METABOLOMIC PROFILING OF CANCER

Abstract
The present invention relates to cancer markers. In particular, the present invention provides metabolites that are differentially present in prostate cancer.
Description
FIELD OF THE INVENTION

The present invention relates to cancer markers. In particular, the present invention provides metabolites that are differentially present in prostate cancer.


BACKGROUND OF THE INVENTION

Afflicting one out of nine men over age 65, prostate cancer (PCA) is a leading cause of male cancer-related death, second only to lung cancer (Abate-Shen and Shen, Genes Dev 14:2410 [2000]; Ruijter et al., Endocr Rev, 20:22 [1999]). The American Cancer Society estimates that about 184,500 American men will be diagnosed with prostate cancer and 39,200 will die in 2001.


Prostate cancer is typically diagnosed with a digital rectal exam and/or prostate specific antigen (PSA) screening. An elevated serum PSA level can indicate the presence of PCA. PSA is used as a marker for prostate cancer because it is secreted only by prostate cells. A healthy prostate will produce a stable amount—typically below 4 nanograms per milliliter, or a PSA reading of “4” or less—whereas cancer cells produce escalating amounts that correspond with the severity of the cancer. A level between 4 and 10 may raise a doctor's suspicion that a patient has prostate cancer, while amounts above 50 may show that the tumor has spread elsewhere in the body.


When PSA or digital tests indicate a strong likelihood that cancer is present, a transrectal ultrasound (TRUS) is used to map the prostate and show any suspicious areas. Biopsies of various sectors of the prostate are used to determine if prostate cancer is present. Treatment options depend on the stage of the cancer. Men with a 10-year life expectancy or less who have a low Gleason number and whose tumor has not spread beyond the prostate are often treated with watchful waiting (no treatment). Treatment options for more aggressive cancers include surgical treatments such as radical prostatectomy (RP), in which the prostate is completely removed (with or without nerve sparing techniques) and radiation, applied through an external beam that directs the dose to the prostate from outside the body or via low-dose radioactive seeds that are implanted within the prostate to kill cancer cells locally. Anti-androgen hormone therapy is also used, alone or in conjunction with surgery or radiation. Hormone therapy uses luteinizing hormone-releasing hormones (LH-RH) analogs, which block the pituitary from producing hormones that stimulate testosterone production. Patients must have injections of LH-RH analogs for the rest of their lives.


While surgical and hormonal treatments are often effective for localized PCA, advanced disease remains essentially incurable. Androgen ablation is the most common therapy for advanced PCA, leading to massive apoptosis of androgen-dependent malignant cells and temporary tumor regression. In most cases, however, the tumor reemerges with a vengeance and can proliferate independent of androgen signals.


The advent of prostate specific antigen (PSA) screening has led to earlier detection of PCA and significantly reduced PCA-associated fatalities. However, the impact of PSA screening on cancer-specific mortality is still unknown pending the results of prospective randomized screening studies (Etzioni et al., J. Natl. Cancer Inst., 91:1033 [1999]; Maattanen et al., Br. J. Cancer 79:1210 [1999]; Schroder et al., J. Natl. Cancer Inst., 90:1817 [1998]). A major limitation of the serum PSA test is a lack of prostate cancer sensitivity and specificity especially in the intermediate range of PSA detection (4-10 ng/ml). Elevated serum PSA levels are often detected in patients with non-malignant conditions such as benign prostatic hyperplasia (BPH) and prostatitis, and provide little information about the aggressiveness of the cancer detected. Coincident with increased serum PSA testing, there has been a dramatic increase in the number of prostate needle biopsies performed (Jacobsen et al., JAMA 274:1445 [1995]). This has resulted in a surge of equivocal prostate needle biopsies (Epstein and Potter J. Urol., 166:402 [2001]). Thus, development of additional serum and tissue biomarkers to supplement PSA screening is needed.


SUMMARY OF THE INVENTION

The present invention relates to cancer markers. In particular, the present invention provides metabolites that are differentially present in prostate cancer.


For example, in some embodiments, the present invention provides a method of diagnosing cancer (e.g., prostate cancer), comprising: detecting the presence or absence of one or more (e.g., 2 or more, 3 or more, 5 or more, 10 or more, etc. measured together in a multiplex or panel format) cancer specific metabolites (e.g., sarcosine, cysteine, glutamate, asparagine, glycine, leucine, proline, threonine, histidine, n-acetyl-aspartic acid (N-acetylaspartate (NAA)), inosine, inositol, adenosine, taurine, creatine, uric acid, glutathione, uracil, kynurenine, glycerol-s-phosphate, glycocholic acid, suberic acid, thymine, glutamic acid, xanthosine, 4-acetamidobutyric acid, citrate, malate, and N-acetylyrosine or thymine) in a sample (e.g., a tissue (e.g., biopsy) sample, a blood sample, a serum sample, or a urine sample) from a subject; and diagnosing cancer based on the presence of the cancer specific metabolite. In some embodiments, the cancer specific metabolite is present in cancerous samples but not non-cancerous samples. In some embodiments, one or more additional cancer markers are detected (e.g., in a panel or multiplex format) along with the cancer specific metabolites. In some embodiments, the panel detects citrate, malate, N-acetyl-aspartic acid, and sarcosine.


The present invention further provides a method of screening compounds, comprising: contacting a cell (e.g., a cancer (e.g., prostate cancer) cell) containing a cancer specific metabolite with a test compound; and detecting the level of the cancer specific metabolite. In some embodiments, the method further comprises the step of comparing the level of the cancer specific metabolite in the presence of the test compound to the level of the cancer specific metabolite in the absence of the cancer specific metabolite. In some embodiments, the cell is in vitro, in a non-human mammal, or ex vivo. In some embodiments, the test compound is a small molecule or a nucleic acid (e.g., antisense nucleic acid, a siRNA, or a miRNA) that inhibits the expression of an enzyme involved in the synthesis or breakdown of a cancer specific metabolite. In some embodiments, the cancer specific metabolite is sarcosine, cysteine, glutamate, asparagine, glycine, leucine, proline, threonine, histidine, n-acetyl-aspartic acid, inosine, inositol, adenosine, taurine, creatine, uric acid, glutathione, uracil, kynurenine, glycerol-s-phosphate, glycocholic acid, suberic acid, thymine, glutamic acid, xanthosine, 4-acetamidobutyric acid, n-acetyl tyrosine or thymine. In some embodiments, the method is a high throughput method.


The present invention further provides a method of characterizing prostate cancer, comprising: detecting the presence or absence of an elevated level of sarcosine in a sample (e.g., a tissue sample, a blood sample, a serum sample, or a urine sample) from a subject diagnosed with cancer; and characterizing the prostate cancer based on the presence or absence of the elevated levels of sarcosine. In some embodiments, the presence of an elevated level of sarcosine in the sample is indicative of invasive prostate cancer in the subject.


Additional embodiments of the present invention are described in the detailed description and experimental sections below.





DESCRIPTION OF THE FIGURES


FIG. 1 shows metabolomic profiling of prostate cancer progression. a, Illustration of the steps involved in metabolomic profiling of prostate-derived tissues. b, Venn diagram representing the distribution of 626 metabolites measured across three classes of prostate-related tissues including benign prostate tissue (n=16), clinically localized prostate cancer (PCA, n=12), and metastatic prostate cancer (Mets, n=14). c, Dendrogram representing unsupervised hierarchical clustering of the prostate-related tissues described in b. N, benign prostate. T, PCA. M, Mets. d, Z-score plots for 626 metabolites monitored in prostate cancer samples normalized to the mean of the benign prostate samples. e, Principal components analysis of prostate tissue samples based on metabolomic alterations.



FIG. 2 shows differential metabolomic alterations characteristic of prostate cancer progression. a, Z-score plot of metabolites altered in localized PCA relative to their mean in benign prostate tissues. b, Same as a but for the comparison between metastatic and PCA, with data relative to the mean of the PCA samples.



FIG. 3 shows integrative analysis of metabolomic profiles of prostate cancer progression and validation of sarcosine as a marker for prostate cancer. a, Network view of the molecular concept analysis for the metabolomic profiles of the “over-expressed in PCA signature”. b, Same as a, but for the metabolomic profiles of the “overexpressed in metastatic samples signature”. c, Sarcosine levels in independent benign, PCA, and metastatic tissues based on isotope dilution GC/MS analysis. d, Boxplot of sarcosine levels based on isotope dilution GC/MS analysis showing normalized sarcosine to alanine levels in urine sediments from biopsy positive and negative individuals (mean±SEM: 0.30±0.13 vs −0.35±0.13, Wilcoxon P=0.0004). e, same as d but for urine supernatants showing elevated sarcosine to creatinine levels in biopsy positive prostate cancer patients compared to biopsy negative controls (mean±SEM: −5.92±0.13 vs. −6.49±0.17, Wilcoxon P=0.0025)



FIG. 4 shows that sarcosine is associated with prostate cancer invasion and aggressiveness. a, Assessment of sarcosine and invasiveness of prostate cancer cell lines and benign epithelial cells. b, (Left panel) Overexpression of EZH2 by adenovirus infection in RWPE cells is associated with increased levels of sarcosine and significant increase in invasion (t-test P=0.0001) compared to vector control. (Right panel) Knockdown of EZH2 by siRNA in DU145 cells is associated with decreased levels of sarcosine and significant decrease in invasion relative to non-target siRNA control (t-test P=0.0115). c, (Left panel) Overexpression of TMPRSS2-ERG or TMPRSS2-ETV1 in RWPE is associated with increased levels of sarcosine (t-test: P=0.0035 and P=0.0016, respectively) and invasion (t-test: P=0.0019 and P=0.0057, respectively) relative to wild type control. (Right panel) Knockdown of TMPRSS2-ERG in VCaP cells is associated with decreased levels of sarcosine and significant decrease in invasion relative to non-target siRNA control (t-test: P=0.0004). d, Assessment of invasion in prostate epithelial cells upon exogenous addition of alanine (circles), glycine (triangles) and sarcosine (squares) measured using a modified Boyden chamber assay. e, Knockdown of GNMT in DU145 cells using GNMT siRNA is associated with a decrease in sarcosine and invasion. (f) Attenuation of GNMT in RWPE cells blocks the ability of exogenous glycine but not sarcosine to induce invasion. g, Immunoblot analysis shows time-dependent phosphorylation of EGFR upon treatment of RWPE cells with 50 μM sarcosine relative to alanine h, Decrease in sarcosine-induced invasion of PrEC prostate epithelial cells upon pretreatment with 10 μM erlotinib (F-test: P=0.0003). DU145 cells serve as a positive control for cell invasion. i, Pre-treatment of RWPE cells with C225 decreases sarcosine-induced invasion relative to sarcosine treatment alone (F-test: P=0.0056).



FIG. 5 shows the relative distributions of standardized peak intensities for metabolites and distribution of tissue specimens from each sample class, across two experimental batches profiled. Samples from each of the three tissue classes were equally distributed across the two batches (X-axis). Y-axis shows the standardized peak intensity (m/z) for the 624 metabolites profiled in 42 tissue samples used in this study.



FIG. 6 shows an outline of steps involved in analysis of the tissue metabolomic profiles.



FIG. 7 shows reproducibility of the metabolomic profiling platform used in the discovery phase.



FIG. 8 shows the relative expression of metastatic cancer-specific metabolites across metastatic tissues from different sites.



FIG. 9 shows an outline of different steps involved in OCM analyses of the metabolomic profiles of localized prostate cancer and metastatic disease.



FIG. 10 shows the reproducibility of sarcosine assessment using isotope-dilution GC-MS. (a) Sarcosine measurement in biological replicates of three prostate-derived cell lines was highly reproducible with a CV of <10%. (b) Sarcosine measurement for 89 prostate derived tissue samples using two independent GC-MS instruments was highly correlated with Rho>0.9.



FIG. 11 shows a comparison of sarcosine levels in tumor bearing tissues and non-tumor controls derived from patients with metastatic prostate cancer using isotope dilution GC/MS. (a) GC/MS trace showing the quantitation of native sarcosine in prostate cancer metastases to the lung. (b) As in (a) but in adjacent control lung tissue. (c) Bar plots showing high levels of sarcosine in metastatic tissues based on isotope dilution GC/MS analysis.



FIG. 12 shows an assessment of sarcosine in urine sediments from men with positive and negative biopsies for cancer. (a) Boxplot showing significantly higher sarcosine levels, relative to alanine, in a batch of 60 urine sediments from 32 biopsy positive and 28 biopsy negative individuals (Wilcoxon rank-sum test: P=0.0188). (b) The Receiver Operator Characteristic (ROC) Curve for the 60 samples in (a) has an AUC f 0.68 (95% CI: 0.54, 0.82). (c) Similar to (a), but in an independent batch of 33 samples (17 biopsy positive and 16 biopsy negative individuals). (d) ROC Curve for the 33 samples in (b) has an AUC of 0.76 (95% CI: 0.59, 0.93). (e) Boxplot for the total set of 93 samples shown in (a) and (c). (f) ROC Curve for the entire dataset (n=93) has an AUC of 0.71 (95% CI: 0.61, 0.82)



FIG. 13 shows an assessment of sarcosine in biopsy positive and negative urine supernatants. (a) Box-plot showing significantly (Wilcoxon rank-sum test: P=0.0025) higher levels of sarcosine relative to creatinine in a batch of 110 urine supernatants from 59 biopsy positive and 51 biopsy negative individuals. (b) Receiver Operator Curve of (a) has an AUC of 0.67 (95% CI: 0.57, 0.77).



FIG. 14 shows confirmation of additional prostate cancer-associated metabolites in prostate-derived tissue samples. (a) Box-plot showing elevated levels of cysteine during progression from benign to clinically localized to metastatic disease (n=5 each, mean±SEM: 6.19±0.13 vs 7.14±0.34 vs 8.00±0.37 for Benign vs PCA vs Mets) (b) same as a, but for glutamic acid (mean±SEM: 9.00±0.26 vs 9.92±0.41 vs 11.15±0.44 for Benign vs PCA vs Mets) (c) same as a, but for glycine (mean±SEM: 8.00±0.06 vs 8.51±0.28 vs 9.28±0.28 for Benign vs PCA vs Mets). (d) same as a, but for thymine (mean±SEM: 1.33±0.15 vs 2.01±0.28 vs 2.27±0.31 for Benign vs PCA vs Mets).



FIG. 15 shows an immunoblot confirmation of EZH2 over-expression and knock-down in prostate-derived cell lines.



FIG. 16 shows real-time PCR-based quantitation of knock-down of the ERG gene fusion product in VCaP cells.



FIG. 17 shows an assessment of internalized sarcosine in prostate and breast epithelial cell lines.



FIG. 18 shows cell cycle analysis and assessment of proliferation in amino acid-treated prostate epithelial cells. (a) Cell cycle profile of untreated prostate cell line RWPE or treated for 24 h with 50 μM of either (b) alanine (c) glycine (d) sarcosine. (e) Assessment of cell numbers using coulter counter for (a-d).



FIG. 19 shows real-time PCR-based quantitation of GNMT knockdown in prostate cell lines. (a) In DU145 cells, siRNA mediated knockdown resulted in approximately 25% decrease in GNMT mRNA levels (b) in RWPE cells, siRNA mediated knockdown resulted in approximately 42% decrease in GNMT mRNA levels.



FIG. 20 shows glycine-induced invasion, but wnot sarcosine-induced invasion is blocked by knock-down of GNMT.



FIG. 21 shows Oncomine concept maps of genes over-expressed in sarcosine treated prostate epithelial cells compared to alanine-treated.



FIG. 22 shows downstream read-outs of the EGFR pathway are activated by sarcosine.



FIG. 23 shows that Erlotinib inhibits sarcosine mediated invasion in PrEC cells. (a) Immunoblot analysis showing inhibition of EGFR phosphorylation by 10 μM Erlotinib. (b) Pre-treatment of PrEC cells with 10 μM Erlotinib results in a significant decrease in sarcosine-induced invasion. (c) colorimetric quantitation of (b).



FIG. 24 shows that Erlotinib inhibits sarcosine mediated invasion in RWPE cells. (a) Pre-treatment of RWPE cells with 10 μM Erlotinib results in a 2-fold decrease in sarcosine-induced invasion.



FIG. 25 shows that C225 inhibits sarcosine mediated invasion in RWPE cells. (a) Pre-treatment of RWPE cells with 50 mg/ml of C225 results in a significant decrease in sarcosine-induced invasion. (b) Immunoblot analysis showing inhibition of EGFR phosphorylation by 50 mg/ml of C225.



FIG. 26 shows that knock-down of EGFR attenuates sarcosine mediated cell invasion. (a) Photomicrograph of cells. (b) Colorometic assessment of invasion. (c) Confirmation of EGFR knock-down by QRT-PCR.



FIG. 27 shows a three dimensional plot of a panel of biomarkers useful to determine cancer tumor aggressivity in a range of tumors from non-aggressive to very aggressive. Benign (diamonds), metastatic (isosceles triangles), GS3 (squares), GS4 (equilateral triangles). X-axis, citrate/malate; Y-axis, NAA; Z-axis, sarcosine. Several metastatic samples are off-scale and are not visible on the graph as presented.





DEFINITIONS

To facilitate an understanding of the present invention, a number of terms and phrases are defined below:


“Prostate cancer” refers to a disease in which cancer develops in the prostate, a gland in the male reproductive system. “Low grade” or “lower grade” prostate cancer refers to non-metastatic prostate cancer, including malignant tumors with low potential for metastasis (i.e. prostate cancer that is considered to be less aggressive). “High grade” or “higher grade” prostate cancer refers to prostate cancer that has metastasized in a subject, including malignant tumors with high potential for metastasis (prostate cancer that is considered to be aggressive).


As used herein, the term “cancer specific metabolite” refers to a metabolite that is differentially present in cancerous cells compared to non-cancerous cells. For example, in some embodiments, cancer specific metabolites are present in cancerous cells but not non-cancerous cells. In other embodiments, cancer specific metabolites are absent in cancerous cells but present in non-cancerous cells. In still further embodiments, cancer specific metabolites are present at different levels (e.g., higher or lower) in cancerous cells as compared to non-cancerous cells. For example, a cancer specific metabolite may be differentially present at any level, but is generally present at a level that is increased by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100%, by at least 110%, by at least 120%, by at least 130%, by at least 140%, by at least 150%, or more; or is generally present at a level that is decreased by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, or by 100% (i.e., absent). A cancer specific metabolite is preferably differentially present at a level that is statistically significant (i.e., a p-value less than 0.05 and/or a q-value of less than 0.10 as determined using either Welch's T-test or Wilcoxon's rank-sum Test). Exemplary cancer specific metabolites are described in the detailed description and experimental sections below.


The term “sample” in the present specification and claims is used in its broadest sense. On the one hand it is meant to include a specimen or culture. On the other hand, it is meant to include both biological and environmental samples. A sample may include a specimen of synthetic origin.


Biological samples may be animal, including human, fluid, solid (e.g., stool) or tissue, as well as liquid and solid food and feed products and ingredients such as dairy items, vegetables, meat and meat by-products, and waste. Biological samples may be obtained from all of the various families of domestic animals, as well as feral or wild animals, including, but not limited to, such animals as ungulates, bear, fish, lagamorphs, rodents, etc. A biological sample may contain any biological material suitable for detecting the desired biomarkers, and may comprise cellular and/or non-cellular material from a subject. The sample can be isolated from any suitable biological tissue or fluid such as, for example, prostate tissue, blood, blood plasma, urine, or cerebral spinal fluid (CSF).


Environmental samples include environmental material such as surface matter, soil, water and industrial samples, as well as samples obtained from food and dairy processing instruments, apparatus, equipment, utensils, disposable and non-disposable items. These examples are not to be construed as limiting the sample types applicable to the present invention.


A “reference level” of a metabolite means a level of the metabolite that is indicative of a particular disease state, phenotype, or lack thereof, as well as combinations of disease states, phenotypes, or lack thereof. A “positive” reference level of a metabolite means a level that is indicative of a particular disease state or phenotype. A “negative” reference level of a metabolite means a level that is indicative of a lack of a particular disease state or phenotype. For example, a “prostate cancer-positive reference level” of a metabolite means a level of a metabolite that is indicative of a positive diagnosis of prostate cancer in a subject, and a “prostate cancer-negative reference level” of a metabolite means a level of a metabolite that is indicative of a negative diagnosis of prostate cancer in a subject. A “reference level” of a metabolite may be an absolute or relative amount or concentration of the metabolite, a presence or absence of the metabolite, a range of amount or concentration of the metabolite, a minimum and/or maximum amount or concentration of the metabolite, a mean amount or concentration of the metabolite, and/or a median amount or concentration of the metabolite; and, in addition, “reference levels” of combinations of metabolites may also be ratios of absolute or relative amounts or concentrations of two or more metabolites with respect to each other. Appropriate positive and negative reference levels of metabolites for a particular disease state, phenotype, or lack thereof may be determined by measuring levels of desired metabolites in one or more appropriate subjects, and such reference levels may be tailored to specific populations of subjects (e.g., a reference level may be age-matched so that comparisons may be made between metabolite levels in samples from subjects of a certain age and reference levels for a particular disease state, phenotype, or lack thereof in a certain age group). Such reference levels may also be tailored to specific techniques that are used to measure levels of metabolites in biological samples (e.g., LC-MS, GC-MS, etc.), where the levels of metabolites may differ based on the specific technique that is used.


As used herein, the term “cell” refers to any eukaryotic or prokaryotic cell (e.g., bacterial cells such as E. coli, yeast cells, mammalian cells, avian cells, amphibian cells, plant cells, fish cells, and insect cells), whether located in vitro or in vivo.


As used herein, the term “processor” refers to a device that performs a set of steps according to a program (e.g., a digital computer). Processors, for example, include Central Processing Units (“CPUs”), electronic devices, or systems for receiving, transmitting, storing and/or manipulating data under programmed control.


As used herein, the term “memory device,” or “computer memory” refers to any data storage device that is readable by a computer, including, but not limited to, random access memory, hard disks, magnetic (floppy) disks, compact discs, DVDs, magnetic tape, flash memory, and the like.


The term “proteomics”, as described in Liebler, D. Introduction to Proteomics: Tools for the New Biology, Humana Press, 2003, refers to the analysis of large sets of proteins. Proteomics deals with the identification and quantification of proteins, their localization, modifications, interactions, activities, and their biochemical and cellular function. The explosive growth of the proteomics field has been driven by novel, high-throughput laboratory methods and measurement technologies, such as gel electrophoresis and mass spectrometry, as well as by innovative computational tools and methods to process, analyze, and interpret huge amounts of data.


“Mass Spectrometry” (MS) is a technique for measuring and analyzing molecules that involves fragmenting a target molecule, then analyzing the fragments, based on their mass/charge ratios, to produce a mass spectrum that serves as a “molecular fingerprint”. Determining the mass/charge ratio of an object is done through means of determining the wavelengths at which electromagnetic energy is absorbed by that object. There are several commonly used methods to determine the mass to charge ration of an ion, some measuring the interaction of the ion trajectory with electromagnetic waves, others measuring the time an ion takes to travel a given distance, or a combination of both. The data from these fragment mass measurements can be searched against databases to obtain definitive identifications of target molecules. Mass spectrometry is also widely used in other areas of chemistry, like petrochemistry or pharmaceutical quality control, among many others.


The term “lysis” refers to cell rupture caused by physical or chemical means. This is done to obtain a protein extract from a sample of serum or tissue.


The term “separation” refers to separating a complex mixture into its component proteins or metabolites. Common laboratory separation techniques include gel electrophoresis and chromatography.


The term “gel electrophoresis” refers to a technique for separating and purifying molecules according to the relative distance they travel through a gel under the influence of an electric current. Techniques for automated gel spots excision may provide data in large dataset format that may be used as input for the methods and systems described herein.


The term “capillary electrophoresis” refers to an automated analytical technique that separates molecules in a solution by applying voltage across buffer-filled capillaries. Capillary electrophoresis is generally used for separating ions, which move at different speeds when the voltage is applied, depending upon the size and charge of the ions. The solutes (ions) are seen as peaks as they pass through a detector and the area of each peak is proportional to the concentration of ions in the solute, which allows quantitative determinations of the ions.


The term “chromatography” refers to a physical method of separation in which the components to be separated are distributed between two phases, one of which is stationary (stationary phase) while the other (the mobile phase) moves in a definite direction. Chromatographic output data may be used for manipulation by the present invention.


The term “chromatographic time”, when used in the context of mass spectrometry data, refers to the elapsed time in a chromatography process since the injection of the sample into the separation device. A “mass analyzer” is a device in a mass spectrometer that separates a mixture of ions by their mass-to-charge ratios.


A “source” is a device in a mass spectrometer that ionizes a sample to be analyzed.


A “detector” is a device in a mass spectrometer that detects ions.


An “ion” is a charged object formed by adding electrons to or removing electrons from an atom.


A “mass spectrum” is a plot of data produced by a mass spectrometer, typically containing m/z values on x-axis and intensity values on y-axis.


A “peak” is a point on a mass spectrum with a relatively high y-value.


