A biopsy-based genomic classifier to predict biochemical failure after definitive radiation without hormone therapy in a prospective cohort of intermediate risk prostate cancer.
Bibliographic record
Abstract
68 Background: Recently, NCCN adopted the Zumsteg-Spratt subclassification to define NCCN favorable and unfavorable intermediate-risk prostate cancer (IR-PCa). NCCN unfavorable disease is recommended to receive combination androgen deprivation therapy (ADT) and radiotherapy. To determine if genomics could help identify a subset who may safely avoid ADT, we evaluated the Decipher genomic classifier (GC) in IR-PCa treated with dose-escalated image-guided radiotherapy (DE-IGRT) alone. Methods: Our cohort comprised of 121 patients with NCCN favorable (N = 49, 40%) and unfavorable (N = 74, 60%) IR-PCa, who received 78 Gy without ADT. Diagnostic needle biopsies with the highest Gleason score (GS) and %tumor involvement were macrodissected for RNA extraction. GC scores were determined from the Decipher prostate cancer classifier assay (GenomeDx Biosciences, San Diego, CA). Primary clinical endpoint was biochemical relapse ([BCR], PSA nadir + 2ng/ml) post-DE-IGRT. We compared association with BCR against known clinicopathologic prognostic indices and the NCCN risk strata. Results: With a median follow up of 7.5y, 24 (19%) patients experienced BCR. Individual clinical indices did not predict BCR-free survival rate (BFS). NCCN risk strata was however associated with a small but significant difference in BFS (5-y 93%, favorable vs 88%, unfavourable, P = 0.046). GC scores stratified 85 (70%), 19 (16%), and 17 (14%) men into low, intermediate, and high risk of recurrence; 5-y BFS were 95%, 89%, and 59%, respectively (P < 0.001). On multivariable analysis, a hazard ratio of 4.71 (95% CI 1.81-12.28, P = 0.0015) for BCR was observed for the GC high risk group compared to low/intermediate; NCCN risk strata and intraductal variant did not achieve significance. Conclusions: In IR-PCa men treated with DE-IGRT monotherapy, Decipher GC was an independent predictor of BCR. While most men in this our cohort were stratified as NCCN unfavorable IR-PCa, the majority were GC low risk with excellent outcomes from DE-IGRT alone. In contrast, a minority with GC high risk had suboptimal outcomes, and may benefit from ADT intensification.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".