antigen identified by






monoclonal






antibodies 16.3A5,






EJ16, EJ30, EL32






and G344)


 


GATCTTGGCTGTATTTA
 83
NM_000611
 604-605
CD59 antigen p18-20






(antigen identified by






monoclonal






antibodies 16.3A5,






EJ16, EJ30, EL32






and G344)


 


GATCCCTGAAGTTGCCC
 84
NM_000611
 604-605
CD59 antigen p18-20






(antigen identified by






monoclonal






antibodies 16.3A5,






EJ16, EJ30, EL32






and G344)


 


GATCCTGTTGGGAAAGA
 85
NM_000611
 604-605
CD59 antigen p18-20






(antigen identified by






monoclonal






antibodies 16.3A5,






EJ16, EJ30, EL32






and G344)


 


GATCTTGGCTGTATTTA
 86
NM_203329
 598-599
CD59 antigen p18-20






(antigen identified by






monoclonal






antibodies 16.3A5,






EJ16, EJ30, EL32






and G344)


 


GATCTGTGCTGACCCCA
 87
NM_002982
 606-607
chemokine (C-C






motif) ligand 2


 


GATCTCTTGGAATGACA
 88
NM_012242
 608-609
dickkopf homolog 1






(Xenopus laevis)


 


GATCACCATCAAGCCAG
 89
NM_012242
 608-609
dickkopf homolog 1






(Xenopus laevis)


 


GATCAAACAGCTCTAGT
 90
NM_016308
 610-611
UMP-CMP kinase


 


GATCCCCTGTTACGACA
 91
NM_014155
 612-613
HSPC063 protein


 


GATCTCTGATTACCAGC
 92
NM_025205
 614-615
mediator of RNA






polymerase II






transcription, subunit






28 homolog (yeast)


 


GATCATTGAACGAGACA
 93
NM_031903
 616-617
mitochondrial






ribosomal protein






L32


 


GATCACAGACCACGAGT
 94
NM_178507
 618-619
NS5ATP13TP2






protein


 


GATCTGCATCAGTTGTA
 95
NM_148170
 620-621
cathepsin C


 


GATCTCTTGCTAGATTT
 96
NM_005059
 622-623
relaxin 2


 


GATCACAAGGCTGCCTG
 97
NM_000405
 624-625
GM2 ganglioside






activator


 


GATCGTTTCTCATCTCT
 98
NM_006432
 626-627
Niemann-Pick






disease, type C2


 


GATCCCCGCGATACTTC
 99
NM_015921
 628-629
chromosome 6 open






reading frame 82


 


GATCTTTTTTTGGATAT
100
NM_181777
 630-631
ubiquitin-conjugating






enzyme E2A (RAD6






homolog)


 


GATCCGAGAGTAAGGAA
101
NM_032488
 632-633
cornifelin


 


GATCATGTGTTTCCATG
102
NM_014435
 634-635
N-acylsphingosine






amidohydrolase (acid






ceramidase)-like


 


GATCTCAGAACAACCTT
103
NM_016029
 636-637
dehydrogenase/






reductase






(SDR family)






member 7


 


GATCTTACCTCCTGATA
104
NM_020467
 638-639
hypothetical protein






from clone 643


 


GATCCCAGACTGGTTCT
105
NM_003782
 640-641
UDP-






Gal:betaGlcNAc beta






1,3-






galactosyltransferase,






polypeptide 4


 


GATCAAGTGCATTTGAC
106
NM_173631
 642-643
zinc finger protein






547


 


GATCAGTGCGTCATGGA
107
NM_005423
 644-645
trefoil factor 2






(spasmolytic protein






1)


 


GATCCAAGAGGAAGAAT
108
NM_014402
 646-647
low molecular mass






ubiquinone-binding






protein (9.5 kD)


 


GATCCAGCAAACAGGTT
109
NM_003851
 648-649
cellular repressor of






E1A-stimulated






genes 1


 


GATCATAGAAGGCTATT
110
NM_181834
 650-651
neurofibromin 2






(bilateral acoustic






neuroma)


 


GATCCCCCTTCATTTGA
111
NM_004862
 652-653
lipopolysaccharide-






induced TNF factor


 


GATCCCAAATTTGAAGT
112
NM_001685
 654-655
ATP synthase, H+






transporting,






mitochondrial F0






complex, subunit F6


 


GATCTGCTTTCTGTAAT
113
NM_002406
 656-657
mannosyl (alpha-1,3-)-






glycoprotein beta-






1,2-N-






acetylglucosaminyl-






transferase


 


GATCACTCCTTATTTGC
114
NM_019021
 658-659
hypothetical protein






FLJ20010


 


GATCACCTTCGACGACT
115
NM_003130
 660-661
sorcin


 


GATCTCTATTGTAATCT
116
NM_002489
 662-663
NADH






dehydrogenase






(ubiquinone) 1 alpha






subcomplex, 4, 9 kDa


 


GATCTCCTGGCTGCAAA
117
NM_138429
 664-665
claudin 15


 


GATCCCAGTCTCTGCCA
118
NM_201397
 666-667
glutathione






peroxidase 1


 


GATCTTCTTTATAATTC
119
NM_004048
 668-66
9beta-2-microglobulin


 


GATCTGTTCAAACAGCA
120
NM_024060
 670-671
hypothetical protein






MGC5395


 


GATCGTGCTCACAGGCA
121
NM_033280
 672-673
SEC11-like 3 (S.






cerevisiae)


 


GATCAATATGTAAATAT
122
NM_020199
 674-675
chromosome 5 open






reading frame 15


 


GATCAGCTTTGCTCCTG
123
NM_207495
 676-677
hypothetical protein






DKFZp686I15217


 


GATCTCTATGGCTGTAA
124
NM_033211
 678-679
hypothetical gene






supported by






AF038182;






BC009203


 


GATCTCAGAACCTCTGT
125
NM_001001436
 680-681
similar to RIKEN






cDNA 4921524J17


 


GATCCAGCCATTACTAA
126
NM_016205
 682-683
platelet derived






growth factor C


 


GATCTTTCCCAAGATTG
127
NM_001001434
 684-685
syntaxin 16


 


GATCGATTCTGTGACAC
128
NM_181726
 686-687
low density






lipoprotein receptor-






related protein






binding protein


 


GATCTATTTTTTCTAAA
129
NM_004125
 688-689
guanine nucleotide






binding protein (G






protein), gamma 10


 


GATCAAGAATCCTGCTC
130
NM_006332
 690-691
interferon, gamma-






inducible protein 30


 


GATCGGTGGAGAACCTC
131
NM_175742
 692-693
melanoma antigen,






family A, 2


 


GATCGGTGGAGAACCTC
132
NM_175743
 694-695
melanoma antigen,






family A, 2


 


GATCGGTGGAGAACCTC
133
NM_153488
 696-697
melanoma antigen,






family A, 2B


 


GATCATGGGTGAGGGGT
134
NM_001483
 698-699
glioblastoma






amplified sequence


 


GATCCCCCTCACCATGA
135
NM_032621
 700-701
brain expressed X-






linked 2


 


GATCAACTAATAGCTCT
136
NM_181892
 702-703
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAAATAAAGTTATA
137
NM_181892
 702-703
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAAGGAGACCCGGA
138
NM_024540
 704-705
mitochondrial






ribosomal protein






L24


 


GATCAAGGAGACCCGGA
139
NM_145729
 706-707
mitochondrial






ribosomal protein






L24


 


GATCCTAAGCCATAGAC
140
NM_025075
 708-709
Ngg1 interacting






factor 3 like 1






binding protein 1


 


GATCCATTGAGCCCAGC
141
NM_181725
 710-711
hypothetical protein






FLJ12760


 


GATCTGAGGGCGTCTTC
142
NM_012153
 712-713
ets homologous






factor


 


GATCTCGGTAGTTACGT
143
NM_012153
 712-713
ets homologous






factor


 


GATCCCAAGATGATTAA
144
NM_014177
 714-715
chromosome 18 open






reading frame 55


 


GATCTCAAACTTGTCTT
145
NM_003350
 716-717
ubiquitin-conjugating






enzyme E2 variant 2


 


GATCATAGTTATTATAC
146
NM_032466
 718-719
aspartate beta-






hydroxylase


 


GATCCCAACTGCTCCTG
147
NM_005947
 720-721
metallothionein 1B






(functional)


 


GATCAAAATGCTAAAAC
148
NM_016311
 722-723
ATPase inhibitory






factor 1


 


GATCTGTTTGTTCCCTG
149
NM_013411
 724-725
adenylate kinase 2


 


GATCAACAGTGGCAATG
150
NM_001001392
 726-727
CD44 antigen






(homing function and






Indian blood group






system)


 


GATCAATAATAATGAGG
151
NM_001001392
 726-727
CD44 antigen






(homing function and






Indian blood group






system)


 


GATCAACTAATAGCTCT
152
NM_181890
 728-729
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAAATAAAGTTATA
153
NM_181891
 730-731
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAAATAAAGTTATA
154
NM_181890
 728-729
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAAATAAAGTTATA
155
NM_181889
 732-733
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAACTAATAGCTCT
156
NM_003340
 734-735
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAACTAATAGCTCT
157
NM_181888
 736-737
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAAATAAAGTTATA
158
NM_181888
 736-737
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAACTAATAGCTCT
159
NM_181891
 730-731
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAACTAATAGCTCT
160
NM_181887
 738-739
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAAATAAAGTTATA
161
NM_181887
 738-739
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAACTAATAGCTCT
162
NM_181886
 740-741
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAAATAAAGTTATA
163
NM_181886
 740-741
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAAATAAAGTTATA
164
NM_003340
 734-735
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAACTAATAGCTCT
165
NM_181889
 732-733
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCTGATTTTTTCCCC
166
NM_145751
 742-743
TNF receptor-






associated factor 4


 


GATCAGAAATGACTGTG
167
NM_018509
 744-745
hypothetical protein






PRO1855


 


GATCACTGAGAAAAAAT
168
NM_152407
 746-747
GrpE-like 2,






mitochondrial (E.






coli)


 


GATCCAAGAGTTTAGTG
169
NM_006807
 748-749
chromobox homolog






1 (HP1 beta homolog






Drosophila)


 


GATCTTTGCTGGCAAGC
170
NM_002954
 750-751
ribosomal protein






S27a


 


GATCCACACTGAGAGAG
171
NM_145864
 752-753
kallikrein 3, (prostate






specific antigen)


 


GATCTGTATTATTAAAT
172
NM_032549
 754-755
IMP2 inner






mitochondrial






membrane protease-






like (S. cerevisiae)


 


GATCTGTTTGTTCCCTG
173
NM_172199
 756-757
adenylate kinase 2


 


GATCCCCTGCCTGGTGC
174
NM_001312
 758-759
cysteine-rich protein






2


 


GATCAACTAATAGCTCT
175
NM_181893
 760-761
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCAAATAAAGTTATA
176
NM_181893
 760-761
ubiquitin-conjugating






enzyme E2D 3






(UBC4/5 homolog,






yeast)


 


GATCTTTTTCAAGTCTT
177
NM_012071
 762-763
COMM domain






containing 3


 


GATCATGTATGAGATAG
178
NM_012460
 764-765
translocase of inner






mitochondrial






membrane 9 homolog






(yeast)


 


GATCCTTCAGGCAGTAA
179
NM_176805
 766-767
mitochondrial






ribosomal protein






S11


 


GATCTTTTTTTGGATAT
180
NM_003336
 768-769
ubiquitin-conjugating






enzyme E2A (RAD6






homolog)


 


GATCCCAGTCTCTGCCA
181
NM_000581
 770-771
glutathione






peroxidase 1


 


GATCAAGACGAGCCTGC
182
NM_004864
 772-773
growth differentiation






factor 15


 


GATCCCAGCTGATGTAG
183
NM_001885
 774-775
crystallin, alpha B


 


GATCATGAAGACCTGCT
184
NM_003754
 776-777
eukaryotic translation






initiation factor 3,






subunit 5 epsilon,






47 kDa


 


GATCTCAAGGTTGATAG
185
NM_003864
 778-779
sin3-associated






polypeptide, 30 kDa


 


GATCACCAGGCTGCCCA
186
NM_148571
 780-781
mitochondrial






ribosomal protein






L27


 


GATCAAAATGCTAAAAC
187
NM_178190
 782-783
ATPase inhibitory






factor 1


 


GATCAAGATGACACTGA
188
NM_004483
 784-785
glycine cleavage






system protein H






(aminomethyl carrier)


 


GATCGGGAACTCCTGCT
189
NM_005952
 786-787
metallothionein 1X


 


GATCTTGTCTTTAAAAC
190
NM_015646
 788-789
RAP1B, member of






RAS oncogene






family


 


GATCCACACACGTTGGT
191
NM_003255
 790-791
inhibitor of






metalloproteinase 2


 


GATCATCAGTCACCGAA
192
NM_000077
 792-793
cyclin-dependent






kinase inhibitor 2A






(melanoma, p16,






inhibits CDK4)


 


GATCCAGTATTCAGTCA
193
NM_002166
 794-795
inhibitor of DNA






binding 2, dominant






negative helix-loop-






helix protein


 


GATCCTTGCAGGGAGCT
194
NM_015343
 796-797
dullard homolog






(Xenopus laevis)


 


GATCTCCTTGCCCCAGC
195
NM_015343
 796-797
dullard homolog






(Xenopus laevis)


 


GATCGCCTAGTATGTTC
196
NM_003897
 798-799
immediate early






response 3


 


GATCAGACTGTATTAAA
197
NM_032052
 800-801
zinc finger protein






278


 


GATCGGCCCTACTAGAT
198
NM_032052
 800-801
zinc finger protein






278


 


GATCTCCCACTGCGGGG
199
NM_032052
 800-801
zinc finger protein






278


 


GATCTGTGATGGTCAGC
200
NM_000232
 802-803
sarcoglycan, beta






(43 kDa dystrophin-






associated






glycoprotein)


 


GATCACTGTGGTATCTA
201
NM_052822
 804-805
secretory carrier






membrane protein 1


 


GATCATCAGTCACCGAA
202
NM_058197
 806-807
cyclin-dependent






kinase inhibitor 2A






(melanoma, p16,






inhibits CDK4)


 


GATCATTTGTTTATTAA
203
NM_022334
 808-809
integrin beta 1






binding protein 1


 


GATCAAATATGTAAAAT
204
NM_004842
 810-811
A kinase (PRKA)






anchor protein 7


 


GATCTCTTGCTAGATTT
205
NM_134441
 812-813
relaxin 2


 


GATCACCTTCGACGACT
206
NM_198901
 814-815
sorcin


 


GATCGGATTGATTAAAA
207
NM_020353
 816-817
phospholipid






scramblase 4


 


GATCTAGTTGGGAGATA
208
NM_153367
 818-819
chromosome 10 open






reading frame 56


 


GATCTTTTTTGGCTACT
209
NM_018424
 820-821
erythrocyte






membrane protein






band 4.1 like 4B


 


GATCACATTTTCTGTTG
210
NM_201436
 822-823
H2A histone family,






member V


 


GATCACCTGGGTTTCTT
211
NM_021999
 824-825
integral membrane






protein 2B


 


GATCTATTAGATTCAAA
212
NM_021105
 826-827
phospholipid






scramblase 1


 


GATCTCTTATTTTACAA
213
NM_000546
 828-829
tumor protein p53






(Li-Fraumeni






syndrome)


 


GATCATAGAAGGCTATT
214
NM_181835
 830-831
neurofibromin 2






(bilateral acoustic






neuroma)


 


GATCTTCCTGGACAGGA
215
NM_152992
 832-833
POM (POM121






homolog, rat) and






ZP3 fusion


 


GATCAAGGACCGGCCCA
216
NM_032391
 834-835
small nuclear protein






PRAC


 


GATCGCATTTTTGTAAA
217
NM_058171
 836-837
inhibitor of growth






family, member 2


 


GATCCATCCTCATCTCC
218
NM_020188
 838-839
DC13 protein


 


GATCGATGGTGGCGCTT
219
NM_138992

beta-site APP-






cleaving enzyme 2


 


GATCTTATAAAAAGAAA
220
NM_017998
 840-841
chromosome 9 open






reading frame 40


 


GATCTGAACGATGCCGT
221
NM_024579
 842-843
hypothetical protein






FLJ23221


 


GATCTCCCCGCCGCAGC
222
NM_015973
 844-845
galanin


 


GATCGTCGTCCAGGCCA
223
NM_032920
 846-847
chromosome 21 open






reading frame 124


 


GATCGTTGGGGAACCCC
224
NM_199483
 848-849
chromosome 20 open






reading frame 24


 


GATCCTATATGTCCTGT
225
NM_152344
 850-851
hypothetical protein






FLJ30656


 


GATCGATGGTTGACAAT
226
NM_004552
 852-853
NADH






dehydrogenase






(ubiquinone) Fe-S






protein 5, 15 kDa






(NADH-coenzyme Q






reductase)


