DNA repair mutations and treatment-emergent small cell neuroendocrine prostate cancer (t-SCNC) as hallmarks of distinct subgroups of metastatic castration resistant prostate cancer (mCRPC): Data from the West Coast Prostate Cancer Dream Team.
Bibliographic record
Abstract
5039 Background: Genomic alterations in DNA repair genes are present in approximately 20-30% of patients with mCRPC. t-SCNC may be increasing in prevalence and is associated with adverse clinical outcomes. Potential overlap between these two subsets of mCRPC was assessed. Methods: Eligible patients (pts) underwent a metastatic tumor core needle biopsy at one of 5 centers. Tumor tissue was sent for consensus pathology call (JH, GT, LT) and targeted next-generation DNA sequencing. Chi-square test was used to compare the frequency of genomic alterations in select DNA repair genes (BRCA1, BRCA2, ATM, CDK12, RAD51, PALB2, FANCA, CHEK2, MLH1, MSH2, MLH3, and MSH6) between tumors with versus those without small cell histology. Frozen tissue from the same metastatic tumor underwent RNA sequencing (RNA-seq). Pts were prospectively followed for overall survival (OS). Results: 119 consecutive biopsies with sufficient tumor to permit histologic assessment and targeted NGS were included, including 14 biopsies (13%) with t-SCNC. In the overall cohort, DNA repair mutations were present in 41 biopsies (34%). By histologic subtype, mutations were present in 40/105 (38%) of adenocarcinoma tumors vs. only 1 of 14 (7%) biopsies with t-SCNC histology (p = 0.047). In contrast, TP53 and/or RB1 loss were enriched in the t-SCNC cohort (83% vs. 34%, p = 0.0015). Unsupervised hierarchical analysis of the transcriptome identified small cell-enriched cluster of biopsies (N = 12) that were mutually exclusive of DNA repair mutations and enriched for E2F transcriptional targets on RNA-seq. The presence of t-SCNC histologic differentiation was associated with worse OS from date of mCRPC (median OS 44.5 vs. 36.6 months; log-rank p = 0.027); the presence of DNA repair mutations was not (p = 0.734). Conclusions: Tumors harboring DNA repair pathway mutations or t-SCNC differentiation may represent distinct disease subsets of mCRPC with differing clinical outcomes. Added together, the two subsets account for approximately 40% of mCRPC pts. Independent prospective validation of these findings is warranted. Clinical trial information: NCT02432001.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".