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Darolutamide plus ADT in patients with metastatic hormone-sensitive prostate cancer (mHSPC) by disease volume: Subgroup analysis of the phase 3 ARANOTE trial.

2025· article· en· W4407700779 on OpenAlexaff
Fred Saad, Neal D. Shore, Egils Vjaters, David Olmos, Natasha Littleton, Anna Liu, Isabella Testa, Mindy Mo, Shankar Srinivasan, Kunhi Parambath Haresh

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsBayer (Canada)Université de Montréal
FundersBayer
KeywordsMedicineProstate cancerOncologySubgroup analysisInternal medicineDocetaxelDiseaseCancerConfidence interval

Abstract

fetched live from OpenAlex

151 Background: Darolutamide (DARO) plus ADT significantly reduced the risk of radiological progression or death by 46% (HR 0.54; 95% CI: 0.41–0.71; P <0.0001) versus placebo (PBO) plus ADT in patients with mHSPC in the ARANOTE trial. The incidence of treatment-emergent adverse events (TEAEs) was low and similar between arms, with fewer patients discontinuing study drug due to TEAEs in the DARO vs PBO arm (6.1% vs 9.0%). Here, we report the efficacy and safety by disease volume in both arms of ARANOTE. Methods: Patients with mHSPC were randomized 2:1 to receive DARO 600 mg twice daily + ADT or PBO + ADT. High-volume (HV) disease was defined by the presence of visceral metastases and/or ≥4 bone lesions with ≥1 beyond the vertebral bodies and pelvis (CHAARTED criteria). The primary endpoint was radiological progression-free survival (rPFS). Secondary endpoints included time to metastatic castration-resistant prostate cancer (mCRPC), time to prostate-specific antigen (PSA) progression, and safety. Results: Of the 669 patients included in the full analysis set, 472 (71%; DARO n=315; PBO n=157) had HV disease and 197 (29%; DARO n=131; PBO n=66) had low-volume (LV) disease. Baseline demographics and patient characteristics were generally balanced between the treatment arms in HV and LV subgroups. Patients with LV disease had better prognostic factors (eg, a higher proportion of patients with ECOG PS 0, Gleason <8, having received prior local therapy, and lower baseline median PSA levels). DARO + ADT improved rPFS vs PBO + ADT in both HV and LV subgroups. In the LV subgroup, DARO + ADT reduced the risk of radiological progression or death by 70% (HR 0.30; 95% CI: 0.15–0.60) with median rPFS not reached in either group. In the HV subgroup, DARO + ADT reduced the risk of radiological progression or death by 40% (HR 0.60; 95% CI: 0.44–0.80) with median rPFS of 30.2 months with DARO vs 19.2 months with PBO. For the secondary endpoints, DARO delayed time to CRPC (HV: HR 0.46; 95% CI: 0.36–0.60; LV: HR 0.21; 95% CI: 0.12–0.37) and time to PSA progression (HV: HR 0.34; 95% CI: 0.25–0.46; LV: HR 0.19; 95% CI: 0.10–0.37) and a higher proportion achieved PSA <0.2 ng/mL with DARO vs PBO (HV: 54.6% vs 15.5%; LV: 82.6% vs 25.4%) in HV and LV subgroups. Incidences of TEAEs were low and similar between treatment groups across the HV and LV subgroups and consistent with the overall population. Lower rates of fatigue and treatment discontinuations due to TEAEs with DARO vs PBO were observed in the LV subgroup: 2.3% vs. 13.8% and 3.1% v 10.8%, respectively. Conclusions: Efficacy outcomes with DARO + ADT in patients with mHSPC were improved vs PBO + ADT regardless of disease volume. DARO + ADT was well-tolerated in both volume subgroups, consistent with the overall population. Patients with LV mHSPC had marked treatment efficacy with minimal treatment burden. Clinical trial information: NCT04736199 .

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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.008
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.052
GPT teacher head0.464
Teacher spread0.412 · 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 designObservational
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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Citations1
Published2025
Admission routes1
Has abstractyes

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