Urine Metabolomic Biomarker Panel for Colorectal Cancer: Presidential Poster Award
Bibliographic record
Abstract
Introduction: Metabolomics is the study of low molecular weight compounds generated by metabolism. There are 3,000 metabolites in human urine which provide can be characterized to provide information about a patient’s current state of health. Colorectal cancer (CRC) is the third leading cause of cancer deaths worldwide in 2015 (World Health Organization, WHO). Population-based screening programs are credited with earlier CRC diagnoses and treatment initiation which reduce mortality rates and improve patient health outcomes. However, recommended screening methods are unsatisfactory for global purposes as they are invasive, resource intensive, suffer from low uptake, or have poor diagnostic performance. Our goal was to identify a urine metabolomics-based biomarker panel for the detection of CRC suitable for global population-based screening. Methods: Prospective urine samples were collected from study participants. Based upon colonoscopy and histopathology results, 400 participants (CRC, 200; Normal, 200) from each country, Canada and Nigeria, were included in the analyses. Targeted liquid chromatography-mass spectrometry (LC-MS) was performed to quantify 140 highly valuable metabolites in each urine sample. Potential biomarkers for CRC were identified by comparing the metabolomic profile from CRC vs. Normal group. A urine metabolomic predictor for CRC was generated for each country using 67% samples (un-blinded training set) with advanced machine learning methods. These were validated using the remaining 33% samples (blinded testing set). Results: A panel of 20 metabolites was identified as possible biomarkers for CRC from the Canadian dataset; a predictor (AUC 0.902) was then built. A panel of 30 metabolites was identified as possible biomarkers for CRC from the Nigerian dataset; a predictor (AUC 0.895) was built. Diacetylspermine and kynurenine were common to both metabolite panels and had very high VIP scores (> 2.0). A single predictor for CRC, using only these two metabolites, was identified with excellent accuracy profiles, respectively: Canada, AUC 0.880; and Nigeria, AUC 0.858. Conclusion: We present a potentially “universal” metabolomic biomarker panel independent of age, diet, and ethnicity that has potential clinical application for population-based CRC screening using easy-to-collect, urine samples. A larger-scale, multi-country trial is planned for further validation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".