 


GATCTTGTACTAACTTA
227
NM_019059
 854-855
translocase of outer






mitochondrial






membrane 7 homolog






(yeast)


 


GATCCCGATGTTCTTAA
228
NM_001806
 856-857
CCAAT/enhancer






binding protein






(C/EBP), gamma


 


GATCCTGTTTAACAAAG
229
NM_015469
 858-859
nipsnap homolog 3A






(C. elegans)


 


GATCACGCACACACAAT
230
NM_198337
 860-861
insulin induced gene






1


 


GATCCAGCCAGACTTGC
231
NM_144772
 862-863
apolipoprotein A-I






binding protein


 


GATCCACACTGGAGAGA
232
NM_003450
 864-865
zinc finger protein






174


 


GATCTCAGTTCTGCGTT
233
NM_004642
 866-867
CDK2-associated






protein 1


 


GATCTACACCTCTTGCC
234
NM_052845
 868-869
methylmalonic






aciduria (cobalamin






deficiency) type B


 


GATCCAGCTGGAAAGCT
235
NM_006406
 870-871
peroxiredoxin 4


 


GATCCTTCAGGCAGTAA
236
NM_022839
 872-873
mitochondrial






ribosomal protein






S11


 


GATCCACACTGAGAGAG
237
NM_001648
 874-875
kallikrein 3, (prostate






specific antigen)


 


GATCACCTTATGGATGT
238
NM_003932
 876-877
suppression of






tumorigenicity 13






(colon carcinoma)






(Hsp70 interacting






protein)


 


GATCTAGTTATTTTAAT
239
NM_172178
 878-879
mitochondrial






ribosomal protein






L42


 


GATCATTGAGAATGCAG
240
NM_206966
 880-881
similar to AVLV472


 


GATCATGCCAAGTGGTG
241
NM_058248
 882-883
deoxyribonuclease II






beta


 


GATCACATTTTCTGTTG
242
NM_201516
 884-885
H2A histone family,






member V


 


GATCAGAAAGAAACCTT
243
NM_006744
 886-887
retinol binding






protein 4, plasma


 


GATCCGTGGCAGGGCTG
244
NM_031901
 888-889
mitochondrial






ribosomal protein






S21


 


GATCCGTGGCAGGGCTG
245
NM_018997
 890-891
mitochondrial






ribosomal protein






S21


 


GATCTATCACCCAAACA
246
NM_198157
 892-893
ubiquitin-conjugating






enzyme E2L 3


 


GATCAAGCGTGCTTTCC
247
NM_000995
 894-895
ribosomal protein






L34


 


GATCAAGCGTGCTTTCC
248
NM_033625
 896-897
ribosomal protein






L34


 


GATCCCTCATCCCTGAA
249
NM_014098
 898-899
peroxiredoxin 3


 


GATCCACCTTGGCCTCC
250
NM_147187
 900-901
tumor necrosis factor






receptor superfamily,






member 10b


 


GATCTTAGGGAGACAAA
251
NM_182529
 902-903
THAP domain






containing 5


 


GATCAAGATACGGAAGA
252
NM_177924
 904-905
N-acylsphingosine






amidohydrolase (acid






ceramidase) 1


 


GATCTGTTTGTTCCCTG
253
NM_001625
 906-907
adenylate kinase 2


 


GATCAGCAAAAGCCAAA
254
NM_201263
 908-909
tryptophanyl tRNA






synthetase 2






(mitochondrial)


 


GATCGGGGGAGGGTAAA
255
NM_004544
 910-911
NADH






dehydrogenase






(ubiquinone) 1 alpha






subcomplex, 10,






42 kDa


 


GATCGTGGAGGAGGGAC
256
NM_016310
 912-913
polymerase (RNA)






III (DNA directed)






polypeptide K, 12.3






kDa


 


GATCACTTTTGAAAGCA
257
NM_018465
 914-915
chromosome 9 open






reading frame 46


 


GATCTGATTTGCTAGTT
258
NM_015147
 916-917
KIAA0582


 


GATCCTAGGGGGTTTTG
259
NM_015147
 916-917
KIAA0582


 


GATCTAAGTTGCCTACC
260
NM_014176
 918-919
HSPC150 protein






similar to ubiquitin-






conjugating enzyme


 


GATCTTTGTTCTTGACC
261
NM_020531
 920-921
chromosome 20 open






reading frame 3


 


GATCTCTTAGCCAGAGG
262
NM_153333
 922-923
transcription






elongation factor A






(SII)-like 8


 


GATCTCTCTCACCTACA
263
NM_003287
 924-925
tumor protein D52-






like 1


 


GATCAGAGGTGAAGGGA
264
NM_007021
 926-927
chromosome 10 open






reading frame 10


 


GATCTCATTGATGTACA
265
NM_032947
 928-929
putative small






membrane protein






NID67


 


GATCTGTGCCGGCTTCC
266
NM_005656
 930-931
transmembrane






protease, serine 2


 


GATCCGTCTGTGCACAT
267
NM_005656
 930-931
transmembrane






protease, serine 2


 


GATCGGCTCTGGGAGAC
268
NM_006315
 932-933
ring finger protein 3


 


GATCGATTAATGAAGTG
269
NM_016326
 934-935
chemokine-like factor


 


GATCCTGGACTGGGTAC
270
NM_006830
 936-937
ubiquinol-






cytochrome c






reductase (6.4 kD)






subunit


 


GATCTTGGAGAATGTGA
271
NM_001216
 938-939
carbonic anhydrase






IX


 


GATCTTTTTTTGGATAT
272
NM_181762
 940-941
ubiquitin-conjugating






enzyme E2A (RAD6






homolog)


 


GATCTAGTTATTTTAAT
273
NM_014050
 942-943
mitochondrial






ribosomal protein






L42


 


GATCTAGTTATTTTAAT
274
NM_172177
 944-945
mitochondrial






ribosomal protein






L42


 


GATCAAGGGACGGCTGA
275
NM_000978
 946-947
ribosomal protein






L23


 


GATCAGAAGGCTCTGGT
276
NM_018442
 948-949
IQ motif and WD






repeats 1


 


GATCAATGTTGAAGAAT
277
NM_018442
 948-949
IQ motif and WD






repeats 1


 


GATCCTGCACTCTAACA
278
NM_203339
 950-951
clusterin






(complement lysis






inhibitor, SP-40,40,






sulfated glycoprotein






2, testosterone-






repressed prostate






message 2,






apolipoprotein J)


 


GATCTGATTATTTACTT
279
NM_004708
 952-953
programmed cell






death 5


 


GATCCTTGAAGGCAGCT
280
NM_197958
 954-955
acheron


 


GATCCCTTTTCTTACTA
281
NM_153713
 956-957
hypothetical protein






MGC46719


 


GATCTGTCCACTTCTGG
282
NM_153713
 956-957
hypothetical protein






MGC46719


 


GATCAGATACCACCAAG
283
NM_001001503
 958-959
NADH






dehydrogenase






(ubiquinone)






flavoprotein 3,






10 kDa


 


GATCCTTTGGATTAATC
284
NM_016138
 960-961
coenzyme Q7






homolog, ubiquinone






(yeast)


 


GATCATTATTTCTGTCT
285
NM_018184
 962-963
ADP-ribosylation






factor-like 10C


 


GATCAGCCCTCAAAGAA
286
NM_018184
 962-963
ADP-ribosylation






factor-like 10C


 


GATCAGCAAAAATAAAG
287
NM_016096
 964-965
HSPC038 protein


 


GATCTCAGCGGCATTAA
288
NM_052951
 966-967
deoxynucleotidyl-






tran sferase,






terminal, in-






teracting protein 1


 


GATCCCTGGAGTGCCTT
289
NM_003226
 968-969
trefoil factor 3






(intestinal)


 


GATCTGTTTCTACCAAT
290
NM_183045
 970-971
ring finger protein






(C3H2C3 type) 6


 


GATCCTGCTGTGAAAGG
291
NM_153750
 972-973
chromosome 21 open






reading frame 81


 


GATCTTGAAAGTGCCTG
292
NM_022130
 974-975
golgi phosphoprotein






3 (coat-protein)


 


GATCAATACAATAACAA
293
NM_003479
 976-977
protein tyrosine






phosphatase type






IVA, member 2


 


GATCTCCTATGAGAACA
294
NM_003479
 976-977
protein tyrosine






phosphatase type






IVA, member 2


 


GATCAATACAATAACAA
295
NM_080391
 978-979
protein tyrosine






phosphatase type






IVA, member 2


 


GATCTCCTATGAGAACA
296
NM_080391
 978-979
protein tyrosine






phosphatase type






IVA, member 2


 


GATCCAACCCTGTACTG
297
NM_177969
 980-981
protein phosphatase






1B (formerly 2C),






magnesium-






dependent, beta






isoform


 


GATCTCTACCATTTAAT
298
NM_001017
 982-983
ribosomal protein






S13


 


GATCCAGAAATACTTAA
299
NM_005410
 984-985
selenoprotein P,






plasma, 1


 


GATCCAATGCTAAACTC
300
NM_005410
 984-985
selenoprotein P,






plasma, 1


 


GATCAAATGAGAATAAA
301
NM_182620
 986-987
family with sequence






similarity 33,






member A


 


GATCCTTGCCACAAGAA
302
NM_004034
 988-989
annexin A7


 


GATCAGACTGTATTAAA
303
NM_032051
 990-991
zinc finger protein






278


 


GATCTCCCACTGCGGGG
304
NM_032051
 990-991
zinc finger protein






278


 


GATCGGCCCTACTAGAT
305
NM_032051
 990-991
zinc finger protein






278


 


GATCAAAAAGCAAGCAG
306
NM_015972
 992-993
polymerase (RNA) I






polypeptide D,






16 kDa


 


GATCACTTCAGCTGCCT
307
NM_019007
 994-995
armadillo repeat






containing, X-linked






6


 


GATCACCGACTGAAAAT
308
NM_002165
 996-997
inhibitor of DNA






binding 1, dominant






negative helix-loop-






helix protein


 


GATCAATGAAGTGAGAA
309
NM_003094
 998-999
small nuclear






ribonucleoprotein






polypeptide E


 


GATCATCTCAGAAGTCT
310
NM_018683
1000-
zinc finger protein





1001
313


 


GATCAGGAAGGACTTGT
311
NM_018683
1000-
zinc finger protein





1001
313


 


GATCATTCCCATTTCAT
312
NM_002583
1002-
PRKC, apoptosis,





1003
WT1, regulator


 


GATCGCTTTCTACACTG
313
NM_006926
1004-
surfactant,





1005
pulmonary-associated






protein A2


GATCAGTTAGCTTTTAT
314
NM_014335
1006-
CREBBP/EP300





1007
inhibitor 1


 


GATCAGTAGTTCAACAG
315
NM_175061
1008-
juxtaposed with





1009
another zinc finger






gene 1


GATCCGATAAGTTATTG
316
NM_004707
1010-
APG12 autophagy





1011
12-like (S. cerevisiae)


 


GATCAGTGGGCACAGTT
317
NM_006818
1012-
ALL1-fused gene





1013
from chromosome 1q


 


GATCAGTGCCAGAAGTC
318
NM_016303
1014-
WW domain binding





1015
protein 5


 


GATCAGAGAAGTAAGTT
319
NM_004871
1016-
golgi SNAP receptor





1017
complex member 1


 


GATCTCACTTTCCCCTT
320
NM_015373
1018-
PKD2 interactor,





1019
golgi and






endoplasmic






reticulum associated






1


 


GATCAGGCAGTTCCTGG
321
NM_213720
1020-
chromosome 22 open





1021
reading frame 16


 


GATCCTTGCCACAAGAA
322
NM_001156
1022-
annexin A7





1023



 


GATCAAGAAAAATAAGG
323
NM_000999
1024-
ribosomal protein





1025
L38


 


GATCGATTTCTTTCCTC
324
NM_021102
1026-
serine protease





1027
inhibitor, Kunitz






type, 2


 


GATCATAGAAGGCTATT
325
NM_181826
1028-
neurofibromin 2





1029
(bilateral acoustic






neuroma)


 


GATCCGGTGCGCCATGT
326
NM_002638
1030-
protease inhibitor 3,





1031
skin-derived






(SKALP)


 


GATCGCAGTTTGGAAAC
327
NM_005461
1032-
v-maf





1033
musculoaponeurotic






fibrosarcoma






oncogene homolog B






(avian)


 


GATCAATTTCAAACCCT
328
NM_005461
1032-
v-maf





1033
musculoaponeurotic






fibrosarcoma






oncogene homolog B






(avian)


 


GATCTCCTATGAGAACA
329
NM_080392
1034-
protein tyrosine





1035
phosphatase type






IVA, member 2


 


GATCAATACAATAACAA
330
NM_080392
1034-
protein tyrosine





1035
phosphatase type






IVA, member 2


 


GATCCTACCACCTACTG
331
NM_018281
1036-
hypothetical protein





1037
FLJ10948


 


GATCATTTGTTTATTAA
332
NM_004763
1038-
integrin beta 1





1039
binding protein 1


 


GATCAAAATGCTAAAAC
333
NM_178191
1040-
ATPase inhibitory





1041
factor 1


 


GATCTGGGGTGGGAGTA
334
NM_002773
1042-
protease, serine, 8





1043
(prostasin)


 


GATCATGCTTGTGTGAG
335
NM_018648
1044-
nucleolar protein





1045
family A, member 3






(H/ACA small






nucleolar RNPs)


 


GATCAAATATGTAAAAT
336
NM_138633
1046-
A kinase (PRKA)





1047
anchor protein 7


 


GATCAGACTTCTCAGCT
337
NM_006856
1048-
activating





1049
transcription factor 7


 


GATCATAGAAGGCTATT
338
NM_181827
1050-
neurofibromin 2





1051
(bilateral acoustic






neuroma)


 


GATCCACCTTGGCCTCC
339
NM_003842
1052-
tumor necrosis factor





1053
receptor superfamily,






member 10b


 


GATCTCTGGCCCCTCAG
340
NM_198527
1054-
Similar to RIKEN





1055
cDNA 1110033O09






gene


 


GATCCTCATTGAGCCAC
341
NM_024866
1056-
adrenomedullin 2





1057



 


GATCCAGTGGGGTCCGG
342
NM_002475
1058-
myosin light chain 1





1059
slow a


 


GATCATTTTGTATTAAT
343
NM_017544
1060-
NF-kappa B





1061
repressing factor


 


GATCAGAAAAAGAAAGA
344
NM_000982
1062-
ribosomal protein





1063
L21


 


GATCCTGTTCCTGTCAC
345
NM_203413
1064-
S-phase 2 protein





1065



 


GATCATGGTTCTCTTTG
346
NM_000202
1066-
iduronate 2-sulfatase





1067
(Hunter syndrome)


 


GATCCTCTGACCGCTGG
347
NM_022365
1068-
DnaJ (Hsp40)





1069
homolog, subfamily






C, member 1


 


GATCTGCTATTGCCAGC
348
NM_016399
1070-
hypothetical protein





1071
HSPC132


 


GATCCTGGAAATTGCAG
349
NM_001233
1072-
caveolin 2





1073



 


GATCAGTCTCAAGTGTC
350
NM_003702
1074-
regulator of G-





1075
protein signalling 20


 


GATCAGGTTAGCAAATG
351
NM_004331
1076-
BCL2/adenovirus





1077
E1B 19 kDa






interacting protein 3-






like


 


GATCAGTATGCTGTTTT
352
NM_004968
1078-
islet cell autoantigen





1079
1, 69 kDa


 


GATCTGGTTTCTAGCAA
353
NM_024096
1080-
XTP3-transactivated





1081
protein A


 


GATCTAATTAAATAAAT
354
NM_000903
1082-
NAD(P)H





1083
dehydrogenase,






quinone 1


 


GATCCTGGGTTTTTGTG
355
NM_017830
1084-
OCIA domain





1085
containing 1


 


GATCACCGACTGAAAAT
356
NM_181353
1086-
inhibitor of DNA





1087
binding 1, dominant






negative helix-loop-






helix protein


 


GATCAGGTAACCAGAGC
357
NM_002488
1088-
NADH





1089
dehydrogenase






(ubiquinone) 1 alpha






subcomplex, 2, 8 kDa


 


GATCAGTGAACACTAAC
358
NM_016645
1090-
mesenchymal stem





1091
cell protein DSC92


 


GATCTCAGATGCTAGAA
359
NM_016567
1092-
BRCA2 and





1093
CDKN1A interacting






protein


 


GATCGCTCTGCCCATGT
360
NM_016567
1092-
BRCA2 and





1093
CDKN1A interacting






protein


 


GATCAGCTCCGTGGGGC
361
NM_152398
1094-
OCIA domain





1095
containing 2


 


