The genomic landscape of metastatic castration-resistant prostate cancers reveals multiple distinct genotypes with potential clinical impact
Bibliographic record
Abstract
Abstract Metastatic castration-resistant prostate cancer (mCRPC) has a highly complex genomic landscape. With the recent development of novel treatments, accurate stratification strategies are needed. Here we present the whole-genome sequencing (WGS) analysis of fresh-frozen metastatic biopsies from 197 mCRPC patients. Using unsupervised clustering based on genomic features, we define eight distinct genomic clusters. We observe potentially clinically relevant genotypes, including microsatellite instability (MSI), homologous recombination deficiency (HRD) enriched with genomic deletions and BRCA2 aberrations, a tandem duplication genotype associated with CDK12 −/− and a chromothripsis-enriched subgroup. Our data suggests that stratification on WGS characteristics may improve identification of MSI, CDK12 −/− and HRD patients. From WGS and ChIP-seq data, we show the potential relevance of recurrent alterations in non-coding regions identified with WGS and highlight the central role of AR signaling in tumor progression. These data underline the potential value of using WGS to accurately stratify mCRPC patients into clinically actionable subgroups.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".