Identifying molecular determinants of response to apalutamide (APA) in patients (pts) with nonmetastatic castration-resistant prostate cancer (nmCRPC) in the SPARTAN trial.
Bibliographic record
Abstract
42 Background: The SPARTAN trial recently demonstrated that addition of APA to androgen deprivation therapy (ADT) improved metastasis-free survival (MFS) and second progression-free survival (PFS2) in nmCRPC pts. We performed transcriptome-wide profiling of available primary tumor samples from pts in SPARTAN to evaluate potential biomarkers of response or resistance to APA+ADT. Methods: Pts included in SPARTAN were at high risk of developing metastasis.We used a commercially available genomic assay (DECIPHER prostate test, GenomeDx Biosciences, Inc., San Diego, CA) to assess gene expression in 233 archived primary tumors from SPARTAN pts. Using a Cox proportional hazard model, we assessed the association between scores and subtypes from previously derived prognostic and predictive gene signatures, such as DECIPHER and basal (BA) vs luminal (LU) subtyping. Results: Pts with high DECIPHER scores had greater treatment effect with APA+ADT than those with low scores. Pts with LU, a subtype known to be sensitive to ADT, greatly benefited from APA+ADT. Pts with BA, typically resistant to ADT, also benefited from APA+ADT. Conclusions: DECIPHER score and BA or LU subtype may be biomarkers of response to APA+ADT. DECIPHER may be useful for identifying pts for early treatment intensification with APA or other agents, and molecular subtyping may be an effective approach for pt selection in trials combining novel therapies with APA. Clinical trial information: NCT01946204. [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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".