GATCATTGCCCAAAGTT
362
NM_152398
1094-
OCIA domain





1095
containing 2


 


GATCTGGCACTGTGGTT
363
NM_000998
1096-
ribosomal protein





1097
L37a


 


GATCTGGCACTGTGGGT
364
NM_000998
1096-
ribosomal protein





1097
L37a


 


GATCTCAGATGCTAGAA
365
NM_078468
1098-
BRCA2 and





1099
CDKN1A interacting






protein


 


GATCGCTCTGCCCATGT
366
NM_078468
1098-
BRCA2 and





1099
CDKN1A interacting






protein


 


GATCTGCTGTGGAATTG
367
NM_172316
1100-
Meis1, myeloid





1101
esotropic viral






integration site 1






homolog 2 (mouse)


 


GATCGTTCTTGATTTTG
368
NM_032476
1102-
mitochondrial





1103
ribosomal protein S6


 


GATCTTGGTTTCATGTG
369
NM_032476
1102-
mitochondrial





1103
ribosomal protein S6


 


GATCATTCTTGATTTTG
370
NM_032476
1102-
mitochondrial





1103
ribosomal protein S6


 


GATCCATATGGAAAGAA
371
NM_014171
1104-
postsynaptic protein





1105
CRIPT


 


GATCTGCCCCCACTGTC
372
NM_138929
1106-
diablo homolog





1107
(Drosophila)


 


GATCGCCTAGTATGTTC
373
NM_052815
1108-
immediate early





1109
response 3


 


GATCAATGCTAATATGA
374
NM_005805
1110-
proteasome





1111
(prosome,






macropain) 26S






subunit, non-ATPase,






14


 


GATCAGCATCAGGCTGT
375
NM_012459
1112-
translocase of inner





1113
mitochondrial






membrane 8 homolog






B (yeast)


 


GATCTGGAAGTGAAACA
376
NM_134265
1114-
WD repeat and SOCS





1115
box-containing 1


 


GATCCACGTGTGAGGGA
377
NM_182640
1116-
mitochondrial





1117
ribosomal protein S9


 


GATCACAGAAAAATTAA
378
NM_182640
1116-
mitochondrial





1117
ribosomal protein S9


 


GATCTCTCTGCGTTTGA
379
NM_012445
1118-
spondin 2,





1119
extracellular matrix






protein


 


GATCTCAGAAGTTTTGA
380
NM_138459
1120-
chromosome 6 open





1121
reading frame 68


 


GATCCGGACTTTTTAAA
381
NM_006339
1122-
high-mobility group





1123
20B


 


GATCATAGTTATTATAC
382
NM_032467
1124-
aspartate beta-





1125
hydroxylase


 


GATCCTGCCCTGCTCTC
383
NM_003145
1126-
signal sequence





1127
receptor, beta






(translocon-






associated protein






beta)


 


GATCGATTGAGAAGTTA
384
NM_012110
1128-
cysteine-rich





1129
hydrophobic domain






2


 


GATCCAAGTACTCTCTC
385
NM_175081
1130-
purinergic receptor





1131
P2X, ligand-gated ion






channel, 5


 


GATCATACACCTGCTCA
386
NM_001009
1132-
ribosomal protein S5





1133



 


GATCCTGGATGCCACGA
387
NM_174889
1134-
hypothetical protein





1135
LOC91942


 


GATCCCTGCCACAAGTT
388
NM_006923
1136-
stromal cell-derived





1137
factor 2


 


GATCAGACGAGGCCATG
389
NM_006107
1138-
cisplatin resistance-





1139
associated






overexpressed protein


 


GATCTTTCAGGAAAGAC
390
NM_033011
1140-
plasminogen





1141
activator, tissue


 


GATCTTTTAAAAATATA
391
NM_001914
1142-
cytochrome b-5





1143



 


GATCGTTTTGTTTTGTT
392
NM_021149
1144-
coactosin-like 1





1145
(Dictyostelium)


 


GATCTATGGCCTCTGGT
393
NM_021643
1146-
tribbles homolog 2





1147
(Drosophila)


 


GATCCTAAATCATTTTG
394
NM_022783
1148-
DEP domain





1149
containing 6


 


GATCTAAGAAGAAACTA
395
NM_005765
1150-
ATPase, H+





1151
transporting,






lysosomal accessory






protein 2


 


GATCTTGGTGTTCAAAA
396
NM_001497
1152-
UDP-





1153
Gal:betaGlcNAc beta






1,4-






galactosyltransferase,






polypeptide 1


 


GATCCCTCATCCCTGAA
397
NM_006793
1154-
peroxiredoxin 3





1155



 


GATCTGCAGTGCTTCAC
398
NM_178181
1156-
CUB domain-





1157
containing protein 1


 


GATCTATGCCCTTGTTA
399
NM_033167
1158-
UDP-





1159
Gal:betaGlcNAc beta






1,3-






galactosyltransferase,






polypeptide 3


 


GATCTATGCCCTTGTTA
400
NM_033169
1160-
UDP-





1161
Gal:betaGlcNAc beta






1,3-






galactosyltransferase,






polypeptide 3


 


GATCAGTTTATTATTGA
401
NM_033169
1160-
UDP-





1161
Gal:betaGlcNAc beta






1,3-






galactosyltransferase,






polypeptide 3


 


GATCTATGCCCTTGTTA
402
NM_033168
1162-
UDP-





1163
Gal:betaGlcNAc beta






1,3-






galactosyltransferase,






polypeptide 3


 


GATCAGTTTATTATTGA
403
NM_033167
1158-
UDP-





1159
Gal:betaGlcNAc beta






1,3-






galactosyltransferase,






polypeptide 3


 


GATCTATGCCCTTGTTA
404
NM_003781
1164-
UDP-





1165
Gal:betaGlcNAc beta






1,3-






galactosyltransferase,






polypeptide 3


 


GATCAGTTTATTATTGA
405
NM_003781
1164-
UDP-





1165
Gal:betaGlcNAc beta






1,3-






galactosyltransferase,






polypeptide 3


 


GATCAGTTTATTATTGA
406
NM_033168
1162-
UDP-





1163
Gal:betaGlcNAc beta






1,3-






galactosyltransferase,






polypeptide 3


 


GATCGAGTCAAGATGAG
407
NM_013442
1166-
stomatin (EPB72)-





1167
like 2


 


GATCACCATGATGCAGA
408
NM_031905
1168-
SVH protein





1169



 


GATCCCGTGTGTGTGTG
409
NM_031905
1168-
SVH protein





1169



 


GATCATGGTTCTCTTTG
410
NM_006123
1170-
iduronate 2-sulfatase





1171
(Hunter syndrome)


 


GATCCGCAGGCAGAAGC
411
NM_002775
1172-
protease, serine, 11





1173
(IGF binding)


 


GATCGATGGTGGCGCTT
412
NM_138991
1174-
beta-site APP-





1175
cleaving enzyme 2


 


GATCTGCATCAGTTGTA
413
NM_001814
1176-
cathepsin C





1177



 


GATCTCTACTACCACAA
414
NM_001908
1178-
cathepsin B





1179



 


GATCTCTACTACCACAA
415
NM_147780
1180-
cathepsin B





1181



 


GATCTCTACTACCACAA
416
NM_147781
1182-
cathepsin B





1183



 


GATCTCTACTACCACAA
417
NM_147782
1184-
cathepsin B





1185



 


GATCTCTACTACCACAA
418
NM_147783
1186-
cathepsin B





1187



 


GATCGATGGTGGCGCTT
419
NM_012105
1188-
beta-site APP-





1189
cleaving enzyme 2


 


GATCTTTCAGGAAAGAC
420
NM_000931
1190-
plasminogen





1191
activator, tissue


 


GATCAAATTGCAAAATA
421
NM_153705
1192-
KDEL (Lys-Asp-





1193
Glu-Leu) containing






2


 


GATCTTATTTTCTGAGA
422
NM_014584
1194-
ERO1-like (S.





1195
cerevisiae)


 


GATCCACAAGGCCTGAG
423
NM_001185
1196-
alpha-2-glycoprotein





1197
1, zinc


 


GATCTAGGCCTCATCTT
424
NM_016352
1198-
carboxypeptidase A4





1199



 


GATCCCTTTGAAATTTT
425
NM_001219
1200-
calumenin





1201



 


GATCTACAACATATAAA
426
NM_020648
1202-
twisted gastrulation





1203
homolog 1






(Drosophila)


 


GATCAGTTTTTTCACCT
427
NM_001901
1204-
connective tissue





1205
growth factor


 


GATCACAGTGTCAGAGA
428
NM_007224
1206-
neurexophilin 4





1207



 


GATCGTTACTATGTGTC
429
NM_004541
1208-
NADH





1209
dehydrogenase






(ubiquinone) 1 alpha






subcomplex, 1,






7.5 kDa


 


GATCATTGACCTCTGTG
430
NM_006459
1210-
SPFH domain family,





1211
member 1


 


GATCTGAAGCCCAGGTT
431
NM_024514
1212-
cytochrome P450,





1213
family 2, subfamily






R, polypeptide 1


 


GATCTGTTAAAAAAAAA
432
NM_147159
1214-
opioid receptor,





1215
sigma 1


 


GATCTTTCAGGAAAGAC
433
NM_000930
1216-
plasminogen





1217
activator, tissue


 


GATCATAAGACAATGGA
434
NM_001657
1218-
amphiregulin





1219
(schwannoma-






derived growth






factor)


 


GATCAGTCTTTATTAAT
435
NM_013995
1220-
lysosomal-associated





1221
membrane protein 2


 


GATCCAGGCTCACTGTG
436
NM_005250
1222-
forkhead box L1





1223



 


GATCAAATAATGCGACG
437
NM_018064
1224-
chromosome 6 open





1225
reading frame 166


 


GATCTTGGTTTTCCATG
438
NM_003000
1226-
succinate





1227
dehydrogenase






complex, subunit B,






iron sulfur (Ip)


 


GATCTGTTAGTCAAGTG
439
NM_005313
1228-
glucose regulated





1229
protein, 58 kDa


 


GATCATTTCTGGTAAAT
440
NM_005313
1228-
glucose regulated





1229
protein, 58 kDa


 


GATCAAAGCACTCTTCC
441
NM_005313
1228-
glucose regulated





1229
protein, 58 kDa


 


GATCATGCCAAGTGGTG
442
NM_021233
1230-
deoxyribonuclease II





1231
beta


 


GATCATCGCCTCCCTGG
443
NM_006216
1232-
serine (or cysteine)





1233
proteinase inhibitor,






clade E (nexin,






plasminogen






activator inhibitor






type 1), member 2


 


GATCACCAGGCTGCCCA
444
NM_016504
1234-
mitochondrial





1235
ribosomal protein






L27


 


GATCGGATGGGCAAGTC
445
NM_002178
1236-
insulin-like growth





1237
factor binding protein






6


 


GATCTCAAGACCAAAGA
446
NM_030810
1238-
thioredoxin domain





1239
containing 5


 


GATCTCACATTGTGCCC
447
NM_014254
1240-
transmembrane





1241
protein 5


 


GATCAGTCTTTATTAAT
448
NM_002294
1242-
lysosomal-associated





1243
membrane protein 2


 


GATCAGAGAAGATGATA
449
NM_000640
1244-
interleukin 13





1245
receptor, alpha 2


 


GATCAGGTAACCAGAGC
450
NM_000591
1246-
CD14 antigen





1247



 


GATCATCAGTAAATTTG
451
NM_031284
1248-
ADP-dependent





1249
glucokinase


 


GATCAATAAAATGTGAT
452
NM_002658
1250-
plasminogen





1251
activator, urokinase


 


GATCCCTCGGGTTTTGT
453
NM_006350
1252-
follistatin





1253



 


GATCTTGCAACTCCATT
454
NM_006350
1252-
follistatin





1253



 


GATCCAGCATGGAGGCC
455
NM_018664
1254- 
Jun dimerization





1255
protein p21SNFT


 


GATCATTGTGAAGGCAG
456
NM_001511
1256-
chemokine (C-X-C





1257
motif) ligand 1






(melanoma growth






stimulating activity,






alpha)


 


GATCTGCCAGCAGTGTT
457
NM_002004
1258-
farnesyl diphosphate





1259
synthase (farnesyl






pyrophosphate






synthetase,






dimethylallyl-






transtransferase,






geranyltrans-






transferase)


 


GATCAGAGGTTACTAGG
458
NM_006408
1260-
anterior gradient 2





1261
homolog (Xenopus






laevis)


 


GATCCACAGGGGTGGTG
459
NM_000602
1262-
serine (or cysteine)





1263
proteinase inhibitor,






clade E (nexin,






plasminogen






activator inhibitor






type 1), member 1


 


GATCACAAGGGGGGGAT
460
NM_016588
1264-
neuritin 1





1265



 


GATCTCTGTTTTGACTA
461
NM_004109
1266-
ferredoxin 1





1267



 


GATCTAACCTGGCTTGT
462
NM_004109
1266-
ferredoxin 1





1267



 


GATCAGCAAGTGTCCTT
463
NM_000935
1268-
procollagen-lysine, 2-





1269
oxoglutarate 5-






dioxygenase 2


 


GATCTAGTGGTTCACAC
464
NM_003236
1270-
transforming growth





1271
factor, alpha


 


GATCAAACAGTTTCTGG
465
NM_016139
1272-
coiled-coil-helix-





1273
coiled-coil-helix






domain containing 2


 


GATCATCAAGAAAAAAG
466
NM_018464
1274-
chromosome 10 open





1275
reading frame 70


 


GATCCCAGAGAGCAGCT
467
NM_002421
1276-
matrix





1277
metalloproteinase 1






(interstitial






collagenase)


 


GATCTTGTGTATTTTTG
468
NM_020440
1278-
prostaglandin F2





1279
receptor negative






regulator


 


GATCTATGTTCTCTCAG
469
NM_013363
1280-
procollagen C-





1281
endopeptidase






enhancer 2


 


GATCAGCAAGTGTCCTT
470
NM_182943
1282-
procollagen-lysine, 2-





1283
oxoglutarate 5-






dioxygenase 2


 


GATCATGTGCTACTGGT
471
NM_003172
1284-
surfeit 1





1285



 


GATCTGTAAATAAAATC
472
NM_130781
1286-
RAB24, member





1287
RAS oncogene






family


 


GATCAGGGCTGAGGGTA
473
NM_000157
1288-
glucosidase, beta;





1289
acid (includes






glucosylceramidase)


 


GATCCTCCTATGTTGTT
474
NM_005551
1290-
kallikrein 2, prostatic





1291



 


GATCAGAGATGCACCAC
475
NM_002997
1292-
syndecan 1





1293



 


GATCTGTCTGTTGCTTG
476
NM_005570
1294-
lectin, mannose-





1295
binding, 1


 


GATCACCATGAAAGAAG
477
NM_003873
1296-
neuropilin 1





1297



 


GATCTGTTAAAAAAAAA
478
NM_005866
1298-
opioid receptor,





1299
sigma 1


 


GATCAATTCCCTTGAAT
479
NM_138322
1300-
proprotein convertase





1301
subtilisin/kexin type






6


 


GATCCCAGACCAACCCT
480
NM_024642
1302-
UDP-N-acetyl-alpha-





1303
D-






galactosamine:poly-






peptide N-






acetylgalactosaminyl-






transferase 12






(GalNAc-T12)


 


GATCATCACAGTTTGAG
481
NM_002425
1304-
matrix





1305
metalloproteinase 10






(stromelysin 2)


 


GATCGGAACAGCTCCTT
482
NM_178154
1306-
fucosyltransferase 8





1307
(alpha (1,6)






fucosyltransferase)


 


GATCGGAACAGCTCCTT
483
NM_178155
1308-
fucosyltransferase 8





1309
(alpha (1,6)






fucosyltransferase)


 


GATCGGAACAGCTCCTT
484
NM_178156
1310-
fucosyltransferase 8





1311
(alpha (1,6)






fucosyltransferase)


 


GATCTGTGGGCCCAGTC
485
NM_004077
1312-
citrate synthase





1313



 


GATCAACCTTAAAGGAA
486
NM_000143
1314-
fumarate hydratase





1315



 


GATCTTCTACTTGCCTG
487
NM_000302
1316-
procollagen-lysine 1,





1317
2-oxoglutarate 5-






dioxygenase 1


 


GATCACCAGCCATGTGC
488
NM_004390
1318-
cathepsin H





1319



 


GATCACCGGAGGTCAGT
489
NM_016026
1320-
retinol





1321







dehydrogenase 11






(all-trans and 9-cis)


 


GATCTATTTTATGCATG
490
NM_020792
1322-
KIAA1363 protein





1323



 


GATCTGTTAAAAAAAAA
491
NM_147157
1324-
opioid receptor,





1325
sigma 1


 


GATCATTTTGGTTCGTG
492
NM_016417
1326-
chromosome 14 open





1327
reading frame 87


 


