Prospective randomized trial of gene expression classifier utility following radical prostatectomy (G-MINOR).
Bibliographic record
Abstract
15 Background: Decipher is a tissue-based genomic classifier (GC) developed and validated in the post-radical prostatectomy (RP) setting as a predictor of metastasis. We conducted the first prospective randomized controlled trial assessing the use of a prostate cancer GC, with a primary objective to determine the impact of test results on adjuvant treatment decisions. Methods: The Genomics in Michigan ImpactiNg Observation or Radiation (G-MINOR) randomized trial enrolled participants across 12 centers between January 2017-August 2018. Eligible patients had undergone RP within 9 months of enrollment, had pT3-4 disease and/or positive surgical margins, and a PSA < 0.1ng/mL. Patients were assigned to either the GC or Usual Care (UC) group using cluster-crossover block randomization. Patients and providers in both arms received a CAPRA-S recurrence risk score. Decipher scores were obtained on RP tissue of all patients, but patients and providers in the UC arm were blinded to the results. The primary endpoint was the impact of impact of GC test result on adjuvant treatment decisions compared to clinical factors alone within 18 months of RP. Results: 356 patients were randomized and 340 had at least 18 months of follow-up. Of these, all but 2 control (UC) patients had sufficient tissue to pass quality control for GC testing. Randomization resulted in 175 (51.5%) GC and 165 (48.5%) UC patients. There were no significant differences in clinical variables or Decipher scores between arms. At 18 months post-RP, 19 (10.9%) patients in the GC group and 12 (7.3%) patients in the UC group had received adjuvant treatment. In the primary analysis, availability of the GC score in the GC arm was significantly associated with adjuvant treatment in GC high-risk patients after controlling for CAPRA-S risk (OR 7.6, 95%CI 1.95-29.6, p = 0.009). In the GC arm, both GC score (OR 8.8, 95%CI 1.9-39.7, p = 0.005) and CAPRA-S score (OR 3.8, 95%CI 1.09-12.9, p = 0.04) were independently associated with adjuvant treatment in a multivariable logistic regression model. Conclusions: In the first ever randomized trial testing the impact of a prostate cancer genomic classifier on treatment decisions, the use of a GC post-RP impacted post-operative treatment in a manner concordant with classifier risk. Further follow-up will be necessary to assess the impact of GC testing on oncologic outcomes. Clinical trial information: NCT02783950. [Table: see text]
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".