Tumor subtype defines distinct pathways of molecular and clinical progression in primary prostate cancer
Bibliographic record
Abstract
BACKGROUNDMolecular characterization of prostate cancer (PCa) has revealed distinct subclasses based on underlying genomic alterations occurring early in the natural history of the disease. However, how these early alterations influence subsequent molecular events and the course of the disease over its long natural history remains unclear.METHODSWe explored the molecular and clinical progression of different genomic subtypes of PCa using distinct tumor lineage models based on human genomic and transcriptomic data. We developed transcriptional classifiers, and defined "early" and "late" categories of molecular subclasses from 8,158 PCa patients. Molecular subclasses were correlated with clinical outcomes and pathologic characteristics using Kaplan-Meier and logistic regression analyses.RESULTSWe identified PTEN and CHD1 alterations as subtype-specific late progression events specifically in ERG-overexpressing (ERG+) and SPOP-mutant tumors, respectively, and 2 distinct progression models consisting of ERG/PTEN (normal to ERG+ to PTEN-deleted) and SPOP/CHD1 (normal to SPOP-mutated to CHD1-deleted) with shared early tumorigenesis but distinct pathways toward progression. We found that within ERG+ and SPOP-mutant subtypes, late events were associated with worse prognosis. Importantly, the clinical and pathologic features associated with distinct late events at radical prostatectomy were strikingly different; PTEN deletions were associated with increased locoregional stage, while CHD1 deletions were only associated with increased grade, despite equivalent metastatic potential.CONCLUSIONThese findings suggest a paradigm in which specific subtypes of PCa follow distinct pathways of progression, at both the molecular and clinical levels. Therefore, the interpretation of common clinical parameters such as locoregional tumor stage may be influenced by the underlying tumor lineage, and potentially influence management decisions.FUNDINGProstate Cancer Foundation, National Cancer Institute, Urology Care Foundation, Damon Runyon Cancer Research Foundation, US Department of Defense, and the AIRC Foundation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".