WO2018184112A1 - Methods of high-throughput mass spectrometry based urine test for colorectal states - Google Patents

Abstract

Methods for the assessment of colorectal polyps in general, and adenomatous polyps in particular, by mass spectrometry based measurement of metabolites in urine are described. In some embodiments, certain metabolites are identified as being reduced in concentration or quantity in subjects with colorectal polyps as compared with subjects without colorectal polyps. The measurement of these metabolites in urine can indicate the presence of, or predisposition to, colorectal polyps in general, or adenomatous polyps in particular, in a subject.

Description

METHODS OF HIGH-THROUGHPUT MASS SPECTROMETRY BASED URINE TEST FOR COLORECTAL STATES

CROSS REFERENCE TO RELATED APPLICATIONS:

This application claims priority of United States Provisional Patent Application

Serial No. 62/482,004, entitled "Methods of High-Throughput Mass Spectrometry Based Urine Test for Colorectal Cancer Screening", filed April 5, 2017, and is hereby incorporated by reference herein in its entirety.

FIELD OF THE INVENTION The present disclosure relates to the assessment of either colorectal cancer or colorectal polyps, or both, by measurement of metabolites in urine.

BACKGROUND

Colorectal cancer (CRC) is a major public health concern as it is globally ranked as the third most frequent form of cancer with the age standardized incidence rate of 17.2 per 100,000 population and represents almost 8.5% of all deaths due to cancer. It is also the third leading cause of cancer-related deaths in the Western world. In 2016, the American Cancer Society estimates that there will be 95,270 new cases of CRC, 39,220 new cases of rectal cancer, and 49,190 deaths from colon or rectal cancer in the United States. In 2016, the Public Health Agency of Canada estimates that 26,100 Canadians will be diagnosed with CRC, which will be the cause of death in 9,000 cases. CRC appears not only in humans but also in animal species, and in both sexes. Among human beings, more than 9 out of 10 people diagnosed with CRC are over the age of 50. However, younger individuals can develop CRC. CRC is largely preventable through population based- and individual based- screening programs. The chance of surviving CRC is closely related to the stage of the disease at diagnosis; the earlier the diagnosis, the greater the likelihood of survival. While CRC and colorectal polyps are unique conditions, in many instances CRC is preceded by colorectal polyps, particularly adenomatous colorectal polyps. If identified early at the colorectal polyp or precancerous lesion stage, CRC is more likely to be curable. Therefore, subjects with CRC and/or colorectal polyps would greatly benefit from early diagnosis.

Current CRC screening methods consist of one or a combination of the followings: fecal occult blood testing (FOBT), flexible sigmoidoscopy, air-contrast barium enema, computerized tomography colonography (CTC) and/or colonoscopy. These current screening methods all have limitations or potential risks that limit their application.

Colonoscopy is currently the standard test for the presence or absence of both CRC and colorectal polyps. However, colonoscopy is expensive, invasive, and can impose unnecessary hazards and risks caused by sedation or the procedure itself. Complications with colonoscopy can include perforation, hemorrhage, respiratory depression, arrhythmias, and infection. In addition, colonoscopy requires considerable physical resources and skilled personnel. A known non-invasive CRC screening method is FOBT. FOBT, however, has very low sensitivity in detection of adenoutomas polyps. FOBT is based on the assumption that cancers will bleed, therefore, can be detected in the stool using chemical or immunological assays, and involves a crude test for the peroxidase-like activity of heme in hemoglobin. However, there are several factors that limit the effectiveness of the fecal-based testing methods as screening tests. First, relatively few individuals complete the standard fecal-based testing, including those known to be at above-average risk for CRC. Second, the fecal-based diagnostic tests have low sensitivity. For example, the guaiac-based fecal test, which tests for hemoglobin, has a sensitivity of approximately 3% for detecting any adenoma and 10-30% for detecting advanced (> 10mm) adenomatous polyps. Newer fecal immunochemical tests, which use antibodies to globin, have reported sensitivities of 13-26% for any adenomatous polyps and 20-67% for advanced adenomatous polyps. Third, the interpretation of these fecal-based test is subjective as the result is a colorimetric change, which means it can be difficult to determine whether the test is truly positive or not.

CTC, or virtual colonoscopy, is a recent non-invasive technique for imaging the colon. However, its performance varies due primarily to technological differences in the subject preparation and the hardware and software used for the analysis. Other limitations of CTC include high false positives (FP) readings, inability to detect flat adenomas, no capacity to remove polyps, repetitive and cumulative radiation doses, and cost.

With advances in the CRC related molecular pathology, several new screening methods based on DNA analysis from stool samples became available. These are typically Polymerase chain reaction (PCR) based assays used to identify mutations known to occur in the adenoma-to-carcinoma sequence, or in familial CRC. Commonly screened gene mutations include KRAS, TP53, APC, as well as assays for micro satellite instability and hypermethylated DNA. However, whether genomics-based tests will result in high diagnostic accuracy for sporadic CRC remains to be seen. Accordingly, there is a strong need to develop improved methods of assessing states of either CRC, or colorectal polyps, or both in a subject.

SUMMARY

Methods for the diagnosis of CRC, colorectal polyps in general and adenomatous polyps in particular by mass spectrometry based measurement of metabolites in urine are described. In some embodiments, certain metabolites are identified as being reduced in concentration or quantity in subjects with either or both CRC and colorectal polyps as compared with subjects without CRC or colorectal polyps. The measurement of these metabolites in urine can indicate the presence of CRC and/or colorectal polyps in general, and adenomatous polyps in particular in a subject.

The present disclosure provides a clinically scalable (high throughput, low cost, and highly sensitive) urine based test for the detection of adenomatous polyps, which will be a suitable tool for population based CRC screening. Broadly stated, in some embodiments, a method is provided for assessing whether a subject has or is predisposed to developing colorectal polyps, said method comprising: (a) providing a urine sample from said subject; (b) obtaining a metabolite profile from said urine sample; (c) comparing said metabolite profile with a reference metabolite profile; and (d) assessing, based on said comparison in step (c), whether said subject has or is predisposed to developing colorectal cancer and/or colorectal polyps; wherein said metabolite profile is obtained using mass spectrometry.

In some embodiments, the method can comprise in step (b), said metabolic profile is obtained by measuring the concentration of one or more metabolites in said urine sample to produce said metabolite profile for said subject; and, in step (c), said reference metabolite profile is determined from the concentration of corresponding metabolites in urine of individuals in a reference population, step (b) comprising measuring the concentration in said urine sample of at least any 1 , 2, 3, 4, 5, 6, 7, 8, 9, or 10 metabolites selected from the group consisting of: Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline, step (b) comprising measuring the concentration in said urine sample of at least any one or more metabolites in a set of metabolites selected from the group consisting of: (i) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline; (ii) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, and 1-Methylnicotinamide ; (iii) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, and Fumaric acid ; (iv) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, and Pantothenic acid; (v) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, and Methylamine; (vi) Succinic acid, Ascorbic acid, Carnitine, Creatine, and Citric acid; (vii)Succinic acid, Ascorbic acid, Carnitine, and Creatine; (viii) Succinic acid, Ascorbic acid, and Carnitine; (vix) Succinic acid and Ascorbic acid; (x) Succinic acid, wherein a reduced urine concentration as compared to the reference metabolite profile of any one or more metabolites selected from the group consisting of Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline is indicative that the subject has or is predisposed to developing colorectal polyps, wherein said colorectal polyps are either adenomatous polyps or hyperplastic polyps and wherein step (b) comprises measuring the concentration in said urine sample of at least any 1 , 2, 3, 4, 5, 6, 7, 8, 9, or 10 metabolites selected from the group consisting of: Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline, wherein said step (b) comprises measuring the concentration in said urine sample of at least any one or more metabolites in a set of metabolites selected from the group consisting of: (i) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline; (ii) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, and 1-Methylnicotinamide; (iii) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, and Fumaric acid; (iv) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, and Pantothenic acid; (v) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, and Methylamine; (vi) Succinic acid, Ascorbic acid, Carnitine, Creatine, and Citric acid; (vii) Succinic acid, Ascorbic acid, Carnitine, and Creatine; (viii) Succinic acid, Ascorbic acid, and Carnitine; (vix) Succinic acid and Ascorbic acid; and (vx)Succinic acid, wherein a reduced urine concentration as compared to the reference metabolite sample of any one or more metabolites selected from the group consisting of Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline is indicative that the subject has or is predisposed to developing colorectal polyps which are either adenomatous polyps or hyperplastic polyps, wherein said reference metabolite profile is obtained using one or more methods selected from the group consisting of: nuclear magnetic resonance spectroscopy; high performance liquid chromatography; thin layer chromatography; electrochemical analysis; mass spectroscopy; Liquid chromatography-mass spectrometry; refractive index spectroscopy; ultra-violet spectroscopy; fluorescent analysis; radiochemical analysis; near-infrared spectroscopy; gas chromatography and light scattering analysis, wherein an assessment is made by also using clinical features of the subject, wherein the clinical features are selected from the group comprising age, sex, smoking status, and a combination thereof, wherein an assessment is made by also using an algorithm, and/or wherein the algorithm is a LASSO algorithm.