The term “m/z” refers to the dimensionless quantity formed by dividing the mass number of an ion by its charge number. It has long been called the “mass-to-charge” ratio.


The term “metabolism” refers to the chemical changes that occur within the tissues of an organism, including “anabolism” and “catabolism”. Anabolism refers to biosynthesis or the buildup of molecules and catabolism refers to the breakdown of molecules.


A “metabolite” is an intermediate or product resulting from metabolism. Metabolites are often referred to as “small molecules”.


The term “metabolomics” refers to the study of cellular metabolites.


A “biopolymer” is a polymer of one or more types of repeating units. Biopolymers are typically found in biological systems and particularly include polysaccharides (such as carbohydrates), and peptides (which term is used to include polypeptides and proteins) and polynucleotides as well as their analogs such as those compounds composed of or containing amino acid analogs or non-amino acid groups, or nucleotide analogs or non-nucleotide groups. This includes polynucleotides in which the conventional backbone has been replaced with a non-naturally occurring or synthetic backbone, and nucleic acids (or synthetic or naturally occurring analogs) in which one or more of the conventional bases has been replaced with a group (natural or synthetic) capable of participating in Watson-Crick type hydrogen bonding interactions. Polynucleotides include single or multiple stranded configurations, where one or more of the strands may or may not be completely aligned with another.


As used herein, the term “post-surgical tissue” refers to tissue that has been removed from a subject during a surgical procedure. Examples include, but are not limited to, biopsy samples, excised organs, and excised portions of organs.


As used herein, the terms “detect”, “detecting”, or “detection” may describe either the general act of discovering or discerning or the specific observation of a detectably labeled composition.


As used herein, the term “clinical failure” refers to a negative outcome following prostatectomy. Examples of outcomes associated with clinical failure include, but are not limited to, an increase in PSA levels (e.g., an increase of at least 0.2 ng ml−1) or recurrence of disease (e.g., metastatic prostate cancer) after prostatectomy.


As used herein, the term “siRNAs” refers to small interfering RNAs. In some embodiments, siRNAs comprise a duplex, or double-stranded region, of about 18-25 nucleotides long; often siRNAs contain from about two to four unpaired nucleotides at the 3′ end of each strand. At least one strand of the duplex or double-stranded region of a siRNA is substantially homologous to, or substantially complementary to, a target RNA molecule. The strand complementary to a target RNA molecule is the “antisense strand;” the strand homologous to the target RNA molecule is the “sense strand,” and is also complementary to the siRNA antisense strand. siRNAs may also contain additional sequences; non-limiting examples of such sequences include linking sequences, or loops, as well as stem and other folded structures. siRNAs appear to function as key intermediaries in triggering RNA interference in invertebrates and in vertebrates, and in triggering sequence-specific RNA degradation during posttranscriptional gene silencing in plants.


The term “RNA interference” or “RNAi” refers to the silencing or decreasing of gene expression by siRNAs. It is the process of sequence-specific, post-transcriptional gene silencing in animals and plants, initiated by siRNA that is homologous in its duplex region to the sequence of the silenced gene. The gene may be endogenous or exogenous to the organism, present integrated into a chromosome or present in a transfection vector that is not integrated into the genome. The expression of the gene is either completely or partially inhibited. RNAi may also be considered to inhibit the function of a target RNA; the function of the target RNA may be complete or partial.


DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to cancer markers. In particular embodiments, the present invention provides metabolites that are differentially present in prostate cancer. Experiments conducted during the course of development of embodiments of the present invention identified a series of metabolites as being differentially present in prostate cancer versus normal prostate. Experiments conducted during the course of development of embodiments of the present invention indentified, for example, sarcosine, cysteine, glutamate, asparagine, glycine, leucine, proline, threonine, histidine, n-acetyl-aspartic acid, inosine, inositol, adenosine, taurine, creatine, uric acid, glutathione, uracil, kynurenine, glycerol-s-phosphate, glycocholic acid, suberic acid, thymine, glutamic acid, xanthosine, 4-acetamidobutyric acid, n-acetyl tyrosine and thymine. Tables 3, 4, 10 and 11 provide additional metabolites present in localized and metastatic cancer. The disclosed markers find use as diagnostic and therapeutic targets. In some embodiments, the present invention provides methods of identifying invasive prostate cancers based on the presence of elevated levels of sarcosine (e.g. in tumor tissue or other bodily fluids).


I. Diagnostic Applications

In some embodiments, the present invention provides methods and compositions for diagnosing cancer, including but not limited to, characterizing risk of cancer, stage of cancer, risk of or presence of metastasis, invasiveness of cancer, etc. based on the presence of cancer specific metabolites or their derivates, precursors, metabolites, etc. Exemplary diagnostic methods are described below.


Thus, for example, a method of diagnosing (or aiding in diagnosing) whether a subject has prostate cancer comprises (1) detecting the presence or absence or a differential level of one or more cancer specific metabolites selected from sarcosine, cysteine, glutamate, asparagine, glycine, leucine, proline, threonine, histidine, n-acetyl-aspartic acid, inosine, inositol, adenosine, taurine, creatine, uric acid, glutathione, uracil, kynurenine, glycerol-s-phosphate, glycocholic acid, suberic acid, thymine, glutamic acid, xanthosine, 4-acetamidobutyric acid, n-acetyl tyrosine, and thymine in a sample from a subject; and b) diagnosing cancer based on the presence, absence or differential level of the cancer specific metabolite. When such a method is used to aid in the diagnosis of prostate cancer, the results of the method may be used along with other methods (or the results thereof) useful in the clinical determination of whether a subject has prostate cancer.


In another example, methods of characterizing prostate cancer comprise detecting the presence or absence or amount of an elevated level of a metabolite, for example sarcosine, in a sample from a subject diagnosed with cancer; and b) characterizing the prostate cancer based on the presence of said elevated levels of the metabolite (e.g. sarcosine).


A. Sample


Any patient sample suspected of containing cancer specific metabolites is tested according to the methods described herein. By way of non-limiting examples, the sample may be tissue (e.g., a prostate biopsy sample or post-surgical tissue), blood, urine, or a fraction thereof (e.g., plasma, serum, urine supernatant, urine cell pellet or prostate cells). In some embodiments, the sample is a tissue sample obtained from a biopsy or following surgery (e.g., prostate biopsy).


In some embodiments, the patient sample undergoes preliminary processing designed to isolate or enrich the sample for cancer specific metabolites or cells that contain cancer specific metabolites. A variety of techniques known to those of ordinary skill in the art may be used for this purpose, including but not limited: centrifugation; immunocapture; and cell lysis.


B. Detection of Metabolites


Metabolites may be detected using any suitable method including, but not limited to, liquid and gas phase chromatography, alone or coupled to mass spectrometry (See e.g., experimental section below), NMR (See e.g., US patent publication 20070055456, herein incorporated by reference), immunoassays, chemical assays, spectroscopy and the like. In some embodiments, commercial systems for chromatography and NMR analysis are utilized.


In other embodiments, metabolites (i.e. biomarkers and derivatives thereof) are detected using optical imaging techniques such as magnetic resonance spectroscopy (MRS), magnetic resonance imaging (MRI), CAT scans, ultra sound, MS-based tissue imaging or X-ray detection methods (e.g., energy dispersive x-ray fluorescence detection).


Any suitable method may be used to analyze the biological sample in order to determine the presence, absence or level(s) of the one or more metabolites in the sample. Suitable methods include chromatography (e.g., HPLC, gas chromatography, liquid chromatography), mass spectrometry (e.g., MS, MS-MS), enzyme-linked immunosorbent assay (ELISA), antibody linkage, other immunochemical techniques, biochemical or enzymatic reactions or assays, and combinations thereof. Further, the level(s) of the one or more metabolites may be measured indirectly, for example, by using an assay that measures the level of a compound (or compounds) that correlates with the level of the biomarker(s) that are desired to be measured.


The levels of one or more of the recited metabolites may be determined in the methods of the present invention. For example, the level(s) of one metabolites, two or more metabolites, three or more metabolites, four or more metabolites, five or more metabolites, six or more metabolites, seven or more metabolites, eight or more metabolites, nine or more metabolites, ten or more metabolites, etc., including a combination of some or all of the metabolites including, but not limited to, sarcosine, cysteine, glutamate, asparagine, glycine, leucine, proline, threonine, histidine, n-acetyl-aspartic acid, inosine, inositol, adenosine, taurine, creatine, uric acid, glutathione, uracil, kynurenine, glycerol-s-phosphate, glycocholic acid, suberic acid, thymine, glutamic acid, xanthosine, 4-acetamidobutyric acid, n-acetyl tyrosine and thymine, may be determined and used in such methods. Determining levels of combinations of the metabolites may allow greater sensitivity and specificity in the methods, such as diagnosing prostate cancer and aiding in the diagnosis of prostate cancer, and may allow better differentiation or characterization of prostate cancer from other prostate disorders (e.g. benign prostatic hypertrophy (BPH), prostatitis, etc.) or other cancers that may have similar or overlapping metabolites to prostate cancer (as compared to a subject not having prostate cancer). For example, ratios of the levels of certain metabolites in biological samples may allow greater sensitivity and specificity in diagnosing prostate cancer and aiding in the diagnosis of prostate cancer and allow better differentiation or characterization of prostate cancer from other cancers or other disorders of the prostate that may have similar or overlapping metabolites to prostate cancer (as compared to a subject not having prostate cancer).


C. Data Analysis


In some embodiments, a computer-based analysis program is used to translate the raw data generated by the detection assay (e.g., the presence, absence, or amount of a cancer specific metabolite) into data of predictive value for a clinician. The clinician can access the predictive data using any suitable means. Thus, in some embodiments, the present invention provides the further benefit that the clinician, who is not likely to be trained in metabolite analysis, need not understand the raw data. The data is presented directly to the clinician in its most useful form. The clinician is then able to immediately utilize the information in order to optimize the care of the subject.


The present invention contemplates any method capable of receiving, processing, and transmitting the information to and from laboratories conducting the assays, information provides, medical personal, and subjects. For example, in some embodiments of the present invention, a sample (e.g., a biopsy or a blood, urine or serum sample) is obtained from a subject and submitted to a profiling service (e.g., clinical lab at a medical facility, etc.), located in any part of the world (e.g., in a country different than the country where the subject resides or where the information is ultimately used) to generate raw data. Where the sample comprises a tissue or other biological sample, the subject may visit a medical center to have the sample obtained and sent to the profiling center, or subjects may collect the sample themselves (e.g., a urine sample) and directly send it to a profiling center. Where the sample comprises previously determined biological information, the information may be directly sent to the profiling service by the subject (e.g., an information card containing the information may be scanned by a computer and the data transmitted to a computer of the profiling center using an electronic communication systems). Once received by the profiling service, the sample is processed and a profile is produced (i.e., metabolic profile), specific for the diagnostic or prognostic information desired for the subject.


The profile data is then prepared in a format suitable for interpretation by a treating clinician. For example, rather than providing raw data, the prepared format may represent a diagnosis or risk assessment (e.g., likelihood of cancer being present) for the subject, along with recommendations for particular treatment options. The data may be displayed to the clinician by any suitable method. For example, in some embodiments, the profiling service generates a report that can be printed for the clinician (e.g., at the point of care) or displayed to the clinician on a computer monitor.


In some embodiments, the information is first analyzed at the point of care or at a regional facility. The raw data is then sent to a central processing facility for further analysis and/or to convert the raw data to information useful for a clinician or patient. The central processing facility provides the advantage of privacy (all data is stored in a central facility with uniform security protocols), speed, and uniformity of data analysis. The central processing facility can then control the fate of the data following treatment of the subject. For example, using an electronic communication system, the central facility can provide data to the clinician, the subject, or researchers.


In some embodiments, the subject is able to directly access the data using the electronic communication system. The subject may chose further intervention or counseling based on the results. In some embodiments, the data is used for research use. For example, the data may be used to further optimize the inclusion or elimination of markers as useful indicators of a particular condition or stage of disease.


When the amount(s) or level(s) of the one or more metabolites in the sample are determined, the amount(s) or level(s) may be compared to prostate cancer metabolite-reference levels, such as prostate-cancer-positive and/or prostate cancer-negative reference levels to aid in diagnosing or to diagnose whether the subject has prostate cancer. Levels of the one or more metabolites in a sample corresponding to the prostate cancer-positive reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of a diagnosis of prostate cancer in the subject. Levels of the one or more metabolites in a sample corresponding to the prostate cancer-negative reference levels (e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels) are indicative of a diagnosis of no prostate cancer in the subject. In addition, levels of the one or more metabolites that are differentially present (especially at a level that is statistically significant) in the sample as compared to prostate cancer-negative reference levels are indicative of a diagnosis of prostate cancer in the subject. Levels of the one or more metabolites that are differentially present (especially at a level that is statistically significant) in the sample as compared to prostate cancer-positive reference levels are indicative of a diagnosis of no prostate cancer in the subject.


The level(s) of the one or more metabolites may be compared to prostate cancer-positive and/or prostate cancer-negative reference levels using various techniques, including a simple comparison (e.g., a manual comparison) of the level(s) of the one or more metabolites in the biological sample to prostate cancer-positive and/or prostate cancer-negative reference levels. The level(s) of the one or more metabolites in the biological sample may also be compared to prostate cancer-positive and/or prostate cancer-negative reference levels using one or more statistical analyses (e.g., t-test, Welch's T-test, Wilcoxon's rank sum test, random forest).


D. Compositions & Kits


Compositions for use (e.g., sufficient for, necessary for, or useful for) in the diagnostic methods of some embodiments of the present invention include reagents for detecting the presence or absence of cancer specific metabolites. Any of these compositions, alone or in combination with other compositions of the present invention, may be provided in the form of a kit. Kits may further comprise appropriate controls and/or detection reagents.


E. Panels


Embodiments of the present invention provide for multiplex or panel assays that simultaneously detect one or more of the markers of the present invention (e.g., sarcosine, cysteine, glutamate, asparagine, glycine, leucine, proline, threonine, histidine, n-acetyl-aspartic acid, inosine, inositol, adenosine, taurine, creatine, uric acid, glutathione, uracil, kynurenine, glycerol-s-phosphate, glycocholic acid, suberic acid, thymine, glutamic acid, xanthosine, 4-acetamidobutyric acid, n-acetyltyrosine and thymine), alone or in combination with additional cancer markers known in the art. For example, in some embodiments, panel or combination assays are provided that detected 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 15 or more, or 20 or more markers in a single assay. In some embodiments, assays are automated or high throughput.


In some embodiments, additional cancer markers are included in multiplex or panel assays. Markers are selected for their predictive value alone or in combination with the metabolic markers described herein. Exemplary prostate cancer markers include, but are not limited to: AMACR/P504S (U.S. Pat. No. 6,262,245); PCA3 (U.S. Pat. No. 7,008,765); PCGEM1 (U.S. Pat. No. 6,828,429); prostein/P501S, P503S, P504S, P509S, P510S, prostase/P703P, P710P (U.S. Publication No. 20030185830); and, those disclosed in U.S. Pat. Nos. 5,854,206 and 6,034,218, and U.S. Publication No. 20030175736, each of which is herein incorporated by reference in its entirety. Markers for other cancers, diseases, infections, and metabolic conditions are also contemplated for inclusion in a multiplex or panel format.


II. Therapeutic Methods

In some embodiments, the present invention provides therapeutic methods (e.g., that target the cancer specific metabolites described herein). In some embodiments, the therapeutic methods target enzymes or pathway components of the cancer specific metabolites described herein.


For example, in some embodiments, the present invention provides compounds that target the cancer specific metabolites of the present invention. The compounds may decrease the level of cancer specific metabolite by, for example, interfering with synthesis of the cancer specific metabolite (e.g., by blocking transcription or translation of an enzyme involved in the synthesis of a metabolite, by inactivating an enzyme involved in the synthesis of a metabolite (e.g., by post translational modification or binding to an irreversible inhibitor), or by otherwise inhibiting the activity of an enzyme involved in the synthesis of a metabolite) or a precursor or metabolite thereof, by binding to and inhibiting the function of the cancer specific metabolite, by binding to the target of the cancer specific metabolite (e.g., competitive or non competitive inhibitor), or by increasing the rate of break down or clearance of the metabolite. The compounds may increase the level of cancer specific metabolite by, for example, inhibiting the break down or clearance of the cancer specific metabolite (e.g., by inhibiting an enzyme involved in the breakdown of the metabolite), by increasing the level of a precursor of the cancer specific metabolite, or by increasing the affinity of the metabolite for its target. Exemplary therapeutic targets include, but are not limited to, glycine-N-methyl transferase (GNMT) and sarcosine.


A. Metabolic Pathways


The metabolic pathways of exemplary cancer specific metabolites are described below. Additional metabolites are contemplated for use in the compositions and methods of the present invention and are described, for example, in the Experimental section below.


i. Sarcosine Metabolism


For example, sarcosine is involved in choline metabolism in the liver. The oxidative degradation of choline to glycine in the mammalian liver takes place in the mitochondria, where it enters by a specific transporter. The two last steps in this metabolic pathway are catalyzed by dimethylglycine dehydrogenase (Me2GlyDH), which converts dimethylglycine into sarcosine, and sarcosine dehydrogenase (SarDH), which converts sarcosine (N-methylglycine) into glycine. Both enzymes are located in the mitochondrial matrix. Accordingly, in some embodiments, therapeutic compositions target Me2GlyDH and/or SarDH. Exemplary compounds are identified, for example, by using the drug screening methods described herein.


ii. Glycholic Acid Metabolism


The end products of cholesterol utilization are the bile acids, synthesized in the liver. Synthesis of bile acids is the predominant mechanisms for the excretion of excess cholesterol. However, the excretion of cholesterol in the form of bile acids is insufficient to compensate for an excess dietary intake of cholesterol. The most abundant bile acids in human bile are chenodeoxycholic acid (45%) and cholic acid (31%). The carboxyl group of bile acids is conjugated via an amide bond to either glycine or taurine before their secretion into the bile canaliculi. These conjugation reactions yield glycocholic acid and taurocholic acid, respectively. The bile canaliculi join with the bile ductules, which then form the bile ducts. Bile acids are carried from the liver through these ducts to the gallbladder, where they are stored for future use. The ultimate fate of bile acids is secretion into the intestine, where they aid in the emulsification of dietary lipids. In the gut the glycine and taurine residues are removed and the bile acids are either excreted (only a small percentage) or reabsorbed by the gut and returned to the liver. This process is termed the enterohepatic circulation.


iii. Suberic Acid Metabolism


Suberic acid, also octanedioic acid, is a dicarboxylic acid, with formula C6H12(COOH)2. The peroxisomal metabolism of dicarboxylic acids results in the production of the mediumchain dicarboxylic acids adipic acid, suberic acid, and sebacic acid, which are excreted in the urine.


iv. Xanthosine Metabolism


Xanthosine is involved in purine nucleoside metabolism. Specifically, xanthosine is an intermediate in the conversion of inosine to guanosine. Xanthylic acid can be used in quantitative measurements of the Inosine monophosphate dehydrogenase enzyme activities in purine metabolism, as recommended to ensure optimal thiopurine therapy for children with acute lymphoblastic leukaemia (ALL).


B. Small Molecule Therapies


In some embodiments, small molecule therapeutics are utilized. In certain embodiments, small molecule therapeutics targeting cancer specific metabolites. In some embodiments, small molecule therapeutics are identified, for example, using the drug screening methods of the present invention.


C. Nucleic acid Based Therapies


In other embodiments, nucleic acid based therapeutics are utilized. Exemplary nucleic acid based therapeutics include, but are not limited to antisense RNA, siRNA, and miRNA. In some embodiments, nucleic acid based therapeutics target the expression of enzymes in the metabolic pathways of cancer specific metabolites (e.g., those described above).


In some embodiments, nucleic acid based therapeutics are antisense. siRNAs are used as gene-specific therapeutic agents (Tuschl and Borkhardt, Molecular Intervent. 2002; 2(3):158-67, herein incorporated by reference). The transfection of siRNAs into animal cells results in the potent, long-lasting post-transcriptional silencing of specific genes (Caplen et al, Proc Natl Acad Sci U.S.A. 2001; 98: 9742-7; Elbashir et al., Nature. 2001; 411:494-8; Elbashir et al., Genes Dev. 2001; 15: 188-200; and Elbashir et al., EMBO J. 2001; 20: 6877-88, all of which are herein incorporated by reference). Methods and compositions for performing RNAi with siRNAs are described, for example, in U.S. Pat. No. 6,506,559, herein incorporated by reference.


In other embodiments, expression of genes involved in metabolic pathways of cancer specific metabolites is modulated using antisense compounds that specifically hybridize with one or more nucleic acids encoding the enzymes (See e.g., Georg Sczakiel, Frontiers in Bioscience 5, d194-201 Jan. 1, 2000; Yuen et al., Frontiers in Bioscience d588-593, Jun. 1, 2000; Antisense Therapeutics, Second Edition, Phillips, M. Ian, Humana Press, 2004; each of which is herein incorporated by reference).


D. Gene Therapy


The present invention contemplates the use of any genetic manipulation for use in modulating the expression of enzymes involved in metabolic pathways of cancer specific metabolites described herein. Examples of genetic manipulation include, but are not limited to, gene knockout (e.g., removing the gene from the chromosome using, for example, recombination), expression of antisense constructs with or without inducible promoters, and the like. Delivery of nucleic acid construct to cells in vitro or in vivo may be conducted using any suitable method. A suitable method is one that introduces the nucleic acid construct into the cell such that the desired event occurs (e.g., expression of an antisense construct). Genetic therapy may also be used to deliver siRNA or other interfering molecules that are expressed in vivo (e.g., upon stimulation by an inducible promoter).


Introduction of molecules carrying genetic information into cells is achieved by any of various methods including, but not limited to, directed injection of naked DNA constructs, bombardment with gold particles loaded with said constructs, and macromolecule mediated gene transfer using, for example, liposomes, biopolymers, and the like. Preferred methods use gene delivery vehicles derived from viruses, including, but not limited to, adenoviruses, retroviruses, vaccinia viruses, and adeno-associated viruses. Because of the higher efficiency as compared to retroviruses, vectors derived from adenoviruses are the preferred gene delivery vehicles for transferring nucleic acid molecules into host cells in vivo. Adenoviral vectors have been shown to provide very efficient in vivo gene transfer into a variety of solid tumors in animal models and into human solid tumor xenografts in immune-deficient mice. Examples of adenoviral vectors and methods for gene transfer are described in PCT publications WO 00/12738 and WO 00/09675 and U.S. Pat. Nos. 6,033,908, 6,019,978, 6,001,557, 5,994,132, 5,994,128, 5,994,106, 5,981,225, 5,885,808, 5,872,154, 5,830,730, and 5,824,544, each of which is herein incorporated by reference in its entirety.


Vectors may be administered to subject in a variety of ways. For example, in some embodiments of the present invention, vectors are administered into tumors or tissue associated with tumors using direct injection. In other embodiments, administration is via the blood or lymphatic circulation (See e.g., PCT publication 99/02685 herein incorporated by reference in its entirety). Exemplary dose levels of adenoviral vector are preferably 108 to 1011 vector particles added to the perfusate.


E. Antibody Therapy


In some embodiments, the present invention provides antibodies that target cancer specific metabolites or enzymes involved in their metabolic pathways. Any suitable antibody (e.g., monoclonal, polyclonal, or synthetic) may be utilized in the therapeutic methods disclosed herein. In preferred embodiments, the antibodies used for cancer therapy are humanized antibodies. Methods for humanizing antibodies are well known in the art (See e.g., U.S. Pat. Nos. 6,180,370, 5,585,089, 6,054,297, and 5,565,332; each of which is herein incorporated by reference).


In some embodiments, antibody based therapeutics are formulated as pharmaceutical compositions as described below. In preferred embodiments, administration of an antibody composition of the present invention results in a measurable decrease in cancer (e.g., decrease or elimination of tumor).


F. Pharmaceutical Compositions


The present invention further provides pharmaceutical compositions (e.g., comprising pharmaceutical agents that modulate the level or activity of cancer specific metabolites. The pharmaceutical compositions of some embodiments of the present invention may be administered in a number of ways depending upon whether local or systemic treatment is desired and upon the area to be treated. Administration may be topical (including ophthalmic and to mucous membranes including vaginal and rectal delivery), pulmonary (e.g., by inhalation or insufflation of powders or aerosols, including by nebulizer; intratracheal, intranasal, epidermal and transdermal), oral or parenteral. Parenteral administration includes intravenous, intraarterial, subcutaneous, intraperitoneal or intramuscular injection or infusion; or intracranial, e.g., intrathecal or intraventricular, administration.


Pharmaceutical compositions and formulations for topical administration may include transdermal patches, ointments, lotions, creams, gels, drops, suppositories, sprays, liquids and powders. Conventional pharmaceutical carriers, aqueous, powder or oily bases, thickeners and the like may be necessary or desirable.


Compositions and formulations for oral administration include powders or granules, suspensions or solutions in water or non-aqueous media, capsules, sachets or tablets. Thickeners, flavoring agents, diluents, emulsifiers, dispersing aids or binders may be desirable.


