BRCA-deficient metastatic prostate cancer has an adverse prognosis and distinct genomic phenotype
Bibliographic record
Abstract
BACKGROUND: Genomic alterations in DNA damage response (DDR) genes are common in metastatic castration-resistant prostate cancer (mCRPC). Understanding how these genomic events impact prognosis and/or treatment response is vital for optimising clinical outcomes. METHODS: Targeted sequencing was performed on 407 plasma samples from 375 men with mCRPC. Using the CLIA-certified PredicineCARE™ cell-free DNA (cfDNA) assay, pathogenic alterations in 152 key genes (including 27 DDR-related genes) were assessed, as was the presence and mechanisms of biallelic loss in BRCA2. FINDINGS: At least one DDR alteration was present in 34.5% (129/375) of patients (including monoallelic alterations). The most frequently altered DDR genes were BRCA2 (19%), ATM (13%), FANCA (5%), CHEK2 (5%) and BRCA1 (3%). Patients with BRCA alterations, especially BRCA2, had significantly worse progression-free survival (PFS) (Hazard ratio (HR) 3.3 [95% CI 1.9-6.0]; Cox regression p < 0.001), overall survival (HR 2.2 [95% CI 1.1-4.5]; Cox regression p = 0.02) and PSA response rates to androgen receptor (AR) pathway inhibitors (32% vs 60%, chi-square p = 0.02). BRCA-deficient tumours were also enriched for alterations within multiple genes including in the AR and PI3K pathways. Zygosity of BRCA2 alterations had no discernible impact on clinical outcomes, with similarly poor PFS for monoallelic vs biallelic loss (median 3.9 months vs 3.4 months vs copy neutral 9.8 months). INTERPRETATION: These data emphasise that the BRCA genes, in particular BRCA2, are key prognostic biomarkers in mCRPC. The clinical utility of BRCA2 as a marker of poor outcomes may, at least in cfDNA assays, be independent of the zygosity state detected. Enrichment of actionable genomic alterations in cfDNA from BRCA-deficient mCRPC may support rational co-targeting strategies in future clinical trials. FUNDING: Several funding sources have supported this study. A full list is provided in the Acknowledgments. No funding was received from Predicine, Inc. during the conduct of the study.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".