Prostate cancer CTC-RNA Assay: A new method for contemporary genomics and precision medicine via liquid biopsy.

Abstract

170 Background: Transcriptome-based analysis has begun to reshape the approach to prostate cancer (PC). Two different gene expression signatures have shown that PC can be divided into 3 subclasses reflecting luminal-basal biology. These subtypes point toward biological drivers that may strongly influence how care should be personalized including optimization of androgen receptor targeted therapy. The majority of work done in this area has been based on tissue-based gene expression. With the advent of newer nanotechnology platforms for isolation of circulating tumor cells (CTCs), profiling of PC gene expression from blood is now possible. Methods: We recruited 34 patients with metastatic castration resistant PC at Cedars-Sinai Medical Center who had available blood specimens prior to initiation of androgen receptor signaling inhibitor (ARSI, e.g. abiraterone, enzalutamide and apalutamide) therapy.We utilized the NanoVelcro Assays which allow for capture and release of CTCs with intact mRNA. Gene sets from the PCS and PAM50 signatures were re-reviewed to optimize signal detection in the blood and enriched for genes upregulated in PC. The NanoString nCounter platform was used for RNA profiling. Results: The final assay was tested in banked blood samples and provided classifications of patients that associated with clinical responsiveness to therapy. Validation was conducted to examine the performance of the CTC-specific PCS/PAM50 panel in public databases (including Prostate Cancer Transcriptome Atlas and GenomeDx). Our pilot study showed that the median overall survival was significantly worse in PCS1 patients. Conclusions: This study shows initial proof of principle that genomic classification in blood is possible using contemporary tool for blood component isolation and RNA profiling. Additional technical and clinical validations are needed prior to widespread implementation, but these methods may make it possible to increase the utilization of genomic classifiers in clinical studies and in practice.