Impact of BRCA alterations on androgen receptor pathway inhibition treatment outcome in advanced prostate cancer.
Bibliographic record
Abstract
Abstract Androgen Receptor Pathway Inhibitors (ARPIs) are frequently used to treat advanced prostate cancer (PCa). Understanding the impact of homologous recombination repair (HRR) alterations on ARPI treatment outcomes is critical for optimizing treatment. We conducted a retrospective analysis of pooled data from five randomized trials of ARPIs (apalutamide (Apa) or abiraterone acetate (Abi)) across PCa disease settings. Patients received ARPI-based regimens or androgen deprivation therapy (ADT) alone. HRR alterations were determined using targeted panel or whole-exome sequencing of diagnostic tissue or plasma samples. Radiographic progression-free survival (rPFS) and overall survival (OS) were compared between biomarker groups. Of 608 patients treated with ARPI-based regimens, BRCA-altered patients were at higher risk of progression (HR=1.8 (1.16–2.79), P-value=0.009) and mortality (HR=1.6 (1.02–2.34), P-value=0.038) than BRCA-wildtype. HRR-altered patients had shorter rPFS than HRR wildtype patients (HR=1.7 (1.2–2.3), P-value=0.001). BRCA-altered patients had shorter OS compared to HRR wildtype patients (HR=1.6 (1.03–2.36) P-value=0.036). The same results were observed in the full cohort of 878 patients treated with next-generation ARPI or ADT alone. In 115 HRR-altered patients, addition of ARPIs to ADT significantly prolonged rPFS (HR=0.42 (0.2–0.89), P-value=0.023) and OS (HR=0.45 (0.23–0.91), P-value=0.025) vs. ADT alone. While patients with HRR gene alterations benefit from the addition of ARPIs to ADT, they generally have worse outcomes with AR directed therapy than HRR wildtype patients, with patients harboring BRCA alterations doing particularly poorly. These findings underline the unmet need for new and combination approaches for these patients.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 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".