GATCACTTGTGTACGAA
493
NM_024641
1328-
mannosidase, endo-





1329
alpha


 


GATCCCTCCACCCCCAT
494
NM_001441
1330-
fatty acid amide





1331
hydrolase


 


GATCCAAAGTCATGTGT
495
NM_058172
1332-
anthrax toxin





1333
receptor 2


 


GATCCATAAATATTTAT
496
NM_058172
1332-
anthrax toxin





1333
receptor 2


 


GATCTGCCTGCATCCTG
497
NM_003225
1334-
trefoil factor 1 (breast





1335
cancer, estrogen-






inducible sequence






expressed in)


 


GATCCAGTGTCCATGGA
498
NM_007085
1336-
follistatin-like 1





1337



 


GATCAATTCCCTTGAAT
499
NM_138324
1338-
proprotein convertase





1339
subtilisinikexin type






6


 


GATCCGTGTGCTTGGGC
500
NM_018143
1340-
kelch-like 11





1341
(Drosophila)


 


GATCCAGGGTCCCCCAG
501
NM_004911
1342-
protein disulfide





1343
isomerase related






protein (calcium-






binding protein,






intestinal-related)


 


GATCATGGGACCCTCTC
502
NM_003032
1344-
sialyltransferase 1





1345
(beta-galactoside






alpha-2,6-






sialyltransferase)


 


GATCATGGGACCCTCTC
503
NM_173216
1346-
sialyltransferase 1





1347
(beta-galactoside






alpha-2,6-






sialyltransferase)


 


GATCTCACTGTTATTAT
504
NM_007115
1348-
tumor necrosis factor,





1349
alpha-induced protein






6


 


GATCCTGTATCCAAATC
505
NM_007115
1348-
tumor necrosis factor,





1349
alpha-induced protein






6


 


GATCAGTTTTCTCTTAA
506
NM_024769
1350-
adipocyte-specific





1351
adhesion molecule


 


GATCTACCAGATAACCT
507
NM_000522
1352-
homeo box A13





1353



 


GATCCTAGTAATTGCCT
508
NM_054034
1354-
fibronectin 1





1355



 


GATCAATGCAACGACGT
509
NM_006833
1356-
COP9 constitutive





1357
photomorphogenic






homolog subunit 6






(Arabidopsis)


 


GATCAATTCCCTTGAAT
510
NM_138325
1358-
proprotein convertase





1359
subtilisin/kexin type






6


 


GATCAATTCCCTTGAAT
511
NM_138323
1360-
proprotein convertase





1361
subtilisin/kexin type






6


 


GATCCCAGAGGGATGCA
512
NM_024040
1362-
CUE domain





1363
containing 2


 


GATCATCAAAAATGCTA
513
NM_017898
1364-
hypothetical protein





1365
FLJ20605


 


GATCCCTCGGGTTTTGT
514
NM_013409
1366-
follistatin





1367



 


GATCTTGCAACTCCATT
515
NM_013409
1366-
follistatin





1367



 


GATCTTGTTAATGCATT
516
NM_001873
1368-
carboxypeptidase E





1369



 


GATCAAAGGTTTAAAGT
517
NM_001627
1370-
activated leukocyte





1371
cell adhesion






molecule


 


GATCACCAAGATGCTTC
518
NM_018371
1372-
chondroitin beta1,4





1373
N-






acetylgalactosaminyl-






transferase


 


GATCAAATGTGCCTTAA
519
NM_014918
1374-
carbohydrate





1375
(chondroitin)






synthase 1


 


GATCTTCGGCCTCATTC
520
NM_017860
1376-
hypothetical protein





1377
FLJ20519


 


GATCCCTTCTGCCCTGG
521
NM_022367
1378-
sema domain,





1379
immunoglobulin






domain (Ig),






transmembrane






domain (TM) and






short cytoplasmic






domain,






(semaphorin) 4A


 


GATCCAACCGACTGAAT
522
NM_006670
1380-
trophoblast





1381
glycoprotein


 


GATCTCTGCAGATGCCA
523
NM_004750
1382-
cytokine receptor-like





1383
factor 1


 


GATCACAAAATGTTGCC
524
NM_001077
1384-
UDP





1385
glycosyltransferase 2






family, polypeptide






B17


 


GATCTCTCTTTCTCTCT
525
NM_031882
1386-
protocadherin alpha





1387
subfamily C, 1


 


GATCTCTCTTTCTCTCT
526
NM_031860
1388-
protocadherin alpha





1389
10


 


GATCTCTCTTTCTCTCT
527
NM_018906
1390-
protocadherin alpha 3





1391



 


GATCTCTCTTTCTCTCT
528
NM_031411
1392-
protocadherin alpha 1





1393



 


GATCACAGGCGTGAGCT
529
NM_032620
1394-
GTP binding protein





1395
3 (mitochondrial)


 


GATCAACATCTTTTCTT
530
NM_004343
1396-
calreticulin





1397



 


GATCTCTGATTTAACCG
531
NM_002185
1398-
interleukin 7 receptor





1399



 


GATCTCTCTTTCTCTCT
532
NM_031497
1400-
protocadherin alpha 3





1401



 


GATCCATTTTTAATGGT
533
NM_198278
1402-
hypothetical protein





1403
LOC255743


 


GATCTTTTCTAAATGTT
534
NM_005699
1404-
interleukin 18





1405
binding protein


 


GATCTCTCTTTCTCTCT
535
NM_031410
1406-
protocadherin alpha 1





1407



 


GATCGGTGCGTTCTCCT
536
NM_005561
1408-
lysosomal-associated





1409
membrane protein 1


 


GATCTTTTCTAAATGTT
537
NM_173042
1410-
interleukin 18





1411
binding protein


 


GATCTTTTCTAAATGTT
538
NM_173043
1412-
interleukin 18





1413
binding protein


 


GATCTCTCTTTCTCTCT
539
NM_031496
1414-
protocadherin alpha 2





1415



 


GATCCTGTTGGATGTGA
540
NM_080927
1416-
discoidin, CUB and





1417
LCCL domain






containing 2


 


GATCTCTCTTTCTCTCT
541
NM_031864
1418-
protocadherin alpha





1419
12


 


GATCTCTCTTTCTCTCT
542
NM_031849
1420-
protocadherin alpha 6





1421



 


GATCCTGTGCTTCTGCA
543
NM_006464
1422-
trans-golgi network





1423
protein 2


 


GATCTCTCTTTCTCTCT
544
NM_031865
1424-
protocadherin alpha





1425
13


 


GATCTGATGAAGTATAT
545
NM_022746
1426-
hypothetical protein





1427
FLJ22390


 


GATCACTTGTCTTGTGG
546
NM_006988
1428-
a disintegrin-like and





1429
metalloprotease






(reprolysin type) with






thrombospondin type






1 motif, 1


 


GATCTTTTCTAAATGTT
547
NM_173044
1430-
interleukin 18





1431
binding protein


 


GATCTCTCTTTCTCTCT
548
NM_031856
1432-
protocadherin alpha 8





1433



 


GATCTCTCTTTCTCTCT
549
NM_031500
1434-
protocadherin alpha 4





1435



 


GATCAGCACTGCCAGTG
550
NM_016592
1436-
GNAS complex locus





1437



 


GATCCGGAAAGATGAAT
551
NM_144640
1438-
interleukin 17





1439
receptor E


 


GATCTCTCTTTCTCTCT
552
NM_031501
1440-
protocadherin alpha 5





1441



 


GATCTCTCTTTCTCTCT
553
NM_031495
1442-
protocadherin alpha 2





1443



 


GATCTAATGTAAAATCC
554
NM_002354
1444-
tumor-associated





1445
calcium signal






transducer 1


 


GATCTTCTTTTGTAATG
555
NM_032780
1446-
transmembrane





1447
protein 25


 


GATCAATAATAATGAGG
556
NM_001001390
1448-
CD44 antigen





1449
(homing function and






Indian blood group






system)


 


GATCAACAGTGGCAATG
557
NM_001001390
1448-
CD44 antigen





1449
(homing function and






Indian blood group






system)


 


GATCAACAGTGGCAATG
558
NM_001001391
1450-
CD44 antigen





1451
(homing function and






Indian blood group






system)


 


GATCAATAATAATGAGG
559
NM_001001391
1450-
CD44 antigen





1451
(homing function and






Indian blood group






system)


 


GATCATTGCTCCTTCTC
560
NM_004872
1452-
chromosome 1 open





1453
reading frame 8


 


GATCTCTGCATTTTATA
561
NM_020198
1454-
GK001 protein





1455



 


GATCTATGAAATCTGTG
562
NM_020198
1454-
GK001 protein





1455



 


GATCTCTCTTTCTCTCT
563
NM_018901
1456-
protocadherin alpha





1457
10


 


GATCACTGGAGCTGTGG
564
NM_002116
1458-
major





1459
histocompatibility






complex, class I, A


 


GATCATCCAGTTTGCTT
565
NM_004540
1460-
neural cell adhesion





1461
molecule 2


 


GATCAAAATTGTTACCC
566
NM_004540
1460-
neural cell adhesion





1461
molecule 2


 


GATCAACAGTGGCAATG
567
NM_001001389
1462-
CD44 antigen





1463
(homing function and






Indian blood group






system)


 


GATCAATAATAATGAGG
568
NM_001001389
1462-
CD44 antigen





1463
(homing function and






Indian blood group






system)


 


GATCAACAGTGGCAATG
569
NM_000610
1464-
CD44 antigen





1465
(homing function and






Indian blood group






system)


 


GATCAATAATAATGAGG
570
NM_000610
1464-
CD44 antigen





1465
(homing function and






Indian blood group






system)


 


GATCCATACTGTTTGGA
571
NM_001792
1466-
cadherin 2, type 1, N-





1467
cadherin (neuronal)


 


GATCTGCATTTTCAGAA
572
NM_015544
1468-
DKFZP564K1964





1469
protein


 


GATCCCATTTTTTGGTA
573
NM_000574
1470-
decay accelerating





1471
factor for






complement (CD55,






Cromer blood group






system)


 


GATCTGCAGTGCTTCAC
574
NM_022842
1472-
CUB domain-





1473
containing protein 1


 


GATCTGTTAAAAAAAAA
575
NM_147160
1474-
opioid receptor,





1475
sigma 1


 


GATCATAGGTCTGGACA
576
NM_014045
1476-
low density





1477
lipoprotein receptor-






related protein 10


 


GATCTAATACTACTGTC
577
NM_001110
1478-
a disintegrin and





1479
metalloproteinase






domain 10


 


GATCTCTTGAGGCTGGG
578
NM_016371
1480-
hydroxysteroid (17-





1481
beta) dehydrogenase






7


 


GATCGTTCATTGCCTTT
579
NM_001746
1482-
calnexin





1483



 


GATCTCTCTTTCTCTCT
580
NM_018900
1484-
protocadherin alpha 1





1485



 


GATCTGACCTGGTGAGA
581
NM_004393
1486-
dystroglycan 1





1487
(dystrophin-






associated






glycoprotein 1)


 


GATCATCTTTCCTGTTC
582
NM_002117
1488-
major





1489
histocompatibility






complex, class I, C


 


GATCGTAAAATTTTAAG
583
NM_003816
1490-
a disintegrin and





1491
metalloproteinase






domain 9 (meltrin






gamma)


 


GATCTCTCTTTCTCTCT
584
NM_018904
1492-
protocadherin alpha





1493
13


 


GATCTCTCTTTCTCTCT
585
NM_018911
1494-
protocadherin alpha 8





1495



 


GATCTCTCTTTCTCTCT
586
NM_018905
1496-
protocadherin alpha 2





1497



 


GATCTCTCTTTCTCTCT
587
NM_018903
1498-
protocadherin alpha





1499
12


 


GATCTCTCTTTCTCTCT
588
NM_018907
1500-
protocadherin alpha 4





1501



 


GATCTCTCTTTCTCTCT
589
NM_018908
1502-
protocadherin alpha 5





1503



 


GATCCGGAAAGATGAAT
590
NM_153480
1504-
interleukin 17





1505
receptor E


 


GATCCGGAAAGATGAAT
591
NM_153483
1506-
interleukin 17





1507
receptor E


 


GATCTCTGTAATTTTAT
592
NM_021923
1508-
fibroblast growth





1509
factor receptor-like 1


 


GATCTAAGAGATTAATA
593
NM_004362
1510-
calmegin





1511









Example 2
Identification of Secreted Proteins by Computational Analysis of MPSS Signature Sequences
Secreted proteins can readily be exploited for blood cancer diagnosis and prognosis. As such, the differentially expressed genes identified in Example 1 were further analyzed to determine how many of the differentially expressed genes encode secreted proteins. Proteins with signal peptides (classical secretory proteins) were predicted using the same criteria described by Chen et al., Mamm Genome, 14: 859-865, 2003, with the SignalP 3.0 server developed by The Center for Biological Sequence Analysis, Lyngby, Denmark (httpcolon double slash www dot cbs dot dtu dot dk/services/SignalP-3.0; see also, J. D. Bendtsen, et al., J. Mol. Biol., 340:783-795, 2004.) and the TMHMM2.0 server (see for example A. Krogh, et al., Journal of Molecular Biology, 305(3):567-580, January 2001; E. L. L. Sonnhammer, et al., In J. Glasgow, T. Littlejohn, F. Major, R. Lathrop, D. Sankoff, and C. Sensen, editors, Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology, pages 175-182, Menlo Park, Calif., 1998. AAAI Press). Putatively nonclassical secretory secreted proteins (without signal peptides) were predicted based on the SecretomeP 1.0 server, (httpcolon double slash www dot cbs dot dtu dot dk/services/SecretomeP-1.0/) and required an odds ratio score>3.
Five hundred and twenty one signatures belonging to 460 genes potentially encoding secreted proteins (Table 3) were identified. Among these, 287 (259 genes) and 234 (201 genes) signatures were overexpressed or underexpressed in CL1 cells compared with LNCaP cells. Thus these proteins can be used in blood diagnostics to follow prostate cancer progression. Additionally, these proteins can be used in other settings, such as for identifying drug side effects.
Example 3
Prostate Cancer Diagnostics Using Multiparameter Analysis
This example describes a multiparameter diagnostic fingerprint using the WDR19 prostate-specific secreted protein in combination with PSA. The WDR19 prostate-specific protein is diagnostically superior to PSA when used alone and further improved prostate cancer detection when used in combination with PSA.
WDR19 was previously identified as relatively tissue-specific by cDNA array studies and Northern blot analysis (see e.g., U.S. Patent Application Publication No. 20020150893). This protein was selected, expressed as protein, purified and antibodies were made against it, all using standard techniques known in the art (the cDNA encoding the WDR19 protein is provided in SEQ ID NO:1, the amino acid sequence is provided in SEQ ID NO:2). The WDR19-specific antibody was shown to be an excellent tissue-specific marker of prostate cancer with staining of the specific epithelial cells being directly proportional to the progression of the cancer. In this regard it is very different from the well-established PSA marker which is not a good prostate tissue cancer marker.
The WDR19 antibodies and those for the well-established PSA prostate cancer blood marker were used to analyze 10 blood samples from normal individuals, 10 blood samples from early prostate cancer patients and 10 blood samples from late prostate cancer patients. The results showed that WDR19 reacted against no normals, against 5/10 early cancers, and against 5/10 late cancers, whereas PSA reacted against no normals, no early cancers and 7/10 late cancers. The two markers together detected all the late cancers. Thus the multiparameter analysis of blood markers (e.g. the analyses of multiple markers) for prostate cancer was far more powerful than using each marker alone.
Accordingly, the results show a molecular blood fingerprint that comprises the WDR19 and PSA proteins. This fingerprint allows superior diagnostic power to PSA alone and further improves prostate cancer detection.
WDR19 was also shown to be an effective histochemical marker for prostate cancer. Two hundred and seventy-five tissue cores that contain both stromal and epithelial cells from cancer patients, 17 from benign prostatic hyperplasia (BPH) and 12 from normal individuals were examined. The mean WDR19 protein staining intensities were 2.52 [standard error (S.E.), 0.05; 95% confidence interval (CI), 2.41-2.61] for prostate cancer; 1.03 BPH (S.E. 0.03; 95% CI, 0.96-1.09); and 1.0 (S.E., 0, 95% CI 1.0-1.0) for normal individuals. Pair-wise comparisons (using independent t-test) demonstrated that WDR19 staining intensity is significantly different between prostate cancer and BPH (mean difference 1.49; P<0.0001) and between prostate cancers and normal (mean difference 1.52; P<0.0001). These data suggested that WDR19, in addition to being a prostate-specific blood biomarker, is a quantitative cancer-specific marker for prostate tissues.
Example 4
Identification of Organ-Specific Secreted Proteins Using MPSS and Computational Analysis
MPSS as described in Example 1 and in the detailed description, was used to identify more than 2 million transcripts from each of the prostate cell lines (see Example 1) and in normal prostate tissue. The MPSS signature sequences from normal prostate were compared against 29 other tissues each with about 1 million or more mRNA transcripts. This comparison revealed that about 300 of these transcripts are organ-specific and about 60 of these organ-specific transcripts are potentially secreted into the blood. (See Table 4).







