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8-year outcomes of enzalutamide (ENZA) versus a non-steroidal anti-androgen (NSAA) for metastatic, hormone-sensitive prostate cancer (ENZAMET; ANZUP 1304).

2025· article· en· W4410811464 on OpenAlexaff
Alison Yan Zhang, Ian D. Davis, Hayley Thomas, Ronan Andrew McLaughlin, Thean Hsiang Tan, David Pook, Gavin Marx, Robert Zielinski, Shahneen Sandhu, Alastair Thomson, M. Neil Reaume, Scott North, John McCaffrey, Nicola Jane Lawrence, Lisa G. Horvath, Simon Chowdhury, Kim N., Martin R. Stockler, Christopher Sweeney

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsBC Cancer AgencyUniversity of AlbertaUniversity of British ColumbiaOttawa Hospital
FundersCancer Council Australia
KeywordsEnzalutamideMedicineProstate cancerOncologyInternal medicineAndrogenHormoneCancerGynecologyAndrogen receptor

Abstract

fetched live from OpenAlex

5090 Background: We previously reported that ENZA improved overall survival (OS) after median follow-up times of 34 and 68 months, in comparison with a NSAA, when added to testosterone suppression, with or without concurrent early docetaxel, for mHSPC. We now report outcomes after median follow-up of 98 months. Methods: Participants (pts) with mHSPC were randomly assigned (1:1) from 31MAR2014-24MAR2017 to treatment with ENZA 160 mg or NSAA, in addition to testosterone suppression. Concurrent early docetaxel was used in 45%. OS was the primary endpoint and analysed with the Kaplan-Meier method, log-rank test for p-values, and Cox regression for hazard ratios (HR). Secondary outcomes included deaths due to prostate cancer (PC) versus (vs) other causes. The numbers of pts experiencing specified adverse events (AE) of grade 3-5 are expressed per 100 person-years of study treatment exposure to account for differing treatment durations. Results: After a median follow-up of 98 months, data cut-off 30JUN2024, death was reported in 285/563 (51%) pts assigned ENZA vs 337/562 (60%) assigned NSAA. OS was longer among those assigned ENZA than NSAA (medians 95 vs 70 months; OS at 96 months 50% vs 40%; HR 0.73, 95% CI 0.63 to 0.86; p=0.0001). Clinical PFS also continued to favour ENZA over NSAA (HR 0.49; 95% CI 0.42 to 0.57; p<0.0001). PC accounted for 468 of all 622 deaths, and were less frequent among those assigned ENZA than NSAA (207 vs 261). Deaths due to other causes accounted for a total of 154 deaths, and were similarly frequent among those assigned ENZA vs NSAA (78 vs 76). Mean duration of study treatment was longer for ENZA than NSAA (58 vs 36 months). 185/562 (33%) remain on ENZA with 88% on full dose. G3-5 AE of interest were reported in the following numbers of pts per 100 years of study treatment with ENZA vs NSAA: cardiac disorder 2.2 vs 2.2, nervous system disorder 2.3 vs 2.0, fall 0.70 vs 0.24. Causes of death according to PSA at 7 months are tabulated below. Among those with PSA at 7 months ≤0.2, deaths were due to PC in 29%, and other causes in 13%. Among those with PSA at 7 months >0.2, deaths were due to PC in 60%, and other causes in 13%. Conclusions: Treatment with enzalutamide continues to confer substantial OS benefits at 8 years. These findings highlight long-term safety, toxicities, non-PC causes of death, and survival outcomes of those with and without PSA ≤0.2 at 7 months. ClinicalTrials.gov Identifier NCT02446405. Clinical trial information: NCT02446405 . Landmark analysis by PSA at 7 months ENZA (N=555) NSAA (N=549) ALL (N=1104) Deaths due to prostate cancer, PSA ≤0.2 100/375 (27%) 87/270 (32%) 187/645 (29%) Deaths due to other causes, PSA ≤0.2 54/375 (14%) 33/270 (12%) 87/645 (13%) Deaths due to prostate cancer, PSA >0.2 104/180 (58%) 170/279 (61%) 274/459 (60%) Deaths due to other causes, PSA >0.2 22/180 (12%) 39/279 (14%) 61/459 (13%)

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.138
GPT teacher head0.517
Teacher spread0.378 · 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 designRandomized trial
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".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

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