The diverse genomic landscape of low−risk prostate cancer.
Bibliographic record
Abstract
74 Background: Among men with clinically low-risk prostate cancer, we have previously documented heterogeneity in terms of clinical characteristics and genomic risk scores. In this study, we aimed to study the underlying tumor biology of this patient population, by interrogating patterns of gene expression among men with clinically low-risk tumors. Methods: Prostate biopsies from 427 patients considered potentially suitable for active surveillance underwent central pathology review and genome-wide expression profiling. These cases were compared to 1290 higher-risk biopsy cases with diverse clinical features from a prospective genomic registry. Average genomic risk (AGR) was determined from 18 published prognostic signatures, and MSigDB Hallmark gene sets were analyzed using bootstrapped clustering methods. These sets were examined in relation to clinical variables and pathologic and biochemical outcomes using multivariable regression analysis. Results: 408 (96%) of biopsies passed RNA quality control. Based on average genomic risk quartiles defined by the high-risk multicenter cases, the UCSF low-risk patients were distributed across the quartiles as 219 (54%), 107 (26%), 61 (15%), and 21 (5%). Unsupervised clustering analysis of the Hallmark gene set scores revealed 3 clusters, which were enriched for the previously described PAM50 luminal A, luminal B and basal subtypes. These three clusters did not associate with existing clinical or known genomic risk characteristics, suggesting a novel and independent classification for low-risk prostate cancer. AGR was associated with both pathological (OR: 1.3, p < 0.001) and biochemical outcomes (OR: 1.5, p = 0.001 ) but the clusters were not. Conclusions: Prostate cancers that are largely homogeneously low-risk by traditional characteristics demonstrate substantial diversity at the level of genomic expression. Molecular sub-stratification of low-risk prostate cancer may facilitate better decision-making with respect to both timing and intensity of cancer surveillance and treatment.
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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 | 0.001 | 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".