Germline genomic patterns are associated with cancer risk, oncogenic pathways and clinical outcomes
Bibliographic record
Abstract
Summary Germline genetic polymorphism is prevalent and inheritable. So far mutations of a handful of genes have been associated with cancer risks. For example, women who harbor BRCA1/2 germline mutations have a 70% of cumulative breast cancer risk; individuals with congenital germline APC mutations have nearly 100% of cumulative colon cancer by the age of fifty. At present, gene-centered cancer predisposition knowledge explains only a small fraction of the inheritable cancer cases. Here we conducted a systematic analysis of the germline genomes of cancer patients (n=9,712) representing 22 common cancer types along with non-cancer individuals (n=16,670), and showed that seven germline genomic patterns, or significantly repeatedly occurring sequential mutation profiles, could be associated with both carcinogenesis processes and cancer clinical outcomes. One of the genomic patterns was significantly enriched in the germline genomes of patients who smoked than in those of non-smoker patients of 13 common cancer types, suggesting that the germline genomic pattern was likely to confer an elevated carcinogenesis sensitivity to tobacco smoke. Several patterns were also associated with somatic mutations of key oncogenic genes and somatic-mutational signatures which are associated with higher genome instability in tumors. Furthermore, subgroups defined by the germline genomic patterns were significantly associated with distinct oncogenic pathways, tumor histological subtypes and prognosis in 12 common cancer types, suggesting that germline genomic patterns enable to inform treatment and clinical outcomes. These results demonstrated that genetic cancer risk and clinical outcomes could be encoded in germline genomes in the form of not only mutated genes, but also specific germline genomic patterns, which provided a novel perspective for further investigation.
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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 | 0.002 |
| 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) | 0.002 | 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".