Homologous Recombination Deficiency and Platinum-Based Therapy Outcomes in Advanced Breast Cancer
Bibliographic record
Abstract
Abstract Purpose: Recent studies have identified mutation signatures of homologous recombination deficiency (HRD) in over 20% of breast cancers, as well as pancreatic, ovarian, and gastric cancers. There is an urgent need to understand the clinical implications of HRD signatures. Whereas BRCA1/2 mutations confer sensitivity to platinum-based chemotherapies, it is not yet clear whether mutation signatures can independently predict platinum response. Experimental Design: In this observational study, we sequenced tumor whole genomes (100× depth) and matched normals (60×) of 93 advanced-stage breast cancers (33 platinum-treated). We computed a published metric called HRDetect, independently trained to predict BRCA1/2 status, and assessed its capacity to predict outcomes on platinum-based chemotherapies. Clinical endpoints were overall survival (OS), total duration on platinum-based therapy (TDT), and radiographic evidence of clinical improvement (CI). Results: HRDetect predicted BRCA1/2 status with an area under the curve (AUC) of 0.94 and optimal threshold of 0.7. Elevated HRDetect was also significantly associated with CI on platinum-based therapy (AUC = 0.89; P = 0.006) with the same optimal threshold, even after adjusting for BRCA1/2 mutation status and treatment timing. HRDetect scores over 0.7 were associated with a 3-month extended median TDT (P = 0.0003) and 1.3-year extended median OS (P = 0.04). Conclusions: Our findings not only independently validate HRDetect, but also provide the first evidence of its association with platinum response in advanced breast cancer. We demonstrate that HRD mutation signatures may offer clinically relevant information independently of BRCA1/2 mutation status and hope this work will guide the development of clinical trials. Clin Cancer Res; 23(24); 7521–30. ©2017 AACR.
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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 | 0.003 |
| 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) | 0.001 | 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".