Characterizing population-wide genomic risk distribution for development of a novel clinical-genomic risk system for prognostication in patients with clinically localized prostate cancer
Bibliographic record
Abstract
PURPOSE: Genomic classifiers are endorsed by guidelines and commonly used to inform prognosis in prostate cancer. We aimed to understand the distribution of genomic risk within the validated staging collaboration for cancer of the prostate (STAR-CAP) and propose a system integrating genomic and clinicopathologic risk. We hypothesized that genomic heterogeneity would have implications on risk estimates and may inform treatment decisions. MATERIALS AND METHODS: Genomic risk was assessed using the Decipher genomic classifier in two separate multi-institutional, prospectively collected population-based registries: (1) Decipher Genomics Resource for Intelligent Discovery (GRID) [n = 50,891] and (2) Michigan Urological Surgery Improvement Collaborative (MUSIC-Decipher) [n = 1602]. The primary endpoint was estimated prostate cancer-specific mortality (PCSM), and secondary endpoint was distant metastasis (DM). Marginal risk estimates provided by STAR-CAP were combined with hazard ratios of Decipher to calculate integrated risk estimates. RESULTS: Median age and PSA was 68 years and 6.2 ng/mL in GRID, and 66 years and 10.5 ng/mL in MUSIC. The GRID population had 50.2%, 18.5%, and 31.4% with low-, intermediate-, and high-Decipher risk, compared to 48.0%, 16.2%, and 35.8% in MUSIC. Decipher-based genomic risk varied across STAR-CAP stages in both registries. Estimates of 10-year PCSM (0.1% to 48.8%) and DM (0.3%-72.9%) varied widely after integration of clinical-genomic risk. Use of an integrated Decipher-STAR-CAP system led to significant stage reclassification, including 23.4% upstaging and 45.6% downstaging at least one stage. CONCLUSIONS: These findings suggest integration of genomic and clinicopathologic risk may lead to more nuanced risk assessment in prostate cancer and may help individualize treatment consideration.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".