AI-Based Pathology classifier Predicts Sensitivity to Enzalutamide in Metastatic Hormone-Sensitive Prostate Cancer: A Biomarker Analysis of the ENZAMET Trial

PURPOSE: The ENZAMET trial established that enzalutamide added to androgen deprivation therapy (ADT) improves overall survival (OS) in metastatic hormone-sensitive prostate cancer (mHSPC), but treatment response is heterogeneous. We tested whether the Artificial Intelligence Pathology Image Classifier (APIC), previously developed and locked in CHAARTED, predicts overall survival benefit from enzalutamide in ENZAMET. EXPERIMENTAL DESIGN: This biomarker study applied APIC to digitized hematoxylin and eosin specimens from ENZAMET (ANZUP 1304, NCT02446405). ENZAMET randomized participants with mHSPC to ADT plus enzalutamide or non-steroidal antiandrogen (NSAA), with early concurrent docetaxel permitted. APIC was applied without modification or retraining. The prespecified primary analysis evaluated the treatment-by-APIC interaction for OS using Cox models adjusted for standard clinical factors. Prespecified sensitivity analysis excluded participants receiving early concurrent docetaxel. RESULTS: Among 1125 participants, 492 with digitized tissue (median follow-up 68.8 months) were eligible. APIC classified 316 (64.0%) as APIC-negative and 176 (36.0%) as APIC-positive. APIC significantly modified enzalutamide benefit (interaction p=0.014). APIC-negative disease showed improved survival with enzalutamide versus NSAA (HR=0.50, 95%CI:0.35-0.73; 60-month OS 76.5% vs 58.3%), while APIC-positive showed limited evidence of benefit (HR=1.04, 95%CI:0.67-1.62; 60-month OS 55.6% vs 58.8%). In low-volume disease, enzalutamide improved survival in APIC-negative (HR=0.31, 95%CI:0.16-0.62) but not APIC-positive (HR=1.04, 95%CI:0.53-2.06; interaction p=0.015). For non-docetaxel-treated participants, enzalutamide benefit was greater in APIC-negative (HR=0.40; 60-month OS 82.0% vs 61.0%) than APIC-positive (HR=0.79; 60-month OS 62.8% vs 57.6%). CONCLUSIONS: The presence of APIC negative disease identified participants with a major improvement in OS with addition of enzalutamide to ADT compared with addition of NSAA.

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Journal
Clinical Cancer Research
Published
2026-06-26
DOI
https://doi.org/10.1158/1078-0432.ccr-26-0871
Primary Topic
Prostate Cancer Treatment and Research
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article
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article

AI-Based Pathology classifier Predicts Sensitivity to Enzalutamide in Metastatic Hormone-Sensitive Prostate Cancer: A Biomarker Analysis of the ENZAMET Trial

Christopher J. Sweeney, Sonia Yip, Vinod Vijay Subhash, Tilak Pathak et al.
Clinical Cancer Research
Prostate Cancer Treatment and Research
article

AI-Based Pathology classifier Predicts Sensitivity to Enzalutamide in Metastatic Hormone-Sensitive Prostate Cancer: A Biomarker Analysis of the ENZAMET Trial

Christopher J. Sweeney, Sonia Yip, Vinod Vijay Subhash, Tilak Pathak, Anant Madabhushi, Heidi Thomas, Samantha R. Oakes, Pingfu Fu, Hui‐Ming Lin, Martin R. Stockler, Ian D. Davis, Arun Azad, Alison Y. Zhang, Lisa Glen Horvath, Anthony M. Joshua, Naoto Tokuyama, James G. Kench, Sebastian Medina, Vaishnavi Putcha
article en

Abstract

PURPOSE: The ENZAMET trial established that enzalutamide added to androgen deprivation therapy (ADT) improves overall survival (OS) in metastatic hormone-sensitive prostate cancer (mHSPC), but treatment response is heterogeneous. We tested whether the Artificial Intelligence Pathology Image Classifier (APIC), previously developed and locked in CHAARTED, predicts overall survival benefit from enzalutamide in ENZAMET. EXPERIMENTAL DESIGN: This biomarker study applied APIC to digitized hematoxylin and eosin specimens from ENZAMET (ANZUP 1304, NCT02446405). ENZAMET randomized participants with mHSPC to ADT plus enzalutamide or non-steroidal antiandrogen (NSAA), with early concurrent docetaxel permitted. APIC was applied without modification or retraining. The prespecified primary analysis evaluated the treatment-by-APIC interaction for OS using Cox models adjusted for standard clinical factors. Prespecified sensitivity analysis excluded participants receiving early concurrent docetaxel. RESULTS: Among 1125 participants, 492 with digitized tissue (median follow-up 68.8 months) were eligible. APIC classified 316 (64.0%) as APIC-negative and 176 (36.0%) as APIC-positive. APIC significantly modified enzalutamide benefit (interaction p=0.014). APIC-negative disease showed improved survival with enzalutamide versus NSAA (HR=0.50, 95%CI:0.35-0.73; 60-month OS 76.5% vs 58.3%), while APIC-positive showed limited evidence of benefit (HR=1.04, 95%CI:0.67-1.62; 60-month OS 55.6% vs 58.8%). In low-volume disease, enzalutamide improved survival in APIC-negative (HR=0.31, 95%CI:0.16-0.62) but not APIC-positive (HR=1.04, 95%CI:0.53-2.06; interaction p=0.015). For non-docetaxel-treated participants, enzalutamide benefit was greater in APIC-negative (HR=0.40; 60-month OS 82.0% vs 61.0%) than APIC-positive (HR=0.79; 60-month OS 62.8% vs 57.6%). CONCLUSIONS: The presence of APIC negative disease identified participants with a major improvement in OS with addition of enzalutamide to ADT compared with addition of NSAA.

Clinical Cancer Research
The University of Sydney (AU), Box Hill Hospital (AU), Garvan Institute of Medical Research (AU), Emory University (US), Royal Prince Alfred Hospital (AU), Peter MacCallum Cancer Centre (AU), The Wallace H. Coulter Department of Biomedical Engineering (US), Cooperative Trials Group for Neuro-Oncology (AU), Chris O’Brien Lifehouse (AU), The Kinghorn Cancer Centre (AU), Australian and New Zealand Urogenital and Prostate Cancer Trials Group (AU), Monash University (AU), The University of Adelaide (AU), Case Western Reserve University (US)
Openalex Percentile: Top 7%
Prostate Cancer Treatment and Research
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