Genomic Profiles of Pathogenic and Moderate-Penetrance Germline Variants Associated With Risk of Early-Onset Lung Adenocarcinoma
Bibliographic record
Abstract
INTRODUCTION: Up to 54% of all lung adenocarcinoma (LADC) cases in Asian populations occur in never-smoking women, suggesting that the impact of smoking and other environmental factors on the risk of early-onset LADC is minimal. Genetic factors may play a crucial role in disease development. METHODS: The prevalence of germline pathogenic variants (GPVs) of 454 hereditary cancer and DNA repair genes was evaluated by whole-exome and whole-genome sequencing of 348 early-onset LADC (aged ≤ 40 y) and 1425 later-onset LADC (aged ≥ 41 y) cases. A case-control study comprising 10,302 LADC cases and 7898 healthy controls was performed to identify moderate-risk genetic factors for the disease. Analysis of somatic mutations in 1278 patients with LADC, including 31 patients with GPVs, was also performed. RESULTS: The frequency of GPVs of TP53 and BRCA2 was significantly higher in those with early-onset LADC than in those with later-onset LADC. The detection rates for TP53 and BRCA2 GPVs were 2.9% and 1.7%, respectively, in patients with early-onset LADC, and 0.14% and 0.21%, respectively, in patients with later-onset LADC. Patients with BRCA1 GPVs exhibited a high incidence of concurrent TP53 somatic mutations. Patients with BRCA2 GPVs exhibited deficient homologous recombination in tumors by means of loss of the wild-type allele. A germline ALKBH2 variant, p.Glu35Alafs∗54, was associated with the risk of early-onset LADC, and patients with a deleterious variant exhibited a correlation between SBS4-related somatic mutations and the Brinkman index. CONCLUSION: TP53 and BRCA2 GPVs and the ALKBH2 novel variant are associated with early-onset LADC in Asians.
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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".