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

Nicholas R Rydzewski,Rahul Aggarwal,Joshua M Lang,Meng Zhang,Felix Y Feng,Gefei Song,Menggang Yu,S Laura Chang,Kyle T Helzer,Eric J Small,William S Chen,Matthew L Bootsma,Martin Sjöström,Erik Peterson,Daniel R Wahl,Hamza Bakhtiar,Raunak M Shrestha,David A Quigley,Shuang G Zhao

doi:10.1038/s41525-021-00239-z

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.

Highlights

Treatment decisions for cancer patients have historically depended on the tumor location and histologic appearance
We examined if the TreAtment Response Generalized Elastic-neT Signatures (TARGETS) predictions could successfully predict cell line drug response in an independent dataset from the Cancer Cell Line Encyclopedia (CCLE)[16]
In GBM, the benefit of Temozolomide is more pronounced in MGMT promoter methylated tumors[20,21,22,23], and we found MGMT-methylated glioblastoma was predicted to be more sensitive to Temozolomide (p < 0.0001)

Summary

INTRODUCTION

Treatment decisions for cancer patients have historically depended on the tumor location and histologic appearance. Multiple anti-neoplastic therapies have been paired with predictive biomarkers for making treatment decisions. This approach has been successful with targeted drug therapies. Since the approval of these agents 20 years ago, the FDA has approved dozens of different targeted therapies, with the number increasing rapidly every year Even among these targeted therapies and among patients who have a mutation known to confer increased sensitivity to the therapy, treatment outcomes can still be heterogeneous. We have leveraged an existing large-scale in-vitro database to train TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We validated these results on three independent cohorts. This pan-cancer, platformindependent approach can be used to better identify responders vs. non-responders and could potentially identify new patient populations which would benefit from specific treatments

RESULTS

DISCUSSION

CODE AVAILABILITY