Broadly stated, in some embodiments, a method is provided for identifying urine metabolites indicative of the presence or absence of colorectal polyps, said method comprising: (a) providing a urine sample from a subject; (b) obtaining a metabolite profile from said urine sample; (c) comparing said metabolite profile with a reference metabolite profile; and (d) identifying, based on said comparison in step (c), one or more metabolites in said metabolite profile that are indicative of the presence of or predisposition to in said subject of colorectal polyps; wherein said metabolite profile is obtained using mass spectrometry.

In some embodiments, the method can comprise wherein said reference metabolite profile is obtained using one or more methods selected from the group consisting of: nuclear magnetic resonance spectroscopy; high performance liquid chromatography; thin layer chromatography; electrochemical analysis; mass spectroscopy; liquid chromatography-mass spectrometry; refractive index spectroscopy; ultra-violet spectroscopy; fluorescent analysis; radiochemical analysis; near-infrared spectroscopy; gas chromatography and light scattering analysis, wherein an identification is made by also using clinical features of the subject, wherein the clinical features are selected from the group comprising age, sex, smoking status, and a combination thereof, wherein an identification is made by also using an algorithm, wherein the algorithm is a LASSO algorithm.

Broadly stated, in some embodiments, a kit is provided for assessing whether a subject has or is predisposed to developing colorectal polyps using mass spectrometry, said kit comprising one or more reagents for detecting the presence and/or concentration and/or amount of one or more metabolites in a urine sample of a subject, and instructions for use of said kit for assessing whether a subject has or is predisposed to developing colorectal polyps.

In some embodiments, the kit can comprise wherein said one or more metabolites are selected from the group consisting of Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1 - Methylnicotinamide, Trigonelline, and a combination thereof.

Broadly stated, in some embodiments, a use of a metabolite profile is provided, comprising one or more of the following metabolites: Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline, for assessing whether a subject has or is predisposed to developing colorectal polyps.

In some embodiments, a use of a urine metabolite profile is provided, the urine metabolite profile comprising one or more of metabolites selected from the group consisting of: Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline, for assessing whether a subject has or is predisposed to developing CRC and/or colorectal polyps.

Broadly stated, in some embodiments, a system is provided, comprising: (a) a colorectal polyps-assessing apparatus including a control unit and a memory unit to assess a colorectal polyp state in a subject; and (b) an information communication terminal apparatus that provides data on the presence and/or concentration and/or amount of metabolites in a urine sample from the subject connected to each other communicatively, wherein the data is obtained using mass spectrometry, wherein the information communication terminal apparatus includes: (a) a data sending unit that transmits the data on the presence and/or concentration and/or amount of metabolites in the sample to the colorectal polyps-assessing apparatus; and (b) an assessment result-receiving unit that receives the assessment result of the colorectal polyps state of the subject transmitted from the colorectal polyps-assessing apparatus, wherein the control unit of the colorectal polyps- assessing apparatus includes: (a) a data-receiving unit that receives the data on the metabolite concentration and/or amount of the sample transmitted from the information communication terminal apparatus; (b) a discriminant value-calculating unit that calculates a discriminant value that is a value of multivariate discriminant, based on both the concentration and/or amount value of the metabolite in the sample received by the data-receiving unit and a multivariate discriminant with the concentration and/or amount of the metabolite as explanatory variable stored in the memory unit; (c) a discriminant value criterion-assessing unit that assesses the colorectal polyps state in the subject, based on the discriminant value calculated by the discriminant value-calculating unit; and (d) an assessment result-sending unit that transmits the assessment result of the subject obtained by the discriminant value criterion-assessing unit to the information communication terminal apparatus.

Broadly stated, in some embodiments, a method is provided for identifying and evaluating effectiveness of pharmaceutical agents and/or surgical treatments and/or physical treatments against colorectal polyps, said method comprising: (a) providing a first urine sample from a subject having colorectal polyps;

(b) obtaining a metabolite profile from said first urine sample, wherein said first metabolite profile is obtained using mass spectrometry; (c) administering one or more pharmaceutical candidates and/or performing one or more physical or surgical treatments to or on said subject; (d) providing a second urine sample from said subject; (e) obtaining a metabolite profile from said second urine sample, wherein said second metabolite profile is obtained using mass spectrometry; (f) comparing said metabolite profile obtained in steps (b) and (e) with a reference metabolite profile; and (g) assessing, based on said comparison in step (f), whether the one or more pharmaceutical candidates and/or treatments is effective against colorectal cancer and/or colorectal polyps.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which illustrate embodiments of the invention by way of example only:

Figure 1 depicts a representative liquid chromatography-mass spectrometry (LCMS) of Calibrant 6 (standard mixture of Succinic acid, Ascorbic acid, Carnitine and corrensponding internal standards );

Figure 2 depicts a representative plate map where the LCMS sequence runs vertically;

Figure 3 is a curve depicting the performance of final MS-based PolypDx™ predictor using three metabolites and three clinical features on the (A) training data, and (B) testing data, including the performance of the fecal based test;

Figure 4 depicts a Passing and Bablok regression analyses of MS- quantified on NMR-quantified data for Succinic acid, N = 685; concentration range 0- 362 μηιοΙ/L; Pearson correlation coefficient r = 0.862, P < 0.0001. (A) Scatter diagram with regression line and confidence bands for regression line. Identity line is dashed. Regression line equation: y = 4.17 + 1.32 x; 95% CI for intercept 2.72 to 5.33 and for slope 1.26 to 1.38 indicated small constant and small proportional difference. Cusum test for linearity indicates significant deviation from linearity (P<0.01). (B) Residual plot presents distribution of difference around fitted regression line;

Figure 5 depicts a Passing and Bablok regression analyses of MS- quantified on NMR-quantified data for Ascorbic acid, N = 685; concentration range 0- 13368 μηιοΙ/L; Pearson correlation coefficient r = 0.800, P < 0.0001. (A) Scatter diagram with regression line and confidence bands for regression line. Identity line is dashed. Regression line equation: y = 2.50 + 1.12 x; 95% CI for intercept 2.50 to 2.50 and for slope 1.06 to 1.19 indicated small constant and small proportional difference. Cusum test for linearity indicates significant deviation from linearity (P<0.01). (B) Residual plot presents distribution of difference around fitted regression line; and

Figure 6 depicts a Passing and Bablok regression analyses of MS- quantified on NMR-quantified data for Carnitine, N = 685; concentration range 0-948 μηιοΙ/Ι_; Pearson correlation coefficient r = 0.921 , P < 0.0001. (A) Scatter diagram with regression line and confidence bands for regression line. Identity line is dashed. Regression line equation: y = 1.73 + 0.99 x; 95% CI for intercept 0.77 to 2.50 and for slope 0.96 to 1.02 indicated small constant and small proportional difference. Cusum test for linearity indicates significant deviation from linearity (P=0.04). (B) Residual plot presents distribution of difference around fitted regression line. DETAILED DESCRIPTION

Metabolomics and Assesment of CRC or Colorectal Polyps

Metabolomics is an emerging field of research downstream from genomics, proteomics and transcriptomics. There are over 40,000 metabolites in the human body whose concentrations provide a snapshot of an individual's current state of health. A metabolome is a quantitative collection of low molecular weight compounds, such as metabolic substrates and products, lipids, small peptides, vitamins, and other protein cofactors, generated by metabolism. A metabolome is downstream from a transcriptome and a proteome and thus any changes from a normal state are amplified and are numerically more tractable. Metabolomics can be a precise, consistent, and quantitative tool to examine and describe cellular growth, maintenance, and function. Relative to adenomatous polyps and CRC, metabolomics has the capacity to detect, not only dysplastic cellular changes of the human mucosa, but also changes in the intestinal microflora. Metabolomics can be performed on urine, serum, tissue, and even on saliva and amniotic fluid. Generally, urine metabolomics represents a much less invasive method of testing compared to tissue or serum metabolomics.