Compositions and formulations for parenteral, intrathecal or intraventricular administration may include sterile aqueous solutions that may also contain buffers, diluents and other suitable additives such as, but not limited to, penetration enhancers, carrier compounds and other pharmaceutically acceptable carriers or excipients.


Pharmaceutical compositions of the present invention include, but are not limited to, solutions, emulsions, and liposome-containing formulations. These compositions may be generated from a variety of components that include, but are not limited to, preformed liquids, self-emulsifying solids and self-emulsifying semisolids.


The pharmaceutical formulations of the present invention, which may conveniently be presented in unit dosage form, may be prepared according to conventional techniques well known in the pharmaceutical industry. Such techniques include the step of bringing into association the active ingredients with the pharmaceutical carrier(s) or excipient(s). In general the formulations are prepared by uniformly and intimately bringing into association the active ingredients with liquid carriers or finely divided solid carriers or both, and then, if necessary, shaping the product.


The compositions of the present invention may be formulated into any of many possible dosage forms such as, but not limited to, tablets, capsules, liquid syrups, soft gels, suppositories, and enemas. The compositions of the present invention may also be formulated as suspensions in aqueous, non-aqueous or mixed media. Aqueous suspensions may further contain substances that increase the viscosity of the suspension including, for example, sodium carboxymethylcellulose, sorbitol and/or dextran. The suspension may also contain stabilizers.


In one embodiment of the present invention the pharmaceutical compositions may be formulated and used as foams. Pharmaceutical foams include formulations such as, but not limited to, emulsions, microemulsions, creams, jellies and liposomes. While basically similar in nature these formulations vary in the components and the consistency of the final product.


Agents that enhance uptake of oligonucleotides at the cellular level may also be added to the pharmaceutical and other compositions of the present invention. For example, cationic lipids, such as lipofectin (U.S. Pat. No. 5,705,188), cationic glycerol derivatives, and polycationic molecules, such as polylysine (WO 97/30731), also enhance the cellular uptake of oligonucleotides.


The compositions of the present invention may additionally contain other adjunct components conventionally found in pharmaceutical compositions. Thus, for example, the compositions may contain additional, compatible, pharmaceutically-active materials such as, for example, antipruritics, astringents, local anesthetics or anti-inflammatory agents, or may contain additional materials useful in physically formulating various dosage forms of the compositions of the present invention, such as dyes, flavoring agents, preservatives, antioxidants, opacifiers, thickening agents and stabilizers. However, such materials, when added, should not unduly interfere with the biological activities of the components of the compositions of the present invention. The formulations can be sterilized and, if desired, mixed with auxiliary agents, e.g., lubricants, preservatives, stabilizers, wetting agents, emulsifiers, salts for influencing osmotic pressure, buffers, colorings, flavorings and/or aromatic substances and the like which do not deleteriously interact with the nucleic acid(s) of the formulation.


Certain embodiments of the invention provide pharmaceutical compositions containing (a) one or more nucleic acid compounds and (b) one or more other chemotherapeutic agents that function by different mechanisms. Examples of such chemotherapeutic agents include, but are not limited to, anticancer drugs such as daunorubicin, dactinomycin, doxorubicin, bleomycin, mitomycin, nitrogen mustard, chlorambucil, melphalan, cyclophosphamide, 6-mercaptopurine, 6-thioguanine, cytarabine (CA), 5-fluorouracil (5-FU), floxuridine (5-FUdR), methotrexate (MTX), colchicine, vincristine, vinblastine, etoposide, teniposide, cisplatin and diethylstilbestrol (DES). Anti-inflammatory drugs, including but not limited to nonsteroidal anti-inflammatory drugs and corticosteroids, and antiviral drugs, including but not limited to ribivirin, vidarabine, acyclovir and ganciclovir, may also be combined in compositions of the invention. Other non-antisense chemotherapeutic agents are also within the scope of this invention. Two or more combined compounds may be used together or sequentially.


Dosing is dependent on severity and responsiveness of the disease state to be treated, with the course of treatment lasting from several days to several months, or until a cure is effected or a diminution of the disease state is achieved. Optimal dosing schedules can be calculated from measurements of drug accumulation in the body of the patient. The administering physician can easily determine optimum dosages, dosing methodologies and repetition rates. Optimum dosages may vary depending on the relative potency of individual oligonucleotides, and can generally be estimated based on EC50s found to be effective in in vitro and in vivo animal models or based on the examples described herein. In general, dosage is from 0.01 μg to 100 g per kg of body weight, and may be given once or more daily, weekly, monthly or yearly. The treating physician can estimate repetition rates for dosing based on measured residence times and concentrations of the drug in bodily fluids or tissues. Following successful treatment, it may be desirable to have the subject undergo maintenance therapy to prevent the recurrence of the disease state, wherein the pharmaceutical composition is administered in maintenance doses, ranging from 0.01 μg to 100 g per kg of body weight, once or more daily, to once every 20 years.


III. Drug Screening Applications

In some embodiments, the present invention provides drug screening assays (e.g., to screen for anticancer drugs). The screening methods of the present invention utilize cancer specific metabolites described herein. As described above, in some embodiments, test compounds are small molecules, nucleic acids, or antibodies. In some embodiments, test compounds target cancer specific metabolites directly. In other embodiments, they target enzymes involved in metabolic pathways of cancer specific metabolites.


In preferred embodiments, drug screening methods are high throughput drug screening methods. Methods for high throughput screening are well known in the art and include, but are not limited to, those described in U.S. Pat. No. 6,468,736, WO06009903, and U.S. Pat. No. 5,972,639, each of which is herein incorporated by reference.


The test compounds of some embodiments of the present invention can be obtained using any of the numerous approaches in combinatorial library methods known in the art, including biological libraries; peptoid libraries (libraries of molecules having the functionalities of peptides, but with a novel, non-peptide backbone, which are resistant to enzymatic degradation but which nevertheless remain bioactive; see, e.g., Zuckennann et al., J. Med. Chem. 37: 2678-85 [1994]); spatially addressable parallel solid phase or solution phase libraries; synthetic library methods requiring deconvolution; the ‘one-bead one-compound’ library method; and synthetic library methods using affinity chromatography selection. The biological library and peptoid library approaches are preferred for use with peptide libraries, while the other four approaches are applicable to peptide, non-peptide oligomer or small molecule libraries of compounds (Lam (1997) Anticancer Drug Des. 12:145).


Examples of methods for the synthesis of molecular libraries can be found in the art, for example in: DeWitt et al., Proc. Natl. Acad. Sci. U.S.A. 90:6909 [1993]; Erb et al., Proc. Nad. Acad. Sci. USA 91:11422 [1994]; Zuckermann et al., J. Med. Chem. 37:2678 [1994]; Cho et al., Science 261:1303 [1993]; Carrell et al., Angew. Chem. Int. Ed. Engl. 33.2059 [1994]; Carell et al., Angew. Chem. Int. Ed. Engl. 33:2061 [1994]; and Gallop et al., J. Med. Chem. 37:1233 [1994].


Libraries of compounds may be presented in solution (e.g., Houghten, Biotechniques 13:412-421 [1992]), or on beads (Lam, Nature 354:82-84 [1991]), chips (Fodor, Nature 364:555-556 [1993]), bacteria or spores (U.S. Pat. No. 5,223,409; herein incorporated by reference), plasmids (Cull et al., Proc. Nad. Acad. Sci. USA 89:18651869 [1992]) or on phage (Scott and Smith, Science 249:386-390 [1990]; Devlin Science 249:404-406 [1990]; Cwirla et al., Proc. Natl. Acad. Sci. 87:6378-6382 [1990]; Felici, J. Mol. Biol. 222:301 [1991]).


In some embodiments, the markers described herein are used to produce a model system for the identification of therapeutic agents for cancer. For example, a cancer-specific biomarker metabolite (for example, sarcosine which activates cell proliferation) can be added to a cell-line to increase the cancer aggressivity of the cell line. The cell line will have an improved dynamic range of response (e.g., ‘readout’) which is useful to screen for anti-cancer agents. While an in vitro example is described, the model assay system may be in vitro, in vivo or ex vivo.


VII. Transgenic Animals

The present invention contemplates the generation of transgenic animals comprising an exogenous gene (e.g., resulting in altered levels of a cancer specific metabolite). In preferred embodiments, the transgenic animal displays an altered phenotype (e.g., increased or decreased presence of metabolites) as compared to wild-type animals. Methods for analyzing the presence or absence of such phenotypes include but are not limited to, those disclosed herein. In some preferred embodiments, the transgenic animals further display an increased or decreased growth of tumors or evidence of cancer.


The transgenic animals of the present invention find use in drug (e.g., cancer therapy) screens. In some embodiments, test compounds (e.g., a drug that is suspected of being useful to treat cancer) and control compounds (e.g., a placebo) are administered to the transgenic animals and the control animals and the effects evaluated.


The transgenic animals can be generated via a variety of methods. In some embodiments, embryonal cells at various developmental stages are used to introduce transgenes for the production of transgenic animals. Different methods are used depending on the stage of development of the embryonal cell. The zygote is the best target for micro-injection. In the mouse, the male pronucleus reaches the size of approximately 20 micrometers in diameter that allows reproducible injection of 1-2 picoliters (pl) of DNA solution. The use of zygotes as a target for gene transfer has a major advantage in that in most cases the injected DNA will be incorporated into the host genome before the first cleavage (Brinster et al., Proc. Natl. Acad. Sci. USA 82:4438-4442 [1985]). As a consequence, all cells of the transgenic non-human animal will carry the incorporated transgene. This will in general also be reflected in the efficient transmission of the transgene to offspring of the founder since 50% of the germ cells will harbor the transgene. U.S. Pat. No. 4,873,191 describes a method for the micro-injection of zygotes; the disclosure of this patent is incorporated herein in its entirety.


In other embodiments, retroviral infection is used to introduce transgenes into a non-human animal. In some embodiments, the retroviral vector is utilized to transfect oocytes by injecting the retroviral vector into the perivitelline space of the oocyte (U.S. Pat. No. 6,080,912, incorporated herein by reference). In other embodiments, the developing non-human embryo can be cultured in vitro to the blastocyst stage. During this time, the blastomeres can be targets for retroviral infection (Janenich, Proc. Natl. Acad. Sci. USA 73:1260 [1976]). Efficient infection of the blastomeres is obtained by enzymatic treatment to remove the zona pellucida (Hogan et al., in Manipulating the Mouse Embryo, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y. [1986]). The viral vector system used to introduce the transgene is typically a replication-defective retrovirus carrying the transgene (Jahner et al., Proc. Natl. Acad. Sci. USA 82:6927 [1985]). Transfection is easily and efficiently obtained by culturing the blastomeres on a monolayer of virus-producing cells (Stewart, et al., EMBO J., 6:383 [1987]). Alternatively, infection can be performed at a later stage. Virus or virus-producing cells can be injected into the blastocoele (Jahner et al., Nature 298:623 [1982]). Most of the founders will be mosaic for the transgene since incorporation occurs only in a subset of cells that form the transgenic animal. Further, the founder may contain various retroviral insertions of the transgene at different positions in the genome that generally will segregate in the offspring. In addition, it is also possible to introduce transgenes into the germline, albeit with low efficiency, by intrauterine retroviral infection of the midgestation embryo (Jahner et al., supra [1982]). Additional means of using retroviruses or retroviral vectors to create transgenic animals known to the art involve the micro-injection of retroviral particles or mitomycin C-treated cells producing retrovirus into the perivitelline space of fertilized eggs or early embryos (PCT International Application WO 90/08832 [1990], and Haskell and Bowen, Mol. Reprod. Dev., 40:386 [1995]).


In other embodiments, the transgene is introduced into embryonic stem cells and the transfected stem cells are utilized to form an embryo. ES cells are obtained by culturing pre-implantation embryos in vitro under appropriate conditions (Evans et al., Nature 292:154 [1981]; Bradley et al., Nature 309:255 [1984]; Gossler et al., Proc. Acad. Sci. USA 83:9065 [1986]; and Robertson et al., Nature 322:445 [1986]). Transgenes can be efficiently introduced into the ES cells by DNA transfection by a variety of methods known to the art including calcium phosphate co-precipitation, protoplast or spheroplast fusion, lipofection and DEAE-dextran-mediated transfection. Transgenes may also be introduced into ES cells by retrovirus-mediated transduction or by micro-injection. Such transfected ES cells can thereafter colonize an embryo following their introduction into the blastocoel of a blastocyst-stage embryo and contribute to the germ line of the resulting chimeric animal (for review, See, Jaenisch, Science 240:1468 [1988]). Prior to the introduction of transfected ES cells into the blastocoel, the transfected ES cells may be subjected to various selection protocols to enrich for ES cells which have integrated the transgene assuming that the transgene provides a means for such selection. Alternatively, the polymerase chain reaction may be used to screen for ES cells that have integrated the transgene. This technique obviates the need for growth of the transfected ES cells under appropriate selective conditions prior to transfer into the blastocoel.


In still other embodiments, homologous recombination is utilized to knock-out gene function or create deletion mutants (e.g., truncation mutants). Methods for homologous recombination are described in U.S. Pat. No. 5,614,396, incorporated herein by reference.


EXPERIMENTAL

The following examples are provided in order to demonstrate and further illustrate certain preferred embodiments and aspects of the present invention and are not to be construed as limiting the scope thereof.


Example 1
A. Methods:

Clinical Samples:


Benign prostate and localized prostate cancer tissues were obtained from a radical prostatectomy series at the University of Michigan Hospitals and the metastatic prostate cancer biospecimens were from the Rapid Autopsy Program, which are both part of University of Michigan Prostate Cancer Specialized Program of Research Excellence (S.P.O.R.E) Tissue Core. Samples were collected with informed consent and prior institutional review board approval at the University of Michigan. Detailed clinical information on each of the tissue samples used in the profiling phase of this study is provided in Table 1. Analogous information for tissues and urine samples used to validate sarcosine are given in Tables 5 and 6 respectively. All the samples were stripped of identifiers prior to metabolomic assessment. For the profiling studies, tissue samples were sent to Metabolon, Inc. without any accompanying clinical information. Upon receipt, each sample was accessioned by Metabolon into a LIMS system and assigned unique 10 digit identifier. The sample was bar coded and this anonymous identifier alone was used to track all sample handling, tasks, results etc. All samples were stored at −80° C. until use.


General Considerations:


The metabolomic profiling analysis of all samples was carried out in collaboration with Metabolon using the following general protocol. All samples were randomized prior to mass spectrometric analyses to avoid any experimental drifts (FIG. 5). A number of internal standards, including injection standards, process standards, and alignment standards were used to assure QA/QC targets were met and to control for experimental variability (see Table 2 for description of standards). The tissue specimens were processed in two batches of 21 samples each. Samples from each of the three tissue diagnostic classes—benign prostate, PCA, and metastatic tumor—were equally distributed across the two batches (FIG. 5). Thus, in each batch there were 8 benign prostates, 6 PCAs, and 7 metastatic tumor samples (FIG. 5). The samples were subsequently processed as described below.


Sample Preparation:


Samples were kept frozen until assays were to be performed. The sample preparation was programmed and automated. It was performed on a MicroLab STAR® sample prep system from Hamilton Company (Reno, Nev.). Sample extraction consisted of sequential organic and aqueous extractions. A recovery standard was introduced at the start of the extraction process. The resulting pooled extract was equally divided into a liquid chromatography (LC) fraction and a gas chromatography (GC) fraction. Samples were dried on a TurboVap® evaporator (Zymark, Claiper Life Science, Hopkinton, Mass.) to remove the organic solvent. Finally, samples were frozen and lyophilized to dryness. As discussed specifically below, all samples were adjusted to final solvent strength and volumes prior to injection. Injection standards were introduced during the final resolvation. In addition to controls and blanks, an additional well-characterized sample (a QC control, for QC verification) was included multiple times into the randomization scheme such that sample preparation and analytical variability could be constantly assessed.


Liquid Chromatography/Mass Spectroscopy (LC/MS):


The LC/MS portion of the platform is based on a Surveyor HPLC and a Thermo-Finnigan LTQ-FT mass spectrometer (Thermo Fisher Corporation, Waltham, Mass.). The LTQ side data was used for compound quantitation. The FT side data, when collected, was used only to confirm the identity of specific compounds. The instrument was set for continuous monitoring of both positive and negative ions. Some compounds are redundantly visualized across more than one of these data-streams, however, not only is the sensitivity and linearity vastly different from interface to interface but these redundancies, in some instances, are actually used as part of the QC program.


The vacuum-dried sample was re-solubilized in 100 μl of injection solvent that contains no less than five injection standards at fixed concentrations. The chromatography was standardized and was never allowed to vary. Internal standards were used both to assure injection and chromatographic consistency. The chromatographic system was operated using a gradient of Acetonitrile (ACN): Water (both solvents were modified by the addition of 0.1% TFA) from 5% to 100% over an 8 minute period, followed by 100% ACN for 8 min. The column was then reconditioned back to starting conditions. The columns (Aquasil C-18, Thermo Fisher Corporation, Waltham, Mass.) were maintained in temperature-controlled chambers during use and were exchanged, washed and reconditioned after every 50 injections. As part of Metabolon's general practice, all columns were purchased from a single manufacturer's lot at the outset of these experiments. All solvents were similarly purchased in bulk from a single manufacturer's lot in sufficient quantity to complete all related experiments. All samples were bar-coded by LIMS and all chromatographic runs were LIMS-scheduled tasks. The raw data files were tracked and processed by their LIMS identifiers and archived to DVD at regular intervals. The raw data was processed as described later.


A similar LC/MS protocol as described above was used to assess sarcosine and creatinine in urine supernatants.


Gas chromatography/Mass Spectrometry (GC/MS):


For the metabolomic profiling studies, the samples destined for GC were re-dried under vacuum desiccation for a minimum of 24 hours prior to being derivatized under dried nitrogen using bistrimethylsilyl-trifluoroacetamide (BSTFA). Samples were analyzed on a Thermo-Finnigan Mat-95 XP (Thermo Fisher Corporation, Waltham, Mass.) using electron impact ionization and high resolution. The column used for the assay was (5% phenyl)-methyl polysiloxane. During the course of the run, temperature was ramped from 40° to 300° C. in a 16 minute period. The resulting spectra were compared against libraries of authentic compounds. As noted above, all samples were scheduled by LIMS and all chromatographic runs were LIMS schedule-based tasks. The raw data files were identified by their LIMS identifiers and archived to DVD at regular intervals. The raw data was processed as described later.


For isotope dilution GC/MS analysis of sarcosine and alanine (in case of urine sediments, FIG. 3d), residual water was removed from the samples by forming an azeotrope with 100 μL of dimethylformamide (DMF), and drying the suspension under vacuum. All of the samples were injected using an on column injector and a Agilent 6890N gas chromatograph equipped with a 15-m DB-5 capillary column (inner diameter, 0.2 mm; film thickness, 0.33 micron; J & W Scientific Folsom, CA) interfaced with a Agilent 5975 MSD mass detector. The t-butyl dimethylsilyl derivatives of sarcosine were quantified by selected ion monitoring (SIM), using isotope dilution electron-impact ionization GC/MS. The levels of alanine and sarcosine that eluted at 3.8 and 4.07 minutes respectively, were quantified using their respective ratio between the ion of m/z 232 derived from native metabolite ([M-O-t-butyl-dimethylsilyl]-) and the ions of m/z 233 and 235 respectively for alanine and sarcosine, derived from the isotopically labeled deuteriated internal standard [2H3] for the compounds. A similar strategy was used for assessment of sarcosine, cysteine, thymine, glycine and glutamic acid in the tissues. The m/z for native and labeled molecular peaks for these compounds were: 158 and 161 (sarcosine), 406 and 407 (cysteine), 432 and 437 (glutamic acid), 297 and 301 (thymine), and 218 and 219 (glycine) respectively. In case of urine supernatants (FIG. 3e), sarcosine was measured and normalized to creatinine Relative area counts for each compound were obtained by manual integration of chromatogram peaks corresponding to each compound using Xcalibur software (Thermo Fisher Corporation, Waltham, Mass.). The data are presented as the log of the ratio, (sarcosine ion counts)/(creatinine ion counts). For metabolite validation, all the samples were assessed by single runs on the instrument except for sarcosine validation of tissues wherein each sample was run in quadruplicates and the average ratio was used for calculate sarcosine levels. The limit of detection (signal/noise>10) was ˜0.1 femtomole for sarcosine using isotope dilution GC/MS.


Metabolomic Libraries:


These were used to search the mass spectral data. The library was created using approximately 800 commercially available compounds that were acquired and registered into the Metabolon LIMS. All compounds were analyzed at multiple concentrations under the conditions as the experimental samples, and the characteristics of each compound were registered into a LIMS-based library. The same library was used for both the LC and GC platforms for determination of their detectable characteristics. These were then analyzed using custom software packages. Initial data visualization used SAS and Spotfire.


Statistical Analysis (See FIG. 6 for Outline):


a) Metabolomic Data


Data Imputation The metabolic data is left censored due to thresholding of the mass spectrometer data. The missing values were input based on the average expression of the metabolite across all subjects. If the mean metabolite measure across samples was greater than 100,000, then zero was imputed, otherwise one half of the minimum measure for that sample was imputed. In this way, it was distinguished which metabolites had missing data due to absence in the sample and which were missing due to instrument thresholds. Sample minimums were used for the imputed values since the mass spectrometer threshold for detection may differ between samples and it was preferred that that threshold level be captured.


Sample Normalization: To reduce between-sample variation the imputed metabolic measures for each tissue sample was centered on its median value and scaled by its interquartile range (IQR).


Analysis:


z-score: This z-score analysis scaled each metabolite according to a reference distribution. Unless otherwise specified, the benign samples were designated as the reference distribution. Thus the mean and standard deviation of the benign samples was determined for each metabolite. Then each sample, regardless of diagnosis, was centered by the benign mean and scaled by the benign standard deviation, per metabolite. In this way, one can look at how the metabolite expressions deviate from the benign state.


Hierarchical Clustering: Hierarchical clustering was performed on the log transformed normalized data. A small value (unity) was added to each normalized value to allow log transformation. The log transformed data was median centered, per metabolite, prior to clustering for better visualization. Pearson's correlation was used for the similarity metric. Clustering was performed using the Cluster program and visualized using Treeview 1. A maize/blue color scheme was used in heat maps of the metabolites.


Comparative Tests: To look at association of metabolite detection with diagnosis, the measure were dichotomized as present or absent (i.e., undetected). Chi-square tests were used to assess difference in rates of presence/absence of measurements for each metabolite between diagnosis groups. To assess the association between metabolite expression levels between diagnosis groups, two-tailed Wilcoxon rank sum tests were used for two-sample tests; benign vs. PCA, PCA vs. Mets. Kruskal-Wallis tests were used for three-way comparisons between all diagnosis groups; benign vs. PCA vs. Mets. Non-parametric tests were used reduce the influence of the imputed values. Tests were run per metabolite on those metabolites that had detectable expression in at least 20% of the samples. Significance was determined using permutation testing in which the sample labels were shuffled and the test was recomputed. This was repeated 1000 times. Tests in which the original statistic was more extreme than the permuted test statistic increased evidence against the null hypothesis of no difference between diagnosis groups. False discovery rates were determined from the permuted P-value using the q-value conversion algorithm of Storey et al 2 as implemented in the R package “q-value”. Pairwise differences in expression in the cell line data and small scale tissue data were tested using two-tailed t-tests with Satterthwaite variance estimation. Comparisons involving multiple cell lines used repeated measures analysis of variance (ANOVA) to adjust for the multiple measures per cell line. Fold change was estimated using ANOVA on a log scale, following the model log(Y)=A+B*Treatment+E. In this way exp(B) is an estimate of (Y|Treatment=1)/(Y|Treatment=0) and the standard error of exp(B) can be estimated from SE(B) using the delta method.


Classification: Metabolites were added to classifiers based on increasing empirical p P-value. Support vector machines (SVM) were used to determine an optimal classifier. Leave-one-out cross validation (LOOCV) was employed to estimate error rates among classifiers. To avoid bias, comparative tests to determine the empirical P-value ranking, were repeated for each leave-one-out sample set. SVM selected the optimal empirical P-value for inclusion in the classifier. Those metabolites that appeared in at least 80% of the LOOCV samples at or below the chosen empirical P-value were selected as the classification set. A principal components analysis was used to help visualize the separation provided by the resulting classification set of metabolites. Principal components one, two, and four were used for plotting.


Validation of Sarcosine in Urine: Urine sediment experiments were performed across three batches; batch-level variation was removed using two adjustments. First, two batches (n=15 and n=18) with available measurements on cell line controls DU145 and RWPE were combined by estimating batch-level differences using only this cell line data in an ANOVA model with the log-transformed ratio of sarcosine to alanine as the response. The second adjustment put the resulting combined batches (n=33) together with the remaining third batch (n=60) by centering (by the median) and scaling (by the median absolute deviation) within each of these two batches. As seen in FIG. 12, the ratio of sarcosine to alanine was predictive of biopsy status not only in the combined dataset but also in each of these two smaller batches separately.


