Targeting defective DNA repair in prostate cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: Prostate cancer is the second leading cause of cancer death in men. Characterization of the genomic landscape of prostate cancer has demonstrated frequent aberrations in DNA repair pathways, identifiable in up to 25% patients with metastatic disease, which may sensitize to novel therapies, including PARP inhibitors and immunotherapy. Here, we summarize the current clinical landscape and future horizons for targeting defective DNA repair pathways in PC. RECENT FINDINGS: Several clinical trials have demonstrated efficacy of different PARP inhibitors in metastatic castration-resistant prostate cancer (mCRPC), most pronounced in those with BRCA mutations. The PROfound trial is the first positive phase 3 biomarker-selected trial to demonstrate improved outcomes with a targeted treatment, Olaparib, in mCRPC. Whilst the Keynote-199 trial failed to demonstrate efficacy of immune-checkpoint inhibitor pembrolizumab in unselected mCRPC patients, there was evidence of response in those harbouring DNA repair defects. SUMMARY: These landmark trials represent a significant advance towards personalization of PC therapy. However, resistance remains inevitable and there is a lack of reliable predictive biomarkers to select patients for treatment. Characterization of resistance mechanisms, and validation of novel biomarkers is critical to maximize clinical benefit and inform novel treatment combinations to improve outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".