TABLE 4







PROSTATE-SPECIFIC PROTEINS POTENTIALLY


SECRETED INTO BLOOD









Accession
SEQ ID



No.
NO:
Annotations/Description





NP_001176
1512
alpha-2-glycoprotein 1, zinc; Alpha-2-glycoprotein, zinc




[Homo sapiens]


NP_001719
1513
basigin isoform 1; OK blood group; collagenase




stimulatory factor; M6 antigen; extracellular matrix




metalloproteinase inducer [Homo sapiens]


NP_940991
1514
basigin isoform 2; OK blood group; collagenase




stimulatory factor; M6 antigen; extracellular matrix




metalloproteinase inducer [Homo sapiens]


NP_004039
1515
beta-2-microglobulin precursor [Homo sapiens]


NP_002434
1516
beta-microseminoprotein isoform a precursor; seminal




plasma beta-inhibin; prostate secreted seminal plasma




protein; immunoglobulin binding factor; prostatic




secretory protein 94 [Homo sapiens]


NP_619540
1517
beta-microseminoprotein isoform b precursor; seminal




plasma beta-inhibin; prostate secreted seminal plasma




protein; immunoglobulin binding factor; prostatic




secretory protein 94 [Homo sapiens]


NP_817089
1518
cadherin-like 26 isoform a; cadherin-like protein VR20




[Homo sapiens]


NP_068582
1519
cadherin-like 26 isoform b; cadherin-like protein VR20




[Homo sapiens]


NP_001864
1520
carboxypeptidase E precursor [Homo sapiens]


NP_004807
1521
chromosome 9 open reading frame 61; Friedreich ataxia




region gene X123 [Homo sapiens]


NP_001271
1522
cold inducible RNA binding protein; Cold-inducible




RNA-binding protein; cold inducible RNA-binding




protein; glycine-rich RNA binding protein [Homo




sapiens]


NP_008977
1523
elastin microfibril interfacer 1; TNF? elastin microfibril




interface located protein; elastin microfibril interface




located protein [Homo sapiens]


NP_004104
1524
fibroblast growth factor 12 isoform 2; fibroblast growth




factor 12B; fibroblast growth factor homologous factor 1;




myocyte-activating factor; fibroblast growth factor FGF-




12b [Homo sapiens]


NP_005962
1525
FXYD domain containing ion transport regulator 3




isoform 1 precursor; phospholemman-like protein; FXYD




domain-containing ion transport regulator 3 [Homo




sapiens]


NP_068710
1526
FXYD domain containing ion transport regulator 3




isoform 2 precursor; phospholemman-like protein; FXYD




domain-containing ion transport regulator 3 [Homo




sapiens]


NP_006352
1527
homeo box B13; homeobox protein HOX-B13 [Homo




sapiens]


NP_002139
1528
homeo box D10; homeobox protein Hox-D10; homeo box




4D; Hox-4


NP_000513
1529
homeobox protein A13; homeobox protein HOXA13;




homeo box 1J; transcription factor HOXA13 [Homo




sapiens]


NP_060819
1530
hypothetical protein FLJ11175 [Homo sapiens]


NP_078985
1531
hypothetical protein FLJ14146 [Homo sapiens]


NP_061894
1532
hypothetical protein FLJ20010 [Homo sapiens]


NP_115617
1533
hypothetical protein FLJ23544; QM gene; DNA segment




on chromosome X (unique) 648 expressed sequence; 60S




ribosomal protein L10; tumor suppressor QM; Wilms




tumor-related protein; laminin receptor homolog [Homo




sapiens]


NP_057582
1534
hypothetical protein HSPC242 [Homo sapiens]


NP_116285
1535
hypothetical protein MGC14388 [Homo sapiens]


NP_116293
1536
hypothetical protein MGC14433 [Homo sapiens]


NP_077020
1537
hypothetical protein MGC4309 [Homo sapiens]


NP_061074
1538
hypothetical protein PRO1741 [Homo sapiens]


NP_563614
1539
hypothetical protein similar to KIAA0187 gene product




[Homo sapiens]


NP_951038
1540
I-mfa domain-containing protein isoform p40 [Homo




sapiens]


NP_005542
1541
kallikrein 2, prostatic isoform 1; glandular kallikrein 2




[Homo sapiens]


NP_004908
1542
kallikrein 4 preproprotein; protease, serine, 17; enamel




matrix serine protease 1; kallikrein-like protein 1; protase;




androgen-regulated message 1 [Homo sapiens]


NP_002328
1543
low density lipoprotein receptor-related protein associated




protein 1; lipoprotein receptor associated protein; alpha-2-




MRAP; alpha-2-macroglobulin receptor-associated




protein 1; low density lipoprotein-related protein-




associated protein 1; low density li


NP_859077
1544
low density lipoprotein receptor-related protein binding




protein [Homo sapiens]


NP_000897
1545
natriuretic peptide receptor A/guanylate cyclase A




(atrionatriuretic peptide receptor A); Natriuretic peptide




receptor A/guanylate cyclase A [Homo sapiens]


NP_085048
1546
Nedd4 family interacting protein 1; Nedd4 WW domain-




binding protein 5 [Homo sapiens]


NP_000896
1547
neuropeptide Y [Homo sapiens]


NP_039227
1548
olfactory receptor, family 10, subfamily H, member 2




[Homo sapiens]


NP_000599
1549
orosomucoid 2; alpha-1-acid glycoprotein, type 2 [Homo




sapiens]


NP_002643
1550
prolactin-induced protein; prolactin-inducible protein




[Homo sapiens]


NP_057674
1551
prostate androgen-regulated transcript 1 protein; prostate-




specific and androgen-regulated cDNA 14D7 protein




[Homo sapiens]


NP_001639
1552
prostate specific antigen isoform 1 preproprotein; gamma-




seminoprotein; semenogelase; seminin; P-30 antigen




[Homo sapiens]


NP_665863
1553
prostate specific antigen isoform 2; gamma-




seminoprotein; semenogelase; seminin; P-30 antigen




[Homo sapiens]


NP_001090
1554
prostatic acid phosphatase precursor [Homo sapiens]


NP_001000
1555
ribosomal protein S5; 40S ribosomal protein S5 [Homo




sapiens]


NP_005658
1556
ring finger protein 103; Zinc finger protein expressed in




cerebellum; zinc finger protein 103 homolog (mouse)




[Homo sapiens]


NP_937761
1557
ring finger protein 138 isoform 2 [Homo sapiens]


NP_002998
1558
semenogelin I isoform a preproprotein [Homo sapiens]


NP_937782
1559
semenogelin I isoform b preproprotein [Homo sapiens]


XP_353669
1560
similar to HIC protein isoform p32 [Homo sapiens]


NP_003855
1561
sin3 associated polypeptide p30 [Homo sapiens]


NP_036581
1562
six transmembrane epithelial antigen of the prostate; six




transmembrane epithelial antigen of the prostate (NOTE:




non-standard symbol and name) [Homo sapiens]


NP_008868
1563
SMT3 suppressor of mif two 3 homolog 2; SMT3




(suppressor of mif two 3, yeast) homolog 2 [Homo




sapiens]


NP_066568
1564
solute carrier family 15 (H+/peptide transporter), member




2 [Homo sapiens]


NP_055394
1565
solute carrier family 39 (zinc transporter), member 2




[Homo sapiens]


NP_003209
1566
telomeric repeat binding factor 1 isoform 2; Telomeric




repeat binding factor 1; telomeric repeat binding protein 1




[Homo sapiens]


NP_110437
1567
thioredoxin domain containing 5 isoform 1; thioredoxin




related protein; endothelial protein disulphide isomerase




[Homo sapiens]


NP_004863
1568
thymic dendritic cell-derived factor 1; liver membrane-




bound protein [Homo sapiens]


NP_665694
1569
TNF receptor-associated factor 4 isoform 2; tumor




necrosis receptor-associated factor 4A; malignant 62;




cysteine-rich domain associated with ring and TRAF




domain [Homo sapiens]


NP_005647
1570
transmembrane protease, serine 2; epitheliasin [Homo




sapiens]


NP_008931
1571
uroplakin 1A [Homo sapiens]


NP_036609
1572
WW domain binding protein 1 [Homo sapiens]


NP_009062
1573
zinc finger protein 75 [Homo sapiens]









Example 5
Comparison of Localized Prostate Cancer and Prostate Cancer Metastases in the Liver
In an additional experiment, the transcriptome from normal prostate tissue was compared to the transcriptome of each of the LNCaP and CL-1 prostate cancer cell lines. The comparison showed that the transcriptomes were distinct for the normal tissue, the early prostate cancer and the late prostate cancer. An additional comparison was carried out between localized prostate cancer and metastases in the liver. About 6,000 genes were identified that were significantly changed between the localized prostate cancer and the metastasized cancer and again, many of the changed genes encoded secreted proteins that can be part of the blood fingerprints indicative of the more advanced disease status of metastases. The metastases-altered blood fingerprints may indicate the site of metastases.
These experiments demonstrate that there are continuous changes in the two types of networks as prostate cancer progresses—from localized to androgen independence to metastases. These graded network transitions suggest that one will be able to detect the very earliest stages of prostate cancer and, accordingly, that the organ-specific, molecular blood fingerprints approach described herein will permit a very early diagnosis of prostate and other types of cancers. These experiments further support the notion that the drug-related side effects that impact the prostate can be identified and monitored using the organ-specific, molecular blood fingerprints.
Example 6
MPSS Analysis in a Yeast Model System
This experiment demonstrates perturbation-specific fingerprints of patterns of gene expression for nuclear, cytoplasmic, membrane-bound and secreted proteins in the yeast metabolic system that converts the sugar galactose into glucose-6-phosphate (the gal system).
The gal systems includes 9 proteins. In the course of studying how this systems works, 9 new strains of yeast were created, each with a different one of the 9 relevant genes destroyed (gene knockouts). Yeast is a single celled eukaryote organism with about 6,000 genes. The expression patterns of each of the 6,000 genes was studied in the wild type yeast and each of the 9 knockout strains. The data from these experiments showed: 1) the wild type and each of knock out strains exhibited statistically significant changes in patterns of gene expression from the wild type strain ranging from 89 to 465 altered patterns of gene expression; 2) each of these patterns of changed gene expression were unique; and 3) on average about 15% of the genes with changed expression patterns encoded proteins that were potentially secreted (as determined by computational analysis from the sequence of the gene). These genes are as follows: (listed by gene name as available through the public yeast genome database at http colon double slash www dot yeastgenome dot org/. The genomic DNA, cDNA and amino acid sequences corresponding to each of the listed genes are publicly available, for example, through the yeast genome database.) YGL102C, YGL069C, YLL044W, YMR321c, YKL153W, YMR195W, YHL015W, YNL096C, YGRO30C, YDR123c, YKL186C, YOR234c, YKL001C, YJL188C, YDL023C, YPL143W, YEL039C, YKL006W, YGR280C, YBR285W, YKR091W, YDR064W, YBR047W, YGR243W, YOR309c, YDR461W, YHR053c, YHR055c, YGR148c, YGL187C, YIL018W, YFR003c, YPL107W, YBR185c, YNR014W, YJL067W, YDR451c, YGL031C, YHR141c, YNL162W, YBR046C, YNL036W, YDL136W, YDL191W, YLR257W, YNL057W, YGL068W, YKR057W, YLR201c, YHL001W, YDR010C, YPL138C, YOR312c, YPL276W, YML114C, YLR327c, YBR191W, YOR257W, YOR096W, YPL223C, YJL136C, YAL044C, YER079W, YMR107W, YPL079W, YDR175c, YGRO35C, YDR153c, YDR337W, YOR167c, YMR194W, YOR194c, YHR090C, YGR110W, YMR242c, YHR198c, YPL177C, YLR164W, YMR143W, YDL083C, YLR325c, YOR203W, YMR193W, YLR062c, YOR383c, YLR300W, YJL079C, YJL158C, YHR139c, YGL032C, YER150W, YNL160W, YDR382W, YMR305c, YKL096W, YKR013W, YCL043C, YLR042c, YDR055W, YPL163C, YEL040W, YJL171C, YLR121c, YDR382W, YLR250W, YGR189c, YJL159W, YMR215W, YDR519W, YIL162W, YKL163W, YDR518W, YDR534c, YPR157W, YML130C, YML128C, YBR092C, YDR032c, YLR120C, YBR093C, YHR215W, YAR071W, YDL130W, YDR144c, YPR123c, YGR174c, YOR327c, YNL058C, YGR265W, YGR160W, YIL117C, YOL053W, YGR236c, YGRO60W, YKL120W, YDL046W, YHR132c, YMR058W, YLR332W, YKR061W, YEL001C, YKL154W, YKL073W, YMR238W, YJR020W, YIL136W, YHL028W, YDL010W, YLR339c, YNL217W, YHR063c.
The different knockout strains can be thought of as analogous to genetic disease mutants. Accordingly, these data further support the notion that each disease has a unique expression fingerprint and that each disease generates unique collections of secreted proteins that constitute molecular fingerprints capable of identifying the corresponding disease.
Example 7
Identification of Prostate-Specific/Enriched Genes Using a 2.5 Fold Over-Expression Cut-Off
Organ specific/enriched expression can be determined by the ratio of the expression (e.g., measured in transcripts per million (tpm)) in a particular organ as compared to other organs. In this example, prostate enriched/specific expression was analyzed by comparing the expression level (tpm counts) of MPSS signature sequences identified from normal prostate tissue to their corresponding expression levels in 33 normal tissues. A particular gene that demonstrated at least a 2.5-fold increase in expression in prostate as compared to all tissues examined (each tissue evaluated individually) was considered to be prostate-specific/enriched. The tissues examined were adrenal gland, bladder, bone marrow, brain (amygdala, caudate nucleus, cerebellum, corpus callosum, hypothalamus, and thalamus), whole fetal brain, heart, kidney, liver (new cloning), lung, mammary gland, monocytes, peripheral blood lymphocytes, pituitary gland, placenta, pancreas, prostate, retina, spinal cord, salivary gland, small intestine, stomach, spleen, testis, thymus, trachea, thyroid, and uterus. This analysis identified 109 unique genes (with mpss signature sequence belonging to class 1-4, i.e. with confirmed match to cDNAs) whose expression was at least 2.5 fold that observed in other normal tissues. The list of prostate-specific/enriched genes is provided in Tables 5A-5D with the expression level in tpm in prostate shown. This list includes KLK2, KLK3, KLK4, TMPRSS2, which are genes previously shown to be prostate-specific.







TABLE 5A







PROSTATE ENRICHED GENES IDENTIFIED BY RATIO SCHEMA (RATIO > 2.5)*













MPSS Sig.







