Computational pathology to predict docetaxel benefit for high-risk localized prostate cancer in NRG/RTOG 0521 (NCT00288080).
1557 Background: The benefit of adding docetaxel (DTX) to standard of care (SOC) for high-risk localized prostate cancer remains debated. The NRG/RTOG 0521 randomized phase III trial demonstrated that docetaxel, when added to SOC—comprising radiotherapy (RT) and long-term androgen deprivation therapy (ADT)—improved overall survival (OS). However, while RTOG 0521 demonstrated improved OS with DTX, the observed improvement did not meet the predetermined threshold for clinical significance, leaving the role of DTX intensification uncertain. Enhanced stratification methods are needed to identify aggressive disease phenotypes and guide patient selection for adjuvant chemotherapy. This study aims to develop and validate a computational AI derived pathology image classifier (APIC) to quantify the tumor-immune microenvironment from diagnostic biopsy specimens and predict DTX benefit in patients from the NRG/RTOG 0521 trial. Methods: The study included patients with available high-quality biopsy images from the NRG/RTOG 0521 trial. Primary outcome was OS, median follow-up was 5.7 years. After segmenting nuclei and identifying lymphocytes, we derived features that captured immune-tumor spatial patterns and nuclear diversity in the tumor microenvironment to construct APIC. DTX benefit was evaluated using Cox proportional hazards models with interaction terms, log-rank tests and Kaplan-Meier analyses by comparing OS between treatment arms within APIC-stratified groups. Results: Among NRG/RTOG 0521 trial participants, 350 patients had evaluable quality biopsy slide images. Half of the SOC (RT+ADT) arm was used for training (84 patients), and 266 patients were used for validation (SOC: 85 patients, and SOC+DTX arm: 181 patients). DTX significantly improved OS in APIC-positive (n = 119, 45%) patients (HR = 0.49, 95% CI: 0.26-0.92, p = 0.023) but not in APIC-negative (n = 147, 55%) patients (HR = 1.17, 95% CI: 0.59-2.3, p = 0.66). APIC-positive patients derived 22% 10-year OS benefit (95% CI: 1.7%-41.6%) from DTX. The 10-year OS was 74% in the DTX arm compared to 52% with RT and ADT alone in the APIC-positive group. A significant interaction (p = 0.024) was observed between APIC status and treatment. Conclusions: We validated APIC as a predictive biomarker for DTX benefit in high-risk localized prostate cancer patients from NRG/RTOG 0521, identifying a subset who achieved significant survival improvement from treatment intensification – a benefit not reached in the unselected trial population. Further investigation is warranted to evaluate APIC's predictive potential of DTX intensification in metastatic disease settings.
Authors
- Amit I. Shah
- Shilpa Gupta (ORCID: https://orcid.org/0000-0002-8775-503X)
- Kamal Hammouda (ORCID: https://orcid.org/0000-0003-1598-4287)
- Tilak Pathak (ORCID: https://orcid.org/0000-0002-9738-4172)
- Michael Straza (ORCID: https://orcid.org/0000-0003-4672-6213)
- Rohann Correa (ORCID: https://orcid.org/0000-0003-0886-1625)
- Tuomas Mirtti (ORCID: https://orcid.org/0000-0003-0455-9891)
- Howard M. Sandler (ORCID: https://orcid.org/0000-0002-8193-5769)
- Jason A. Efstathiou (ORCID: https://orcid.org/0000-0003-0996-0350)
- M.A. Hallman (ORCID: https://orcid.org/0000-0002-2825-0656)
- Pingfu Fu (ORCID: https://orcid.org/0000-0002-2334-5218)
- Richard C. Jordan (ORCID: https://orcid.org/0000-0002-2558-0537)
- Naoto Tokuyama (ORCID: https://orcid.org/0000-0002-7784-4901)
- Susan Chafe (ORCID: https://orcid.org/0009-0007-9227-7611)
- Priti Lal (ORCID: https://orcid.org/0000-0003-2193-9968)
- Felix Y. Feng (ORCID: https://orcid.org/0000-0001-9756-4037)
- Sebastián Medina (ORCID: https://orcid.org/0000-0002-7000-3499)
- Karen E. Hoffman
- Stephanie L. Pugh (ORCID: https://orcid.org/0000-0002-5977-8912)
Institutions
- Cedars-Sinai Medical Center (US)
- Fox Chase Cancer Center (US)
- Cleveland Clinic (US)
- The University of Texas MD Anderson Cancer Center (US)
- Emory University (US)
- University of California, San Francisco (US)
- Medical College of Wisconsin (US)
- Helsinki University Hospital (FI)
- London Health Sciences Centre (CA)
- The Wallace H. Coulter Department of Biomedical Engineering (US)
- Massachusetts General Hospital (US)
- WellSpan Health (US)
- NRG Oncology (US)
- Case Western Reserve University (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Journal of Clinical Oncology
- Published
- 2025-05-28
- DOI
- https://doi.org/10.1200/jco.2025.43.16_suppl.1557
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- California Breast Cancer Research Program
- National Heart, Lung, and Blood Institute
- National Institute of Biomedical Imaging and Bioengineering
- DOD Prostate Cancer Research Program
- DOD Peer Reviewed Cancer Research Program