Urine metabolomics as a predictor of patient tolerance and response to adjuvant chemotherapy in colorectal cancer
Bibliographic record
Abstract
Colorectal cancer is the third leading cause of cancer-associated mortality in the western world. The ability to predict a patient's response to chemotherapy may be of great value for clinicians and patients when planning cancer treatment. The aim of the current study was to develop a urine metabolomics-based biomarker panel to predict adverse events and response to chemotherapy in patients with colorectal cancer. A retrospective chart review of patients diagnosed with stage III or IV colorectal cancer between 2008 and 2012 was performed. The exclusion criteria included chemotherapy for palliation and patients living outside of Alberta. Data was collected concerning the chemotherapy regimen, adverse events associated with chemotherapy, disease progression and recurrence and 5-year survival. Adverse events were subdivided as follows: Delays in treatment, dose reductions, hospitalizations and chemotherapy regime changes. Patients provided urine samples for analysis prior to any intervention. Nuclear magnetic resonance (NMR) spectra of urine samples were acquired. The 1H NMR spectrum of each urine sample was analyzed using Chenomx NMRSuite v7.0. Using machine learning, predictors were generated and evaluated using 10-fold cross-validation. Urine spectra were obtained for 62 patients. The best predictors resulted in area under the receiver operating characteristic curve values of: 0.542 for chemotherapy dose reduction, 0.612 for 5-year survival, 0.650 for cancer recurrence and 0.750 for treatment delay. Therefore, predictors were developed for response to and adverse events from chemotherapy for patients with colorectal cancer patients. The predictor for treatment delay has the most promise, and further studies will aid its refinement and improvement of its accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".