CRISPR screens reveal genetic determinants of PARP inhibitor sensitivity and resistance in prostate cancer
Bibliographic record
Abstract
ABSTRACT Prostate cancer (PCa) harboring BRCA1/2 mutations is often exquisitely sensitive to PARP inhibition. However, genomic alterations in other DNA damage response genes have not been consistently predictive of clinical response to PARP inhibitors (PARPis). Here, we perform genome-wide CRISPR-Cas9 knockout screens in BRCA1/2-proficient PCa cell lines and identify novel genes whose loss has a profound impact on PARPi sensitivity and resistance. Specifically, MMS22L deletion, frequently observed (up to 14%) in PCa, renders cells hypersensitive to PARPis by disrupting RAD51 loading required for homologous recombination repair, although this response is TP53-dependent. Unexpectedly, loss of CHEK2 confers resistance rather than sensitivity to PARPis in PCa cells through increased expression of BRCA2, a target of CHEK2-TP53-E2F7-mediated transcriptional repression. Combined PARP and ATR inhibition overcomes PARPi resistance caused by CHEK2 loss. Our findings may inform the use of PARPis beyond BRCA1/2-deficient tumors and support reevaluation of currently used biomarkers for PARPi treatment in PCa.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".