Genomic Alterations to Guide Treatment Selection in Metastatic Prostate Cancer
Bibliographic record
Abstract
Treatment options for men with metastatic prostate cancer have greatly expanded in the last decade. Androgen receptor pathway inhibitors, taxane cytotoxic therapy, poly(ADP-ribose) polymerase inhibitors, and radionuclide theranostics against prostate-specific membrane antigen have collectively contributed to incremental improvements in both quality and longevity of life for patients with metastatic castration-resistant prostate cancer (mCRPC). Despite these successes, few studies inform on optimal therapy selection and sequencing across this crowded treatment landscape. Genomic analysis of both tissue and liquid biopsy specimens shows promise in bridging this practice gap, with alterations in several key prostate cancer driver genes demonstrating clear associations with clinical outcomes, as well as informing use of novel precision medicine targeted therapies. In this review, we evaluate the current evidence of genomic alterations in various oncogenic signaling pathways as clinical biomarkers in mCRPC, focusing on correlative studies that analyzed outcomes based on findings in plasma cell-free DNA. We highlight the pitfalls of interpreting genomic findings in samples with substandard tumor content, and suggest pathologic and disease factors to consider when embarking upon tumor genotyping to guide treatment decisions in metastatic prostate cancer. As access to life-prolonging therapies improves, and barriers to cost-effective genotyping and reliable data interpretation are overcome, we anticipate that predictive and prognostic biomarkers that inform on disease biology, drug sensitivity, and therapy resistance will inevitably be integrated into the routine care of patients with metastatic prostate cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".