A comprehensive genomic framework for identifying genes predisposing to homologous recombination repair deficient breast cancer
Bibliographic record
Abstract
Abstract Background Patients with clinical characteristics of increased cancer susceptibility without an identified genetic lesion are regularly seen in clinics. Case-control studies and matched normal/tumour sequencing have advanced the discovery of Cancer Susceptibility Genes (CSGs), with limitations when used independently. We reasoned that combining these strategies alongside mutational signatures and clinical data could improve CSGs identification. Methods Using breast cancer exome data from The Cancer Genome Atlas (TCGA-BRCA), we developed a genomic framework that evaluates exome-wide associations of Germline Pathogenic Variants (GPVs) with somatic second hits, within the context of the Homologous Recombination Repair Deficiency (HRD) mutational signature 3 (Sig3). This is complemented by clinico-genomic analysis evaluating clinical and biological plausibility. Results Our framework confirmed significant associations with Sig3 of BRCA1/2 GPVs with second hits, validating its performance. THBS4 also reached significance but co-occurred with other HRD-related events. Borderline significance was observed for KIF13B and TESPA1 . The clinico-genomics approach further identified KIF13B and TESPA1 , as well as RAD51B and other Fanconi Anemia pathway-related genes, which deserve further validation. Conclusions Our framework strengthens identification of candidate HRD-related breast CSGs through combined statistical and clinico-genomics analyses. It is adaptable to other mutational signatures/cancer types and will benefit from larger and well-annotated datasets.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".