SEQ ID

Genbank
Genbank SEQ ID
Tissue Names


MPSS Signature
NO:
Name
Accession No.
NOs:
Description





GATCTCAGAACAACCTT
1688
DHRS7
BC000637
1797-1798
Dehydrogenase/reductase (SDR







family) member 7


 


GATCCAGCCCAGAGACA
1689
NPY
BCO29497
1799-1800
Neuropeptide Y


 


GATCACTCCTTATTTGC
1690
FLJ20010
AW172826
1801
Hypothetical protein FLJ20010


 


GATCCCTCTCCTCTCTG
1691
C9orf61
BI771919
1802
Chromosome 9 open reading







frame 61


 


GATCTGACTTTTTACTT
1692
Lrp2bp
BU853306
1803
Ankyrin repeat domain 37


 


GATCGTTAGCCTCATAT
1693
HOXB13
BC007092
1804-1805
Homeo box B13


 


GATCACAAGGAATCCTG
1694
CREB3L4
BC038962
1806-1807
CAMP responsive element







binding protein 3-like 4


 


GATCTCATGGATGATTA
1695
LEPREL1
BC005029
1808-1809
Leprecan-like 1


 


GATCCAGAAATAAAGTC
1696
KLK4
CB051271
1810
Kallikrein 4 (prostase, enamel







matrix, prostate)


 


GATCTCACAGAAGATGT
1697
MGC35558
NM_145013
1811-1812
Chromosome 11 open reading







frame 45


 


GATCCAAAATCACCAAG
1698
HAX1
BU157155
1813
HCLS1 associated protein X-1


 


GATCCTGGGCTGGAAGG
1699
0
AW207206
1814
Hypothetical gene supported by







AY338954


 


GATCCAGATGCAGGACT
1700
0
BC013389
1815
LOC440156


 


GATCTGTGCTCATCTGT
1701
TMEM16G
BCO28162
1816-1817
Transmembrane protein 16G


 


GATCATTTTATATCAAT
1702
MGC31963
BX099160
1818
Chromosome 1 open reading







frame 85


 


GATCCACACTGAGAGAG
1703
KLK3
BC005307
1819-1820
Kallikrein 3, (prostate specific







antigen)


 


GATCCGTCTGTGCACAT
1704
TMPRSS2
NM_005656
1821-1822
Transmembrane protease, serine







2


 


GATCATTGTAGGGTAAC
1705
LOC221442
BCO26923
1823
Hypothetical protein







LOC221442


 


GATCAGCCCTCAAAAAA
1706
ARL10C
BU159800
1824
ADP-ribosylation factor-like 8B


 


GATCTGGATTCAGGACC
1707
MGC13102
NM_032323
1825-1826
Hypothetical protein







MGC13102


 


GATCAAAAATAAAATGT
1708
0
A1954252
1827
Hypothetical gene supported by







AK022914; AK095211;







BC016035; BC041856;







BX248778


 


GATCCGCTCTGGTCAAC
1709
SEPX1
BQ941313
1828
Selenoprotein X, 1


 


GATCCCTCAAGACTGGT
1710
ACPP
BC007460
1829-1830
Acid phosphatase, prostate


 


GATCCACAAAGACGAGG
1711
BIN3
BI911790
1831
Bridging integrator 3


 


GATCTCTCTGCGTTTGA
1712
SPON2
BC002707
1832-1833
Spondin 2, extracellular matrix







protein


 


GATCTCAACCTCGCTTG
1713
0
AK026938
1834
Hypothetical gene supported by







AL713796


 


GATCAAGTTCCCGCTGG
1714
RPL18A
BG818587
1835
Ribosomal protein L18a


 


GATCATAATGAGGTTTG
1715
ABCC4
NM_005845
1836-1837
ATP-binding cassette, sub-







family C (CFTR/MRP), member







4


 


GATCGGTGACATCGTAA
1716
RPS11
AA888242
1838
Ribosomal protein Sll


 


GATCCACCAGCTGATAA
1717
NSEP1
CN353139
1839
Y box binding protein 1


 


GATCAACACACTTTATT
1718
F1122955
AA256381
1840
Hypothetical protein F1122955


 


GATCCCTTCCTTCCTCT
1719
HOXD11
AA513505
1841
Homeo box Dll


 


GATCAGGACACAGACTT
1720
ORM1
BG564253
1842
Orosomucoid 1


 


GATCCTGCAATCTTGTA
1721
HTPAP
AI572087
1843
Phosphatidic acid phosphatase







type 2 domain containing 1B


 


GATCCTCCTATGTTGTT
1722
KLK2
AA259243
1844
Kallikrein 2, prostatic


 


GATCTGTACCTTGGCTA
1723
SLC2A12
AI675682
1845
Solute carrier family 2







(facilitated glucose transporter),







member 12


 


GATCGGGGCAAGAGAGG
1724
NDRG1
NM_006096
1846-1847
N-myc downstream regulated







gene 1


 


GATCCCCTCCCCTCCCC
1725
NPR1
NM_000906
1848-1849
Natriuretic peptide receptor







A/guanylate cyclase A







(atrionatriuretic peptide receptor







A)


 


GATCCTACAAAGAAGGA
1726
F1121511
NM_025087
1850-1851
Hypothetical protein F1121511


 


GATCATTTGCAGTTAAG
1727
FOXA1
NM_004496
1852-1853
Forkhead box A1


 


GATCTGTCTCCTGCTCT
1728
ENPP3
AI535878
1854
Ectonucleotide







pyrophosphatase/phosphodiester-







ase 3


 


GATCCTTCCCAAGGTAC
1729
GATA2
NM_032638
1855-1856
GATA binding protein 2


 


GATCTTGTTGAAGTCAA
1730
ARG2
BX331427
1857
Arginase, type II


 


GATCGCACCACTGTACA
1731
XPO1
AI569484
1858
Exportin 1 (CRM1 homolog,







yeast)


 


GATCATTTTCTGCTTTA
1732
ASB3
BC009569
1859-1860
Ankyrin repeat and SOCS box-







containing 3


 


GATCCCCACACTTGTCC
1733
0
AK000028
1861
Hypothetical LOC90024


 


GATCTGGAATTGTCATA
1734
KLF3
BX100634
1862
Kruppel-like factor 3 (basic)


 


GATCAATAAGCTTTAAA
1735
TGM4
BC007003
1863-1864
Transglutaminase 4 (prostate)


 


GATCAATGTTTGTAGAT
1736
FLJ16231
NM_001008401
1865-1866
FLJ16231 protein


 


GATCTACATGTCTATCA
1737
BLNK
BX113323
1867
B-cell linker


 


GATCTGTTTTAAATGAG
1738
SLC14A1
NM_015865
1868-1869
Solute carrier family 14 (urea







transporter), member 1 (Kidd







blood group)


 


GATCAAAAAATGCTGCA
1739
PTPLB
AI017286
1870
Protein tyrosine phosphatase-







like (proline instead of catalytic







arginine), member b


 


GATCATGTCTTCATTTT
1740
OR51E2
NM_030774
1871-1872
Olfactory receptor, family 51,







subfamily E, member 2


 


GATCCCTCCACCCCCAT
1741
FAAH
NM_001441
1873-1874
Fatty acid amide hydrolase


 


GATCCTAAGCCATAAAT
1742
STAT6
AL044554
1875
Signal transducer and activator







of transcription 6, interleukin-4







induced


 


GATCATCGTCCTCATCG
1743
ANKH
CB049466
1876
Ankylosis, progressive homolog







(mouse)


 


GATCATCATTTGTCATT
1744
DSCR1L2
AW575747
1877
Down syndrome critical region







gene 1-like 2


 


GATCTAATTTGAAAAAC
1745
TRPM8
NM_024080
1878-1879
Transient receptor potential







cation channel, subfamily M,







member 8


 


GATCTTCCTTGTATCAT
1746
TMC4
AV724505
1880
Transmembrane channel-like 4


 


GATCTCCCCCATGCCTG
1747
ZNF589
BC005859
1881-1882
Zinc finger protein 589


 


GATCAAATTTAGTATTT
1748
LRRK1
BC005408
1883-1884
Leucine-rich repeat kinase 1


 


GATCTGCCTTATAAACA
1749
STEAP2
AA177004
1885
Six transmembrane epithelial







antigen of the prostate 2


 


GATCAGAAAATGAGCTC
1750
SAFB2
BC001216
1886
Scaffold attachment factor B2


 


GATCACCGTGGAGGTTA
1751
CPE
BG707154
1887
Carboxypeptidase E


 


GATCCCTCTGTGCTTCT
1752
GNB2L1
AA024878
1888
Guanine nucleotide binding







protein (G protein), beta







polypeptide 2-like 1


 


GATCTCATTTTTAGAGC
1753
LOC92689
BU688574
1889
Hypothetical protein BC001096


 


GATCATCACATTTCGTG
1754
DLG1
BC042118
1890
Discs, large homolog 1







(Drosophila)


 


GATCATTTTCTGCTTCA
1755
SEMG1
NM_003007
1891-1892
Semenogelin I


 


GATCAATGAAGGAGAGA
1756
SPATA13
BM875598
1893
Spermatogenesis associated 13


 


GATCCCAACTACTCGGG
1757
LOC157657
NM_177965
1894-1895
Chromosome 8 open reading







frame 37


 


GATCAGTTTTTCTGTAA
1758
KIAA1411
CA433208
1896
KIAA1411


 


GATCAAAATTTTAAAAA
1759
MGC20781
BM984931
1897
5′-nucleotidase, cytosolic III-like


 


GATCACCCTTCTCTTCC
1760
LOC255189
BC035335
1898-1899
Phospholipase A2, group IVF


 


GATCCTGGGTACTGAAA
1761
ERBB2
BC080193
1900
V-erb-b2 erythroblastic







leukemia viral oncogene







homolog 2, neuro/glioblastoma







derived oncogene homolog







(avian)


 


GATCGTTCTAAGAGTGT
1762
ZFP64
NM_199427
1901-1902
Zinc finger protein 64 homolog







(mouse)


 


GATCATCATCAAGGGCT
1763
SUHW2
BC042370
1903
Suppressor of hairy wing







homolog 2 (Drosophila)


 


GATCAAAATGATTTTCA
1764
ELOVL7
AL137506
1904-1905
ELOVL family member 7,







elongation of long chain fatty







acids (yeast)


 


GATCTGATTTTTTTCCC
1765
TRAF4
AI888175
1906
TNF receptor-associated factor 4


 


GATCCCATTTCTCACCC
1766
SLC39A2
AI669751
1907
Solute carrier family 39 (zinc







transporter), member 2


 


GATCCTCCCGCCTTGCC
1767
HNF4G
AI088739
1908
Hepatocyte nuclear factor 4,







gamma


 


GATCTTTCTTTTTTTGT
1768
SLC22A3
BC070300
1909
Solute carrier family 22







(extraneuronal monoamine







transporter), member 3


 


GATCTTAACTGTCTCCT
1769
HIST2H2BE
BC005827
1910
Histone 2, H2be


 


GATCAGTTTGATTCTGT
1770
AMD1
BC041345
1911-1912
Adenosylmethionine







decarboxylase 1


 


GATCATGATGTAGAGGG
1771
TYMS
BX390036
1913
Thymidylate synthetase


 


GATCGCACCACTACAGT
1772
PHC3
AK022455
1914
Polyhomeotic like 3







(Drosophila)


 


GATCTCAAAGTGCCTTC
1773
SARG
AL832940
1915-1916
Chromosome 1 open reading







frame 116


 


GATCAATGTCAAACTTC
1774
MTERF
BC000965
1917-1918
Mitochondrial transcription







termination factor


 


GATCTCCCAGAGTCTAA
1775
CYP4F8
NM 007253
1919-1920
Cytochrome P450, family 4,







subfamily F, polypeptide 8


 


GATCCTGATGGCTGTGT
1776
PPAP2A
AK124401
1921
Phosphatidic acid phosphatase







type 2A


 


GATCACTTCCCGCAGTC
1777
KIAA0056
BC011408
1922-1923
KIAA0056 protein


 


GATCTCAAAGGAACCAA
1778
MSMB
AA469293
1924
Microseminoprotein, beta-


 


GATCTGTGCCAGGGTTA
1779
VEGF
AK056914
1925
Vascular endothelial growth







factor


 


GATCTCTTTTTATTTAA
1780
CDH1
NM 004360
1926-1927
Cadherin 1, type 1, E-cadherin







(epithelial)


 


GATCTCCAGCACCAATC
1781
TARP
BC062761
1928-1929
TCR gamma alternate reading







frame protein


 


GATCTGGCGCTTGGGGG
1782
RFP2
NM_001007278
1930-1931
Ret finger protein 2


 


GATCCCGACGGGGGCAT
1783
MESP1
NM 018670
1932-1933
Mesoderm posterior 1 homolog







(mouse)


 


GATCCCGGGCCGTTATC
1784
TRPM4
AA026974
1934
Transient receptor potential







cation channel, subfamily M,







member 4


 


GATCTTTCTCAAAATAT
1785
PAK1IP1
A1468032
1935
PAK1 interacting protein 1


 


GATCGTGACGCTTAATA
1786
HNRPA1
CF122297
1936
Heterogeneous nuclear







ribonucleoprotein Al


 


GATCGCATAATTTTTAA
1787
ZNF207
CB053869
1937
Zinc finger protein 207


 


GATCCCAACACTGAAGG
1788
WNK4
NM_032387
1938-1939
WNK lysine deficient protein







kinase 4


 


GATCTTAAAAACTGCAG
1789
APXL2
BQ448015
1940
Apical protein 2


 


GATCATTTTTTCTATCA
1790
MED28
A1554477
1941
Mediator of RNA polymerase II







transcription, subunit 28







homolog (yeast)


 


GATCCCATTGTGTGTAT
1791
LOC285300
AK095655
1942
Hypothetical protein







LOC285300


 


GATCTCAAAGGAAAAAA
1792
0
AW291753
1943
Transcribed locus


 


GATCTTCTGTTATATTT
1793
0
BM023121
1944
Full length insert cDNA clone







ZD79H10


 


GATCCACAACATACAGC
1794
0
AY338953
1945
Prostate-specific P712P mRNA







sequence


 


GATCTGTGCAGTTGTAA
1795
0
AY533562
1946
KLK16 mRNA, partial sequence


 


