Bibliographic record
Abstract
Several genomic studies have identified DNA repair gene defects in prostate cancer in the last 5 years. The mechanisms by which these DNA repair defects promote carcinogenesis and tumor progression in the prostate have not been fully elucidated, but their presence in at least 20-25% of metastatic castration-resistant prostate cancers (CRPCs) provides an opportunity for a therapeutic strategy that turns a tumor strength into its weakness and may lead to arguably the first molecularly stratified treatment for this disease.Poly(ADP-ribose) polymerase (PARP) inhibitors have been developed as an anticancer synthetic lethal therapeutic strategy for tumors with impaired homologous recombination DNA repair, based on a synthetic lethal effect. Poly(ADP-ribose) polymerase inhibitors have shown to induce significant tumor responses in cancer patients carrying germline BRCA1/2 mutations. Recent evidence from a phase II clinical trial supports further testing of PARP inhibitors for the treatment of metastatic CRPC with either germline or somatic defects in BRCA2, ATM, PALB2, and other DNA repair genes.We review the current evidence of how this strategy is relevant for the treatment of advanced prostate cancers, the available data from trials with PARP inhibitors in metastatic CRPC, and the ongoing studies analyzing combinations of these drugs with other therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.006 | 0.004 |
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".