Abstract 1481: A microRNA classifier for predicting neuroendocrine differentiation in castration-resistant prostate cancer patients
Authors: Saini Sharanjot , Sreekumar Amritha , Lee Jin Tae , Patel Nikhil , Sharma Ashok 
Published: June 2022

Abstract
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 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. We demonstrated that progression of CRPC-Adeno to CRPC-NE states is associated with a characteristic set of miRNA alterations. Here we validate a ‘novel miRNA classifier’ to robustly stratify CRPC-NE tumors from CRPC-Adenocarcinomas in independent clinical cohorts and deduce the optimal miRNA genes required for NEPC diagnosis.
Methods: Human FFPE sections from CRPC-Adeno and CRPC-NE patients 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.
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 employing clinical samples from two independent sites. We propose this miRNA classifier as an important tool for diagnosing NED in CRPC patients.
Citation Format: Sharanjot Saini, Amritha Sreekumar, Jin Tae Lee, Nikhil Patel, Ashok Sharma. A microRNA classifier for predicting neuroendocrine differentiation in castration-resistant prostate cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1481.