Long-term androgen deprivation therapy (LT-ADT) with radiotherapy is standard-of-care for high-risk localized prostate cancer, with abiraterone added for clinically very high-risk disease. Given the toxicity and cost of abiraterone, a predictive biomarker to refine patient selection is needed. We evaluated a digital pathology multimodal artificial intelligence (MMAI) model, previously validated as a prognostic biomarker, for prediction of abiraterone benefit amongst non-metastatic clinically very high-risk prostate cancer.
MMAI scores were generated for patients enrolled in two sequential abiraterone trials (no shared controls) in the STAMPEDE platform protocol (NCT00268476) using digital pathology images, prostate specific antigen (PSA), tumor stage, and age. We applied the previously-established 75th percentile threshold to classify patients as MMAI very high-risk or standard high-risk. The primary endpoint was metastasis-free survival (MFS). Treatment effects and risk estimates were obtained using Cox regression and Kaplan-Meier method, respectively. Prediction was assessed using a treatment-by-biomarker interaction Cox model.
In total, 1137 patients randomized to LT-ADT (N=583) or LT-ADT with abiraterone (N=554), were included. The MMAI very high-risk group (N=268) demonstrated significant MFS improvement from adding abiraterone (HR 0.47; 95% CI 0.31-0.70), with 5-year MFS increasing from 62% (95% CI 53-70%) in LT-ADT to 81% (95% CI 74-87%) in LT-ADT with abiraterone. Limited abiraterone benefit was observed in the MMAI standard high-risk group (N=869; HR 0.83; 95% CI 0.63-1.09), with a 5-year MFS of 82% (95% CI 78-85%) versus 84% (95% CI 80-87%, interaction p-value=0.02). This differential effect was consistent in local node-negative and node-positive subgroups.
In this post-hoc study of randomized clinical trial data, a locked digital pathology MMAI test displayed a strong prognostic association and predicted abiraterone efficacy in very high-risk, non-metastatic prostate cancer. This biomarker could be implemented clinically to maximize benefit from treatment intensification whilst avoiding unnecessary toxicity.
Annals of oncology : official journal of the European Society for Medical Oncology. 2026 Jun 05 [Epub ahead of print]
C T A Parker, H-C Huang, E Grist, L Mendes, R Yamashita, D Croucher, A Sachdeva, L Murphy, V Y T Liu, S Santos Vidal, T Todorovic, S Lall, M Goncalves, S Thakali, A Wingate, L Zakka, D Wetterskog, K Nowakowska, C L Amos, STAMPEDE Collaborators , D M Berney, P T Tran, D E Spratt, M R Sydes, L C Brown, S G Zhao, P Nguyen, N W Clarke, C J Sweeney, E L Stewart, M K B Parmar, A Esteva, N D James, G Attard
Cancer Institute, University College London; London, UK., Artera Inc.; Los Altos, California, USA., Departments of Surgery and Urology, The Christie and Salford Royal Hospitals; Manchester, UK; Genitourinary Cancer Research Group, Division of Cancer Sciences, Manchester Cancer Research Centre, The University of Manchester; Manchester, UK., MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London; London, UK., Barts Cancer Institute, Queen Mary University of London; London, UK., The University of Texas MD Anderson Cancer Center; Houston, TX, USA., UH Seidman Cancer Center, Case Western Reserve University; Cleveland, OH, USA., Department of Human Oncology, University of Wisconsin; Madison, WI, USA., Mass General Brigham Department of Radiation Oncology/Harvard Medical School; Boston, MA, USA., The University of Adelaide South Australian immunoGENomics Cancer Institute (SAiGENCI); Adelaide, Australia., Royal Marsden NHS Foundation Trust and Institute of Cancer Research; London, UK., Cancer Institute, University College London; London, UK; University College London Hospitals; London, UK. Electronic address: .