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 solution for clinical implementation.