The Germline and Somatic Origins of Prostate Cancer Heterogeneity
Bibliographic record
Abstract
Abstract Newly diagnosed prostate cancers differ dramatically in mutational composition and lethality. The most accurate clinical predictor of lethality is tumor tissue architecture, quantified as tumor grade. To interrogate the evolutionary origins of prostate cancer heterogeneity, we analyzed 666 prostate tumor whole genomes. We identified a compendium of 223 recurrently mutated driver regions, most influencing downstream mutational processes and gene expression. We identified and validated individual germline variants that predispose tumors to acquire specific somatic driver mutations: these explain heterogeneity in disease presentation and ancestry differences. High-grade tumors have a superset of the drivers in lower-grade tumors, including increased frequency of BRCA2 and MYC mutations. Grade-associated driver mutations occur early in tumor evolution, and their earlier occurrence strongly predicts cancer relapse and metastasis. Our data suggest high- and low-grade prostate tumors both emerge from a common premalignant field, influenced by germline genomic context and stochastic mutation timing. Significance: This study uncovered 223 recurrently mutated driver regions using the largest cohort of prostate tumors to date. It reveals associations between germline SNPs, somatic drivers, and tumor aggression, offering significant insights into how prostate tumor evolution is shaped by germline factors and the timing of somatic mutations.
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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 | 0.000 |
| 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) | 0.000 | 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".