Genetic analysis of lung cancer reveals novel susceptibility loci and germline impact on somatic mutation burden
Bibliographic record
Abstract
ABSTRACT Germline genetic variants are involved in lung cancer (LC) susceptibility. Previous genome-wide association studies (GWAS) have implicated genes involved in smoking propensity and DNA repair but further work is required to identify additional LC susceptibility variants and to investigate LC disease development dynamics. We have undertaken a family history-based genome-wide association (GWAx) study of LC, analysing 48,843 European cases with a parent/sibling with LC compared to 195,387 controls from the UK Biobank. This was meta-analysed with previously described LC GWAS results. We performed Polygenic Risk Scores (PRS) analyses and further evaluated the PRS influence on the somatic environment in exome (N=736) and genome sequencing (N=61) profiled cohorts. Eight novel loci were identified including DNA repair genes ( CHEK1, MDM4 ), metabolic genes ( CYP1A1 ) and variants that were also associated with smoking propensity, such as both subunits of the neuronal α4β2 nicotinic acetylcholine receptor ( CHRNA4 and CHRNB2) . PRS analysis demonstrated that variants related to eQTLs and/or smoking propensity are enriched for susceptibility variants, including variants below genome-wide significant threshold. PRS of LC variants related to smoking propensity were associated with somatic mutation burden in two case cohorts, with individuals with higher polygenic genetic risk having increased numbers of somatic mutations in their lung tumours. This study has expanded the number of susceptibility loci linked with LC and provided insights into the molecular mechanisms by which these susceptibility variants contribute to the development of lung cancer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".