An AI-based pathology classifier to predict benefit from enzalutamide in metastatic hormone-sensitive prostate cancer (mHSPC) from ENZAMET (ANZUP 1304).

S Sebastian R. Medina (Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, GA) N Naoto Tokuyama (Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, GA) V Vaishnavi Putcha (Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, GA) T Tilak Pathak P Pingfu Fu (6Case Western Reserve University, Cleveland, United States) H Hayley Thomas V Vinod Subhash (ANZUP Cancer Clinical Trials Group, Sydney, Australia) S Sonia Yip (NHMRC Clinical Trials Centre, The University of Sydney, Sydney, NSW, Australia) H Hui-Ming Lin (Garvan Institute of Medical Research, Sydney, NSW, Australia) L Lisa Horvath J James G. Kench (Royal Prince Alfred Hospital, Sydney, Australia) A Alison Yan Zhang (Macquarie University, Sydney, NSW, Australia) M Martin R. Stockler A Anthony M. Joshua (Immunology Division, Garvan Institute of Medical Research) A Arun Azad (Peter MacCallum Cancer Center, Melbourne, Australia) S Samantha Richelle Oakes (Australian & New Zealand Urogenital and Prostate (ANZUP) Cancer Trials Group, Camperdown, Australia) I Ian D. Davis (School of Medicine, Monash University) C Christopher Sweeney (South Australian Immunogenomics Cancer Institute, Adelaide University, Adelaide, SA, Australia) A Anant Madabhushi

Abstract

108 Background: The ENZAMET trial established that adding enzalutamide (ENZ) to androgen deprivation therapy (ADT) improves overall survival (OS) in mHSPC. However, heterogeneity in treatment response and toxicity complicate clinical decision-making. We evaluated a previously developed Artificial Intelligence Pathology Image Classifier (APIC) to determine whether it could identify ENZAMET participants (pts) more likely to benefit from adding ENZ rather than NSAA to ADT. Methods: This AI biomarker study analyzed digitized H&E tumor specimens from ENZAMET (ANZUP 1304, NCT02446405), a phase 3 trial randomizing pts with mHSPC to ADT plus ENZ or a non-steroidal antiandrogen (NSAA), with early docetaxel (EDx) permitted. APIC, a model quantifying nuclear morphology and tumor-immune architecture validated in CHAARTED (Medina et al, Clin Can Res 2025), was applied without modification. The primary multivariable analysis evaluated the treatment-APIC interaction for OS using Cox models adjusted for disease volume (CHAARTED criteria), EDx, and age. Sensitivity analyses excluded pts receiving EDx. APIC associations with 18 circulating immune markers were explored. All tests were two-sided with p<0.05 considered significant. Results: Among 393 evaluable pts (median follow-up 70 months), 248 (63%) were APIC-negative and 145 (37%) APIC-positive. APIC significantly modified ENZ benefit (interaction p=0.010). APIC-negative was associated with improved OS with ENZ versus NSAA (HR 0.42, p<0.001; 5-year OS 82% vs 59%), while APIC-positive showed no benefit (HR 0.98, p=0.92; 5-year OS 57% vs 57%). APIC-treatment interaction was significant (p=0.02) in the multivariable model adjusted for clinical covariates. In low-volume disease, ENZ improved OS in APIC-negative (HR 0.19, p=0.0001) but not APIC-positive (HR 1.12, p=0.8; interaction p=0.002). Excluding EDx use (n=227), APIC-negative was associated with ENZ benefit (HR 0.29, p<0.001; 5-year OS 88% vs 60%, interaction p=0.043), while no significant benefit was observed for APIC-positive (HR 0.75, p=0.39; 5-year OS 61% vs 54%) (Table). Analysis of circulating immune markers identified elevated plasma myeloid progenitor inhibitory factor 1 (MPIF1) in APIC-positive pts (1.32-fold, 95% CI 1.11–1.58, p=0.002). Conclusions: APIC status was associated with benefit of ENZ for pts with mHSPC. APIC might help guide treatment selection for pts with mHSPC considered for androgen receptor pathway inhibitors and/or docetaxel. Clinical trial information: NCT02446405 . APIC Status HR (95% CI) P Value Interaction P Biomarker cohort (n=393) Negative 0.42 (0.27–0.64) <0.001 0.01 Positive 0.98 (0.61–1.56) 0.9 Low-Volume Disease Subgroup (n=209) Negative 0.19 (0.08–0.44) 0.0001 0.002 Positive 1.12 (0.55–2.29) 0.8 No docetaxel cohort (n=227) Negative 0.29 (0.15–0.56) <0.001 0.04 Positive 0.75 (0.40–1.44) 0.4

Article Details

Volume / Issue Vol. 44, Issue 16_suppl
Published June 01, 2026
Pages 108-108
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (19)

S

Sebastian R. Medina

Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, GA

N

Naoto Tokuyama

Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, GA

V

Vaishnavi Putcha

Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, GA

T

Tilak Pathak

P

Pingfu Fu

6Case Western Reserve University, Cleveland, United States

H

Hayley Thomas

V

Vinod Subhash

ANZUP Cancer Clinical Trials Group, Sydney, Australia

S

Sonia Yip

NHMRC Clinical Trials Centre, The University of Sydney, Sydney, NSW, Australia

H

Hui-Ming Lin

Garvan Institute of Medical Research, Sydney, NSW, Australia

L

Lisa Horvath

J

James G. Kench

Royal Prince Alfred Hospital, Sydney, Australia

A

Alison Yan Zhang

Macquarie University, Sydney, NSW, Australia

M

Martin R. Stockler

A

Anthony M. Joshua

Immunology Division, Garvan Institute of Medical Research

A

Arun Azad

Peter MacCallum Cancer Center, Melbourne, Australia

S

Samantha Richelle Oakes

Australian & New Zealand Urogenital and Prostate (ANZUP) Cancer Trials Group, Camperdown, Australia

I

Ian D. Davis

School of Medicine, Monash University

C

Christopher Sweeney

South Australian Immunogenomics Cancer Institute, Adelaide University, Adelaide, SA, Australia

A

Anant Madabhushi