The present invention uses urine metabolomics to identify subjects having or at risk of developing CRC and/or colorectal polyps. This is beneficial in the management of the risk of CRC and/or colorectal polyps, both in prevention and treatment. The use of urine metabolomics in the present invention has a number of potential benefits. Urine is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. There are over 2,651 metabolites in human urine samples, including amino acids, nucleic acids, carbohydrates, organic acids, vitamins, lipids, minerals, food additives, drugs, toxins, pollutants and other chemicals (with a molecular weight <2000 Da) that humans ingest, metabolize, catabolize or come into contact with. Urine is also cost efficient compared to the existing methods for assessing presence or absence of CRC or colorectal polyps. The invention also permits monitoring of individual susceptibility to CRC prior to resorting to, or in combination with, conventional screening methods, and provides for population-based monitoring of CRC and/or colorectal polyps. A wide range of analytical techniques to assay and quantitate components of a metabolome and to extract useful metabolite profiles from the data are available, including e.g. liquid and gas chromatography coupled with mass spectrometry (LCMS or GCMS), nuclear magnetic resonance (NMR) spectroscopy, high performance liquid chromatography (HPLC), thin layer chromatography (TLC), electrochemical analysis, refractive index spectroscopy, ultra-violet spectroscopy, fluorescent analysis, radiochemical analysis, near-infrared spectroscopy and light scattering analysis. The outputs from such analytical techniques can be further analyzed using multivariate analysis such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares (OPLS). One or more metabolite profiles obtained from the described analysis based on a reference population of known CRC and/or colorectal polyp status can be used as a reference to assess the presence or absence of CRC or colorectal polyps in a subject or a predisposition thereto. In the present disclosure, methods for the diagnosis of CRC, colorectal polyps in general and adenomatous polyps in particular by LCMS based measurement of metabolites in urine are described. This developement of the method can be performed by: (a) generating a metabolomic library of a reference population. A reference population may be composed of healthy subjects (i.e. subjects known or assessed by other means to be free of CRC and/or colorectal polyps), and subjects already identified to have or to be predisposed to developing CRC or colorectal polyps, (b) identifiying specific metabolomic biomarkers that are related to CRC or colorectal polyps, (c) developing an analytical method to quantify the identified metabolites concentration in urine, (d) developing an algorithm that can predict whether a subject has or is predisposed to developing CRC and/or colorectal polyps based on the quantified metabolites concentration values. Later, this assessment can be performed by: (a) providing a urine sample from a subject that is suspected to have or be predisposed to developing CRC and/or colorectal polyps; (b) obtaining a metabolite profile from said urine sample using the developed analytical method; (c) running the algorithm with the quantified metabolite concentration values for the said subject; and (d) generating a report that will indicate whether the said subject has or is predisposed to developing CRC and/or colorectal polyps.

Providing and Processing Urine Samples

Urine samples can be collected from subjects that are known or suspected to have CRC or colorectal polyps, and from subjects without CRC or colorectal polyps, by known protocols. The subjects of this invention include both sexes of animal species that are susceptible to CRC and/or colorectal polyps, including humans.

In addition to providing a urine sample, subjects can take a FOBT, fecal immune testing (FIT), and/or a colonoscopy, the results of which can be used to determine classification of subjects into one of the groups of: subjects without CRC and/or colorectal polyps (normal group); subjects having colorectal polyps in general (polyp group). Pathology of resected surgical specimens can be used as the standard to classify subjects into a group where subjects have CRC (CRC group). Relevant clinical information such as age, gender, family history, comorbidities, medications etc. can be obtained from study questionnaires and subjects' medical charts, which could also be used to determine classification of subjects. Such testing can be used in the development of reference urine metabolite profiles and can also be used as an adjunct to screening test subjects by the methods of the invention to confirm or further refine a diagnosis of CRC and/or colorectal polyps. Urine samples can be collected from subjects any time, e.g. during routine screening or in connection with a regular check-up or visit to a physician, or prior to or together with administration of treatment, such as the administration of a medicine or performance of surgery. Urine samples can be collected one or more times for a separate or combined analysis, e.g. 15-700 ml each time. Urine sample collection containers can vary in size and shape, but ideally can accommodate e.g. 20-1 ,000 ml of urine sample. Typically, the container is sterile. If desired, sample containers can be pre-filled or treated with agents for preventing contamination of the sample by microorganisms such as bacteria and fungi while a sample is waiting to be stored, or such agents can be added after sample collection. Metabolomic analysis of the collected urine samples may occur immediately or the samples may be processed for storage and later analysis. For example, the whole or part of the sample could be stored in a freezer at -5~10 °C within 0~48 hours of collection, or could be frozen at -120~ -10 °C within 0-48 hours of collection, or could be processed with chemicals for future analysis or use before being stored. If samples have been stored frozen, they may be thawed (e.g. at room temperature for 12-48 hours), prior to analysis.

For Example, 986 urine samples were acquired previously as part of a regional colon cancer screening program in Edmonton, Canada (SCOPE®, Stop Colorectal Cancer through Prevention and Education). Study participants of average or increased colorectal carcinoma risk were recruited. On day of entry, participants provided a midstream urine sample, and completed a demographic survey. Colonoscopy was performed 2-6 weeks after the urine collection as the reference standard. Participants were excluded if they were under 40 or over 75 years of age or had findings of colonic or ileal disease at the time of colonoscopy. In some embodiments, urine samples may be processed prior to analysis. For example for LCMS acquisition, a simple approach of dilution and filtration can be used for sample preparation. Urine samples can be centrifuged at 10,000 g for 3 mins. 10 μΙ_ of each urine supernatant can then be added to proper container. 10 μΙ_ internal standards (ISTD) can be added to each sample to account for matrix effect and facilitate the absolute quantification. The mixture can then be extracted with 200 μΙ_ of extraction solvent (water with 10 mM Ammonium formate, pH3) and filtered through 0.45 μηι member filter before LCMS injection.

Generating a metabolomic library of a reference population A reference population may be composed of healthy subjects (i.e. subjects known or assessed by other means to be free of CRC and/or colorectal polyps), and subjects already identified to have or to be predisposed to developing CRC or colorectal polyps, or both.

In one embodiment, 685 urine samples were acquired previously as part of a regional colon cancer screening program in Edmonton, Canada (SCOPE®, Stop Colorectal Cancer through Prevention and Education). Colonscopy and pathologic were performed to confirm whether a recuited participant is normal or polyps. There are a number of types of colorectal polyps. While CRC and adenomatous polyps are distinct states adenomatous polyps are known to be a precursor to full-blown CRC. Other types of polyps may not themselves have malignant potential. For instance, unlike adenomatous polyps, hyperplastic polyps have been historically recognized as benign growths of the colon that have no malignant potential— i.e. they were thought to be innocent bystanders. For the purpose of this study, subjects with adenomatous polyps and CRC are classified as "Polyp" while subjects with no polyps and hyperplastic only polyps as "Normal". The distribution of 685 participants' colonoscopy results along with clinic feature statistic are shown in Table 1.

Table 1. Participant Statistics

Label Colonoscopy results Age Sex Smoker Gl Bleeding

Polyp Adenoma (n) = 154 μ = 59.9 F = 60 Yes = 26 Yes = 4 n=155 CRC (n) = 1 σ = 7.4 M = 95 Ex-smoker = 4 No = 151

No = 1 19 Unknown = 0

Unknown = 6

Normal Normal (n) = 446 μ = 56.1 F = 308 Yes = 50 Yes = 9 n=530 Hyperplastic (n) =84 σ = 8.2 M = 222 Ex-smoker = 12 No = 520

No = 449 Unknown = 1

Unknown = 19

Quantification of metabolites, e.g. by concentration or in absolute amount, can be done once the analysis data is available from, for example, but not limited to, GCMS, LCMS, HPLC, NMR spectroscopy, TLC, electrochemical analysis, refractive index spectroscopy, ultra-violet spectroscopy, fluorescent analysis, radiochemical analysis, near-infrared spectroscopy and light scattering analysis. Generally, the more analytical techniques that are used, metabolomic coverage can be improved.

