Association of Molecular Subtypes With Differential Outcome to Apalutamide Treatment in Nonmetastatic Castration-Resistant Prostate Cancer
Bibliographic record
Abstract
IMPORTANCE: There is a need to identify prognostic biomarkers to guide treatment intensification in patients with nonmetastatic castration-resistant prostate cancer (nmCRPC). OBJECTIVE: To examine whether molecular subtypes predict response to apalutamide, using archived primary tumor samples from the randomized, double-blind, phase 3 SPARTAN trial. DESIGN, SETTING, AND PARTICIPANTS: In this cohort study, gene expression data from 233 archived samples from patients with nmCRPC enrolled in the SPARTAN trial were generated using a human exon microarray. The present analysis was conducted from May 10, 2018, to October 15, 2020. INTERVENTIONS: Patients were randomized (2:1) to apalutamide, 240 mg/d, with androgen deprivation therapy (apalutamide+ADT) or placebo+ADT. MAIN OUTCOMES AND MEASURES: Patients were stratified into high-risk and low-risk categories for developing metastases based on genomic classifier (GC) scores for high (GC >0.6) and low to average (GC≤0.6) and into basal and luminal subtypes; associations between these molecular subtypes and metastasis-free survival (MFS), overall survival (OS), and progression-free survival 2 (PFS2) were evaluated using Cox proportional hazards regression and Kaplan-Meier analysis. RESULTS: Median age of the 233 included patients was 73 (range, 49-91) years. A total of 116 of 233 patients (50%) in the SPARTAN biomarker subset had high GC scores. Although all patients receiving apalutamide+ADT had improved outcomes, having high GC scores was associated with the greatest improvement in MFS (hazard ratio [HR], 0.21; 95% CI, 0.11-0.40; P < .001), OS (HR, 0.52; 95% CI, 0.29-0.94; P = .03), and PFS2 (HR, 0.39; 95% CI, 0.23-0.67; P = .001) vs placebo+ADT. In total, 152 of 233 patients (65%) had the basal molecular subtype. Although there were no significant differences in MFS, PFS2, or OS between patients with the luminal vs basal subtype in the placebo+ADT arm, patients with the luminal subtype in the apalutamide+ADT arm had a significantly longer MFS (apalutamide+ADT: HR, 0.40; 95% CI, 0.18-0.91; P = .03; placebo+ADT: HR, 0.66; 95% CI, 0.33-1.31; P = .23) compared with patients with basal subtype; similar trends were observed for OS (apalutamide+ADT: HR, 0.50; 95% CI, 0.25-0.98; P = .04; placebo+ADT: HR, 0.78; 95% CI, 0.38-1.60; P = .50), and PFS2 (apalutamide+ADT: HR, 0.71; 95% CI, 0.42-1.22; P = .22; placebo+ADT: HR, 0.72; 95% CI, 0.38-1.39; P = .33). In regression analysis, the luminal-basal subtype score was significantly associated with MFS in patients receiving apalutamide+ADT (HR, 2.65; 95% CI, 1.15-6.08; P = .02), whereas GC score was significantly associated with MFS in placebo+ADT recipients (HR, 2.09; 95% CI, 1.02-4.27; P = .04). CONCLUSIONS AND RELEVANCE: The findings of this study suggest that the GC score and basal-luminal subtype derived from archived tumor specimens may be biomarkers of response to apalutamide+ADT in the nmCRPC setting. Although overall, the addition of apalutamide to ADT was beneficial, higher-risk and luminal subtypes appeared to benefit most. Obtaining GC scores may be useful for identifying patients for early treatment intensification with apalutamide, and basal-luminal subtyping may be a beneficial approach for patient selection for further treatment intensification in trials combining novel therapies with apalutamide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".