Discovery and Features of an Alkylating Signature in Colorectal Cancer
Bibliographic record
Abstract
Abstract Several risk factors have been established for colorectal cancer, yet their direct mutagenic effects in patients' tumors remain to be elucidated. Here, we leveraged whole-exome sequencing data from 900 colorectal cancer cases that had occurred in three U.S.-wide prospective studies with extensive dietary and lifestyle information. We found an alkylating signature that was previously undescribed in colorectal cancer and then showed the existence of a similar mutational process in normal colonic crypts. This alkylating signature is associated with high intakes of processed and unprocessed red meat prior to diagnosis. In addition, this signature was more abundant in the distal colorectum, predicted to target cancer driver mutations KRAS p.G12D, KRAS p.G13D, and PIK3CA p.E545K, and associated with poor survival. Together, these results link for the first time a colorectal mutational signature to a component of diet and further implicate the role of red meat in colorectal cancer initiation and progression. Significance: Colorectal cancer has several lifestyle risk factors, but the underlying mutations for most have not been observed directly in tumors. Analysis of 900 colorectal cancers with whole-exome sequencing and epidemiologic annotations revealed an alkylating mutational signature that was associated with red meat consumption and distal tumor location, as well as predicted to target KRAS p.G12D/p.G13D. This article is highlighted in the In This Issue feature, p. 2355
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".