Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures.

Abstract

We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.

• Authors: Nicholas R Rydzewski, Erik Peterson, Joshua M Lang, Menggang Yu, S Laura Chang, Martin Sjöström, Hamza Bakhtiar, Gefei Song, Kyle T Helzer, Matthew L Bootsma, William S Chen, Raunak M Shrestha, Meng Zhang, David A Quigley, Rahul Aggarwal, Eric J Small, Daniel R Wahl, Felix Y Feng, Shuang G Zhao
• Journal: NPJ genomic medicine (NPJ Genom Med) Vol. 6 Issue 1 Pg. 76 (Sep 21 2021) ISSN: 2056-7944 [Electronic] England
• PMID: 34548481 (Publication Type: Journal Article)
• Copyright: © 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.