Germline Predisposition to Oncogenic Alkylating Damage in Colorectal Cancer
Bibliographic record
Abstract
BACKGROUND: Red meat consumption is a risk factor for colorectal cancer and has been linked to tumor alkylating DNA damage. rs16906252-T is a cis expression quantitative trait locus variant associated with silencing of MGMT, a central alkylating damage repair gene. We hypothesize that rs16906252-T carriers are predisposed to alkylating damage mutations. METHODS: We conducted mutational signature deconvolution of colorectal cancer whole-exome sequencing data from The Cancer Genome Atlas (n = 540), the Nurses' Health Study / Health Professionals Follow-Up Study (NHS/HPFS, n = 900), as well as non-Western samples from the Pan-Cancer Analysis of Whole Genomes (Colorectal Adenocarcinoma in China cohort, n = 295) and examined the relationship of rs16906252-T with putative alkylation-dependent tumor mutations. Leveraging lifestyle data from the NHS/HPFS, we also investigated the interaction between red meat consumption and rs16906252-T. RESULTS: Among patients with colorectal cancer, rs16906252-T carriers exhibited higher tumor alkylating damage compared with noncarriers. In the general population, rs16906252-T is largely absent in individuals with East Asian ancestries, and we consistently find a negligible contribution of alkylating damage in patients with colorectal cancer with East Asian ancestries. We show that the alkylating mutational signature's carcinogenicity is mainly mediated by KRAS G12D and G13D mutations. We also observe a synergistic effect of rs16906252-T with high prediagnosis red meat intake for tumor alkylating damage. CONCLUSIONS: MGMT rs16906252-T carriers are predisposed to colorectal cancer oncogenic alkylating damage which is potentiated by red meat intake. IMPACT: Our results support a causal relationship between red meat and colorectal cancer and may inform tailored dietary and screening guidelines for colorectal cancer prevention.
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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) | 0.004 | 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".