Urine supernatant experiments measured sarcosine in relation to creatinine Analysis was performed using a log base 2 scale to indicate fold change from creatinine Urine sediments and supernatants were tested for differences between biopsy status using a two-tailed Wilcoxon rank-sum test. Associations with clinical parameters were assessed by Pearson correlation coefficients for continuous variables and two-tailed Wilcoxon rank-sum tests for categorical variables.


b) Gene Expression:


Expression profiling of sarcosine-treated PrEC prostate epithelial cells. Expression profiling of PrEC cells treated with either 50 μM alanine or sarcosine for 6 h, was performed using the Agilent Whole Human Genome Oligo Microarray (Santa Clara, Calif.). Total RNA isolated using Trizol from the treated cells was purified using the Qiagen RNAeasy Micro kit (Valencia, Calif.). Total RNA from untreated PrEC cells were used as the reference. One μg of total RNA was converted to cRNA and labeled according to the manufacturer's protocol (Agilent). Hybridizations were performed for 16 hrs at 65° C., and arrays were scanned on an Agilent DNA microarray scanner. Images were analyzed and data extracted using Agilent Feature Extraction Software 9.1.3.1, with linear and lowess normalization performed for each array. A technical replicate was included for each of the two treatments. Fold change was determined as the ratio of sarcosine to alanine for each of two replicates. Genes considered further showed a two fold change, either up or down, in both replicates.


Mapping of “Omics” data to a common identifier. The metabolites profiled in example were mapped to the metabolic maps in KEGG using their compound IDs, followed by identification of all the anabolic and catabolic enzymes in the mapped pathways. This was followed by retrieval of the official enzyme commission number (EC number) for the enzymes that were mapped to its official gene ID using KEGG's DBGET integrated data retrieval system.


Enrichment of Molecular Concepts. In order to explore the network of interrelationships among various molecular concepts and the integrated data (containing information from metabolome), the Oncomine Concepts Map bioinformatics tool was used (Rhodes et al., Neoplasia 9, 443-454 (2007); Tomlins et al., Nat Genet. 39, 41-51 (2007)). In addition to being the largest collection of gene sets for association analysis, the Oncomine Concepts Map (OCM) is unique in that computes pair-wise associations among all gene sets in the database, allowing for the identification and visualization of “enrichment networks” of linked concepts. Integration with the OCM allows one to systematically link molecular signatures (i.e., in this case metabolomic signatures) to over 14,000 molecular concepts. To study the enrichments resulting from the metabolomic data alone involved generation of a list of gene IDs from the metabolites that were significant with a P-value less than 0.05 for the comparisons being made. This signature was used to seed the analysis. On a similar note for gene expression-based enrichment analysis, we used gene IDs for transcripts that were significant (p<0.05) for the comparisons being made. Once seeded, each pair of molecular concepts was tested for association using Fisher's exact test. Each concept was then analyzed independently and the most significant concept reported. Results were stored if a given test had an odds ratio>1.25 and P-value<0.01. Adjustment for multiple comparisons was made by computing q-values for all enrichment analyses. All concepts that had a P-value less than 1×10−4 were considered significant. Additionally, OCM was used to reveal the biological nuance underlying sarcosine-induced invasion of prostate epithelial cells. For this the list of genes that were up regulated by 2-fold upon sarcosine treatment compared to alanine treatment, in both the replicates were used for the enrichment.


B. Results

A number of groups have employed gene expression microarrays to profile prostate cancer tissues (Dhanasekaran et al., Nature 412, 822-826. (2001); Lapointe et al., Proc Natl Acad Sci USA 101, 811-816 (2004); LaTulippe et al., Cancer Res 62, 4499-4506 (2002); Luo et al., Cancer Res 61, 4683-4688. (2001); Luo et al., Mol Carcinog 33, 25-35. (2002); Magee et al., Cancer Res 61, 5692-5696. (2001); Singh et al., Cancer Cell 1, 203-209. (2002); Welsh et al., Cancer Res 61, 5974-5978. (2001); Yu et al., J Clin Oncol 22, 2790-2799 (2004)) as well as other tumors (Golub, T. R., et al. Science 286, 531-537 (1999); Hedenfalk et al. The New England Journal of Medicine 344, 539-548 (2001); Perou et al., Nature 406, 747-752 (2000); Alizadeh et al., Nature 403, 503-511 (2000)) at the transcriptome level, and to a more limited extent, at the proteome level (Ahram et al., Mol Carcinog 33, 9-15 (2002); Hood et al., Mol Cell Proteomics 4, 1741-1753 (2005); Prieto et al., Biotechniques Suppl, 32-35 (2005); Varambally et al., Cancer Cell 8, 393-406 (2005); Martin et al., Cancer Res 64, 347-355 (2004); Wright et al., Mol Cell Proteomics 4, 545-554 (2005); Cheung et al., Cancer Res 64, 5929-5933 (2004)). However, in contrast to genomics and proteomics, metabolomics (i.e., examining metabolites with a global, unbiased perspective) is an emerging science, and represents the distal read-out of the cellular state as well as associated pathophysiology. As part of a systems biology perspective, metabolomic profiling is a useful complement to other approaches.


Metabolomic profiling has long relied on the use of high pressure liquid chromatography (HPLC), nuclear magnetic resonance (NMR) (Brindle et al., J Mol Recognit 10, 182-187 (1997)), mass spectrometry (Gates and Sweeley, Clin Chem 24, 1663-1673 (1978)) (GC/MS and LC/MS) and Enzyme Linked Immuno Sorbent Assay (ELISA). Using such techniques in a focused approach, most of the early studies on neoplastic metabolism have interrogated tumor adaptation to hypoxia (Dang and Semenza, Trends Biochem Sci 24, 68-72 (1999); Kress et al., J Cancer Res Clin Oncol 124, 315-320 (1998)). These investigations revealed heterogeneity within the tumor constituted by varying gradients of metabolites (e.g., glucose or oxygen) and growth factors, which allow neoplastic cells to thrive under conditions of low oxygen tension (Dang and Semenza, supra). Among these targeted approaches are studies that have implicated citrate and choline in the process of prostate cancer progression (Mueller-Lisse et al., European radiology 17, 371-378 (2007); Wu et al., Magn Reson Med 50, 1307-1311 (2003)). Multiple groups have also used cell line models to understand changes in the energy utilization pathways with different degrees of tumor aggressiveness (Vizan et al., Cancer Res 65, 5512-5515 (2005); Al-Saffar et al., Cancer Res 66, 427-434 (2006)). Ramanathan et al. have used metabolic profiling as a tool to correlate different stages of tumor progression with bioenergetic pathways (Proc Natl Acad Sci USA 102, 5992-5997 (2005). More recently, holistic interrogation of the metabolome using nuclear magnetic resonance (Wu et al., supra; Cheng et al., Cancer Res 65, 3030-3034 (2005); Burns et al., Magn Reson Med 54, 34-42 (2005); Kurhanewicz et al., J Magn Reson Imaging 16, 451-463 (2002)) and gas chromatography, coupled with time-of-flight mass spectrometry (Denkert et al., Cancer Res 66, 10795-10804 (2006); Ippolito et al., Proc Natl Acad Sci USA 102, 9901-9906 (2005)), have revealed the power of metabolomic signatures in classifying tumor populations. Despite this increase in power, however, the number of metabolites monitored in these studies is limited.


Prostate cancer is the second most common cause of cancer-related death in men in the western world and afflicts one out of nine men over the age of 65 (Abate-Shen and Shen, Genes Dev 14, 2410-2434 (2000); Ruijter et al, Endocr Rev 20, 22-45 (1999)). To better understand the complex molecular events that characterize prostate cancer initiation, unregulated growth, invasion, and metastasis, it is important to delineate the distinct sets of genes, proteins, and metabolites that dictate its progression from precursor lesion, to localized disease, and subsequent metastasis. With the advent of global profiling strategies, such a systematic analysis of molecular alterations is now possible.


In order to profile the metabolome during prostate cancer progression, a combination of liquid and gas chromatography, coupled with mass spectrometry, was used to interrogate the relative levels of metabolites across 42 prostate-related tissue specimens. FIG. 1a outlines the strategy employed for metabolomic profiling. Specifically, this study included benign adjacent prostate specimens (n=16), clinically localized prostate cancers (PCA, n=12), and metastatic prostate cancers (Mets, n=14) (FIG. 1b). Additionally, selection of metastatic tissue samples from different sites minimized the contribution from nonprostatic tissue (see Table 1 for clinical information). Tissue specimens were processed in two groups, each of which were comprised of equally distributed specimens from the three classes (FIG. 5). The technology component of the metabolomics platform used in this study is described in Lawton et al. (Pharmacogenomics 9: 383 (2008)) and outlined in FIG. 1a. This process involved: sample extraction, separation, detection, spectral analysis, data normalization, delineation of class-specific metabolites, pathway mapping, validation and functional characterization of candidate metabolites (FIG. 6 provides an outline of the data analysis strategy). The reproducibility of the profiling process was addressed at two levels; one by measuring only instrument variation, and the other by measuring overall process variation (refer to Table 2 for a list of controls used to assess reproducibility). Instrument variation was measured from a series of internal standards (n=14 in this study) added to each sample just prior to injection. The median coefficient of variation (CV) value for the internal standard compounds was 3.9%. To address overall process variability, metabolomic studies were augmented to include a set of nine experimental sample technical replicates (also called matrix, abbreviated as MTRX), which were spaced evenly among the injections for each day. Reproducibility analysis for the n=339 compounds detected in each of these nine replicate samples gave a measure of the combined variation for all process components including extraction, recovery, derivatization, injection, and instrument steps. The median CV value for the experimental sample technical replicates (tissue profiling part of this study) was 14.6%. FIG. 7 shows the reproducibility of these experimental-sample technical replicates; Spearman's rank correlation coefficient between pairs of technical replicates ranged from 0.93 to 0.97.


The above authenticated process was used to quantify the metabolomic alterations in prostate-derived tissues. In total, high throughput profiling of prostate tissues identified 626 metabolites (175 named, 19 isobars, and 432 metabolites without identification) that were quantitatively detected in the tissue samples across the three tissue classes (see Table 3 for a complete list of metabolites profiled). Of these, 515 metabolites were shared across all the three classes (FIG. 1b). There were 60 metabolites found in PCA and/or metastatic tumors but not in benign prostate.


Three analyses were performed to provide a global perspective of the data. The first employed unsupervised hierarchical clustering on the normalized data (refer to FIG. 6 for detailed outline of data analysis methods for procedural details). This analysis separated the metastatic samples from both the benign and PCA tissues, but it did not accurately cluster the clinically localized prostate cancers from the benign prostates (FIG. 1c). This indicated a higher degree of metabolomic alteration in the metastatic samples relative to benign and PCA specimens highlighted by the heat map representation of the data. This finding is consistent with earlier observations based on gene expression analyses (Dhanasekaran et al., supra; Tomlins et al., Nat Genet. 39, 41-51 (2007). Further, as shown in FIG. 8, this pattern of metabolomic alterations was shared across multiple metastatic samples derived from different sites of origin.


In the second analysis, each metabolite was centered on the mean and scaled on the standard deviation of the normalized benign metabolite levels to create z-scores based on the distribution of the benign samples (see FIG. 6 and methods for details). FIG. 1d shows the 626 metabolites plotted on the vertical-axis, and the benign-based z-score for each sample plotted on the horizontal-axis for each class of sample. As illustrated by the figure, changes in metabolomic content occur most robustly in metastatic tumors (z-score range: −13.6 to 81.9). In particular, there were 105 metabolites that had a z-score of two or greater in at least 33% of the metastatic samples analyzed. In contrast, the changes in clinically localized prostate cancer samples were less than in metastatic disease (z-score range: −7.7-45.8) such that only 38 metabolites had a z-score of two or greater in at least 33% of the samples.


To investigate the classification potential of metabolomic profiles, the third analysis used a support vector machine (SVM) classification algorithm with leave-one out cross-validation (see Methods). This predictor correctly identified all of the benign and metastatic samples, with misclassification of 2/12 PCA samples as benign. The two misclassified cancer samples had a low Gleason grade of 3+3, which indicates less aggressive tumors. In addition, a list of 198 metabolites that were significant at a P=0.05 level in at least 80% of the leave-one-out cross-validated datasets was generated. (See Table 4 for the list of 198 metabolites). For visualization, principal component analysis was employed on this data matrix of 198 metabolites (FIG. 1e). The resulting figure was similar to the classification obtained using SVM; the samples were well delineated using only three principal components.


To further delineate the metabolomic elements that distinguish the three classes of samples analyzed, differential alterations between the PCA and benign samples were selected using a Wilcoxon rank-sum test coupled with a permutation test (n=1,000). A total of 87/518 metabolites were differential across these two classes at a P-value cutoff of 0.05, corresponding to a false discovery rate of 23%. For visualizing the relationship between 87 dysregulated metabolites across disease states, hierarchical clustering was used to arrange the metabolites based on their relative levels across samples. Among the perturbed metabolites, 50 were elevated in PCA while 37 were downregulated. FIG. 2a displays the relative levels of the 37 named metabolites that were differential between benign prostate and PCA. Among the up-regulated metabolites were a number of amino acids, namely cysteine, glutamate, asparagine, glycine, leucine, proline, threonine, and histidine or their derivatives like sarcosine, n-acetyl-aspartic acid, etc. Those that were down-regulated included inosine, inositol, adenosine, taurine, creatinine, uric acid, and glutathione.


A similar approach was used to identify differential metabolites in metastatic prostate cancer and resulted in 124 metabolites that were elevated in the metastatic state compared to the organ-confined state, with 102 compounds down-regulated and 289/518 (56%) unchanged (at a P-value cutoff of 0.05, corresponding to an false discovery rate of 4%). FIG. 2b displays the levels of the 81 named metabolites that were dysregulated during cancer progression. This includes metabolites that were only detected in metastatic prostate cancer: 4-acetamidobutryic acid, thymine, and two unnamed metabolites. A subset of six metabolites was significantly elevated upon disease advancement. These included sarcosine, uracil, kynurenine, glycerol-3-phosphate, leucine and proline. By virtue of their occurrence in a subset of the PCA samples and a majority of the metastatic samples, these metabolites serve as biomarkers for progressive disease


Upon defining class-specific metabolomic patterns, these changes were evaluated in the context of biochemical pathways and delineation of altered biochemical processes during prostate cancer development and progression. The metabolomic profiles were first mapped to their respective pathways as outlined in the Kyoto Encyclopedia of Genes and Genomes (KEGG, release 41.1). This revealed an increase in amino acid metabolism and nitrogen breakdown pathways during cancer development, supporting the gene expression based prediction of androgen-modulated increased protein synthesis as an early event during prostate cancer development (Tomlins et al, 2007; supra). These trends persisted, and even increased, during the progression to the metastatic disease.


Additionally, the class-specific coordinated metabolite patterns were examined using the bioinformatics tool, Oncomine Concept Maps that permitted systematic linkages of metabolomic signatures to molecular concepts, generating novel hypotheses about the biological progression of prostate cancer (refer to FIG. 9 for an outline of the analyses for localized prostate cancer and metastatic prostate cancer and to the Methods for a description of OCM) (Rhodes et al., Neoplasia 9, 443-454 (2007)). Consistent with the KEGG analysis, the Oncomine analysis expanded upon this theme and (FIG. 3a) and identified an enrichment network of amino acid metabolism in these specimens (FIG. 3a). These included the most enriched GO Biological processes; amino acid metabolism (P=6×10−13) and KEGG pathway for glutamate metabolism (P=6.1×10-24). KEGG pathways for glycine, serine and threonine metabolism (P=2.8×10−14), alanine and aspartate metabolism (P=3.3×10−11), arginine and proline metabolism (P=2.3×10−10) and urea cycle and metabolism of amino groups (P=1.7×10-6) also showed strong enrichment.


The metabolomic profiles for compounds “over-expressed in metastatic samples” (FIG. 3b) showed strong enrichment for elevated methyltransferase activity (FIG. 3b). This increased methylation potential was supported by multiple enrichments of S-adenosyl methionine (SAM) mediated methyltransferase activity including: the enriched InterPro concept, SAM binding motif (P=1.1×10−11) and GO Molecular Function, methyltransferase activity (P=7.7×10−8). These enrichments were a result of significant elevation in the levels of S-adenosyl methionine (P=0.004) in the metastatic samples compared to the PCA samples. The resulting enhancement in the methylation potential of the tumor was further supported by additional concepts that described increased chromatin modification (GO Biological Process, P=2.9×10−6), involvement of SET domain containing proteins (InterPro, P=7.4×10−7) and histone-lysine N-methyltransferase activity (GO Molecular Function, P=6.3×10−6) in the metastatic samples (FIG. 3b). This corroborates earlier studies showing elevation of the SET domain containing histone methyltransferase EZH2 in metastatic tumors (Rhodes et al., Neoplasia 9, 443-454 (2007); Varambally et al., Nature 419, 624-629 (2002); van der Vlag and Otte, Nat Genet. 23, 474-478. (1999); Laible et al., Embo J 16, 3219-3232. (1997); Cao et al., Science 298, 1039-1043. (2002); Kleer et al., Proc Natl Acad Sci USA 100, 11606-11611 (2003).


In light of the enrichment of the amino acid precursors and the methylation potential of the tumor, metabolomic biomarkers that typified both of these mechanisms were characterized. The amino acid metabolite sarcosine, an N-methyl derivative of glycine, fit this criteria in that it is methylated and expected to increase in the presence of an excess amino acid pool and increased methylation (Mudd et al., Metabolism: clinical and experimental 29, 707-720 (1980)). Indeed, the metabolomic profile of metastatic samples showed markedly elevated levels of sarcosine in 79% of the specimens analyzed (Chi-Square test, P=0.0538), whereas 42% of the PCA samples showed a step-wise increase in the levels of this metabolite (FIG. 2a-b). None of the benign samples had detectable levels of sarcosine.


The level of sarcosine in the metastatic samples was significantly greater than PCA samples (Wilcoxon rank-sum test, P=0.005) (FIG. 2b), rendering it clinically useful as a metabolite marker, and for the monitoring of disease progression and aggressiveness. For confirmation, a highly sensitive and specific isotope dilution GC/MS method of accurately quantifying sarcosine from tissue and cellular samples (limit of detection=0.1 fmole) was developed. FIG. 10 illustrates the reproducibility of the GC-MS platform using both prostate-derived cell lines and tissues.


Using this method, the utility of sarcosine as a biomarker was validated in an independent set of 89 tissue samples (25 benign, 36 PCA and 28 metastatic prostate cancers (see Table 5 for sample information). As shown in FIG. 3c, sarcosine levels were significantly elevated in PCA samples compared to benign samples (Wilcoxon rank-sum, P=4.34×10−11). Additionally, sarcosine levels displayed an even greater elevation in the metastatic samples compared to organ-confined disease (Wilcoxon rank-sum, P=6.02×10−11). No association of sarcosine with the site of tumor growth was evident, as noted by its absence in organs derived from metastatic patients that were negative for neoplasm (FIG. 11. a-c). The increase of four additional metabolites in prostate cancer progression were validated these using targeted mass spectrometric assays. As shown in FIG. 14, levels of cysteine, glutamic acid, glycine and thymine were all elevated upon progression from benign to localized prostate cancer and advancement into metastatic disease.


A biomarker panel for early disease detection was developed. As a first step, the ability of sarcosine to function as a non-invasive prostate cancer marker, in the urine of biopsy positive and negative individuals was assayed. Sarcosine was independently assessed in both urine sediments and supernatants derived from this clinically relevant cohort (203 samples derived from 160 patients, with 43 patients contributing both urine sediment and supernatant, see Table 6 for clinical information). Sarcosine levels were reported as a log ratio to either alanine levels (in case of urine sediments) or creatinine levels (in case of urine supernatants). Both alanine and creatinine served as controls for variations in urine concentration. The average standardized (to alanine or creatinine) log ratio for sarcosine was significantly higher in both the urine sediments (n=49) and supernatants (n=59) derived from biopsy-proven prostate cancer patients as compared to biopsy negative controls (n=44 urine sediments and n=51 urine supernatants, FIG. 3d, for urine sediment, Wilcoxon P=0.0004 and FIG. 3e, for urine supernatant, Wilcoxon P=0.0025). As shown in FIG. 12f, receiver operator characteristic (ROC) curves for sarcosine assessment in urine sediments (n=93) gave an AUC of 0.71. Similarly, sarcosine assessment in urine supernatants (n=110) resulted in a comparable AUC of 0.67 (FIG. 13b), indicated that sarcosine finds use as a non-invasive marker for detection of prostate cancer. Further sarcosine levels, both in urine sediment and supernatant were not correlated to various clinical parameters like age, PSA and gland weight (Table 7). As a single marker, these performance criteria are equal or superior to currently available prostate cancer biomarkers.


To investigate the biological role of sarcosine elevation in prostate cancer, prostate cancer cell lines VCaP, DU145, 22RV1 and LNCaP and their benign epithelial counterparts, primary benign prostate epithelial cells PrEC and immortalized benign RWPE prostate cells were used. The sarcosine levels of these cell lines was analyzed using isotope dilution GC/MS and cellular invasion was assayed using a modified Boyden chamber matrigel invasion assay (Kleer et al., Proc Natl Acad Sci USA 100, 11606-11611 (2003). As shown in FIG. 4a, the prostate cancer cell lines displayed significantly higher levels of sarcosine (P=0.0218, repeated measures ANOVA) compared to their benign epithelial counterparts (mean±SEM in fmoles/million cells: 9.3±1.04 vs. 2.7±1.49). Further, sarcosine levels in these cells correlated well with the extent of their invasiveness (FIG. 4a, Spearman's correlation coefficient: 0.943, P=0.0048).


Based on earlier findings that EZH2 over-expression in benign cells could mediate cell invasion and neoplastic progression (Varambally et al., 2002, supra; Kleer et al., 2003, supra), sarcosine levels were compared to EZH2 expression. Sarcosine levels were elevated by 4.5 fold upon EZH2-induced invasion in benign prostate epithelial cells. By contrast, DU145 cells are an invasive prostate cancer cell line in which transient knock-down of EZH2 attenuated cell invasion that was accompanied by approximately 2.5 fold decrease in sarcosine levels (FIG. 4B and FIG. 15). Thus, over-expression of oncogenic EZH2 induces sarcosine production while knock-down of EZH2 attenuates sarcosine production. The association of sarcosine with prostate cancer was further strengthened by studies using TMPRSS2-ERG and TMPRSS2-ETV1 gene fusion models of prostate cancer. Recurrent gene fusions involving ETS family of transcription factors (ERG and ETV1) with TMPRSS2 are integral for prostate cancer development (Tomlins et al., Cancer Res 66, 3396-3400 (2006); Tomlins et al., Science 310, 644-648 (2005)). Sarcosine levels upon constitutive over-expression or attenuation of the fusion products in prostate-derived cell lines were tested. Both TMPRSS2-ERG and TMPRSS2-ETV1 induced invasion (P=0.0019 for TMPRSS2-ERG vs control, and 0.0057 for TMPRSS2-ETV1 vs control) associated with a 3-fold sarcosine elevation in benign prostate epithelial cells (FIG. 4c, over-expression, mean±SEM in fmoles/million cells: 3.3±0.1 for TMPRSS2-ERG and 3.4±0.2 for TMPRSS2-ETV1 vs 0.5±0.3 for control, P=0.0035 for ERG vs control and 0.0016 for ETV1 vs control). Similarly, knock-down of TMPRSS2-ERG gene fusion in VCaP cells (which harbor this gene fusion) resulted in >3 fold decrease in the levels of the metabolite with a similar decrease in the invasive phenotype (FIG. 4c, knock-down, see FIG. 16 for transcript levels of ERG upon siRNA-mediated knock-down).


Taken together, the results indicate that sarcosine levels were associated with cancer cell invasion. To determine if sarcosine plays a role in this process, it was added directly to non-invasive benign prostate epithelial cells. Alanine (an isomer of sarcosine) was used as a control for these experiments. Intracellular sarcosine levels were highly elevated, as assessed by isotope dilution GC-MS, confirming sarcosine uptake by the cells (FIG. 17). The addition of sarcosine imparted an invasive phenotype to benign prostate epithelial cells (FIG. 4d, increased invasion upon sarcosine addition compared to control, 25 μM: 1.64-fold, p=0.065 and 50 μM: 2.57-fold, P<0.001). Similar results were obtained with primary prostate epithelial cells and benign immortalized breast epithelial cells. Exposure of the cells to the amino acids did not affect their ability to progress through the different stages of cell cycle (FIG. 18a-d) or affect proliferation (FIG. 18e). Notably, glycine (the precursor of sarcosine) also induced invasion in these cells, although to a lesser degree than sarcosine (FIG. 4d). The present invention is not limited to a particular mechanism. Indeed, an understanding of the mechanism is not necessary to practice the present invention. Nonetheless, it is contemplated that this indicated that glycine was being converted to sarcosine by the cell thus leading to invasion. To test this hypothesis, we blocked the conversion of glycine to sarcosine using RNA interference-mediated knock-down of glycine-N-methyl transferase (GNMT) (Takata et al., Biochemistry 42, 8394-8402 (2003)), the enzyme responsible for converting glycine to sarcosine, in invasive DU145 prostate cancer cells (FIG. 19). GNMT knockdown resulted in a significant reduction in invasion (P=0.0073, t-test) with a concomitant 3-fold decrease in the intracellular sarcosine levels compared to control non-target siRNA-transfected cells (FIG. 4e, 10.2 vs 31.9 fmoles/million cells). In a similar knockdown experiment performed in benign prostate epithelial cells (FIG. 19, RWPE), it was demonstrated that attenuation of GNMT did not affect the ability of exogenous sarcosine to induce invasion (FIG. 4f and FIG. 20a,b, mean±SEM for sarcosine addition, 0.64±0.07 vs 0.65±0.05, for GNMT knockdown vs control non-target siRNA-transfected cells). In this case, the ability of exogenous glycine to induce invasion was significantly hampered (FIG. 4f and FIG. 20a,b, mean±SEM for glycine addition, 0.20±0.03 vs 0.46±0.04, for GNMT knockdown vs control non-target siRNA-transfected cells, P=0.0082). These findings substantiate the role of sarcosine in mediating tumor invasion and may provide a biological explanation for why it is elevated in invasive prostate cancer.