In one embodiment, 70 metabolites were quantified by performing NMR on the 685 urine samples. This was first performed in 2010 using the targeted profiling techniques of Chenomx NMR Suite v7.7 (Chenomx, Inc., Edmonton, Canada). In a later consistency study, re-quantification was carried out using the same NMR spectra and same protocol, but by different operators and at different time points. The analyzed concentration data across operators and time was compared. The consistency of the analyzed NMR results was found to be dependent on the metabolite identity with the more difficult to profile metabolites being more inconsistent. The identified 70 metabolites were classified into one of four consistency groups: Excellent, Good, Fair, and Poor.

Identifiying specific metabolomic biomarkers that are related to CRC or colorectal polyps.

The quantification data can be used to identify and to set a standard to determine a reference metabolite profile based on urine samples obtained from subjects known to be free of CRC and/or colorectal polyps.

In one embodiment, to have a more robust dataset to start and minimize the potential misleading result when combined with analysis of data over time (i.e., batch effect), 13 out of the 70 metabolites that were rated "poor" were removed. Further, any metabolites that was zero for more than 20% of the sample size was not considered (e.g. 3-hydroxymandelic acid). Important features that could distinguish those with adenomatous polyps from those without, were identified using the rest of the reliable metabolite abundance information. After processing, metabolites were ranked using p-value (via the Wilcoxon signed-rank test), as listed in Table 2. The 3 metabolites that had a p-value less than 0.05 were: Succinic acid, Ascorbic acid, and Carnitine.

Table 2: Top 10 p-values for metabolites in NMR data using the Wilcoxon signed-rank test. p-value Metabolite

0.0059 Succinic acid

0.0100 Ascorbic acid

0.0280 Carnitine

0.0595 Creatine

0.0739 Citric acid

0.0861 Methylamine

0.0945 Pantothenic acid

0.1 198 Fumaric acid

0.1346 1 -Methylnicotinamide

0.1703 Trigonelline

Generally, the more metabolites that are assessed, the more accurate the assessment of CRC and/or colorectal polyps will be. In exemplary embodiments, more than 70 metabolites were considered, and 3 metabolites can be used to assess whether a subject has or is predisposed to developing CRC or colorectal polyps. However, this is not intended to limit the scope of the invention to the measurement of 3 metabolites as the more metabolites that are assessed, the more accurate the assessment can be. Indeed, other, or additional urine metabolites beyond these metabolites identified can be included in the metabolite profile. However, as noted above, this can involve greater effort and cost. In many instances, a less accurate, specific, or detailed assessment may be sufficient, particularly if the assessment is only preliminary in nature, or is to be conducted together with or followed by another diagnostic test, such as colonoscopy. Once assessed or screened, subjects with a positive result and presenting a risk of adenomatous polyps and/or CRC can be referred or directed to a colonoscopy. In some cases, during the colonoscopy, the adenomatous polyps can be removed, thus preventing the progression into CRC. Those patients with negative results can continue with regular repeated screening with the urine test. Further, a test involving the assessment of fewer metabolites may be more readily reduced to a simplified kit or test that can be used by a subject at home, or by a medical practitioner at the point of care, without need for sending a urine sample to a laboratory for analysis.

Developing an analytical method to quantify the identified metabolites concentration in urine.

The analytical techniques that make it possible to obtain metabolite profiles from the urine samples can include one or a combination of, but not limited to, mass spectrometry (MS) coupled with gas chromatography (GCMS) or liquid chromatography (LCMS), HPLC, NMR spectroscopy, TLC, electrochemical analysis, refractive index spectroscopy, ultra-violet spectroscopy, fluorescent analysis, radiochemical analysis, near-infrared spectroscopy and light scattering analysis. In some embodiments, targeted Liquid chromatography-mass spectrometry (LCMS/MS) methods can be developed to quantify key metabolites (for example, Succinic acid, Ascorbic acid, and Carnitine) in urine samples using multiple reaction monitoring (MRM). Commercially available standards for Succinic acid, Ascorbic acid, and Carnitine were obtained. In addition, the isotopic labeled internal standards Succinic acid-D4, Ascorbic acid-13C, and Carnitine-D9 were also obtained. Stock solutions of individual compounds were made by dissolving proper amount of standards in MS grade water. Calibration solutions (Cal1-Cal8) at concentrations of 5 μΜ, 10 μΜ, 100 μΜ, 200 μΜ, 400 μΜ, 600 μΜ, 800 μΜ and 1000 μΜ were prepared by mixing the stock solutions of Succinic acid, Ascorbic acid, and Carnitine in water. The internal standard solution (ISTD) with 100 μΜ of Succinic acid-D4, 200 μΜ of Ascorbic acid-13C, and 100 μΜ of Carnitine-D9 was prepared by mixing the stock solutions of isotopic labeled internal standards in water. Calibrant solutions and the internal standard solution were aliquoted and stored at -80 °C until they used. In one embodiment, MS parameter optimization can be performed on an AB Sciex 4000 Qtrap for each metabolite using a standard solution of 5 μΜ compound in 1 :1 water:acetonitrile buffer with 0.1 % formic acid. For each compound, two of the most abundant MRM pairs can be chosen and the corresponding MS parameters, such as Declustering Potential (DP), Collision Energy (CE) and Collision Cell Exit (CXP) can be optimized. All of the MS parameters are summarized in Table 3. Succinic acid and Ascorbic acid were monitored in the negative mode, while Carnitine was monitored in the positive mode. MRM pair 1 (e.g. succinic acid 1) was used for quantification analysis, MRM pair 2 (e.g. succinic acid 2) was monitored for identification. A presentative LCMS chromatograph of Calibrant 6 is shown in Figure 1. Succinic acid and Ascorbic acid were baseline separated.

Table 3. Optimized MS parameters for each compound. MRM pair 1 is used for quantitation and MRM pair 2 is for qualification.

In some embodiments, LCMS spectra can be acquired on an AB Sciex 4000 Qtrap paired to an Agilent UHPLC 1290. An isocratic LC separation of the targeted metabolites (Succinic acid, Ascorbic acid, and Carnitine) can be performed on a Waters ACQUITY UPLC BEH C18 column (2.1 mm x 150mm, 1 .7μηι) with 95:5 water:acetonitrile (10 mM Ammonium formate, pH3) as mobile phase with a flowrate of 0.3 ml/min. The injection volume can be 5 μί and overall LC run time can be 3 min. MRM detection was under optimal parameters for each of the analytes. Metabolite quantification can be achieved using the AB Sciex Analyst software version. During quantification, each metabolite can be identified using the internal standard and quantified using the established calibration curve. Metabolite concentrations that are below the lower limit of detection (LLOD) can be replaced by half of the limit of detection for statistical analysis.

Sample processing validation can be performed using laboratory generated pooled urine samples, that also served as quality controls. LCMS analysis can be done on non-spiked urine, spiked urine and post-spiked urine samples in triplicates. Extraction recoveries and accuracies were calculated for each metabolite and summarized in Table 4. All metabolites were within the range of 90% - 1 10%.

Table 4. Extraction recoveries and accuracies for each metabolite.

a. Recovery (%) = (Response (spiked sample))/(Response (post-spiked sample)) x 100

b. Accuracy (%) = (spiked sample-upspiked sample)/(spiked amount) x 100

For example, 986 urine samples were randomized and run using the developed LCMS method with a 96 well plate format. Each plate contains 1 blank, 1 ISTD, 8 Calibrants, 6 QCs (laboratory generated pooled urine samples) and 80 urine samples from participants. A representative plate map was shown in Figure 2. For each plate, a set of calibration curves was generated and used. Linear regression (R2) for the 3 metabolites is greater than 0.99 for all plates. The LLOQ was the lowest calibrant point at 5 uM. Metabolite concentrations that were below the LLOQ were replaced by half of the limit of detection for statistical analysis. 6 QC samples were put in each plate to access the coefficient of variation (CV%) with the plate and across the 13 different plates. CV% of QC samples for each metabolite within each plate was calculated as the standard deviation and summarized in the Table 5. Notably, the CV% for each metabolite with the plate is less than 15%. The averages of the measured metabolite concentration in QC samples are also listed in Table 5. The concentrations of Succinic acid, Ascorbate acid, and Carnitine were consistent across the 13 plates within acceptable ranges. Table 5. CV% of QC samples for each metabolite within each plate.