GATCTACTATGCCAAAT
1796
0
BC030554
1947
(clone HGT25) T cell receptor







gamma-chain mRNA, V region





*ratio of prostate expression in tpm to other organs greater than 2.5













TABLE 5B







PROSTATE ENRICHED GENES IDENTIFIED


BY RATIO SCHEMA (RATIO >2.5)*















SignalP3.0



Genbank


prediction


Genbank
SEQ ID

SignalP3.0 prediction
Signal peptide


Accession No.
NOs:
Name
Prediction
probability














BC000637
1797-1798
DHRS7
Signal peptide
0.999


BC029497
1799-1800
NPY
Signal peptide
0.998


AW172826
1801
FLJ20010
Non-secretory protein
0.001


BI771919
1802
C9orf61
Signal peptide
0.994


BU853306
1803
Lrp2bp
Non-secretory protein
0


BC007092
1804-1805
HOXB13
Non-secretory protein
0


BC038962
1806-1807
CREB3L4
Non-secretory protein
0


BC005029
1808-1809
LEPREL1
Signal peptide
0.995


CB051271
1810
KLK4
Signal peptide
0.988


NM_145013
1811-1812
MGC35558
Signal peptide
0.935


BU157155
1813
HAX1
Non-secretory protein
0.001


AW207206
1814
0
Non-secretory protein
0.001


BC013389
1815
0
Non-secretory protein
0


BC028162
1816-1817
TMEM16G
Non-secretory protein
0.001


BX099160
1818
MGC31963
Signal peptide
0.994


BC005307
1819-1820
KLK3
Signal peptide
0.992


NM_005656
1821-1822
TMPRSS2
Non-secretory protein
0


BC026923
1823
LOC221442
Signal anchor
0.01


BU159800
1824
ARL10C
Non-secretory protein
0


NM_032323
1825-1826
MGC13102
Non-secretory protein
0


AI954252
1827
0
Non-secretory protein
0.128


BQ941313
1828
SEPX1
Non-secretory protein
0


BC007460
1829-1830
ACPP
Signal peptide
1


BI911790
1831
BIN3
Non-secretory protein
0


BC002707
1832-1833
SPON2
Signal peptide
0.998


AK026938
1834
0
Signal peptide
0.587


BG818587
1835
RPL18A
Non-secretory protein
0


NM_005845
1836-1837
ABCC4
Non-secretory protein
0


AA888242
1838
RPS11
Non-secretory protein
0


CN353139
1839
NSEP1
Non-secretory protein
0.001


AA256381
1840
FLJ22955
Non-secretory protein
0.06


AA513505
1841
HOXD11
Non-secretory protein
0


BG564253
1842
ORM1
Signal peptide
1


AI572087
1843
HTPAP
Non-secretory protein
0.021


AA259243
1844
KLK2
Signal peptide
0.985


AI675682
1845
SLC2A12
Non-secretory protein
0


NM_006096
1846-1847
NDRG1
Non-secretory protein
0


NM_000906
1848-1849
NPR1
Signal peptide
0.997


NM_025087
1850-1851
FLJ21511
Non-secretory protein
0.005


NM_004496
1852-1853
FOXA1
Non-secretory protein
0


AI535878
1854
ENPP3
Non-secretory protein
0.069


NM_032638
1855-1856
GATA2
Non-secretory protein
0


BX331427
1857
ARG2
Non-secretory protein
0.014


AI569484
1858
XPO1
Non-secretory protein
0


BC009569
1859-1860
ASB3
Non-secretory protein
0


AK000028
1861
0
Non-secretory protein
0.001


BX100634
1862
KLF3
Non-secretory protein
0


BC007003
1863-1864
TGM4
Non-secretory protein
0


NM_001008401
1865-1866
FLJ16231
Non-secretory protein
0


BX113323
1867
BLNK
Non-secretory protein
0


NM_015865
1868-1869
SLC14A1
Non-secretory protein
0


AI017286
1870
PTPLB
Non-secretory protein
0.06


NM_030774
1871-1872
OR51E2
Non-secretory protein
0.008


NM_001441
1873-1874
FAAH
Signal peptide
0.805


AL044554
1875
STAT6
Non-secretory protein
0


CB049466
1876
ANKH
Non-secretory protein
0.001


AW575747
1877
DSCR1L2
Non-secretory protein
0


NM_024080
1878-1879
TRPM8
Non-secretory protein
0


AV724505
1880
TMC4
Non-secretory protein
0


BC005859
1881-1882
ZNF589
Non-secretory protein
0


BC005408
1883-1884
LRRK1
Non-secretory protein
0


AA177004
1885
STEAP2
Non-secretory protein
0


BC001216
1886
SAFB2
Non-secretory protein
0


BG707154
1887
CPE
Signal peptide
1


AA024878
1888
GNB2L1
Non-secretory protein
0


BU688574
1889
LOC92689
Non-secretory protein
0


BC042118
1890
DLG1
Non-secretory protein
0


NM_003007
1891-1892
SEMG1
Signal peptide
0.922


BM875598
1893
SPATA13
Non-secretory protein
0


NM_177965
1894-1895
LOC157657
Non-secretory protein
0


CA433208
1896
KIAA1411
Non-secretory protein
0


BM984931
1897
MGC20781
Non-secretory protein
0


BC035335
1898-1899
LOC255189
Non-secretory protein
0


BC080193
1900
ERBB2
Non-secretory protein
0


NM_199427
1901-1902
ZFP64
Non-secretory protein
0


BC042370
1903
SUHW2
Non-secretory protein
0


AL137506
1904-1905
ELOVL7
Non-secretory protein
0


AI888175
1906
TRAF4
Non-secretory protein
0


AI669751
1907
SLC39A2
Signal peptide
0.982


AI088739
1908
HNF4G
Non-secretory protein
0.001


BC070300
1909
SLC22A3
Signal anchor
0.097


BC005827
1910
HIST2H2BE
Non-secretory protein
0


BC041345
1911-1912
AMD1
Non-secretory protein
0


BX390036
1913
TYMS
Non-secretory protein
0


AK022455
1914
PHC3
Non-secretory protein
0


AL832940
1915-1916
SARG
Non-secretory protein
0


BC000965
1917-1918
MTERF
Non-secretory protein
0


NM_007253
1919-1920
CYP4F8
Signal peptide
1


AK124401
1921
PPAP2A
Non-secretory protein
0.348


BC011408
1922-1923
KIAA0056
Non-secretory protein
0


AA469293
1924
MSMB
Signal peptide
0.997


AK056914
1925
VEGF
Non-secretory protein
0


NM_004360
1926-1927
CDH1
Signal peptide
0.896


BC062761
1928-1929
TARP
Non-secretory protein
0


NM_001007278
1930-1931
RFP2
Non-secretory protein
0


NM_018670
1932-1933
MESP1
Signal anchor
0.004


AA026974
1934
TRPM4
Non-secretory protein
0


AI468032
1935
PAK1IP1
Non-secretory protein
0.001


CF122297
1936
HNRPA1
Non-secretory protein
0


CB053869
1937
ZNF207
Non-secretory protein
0


NM_032387
1938-1939
WNK4
Non-secretory protein
0


BQ448015
1940
APXL2
Non-secretory protein
0


AI554477
1941
MED28


AK095655
1942
LOC285300


AW291753
1943
0


BM023121
1944
0


AY338953
1945
0


AY533562
1946
0


BC030554
1947
0





*ratio of prostate expression in tpm to other organs greater than 2.5













TABLE 5C







PROSTATE ENRICHED GENES IDENTIFIED


BY RATIO SCHEMA (RATIO >2.5)*

















TMHMM 2.0





SignalP3.0
SecretomeP2.0
prediction



Genbank

prediction
prediction
Pred trans-


Genbank
SEQ ID

Max cleavage
Secreted
membrane


Accession No.
NOs:
name
site probability
potential (Odds)
domains















BC000637
1797-1798
DHRS7
0.599 between
6.3
1





pos. 28 and 29


BC029497
1799-1800
NPY
0.520 between
6.09
1





pos. 28 and 29


AW172826
1801
FLJ20010
0.000 between
6.06
0





pos. 46 and 47


BI771919
1802
C9orf61
0.534 between
5.9
2





pos. 29 and 30


BU853306
1803
Lrp2bp
0.000 between
5.62
0





pos. 55 and 56


BC007092
1804-1805
HOXB13
0.000 between
5.14
0





pos. −1 and 0


BC038962
1806-1807
CREB3L4
0.000 between
4.72
0





pos. −1 and 0


BC005029
1808-1809
LEPREL1
0.991 between
4.59
0





pos. 24 and 25


CB051271
1810
KLK4
0.401 between
4.57
1





pos. 29 and 30


NM_145013
1811-1812
MGC35558
0.901 between
4.47
0





pos. 22 and 23


BU157155
1813
HAX1
0.001 between
4.41
0





pos. 18 and 19


AW207206
1814
0
0.001 between
4.39
0





pos. 20 and 21


BC013389
1815
0
0.000 between
4.3
0





pos. 27 and 28


BC028162
1816-1817
TMEM16G
0.001 between
4.29
7





pos. 22 and 23


BX099160
1818
MGC31963
0.855 between
4.22
2





pos. 35 and 36


BC005307
1819-1820
KLK3
0.525 between
3.938
0





pos. 23 and 24


NM_005656
1821-1822
TMPRSS2
0.000 between
3.86
1





pos. −1 and 0


BC026923
1823
LOC221442
0.004 between
3.81
0





pos. 50 and 51


BU159800
1824
ARL10C
0.000 between
3.76
0





pos. 35 and 36


NM_032323
1825-1826
MGC13102
0.000 between
3.69
5





pos. −1 and 0


AI954252
1827
0
0.121 between
3.58
0





pos. 42 and 43


BQ941313
1828
SEPX1
0.000 between
3.49
0





pos. 13 and 14


BC007460
1829-1830
ACPP
0.975 between
3.49
1





pos. 32 and 33


BI911790
1831
BIN3
0.000 between
3.41
0





pos. −1 and 0


BC002707
1832-1833
SPON2
0.829 between
3.06
0





pos. 26 and 27


AK026938
1834
0
0.568 between
3.02
0





pos. 27 and 28


BG818587
1835
RPL18A
0.000 between
2.8
0





pos. 24 and 25


NM_005845
1836-1837
ABCC4
0.000 between
2.67
11





pos. −1 and 0


AA888242
1838
RPS11
0.000 between
2.64
0





pos. −1 and 0


CN353139
1839
NSEP1
0.000 between
2.35
0





pos. 25 and 26


AA256381
1840
FLJ22955
0.038 between
2.19
1





pos. 15 and 16


AA513505
1841
HOXD11
0.000 between
2.14
0





pos. 20 and 21


BG564253
1842
ORM1
0.923 between
2.03
0





pos. 18 and 19


AI572087
1843
HTPAP
0.009 between
2.01
4





pos. 63 and 64


AA259243
1844
KLK2
0.455 between
1.81
0





pos. 17 and 18


AI675682
1845
SLC2A12
0.000 between
1.79
12





pos. 51 and 52


NM_006096
1846-1847
NDRG1
0.000 between
1.76
0





pos. −1 and 0


NM_000906
1848-1849
NPR1
0.960 between
1.75
0





pos. 32 and 33


NM_025087
1850-1851
FLJ21511
0.005 between
1.75
10





pos. 20 and 21


NM_004496
1852-1853
FOXA1
0.000 between
1.71
0





pos. −1 and 0


AI535878
1854
ENPP3
0.036 between
1.69
1





pos. 42 and 43


NM_032638
1855-1856
GATA2
0.000 between
1.65
0





pos. 22 and 23


BX331427
1857
ARG2
0.013 between
1.56
0





pos. 36 and 37


AI569484
1858
XPO1
0.000 between
1.54
0





pos. −1 and 0


BC009569
1859-1860
ASB3
0.000 between
1.53
0





pos. −1 and 0


AK000028
1861
0
0.000 between
1.46
0





pos. 22 and 23


BX100634
1862
KLF3
0.000 between
1.4
0





pos. −1 and 0


BC007003
1863-1864
TGM4
0.000 between
1.36
0





pos. −1 and 0


NM_001008401
1865-1866
FLJ16231
0.000 between
1.21
0





pos. −1 and 0


BX113323
1867
BLNK
0.000 between
1.21
0





pos. −1 and 0


NM_015865
1868-1869
SLC14A1
0.000 between
1.2
8





pos. −1 and 0


AI017286
1870
PTPLB
0.028 between
1.2
4





pos. 63 and 64


NM_030774
1871-1872
OR51E2
0.003 between
1.2
7





pos. 22 and 23


NM_001441
1873-1874
FAAH
0.549 between
1.2
1





pos. 28 and 29


AL044554
1875
STAT6
0.000 between
1.17
0





pos. −1 and 0


CB049466
1876
ANKH
0.000 between
1.15
8





pos. 26 and 27


AW575747
1877
DSCR1L2
0.000 between
1.12
0





pos. −1 and 0


NM_024080
1878-1879
TRPM8
0.000 between
1.07
8





pos. −1 and 0


AV724505
1880
TMC4
0.000 between
1.06
8





pos. −1 and 0


BC005859
1881-1882
ZNF589
0.000 between
0.99
1





pos. −1 and 0


BC005408
1883-1884
LRRK1
0.000 between
0.99
0





pos. −1 and 0


AA177004
1885
STEAP2
0.000 between
0.95
6





pos. −1 and 0


BC001216
1886
SAFB2
0.000 between
0.95
0





pos. −1 and 0


BG707154
1887
CPE
0.859 between
0.93
0





pos. 27 and 28


AA024878
1888
GNB2L1
0.000 between
0.92
0





pos. 33 and 34


BU688574
1889
LOC92689
0.000 between
0.91
0





pos. −1 and 0


BC042118
1890
DLG1
0.000 between
0.87
0





pos. −1 and 0


NM_003007
1891-1892
SEMG1
0.515 between
0.85
0





pos. 23 and 24


BM875598
1893
SPATA13
0.000 between
0.81
0





pos. −1 and 0


NM_177965
1894-1895
LOC157657
0.000 between
0.81
0





pos. −1 and 0


CA433208
1896
KIAA1411
0.000 between
0.8
0





pos. −1 and 0


BM984931
1897
MGC20781
0.000 between
0.79
0





pos. 25 and 26


BC035335
1898-1899
LOC255189
0.000 between
0.78
0





pos. 23 and 24


BC080193
1900
ERBB2
0.000 between
0.74
2





pos. −1 and 0


NM_199427
1901-1902
ZFP64
0.000 between
0.68
0





pos. −1 and 0


BC042370
1903
SUHW2
0.000 between
0.67
0





pos. −1 and 0


AL137506
1904-1905
ELOVL7
0.000 between
0.67
7





pos. −1 and 0


AI888175
1906
TRAF4
0.000 between
0.63
0





pos. −1 and 0


AI669751
1907
SLC39A2
0.297 between
0.62
8





pos. 23 and 24


AI088739
1908
HNF4G
0.001 between
0.59
0





pos. 21 and 22


BC070300
1909
SLC22A3
0.048 between
0.58
12





pos. 33 and 34


BC005827
1910
HIST2H2BE
0.000 between
0.58
0





pos. −1 and 0


BC041345
1911-1912
AMD1
0.000 between
0.58
0





pos. −1 and 0


BX390036
1913
TYMS
0.000 between
0.57
0





pos. −1 and 0


AK022455
1914
PHC3
0.000 between
0.57
0





pos. −1 and 0


AL832940
1915-1916
SARG
0.000 between
0.56
0





pos. 21 and 22


BC000965
1917-1918
MTERF
0.000 between
0.56
0





pos. 14 and 15


NM_007253
1919-1920
CYP4F8
0.781 between
0.56
1





pos. 36 and 37


AK124401
1921
PPAP2A
0.226 between
0.53
5





pos. 30 and 31


BC011408
1922-1923
KIAA0056
0.000 between
0.52
0





pos. −1 and 0


AA469293
1924
MSMB
0.928 between
0.51
1





pos. 20 and 21


AK056914
1925
VEGF
0.000 between
0.485
0





pos. −1 and 0


NM_004360
1926-1927
CDH1
0.487 between
0.36
1





pos. 22 and 23


BC062761
1928-1929
TARP
0.000 between
0.35
1





pos. 20 and 21


NM_001007278
1930-1931
RFP2
0.000 between
0.32
1





pos. 24 and 25


NM_018670
1932-1933
MESP1
0.002 between
0.31
0





pos. 20 and 21


AA026974
1934
TRPM4
0.000 between
0.3
5





pos. −1 and 0


AI468032
1935
PAK1IP1
0.000 between
0.27
0





pos. 25 and 26


CF122297
1936
HNRPA1
0.000 between
0.22
0





pos. 32 and 33


CB053869
1937
ZNF207
0.000 between
0.21
0





pos. −1 and 0


NM_032387
1938-1939
WNK4
0.000 between
0.2
0





pos. −1 and 0


BQ448015
1940
APXL2
0.000 between
0.19
0





pos. 41 and 42


AI554477
1941
MED28

#N/A
#N/A


AK095655
1942
LOC285300

#N/A
#N/A


AW291753
1943
0

#N/A
#N/A


BM023121
1944
0

#N/A
#N/A


AY338953
1945
0

#N/A
#N/A


AY533562
1946
0

#N/A
#N/A


BC030554
1947
0

#N/A
#N/A





*ratio of prostate expression in tpm to other organs greater than 2.5













TABLE 5D







PROSTATE ENRICHED GENES IDENTIFIED


BY RATIO SCHEMA (RATIO >2.5)*













Genbank



Prostate


Genbank
SEQ ID

NN-

Expression


Accession No.
NOs:
name
score
Odds
(tmp)