To determine the pathways that sarcosine activates in order to mediate invasion, gene expression analysis of sarcosine-treated prostate epithelial cells was compared to alanine-treated cells. Oncomine Concepts was used to evaluate whether the genes induced by sarcosine map to other molecular concepts (FIG. 21 and Table 8). Concepts of interest that were found to be significantly associated with sarcosine-induced genes included: (1) genes associated with estrogen receptor (ER) positive breast tumors, (2) genes associated with metastatic or aggressive variants of melanoma, and (3) genes associated with EGF receptor pathway activation in tumors).


As the EGFR pathway and a number of its downstream mediators, including src and p38MAPK, have been implicated in ER positive breast cancer (Gross and Yee, Breast Cancer Res 4, 62-64 (2002); Lazennec et al., Endocrinology 142, 4120-4130 (2001); Rakovic et al., Arch Oncol 14, 146-150 (2006)) and invasive melanoma (Fagiani et al., Cancer Res 67, 3064-3073 (2007)), this pathway was examined in the context of sarcosine-induced cell invasion. Immunoblot analyses confirmed a time-dependent increase in EGFR (FIG. 4g) and src phosphorylation (FIG. 22) in sarcosine-treated prostate epithelial cells (PrEC) compared to alanine-treated controls. Concordant with this was the finding of phosphorylation of p38MAPK in these samples (FIG. 22). It was reported that p38MAPK played a significant role in EGFR-Src-mediated invasion (Park et al., Cancer Res 66, 8511-8519 (2006); Hiscox et al., Clin Exp Metastasis 24, 157-167 (2007); Hiscox et al., Breast Cancer Res Treat 97, 263-274 (2006)). Also total EGFR levels were elevated upon treatment with alanine or sarcosine. The invasion induced by sarcosine was decreased by 70% (P=0.0003) upon pre-treatment of PrEC cells with 10 μM concentration of erlotinib, a small molecule inhibitor of EGFR56-58 (FIG. 4h and FIG. 23, a-c). Similar attenuation of sarcosine-induced invasion was also seen in the immortalized prostate epithelial cells RWPE (t-test: P=0.00007, See FIG. 26). This observation was further strengthened using both antibody-mediated inhibition of EGFRactivity and siRNA-mediated knock-down of receptor levels. Specifically, 50 μg/ml of C225 completely blocked sarcosine induced invasion in RWPE (FIG. 4i and FIG. 25a,b) and PrEC cells. Similar attenuation of sarcosine-induced invasion was obtained using siRNA-mediated knock-down of EGFR compared to non-target control (P=0.0058, FIG. 25a-c).


Changes in metabolic activity and cancer progression are highly interrelated events. Changes in the levels of sarcosine reflect the inherent changes in the biochemistry of the tumor as it develops and progresses to a more advanced state. This is evident from data described above where cancer progression has been shown to be associated with an increase in amino acid metabolism and methylation potential of the tumor. Furthermore, one of the factors leading to an increased methylation potential is the increase in levels of S-adenosyl methionine (SAM) and its pathway components during tumor progression. This translates into elevated levels of methylated metabolites like N-methyl-glycine (sarcosine), methyl-guanosine, methyl-adenosine (known markers of DNA methylation) etc. in tumors compared to their benign counterparts. Notably, one of the major pathways for sarcosine generation involves the transfer of the methyl group from SAM to glycine, a reaction catalyzed by glycine-N-methyl transferase (GNMT). Using siRNA directed against GNMT, it was shown that sarcosine generation is important for the cell invasion process. This supports the hypothesis that elevated levels of sarcosine are a result of a change in the tumor's metabolic activity that is closely associated with the process of tumor progression. Sarcosine produced from tumor progression-associated changes in metabolic activity, by itself promotes tumor invasion.


The data described herein shows that sarcosine levels are reflective of two important hallmarks associated with prostate cancer development; namely increased amino acid metabolism and enhanced methylation potential leading to epigenetic silencing. The former is evident from the metabolomic profiles of localized prostate cancer that show high levels of multiple amino acids. This is also well corroborated by gene expression studies (Tomlins et al., Nat Genet, 2007. 39(1): 41-51) that describe increased protein biosynthesis in indolent tumors. Increased methylation has been known to play a major role in epigenetic silencing. Increased levels of EZH2, a methyltransferase belonging to the polycomb complex, are found in aggressive prostate cancer and metastatic disease (Varambally et al., Nature, 2002. 419(6907):624-9). The current study expands understanding in this realm by implicating tumor progression to be associated with elevated methylation potential. This is supported by the finding of elevated levels of S-adenosyl methionine (the major methylation currency of the cell) and its associated pathway components during prostate cancer progression. This is further reflected by elevated levels of methylated metabolites in the dataset. Included among these is the methylated derivative of glycine (i.e., sarcosine) that shows a progressive elevation in its levels from localized tumor to metastatic disease. Notably, one of the major pathways for sarcosine generation involves the methylation reaction wherein the enzyme glycine-N-methyltransferase catalyses the transfer of methyl groups from SAM to glycine (an essential amino acid). Thus elevated levels of sarcosine can be attributed to an increase in both amino acid levels (in this case glycine) and an increase in methylation, both of which form the hallmarks of prostate cancer progression.


This Example describes unbiased metabolomic profiling of prostate cancer tissues to shed light into the metabolic pathways and networks dysregulated during prostate cancer progression. The present invention is not limited to a particular mechanism. Indeed, an understanding of the mechanism is not necessary to practice the present invention. Nonetheless, it is contemplated that the dysregulation of the metabolome during tumor progression could result from a myriad of causes that include perturbation in activities of their regulatory enzymes, changes in nutrient access or waste clearance during tumor development/progression











TABLE 1






Characteristic
Value+















Benign: Benign adjacent prostate tissues from patients with


prostate cancer










No. of patients
16text missing or illegible when filed



Age at biopsy (years)
  56 ± 6.7 [40, 63]



Race




White (non-Hispanic origin)
12 (92.3%)



Other
 1 (7.7%)







PCA: Patients with clinically localized prostate cancer










No. of patients
11text missing or illegible when filed



Age at biopsy (years)
  57 ± 7.7 [40, 63]



Sample Gleason Grade (minor + major)




3 + 3
 3 (25%)



3 + 4
 5 (41.7%)



4 + 3
 3 (25%)



4 + 4
 1 (8.3%)



Baseline PSA
10.4 ± 8.1 [2.4, 24.6]



Stage




T2a
 3 (30%)



T2b
 4 (40%)



T3a
 2 (20%)



T3b
 0 (0%)



T4
 1 (10%)



Race




White (non-Hispanic origin) (%)
 8 (80%)



Other (%)
 2 (20%)







Mets: Patients with metastatic prostate cancer.










No. of patients
13text missing or illegible when filed



Age at death (years)
 66 ± 12.1 [40, 82]



Sample Location




Soft tissue
 4 (28.6%)



Liver
 8 (57.1%)



Rib
 1 (7.1%)



Diaphragm
 1 (7.1%)



Race




White (non-Hispanic origin) (%)
13 (100%)






text missing or illegible when filed indicates data missing or illegible when filed
















TABLE 2





Standard
Description
Purpose







MTRX
Large pool of human
Assure all aspects of profiling



plasma maintained
process are within specifications



at Metabolon,




characterized




extensively



PRCS
Aliquot of ultra-pure
Process blank to assess contribution



water
to compound signals from process


SOLV
Aliquot of extraction
Solvent blank used to segregate



solvents
contamination sources in extraction


DS
Derivatization
Assess variability of derivatization



Standard
for GC/MS samples


IS
Internal Standard
Assess variability/performance of




instrument


RS
Recovery Standard
Assess variability; verify




performance of




extraction/instrumentation
















TABLE 3







List of named metabolites and isobars measured in benign,


PCA and metastatic prostate cancer tissues using either


liquid chromatography (LC) or gas phase chromatography


(GC) coupled to mass spectrometry








Mass



spectrometry



method



used for



identification






Biochemical


GC
1,5-anhydroglucitol (1,5-AG)


LC
1-Methyladenosine (1mA)


GC
2-Aminoadipate


LC
2′-Deoxyuridine-5′-triphosphate (dUTP)


GC
2-Hydroxybutyrate (AHB)


LC
2-Hydroxybutyrate (AHB)


LC
3-Methyl-2-oxopentanoate


LC
3-Methylhistidine (1-Methylhistidine)


GC
3-Phosphoglycerate


GC
3,4-Dihydroxyphenylethyleneglycol (DOPEG)


LC
4-Acetamidobutanoate


LC
4-Guanidinobutanoate


LC
4-Methyl-2-oxopentanoate


GC
5-Hydroxyindoleacetate (5-HIA)


LC
5-Hydroxytryptophan


LC
5-Methylthioadenosine (MTA)


LC
5-Sulfosalicylate


LC
5,6-Dihydrothymine


GC
5,6-Dihydrouracil


LC
6-Phosphogluconate


LC
Acetylcarnitine (ALC; C2 AC)


GC
Aconitate


GC
Adenine


LC
Adenosine


LC
α-Ketoglutarate


GC
Alanine


LC
Alanylalanine


GC
Arachidonate (20:4n6)


LC
Argininosuccinate


GC
Ascorbate (Vitamin C)


GC
Asparagine


GC
Aspartate


LC
Assymetric Dimethylarginine (ADMA)


GC
α-Tocopherol


LC
Azelate (Nonanedioate)


GC
β-Alanine


GC
β-aminoisobutyrate


GC
β-Hydroxybutyrate (BHBA)


LC
Bicine


LC
Biliverdin


LC
Biotin


LC
Bradykinin


GC
Cadaverine


LC
Caffeine


LC
Carnitine


LC
Catechol


GC
Cholesterol


LC
Ciliatine (2-Aminoethylphosphonate)


GC
Citrate


GC
Citrulline


LC
Creatinine


GC
Cystathionine


GC
Cysteine


LC
Cytidine


LC
Cytidine monophosphate (CMP)


LC
Deoxyuridine


LC
Dihydroxyacetonephosphate (DHAP)


GC
Dimethylbenzimidazole


GC
Erythritol


LC
Ethylmalonate


GC
Fructose


GC
Fructose-6-phosphate


GC
Fumarate (trans-Butenedioate)


GC
Glucose


LC
γ-Glutamylcysteine


LC
γ-Glutamylglutamine


GC
Glutamate


GC
Glutamine


LC
Glutarate (Pentanedioate)


LC
Glutathione reduced (GSH)


GC
Glycerate


GC
Glycerol


GC
Glycerol-3-phosphate (G3P)


LC
Glycerophosphorylcholine (GPC)


GC
Glycine


LC
Glycocholate (GCA)


GC
Guanine


LC
Guanosine


GC
Heptadecanoate (Margarate; 17:0)


LC
Hexanoylcarnitine (C6 AC)


LC
Hippurate (Benzoylglycine)


LC
Histamine


GC
Histidine


LC
Histidinol


LC
Homocysteine


LC
Homoserine lactone


LC
Hydroxyphenylpyruvate


GC
Hydroxyproline


GC
Hypotaurine


LC
Hypoxanthine


GC
Imidazolelactate


LC
Indolelactate


LC
Inosine


LC
Indoxylsulfate


GC
Inositol-1-phosphate (I1P)


GC
Isoleucine


LC
Kynurenate


LC
Kynurenine


GC
Lactate


GC
Laurate (12:0)


GC
Leucine


GC
Linoleate (18:2n6)


GC
Lysine


GC
Malate


GC
Mannose


GC
Mannose-6-phosphate


LC
Methionine


LC
Methylglutarate


GC
myo-Inositol


GC
Myristate (14:0)


LC
N-6-trimethyllysine


LC
N-Acetylaspartate (NAA)


GC
N-Acetylgalactosamine


GC
N-Acetylglucosamine


GC
N-Acetylglucosaminylamine


LC
N-Acetylneuraminate


LC
N-Carbamoylaspartate


LC
Nicotinamide


LC
Nicotinamide adenine dinucleotide (NAD+)


LC
Nicotinamide Ribonucleotide (NMN)


GC
Octadecanoic acid


LC
Ofloxacin


GC
Oleate (18:1n9)


GC
Ornithine


LC
Orotidine-5′-phosphate


GC
Orthophosphate (Pi)


LC
Oxalate (Ethanedioate)


GC
Oxoproline


GC
Palmitate (16:0)


GC
Palmitoleate (16:1n7)


LC
Pantothenate


LC
Paraxanthine


LC
Phenylalanine


GC
Phosphoenolpyruvate (PEP


GC
Phosphoethanolamine


LC
Phosphoserine


GC
p-Hydroxyphenylacetate (HPA)


GC
p-Hydroxyphenyllactate (HPLA)


LC
Picolinate


LC
Pipecolate


GC
Proline


GC
Putrescine


LC
Pyridoxamine


GC
Pyrophosphate (PPi)


LC
Quinolinate


LC
Riboflavin (Vitamin B2)


GC
Ribose


LC
S-Adenosylhomocysteine (SAH)


LC
S-Adenosylmethionine (SAM)


GC
Sarcosine (N-Methylglycine)


GC
Serine


GC
Sorbitol


GC
Spermidine


GC
Spermine


LC
Suberate (Octanedioate)


GC
Succinate


GC
Sucrose/Maltose


LC
Tartarate


LC
Taurine


LC
trans-2,3,4-Trimethoxycinnamate


GC
Threonine


GC
Thymine


LC
Thyroxine


LC
Topiramate


LC
Tryptophan


LC
Tyrosine


LC
UDP-N-acetylmuraminate (UDP-MurNAc)


GC
Uracil


LC
Urate


GC
Urea


LC
Uridine


GC
Valine


LC
Xanthine


LC
Xanthosine


GC
Xylitol



ISOBARS


LC
Isobar includes mannose, fructose, glucose, galactose


LC
Isobar includes arginine, N-alpha-acetyl-ornithine


LC
Isobar includes D-fructose 1-phosphate, beta-D-fructose 6-



phosphate


LC
Isobar includes D-saccharic acid, 1,5-anhydro-D-glucitol


LC
Isobar includes 2-aminoisobutyric acid, 3-amino-



isobutyrate


LC
Isobar includes gamma-aminobutyryl-L-histidine


LC
Isobar includes glutamic acid, O-acetyl-L-serine


LC
Isobar includes L-arabitol, adonitol


LC
Isobar includes L-threonine, L-allothreonine, L-



homoserine


LC
Isobar includes R,S-hydroorotic acid, 5,6-dihydroorotic



acid


LC
Isobar includes inositol 1-phosphate, mannose 6-phosphate


LC
Isobar includes maltotetraose, stachyose


LC
Isobar includes 1-kestose, maltotriose, melezitose


LC
Isobar includes N-acetyl-D-glucosamine, N-acetyl-D-



mannosamine


LC
Isobar includes D-arabinose 5-phosphate, D-ribulose 5-



phosphate


LC
Isobar includes Gluconic acid, DL-arabinose, D-ribose


LC
Isobar includes Maltotetraose, stachyose


LC
Isobar includes valine, betaine


LC
Isobar includes glycochenodeoxycholic



acid/glycodeoxycholic acid
















TABLE 4







List of 198 metabolites that make up the three-class-predictor


derived from LOOCV










Permuted
LOOCV


Metabolite
P-value
Frequency












1,5-anhydroglucitol (1,5-AG)
<0.001
100.00%


1-Methyladenosine (1mA)
<0.001
100.00%


2-Hydroxybutyrate (AHB)
<0.001
100.00%


4-Acetamidobutanoate
<0.001
100.00%


5-Hydroxyindoleacetate (5-HIA)
0.002
100.00%


Adenosine
<0.001
100.00%


Arachidonate (20:4n6)
0.005
100.00%


Aspartate
0.001
100.00%


Assymetric Dimethylarginine (ADMA)
0.001
100.00%


β-aminoisobutyrate
<0.001
100.00%


Bicine
<0.001
100.00%


Biliverdin
0.003
83.30%


Bradykinin hydroxyproline
<0.001
100.00%


Caffeine
0.007
97.60%


Catechol
<0.001
100.00%


Ciliatine (2-Aminoethylphosphonate)
<0.001
100.00%


Citrate
<0.001
100.00%


Creatinine
0.008
85.70%


Cysteine
<0.001
100.00%


Dehydroepiandrosterone sulfate (DHEA-S)
<0.001
100.00%


Erythritol
<0.001
100.00%


Ethylmalonate
<0.001
100.00%


Fumarate (trans-Butenedioate)
0.004
100.00%


γ-Glutamylglutamine
<0.001
100.00%


Glutamate
0.01
85.70%


Glutathione reduced (GSH)
<0.001
100.00%


Glycerol
<0.001
100.00%


Glycerol-3-phosphate (G3P)
<0.001
100.00%


Glycine
0.008
97.60%


Glycocholate (GCA)
0.002
100.00%


Guanosine
<0.001
100.00%


Heptadecanoate (Margarate; 17:0)
<0.001
100.00%


Hexanoylcarnitine (C6 AC)
<0.001
100.00%


Histamine
0.003
100.00%


Histidine
0.002
100.00%


Homocysteine
<0.001
100.00%


Homoserine lactone
0.001
100.00%


Hydroxyphenylpyruvate
<0.001
100.00%


Inosine
<0.001
100.00%


Inositol-1-phosphate (I1P)
<0.001
100.00%


Kynurenine
<0.001
100.00%


Laurate (12:0)
<0.001
100.00%


Leucine
<0.001
100.00%


Linoleate (18:2n6)
<0.001
100.00%


Methylglutarate
0.002
100.00%


myo-Inositol
<0.001
100.00%


Myristate (14:0)
<0.001
100.00%


N-6-trimethyllysine
0.001
100.00%


N-Acetylaspartate (NAA)
0.003
100.00%


N-Acetylgalactosamine
<0.001
100.00%


N-Acetylglucosamine
<0.001
100.00%


N-Acetylglucosaminylamine
0.002
100.00%


Nicotinamide
<0.001
100.00%


Nicotinamide adenine dinucleotide (NAD+)
0.002
100.00%


Octadecanoic acid
<0.001
100.00%


Oleate (18:1n9)
<0.001
100.00%


Orthophosphate (Pi)
<0.001
100.00%


Palmitate (16:0)
<0.001
100.00%


Palmitoleate (16:1n7)
<0.001
100.00%


Pantothenate
0.004
92.90%


Phosphoserine
<0.001
100.00%


p-Hydroxyphenyllactate (HPLA)
<0.001
100.00%


Pipecolate
<0.001
100.00%


Proline
<0.001
100.00%


Putrescine
<0.001
100.00%


Pyridoxamine
0.001
95.20%


Riboflavin (Vitamin B2)
<0.001
100.00%


Ribose
<0.001
100.00%


S-Adenosylmethionine (SAM)
0.001
100.00%


Sarcosine (N-Methylglycine)
<0.001
100.00%


Sorbitol
0.001
100.00%


Spermidine
<0.001
100.00%


Spermine
<0.001
100.00%


Taurine
<0.001
100.00%


Thymine
<0.001
100.00%


Tryptophan
<0.001
100.00%


Uracil
<0.001
100.00%


Urate
<0.001
100.00%


Urea
<0.001
100.00%


Uridine
<0.001
100.00%


Valine
<0.001
100.00%


Xanthine
<0.001
100.00%


Xanthosine
<0.001
100.00%


Isobars and Un-named


Isobar includes mannose, fructose, glucose,
0.001
100.00%


galactose


Isobar includes arginine, N-alpha-acetyl-ornithine
0.005
83.30%


Isobar includes D-saccharic acid, 1,5-anhydro-
<0.001
100.00%


D-glucitol


Isobar includes 2-aminoisobutyric acid,
<0.001
100.00%


3-aminoisobutyrate


Isobar includes L-arabitol, adonitol
<0.001
100.00%


Isobar includes inositol 1-phosphate,
<0.001
100.00%


mannose 6-phosphate


Isobar includes Maltotetraose, stachyose
0.003
100.00%


X-1104
<0.001
100.00%


X-1111
<0.001
100.00%


X-1114
0.002
100.00%


X-1142
0.004
100.00%


X-1186
0.001
97.60%


X-1329
<0.001
100.00%


X-1333
0.002
100.00%


X-1342
0.003
100.00%


X-1349
<0.001
100.00%


X-1351
<0.001
100.00%


X-1465
<0.001
100.00%


X-1575
0.01
100.00%


X-1576
<0.001
100.00%


X-1593
0.003
100.00%


X-1595
<0.001
100.00%


X-1597
0.001
100.00%


X-1608
0.005
100.00%


X-1609
0.002
100.00%


X-1679
<0.001
100.00%


X-1843
<0.001
100.00%


X-1963
<0.001
100.00%


X-1977
<0.001
100.00%


X-1979
0.005
92.90%


X-2055
0.008
83.30%


X-2074
<0.001
100.00%


X-2105
0.005
90.50%


X-2108
0.005
100.00%


X-2118
<0.001
100.00%


X-2141
0.007
88.10%


X-2143
0.002
100.00%


X-2181
<0.001
100.00%


X-2237
0.001
100.00%


X-2272
<0.001
100.00%


X-2292
<0.001
100.00%


X-2466
<0.001
100.00%


X-2548
0.003
97.60%


X-2607
0.005
100.00%


X-2688
0.001
100.00%


X-2690
<0.001
100.00%


X-2697
0.001
100.00%


X-2766
<0.001
100.00%


X-2806
<0.001
100.00%


X-2867
<0.001
100.00%


X-2973
<0.001
100.00%


X-3003
0.001
100.00%


X-3044
0.001
100.00%


X-3056
<0.001
100.00%


X-3102
<0.001
100.00%


X-3129
<0.001
100.00%


X-3138
<0.001
100.00%


X-3139
<0.001
100.00%


X-3176
<0.001
100.00%


X-3220
0.001
100.00%


X-3238
<0.001
100.00%


X-3379
<0.001
100.00%


X-3390
<0.001
100.00%


X-3489
0.001
100.00%


X-3771
<0.001
100.00%


X-3778
<0.001
100.00%


X-3807
<0.001
100.00%


X-3808
<0.001
100.00%


X-3810
<0.001
100.00%


X-3816
<0.001
100.00%


X-3833
0.002
100.00%


X-3893
<0.001
100.00%


X-3952
0.001
100.00%


X-3955
<0.001
100.00%


X-3960
<0.001
100.00%


X-3992
<0.001
100.00%


X-3997
0.002
100.00%


X-4013
<0.001
100.00%


X-4015
<0.001
100.00%


X-4018
<0.001
100.00%


X-4027
<0.001
100.00%


X-4051
<0.001
100.00%


X-4075
<0.001
100.00%


X-4084
<0.001
100.00%


X-4096
<0.001
100.00%


X-4117
0.003
100.00%


X-4365
<0.001
100.00%


X-4428
0.002
100.00%


X-4514
<0.001
100.00%


X-4567
0.003
95.20%


X-4611
<0.001
100.00%


X-4615
<0.001
100.00%


X-4616
0.005
95.20%


X-4617
0.001
100.00%


X-4620
<0.001
100.00%


X-4624
0.003
85.70%


X-4649
<0.001
100.00%


X-4866
0.001
100.00%


X-4869
<0.001
100.00%


X-5107
0.001
100.00%


X-5109
0.004
100.00%


X-5110
0.004
81.00%


X-5128
<0.001
100.00%


X-5187
<0.001
100.00%


X-5207
<0.001
100.00%


X-5208
<0.001
100.00%


X-5209
<0.001
100.00%


X-5210
<0.001
100.00%


X-5212
<0.001
100.00%


X-5214
0.003
100.00%


X-5215
<0.001
100.00%


X-5229
0.003
100.00%


X-5232
0.002
97.60%




















TABLE 5








Number
Number



Tissue type
of samples
of patients




















Benign adjacent prostate tissue
25
20



Local tumor (PCA) tissue
36
36



Metastatic tumor tissue
28
19



Metastasis site: adrenal
1
1



Liver
14
12



Lung
1
1



Mesentary
2
1



Pancreas
1
1



Periaortic lymph
3
2



Soft tissue
2
2



Unknown
4
4



















TABLE 6






Urine Supernatant
Urine Sediment


Characteristic
Samples (n = 110)
Samples (n = 93)