Developing an algorithm that can predict whether a subject has or is predisposed to developing CRC and/or colorectal polyps Levels of the specific metabolites over or below a determined critical value, either in concentration or in amount, can indicate the presence of CRC or colorectal polyps in general and adenomatous polyps in particular. The concentrations or the amount of the metabolites can be interpreted independently using an individual cut-off for each metabolite or they can be interpreted collectively. Metabolite concentrations or amounts obtained can be used as they are (i.e. as the raw data) or be normalized. For example, the concentration or amount of a metabolite can be log-transformed to normalize the concentrations or amounts to the concentration or the amount of other metabolites. The metabolites can also be normalized to the concentration of all metabolites minus the concentration of selected compounds such as e.g. urea to obtain similar results.

Multivariate statistical analysis can be applied to the collected data or complex spectral data to identify differences arising between the groups of data sets obtained from the urine sample. The metabolite measurements in samples from subject having CRC or colorectal polyps in general or adenomatous polyps specifically can be compared to metabolite measurements in samples from subjects without CRC or colorectal polyps to identify metabolites that significantly contribute to the separation of different groups. Data comparison can be performed using any appropriate tools that fulfill the purpose. The tools include PCA, PLS-DA, OPLS and support vector machines (SVM), and softwares that can perform one or more of such analyses, e.g., Simca-P+, can be used. These are statistical methods of compressing multi-dimensional data down to two or three main components. PLS- DA and OPLS are supervised, that is, they take into account the class assignments, while PCA is unsupervised and can be influenced by many factors such as gender, comorbidities etc. An optimized multivariate cut-off for the underlying combination of metabolites can be used to discriminate a cancerous or pre-cancerous state from a healthy state. Upon determination of which specific metabolites are the significant contributors to the data separation between the CRC group and the normal group samples or the polyp group and the normal group samples or the adenoma group and the normal group samples, one or more profiles of these specific metabolites can be established. One or more metabolite profiles, or a combination thereof, can be used as a reference metabolite profile to assess CRC or colorectal polyps in general or adenomatous polyps in particular in a subject. In some embodiments, the top 10 metabolites can be used in separating normal group from polyp group, for example: Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1 - Methylnicotinamide, and Trigonelline

However, not all features of the metabolite analysis results are always required for a proper assessment of CRC, colorectal polyps in general or adenomatous polyps specifically. Since there would be an incremental cost to obtaining more information about a subject's urine metabolite profile, it may be beneficial to use the minimal number of metabolites possible. In order to determine which specific metabolites are the strongest contributors to the data separation between the CRC group and the normal group samples or the polyp group and the normal group samples or the adenoma group and the normal group samples, further data analysis can be performed.

There are many ways to evaluate a selected metabolite profile to assess whether a subject has or is predisposed to developing CRC and/or colorectal polyps. The values measured for metabolites can be mathematically combined and the combined value can be correlated to the underlying states question. Metabolite values may be combined by any appropriate mathematical method. Mathematical methods for correlating a metabolite combination to a disease can employ methods such as, but not limited to, discriminant analysis (DA) (i.e. linear-, quadratic-, regularized-DA), Kernel Methods (i.e. SVM), Nonparametric Methods (i.e. k-Nearest- Neighbor Classifiers), PLS (Partial Least Squares), Tree-Based Methods (i.e. Logic Regression, CART, Random Forest Methods, Boosting/Bagging Methods), Generalized Linear Models (i.e. Logistic Regression), Principal Components based Methods (i.e. SIMCA), Generalized Additive Models, Fuzzy Logic based Methods, Neural Networks and Genetic Algorithms based Methods. For the SVM model, the linear coefficients of each feature in an SVM classifier can be used to select the most important features. Those features that had the largest absolute value can be selected, and the SVM model can be re-calculated using only the selected features and the training set if necessary. When comparing test results from two different populations, for example, one with a disease and the other without the disease, a perfect separation between the two groups is rarely observed. Indeed, the distribution of the test results will overlap. Therefore, when a cut-off point or criterion value to discriminate between the two populations is selected and applied, there will be some cases with the disease correctly classified as positive (True Positive fraction), but some cases with the disease will be classified as negative (False Negative fraction). On the other hand, some cases without the disease will be correctly classified as negative (True Negative fraction), but some cases without the disease will be classified as positive (False Positive fraction). The performance of such a test, or the accuracy of a test to discriminate diseased groups from healthy groups, can be evaluated using tools such as ROC curve analysis. The ROC curve is a graphical representation of the spectrum of sensitivities and specificities generated using the various cut-offs, using the sensitivity as the y-axis and 1 -specificity as the x-axis. In an ROC curve the true positive rate (Sensitivity) is plotted in function of the FP rate (100-Specificity) for different cut-off points. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. A test with perfect discrimination (no overlap in the two distributions) has a ROC curve that passes through the upper left corner (100% sensitivity, 100% specificity). Therefore, qualitatively, the closer the plot is to the upper left corner, the higher the overall accuracy of the test. Area under the ROC curve (AUC) reflects the accuracy of the test and is displayed on the left lower corner of the plot.

Standard machine learning methodology of using an external data set can be used to evaluate how well the predictor could predict labels for new unlabeled instances. A dataset can be divided into two thirds training data and one third testing data. These two data sets can be balanced for age, sex, and class distribution. In one example, the MS quantifications were log-transformed and were used in conjunction with three clinical features (age, sex, and smoking status) along with a label (specifically, "Polyp" or "Normal") to train a predictor using the LASSO algorithm. Unlike a previous study, the Gl bleeding feature was not used in this predictor. This is because patients with Gl bleeding would be referred for colonoscopy, regardless of the outcome of the prediction algorithm. The trained predictor was then evaluated on the testing data set using sensitivity, specificity and AUC of the Receiver Operating Characteristic (ROC) curve. Figure 3 shows the ROC curve of the predictor's performance on the training and testing data. An AUC of 0.687 was achieved on the training set and an AUC of 0.692 was achieved on the testing set. The AUC of 0.692 has the advantage of being higher than the AUC of the NMR test at 0.670 as MS can be more sensitive, and thus more accurate, in a lower concentration range.

Further, using MS can allow for development and validation of a clinically scalable test for the detection of Adenomatous polyps, which would be suitable for population-based colorectal cancer screening. Compared to other methods, MS is sensitive, high throughput, and cost-effective. In addition, the ability to develop multianalyte panels using a single MS method offers additional time, labor, and expense savings, for which immunosuppressant assays are a great example. The prediction threshold for the developed algorithm can be adjustable, to vary the tradeoff between sensitivity and specificity. As sensitivity increases, more samples are being predicted as positive (i.e. requiring colonoscopy). Meanwhile the specificity drops. To optimize the predictor to a specific market, requirements for the test's sensitivity, specificity, or Predictive Rate must be met. For example, predictive performance of a PolypDx™ test was compared against the given requirements. A prediction threshold on the training data results (along the ROC curve) was chosen, and then evaluated on the testing data set. The results are summarized in Table 6. The thresholds tested were sensitivity at 70, 80, and 90%, and specificity at 70, 80 and 90%. The results show that this protocol for picking a threshold generalizes well to the testing set. This is likely due to the nature of the LASSO linear predictor. For more complex predictors, such as Random Forests, this threshold picking may not generalize well. Table 6. Generalizability of prediction threshold from training set to testing set. When picking a threshold on the training set, the performance on the testing set with the same threshold produces similar performance.

