Preclinical patient‐derived modeling of castration‐resistant prostate cancer facilitates individualized assessment of homologous recombination repair deficient disease
Bibliographic record
Abstract
The use of mutation analysis of homologous recombination repair (HRR) genes to estimate PARP-inhibition response may miss a larger proportion of responding patients. Here, we provide preclinical models for castration-resistant prostate cancer (CRPC) that can be used to functionally predict HRR defects. In vitro, CRPC LNCaP sublines revealed an HRR defect and enhanced sensitivity to olaparib and cisplatin due to impaired RAD51 expression and recruitment. Ex vivo-induced castration-resistant tumor slice cultures or tumor slice cultures derived directly from CRPC patients showed increased olaparib- or cisplatin-associated enhancement of residual radiation-induced γH2AX/53BP1 foci. We established patient-derived tumor organoids (PDOs) from CRPC patients. These PDOs are morphologically similar to their primary tumors and genetically clustered with prostate cancer but not with normal prostate or other tumor entities. Using these PDOs, we functionally confirmed the enhanced sensitivity of CRPC patients to olaparib and cisplatin. Moreover, olaparib but not cisplatin significantly decreased the migration rate in CRPC cells. Collectively, we present robust patient-derived preclinical models for CRPC that recapitulate the features of their primary tumors and enable individualized drug screening, allowing translation of treatment sensitivities into tailored clinical therapy recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".