BC000637
1797-1798
DHRS7
0.92
6.302
754


BC029497
1799-1800
NPY
0.911
6.099
642


AW172826
1801
FLJ20010
0.911
6.061
92


BI771919
1802
C9orf61
0.906
5.902
91


BU853306
1803
Lrp2bp
0.895
5.626
95


BC007092
1804-1805
HOXB13
0.875
5.145
344


BC038962
1806-1807
CREB3L4
0.866
4.721
334


BC005029
1808-1809
LEPREL1
0.857
4.594
118


CB051271
1810
KLK4
0.856
4.575
360


NM_145013
1811-1812
MGC35558
0.86
4.477
53


BU157155
1813
HAX1
0.854
4.412
67


AW207206
1814
0
0.854
4.391
279


BC013389
1815
0
0.85
4.304
64


BC028162
1816-1817
TMEM16G
0.843
4.293
281


BX099160
1818
MGC31963
0.846
4.222
53


BC005307
1819-1820
KLK3
0.838
3.938
24771


NM_005656
1821-1822
TMPRSS2
0.816
3.861
1425


BC026923
1823
LOC221442
0.8
3.812
104


BU159800
1824
ARL10C
0.822
3.76
167


NM_032323
1825-1826
MGC13102
0.788
3.699
238


AI954252
1827
0
0.814
3.589
159


BQ941313
1828
SEPX1
0.798
3.492
56


BC007460
1829-1830
ACPP
0.815
3.495
55


BI911790
1831
BIN3
0.806
3.41
54


BC002707
1832-1833
SPON2
0.766
3.063
873


AK026938
1834
0
0.769
3.025
304


BG818587
1835
RPL18A
0.768
2.806
58


NM_005845
1836-1837
ABCC4
0.747
2.671
454


AA888242
1838
RPS11
0.754
2.645
50


CN353139
1839
NSEP1
0.733
2.358
179


AA256381
1840
FLJ22955
0.688
2.196
57


AA513505
1841
HOXD11
0.715
2.142
99


BG564253
1842
ORM1
0.691
2.034
180


AI572087
1843
HTPAP
0.677
2.013
332


AA259243
1844
KLK2
0.676
1.816
7988


AI675682
1845
SLC2A12
0.499
1.792
127


NM_006096
1846-1847
NDRG1
0.667
1.765
2688


NM_000906
1848-1849
NPR1
0.658
1.755
150


NM_025087
1850-1851
FLJ21511
0.605
1.756
230


NM_004496
1852-1853
FOXA1
0.627
1.711
793


AI535878
1854
ENPP3
0.635
1.693
54


NM_032638
1855-1856
GATA2
0.598
1.659
238


BX331427
1857
ARG2
0.621
1.56
150


AI569484
1858
XPO1
0.604
1.54
68


BC009569
1859-1860
ASB3
0.607
1.538
2781


AK000028
1861
0
0.595
1.466
55


BX100634
1862
KLF3
0.581
1.401
136


BC007003
1863-1864
TGM4
0.59
1.368
5602


NM_001008401
1865-1866
FLJ16231
0.55
1.21
254


BX113323
1867
BLNK
0.559
1.211
183


NM_015865
1868-1869
SLC14A1
0.335
1.208
255


AI017286
1870
PTPLB
0.457
1.201
102


NM_030774
1871-1872
OR51E2
0.522
1.208
420


NM_001441
1873-1874
FAAH
0.535
1.206
476


AL044554
1875
STAT6
0.547
1.174
71


CB049466
1876
ANKH
0.335
1.153
58


AW575747
1877
DSCR1L2
0.471
1.123
225


NM_024080
1878-1879
TRPM8
0.519
1.077
267


AV724505
1880
TMC4
0.402
1.064
120


BC005859
1881-1882
ZNF589
0.491
0.992
156


BC005408
1883-1884
LRRK1
0.499
0.999
202


AA177004
1885
STEAP2
0.482
0.954
2156


BC001216
1886
SAFB2
0.427
0.954
76


BG707154
1887
CPE
0.464
0.933
148


AA024878
1888
GNB2L1
0.465
0.921
59


BU688574
1889
LOC92689
0.461
0.918
82


BC042118
1890
DLG1
0.457
0.872
50


NM_003007
1891-1892
SEMG1
0.447
0.853
4660


BM875598
1893
SPATA13
0.422
0.812
79


NM_177965
1894-1895
LOC157657
0.434
0.819
92


CA433208
1896
KIAA1411
0.427
0.809
69


BM984931
1897
MGC20781
0.417
0.795
117


BC035335
1898-1899
LOC255189
0.49
1.04
56


BC080193
1900
ERBB2
0.377
0.743
1770


NM_199427
1901-1902
ZFP64
0.374
0.688
80


BC042370
1903
SUHW2
0.364
0.678
587


AL137506
1904-1905
ELOVL7
0.322
0.673
256


AI888175
1906
TRAF4
0.343
0.631
50


AI669751
1907
SLC39A2
0.34
0.629
60


AI088739
1908
HNF4G
0.32
0.593
225


BC070300
1909
SLC22A3
0.294
0.581
77


BC005827
1910
HIST2H2BE
0.306
0.587
912


BC041345
1911-1912
AMD1
0.317
0.588
438


BX390036
1913
TYMS
0.306
0.571
67


AK022455
1914
PHC3
0.287
0.57
105


AL832940
1915-1916
SARG
0.302
0.563
158


BC000965
1917-1918
MTERF
0.3
0.56
190


NM_007253
1919-1920
CYP4F8
0.28
0.566
54


AK124401
1921
PPAP2A
0.211
0.533
75


BC011408
1922-1923
KIAA0056
0.281
0.527
287


AA469293
1924
MSMB
0.27
0.517
275


AK056914
1925
VEGF
0.256
0.485
202


NM_004360
1926-1927
CDH1
0.179
0.362
192


BC062761
1928-1929
TARP
0.174
0.353
564


NM_001007278
1930-1931
RFP2
0.162
0.322
192


NM_018670
1932-1933
MESP1
0.154
0.315
133


AA026974
1934
TRPM4
0.147
0.305
290


AI468032
1935
PAK1IP1
0.13
0.271
74


CF122297
1936
HNRPA1
0.106
0.228
104


CB053869
1937
ZNF207
0.099
0.212
72


NM_032387
1938-1939
WNK4
0.089
0.201
100


BQ448015
1940
APXL2
0.083
0.19
244


AI554477
1941
MED28


700


AK095655
1942
LOC285300


84


AW291753
1943
0


310


BM023121
1944
0


178


AY338953
1945
0


166


AY533562
1946
0


67


BC030554
1947
0


66





*ratio of prostate expression in tpm to other organs greater than 2.5






Additional analysis was carried out to determine the secretion potential of the prostate-specific genes identified. The analysis programs used included SignalP 3.0, Secretome 2.0 and TMHMM 2.0 (see http colon double slash www dot cbs dot dtu dot dk/services/). The SignalP analysis identifies classical secreted proteins and was conducted using the classical secretion pathway prediction as described at http colon double slash www dot cbs dot dtu dot dk/services/SignalP/ (see Jannick Dyrlov Bendtsen, et al. J. Mol. Biol., 340:783-795, 2004; Henrik Nielsen et al., Protein Engineering, 10:1-6, 1997; Henrik Nielsen and Anders Krogh. Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology (ISMB 6), AAAI Press, Menlo Park, Calif., pp. 122-130, 1998). The Secretome2.0 analysis identifies nonclassical secreted proteins (see J. Dyrløv Bendtsen, et al., Protein Eng. Des. Sel., 17(4):349-356, 2004). TMHMM uses hidden Markov model for three-state (TM-helix, inside, outside) topology prediction of transmembrane proteins (see Erik L. L. Sonnhammer, et al., Proc. of Sixth Int. Conf. on Intelligent Systems for Molecular Biology, p. 175-182 Ed. J. Glasgow, T. Littlejohn, F. Major, R. Lathrop, D. Sankoff, and C. Sensen, Menlo Park, Calif.: AAAI Press, 1998). According to the SignalP analysis method, proteins with an odds scoring 3 or higher have a high confidence of being secreted. However, it should be noted that several proteins scoring well below 3 by this method are known to be secreted proteins detected in the blood (see e.g., Table 5, KLK2). Further, these analyses do not take into account proteins that may be shed.
In summary, this example identifies prostate-specific and potentially secreted prostate-specific proteins that can be used in diagnostic panels for the detection of diseases of the prostate.
Example 8
Prostate Cancer Diagnostics Using Multiparameter Analysis
This example describes a multiparameter diagnostic fingerprint using the NDRG1 prostate-specific protein in combination with PSA. The NDRG1 prostate-specific protein further improved prostate cancer detection when used in combination with PSA.
Commercially available antibodies specific for numerous proteins encoded by prostate-specific genes as described in Table 5 were used to determine which proteins would be useful in a multiparameter diagnostic assay for prostate cancer. Most of the commercially available antibodies were not suitable (e.g., were not sensitive enough or showed non-specific binding). However, the antibody available for NDRG1 (anti-NDRG1(C terminal) poly IgY; Cat #A22272B; GenWay Inc) was shown to specifically bind to NDRG1 from serum. NDRG1 is a member of the N-myc downregulated gene (NDRG) family that belongs to the alpha/beta hydrolase superfamily. It is classified as a tumor suppressor and heavy metal-response protein. Its expression is modulated by diverse physiological and pathological conditions including hypoxia, cellular differentiation, heavy metal, N-myc and neoplasia (Lachat P, et al.; Histochem Cell Biol. 2002 November; 118(5):399-408).
NDRG1 protein expression was analyzed in serum samples from 18 advanced prostate cancer patients, 21 prostate cancer patients with localized cancer, and 22 normal controls. Western blot analysis was used to measure serum protein expression as follows: Serum was diluted (1:10) with lysis buffer (50 mM Hepes, pH 7.4, 4 mM EDTA, 2 mM EGTA, 2 μM PMSF, 20 μg/ml, leupeptine (or 1× protease inhibitor cocktail), 1 mM Na3VO4, 10 mM NaF, 2 mM Na pyrophosphate, 1% Triton X-100). Protein concentration was determined using the Bio-Rad protein assay kit. Serum proteins (50 μg) were subjected to SDS-PAGE electrophoresis and transferred to a PVDF membrane (Hybond-P, Amersham Pharmacia Biotech, Piscataway, N.J.). The membrane was blocked with 4% non-fat milk in TBS (25 mM Tris, pH 7.4, 125 mM NaCl) for 1 h at room temperature, followed by incubation with primary antibodies against NDRG1 IgY (1:500) overnight at 4° C. The membranes were washed 3 times with TBS, and then incubated with horseradish peroxidase conjugated anti-rabbit IgY (1:16,000) for 1 h. The immunoblot was then washed five times with TBS and developed using an ECL (Amersham). The intensities of the single band corresponding to the NDRG1 protein were then scored. The results are summarized in Table 6 together with serum PSA measurements performed using a commercial ELISA kit.







TABLE 6







COMBINED ANALYSIS OF NDRG1 AND PSA SERUM EXPRESSION


INCREASES PROSTATE CANCER DIAGNOSIS CONFIDENCE.












NDRG-1
PSA
serum
serum


cancer
intensity 
values
diagnosis
diagnosis


status
(scores*)
(ng/ml)
by PSA
by NDRG1





Advanced
3
70.48
identified as
identified as 





cancer by 
cancer by 





PSA assay
NDRG1 assay


Advanced
4
127.3 
identified as
identified as 





cancer by 
cancer by 





PSA assay
NDRG1 assay


Advanced
4
422.1 
identified as
identified as 





cancer by 
cancer by 





PSA assay
NDRG1 assay


Advanced
4
1223   
identified as
identified as 





cancer by 
cancer by 





PSA assay
NDRG1 assay


Advanced
4
71.28
identified as
identified as 





cancer by 
cancer by 





PSA assay
NDRG1 assay


Advanced
2
133.2 
identified as
missed by 





cancer by 
NDRG1 assay





PSA assay



Advanced
4
353.7 
identified as
identified as





cancer by 
cancer by 





PSA assay
NDRG1 assay


Advanced
1
73.95
identified as
missed by 





cancer by 
NDRG1 assay





PSA assay



Advanced
3
454.8 
identified as
identified as 





cancer by 
cancer by 





PSA assay
NDRG1 assay


Advanced
4
474   
identified as
identified as 





cancer by 
cancer by 





PSA assay
NDRG1 assay


Advanced
6
150.1 
identified as
identified as 





cancer by 
cancer by 





PSA assay
NDRG1 assay


Advanced
0
1375   
identified as
missed by 





cancer by 
NDRG1 assay





PSA assay



Advanced
6
71.28
identified as
identified as 





cancer by 
cancer by 





PSA assay
NDRG1 assay


Advanced
6
4066   
identified as
identified as 





cancer by 
cancer by 





PSA assay
NDRG1 assay


Advanced
4
1199   
identified as
identified as 





cancer by 
cancer by 





PSA assay
NDRG1 assay


Advanced
1
38.14
identified as
missed by 





cancer by 
NDRG1 assay





PSA assay



Advanced
6
552.6 
identified as
identified as 





cancer by 
cancer by 





PSA assay
NDRG1 assay


Advanced
5
321  
identified as
identified as 





cancer by 
cancer by 





PSA assay
NDRG1 assay


Primary
−1 
14.2 
possibly cancer



Primary
2
 6.27
Grey Zone of






diagnosis by 






Psa



Primary
2
9.2
Grey Zone of






diagnosis by 






Psa



Primary
1
 8.57
Grey Zone of






diagnosis by 






Psa



Primary
0
 5.67
Grey Zone of






diagnosis by 






Psa



Primary
2
11.3 
possibly cancer



Primary
0
 4.58
Grey Zone of






diagnosis by 






Psa



Primary
0
 5.67
Grey Zone of






diagnosis by 






Psa



Primary
−1 
 6.48
Grey Zone of






diagnosis by 






Psa



Primary           Primary 
3           3 
12.71            4.93 
possibly cancer           Grey Zone of diagnosis by Psa 






Primary
1
 3.16
Grey Zone of






diagnosis by 






Psa



Primary
1
 4.87
Grey Zone of






diagnosis by 






Psa



Primary
1
 4.66
Grey Zone of






diagnosis by 






Psa



Primary
1
 6.87
Grey Zone of






diagnosis by 






Psa



Primary
0
 3.91
Grey Zone of






diagnosis by 






Psa



Primary
0
 6.48
Grey Zone of






diagnosis by 






Psa



Primary
2
13.1 
possibly cancer



Primary
0
 4.58
Grey Zone of






diagnosis by 






Psa



Primary
1
 4.72
Grey Zone of






diagnosis by 






Psa



Primary
4
12.71
possibly cancer






Normal
−1 
0.8
Normal
normal


Normal
−1 
0.8
Normal
normal


Normal
0
0.6
Normal
normal


Normal
1
1  
Normal
normal


Normal
−1 
1.2
Normal
normal


Normal
−1 
 1.91
Normal
normal


Normal
2
0.6
Normal
normal


Normal
−1 
0.3
Normal
normal


Normal
0
1  
Normal
normal


Normal
−1 
0.4
Normal
normal


Normal
−1 
0.8
Normal
normal


Normal
0
1  
Normal
normal


Normal
1
0.8
Normal
normal


Normal
2
0.6
Normal
normal


Normal
1
0.5
Normal
normal


Normal
1
1  
Normal
normal


Normal
−1 
0.7
Normal
normal


Normal
−1 
1.2
Normal
normal


Normal
−1 
1.1
Normal
normal


Normal
0
0.8
Normal
normal


Normal
0
0.7
Normal
normal


Normal
0
0.6
Normal
normal





*scores: no expression, −1;


no expression to very faint, 0;


expression levels then scored from 1 to 6 by intensities






PSA was detected in 100% of the advanced prostate cancers. NDRG1 was detected in 14 out of 18 advanced cancers (78%) (see Table 6, scores greater than 3). Serum PSA levels below 15 ng/ml, particularly, levels between 4-10 ng/ml (often referred to as the ‘grey zone’ in the PSA assay) cannot reliably detect prostate cancer as PSA levels in this range may be the result of other factors such as infection (prostatitis) or benign prostatic hyperplasia (BPH), a common condition in older men. Additionally, the normal range of PSA values increases with patient age. NDRG1 detection in serum reinforced the diagnosis of three prostate cancer patients with PSA levels between 4.9 ng/ml and 15 ng/ml. In these three patients, the NDRG1 scores were 3 or 4, significantly higher than the NDRG1 scores in a cohort of 22 normal individuals (average 0.09, range −1 to 2).
Thus, this example illustrates that the use of two or more prostate specific/enriched cancer markers such as NDRG1 and PSA can improve prostate cancer diagnosis to reduce false positive and false negative rates.
From the foregoing it will be appreciated that, although specific embodiments of the invention have been described herein for purposes of illustration, various modifications may be made without deviating from the spirit and scope of the invention. Accordingly, the invention is not limited except as by the appended claims.

## Claims

1. A method for determining the presence or absence of a drug side effect in a subject taking the drug comprising,
(a) detecting a level of each of a plurality of organ-specific proteins in a test blood sample from the subject, wherein the plurality of organ-specific proteins are secreted from the same organ, wherein the organ-specific proteins comprises at least 5 organ-specific proteins;
(b) comparing said level of each of the plurality of organ-specific proteins in the test blood sample from the subject to a level of each of the plurality of organ-specific proteins in a control sample of blood from one or more subjects who have not taken the drug;
wherein a statistically significant altered level of one or more of the plurality of organ-specific proteins in the test blood sample as compared to the control sample is indicative of the presence or absence of a drug side effect.

2. The method of claim 1 wherein the level of each of the plurality of organ-specific proteins is detected using a method selected from the group consisting of mass spectrometry, and an immunoassay.

3. The method of claim 2 wherein the level of each of the plurality of organ-specific proteins is measured using tandem mass spectrometry.

4. The method of claim 2 wherein the level of each of the plurality of organ-specific proteins is measured using ELISA.

5. The method of claim 2 wherein the level of each of the plurality of organ-specific proteins is measured using an antibody array.

6. The method of claim 1 wherein the organ-specific proteins comprise liver-specific proteins.

7. The method of claim 1 wherein the organ-specific proteins comprise kidney-specific proteins.

8. The method of claim 1 wherein the organ-specific proteins are from an organ other than the expected therapeutic target of the drug.

9. The method of claim 1, wherein the organ-specific proteins comprise at least 10 organ-specific proteins.

10. The method of claim 1, wherein the organ-specific proteins comprise at least 20 organ-specific proteins.

11. The method of claim 1, wherein the monitoring is performed during development of the drug.

12. The method of claim 1, wherein the monitoring is performed after approval of the drug.

13. A method for determining the presence or absence of a drug side effect of a drug for which a first organ is expected to be the therapeutic target, wherein the side effect is associated with a second organ different from the first organ, and wherein the second organ is not the therapeutic target of the drug, the method comprising,
(a) detecting a level of each of a plurality of organ-specific proteins in a test blood sample from a subject taking the drug, wherein the plurality of organ-specific proteins are secreted from the second organ;
(b) comparing the level of each of the plurality of the organ-specific proteins in the test blood sample to a level of each of the plurality of the organ-specific proteins in a control sample of blood from one or more subjects who have not taken the drug, wherein the organ-specific proteins comprises at least 5 organ-specific proteins;
wherein a statistically significant altered level of one or more of the plurality of the organ-specific proteins in the test blood sample as compared to the control sample is indicative of the presence of a drug side effect,
wherein the monitoring is performed during development of the drug.

14. A method for determining the presence or absence of a drug side effect of a drug for which a first organ is expected to be the therapeutic target, wherein the side effect is associated with a second organ different from the first organ, and wherein the second organ is not the therapeutic target of the drug, the method comprising,
(a) detecting a level of each of a plurality of organ-specific proteins in a test blood sample from a subject taking the drug, wherein the plurality of organ-specific proteins are secreted from the second organ;
(b) comparing the level of each of the plurality of the organ-specific proteins in the test blood sample to a level of each of the plurality of the organ-specific proteins in a control sample of blood from one or more subjects who have not taken the drug, wherein the organ-specific proteins comprises at least 5 organ-specific proteins;
wherein a statistically significant altered level of one or more of the plurality of the organ-specific proteins in the test blood sample as compared to the control sample is indicative of the presence of a drug side effect,
wherein the monitoring is performed after approval of the drug.