A microRNA-based classifier for identification of neuroendocrine differentiation in metastatic castration-resistant prostate cancer.

149 Background: Therapy-induced neuroendocrine prostate cancer (NEPC) is an extremely aggressive variant of castration-resistant prostate cancer (CRPC) that is increasing in incidence with the widespread use of second generation of androgen receptor (AR)-pathway inhibitors (APIs) such as enzalutamide (ENZ) and abiraterone. This aggressive variant arises from CRPC-Adenocarcinomas (CRPC-Adeno) via a reversible trans-differentiation process, referred to as neuroendocrine differentiation (NED) wherein cells undergo a lineage switch and exhibit neuroendocrine (NE) features, characterized by expression of neuronal markers such as enolase 2 (ENO2), chromogranin A (CHGA) and synaptophysin (SYP). Currently, histopathological assessment combined with immunohistochemical detection in PCa tissues/serum levels of neuronal markers including SYP, NSE, CHGA and CD56 is currently used to monitor NED in CRPC patients. However, these markers are not sufficiently specific, highlighting the urgent need of novel molecular markers to assess emergence of NED in CRPC patients. Recently, we demonstrated that progression of advanced CRPC with adenocarcinoma characteristics to CRPC-NE is associated with a characteristic set of miRNA alterations that promote plasticity of advanced PCa to NEPC (Bhagirath et al., Oncogene, 2020). Importantly, we could develop a ‘novel miRNA classifier’ to robustly stratify CRPC-NE tumors from CRPC-Adenocarcinomas. Here we further validate the classifier in independent clinical cohorts and deduce the optimal miRNA genes required for NEPC diagnosis. Methods: Human clinical samples with corresponding clinical information were procured from two independent sites of Prostate Cancer Biorepository Network (PCBN). FFPE sections from clinical samples were microdissected, RNA were extracted and small RNA sequencing was performed using an Illumina NextSeq 500 platform. Sequencing data were analyzed and machine learning algorithms were applied. The performance of classifier was measured using receiver operating characteristic (ROC) analysis with area under the curve (AUC) as the primary evaluation metric. Results: Unsupervised analysis of sequencing data by principal component analyses (PCA) revealed distinct clustering of the CRPC-NE tumors from CRPC-Adenocarcinomas based on miRNA profiles suggesting that miRNA profiles can be used to stratify these tumor types. We applied the ‘43-miRNA classifier data’ we deduced earlier to these validation cohorts. Our analyses showed that a set of 5 miRNAs of the classifier are important in distinguishing between CRPC-Adeno vs CRPC-NE with an AUC=0.8318. Conclusions: A ‘5- miRNA’ classifier was validated to be of significance in two independent validation cohorts. We propose this miRNA classifier as an important tool for diagnosing NED in CRPC patients.