Biopsy Negative




No. of patients
51*
44 **


Age at biopsy (years)
63.4 ± 9.9 [42, 82]
60.7 + 9.6 [40, 77]


Baseline PSA (ng/ml)
 6.1 ± 3.8 [0.8, 20.8]
 5.3 + 2.3 [1.1, 10.0]


Biopsy Positive


No. of patients
59 #
49 ##


Age at biopsy (years)
68.0 ± 8.9 [51, 85]
63.8 + 9.3 [47, 81]


Baseline PSA (ng/ml)
11.9 ± 19.6 [2.7, 111]
11.4 + 23.5




[2.7, 111.0]


Gleason Sum


 6
25 (42.4%)
19 (41.3%)


 7
25 (42.4%)
20 (43.5%)


 8
 3 (5.1%)
 2 (4.4%)


 9
 5 (8.5%)
 5 (10.9%)


10
 1 (1.7%)
 0 (0%)


Maximum tumor
 1.7 ± 1.0 [0.5, 4.3]


diameter


Gland weight
49.1 + 12.2 [28.2, 75.1]
49.9 + 14.6




[28.2, 77.6]




















TABLE 7








Urine





Supernatant
Urine Sediment



Characteristic+
Samples
Samples
















Correlation with Sarcosine (log2)











Age
0.18
  0.19



PSA (log)
0.22
−0.06



Gland weight
−0.09  
−0.17







Two-tailed Wilcoxon rank-sum test of sarcosine (log2)











Diagnosis (neg v pos)
P = 0.0025
P = 0.0004



Gleason (6 v 7+)
P = 0.5756
P = 0.6880





















TABLE 8





Concept Type
OCM #
Concept
Odds Ratio
P-Value



















Oncomine Gene
58926356
Melanoma Type - Top 20% over-
2.07
8.50E−08


Expression

expressed in Lymph Node Metastasis,


Signatures

Metastatic Growth Phase Melanoma,




etc (


Oncomine Gene
142671
Human Primary Mammary Epithelial
2.31
3.60E−07


Expression

Cells Oncogene Transfected - Top 10%


Signatures

under-expressed in c-Src (Bild)


Oncomine Gene
142668
Human Primary Mammary Epithelial
2.11
8.10E−06


Expression

Cells Oncogene Transfected - Top 10%


Signatures

under-expressed in activated B-Cate


Oncomine Gene
58926376
Melanoma Type - Top 20% over-
1.82
1.30E−05


Expression

expressed in Lymph Node Metastasis,


Signatures

Metastatic Growth Phase Melanoma,




etc (


Oncomine Gene
58926256
Melanoma Type - Top 10% over-
2.04
2.60E−05


Expression

expressed in Lymph Node Metastasis,


Signatures

Metastatic Growth Phase Melanoma,




etc (


Oncomine Gene
22210256
Breast Carcinoma Estrogen Receptor
2.14
7.40E−05


Expression

Status - Top 10% over-expressed in


Signatures

Positive (Miller)


Oncomine Gene
131268
Breast Carcinoma Estrogen Receptor
2
1.30E−04


Expression

Status - Top 10% over-expressed in 1


Signatures

(vandeVijver)


Oncomine Gene
58926386
Melanoma Type - Top 20% over-
1.68
1.40E−04


Expression

expressed in Lymph Node Metastasis,


Signatures

Metastatic Growth Phase Melanoma,




etc (


Oncomine Gene
125063
Prostate Biochemical Recurrence - 5
2.12
1.40E−04


Expression

years - Top 10% over-expressed in


Signatures

positive (Glinsky)


Oncomine Gene
142672
Breast Carcinoma Recurrence after
2
1.80E−04


Expression

Tamoxifen Treatment - Top 10% under-


Signatures

expressed in positive (Ma)


Oncomine Gene
22234886
Breast Carcinoma Type - Top 10% over-
2
2.70E−04


Expression

expressed in Invasive Ductal (Radvanyi)


Signatures


Oncomine Gene
125058
Breast Carcinoma Estrogen Receptor
2.03
3.50E−04


Expression

Status - Top 10% over-expressed in


Signatures

positive (Wang)


Oncomine Gene
22210326
Breast Carcinoma Estrogen Receptor
2.02
3.70E−04


Expression

Status - Top 10% over-expressed in


Signatures

Positive (Hess)


Oncomine Gene
23655516
ER+ Breast Carcinoma AGTR1 Over-
2.02
4.30E−04


Expression

expression - Top 10% over-expressed in


Signatures

High (Wang)


Oncomine Gene
140005
ER− Breast Carcinoma Disease Free
1.97
6.40E−04


Expression

Survival - 5 years - Top 10% over-


Signatures

expressed in Relapse (Wang)


Oncomine Gene
22229586
Glioblastoma Type - Top 10% over-
1.78
7.70E−04


Expression

expressed in Glioblastoma Primary Cell


Signatures

Line - with EGF and FGF (Lee)


Oncomine Gene
58926286
Melanoma Type - Top 10% over-
1.78
8.00E−04


Expression

expressed in Lymph Node Metastasis,


Signatures

Metastatic Growth Phase Melanoma,




etc (


Oncomine Gene
140596
Wilms Tumor Disease-free Survial - 2
1.97
0.001


Expression

years - Top 10% over-expressed in


Signatures

Relapse (Williams)


Oncomine Gene
142607
Human Primary Mammary Epithelial
1.72
0.001


Expression

Cells Oncogene Transfected - Top 10%


Signatures

over-expressed in activated H-Ras (B


Oncomine Gene
125050
Acute Myeloid Leukemia N-RAS
1.89
0.001


Expression

Mutation - Top 10% over-expressed in


Signatures

positive (Valk)


Oncomine Gene
142593
Human Primary Mammary Epithelial
1.98
0.002


Expression

Cells Oncogene Transfected - Top 5%


Signatures

over-expressed in activated B-Catenin


Oncomine Gene
135851
Acute Myeloid Leukemia N-RAS
1.85
0.002


Expression

Mutation - Top 10% under-expressed in


Signatures

positive (Valk)


Oncomine Gene
22228926
Breast Carcinoma Her2 Status - Top 5%
1.99
0.002


Expression

over-expressed in Positive (Finak)


Signatures


Oncomine Gene
142599
Human Primary Mammary Epithelial
1.93
0.003


Expression

Cells Oncogene Transfected - Top 5%


Signatures

under-expressed in activated B-Caten


Oncomine Gene
122487
Breast Carcinoma Estrogen Receptor
1.99
0.004


Expression

Status - Top 5% over-expressed in 1


Signatures

(vandeVijver)


Oncomine Gene
8445432
head and neck squamous cell
2.26
0.005


Expression

carcinoma P-Tyr-1173 EGFR


Signatures

Immunohistochemistry - Top 5% over-




expressed in Ve


Oncomine Gene
22233006
Breast Carcinoma HER2/neu Status -
1.57
0.008


Expression

Top 10% over-expressed in Positive


Signatures

(Richardson)









Table 9 below includes analytical characteristics of each of the unnamed metabolites listed in Table 4 above. The table includes, for each listed Metabolite ‘X’, the compound identifier (COMP_ID), retention time (RT), retention index (RI), mass, quant mass, and polarity obtained using the analytical methods described above. “Mass” refers to the mass of the C12 isotope of the parent ion used in quantification of the compound. The values for “Quant Mass” give an indication of the analytical method used for quantification: “Y” indicates GC-MS and “1” indicates LC-MS. “Polarity” indicates the polarity of the quantitative ion as being either positive (+) or negative (−).









TABLE 9







Analytical characteristics of unnamed metabolites.