Training Set Testing Set

Threshold Sensitivity Specificity Positive Sensitivity Specificity Positive

Criteria Predictive Predictive

Rate* Rate

Sensitivity 69.9% 59.0% 33.2% 66.7% 55.2% 30.6%

= 70%

Sensitivity 79.6% 42.1 % 28.6% 82.4% 36.0% 27.6%

= 80%

Sensitivity 90.3% 20.9% 24.9% 92.2% 19.2% 25.3%

= 90%

Specificity 59.2% 70.1 % 36.5% 56.9% 70.9% 35.4%

= 70%

Specificity 46.6% 80.0% 40.3% 49.0% 80.8% 43.1 %

= 80%

Specificity 31 .1 % 88.1 % 43.2% 43.1 % 91 .3% 59.5%

= 90%

* Predictive Rate = True Positive/Test Positive

Permutation tests were also performed to determine whether the MS- based predictor was indeed finding useful patterns. This involved randomizing the labels in the training set, then running the training/testing workflow. The result of this analysis is expected to be worse than the performance of the predictor, as the labels of the patients were nonsense. This was repeated 100 times. Of 100 permutation tests, none of the AUCs were better than of the value 0.692 based on the original un- permuted data. This supports the findings that the predictor performance is not due to random chance - i.e., the chance of the null hypothesis (that we would see this 0.692 AUC performance, by chance alone) is p < 0.01 .

In some embodiments, the reference metabolic profile can be directed to assessing whether a subject has or is predisposed to developing CRC, and includes measurements of concentrations in a urine sample of at least any 1 , 2, 3, 4, 5, 6, 7, 8, 9, or 10 metabolites selected from the group consisting of: Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1 -Methylnicotinamide, and Trigonelline.

Generally, if fewer than 10 of these metabolites are to be used in the reference metabolite profile; preference will be given to Succinic acid, Ascorbic acid, and Carnitine. These three metabolites demonstrated acceptable sensitivity and specificity, to develop a reference profile. However, this does not mean that these three metabolites are required to be used in a reference metabolite profile, either alone or in any combination. While the examples provided herein suggest that these three metabolites could be the most relevant, it does not necessarily mean that they must be included for the method to be predictive. In various embodiments, the reference profile for detecting CRC may include one or more metabolites in a set of metabolites selected from the group consisting of: a. Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1 -Methylnicotinamide, and Trigonelline; b. Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, and 1 -Methylnicotinamide; c. Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, and Fumaric acid; d. Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, and Pantothenic acid; e. Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, and Methylamine; f. Succinic acid, Ascorbic acid, Carnitine, Creatine, and Citric acid; g. Succinic acid, Ascorbic acid, Carnitine, and Creatine; h. Succinic acid, Ascorbic acid, and Carnitine; i. Succinic acid and Ascorbic acid; j. Succinic acid.

In some embodiments of the invention, it is the concentration (e.g. measured in μΜ) of the urine metabolites that is measured, and a higher or lower concentration of the metabolite in the urine of a test subject relative to that in reference metabolite profile (based either on raw or normalized concentrations) is indicative of colorectal polyps.

In some embodiments, a reduced concentration of any one or more metabolites selected from the group consisting of Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1 - Methylnicotinamide, and Trigonelline is indicative that the subject has or is predisposed to developing colorectal polyps. It will be understood that by "reduced" it is meant that the concentration of a metabolite in the urine of a subject that has or is predisposed to developing colorectal polyps is lower than in the urine of subjects that do not have or are not predisposed to colorectal polyps.

A reference metabolite profile that is diagnostic of colorectal polyps may be different than a reference metabolite profile for CRC per se. That is, the reference diagnostic profile may be made up of a different set of relevant metabolites, and different relative concentrations of these metabolites may be relevant.

In certain embodiments, the reference metabolite profile can be for colorectal polyps, for example, adenomatous polyps and includes concentrations of at least any 1 , 2, 3, 4, 5, 6, 7, 8, 9, or 10 metabolites selected from the group consisting of: Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline.

In some embodiments, a reduced concentration of any one or more metabolites selected from the group consisting of Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1 - Methylnicotinamide, and Trigonelline is indicative that the subject has or is predisposed to developing adenomatous polyps. As above, "reduced" is relative to a corresponding urine metabolite concentration of healthy subjects.

In some embodiments, the reference metabolite profile is designed to identify subjects having or predisposed to colorectal polyps, but not necessarily to distinguish one type of polyp from another. For instance, the polyp may be adenomatous or hyperplastic, but the reference diagnostic profile does not necessarily distinguish between the two.

In certain embodiments, the reference metabolite profile can be for colorectal polyps that are either adenomatous polyps or hyperplastic polyps and includes urine concentrations of at least any 1 , 2, 3, 4, 5, 6, 7, 8, 9, or 10 metabolites selected from the group consisting of: Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1 - Methylnicotinamide, and Trigonelline.

As above, acceptable specificity and sensitivity for assessment of the presence or predisposition of colorectal polyps was demonstrated with a profile based on only Succinic acid, Ascorbic acid, and Carnitine, and fewer than these three may be used. Thus, if fewer than all of the 10 metabolites referred to above are included in the reference metabolite profile, the profile may include one or more metabolites in a set of metabolites selected from the group consisting of: a. Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline; b. Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, and 1-Methylnicotinamide ; c. Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, and Fumaric acid ; d. Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, and Pantothenic acid; e. Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, and Methylamine; f. Succinic acid, Ascorbic acid, Carnitine, Creatine, and Citric acid; g. Succinic acid, Ascorbic acid, Carnitine, and Creatine; h. Succinic acid, Ascorbic acid, and Carnitine; i. Succinic acid and Ascorbic acid; and j. Succinic acid.

In some embodiments, a reduced concentration of any one or more metabolites selected from the group consisting of Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1 - Methylnicotinamide, and Trigonelline is indicative that the subject has or is predisposed to developing colorectal polyps which are can be adenomatous polyps. As above, "reduced" is relative to a corresponding urine metabolite concentration of healthy subjects. Assessing whether a subject has or is predisposed to developing CRC and/or colorectal polyps

The invention provides methods for assessing whether a subject has or is predisposed to developing CRC and/or colorectal polyps, the method comprising: (a) providing a urine sample from said subject; (b) obtaining a metabolite profile from said urine sample; (c) comparing said metabolite profile with a reference metabolite profile; and (d) assessing, based on said comparison in step (c), whether said subject has or is predisposed to developing CRC and/or colorectal polyps. Urine samples can be obtained as described above. The metabolite profile from the subject contains the corresponding information concerning the subject's urine sample as contained in the selected reference metabolite profile, as described above. Comparison of the metabolite profile from the subject to the reference metabolite profile allows for assessment of whether the subject has or is predisposed to developing CRC and/or colorectal polyps.

Merely by way of an illustrative example, the method might be a method for assessing whether a subject has or is predisposed to developing CRC and/or colorectal polyps. A urine sample could be taken and concentrations of the following metabolites measured: Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline. The concentration of each of these metabolites in the subject's urine is then compared to the concentrations of the corresponding metabolites in the reference metabolite profile. Detection of a lower concentration of any one or more of Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline in the subject's metabolite profile than in the reference metabolite profile may indicate that the subject has or is predisposed to developing CRC and/or colorectal polyps.

Diagnostic kits The invention also provides kits for assessing whether a subject has or is predisposed to developing CRC and/or colorectal polyps. Such kits may comprise one or more reagents for detecting the presence and/or concentration of one or more metabolites in a urine sample of a subject, and may include instructions for use of the kit for assessing whether a subject has or is predisposed to developing CRC and/or colorectal polyps. The most reliable results are likely obtained when urine samples are processed, e.g. by LCMS, in a laboratory setting. For instance, a urine sample might be obtained from a subject in the office of a medical practitioner and then sent to a hospital or commercial medical laboratory for further testing. However, in many instances, it may be desirable to provide immediate results in a clinician's office or to permit a subject to conduct testing at home. The need for a test that is portable, pre-packaged, disposable, usable by a subject without assistance or direction, etc. may in some instances be of more importance than a high degree of accuracy. In many instances, particularly where there will be follow-up with a medical practitioner, a preliminary test, even one with reduced sensitivity and/or specificity may be sufficient. Thus, an assay presented in kit form may involve detection and measurement of a relatively small number of metabolites, to reduce the complexity and cost of the assay.

