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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".