Genomic hallmarks of homologous recombination deficiency in invasive breast carcinomas
Bibliographic record
Abstract
Therapeutic strategies targeting Homologous Recombination Deficiency (HRD) in breast cancer requires patient stratification. The LST (Large-scale State Transitions) genomic signature previously validated for triple-negative breast carcinomas (TNBC) was evaluated as biomarker of HRD in luminal (hormone receptor positive) and HER2-overexpressing (HER2+) tumors. The LST genomic signature related to the number of large-scale chromosomal breakpoints in SNP-array tumor profile was applied to identify HRD in in-house and TCGA sets of breast tumors, in which the status of BRCA1/2 and other genes was also investigated. In the in-house dataset, HRD was predicted in 5% (20/385) of sporadic tumors luminal or HER2+ by the LST genomic signature and the inactivation of BRCA1, BRCA2 or RAD51C confirmed this prediction in 75% (12/16) of the tested cases. In 14% (6/43) of tumors occurring in BRCA1/2 mutant carriers, the corresponding wild-type allele was retained emphasizing the importance of determining the tumor status. In the TCGA luminal and HER2+ subtypes HRD incidence was estimated at 5% (18/329, 95%CI: 5-8%) and 2% (1/59, 95%CI: 2-9%), respectively. In TNBC cisplatin-based neo-adjuvant clinical trials, HRD is shown to be a necessary condition for cisplatin sensitivity. This analysis demonstrates the high performance of the LST genomic signature for HRD detection in breast cancers, which suggests its potential as a biomarker for genetic testing and patient stratification for clinical trials evaluating platinum salts and PARP inhibitors.
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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 | 0.002 |
| 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 | 0.001 | 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".