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

Résumé fourni par la source

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.

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