AI-Powered cellular morphometric biomarkers discovered in needle biopsy of prostatic cancer predict neoadjuvant androgen deprivation therapy response and prognosis: an international multicenter retrospective study

作者：Hong Yan, Aiqin Mao, Dan Li, Manuel Jesús Pérez‐Baena, Alejandro Jiménez‐Navas, Dawei Wang, Ryan Hong, Weidong Cai, Jesús Pérez‐Losada, Kuang‐Yu Jen, Sen Wang, Shan Peng, Mary Helen Barcellos‐Hoff, Jian‐Hua Mao, Yao Fu, Kenneth A. Iczkowski, Shuchi Gulati, Hang‐Seok Chang · 发表于：medRxiv · 年份：2024 · DOI：10.1101/2024.11.17.24317411 · 被引用次数：2 · 研究领域：Prostate Cancer Treatment and Research、Prostate Cancer Diagnosis and Treatment、AI in cancer detection

It is imperative to identify patients with prostate cancer (PCa) who will benefit from androgen receptor signaling inhibitors that can impact quality of life upon prolonged use. Using our extensively-validated artificial-intelligence technique: cellular morphometric biomarker via machine learning (CMB-ML), we identified 13 CMBs from whole slide images of needle biopsies from the trial specimens ( NCT02430480 , n=37) that accurately predicted response to neoadjuvant androgen deprivation therapy (NADT) (AUC: 0.980). Notably, 13-CMB model stratified PCa patients into responder and non-responder groups after NADT treatment in an independent hospital cohort (n=122) that significantly associated with pathologic complete response (p=0.0005), biochemical-recurrence-free survival (p=0.024) and mTOR signaling pathway (p=0.03), suggesting potentially more clinical benefit from mTOR inhibitors in non-responder group. Additionally, genetic and genomic analysis revealed interplay between genetic variants and CMBs on NADT resistance, and provided molecular annotations for CMBs. Overall, prospective clinical implementation of 13-CMB model could assist precision care of PCa patients. Significance: We describe a highly accurate CMB model to predict the therapeutic benefit in prostate cancer patients and uncover the complex interplay between genetic variants and CMBs on NADT resistance. Our model relies only on widely available needle biopsy specimens and provides a robust and cost-effective so...