Smoking habit and long-term colorectal cancer incidence by exome-wide mutational and neoantigen loads: evidence based on the prospective cohort incident-tumour biobank method
Bibliographic record
Abstract
Objective To test the hypothesis that the association of smoking with long-term colorectal cancer incidence may be stronger for tumours with higher mutational and neoantigen loads. Methods and analysis In the Nurses’ Health Study (1980–2012) and the Health Professionals Follow-up Study (1986–2012), our novel prospective cohort incident-tumour biobank method (PCIBM) used 3053 incident colorectal carcinoma cases including 752 cases with whole-exome sequencing data. Using the multivariable duplication-method Cox regression model with the inverse probability weighting to adjust for the selection bias due to tissue availability, we assessed a differential association of cigarette smoking with colorectal carcinoma incidence by an exome-wide tumour mutational burden (e-TMB) or neoantigen load. Results The association of pack-years smoked with colorectal cancer incidence differed by e-TMB (P heterogeneity <0.001). Multivariable-adjusted HRs for e-TMB-high (≥10 mutations/megabase) tumours were 1.28 (95% CI 0.72 to 2.28) and 2.56 (95% CI 1.61 to 4.07) for 1–19 and ≥20 pack-years (vs 0 pack-years; P trend <0.001), respectively. In contrast, pack-years smoked were not associated with e-TMB-low tumour incidence (P trend =0.67). A similar differential association was observed for the neoantigen load (P heterogeneity =0.017). The differential association by e-TMB appeared consistent in the strata of CpG island methylator phenotype status, BRAF mutation or lymphocytic infiltrates. Conclusions Smoking is more strongly associated with the long-term incidence of colorectal carcinoma harbouring higher mutational and neoantigen loads. Our PCIBM-based evidence supports the immunosuppressive effect of smoking and the potential of smoking cessation in improving antitumour immunity for cancer prevention and treatment.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".