Any form of urine assay capable of detecting urine metabolites as described herein may be used. Typically, the assay will quantitate the urine metabolites to some extent e.g. whether they are higher or lower in concentration or in amount than a predetermined threshold value. Such kits may take the form of a test strip, dip stick, cassette, cartridge, chip-based or bead-based array, multi-well plate, or series of containers, or the like. One or more reagents are provided to detect the presence and/or concentration and/or amount of selected urine metabolites. The subject's urine may be dispensed directly onto the assay or indirectly from a stored or previously obtained sample. The presence or absence of a metabolite above or below a pre-determined threshold may be displayed e.g. by a chromogenic, fluorogenic, electrochemiluminescent or other output, e.g. as in an enzyme immunoassay (EIA) such as an enzyme-linked immunoassay (ELISA). In an embodiment, a kit may comprise a solid substrate, such as e.g. a chip, slide, array, etc., with reagents capable of detecting and/or quantitating one or more urine metabolites immobilized at predetermined locations on the substrate. By way of an illustrative example, a chip can be provided with reagents immobilized at discrete, predetermined locations for detecting and quantitating in a urine sample the concentration of Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline, any number thereof, or any combination thereof. As discussed above, reduced levels of these metabolites were found in the urine of subjects with adenomatous polyps. The chip may be configured such that a detectable output (e.g. colour change) is provided only if the concentration of one or more of these metabolites is over a threshold value, the threshold value being selected to distinguish between a metabolite concentration indicative of healthy subjects and those having or predisposed to developing CRC and/or colorectal polyps. Thus, the presence of a detectable output such as a colour change provides an immediate indication that the urine sample contains significantly reduced levels of one or more relevant urine metabolites, indicating that the subject has or is predisposed to developing CRC and/or colorectal polyps.

Systems for Performing the Assessment of CRC or Colorectal Polyps In an embodiment, the invention provides a system for assessing whether a subject has or is predisposed to developing CRC and/or colorectal polyps. Such a system may comprise: a CRC- and/or colorectal polyps-assessing apparatus including a control unit and a memory unit to assess a CRC and/or a colorectal polyp state in a subject; and an information communication terminal apparatus that provides data on the presence and/or concentration and/or amount of metabolites in a urine sample from the subject connected to each other communicatively, wherein the information communication terminal apparatus includes: a data sending unit that transmits the data on the presence and/or concentration and/or amount of metabolites in the sample to the CRC- and/or colorectal polyps-assessing apparatus; and an assessment result-receiving unit that receives the assessment result of the CRC and/or colorectal polyps state of the subject transmitted from the CRC- and/or colorectal polyps-assessing apparatus, wherein the control unit of the CRC- and/or colorectal polyps- assessing apparatus includes: a data-receiving unit that receives the data on the metabolite concentration and/or amount of the sample transmitted from the information communication terminal apparatus; a discriminant value-calculating unit that calculates a discriminant value that is a value of multivariate discriminant, based on both the concentration and/or amount value of the metabolite in the sample received by the data-receiving unit and a multivariate discriminant with the concentration and/or amount of the metabolite as explanatory variable stored in the memory unit; a discriminant value criterion-assessing unit that assesses the CRC or colorectal polyps state in the subject, based on the discriminant value calculated by the discriminant value-calculating unit; and an assessment result-sending unit that transmits the assessment result of the subject obtained by the discriminant value criterion-assessing unit to the information communication terminal apparatus.

Evaluation of Efficacy of Pharmaceutical Agents and/or Physical Treatments and/or Surgical Treatment

Metabolomic analysis is ideal for identification of and evaluation of the effects of potential pharmaceutical agents and/or new physical and/or surgical treatments against CRC, colorectal polyps and/or adenomatous polyps. Urine samples can be taken one or more times, by methods described previously herein, from a subject before and after treatment. The treatment can include administration of one or more pharmaceutical agents at one or more doses, and/or carrying out one or more physical and/or surgical treatments, to or on a subject. The administration of pharmaceutical agents can be made in many different ways including, but not limited to, injection, oral administration, patch or ointment application. The metabolite profiles obtained from the samples can be compared with each other and/or with the metabolite profile from subjects without CRC and/or colorectal polyps. The comparison can indicate the efficacy of the pharmaceutical agents and/or the physical treatment and/or surgical treatment through changes of the metabolite profile in urine samples of the subject. Also, comorbidities and medications of a subject can be studied in subsequent analyses to determine their effects on the metabolomic test results and specifically whether they contribute to discordant results. In addition, the metabolite profiles of the CRC samples can be correlated with operative and histological findings to determine whether CRC location or stage can change a metabolite profile. This invention is further illustrated by the following non-limiting examples.

Example 1. Comparison of the MS-based metabolomics test with NMR-based tests. The concentration values of 685 samples measured by LCMS were compared with the NMR quantifications using Passing and Bablok regression. The correlation plots between MS quantifications and NMR quantifications for each of the three metabolites are shown in Figures 4 - 6. For all three metabolites, there was a strong positive correlation of MS data on NMR data (R>0.8, P <0.01). Regression line equation for Ascorbic Acid is y = 2.50 + 1.12 x; 95% CI for intercept 2.50 to 2.50 and for slope 1.06 to 1.19 indicated small constant and small proportional difference. Regression line equation for Carnitine is y = 1.73 + 0.99 x; 95% CI for intercept 0.77 to 2.50 and for slope 0.96 to 1.02 indicated small constant and no proportional difference. Regression line equation for Succinic Acid is y = 4.17 + 1.32 x; 95% CI for intercept 2.72 to 5.33 and for slope 1.26 to 1.38 indicated small constant and small proportional difference. For all three metabolites, within the 95% CI the two methods were not identical; however the values measured from both methods were comparable.

For the test performance comparison, a NMR predictor was also built and evaluated using the same analysis workflow of building the MS predictor. The AUC of the NMR test is 0.670 which is slightly lower than the AUC of MS based test at 0.692. This might be due to the fact MS is more sensitive in the lower concentration range.

Example 2. Comparison of the urine-based metabolomics test with commercially available fecal-based tests. The diagnostic accuracies of our developed MS-based test for colonic adenomatous polyps were compared with the three fecal-based (one fecal-guaiac and two fecal-immune) tests. The sensitivity and specificity for each test on the same 685 samples set are calculated for adenomatous polyp detection. The sensitivities for polyp detection by Fecal Guaiac Hemll®, Fecal Immune ICT® and Fecal Immune MagSt® are 2.6%, 13.2% and 17.6%, with specificities of 99.0%, 97.1 % and 94.2%, respectively. All three fecal based tests offer high specificity for polyps but a very low sensitivity (< 18%) which questions their use for polyp detection and early cancer screening. These fecal-based tests focus on colon cancer detection, not polyp presence or predisposition. MS-based PolypDx™ demonstrates a much higher sensitivity that is designed for adenomatous polyps detection and would serve as a better population based screening tool for CRC. Figure 3 also shows how well these three fecal-based tests compared to MS-based PolypDx™ predictor's performance. Since none of the fecal tests have an adjustable threshold, each corresponds to a point in the ROC space. All three fecal tests lie on/below our urine-based predictor's ROC curve, which indicates that the MS-based PolypDx™ predictor always outperforms the fecal tests.

The metabolites and clinical features used in the MS-based PolypDx™ algorithm are summarized in Table 7. Correlations were calculated by encoding those patients who require colonoscopy as "1 " and those that did not as "0". Higher concentrations of 3 metabolites of interest were inversely correlated with the need for colonoscopy (e.g. lower concentration of each metabolite indicates the patient is more likely to require colonoscopy). Since the sex feature was encoded with the males as being 1 , and females 0, the patient being male is directly correlated with the need for colonoscopy (i.e. males are more likely to need colonoscopy). Age is also directly correlated with colonoscopy, with older patients more likely to need colonoscopy. Finally, the fact that a patient smokes is directly correlated with the need for colonoscopy (i.e. smokers are more likely to develop polyps). Although none of the correlations for each feature have a large absolute value, the importance lies in the linear combination of the features, via the LASSO algorithm. Table 7. Further Information about features used in the PolypDx™ MS predictor.

Feature PubChem HMDB Correlation

CID

Smoker N/A N/A 0.09

Age N/A N/A 0.13

Sex N/A N/A 0.17

Succinic Acid 1 1 10 HMDB00254 -0.16

Ascorbic Acid 54670067 HMDB00044 -0.15

Carnitine 2724480 HMDB00062 -0.13

The citation of any publication herein is for its disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this invention belongs. As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural reference unless the context clearly dictates otherwise. Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it is readily apparent to those of ordinary skill in the art in light of the teachings of this invention that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims.

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Claims

CLAIMS We claim:

1 . A method for assessing whether a subject has or is predisposed to developing colorectal polyps, said method comprising:

(a) providing a urine sample from said subject;

(b) obtaining a metabolite profile from said urine sample;

(c) comparing said metabolite profile with a reference metabolite profile; and

(d) assessing, based on said comparison in step (c), whether said subject has or is predisposed to developing colorectal polyps; wherein said metabolite profile is obtained using mass spectrometry.

