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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".