Abstract B025: MicroRNA regulators of neuroendocrine differentiation of prostate cancer

Abstract

Abstract 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). There is an urgent need of identifying novel molecular markers to assess emergence of NED in CRPC patients. We demonstrated that progression of advanced CRPC with adenocarcinoma characteristics (CRPC-Adeno) to therapy-induced, androgen-independent NE (CRPC-NE) states is associated with a characteristic set of miRNA alterations that promote plasticity of advanced prostate adenocarcinomas 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. We further study the functional significance of miR-28-3p, a miRNA identified by sequencing, in NEPC. Methods: Human clinical samples with corresponding clinical information were procured from two independent sites of Prostate Cancer Biorepository Network (PCBN). Samples included human CRPC-adeno vs CRPC-NE. FFPE sections from these 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 (random forest machine learning technique with leave-pair-out cross validation (LPOCV)). The performance of classifier was measured using receiver operating characteristic (ROC) analysis with area under the curve (AUC) as the primary evaluation metric. The functional significance of miR-28-3p was studied by its overexpression and knockdown in PCa cell lines followed by functional assays. 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. 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. Further, miR 28-3p potentially has a biphasic role in PCa, where it is oncogenic in early stages and acts as a tumor suppressor in late stage PCa. 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. Citation Format: Amritha Sreekumar, Sharanjot Saini. MicroRNA regulators of neuroendocrine differentiation of prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr B025.