Identification, incidence and clinical outcomes of patients (pts) with hypermutated prostate cancer (PC).
Bibliographic record
Abstract
5072 Background: A small proportion of metastatic PC exhibit outlier somatic mutation (mut) rates exceeding the average of 4.4 mut/Mb. The incidence, clinical course and treatment response of pts with hypermutation (HM) is poorly characterised. Methods: We performed targeted sequencing from a panel of PC genes using plasma cell-free DNA samples collected from metastatic castration-resistant prostate cancer (mCRPC) pts and calculated somatic mutation burden. HM samples were additionally subjected to whole exome sequencing to determine trinucleotide mutational signatures and microsatellite instability (MSI). Clinical data was retrospectively collected and compared to a control cohort of 199 mCRPC pts. Results: 671 samples from 434 pts had ctDNA > 2% and were evaluable. 32 samples from 24 pts had > 11 mut/Mb and fell above the 95th percentile for mutation burden with a median mutation burden of 34 mut/Mb. 11 pts had deleterious mutations or homozygous deletions in mismatch repair (MMR) genes and 4 further pts had evidence of MMR deficiency (MMRd) from mutational signatures and MSI status. The remaining 9 pts had either BRCA2 mutations (n = 4), Kataegis (localized hypermutation, n = 3), or undefined causes for HM (n = 2). The incidence of MMRd was 3.5% (15/434), and germline MMRd was 0.2% (1/434). For MMRd pts with available clinical data (10/15) at diagnosis, the median age was 73.6 y, 70% had Gleason score ≥8, and 50% presented with M1 disease. Comparing the MMRd with the control cohort, median time from ADT to CRPC was 9.1 m (95% CI 6.9–11.4) vs. 18.2 m (95% CI 15.1–21.3), p = 0.001; median time from CRPC to death was 13.1 m (95% CI 0.3–25.9) vs. 40.1 m (95% CI 32.4–47.8), p < 0.001. Conclusions: HM and MMRd can be identified using liquid biopsy and could help to select pts for immunotherapy.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".