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Which patients with metastatic hormone-sensitive prostate cancer (mHSPC) benefit more from androgen receptor pathway inhibitors (ARPIs)? STOPCAP meta-analyses of individual participant data (IPD).

2025· article· en· W4407700221 on OpenAlexaff
David J. Fisher, Claire L. Vale, Larysa Rydzewska, Peter J. Godolphin, Neeraj Agarwal, Gerhardt Attard, Kim N., Noel W. Clarke, Ian D. Davis, Karim Fizazi, Silke Gillessen, Nicholas D. James, David Matheson, Robert Oldroyd, Mahesh Parmar, Christopher Sweeney, Bertrand Tombal, Ian R. White, Jayne F. Tierney

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of British Columbia
FundersMedical Research Council
KeywordsMedicineAndrogen receptorProstate cancerOncologyInternal medicineEnzalutamideMeta-analysisHormoneCancer

Abstract

fetched live from OpenAlex

20 Background: Clinical features of people with mHSPC may affect their outcomes from the addition of ARPIs to androgen deprivation therapy (ADT). The STOPCAP Collaboration is seeking IPD to reliably investigate potential ARPI effect modifiers and determine who benefits more from an ARPI vs docetaxel plus ADT doublet. Methods: Full methods are in registered protocols (CRD42023431331; CRD4202540066). We sought IPD for completed trials examining effects of ARPIs for mHSPC. Initially, we examined ARPI effects using intention-to-treat, two-stage, common-effect meta-analysis of hazard ratios (HRs), adjusted for a core set of covariates and use of concomitant docetaxel. Main effects were based on overall survival (OS). Interaction effects were based on progression-free survival (PFS) to maximise power, then OS whenever PFS interactions were found (P<0.10). Within clinically-relevant subgroups, ARPI and docetaxel doublet effects were compared using two-stage, contrast-based, random-effects network meta-analysis (NMA). Results: By October 2024, we had updated IPD from five trial comparisons: LATITUDE, STAMPEDE A vs G, SWOG-1216, ENZAMET, and STAMPEDE A vs J. Based on these (2882 events/5472 pts), adding an ARPI to ADT improved OS (HR=0.69, 95% CI=0.64-0.74). Four trial comparisons (excluding SWOG-1216) provided PFS data (2781 events/4161 pts) and showed improved PFS (HR=0.49, 95% CI=0.45-0.53). The relative benefit of ARPIs on PFS increased with younger age (interaction p=0.034), higher BMI (interaction p=0.048), and lower burden of metastases (interaction p=0.096). These effects were similar for OS (age interaction p=0.035; BMI interaction p=0.031; volume interaction p=0.25). The age effect was most pronounced in the abiraterone trials. Combining IPD from the ARPI + ADT and docetaxel + ADT trials (GETUG-AFU-15, CHAARTED, STAMPEDE A vs C) in NMA suggested that overall, an ARPI doublet may improve OS more than a docetaxel doublet (HR=0.85, 95% CI=0.70-1.03). However, when the NMA was confined to participants with high-volume, synchronous disease, where docetaxel is most efficacious (but excluding SWOG-1216, for which these data were not available), effects on OS were: HR=0.89, 95% CI=0.74-1.06. Conclusions: Our preliminary results suggest that people with mHSPC who are younger, have a higher BMI, or have low volume disease, may benefit more from ARPIs. ARPI and docetaxel doublets seem similarly effective in high-volume, synchronous disease. We will present updated analyses, incorporating recently received PEACE 1 IPD, for a clearer picture of ARPI effects, including subgroup-specific effects. Ongoing collection of IPD from other key trials will allow robust comparison of ARPI doublet with triplet therapy (including docetaxel), guiding more personalised treatment.

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 imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.045
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.038
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.382
GPT teacher head0.520
Teacher spread0.138 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

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Citations13
Published2025
Admission routes1
Has abstractyes

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