COMP_ID
Metabolite
RT
RI
MASS
QUANT_MASS
Polarity
















5669
X-1104
2.43
2410
201
1



5689
X-1111
2.69
2700
148.1
1
+


5702
X-1114
2.19
2198
104.1
1
+


5765
X-1142
8.54
8739
163
1



5797
X-1186
8.83
9000
529.6
1
+


6379
X-1329
2.69
2791
210.1
1
+


6396
X-1333
3.05
3794
321.9
1
+


6413
X-1342
9.04
9459.4
265.2
1
+


6437
X-1349
3.5
3876
323.9
1
+


6443
X-1351
1.77
1936.5
177.9
1
+


6787
X-1465
3.45
3600
162.1
1
+


6997
X-1575
2.25
2243.5
219.1
1
+


7002
X-1576
2.51
2530
247.1
1
+


7018
X-1593
2.67
2690
395.9
1



7023
X-1595
3.14
3400
290.1
1
+


7029
X-1597
3.66
4100
265.9
1
+


7073
X-1608
8.08
8253
348.1
1



7081
X-1609
8.31
8529
378
1
+


7272
X-1679
8.52
8705.8
283.1
1



7672
X-1843
3.25
3295
288.7
1



8107
X-1963
13.15
13550.8
464.1
1
+


8189
X-1977
3.56
4060
260.9
1
+


8196
X-1979
1.52
1690.3
199
1



8669
X-2055
1.37
1502
269.9
1
+


8796
X-2074
2.24
2380.9
280.1
1
+


8991
X-2105
8.15
8442
433.6
1
+


9007
X-2108
8.76
8800
277.1
1
+


9038
X-2118
13.1
13367.8
547.1
1
+


9137
X-2141
9.39
9605
409.1
1
+


9143
X-2143
10.11
10327
585.1
1
+


9458
X-2181
8.37
8715.5
298
1
+


10047
X-2237
10.14
10039
453.1
1
+


10286
X-2272
7.96
8377
189.1
1



10424
X-2292
2.4
2900
343.9
1



10774
X-2466
9.19
8760
624.8
1
+


10850
X-2548
5.97
6430
202.9
1



11173
X-2607
10.01
10354
578.2
1
+


11222
X-2688
1.42
1614
182
1



11235
X-2690
1.62
1786.2
441.1
1
+


11262
X-2697
3.77
4241.2
209.9
1
+


11544
X-2766
8.09
8395
397
1
+


11770
X-2806
1.38
1491
185.1
1
+


12298
X-2867
9.65
9908
235.3
1
+


12593
X-2973
4.74
1213.4
281
Y
+


12603
X-2980
5.17
1261.3
266.1
Y
+


12626
X-3003
6.79
1446.6
218.1
Y
+


12682
X-3044
1.52
1615.3
150.1
1
+


12720
X-3056
9.19
9432
185.2
1
+


12770
X-3090
11.31
1954.7
243.1
Y
+


12784
X-3102
11.99
2028.2
217.1
Y
+


12785
X-3103
12.09
2039.2
290.1
Y
+


12912
X-3129
8.8
9012
337.1
1
+


13018
X-3138
8.63
8749
229.2
1
+


13024
X-3139
8.82
8934.5
176.1
1
+


13179
X-3176
1.42
1750
132
1
+


13262
X-3220
3.73
4044.1
233.1
1
+


13328
X-3238
11.77
11827.4
220
1
+


13810
X-3379
1.51
1539
414.1
1
+


13853
X-3390
8.14
8800
595.9
1



14368
X-3489
3.26
3840
226
1
+


15057
X-3771
1.68
1761
227
1



15098
X-3778
7.37
7200
307.3
1
+


15211
X-3807
3
3398.5
245
1
+


15213
X-3808
3.28
3719
288.8
1



15215
X-3810
3.74
4500
188.1
1



15227
X-3816
4.16
5310
173.1
1



15255
X-3833
8.81
9100
261.1
1



15374
X-3893
3.26
3724.5
409
1
+


15532
X-3952
8.7
9150
297.2
1
+


15535
X-3955
8.68
8951.7
357.1
1



15571
X-3960
8.49
8744.1
417.1
1
+


16002
X-3992
1.4
1600
129.2
1



16027
X-3997
2.87
2876
564.9
1



16057
X-4013
8.05
8399.5
547
1



16062
X-4015
7.37
1498.4
160
Y
+


16062
X-4015
7.37
1497.8
160
Y
+


16068
X-4018
8.35
8589.3
664
1



16082
X-4027
8.67
1650.2
274.1
Y
+


16116
X-4051
11.56
1970.2
357.1
Y
+


16131
X-4075
13.27
2171.5
103
Y
+


16143
X-4084
14.98
2393.9
441.3
Y
+


16186
X-4096
8.6
8763.6
318.2
1
+


16219
X-4117
14.7
15040.2
260.3
1
+


16666
X-4365
11.05
1892.9
204
Y
+


16705
X-4428
7.92
8236.5
229.2
1
+


16821
X-4498
7.06
1434.9
103
Y
+


16822
X-4499
7.22
1453
189
Y
+


16829
X-4503
8.39
1589.3
227.2
Y
+


16831
X-4504
8.46
1597.1
244.1
Y
+


16837
X-4507
8.89
1644.9
245
Y
+


16853
X-4514
10.31
1812.3
342.2
Y
+


16866
X-4523
12.46
2048.1
258.1
Y
+


16925
X-4567
3.5
3910.5
203.2
1
+


16984
X-4599
7.42
1471.1
113
Y
+


17028
X-4611
8.07
1546.6
292.1
Y
+


17043
X-4615
7.93
8250
222.1
1
+


17044
X-4616
8.12
8427
276.2
1
+


17048
X-4617
8.39
8588
241.3
1
+


17050
X-4618
8.93
1651.1
349.2
Y
+


17053
X-4620
8.82
9001
312.1
1
+


17064
X-4624
10.01
1779.1
342.2
Y
+


17064
X-4624
10.01
1779.2
342.2
Y
+


17072
X-4628
10.11
1786.4
267.1
Y
+


17074
X-4629
10.29
1806.9
274.1
Y
+


17086
X-4637
11.95
1988.1
193
Y
+


17088
X-4639
12.87
2092.4
156.1
Y
+


17130
X-4649
5.33
5997
164.1
1
+


17444
X-4866
9.18
9069
506.7
1
+


17454
X-4869
10.25
10112.8
534.5
1
+


17844
X-5107
11.87
11986
516.7
1
+


17846
X-5109
12.12
12248.5
560.7
1
+


17847
X-5110
12.24
12350.5
582.6
1
+


17862
X-5128
3.12
3462.8
558
1



17919
X-5187
3.53
3985.5
489.1
1
+


17960
X-5207
7.41
1493.6
151
Y
+


17962
X-5208
7.83
1542.3
84
1


17969
X-5209
8.1
1573.6
218.2
Y
+


17971
X-5210
8.47
1616.4
254.1
Y
+


17977
X-5212
8.88
1665.1
306.1
Y
+


17979
X-5214
11.54
1960
117
Y
+


17980
X-5215
11.98
2008
163
Y
+


17989
X-5229
7.13
1461.6
211.1
Y
+


18017
X-5232
12.19
2031.5
134
Y
+


18232
X-5403
5.92
1301.2
319
Y
+


18251
X-5409
7.46
1477.9
128
Y
+


18253
X-5410
7.53
1484
259.1
Y
+


18257
X-5412
7.98
1538.7
128.9
Y
+


18264
X-5414
8.59
1608.2
217.1
Y
+


18265
X-5415
8.83
1639.9
205
Y
+


18271
X-5418
9.01
1659.7
117
Y
+


18272
X-5419
9.05
1664.1
349.2
Y
+


18273
X-5420
9.09
1669
417.1
Y
+


18307
X-5431
11.53
1946.5
453.2
Y
+


18309
X-5433
11.6
1953.5
294
Y
+


18316
X-5437
12.17
2017.3
337.1
Y
+


18388
X-5491
8.3
1575.3
129
Y
+


18390
X-5492
8.39
1584.6
122
Y
+


18419
X-5506
8.66
1616
334.1
Y
+


18430
X-5511
9.73
1745
128.9
Y
+


18438
X-5518
11.94
1991.3
331.1
Y
+


18442
X-5522
13.05
2119.8
259
Y
+


19954
X-6906
9.13
1675.7
175
Y
+


19960
X-6912
9.5
1721.6
292.1
Y
+


19965
X-6928
10.04
1785.5
117
Y
+


19969
X-6931
10.35
1819.6
267.1
Y
+


19973
X-6946
10.76
1865
281.1
Y
+


19984
X-6956
11.65
1961
323.1
Y
+


19990
X-6962
11.9
1986.5
267.1
Y
+


19997
X-6969
12.36
2040
584.4
Y
+


20014
X-6985
13.75
2209.4
277.1
Y
+


20020
X-6991
13.97
2238.8
292.1
Y
+


22308
X-8886
8.24
1589.9
198.1
Y
+


22320
X-8889
8.62
1634.3
521.2
Y
+


22494
X-8994
10.76
1878.7
447.2
Y
+


22548
X-9026
8.45
1599.5
156
Y
+


22570
X-9033
9.61
1735.6
217.1
Y
+


22881
X-9287
9.1
1656.8
271
Y
+


24074
X-9706
4.39
1107
190
Y
+


24076
X-9726
4.91
1167.5
245
Y
+


24332
X-10128
8.8
1613.2
231
Y
+


24469
X-10266
9.17
1655
328
Y
+


25401
X-10359
9.85
1734.3
292.1
Y
+


25402
X-10360
10.23
1781.9
204
Y
+


25449
X-10385
13.25
2128.9
254
Y
+


25607
X-10437
8.43
1596.4
331.1
Y
+


27883
X-10604
10.7
1854.2
173
Y
+


27884
X-10605
11.07
1892.6
173
Y
+


30275
X-10738
11.67
1986.1
382.1
Y
+


30276
X-10739
11.79
1999
469.2
Y
+


31022
X-10831
10.33
1818.4
257.1
Y
+


31041
X-10835
10.7
1858.4
358.2
Y
+


31053
X-10841
11.6
1952
257.1
Y
+


31203
X-10850
10.25
1817
179
Y
+


31489
X-10914
6.82
1389
241.1
Y
+


31750
X-11011
10.07
1777
287.1
Y
+


31751
X-11012
10.48
1825
175
Y
+


31754
X-11015
12.67
2071
285
Y
+


31757
X-11018
13.68
2200
599.7
Y
+


32026
X-11072
10.15
1802
287.2
Y
+


32120
X-11096
8.4
1596
103.1
Y
+


32127
X-11103
9.48
1732
217.1
Y
+


32550
X-02272_201
1.97
1958
189
1



32557
X-06126_201
2.69
2684
203.1
1



32562
X-11245
3.91
3902
238.3
1



32578
X-11261
3.69
3600
286.2
1
+


32599
X-11282
4.77
4763
254.8
1



32631
X-11314
0.64
634
243
1
+


32649
X-11332
0.92
935
212.1
1
+


32650
X-11333
1
1019
212.1
1
+


32651
X-11334
0.96
982
259.1
1
+


32652
X-11335
0.97
991
229.2
1
+


32653
X-03249_200
1.03
1049
141.1
1
+


32664
X-11347
2.6
2641
413
1
+


32665
X-11348_200
2.62
2664
160.1
1
+


32669
X-11352
0.86
879
189.2
1
+


32672
X-02546_200
0.75
764
129.2
1
+


32674
X-11357
1.71
1750
232.1
1
+


32675
X-03951_200
1.87
1912
367.1
1
+


32709
X-03056_200
2.21
2264
185.2
1
+


32714
X-11397
2.59
2634
300.1
1
+


32735
X-01911_200
4.26
4275
464.1
1
+


32738
X-11421
4.54
4575
314.2
1
+


32740
X-11423
1.05
1038
260.1
1



32754
X-11437
2.89
2888
231
1



32761
X-11444
3.99
3983
541.2
1



32767
X-11450
4.11
4103
224.2
1



32769
X-11452
4.12
4109
352.1
1



32781
X-11464
2.96
3014
402.4
1
+


32787
X-11470
4.16
4151
525.2
1



32792
X-11475
4.25
4240
383.2
1



32807
X-11490
4.77
4762
279.8
1



32827
X-11510
3.92
3925
385.2
1



32878
X-11561
1.26
1252
267.1
1



32881
X-11564
1.2
1188
177.1
1



32910
X-11593
0.79
790
189.2
1



32937
X-03951_201
1.77
1773
365.2
1



32957
X-11640
3.78
3776
377.1
1



32978
X-11656
0.6
612
227
1
+


32996
X-11668
1.37
1367
215.2
1



33009
X-01981_200
1.19
1199
158.2
1
+


33014
X-10457_200
1.47
1515
261.2
1
+


33031
X-11687
2.16
2182
384.1
1
+


33033
X-11689
3.11
3142
432.2
1
+


33090
X-11745
8.37
1581
311.1
Y
+


33094
X-11749
9.12
1668
218.2
Y
+


33100
X-11755
10.39
1820
318.2
Y
+


33103
X-11758
11.3
1917
397.2
Y
+


33106
X-11761
11.97
1991
469.4
Y
+


33127
X-11782
13.71
2205
294.2
Y
+


33171
X-11826
1.48
1489
194.1
1



33188
X-11843
2.69
2710
230.1
1



33195
X-11850
3.2
3228
226.1
1



33280
X-11935
1.88
1945
298.1
1
+


33281
X-11936
2.07
2150
312.1
1
+


33290
X-11945
1.83
1896
283.1
1
+


33291
X-11946
1.52
1595
259.2
1
+


33295
X-11949
3.76
3830
220.1
1
+


33325
X-11979
2.01
2088
251.1
1
+


33347
X-12001
1.57
1592
229.2
1



33352
X-12006
2.18
2201
310.2
1



33356
X-12010
1.68
1707
203.1
1



33359
X-12013
2.07
2094
242.1
1



33361
X-12015
1.3
1318
216.2
1



33393
X-12042
1.31
1313
294
1



33398
X-12047
2.65
2660
362.2
1
+


33405
X-12053
3.24
3272
476.3
1
+


33511
X-12096
1.53
1578
174.2
1
+


33512
X-12097
1.48
1526
174.2
1
+


33514
X-12099
1.35
1384
262.1
1
+


33515
X-12100
1.76
1793
221.1
1
+


33516
X-12101
1.6
1646
164.1
1
+


33519
X-12104
1.72
1755
271.1
1
+


33523
X-12108
1.42
1468
160.2
1
+


33528
X-12113
1.69
1728
321
1
+


33530
X-12115
1.54
1587
260.2
1
+


33532
X-12117
1.44
1486
204.2
1
+


33537
X-12122
1.76
1795
276.2
1
+


33539
X-12124
1.4
1442
469.1
1
+


33542
X-12127
1.22
1235
226.1
1
+


33543
X-12128
1.69
1725
162.1
1
+


33546
X-12131
3
3104
340.1
1
+


33590
X-12170_200
2.45
2534
181.1
1
+


33594
X-12173
1.41
1500
202.2
1
+


33609
X-12188
2.83
2866
228.2
1



33614
X-12193
3.45
3533
220
1
+


33620
X-12199
2.94
3038
263.1
1
+


33627
X-12206
0.64
654
255.1
1



33632
X-12211
2.55
2582
295.2
1



33633
X-12212
3.57
3607
229.1
1



33637
X-12216
1.68
1701
228.1
1



33638
X-12217
2.32
2343
203.1
1



33646
X-12225
0.97
1009
143.2
1
+


33658
X-12236
1.31
1321
245.1
1



33665
X-12243
3.45
3533
279.1
1
+


33669
X-12247
0.82
823
166.1
1



33676
X-12254
2.57
2604
240
1



33683
X-12261
1.83
1850
258.1
1



33704
X-12282
1.31
1341
166.1
1
+


33728
X-12306
2.34
2364
247.1
1



33745
X-12323
1.31
1327
230.2
1



33764
X-12339
1.02
1055
174.1
1
+


33765
X-12340
3.3
3391
278
1
+


33774
X-12349
0.71
699
222.2
1



33786
X-12358
2.78
2796
239.9
1
+


33787
X-12359
1.42
1451
218.1
1
+


33792
X-12364
1.79
1800
204.1
1
+


33804
X-12376
1.48
1514
245.2
1
+


33807
X-12379
3.29
3304
297.2
1
+


33814
X-12386
1
1001
216.3
1



33835
X-12407
1.9
1902
205.1
1



33839
X-12411
1.08
1077
195.2
1



33903
X-12458
0.69
700
189.1
1
+


33910
X-12465
1.41
1475
248.2
1
+


34041
X-12511
4.61
4697
202.1
1
+


34094
X-12534
9.11
1687
185.1
Y
+


34123
X-12556
6.61
1374
116.9
Y
+


34124
X-12557
10.12
1782
287
Y
+


34137
X-12570
9.83
1748
312
Y
+


34138
X-12571
2.36
2400
256.1
1
+


34146
X-12579
6.89
1406
393
Y
+


34170
X-12602
1.42
1456
204.2
1
+


34197
X-12603
1.99
1878
397.3
1



34200
X-12606
1.78
1673
353.2
1



34205
X-12611
1.82
1860
290.2
1
+


34206
X-12612
2.96
3020
416.2
1
+


34223
X-12629
3.33
3396
520.3
1
+


34229
X-12632
3.23
3290
490.3
1
+


34231
X-12634
3.35
3409
548.3
1
+


34235
X-12636
3.86
3890
259.2
1
+


34253
X-12650
3.11
3147
446.2
1
+


34268
X-12663
11.07
1895
359.2
Y
+


34289
X-12680
0.81
819
229.3
1
+


34290
X-12681
0.92
931
176.2
1
+


34291
X-12682
0.93
939
589.2
1
+


34292
X-12683
0.99
1004
675.1
1
+


34294
X-12685
1.05
1060
154.2
1
+


34295
X-12686
1.09
1101
181.1
1
+


34297
X-12688
1.2
1210
203.2
1
+


34298
X-12689
1.17
1183
278.2
1
+


34299
X-12690
1.35
1386
346.1
1
+


34300
X-12691
1.35
1405
360.2
1
+


34304
X-12694
0.72
719
105.1
1



34305
X-12695
0.72
722
144.1
1



34310
X-12700
1.07
1060
227.1
1



34311
X-12701
1.08
1100
319.1
1



34314
X-12704
1.23
1252
274
1



34316
X-12706
1.27
1280
223
1



34318
X-12708
1.28
1295
269
1



34322
X-12712
1.65
1690
219
1



34323
X-12713
1.62
1645
263.1
1



34325
X-12715
1.68
1700
279.1
1



34327
X-12717
1.68
1717
194.1
1



34332
X-12722
1.89
1915
249.1
1



34336
X-12726
2.01
1993
233.1
1



34339
X-12729
2.1
2077
228.1
1



34343
X-12733
2.1
2079
339.2
1



34349
X-12739
2.44
2414
241.2
1



34350
X-12740
2.52
2499
287.1
1



34352
X-12742
2.56
2534
241.2
1



34353
X-12743
2.57
2544
302.2
1



34355
X-12745
2.54
2541
350.1
1



34358
X-12748
1.49
1538
322.1
1
+


34359
X-12749
1.51
1562
262.1
1
+


34360
X-12750
1.53
1580
276.2
1
+


34362
X-12752
1.66
1696
262.2
1
+


34370
X-12760
1.98
2001
302.2
1
+


34372
X-12762
1.96
1990
396.1
1
+


34375
X-12765
2.04
2067
281.2
1
+


34485
X-12802
2.72
2731
318.2
1
+


34497
X-12814
2.59
2597
405.2
1



34498
X-12815
2.65
2659
271.1
1



34503
X-12820
2.72
2727
405.2
1



34505
X-12822
2.78
2786
389.1
1



34511
X-12828
2.99
2995
237.2
1



34524
X-12841
3.9
3937
200.2
1



34526
X-12843
3.9
3938
347.2
1



34527
X-12844
4.12
4168
539.3
1



34528
X-12845
4.19
4234
461.3
1



34529
X-12846
4.17
4218
481.3
1



34530
X-12847
4.19
4240
227.1
1



34531
X-12848
4.24
4288
350.1
1



34532
X-12849
4.69
4726
331.2
1



34533
X-12850
4.82
4847
263.8
1










Example 2
Biomarkers of Tumor Aggressiveness

This example describes biomarkers that are useful in combination to distinguish prostate cancer tumors based on the level of tumor aggressiveness. The tissue samples used in the analysis ranged from non-aggressive (i.e., benign) to extremely aggressive (i.e., metastatic). Biomarkers were measured in benign prostate tissues (N=16), Gleason score major 3 (GS3) tumors (N=8), Gleason score major 4 (GS 4) tumors (N=4) and metastatic tumors (N=14). The levels of a four biomarker panel comprised of citrate, malate, N-acetylaspartate (NAA) and sarcosine (methylglycine) were measured in each sample. The ratio of the biomarkers citrate and malate was determined (citrate/malate). The results of the analysis show that a metabolite panel can be used to distinguish between more aggressive and less aggressive tumors and are presented in FIG. 29). The metastatic tumors (most aggressive) were grouped together and were separated from the benign (non-aggressive) samples. The GS3 and GS4 samples were intermediate to the metastatic and benign, with GS4 more aggressive than GS3. The GS4 samples were closer to the metastatic samples while the GS3 were closer to the benign samples. Three GS3 samples (denoted by numbered arrows on the figure) were more closely associated with the more aggressive tumors (GS4 and metastatic). The biomarker analysis predicts that these tumors were more aggressive (higher aggressivity) than the GS3 samples that were more closely associated with the benign tissue. This prediction was supported by the clinical data associated with these samples. Based upon the clinical data, samples #1 and #2 had extra-prostatic extensions; clinically tissues were judged to be more aggressive if they have extra-prostatic extensions. None of the samples that clustered more closely to the benign samples had extra-prostatic extensions. Taken together, these results show that a metabolite panel can be used to distinguish benign from cancer tumors and to distinguish more aggressive from less aggressive tumors (i.e., determine cancer tumor aggressiveness).


The markers selected in the panel presented are an example of a biomarker panel combining sarcosine with other mechanism-based biomarkers. NAA is a membrane associated prostate-specific marker and citrate and malate are intermediates of the TCA cycle. In addition, this result illustrates the utility of biomarker ratios. Different combinations of metabolites, differing in number and composition and selected from the biomarkers described herein or elsewhere (e.g., PCT US2007/078805, herein incorporated by reference in its entirety), may also be used to generate panels of metabolites that are useful for predicting tumor aggressiveness.


Example 3
Biomarkers Discovered in Urine
I. General Methods

A. Identification of Metabolic Profiles for Prostate Cancer


Each sample was analyzed to determine the concentration of several hundred metabolites. Analytical techniques such as GC-MS (gas chromatography-mass spectrometry) and UHPLC-MS (ultra high performance liquid chromatography-mass spectrometry) were used to analyze the metabolites. Multiple aliquots were simultaneously, and in parallel, analyzed, and, after appropriate quality control (QC), the information derived from each analysis was recombined. Every sample was characterized according to several thousand characteristics, which ultimately amount to several hundred chemical species. The techniques used were able to identify novel and chemically unnamed compounds.


B. Statistical Analysis


The data was analyzed using T-tests to identify molecules (either known, named metabolites or unnamed metabolites) present at differential levels in a definable population or subpopulation (e.g., biomarkers for prostate cancer biological samples compared to control biological samples) useful for distinguishing between the definable populations (e.g., prostate cancer and control, low grade prostate cancer and high grade prostate cancer). Other molecules (either known, named metabolites or unnamed metabolites) in the definable population or subpopulation were also identified. In some analyses the data was normalized according to creatinine levels in the samples while in other analyses the samples were not normalized. Results of both analyses are included.


C. Biomarker Identification


Various peaks identified in the analyses (e.g. GC-MS, UHPLC-MS, MS-MS), including those identified as statistically significant, were subjected to a mass spectrometry based chemical identification process. Biomarkers were discovered by (1) analyzing urine samples from different groups of human subjects to determine the levels of metabolites in the samples and then (2) statistically analyzing the results to determine those metabolites that were differentially present in the two groups.


Biomarkers that Distinguish Cancer from Non-Cancer:


The urine samples used for the analysis were from 51 control individuals with negative biopsies for prostate cancer, and 59 individuals with prostate cancer. After the levels of metabolites were determined, the data was analyzed using the Wilcoxon test to determine differences in the mean levels of metabolites between two populations (i.e., Prostate cancer vs. Control).


As listed below in Table 10, biomarkers were discovered that were differentially present between plasma samples from subjects with prostate cancer and Control subjects with negative prostate biopsies (i.e. not diagnosed with prostate cancer).


Table 10 includes, for each listed biomarker, the p-value determined in the statistical analysis of the data concerning the biomarkers, the compound ID useful to track the compound in the chemical database and the analytical platform used to identify the compounds (GC refers to GC/MS and LC refers to UHPLC/MS/MS2). P-values that are listed as 0.000 are significant at p<0.0001.









TABLE 10







Biomarkers useful to distinguish cancer from non-cancer.















% change


COMP_ID
COMPOUND
LIB_ID
p-value
in PCA














34404
1,3-7-trimethyluric acid
LCneg
0.0457
−61.6700


32391
1,3-dimethylurate
GC
0.0188
264.8018


34400
1-7-dimethylurate
LCneg
0.0442
−55.8508


15650
1-methyladenosine
LCpos
0.0156
61.7971


31609
1-methylguanosine
LCpos
0.0181
10.9223


34395
1-methylurate
LCpos
0.047
−30.4105


22030
2-hydroxyisobutyrate
GC
0.0039
62.9593


1432
2-hydroxyphenylacetate
LCneg
0.0344
59.6277


32776
2-methylbutyroylcarnitine-
LCpos
0.0444
72.8112


1431
3-(4-hydroxyphenyl)lactate
GC
0.003
33.8077


18296
3-4-dihydroxyphenylacetate
GC
0.001
147.8039


1566
3-amino-isobutyrate
GC
0.0167
272.4645


32654
3-dehydrocarnitine-
LCpos
0.0188
56.2816


32397
3-hydroxy-2-ethylpropionate
GC
0.0477
40.3754


531
3-hydroxy-3-methylglutarate
GC
4.03E−05
37.8097


15673
3-hydroxybenzoate
LCneg
3.00E−04
196.7772


12017
3-methoxytyrosine
LCpos
0.0069
95.6504


31940
3-methylcrotonylglycine
LCpos
0.0102
62.5089


1557
3-methylglutarate
GC
0.0134
36.0177


15677
3-methylhistidine
LCneg
0.0203
−42.0713


3155
3-ureidopropionate
LCpos
0.0056
68.9399


1558
4-acetamidobutanoate
LCpos
0.0143
77.3732


22115
4-acetylphenyl-sulfate
LCneg
0.0467
100.8052


21133
4-hydroxybenzoate
GC
0.0049
62.6825


1568
4-hydroxymandelate
GC
0.0091
120.1023


541
4-hydroxyphenylacetate
GC
0.0036
85.2767


22118
4-ureidobutyrate
LCpos
0.0134
67.8751


1418
5,6-dihydrothymine
GC
0.0057
140.1535


1559
5,6-dihydrouracil
GC
0.004
80.4881


437
5-hydroxyindoleacetate
GC
1.00E−04
61.2357


1419
5-methylthioadenosine (MTA)
LCpos
5.00E−04
20.5901


1494
5-oxoproline
LCpos
0.0047
17.9299


31580
7-methylguanosine
GC
1.00E−04
75.7087


554
adenine
GC
1.00E−04
46.4734


555
adenosine
LCpos
0.0011
30.8684


2831
adenosine-3′,5′-cyclic-monophosphate
LCpos
0.0038
75.5601



(cAMP)


