AI-discovered cellular morphometric biomarkers in needle biopsy of prostate cancer predict neoadjuvant androgen deprivation therapy response and enable therapeutic targeting of mTOR in androgen deprivation therapy-resistant tumors
Year: 2026
Type: article
Abstract: It is imperative to identify patients with prostate cancer (PCa) who will not benefit from androgen receptor signaling inhibitors and to improve their clinical outcomes. Using artificial intelligence (AI), in this multicenter cohort study of 623 PCa patients, we identified 13 cellular morphometric biomarkers (CMBs), as a New Approach Methodology (NAM), from whole slide images of needle biopsies in clinical trial specimens (NCT02430480, n = 37) that accurately predicted response to neoadjuvant androgen deprivation therapy (NADT) plus enzalutamide (AUC: 0.981, 95% CI [0.979, 0.983]). Importantly, the 13-CMB model stratified PCa patients into responders and non-responders after NADT across two independent hospital cohorts. In one cohort (n = 122), the model identified groups with significantly different pathologic complete response (pCR) (p = 0.0005) and biochemical recurrence-free survival (BCRFS) (p = 0.024). In the second cohort (n = 60), the model similarly distinguished patients with significantly different BCRFS (p = 0.031). The 13-CMB model also stratified PCa patients in the TCGA-PRAD cohort (n = 396) with distinct progression-free survival (p = 0.0017). Importantly, across hospital cohorts and the TCGA-PRAD cohort, the 13-CMB model demonstrated significant and independent clinical value after adjustment for established clinical factors and commonly used genomic biomarkers, including Decipher and Oncotype DX. Furthermore, CMBs accurately predicted the molecular differences between stratified patient groups and the potential benefit from mTOR inhibitors in non-responders, which were validated through IHC staining and patient-derived organoids (n = 8), respectively. Overall, our AI-powered CMB model, relying only on routine needle biopsy specimens, could potentially serve as a robust solution for precision management of PCa patients.
Source: Cancer Letters
Authors: Hong Yan, April W. Mao, Dan Li, Guangbo Fu, Manuel Jesús Pérez-Baena, Alejandro Jiménez-Navas, Dawei Wang, Ryan Hong, Weidong Cai, Jesus Pérez-Losada, Kuang-Yu Jen, Sen Wang, Shan Peng, Mary Helen Barcellos-Hoff, Han Shen, Ning Lin, Jian-Hua Mao, Yao Fu, Kenneth A. Iczkowski, Shuchi Gulati, Hang Chang
Institutions: Second Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Lawrence Berkeley National Laboratory, University of California, Los Angeles, Second Affiliated Hospital of Zhejiang University, Zhejiang University, Huaian First People’s Hospital, Consejo Superior de Investigaciones Científicas, Universidad de Salamanca, Instituto de Investigación Biomédica de Salamanca, Centro de Investigación del Cáncer, Instituto de Biología Molecular y Celular del Cáncer de Salamanca, The University of Sydney, University of California, Davis, Nanjing Drum Tower Hospital, University of California, San Francisco, UCSF Helen Diller Family Comprehensive Cancer Center, First Affiliated Hospital of Anhui Medical University, University of California Davis Medical Center
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,960
OpenAlex ID: W7140176171