Genomic validation of three-tiered sub-classification of high-risk prostate cancer.
Bibliographic record
Abstract
17 Background: Recent data and National Comprehensive Cancer Network (NCCN) guidelines suggest that high-risk prostate cancer (cT3-4, Gleason score ≥ 8, or prostate-specific antigen [PSA] > 20 ng/mL) is a heterogenous group in terms of long-term patient outcomes. We sought to determine whether sub-classification of high-risk prostate cancer based on clinical factors correlates with genomic markers of risk. Methods: We identified 3,220 patients with NCCN unfavorable intermediate-risk (n=2,000) or high-risk (n=1,220) prostate cancer. We defined the following sub-classification of high-risk prostate cancer based on previously published data: favorable high-risk (cT1c, Gleason 6, and PSA > 20 ng/mL or cT1c, Gleason 4+4=8, PSA < 10 ng/mL); very high-risk (cT3b-T4 or primary Gleason pattern 5); and standard high-risk (all others with cT3a, Gleason score ≥ 8, or PSA > 20 ng/mL). We used a set of 37 previously published genomic classifiers, including the 22-gene Decipher assay, to determine whether high-risk genomic features correlated with the clinical sub-classification of high-risk prostate cancer. Results: Among those with favorable high-risk, standard high-risk, and very high-risk prostate cancer, 50.4%, 64.2%, 81.6% had a high-risk Decipher score, respectively (p < 0.001). Among 36 other genomic signatures, 33 had a similar increasing trend across the three sub-classes of high-risk (p < 0.05 after correction for multiple hypothesis testing). Patients in the three sub-classes of high-risk disease were positive for a median number of 5, 7, and 14 high-risk signatures. Under a novel clinical-genomic risk group classification (Spratt et al., 2017), 27.5%, 19.7%, and 7.0% of patients with favorable, standard, or very high-risk disease would be re-classified as intermediate-risk, respectively. In comparison, among those with unfavorable intermediate-risk prostate cancer, 38.2% had a high-risk Decipher score and would be re-classified as clinical-genomic high-risk. Conclusions: Genomic markers of risk correlate with the clinical sub-classification of high-risk prostate cancer into favorable high-risk, standard high-risk, and very high-risk disease, validating the prognostic utility of this stratification.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".