1126
alanineQUM
GC
0.0419
66.0477


22808
allantoin
GC
0.0085
47.1337


15142
allo-threonine
GC
0.0148
198.5838


31591
androsterone sulfate
LCneg
0.016
96.0684


575
arabinose
GC
2.00E−04
67.9778


15964
arabitol
GC
7.00E−04
46.2583


1640
ascorbate (Vitamin C)
GC
0.0327
55.6234


18362
azelate (nonanedioate)
LCneg
0.0478
118.3270


3141
betaine
LCpos
0.0093
91.2635


569
caffeine
LCpos
0.0179
−70.6204


15506
choline
LCpos
0.0016
45.0093


12025
cis-aconitate
LCpos
0.0364
22.2510


22158
citramalate
GC
4.00E−04
59.4381


1564
citrate
GC
0.0019
139.2617


2132
citrulline
GC
4.00E−04
93.6606


27718
creatine
LCpos
4.00E−04
43.7043


20700
cyanurate
GC
0.0139
0.0000


31454
cystine
GC
0.0026
170.2201


32425
dehydroisoandrosterone sulfate (DHEA-S)
LCneg
0.0291
162.9464


15743
dimethylarginine
LCpos
2.00E−04
42.3710


5086
dimethylglycine
GC
0.0294
105.5877


32511
EDTA*
LCneg
0.005
−10.4294


20699
erythritol
GC
2.45E−05
54.8561


33477
erythronate*
GC
3.10E−05
34.5359


577
fructose
GC
0.0373
152.8917


1643
fumarate
GC
3.81E−05
61.1976


1117
galactitol-dulcitol-
GC
0.049
−30.9639


34456
gamma-glutamylisoleucine*
LCpos
0.0032
12.7695


18369
gamma-glutamylleucine
LCpos
5.00E−04
202.0740


33422
gammaglutamylphenylalanine
LCpos
0.0013
170.8455


2734
gamma-glutamyltyrosine
LCpos
6.00E−04
199.6524


18280
gentisate
LCneg
0.0254
84.1857


1476
glucarate (saccharate)
GC
0.0163
93.0656


587
gluconate
GC
1.00E−04
49.6957


18534
glucosamine
GC
1.00E−04
56.1753


20488
glucose
GC
1.00E−04
57.0890


15443
glucuronate
GC
6.00E−04
49.1315


57
glutamate
GC
0.0332
15.2177


32393
glutamylvaline
LCpos
7.00E−04
82.6082


15990
glycerophosphorylcholine (GPC)
LCpos
0.0092
22.5740


11777
glycineQUM
GC
0.01
47.6937


15737
glycolate (hydroxyacetate)
GC
0.0125
115.3677


22171
glycylproline
LCpos
0.0156
64.5671


12359
guanidinoacetate
GC
3.00E−04
186.4843


418
guanine
GC
0.0129
80.4718


33454
gulono-1-4-lactone
GC
5.00E−04
39.8172


15753
hippurate
LCpos
0.032
50.4495


1101
homovanillate (HVA)
GC
0.0044
34.8863


3127
hypoxanthine
LCpos
0.0266
25.2729


15716
imidazole lactate
LCpos
4.00E−04
47.0735


33846
indoleacetate*
LCpos
0.0345
88.8776


18349
indolelactate
GC
0.0038
132.9586


33441
isobutyrylcarnitine
LCpos
0.0017
75.8028


1125
isoleucine
LCpos
0.0036
27.0710


34407
isovalerylcarnitine
LCpos
0.0046
42.2654


1417
kynurenate
LCneg
0.025
39.6023


15140
kynurenine
LCpos
0.0095
141.9643


11454
lactose
GC
0.0075
125.7434


60
leucine
LCpos
0.0088
26.6660


584
mannose
GC
0.0294
177.4984


18493
mesaconate (methylfumarate)
GC
0.008
85.1195


1302
methionine
GC
0.002
64.4250


34285
monoethanolamine
GC
0.0024
52.3196


33953
N-acetylarginine
LCneg
0.0014
116.6228


33942
N-acetylasparagine
LCpos
0.0134
79.3354


32195
N-acetylaspartate (NAA)
GC
0.0011
69.7707


15720
N-acetylglutamate
LCpos
0.009
41.1751


33943
N-acetylglutamine
LCneg
0.0294
65.6816


33946
N-acetylhistidine
LCneg
0.0046
81.9682


33967
N-acetylisoleucine
LCpos
0.0055
36.8144


1587
N-acetylleucine
LCpos
0.0042
107.1016


1592
N-acetylneuraminate
GC
0.0028
149.4873


33950
N-acetylphenylalanine
LCpos
0.0012
76.0267


33939
N-acetylthreonine
LCpos
0.026
89.8599


32390
N-acetyltyrosine
LCpos
3.00E−04
148.0601


1591
N-acetylvaline
GC
0.0035
148.2682


31850
N-butyrylglycine
LCneg
0.0356
46.9738


1598
N-tigloylglycine
LCpos
0.0186
36.7886


33936
octanoylcarnitine
LCpos
0.0063
32.2576


1505
orotate
GC
1.00E−04
57.3419


32558
p-cresol sulfate*
LCneg
0.0203
67.1842


32718
phenylacetylglutamine-
LCpos
0.0177
42.1472


33945
phenylacetylglycine
LCpos
0.0049
102.7455


64
phenylalanine
LCpos
0.0137
70.3716


11438
phosphate
GC
0.0112
66.4883


1512
picolinate
GC
0.0401
23.7291


1898
proline
GC
0.0084
49.8421


33442
pseudouridine
LCpos
0.0069
18.3476


1651
pyridoxal
LCpos
0.0212
77.6885


599
pyruvate
GC
0.0104
68.1170


18335
quinate
GC
0.0412
40.7535


1899
quinolinate
LCpos
0.0068
81.2769


27731
ribonate
GC
4.00E−04
61.5332


15948
S-adenosylhomocysteine (SAH)
LCpos
0.0108
84.3170


1516
sarcosineQUM
GC
0.0073
103.7037


32379
scyllo-inositol
GC
0.0435
154.8068


1648
serine
GC
3.00E−04
49.1580


485
spermidine
LCpos
0.0459
−81.3755


2125
taurine
GC
0.0334
172.8511


12360
tetrahydrobiopterin
GC
0.0116
69.2047


27738
threonate
GC
0.0012
51.7428


1284
threonine
GC
0.0056
139.5883


604
thymine
GC
0.0034
161.2888


6104
tryptamine
LCpos
0.0372
62.1316


54
tryptophan
LCpos
0.0091
70.7395


1603
tyramine
LCpos
0.0493
35.8870


1299
tyrosine
GC
0.0011
58.4261


605
uracil
GC
0.0015
129.5276


607
urocanate
LCpos
0.0072
68.0070


34406
valerylcarnitine
LCpos
0.0306
120.0406


1649
valine
LCpos
2.00E−04
54.9329


1567
vanillylmandelate-VMA-
LCneg
0.0443
49.0489


3147
xanthine
LCpos
0.0331
44.5844


15136
xanthosine
LCpos
0.0156
85.5165


15679
xanthurenate
LCpos
0.0077
27.7713


15835
xylose
GC
0.0137
81.6462


32735
X-01911_200
LCpos
0.0143
234.5459


33009
X-01981_200
LCpos
0.0017
48.0588


32550
X-02272_201
LCneg
0.0247
51.0244


32672
X-02546_200
LCpos
5.00E−04
79.4250


32709
X-03056_200
LCpos
0.0142
15.1147


32653
X-03249_200
LCpos
0.0051
100.7635


32675
X-03951_200
LCpos
6.00E−04
22.8452


32937
X-03951_201
LCneg
4.00E−04
27.1295


32557
X-06126_201
LCneg
0.023
106.4585


24332
X-10128
GC
2.00E−04
52.5090


24469
X-10266
GC
0.0032
38.3625


25401
X-10359
GC
0.0024
33.6027


25402
X-10360
GC
0.0262
44.6591


25449
X-10385
GC
0.0136
49.8885


25607
X-10437
GC
0.0474
86.7596


33014
X-10457_200
LCpos
0.0476
22.6361


27883
X-10604
GC
0.0077
43.5902


27884
X-10605
GC
3.00E−04
40.8850


30275
X-10738
GC
0.0049
55.5093


30276
X-10739
GC
0.0034
82.2508


31022
X-10831
GC
7.00E−04
67.9439


31041
X-10835
GC
0.0051
108.0205


31053
X-10841
GC
0.007
66.8101


31203
X-10850
GC
0.0224
96.3934


31489
X-10914
GC
0.0041
33.6270


31750
X-11011
GC
1.00E−04
51.1781


31751
X-11012
GC
1.00E−04
42.1647


31754
X-11015
GC
0.002
43.7399


31757
X-11018
GC
0.0188
209.6372


32026
X-11072
GC
0.038
167.5549


32120
X-11096
GC
0.0025
258.5659


32127
X-11103
GC
0.026
288.9233


32562
X-11245
LCneg
0.0419
116.4416


32578
X-11261
LCpos
0.0357
53.5881


32599
X-11282
LCneg
0.0211
124.6693


32649
X-11332
LCpos
0.0303
−41.3196


32650
X-11333
LCpos
0.0359
53.6853


32664
X-11347
LCpos
1.00E−04
30.8069


32665
X-11348_200
LCpos
6.00E−04
37.7556


32669
X-11352
LCpos
0.0163
51.3693


32674
X-11357
LCpos
0.0314
55.2106


32714
X-11397
LCpos
0.038
126.7154


32738
X-11421
LCpos
0.0318
69.8841


32740
X-11423
LCneg
0.0151
15.7989


32761
X-11444
LCneg
3.00E−04
33.3214


32767
X-11450
LCneg
0.0461
86.9345


32769
X-11452
LCneg
0.0055
95.2700


32781
X-11464
LCpos
0.0435
53.2915


32787
X-11470
LCneg
0.027
13.3518


32792
X-11475
LCneg
0.0032
292.2009


32807
X-11490
LCneg
0.0092
91.7365


32881
X-11564
LCneg
8.00E−04
31.9184


32910
X-11593
LCneg
0.0435
45.1354


32957
X-11640
LCneg
0.0209
111.1731


32996
X-11668
LCneg
0.0196
39.8008


33031
X-11687
LCpos
0.0016
27.7502


33033
X-11689
LCpos
0.0199
46.8620


33090
X-11745
GC
0.0318
35.4414


33094
X-11749
GC
0.0082
63.4649


33100
X-11755
GC
0.0023
48.7368


33103
X-11758
GC
0.0157
30.5194


33106
X-11761
GC
0.0034
61.6069


33127
X-11782
GC
0.0083
314.9654


33171
X-11826
LCneg
0.0042
178.7640


33188
X-11843
LCneg
0.0076
460.0511


33195
X-11850
LCneg
0.0394
210.3870


33280
X-11935
LCpos
0.0016
19.1957


33281
X-11936
LCpos
0.0151
12.3351


33290
X-11945
LCpos
0.0012
32.5289


33291
X-11946
LCpos
0.0439
90.4452


33325
X-11979
LCpos
0.0052
22.8598


33347
X-12001
LCneg
0.0019
170.7811


33352
X-12006
LCneg
2.00E−04
25.9733


33356
X-12010
LCneg
0.0078
72.4838


33359
X-12013
LCneg
0.022
405.5324


33393
X-12042
LCneg
0.0095
93.4761


33398
X-12047
LCpos
0.0046
48.5667


33405
X-12053
LCpos
0.0276
70.0004


33511
X-12096
LCpos
0.0266
38.6810


33512
X-12097
LCpos
0.0333
58.4217


33514
X-12099
LCpos
0.0072
47.4618


33515
X-12100
LCpos
0.0089
21.6757


33516
X-12101
LCpos
1.00E−04
83.2818


33519
X-12104
LCpos
0.0177
11.4120


33523
X-12108
LCpos
0.026
44.2185


33528
X-12113
LCpos
0.025
146.1043


33532
X-12117
LCpos
0.0483
21.8348


33537
X-12122
LCpos
0.0029
66.5031


33539
X-12124
LCpos
9.00E−04
29.0229


33542
X-12127
LCpos
0.0068
123.3782


33543
X-12128
LCpos
0.0167
43.0535


33546
X-12131
LCpos
0.0086
0.0000


33590
X-12170_200
LCpos
0.003
23.1150


33594
X-12173
LCpos
0.0417
−52.8764


33609
X-12188
LCneg
0.0277
80.8620


33614
X-12193
LCpos
0.0114
140.4048


33620
X-12199
LCpos
0.0109
195.2826


33627
X-12206
LCneg
0.0095
15.5730


33632
X-12211
LCneg
0.0038
217.1225


33633
X-12212
LCneg
0.0361
220.1253


33638
X-12217
LCneg
0.0266
42.5603


33646
X-12225
LCpos
6.00E−04
20.7575


33658
X-12236
LCneg
0.0258
109.4350


33669
X-12247
LCneg
0.0156
38.0283


33676
X-12254
LCneg
0.0315
229.5867


33683
X-12261
LCneg
0.0224
215.2098


33704
X-12282
LCpos
0.0032
78.5452


33728
X-12306
LCneg
0.0356
115.0007


33745
X-12323
LCneg
0.0191
36.7940


33764
X-12339
LCpos
0.023
50.4166


33765
X-12340
LCpos
0.0386
131.2436


33786
X-12358
LCpos
0.0019
39.9305


33787
X-12359
LCpos
0.0022
108.4776


33792
X-12364
LCpos
0.015
52.5728


33804
X-12376
LCpos
0.0037
52.2176


33807
X-12379
LCpos
0.0335
84.0021


33814
X-12386
LCneg
0.0028
79.8037


33835
X-12407
LCneg
0.0419
102.2921


33839
X-12411
LCneg
0.0469
181.1927


33903
X-12458
LCpos
0.0454
3.8204


34041
X-12511
LCpos
0.014
67.0961


34094
X-12534
GC
0.0114
23.0764


34123
X-12556
GC
0.0014
38.9741


34124
X-12557
GC
0.0069
133.5437


34137
X-12570
GC
6.00E−04
23.4172


34146
X-12579
GC
0.0166
36.6870


34197
X-12603
LCneg
0.0486
93.9915


34200
X-12606
LCneg
0.0239
84.7583


34205
X-12611
LCpos
0.0024
36.6540


34206
X-12612
LCpos
0.0403
100.6866


34223
X-12629
LCpos
0.0228
64.2063


34229
X-12632
LCpos
0.0345
65.5474


34231
X-12634
LCpos
0.0339
74.2212


34235
X-12636
LCpos
0.0113
30.6322


34253
X-12650
LCpos
0.0228
70.5815


34268
X-12663
GC
0.0186
149.0884


34289
X-12680
LCpos
0.0249
116.7362


34290
X-12681
LCpos
0.0345
53.3469


34291
X-12682
LCpos
0.0266
25.1312


34292
X-12683
LCpos
0.0025
36.9150


34294
X-12685
LCpos
0.0474
70.8178


34295
X-12686
LCpos
0.0052
15.6282


34297
X-12688
LCpos
0.0029
124.9182


34298
X-12689
LCpos
0.0256
20.8243


34299
X-12690
LCpos
0.0019
16.8796


34300
X-12691
LCpos
0.016
81.0894


34304
X-12694
LCneg
0.0292
30.3117


34305
X-12695
LCneg
0.0083
51.2191


34310
X-12700
LCneg
0.005
85.1265


34311
X-12701
LCneg
0.0451
63.6861


34314
X-12704
LCneg
0.0252
243.6844


34316
X-12706
LCneg
0.0413
156.8494


34318
X-12708
LCneg
0.015
79.9730


34322
X-12712
LCneg
0.0487
79.2438


34325
X-12715
LCneg
0.0049
55.2094


34327
X-12717
LCneg
0.012
203.4073


34336
X-12726
LCneg
0.0146
66.2239


34339
X-12729
LCneg
0.0299
117.3626


34343
X-12733
LCneg
0.0108
43.8603


34349
X-12739
LCneg
0.0014
89.0934


34350
X-12740
LCneg
0.0282
405.1284


34352
X-12742
LCneg
0.0199
70.2457


34353
X-12743
LCneg
6.38E−06
70.0243


34355
X-12745
LCneg
0.0045
1230.4546


34358
X-12748
LCpos
1.09E−05
68.9382


34359
X-12749
LCpos
0.0196
14.6434


34360
X-12750
LCpos
0.0452
34.9301


34362
X-12752
LCpos
0.002
28.4767


34370
X-12760
LCpos
0.007
41.6076


34375
X-12765
LCpos
0.0016
57.1255


34485
X-12802
LCpos
0.0031
47.2186


34497
X-12814
LCneg
0.0349
216.9783


34498
X-12815
LCneg
0.0497
98.1436


34503
X-12820
LCneg
0.0467
348.8805


34505
X-12822
LCneg
0.012
64.5382


34511
X-12828
LCneg
0.0107
74.3241


34524
X-12841
LCneg
0.0049
165.1258


34526
X-12843
LCneg
0.0018
432.1185


34527
X-12844
LCneg
0.0029
30.9475


34528
X-12845
LCneg
0.0161
162.3770


34529
X-12846
LCneg
0.0306
27.5410


34530
X-12847
LCneg
0.0306
254.3334


34531
X-12848
LCneg
0.0147
259.3802


34532
X-12849
LCneg
0.022
232.6990


34533
X-12850
LCneg
0.0106
152.3123


12603
X-2980
GC
0.0435
150.0623


12770
X-3090
GC
0.047
49.3716


16062
X-4015
GC
5.00E−04
97.5835


16821
X-4498
GC
5.00E−04
59.0953


16822
X-4499
GC
2.00E−04
65.9952


16829
X-4503
GC
0.0389
448.9493


16831
X-4504
GC
0.0017
34.7506


16837
X-4507
GC
0.0104
33.7584


16866
X-4523
GC
2.00E−04
163.4988


16984
X-4599
GC
0.0033
76.7293


17050
X-4618
GC
0.0085
32.9874


17064
X-4624
GC
0.0052
55.2961


17072
X-4628
GC
0.0075
272.1564


17074
X-4629
GC
1.00E−04
57.5233


17086
X-4637
GC
6.00E−04
181.6876


17088
X-4639
GC
0.0064
88.5308


18232
X-5403
GC
0.0032
32.1164


18251
X-5409
GC
0.0042
39.1551


18253
X-5410
GC
0.017
355.5448


18257
X-5412
GC
0.0104
48.5322


18264
X-5414
GC
0.0032
135.2663


18265
X-5415
GC
0.0171
40.2508


18271
X-5418
GC
3.00E−04
65.0484


18272
X-5419
GC
0.0082
49.3174


18273
X-5420
GC
2.00E−04
50.7034


18307
X-5431
GC
0.0046
267.5213


18309
X-5433
GC
0.0094
131.5460


18316
X-5437
GC
0.0075
142.7695


18388
X-5491
GC
4.19E−05
58.3225


18390
X-5492
GC
8.00E−04
46.4359


18419
X-5506
GC
0.027
65.4907


18430
X-5511
GC
0.0199
107.8683


18438
X-5518
GC
0.0117
1692.6298


18442
X-5522
GC
0.002
45.8239


19954
X-6906
GC
1.00E−04
34.3189


19960
X-6912
GC
0.0031
36.2744


19965
X-6928
GC
0.0191
38.2332


19969
X-6931
GC
0.0136
225.7159


19973
X-6946
GC
0.003
126.2096


19984
X-6956
GC
4.00E−04
77.8832


19990
X-6962
GC
0.0149
42.7975


19997
X-6969
GC
0.0037
545.8663


20014
X-6985
GC
0.0474
106.4077


20020
X-6991
GC
0.015
49.2941


22308
X-8886
GC
0.0452
118.3757


22494
X-8994
GC
0.017
567.8661


22548
X-9026
GC
0.002
125.0265


22570
X-9033
GC
0.0329
85.2545


22881
X-9287
GC
0.0101
85.5217


24074
X-9706
GC
0.0042
46.6887


24076
X-9726
GC
0.0331
50.6677









The cancer status (i.e. non-cancer or cancer) of individual subjects was determined using the biomarkers sarcosine and N-acetyl tyrosine. Using these two markers in combination resulted in cancer diagnosis with 83% sensitivity and 49% specificity. Assuming a 30% prevalence of cancer in a PSA positive population, these biomarkers gave a Negative Predictive Value (NPV) of 87% and a Positive Predictive Value (PPV) of 41%.


Biomarkers that Distinguish Less Aggressive Cancer from More Aggressive Cancer:


The urine samples used for the analysis were obtained from individuals diagnosed with prostate cancer having biopsy scores of GS major 3 or GS major 4 and above. GSmajor3 indicates a lower grade of cancer that is typically less aggressive while GS major 4 indicates a higher grade of cancer that is typically more aggressive. In this analysis the GS major 3 subjects (N=45) were compared to subjects with a GS major 4 (N=13). After the levels of metabolites were determined, the data was analyzed using the Wilcoxon test to determine differences in the mean levels of metabolites between two populations (i.e., Prostate cancer vs. Control).


As listed below in Table 11, biomarkers were discovered that were differentially present between urine samples from subjects with less aggressive/lower grade prostate cancer and subjects with more aggressive/higher grade prostate cancer.


Table 11 includes, for each listed biomarker, the p-value determined in the statistical analysis of the data concerning the biomarkers, the compound ID useful to track the compound in the chemical database and the analytical platform used to identify the compounds (GC refers to GC/MS and LC refers to UHPLC/MS/MS2). P-values that are listed as 0.000 are significant at p<0.0001.









TABLE 11







Biomarkers that distinguish less aggressive from more aggressive prostate


cancer.















% Change in


COMP_ID
COMPOUND
Platform
p-value
Aggressive PCA














34404
1,3-7-trimethyluric acid
LCneg
0.0057
−66.55113998


34400
1-7-dimethylurate
LCneg
0.001
−62.28917254


15650
1-methyladenosine
LCpos
0.0254
43.02217774


34395
1-methylurate
LCpos
4.00E−04
−49.79665561


34389
1-methylxanthine
LCpos
0.0138
−67.90592259


15667
2-isopropylmalate
LCneg
0.0469
166.2876883


18296
3-4-dihydroxyphenylacetate
GC
0.0014
123.2216303


27672
3-indoxyl-sulfate
LCneg
0.0138
−23.7469546


12017
3-methoxytyrosine
LCpos
0.0113
86.24357623


15677
3-methylhistidine
LCneg
0.0059
102.3968054


32445
3-methylxanthine
LCpos
0.0132
−72.50497601


3155
3-ureidopropionate
LCpos
0.022
27.56547555


1558
4-acetamidobutanoate
LCpos
0.0166
59.98174305


15681
4-guanidinobutanoate
LCpos
0.0297
174.6765122


21133
4-hydroxybenzoate
GC
0.01
71.09064956


1568
4-hydroxymandelate
GC
0.0208
89.80468995


22118
4-ureidobutyrate
LCpos
0.017
60.30878737


437
5-hydroxyindoleacetate
GC
0.0226
84.94805375


1494
5-oxoproline
LCpos
0.0056
−29.70497615


31580
7-methylguanosine
GC
0.0347
84.95194026


555
adenosine
LCpos
0.0111
79.86819651


2831
adenosine-3′,5′-cyclic-
LCpos
0.0136
53.42430461



monophosphate (cAMP)


15142
allo-threonine
GC
5.00E−04
307.6014316


575
arabinose
GC
0.0079
148.4557


15964
arabitol
GC
0.0441
98.60829547


1640
ascorbate (Vitamin C)
GC
0.045
175.9986664


18362
azelate (nonanedioate)
LCneg
0.0186
207.3082051


3141
betaineQUM
LCpos
0.0019
111.1077205


569
caffeine
LCpos
0.0075
−81.71522011


12025
cis-aconitate
LCpos
0.0369
−25.83372809


1564
citrate
GC
0.0153
159.3164801


27718
creatine
LCpos
0.0062
239.6294824


513
creatinine
LCpos
0.0291
77.95100223


32425
dehydroisoandrosterone sulfate
LCneg
0.0272
153.7895042



(DHEA-S)


5086
dimethylglycine
GC
0.0084
89.87003058


1643
fumarate
GC
0.023
−27.15601216


1117
galactitol-dulcitol-
GC
0.0036
352.7349757


34456
gamma-glutamylisoleucine*
LCpos
0.0198
83.47303345


18369
gamma-glutamylleucine
LCpos
8.00E−04
100.8835487


33422
gammaglutamylphenylalanine
LCpos
8.00E−04
116.4623197


2734
gamma-glutamyltyrosine
LCpos
0.0018
199.6523546


1476
glucarate (saccharate)
GC
0.0413
78.73546464


587
gluconate
GC
0.0337
135.3595762


15443
glucuronate
GC
0.048
79.98123372


32393
glutamylvaline
LCpos
0.005
53.61399238


15365
glycerol 3-phosphate (G3P)
GC
0.0095
96.65755153


15990
glycerophosphorylcholine (GPC)
LCpos
0.043
−30.99560024


11777
glycine
GC
0.0047
51.51603573


15737
glycolate (hydroxyacetate)
GC
0.0219
103.7720467


22171
glycylproline
LCpos
0.0081
81.31832313


12359
guanidinoacetate
GC
0.0015
163.1261154


33454
gulono-1-4-lactone
GC
0.0413
61.59491649


1101
homovanillate (HVA)
GC
0.0081
87.32242401


21025
iminodiacetate-IDA-
GC
0.021
44.48398584


33846
indoleacetate*
LCpos
0.0362
105.8783175


18349
indolelactate
GC
0.0332
101.7860312


33441
isobutyrylcarnitine
LCpos
0.0279
55.35226019


12110
isocitrate
LCpos
0.0422
−41.41198939


1125
isoleucine
LCpos
0.0208
54.70179416


15140
kynurenine
LCpos
0.0191
132.392076


527
lactate
GC
0.0337
−29.28603115


11454
lactose
GC
0.0117
108.8417975


60
leucine
LCpos
0.0332
44.16653491


584
mannose
GC
0.0158
108.0495974


18493
mesaconate (methylfumarate)
GC
0.0452
−48.02028356


1302
methionine
GC
0.01
93.23111101


34285
monoethanolamine
GC
0.0363
159.4495524


33953
N-acetylarginine
LCneg
0.0317
85.9617038


32195
N-acetylaspartate (NAA)
GC
0.0379
94.62417064


33946
N-acetylhistidine
LCneg
0.0058
59.11465726


1587
N-acetylleucine
LCpos
0.0227
85.37871881


33950
N-acetylphenylalanine
LCpos
0.0095
66.64423652


33939
N-acetylthreonine
LCpos
0.0332
78.16412969


32390
N-acetyltyrosine
LCpos
0.0057
133.7952527


1591
N-acetylvaline
GC
0.0463
66.01491718


18254
paraxanthine
LCpos
0.0219
−63.90495686


33945
phenylacetylglycine
LCpos
0.006
90.17463794


64
phenylalanine
LCpos
0.0254
57.32016167


33442
pseudouridine
LCpos
0.0231
54.52078056


1651
pyridoxal
LCpos
0.0268
54.86441025


599
pyruvate
GC
0.0071
62.1494331


1899
quinolinate
LCpos
0.006
61.91679621


27731
ribonate
GC
0.0394
100.3888599


15948
S-adenosylhomocysteine (SAH)
LCpos
0.0344
62.81234124


1516
sarcosine
GC
0.0021
89.65517241


1648
serine
GC
0.0337
80.59915169


603
spermine
LCpos
0.0247
−78.26667362


18392
theobromine
LCpos
0.0165
−80.1429027


27738
threonate
GC
0.0396
94.31081416


1284
threonine
GC
0.0118
77.88106938


604
thymine
GC
0.0157
71.13143504


54
tryptophan
LCpos
0.0162
80.30828074


1299
tyrosine
GC
0.008
99.33740457


605
uracil
GC
0.0318
75.86987921


32701
urate-
LCpos
0.0482
−49.86065084


607
urocanate
LCpos
0.0219
55.53807526


1649
valine
LCpos
0.0266
132.4327688


15835
xylose
GC
0.0219
79.58039821


32672
X-02546_200
LCpos
0.0124
39.92995063


32653
X-03249_200
LCpos
0.0347
50.52155844


32675
X-03951_200
LCpos
0.0461
77.31945011


32937
X-03951_201
LCneg
0.0404
84.92252578


24469
X-10266
GC
0.0276
73.92296217


25402
X-10360
GC
0.0347
79.71371779


33014
X-10457_200
LCpos
0.0369
26.87901527


27884
X-10605
GC
0.0379
117.0583917


31751
X-11012
GC
0.0266
126.3470402


31754
X-11015
GC
0.0396
60.66427028


32026
X-11072
GC
0.0204
111.0816308


32120
X-11096
GC
0.002
246.5355958


32562
X-11245
LCneg
0.022
147.5795427


32631
X-11314
LCpos
0.0347
−38.84300738


32649
X-11332
LCpos
0.0059
104.0484707


32651
X-11334
LCpos
0.0321
69.54121645


32652
X-11335
LCpos
0.0379
65.56679429


32665
X-11348_200
LCpos
0.0369
71.33451227


32714
X-11397
LCpos
0.0277
−67.48708723


32754
X-11437
LCneg
0.0047
1257.122467


32767
X-11450
LCneg
0.0363
79.38640823


32792
X-11475
LCneg
0.0031
366.4908828


32807
X-11490
LCneg
0.0466
84.13891831


32827
X-11510
LCneg
0.015
137.5062988


32878
X-11561
LCneg
0.0347
39.08827189


32978
X-11656
LCpos
0.045
−55.75256194


33171
X-11826
LCneg
0.0064
144.2554847


33280
X-11935
LCpos
0.0293
61.44828759


33281
X-11936
LCpos
0.0266
53.18088504


33290
X-11945
LCpos
0.0461
51.88262935


33291
X-11946
LCpos
0.0433
57.82662663


33295
X-11949
LCpos
0.0321
−26.25001217


33325
X-11979
LCpos
0.0278
48.01647625


33352
X-12006
LCneg
0.0304
73.56750455


33356
X-12010
LCneg
0.0083
233.0064131


33361
X-12015
LCneg
0.0158
106.0732039


33393
X-12042
LCneg
0.0173
74.91590711


33398
X-12047
LCpos
0.0219
55.34246459


33514
X-12099
LCpos
0.0129
47.01102723


33516
X-12101
LCpos
0.0103
−36.00760478


33530
X-12115
LCpos
0.0441
−33.02940864


33537
X-12122
LCpos
0.0253
49.52870476


33539
X-12124
LCpos
0.0347
46.14882349


33542
X-12127
LCpos
0.0254
89.89660466


33543
X-12128
LCpos
0.0034
−55.28552444


33609
X-12188
LCneg
0.0071
−77.72107587


33614
X-12193
LCpos
0.0063
116.7744629


33620
X-12199
LCpos
0.0254
161.7656256


33632
X-12211
LCneg
0.0216
203.3196007


33633
X-12212
LCneg
0.033
280.5910199


33637
X-12216
LCneg
0.0118
−52.22252608


33638
X-12217
LCneg
0.0482
−39.44206727


33646
X-12225
LCpos
0.0075
59.98551337


33665
X-12243
LCpos
0.0253
−47.60623384


33676
X-12254
LCneg
0.0191
415.8798474


33704
X-12282
LCpos
0.0059
58.42472716


33764
X-12339
LCpos
0.0413
40.70759506


33774
X-12349
LCneg
0.0198
−25.18575014


33787
X-12359
LCpos
0.0111
93.83073384


33804
X-12376
LCpos
0.0124
58.66527499


33814
X-12386
LCneg
0.0136
108.2300401


33835
X-12407
LCneg
0.0489
55.24997178


33839
X-12411
LCneg
0.019
87.92801957


33910
X-12465
LCpos
0.0218
0


34041
X-12511
LCpos
0.0179
89.02312659


34094
X-12534
GC
0.0369
15.74666369


34123
X-12556
GC
0.0386
55.12702293


34137
X-12570
GC
0.029
72.94401006


34138
X-12571
LCpos
0.0461
−51.97060823


34170
X-12602
LCpos
0.0327
33.15918309


34268
X-12663
GC
0.0265
82.0191453


34289
X-12680
LCpos
0.045
93.83428843


34290
X-12681
LCpos
0.0431
67.59059032


34292
X-12683
LCpos
0.0468
76.11571819


34294
X-12685
LCpos
0.0128
114.0988325


34295
X-12686
LCpos
0.0461
54.50094449


34297
X-12688
LCpos
0.0084
100.1303934


34299
X-12690
LCpos
0.0353
74.54432605


34300
X-12691
LCpos
0.0325
67.30133053


34305
X-12695
LCneg
0.0321
52.64061636


34310
X-12700
LCneg
0.0073
102.1108558


34311
X-12701
LCneg
0.0428
159.9798899


34322
X-12712
LCneg
0.0362
107.510855


34323
X-12713
LCneg
0.0253
141.1585404


34332
X-12722
LCneg
0.0181
120.1175671


34339
X-12729
LCneg
0.0428
210.5959332


34343
X-12733
LCneg
0.0037
−57.78309079


34349
X-12739
LCneg
0.0198
−37.87433792


34350
X-12740
LCneg
0.0158
441.3133411


34352
X-12742
LCneg
0.0307
−48.53620833


34353
X-12743
LCneg
0.0138
155.1605436


34355
X-12745
LCneg
0.0354
471.2309818


34358
X-12748
LCpos
0.0461
−13.09684771


34359
X-12749
LCpos
0.0242
−23.31492948


34360
X-12750
LCpos
0.0297
26.42009682


34372
X-12762
LCpos
0.0412
178.3117468


34497
X-12814
LCneg
0.04
170.9153319


34498
X-12815
LCneg
0.0242
98.14355773


34505
X-12822
LCneg
0.0325
43.0072576


34524
X-12841
LCneg
0.0182
189.4742509


34526
X-12843
LCneg
0.0066
118.568709


34528
X-12845
LCneg
0.023
162.3770256


34532
X-12849
LCneg
0.0143
173.837207


34533
X-12850
LCneg
0.0233
138.2604803


12785
X-3103
GC
0.0482
−47.31496658


16062
X-4015
GC
0.0037
43.60275909


16831
X-4504
GC
0.0321
120.6164818


17086
X-4637
GC
0.0028
281.0902182


18251
X-5409
GC
0.0191
71.87489485


18264
X-5414
GC
0.015
90.0100388


18265
X-5415
GC
0.0413
101.7549199


18316
X-5437
GC
0.0053
128.193364


18388
X-5491
GC
0.023
−31.91685364


19960
X-6912
GC
0.0242
129.4486593


19965
X-6928
GC
0.0317
125.0950831


19969
X-6931
GC
0.0278
180.8662725


19973
X-6946
GC
0.0061
149.537457


19990
X-6962
GC
0.0413
34.36068338


19997
X-6969
GC
0.0145
545.8663231


22320
X-8889
GC
0.0441
41.201698


22494
X-8994
GC
0.0236
805.8059769


22570
X-9033
GC
0.0219
−94.82653652


24074
X-9706
GC
0.0482
35.47108011









All publications, patents, patent applications and accession numbers mentioned in the above specification are herein incorporated by reference in their entirety. Although the invention has been described in connection with specific embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications and variations of the described compositions and methods of the invention will be apparent to those of ordinary skill in the art and are intended to be within the scope of the following claims.

Claims
  • 1-20. (canceled)
  • 21. A method of diagnosing cancer, comprising: a) detecting the presence or level of one or more cancer specific metabolites selected from the group consisting of sarcosine, glutamate, glycine, cysteine, asparagine, leucine, proline, threonine, histidine, n-acetyl-aspartic acid, inosine, inositol, adenosine, taurine, creatine, uric acid, glutathione, uracil, kynurenine, glycerol-s-phosphate, glycocholic acid, suberic acid, xanthosine, 4-acetamidobutyric acid, and thymine in a sample from a subject; and b) diagnosing cancer based on the presence or level of said cancer specific metabolite.
  • 22. The method of claim 21, wherein said cancer is prostate cancer.
  • 23. The method of claim 21, wherein said cancer specific metabolite is present or elevated in cancerous samples but not non-cancerous samples.
  • 24. The method of claim 21, wherein said sample is selected from the group consisting of a tissue sample, a blood sample, a serum sample, and a urine sample.
  • 25. The method of claim 24, wherein said tissue sample is a biopsy sample.
  • 26. The method of claim 21, wherein the sample is analyzed using one or more techniques selected from the group consisting of gas chromatography, liquid chromatography, mass spectrometry, ELISA, and antibody linkage.
  • 27. The method of claim 21, wherein said cancer specific metabolite further comprises one or more cancer specific metabolites selected from the group consisting of citrate, malate and N-acetyl tyrosine.
  • 28. The method of claim 21, wherein said one or more cancer specific metabolites is two or more markers.
  • 29. The method of claim 21, wherein said one or cancer specific metabolites is three or more markers.
  • 30. The method of claim 21, wherein said one or more cancer specific metabolites are sarcosine, glutamate, and glycine.
  • 31. A method of diagnosing cancer, comprising: a) detecting the presence or level of sarcosine, glutamate, and glycine in a sample from a subject; and b) diagnosing cancer based on the presence or level of said cancer specific metabolite.
  • 32. A method of characterizing prostate cancer, comprising: a) detecting the presence or absence of an elevated level of sarcosine and one or more cancer specific metabolites selected from the group consisting of glutamate, glycine, cysteine, asparagine, leucine, proline, threonine, histidine, n-acetyl-aspartic acid, inosine, inositol, adenosine, taurine, creatine, uric acid, glutathione, uracil, kynurenine, glycerol-s-phosphate, glycocholic acid, suberic acid, xanthosine, 4-acetamidobutyric acid, and thymine in a sample from a subject diagnosed with cancer; and b) characterizing said prostate cancer based on the presence of said elevated levels of sarcosine and said one more cancer specific metabolites.
  • 33. The method of claim 32, wherein the presence of an elevated level of sarcosine and said one or more cancer specific metabolites in said sample is indicative of invasive prostate cancer in said subject.
  • 34. The method of claim 32, wherein said sample is selected from the group consisting of a tissue sample, a blood sample, a serum sample, and a urine sample.
  • 35. The method of claim 32, wherein said one or more cancer specific metabolites are sarcosine, glutamate, and glycine.
Parent Case Info

This application is a continuation of U.S. patent application Ser. No. 12/192,539, filed Aug. 15, 2008 which claims priority to U.S. Provisional Patent Application Ser. No. 60/956,239, filed Aug. 16, 2007, U.S. Provisional Patent Application Ser. No. 61/075,540, filed Jun. 25, 2008, and U.S. Provisional Patent Application Ser. No. 61/133,279, filed Jun. 27, 2008, each of which are herein incorporated by reference in its entirety. U.S. patent application Ser. No. 12/192,539 is also a continuation in part of International Patent Application Serial Number PCT/2007/078805, filed Sep. 18, 2007, which claims priority to U.S. Provisional Patent Application Ser. No. 60/845,600, filed Sep. 19, 2006, each of which are herein incorporated by reference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with government support under CA084986, CA111275 and CA133458 awarded by the National Institutes of Health. The Government has certain rights in the invention.

Provisional Applications (4)
Number Date Country
60956239 Aug 2007 US
61075540 Jun 2008 US
61133279 Jun 2008 US
60845600 Sep 2006 US
Continuations (1)
Number Date Country
Parent 12192539 Aug 2008 US
Child 13958158 US
Continuation in Parts (1)
Number Date Country
Parent PCT/US2007/078805 Sep 2007 US
Child 12192539 US