2. The method according to claim 1 , wherein: in step (b), said metabolic profile is obtained by measuring the concentration of one or more metabolites in said urine sample to produce said metabolite profile for said subject; and, in step (c), said reference metabolite profile is determined from the concentration of corresponding metabolites in urine of individuals in a reference population.

3. The method according to claim 2, which is a method for assessing whether the subject has or is predisposed to developing colorectal polyps, and wherein step (b) comprises measuring the concentration in said urine sample of at least any 1 , 2, 3, 4, 5, 6, 7, 8, 9, or 10 metabolites selected from the group consisting of: Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline.

4. The method according to claim 3, wherein said step (b) comprises measuring the concentration in said urine sample of at least any one or more metabolites in a set of metabolites selected from the group consisting of:

(i) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1- Methylnicotinamide, and Trigonelline;

(ii) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, and 1- Methylnicotinamide ;

(iii) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, and Fumaric acid ;

(iv) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, and Pantothenic acid;

(v) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, and Methylamine;

(vi) Succinic acid, Ascorbic acid, Carnitine, Creatine, and Citric acid;

(vii) Succinic acid, Ascorbic acid, Carnitine, and Creatine;

(viii) Succinic acid, Ascorbic acid, and Carnitine;

(vix) Succinic acid and Ascorbic acid; (x) Succinic acid.

5. The method according to any one of claims 3 to 4, wherein a reduced urine concentration of any one or more metabolites selected from the group consisting of Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid,

Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline is indicative that the subject has or is predisposed to developing colorectal polyps.

6. The method according to claim 1 , wherein said colorectal polyps are either adenomatous polyps or hyperplastic polyps and wherein step (b) comprises measuring the concentration in said urine sample of at least any 1 , 2, 3, 4, 5, 6, 7, 8, 9, or 10 metabolites selected from the group consisting of: Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1- Methylnicotinamide, and Trigonelline.

7. The method according to claim 6, wherein said step (b) comprises measuring the concentration in said urine sample of at least any one or more metabolites in a set of metabolites selected from the group consisting of:

(i) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1- Methylnicotinamide, and Trigonelline;

(ii) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, and 1- Methylnicotinamide ;

(iii) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, and Fumaric acid ; (iv) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, and Pantothenic acid;

(v) Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, and Methylamine;

(vi) Succinic acid, Ascorbic acid, Carnitine, Creatine, and Citric acid;

(vii) Succinic acid, Ascorbic acid, Carnitine, and Creatine;

(viii) Succinic acid, Ascorbic acid, and Carnitine; (vix) Succinic acid and Ascorbic acid; and

(vx) Succinic acid.

8. The method according to either one of claim 6 or 7, wherein a reduced urine concentration as compared to the reference metabolite profile of any one or more metabolites selected from the group consisting of Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1- Methylnicotinamide, and Trigonelline is indicative that the subject has or is predisposed to developing colorectal polyps which are either adenomatous polyps or hyperplastic polyps.

9. The method according to any one of claims 1 to 8, wherein said reference metabolite profile is obtained using one or more methods selected from the group consisting of: nuclear magnetic resonance spectroscopy; high performance liquid chromatography; thin layer chromatography; electrochemical analysis; mass spectroscopy; Liquid chromatography-mass spectrometry; refractive index spectroscopy; ultra-violet spectroscopy; fluorescent analysis; radiochemical analysis; near-infrared spectroscopy; gas chromatography and light scattering analysis.

10. The method according to any one of claims 1 to 9, wherein an assessment is made by also using clinical features of the subject.

1 1. The method according to claim 10, wherein the clinical features are selected from the group comprising age, sex, smoking status, and a combination thereof.

12. The method according to any one of claims 1 to 10, wherein an assessment is made by also using an algorithm.

13. The method according to claim 12, wherein the algorithm is a LASSO algorithm.

14. A method for identifying urine metabolites indicative of the presence or absence of colorectal polyps, said method comprising:

(a) providing a urine sample from a subject;

(b) obtaining a metabolite profile from said urine sample;

(c) comparing said metabolite profile with a reference metabolite profile; and

(d) identifying, based on said comparison in step (c), one or more metabolites in said metabolite profile that are indicative of the presence of or predisposition to in said subject of colorectal polyps; wherein said metabolite profile is obtained using mass spectrometry.

15. The method according to claim 14, wherein said reference metabolite profile is obtained using one or more methods selected from the group consisting of: nuclear magnetic resonance spectroscopy; high performance liquid chromatography; thin layer chromatography; electrochemical analysis; mass spectroscopy; liquid chromatography-mass spectrometry; refractive index spectroscopy; ultra-violet spectroscopy; fluorescent analysis; radiochemical analysis; near-infrared

spectroscopy; gas chromatography and light scattering analysis.

16. The method according to either one of claims 14 or 15, wherein an identification is made by also using clinical features of the subject.

17. The method according to claim 16, wherein the clinical features are selected from the group comprising age, sex, smoking status, and a combination thereof.

18. The method according to any one of claims 14 to 17, wherein an identification is made by also using an algorithm.

19. The method according to claim 18, wherein the algorithm is a LASSO algorithm.

20. A kit for assessing whether a subject has or is predisposed to developing colorectal polyps using mass spectrometry, said kit comprising one or more reagents for detecting the presence and/or concentration and/or amount of one or more metabolites in a urine sample of a subject, and instructions for use of said kit for assessing whether a subject has or is predisposed to developing colorectal polyps.

21. A kit according to claim 20, wherein said one or more metabolites are selected from the group consisting of Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1- Methylnicotinamide, Trigonelline, and a combination thereof.

22. A use of a metabolite profile comprising one or more of the following metabolites:

Succinic acid, Ascorbic acid, Carnitine, Creatine, Citric acid, Methylamine, Pantothenic acid, Fumaric acid, 1-Methylnicotinamide, and Trigonelline, for assessing whether a subject has or is predisposed to developing colorectal polyps.

23. A system comprising:

(a) a colorectal polyps-assessing apparatus including a control unit and a memory unit to assess a colorectal polyp state in a subject; and

(b) an information communication terminal apparatus that provides data on the presence and/or concentration and/or amount of metabolites in a urine sample from the subject connected to each other communicatively, wherein the data is obtained using mass spectrometry, wherein the information communication terminal apparatus includes:

(a) a data sending unit that transmits the data on the presence and/or concentration and/or amount of metabolites in the sample to the colorectal polyps-assessing apparatus; and (b) an assessment result-receiving unit that receives the assessment result of the colorectal polyps state of the subject transmitted from the colorectal polyps-assessing apparatus, wherein the control unit of the colorectal polyps-assessing apparatus includes:

(a) a data-receiving unit that receives the data on the metabolite concentration and/or amount of the sample transmitted from the information communication terminal apparatus;

(b) a discriminant value-calculating unit that calculates a discriminant value that is a value of multivariate discriminant, based on both the concentration and/or amount value of the metabolite in the sample received by the data-receiving unit and a multivariate discriminant with the concentration and/or amount of the metabolite as explanatory variable stored in the memory unit;

(c) a discriminant value criterion-assessing unit that assesses the colorectal polyps state in the subject, based on the discriminant value calculated by the discriminant value-calculating unit; and

(d) an assessment result-sending unit that transmits the assessment result of the subject obtained by the discriminant value criterion-assessing unit to the information communication terminal apparatus.

24. A method for identifying and evaluating effectiveness of

pharmaceutical agents and/or surgical treatments and/or physical treatments against colorectal polyps, said method comprising: (a) providing a first urine sample from a subject having colorectal polyps;

(b) obtaining a metabolite profile from said first urine sample, wherein said first metabolite profile is obtained using mass spectrometry;

(c) administering one or more pharmaceutical candidates and/or performing one or more physical or surgical treatments to or on said subject;

(d) providing a second urine sample from said subject in step (c);

(e) obtaining a metabolite profile from said second urine sample, wherein said second metabolite profile is obtained using mass spectrometry;

(f) comparing said metabolite profile obtained in steps (b) and (e) with a reference metabolite profile; and

(g) assessing, based on said comparison in step (f), whether the one or more pharmaceutical candidates and/or treatments is effective against colorectal polyps.