Urine Metabolomics and Colorectal Cancer Screening

This is a prospective, multi-centered study to assess whether urine metabolomics can play a role in the screening of colorectal cancer (CRC). Urine samples will be collected from 1000 patients going through an established CRC screening program, and from a further 500 patients who already have a diagnosis of CRC. Using nuclear magnetic resonance (NMR) spectroscopy, the 1H NMR spectrum of urine samples will be analyzed for specific metabolites, and establish the metabolomic signature of colorectal cancer. The results from metabolomic urinalysis of this screening cohort will be compared with results from colonoscopy, histological descriptions, fecal occult blood testing (FOBT), and fecal immune testing (FIT) to assess the accuracy of urine metabolomics in identifying patients with polyps and malignancies. The urine metabolomic results from the colorectal cancer group will be correlated with operative, histological and clinical staging to define the role of urine metabolomics in assessing colorectal cancer type, location and stage. Additionally approximately 300 urine samples from breast cancer patients and 300 from prostate cancer patients will be collected to validate that the colorectal cancer signature is unique.

Sex

All

Minimum age

18 Years

Maximum age

90 Years

Inclusion Criteria:

For Screening group (normal colonoscopy \& colonic polyps):

* asymptomatic, 50-75 year old, without personal or family history of CRC/polyps
* asymptomatic, 40-75 year old, known personal or first-degree family history of either CRC or polyps

For Cancer group:

* any patient with diagnosis of colorectal cancer
* any patient with diagnosis of prostate cancer
* any patient with diagnosis of breast cancer

Exclusion Criteria:

For screening group:

* hematochezia
* inflammatory bowel disease
* on anticoagulation for reasons other than atrial fibrillation
* significant co-morbidities

For Cancer group:

* already had neoadjuvant treatment at time of urine collection
* no invasive cancer at time of urine collection

• DERIVEDEisner R, Greiner R, Tso V, Wang H, Fedorak RN. A machine-learned predictor of colonic polyps based on urinary metabolomics. Biomed Res Int. 2013;2013:303982. doi: 10.1155/2013/303982. Epub 2013 Nov 7. PMID 24307992