Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling.

Abstract

Simple SummaryWhile patient datasets such as The Cancer Genome Atlas (TCGA) often contain a plethora of “-omics” data, the corresponding drug response information are limited and not suited for novel drug discovery. By integrating in vitro high throughput drug screening data and patient tumor molecular information, we created a virtual drug screening pipeline that enables drug discovery with simultaneous biomarker identification for a patient population. Using triple-negative breast cancer (TNBC) as our population of interest, we demonstrated the pipeline from lead identification, to biomarker discovery, to in vitro and in vivo validation of the compound AZD-1775.(1) Background: Drug imputation methods often aim to translate in vitro drug response to in vivo drug efficacy predictions. While commonly used in retrospective analyses, our aim is to investigate the use of drug prediction methods for the generation of novel drug discovery hypotheses. Triple-negative breast cancer (TNBC) is a severe clinical challenge in need of new therapies. (2) Methods: We used an established machine learning approach to build models of drug response based on cell line transcriptome data, which we then applied to patient tumor data to obtain predicted sensitivity scores for hundreds of drugs in over 1000 breast cancer patients. We then examined the relationships between predicted drug response and patient clinical features. (3) Results: Our analysis recapitulated several suspected vulnerabilities in TNBC and identified a number of compounds-of-interest. AZD-1775, a Wee1 inhibitor, was predicted to have preferential activity in TNBC (p < 2.2 × 10−16) and its efficacy was highly associated with TP53 mutations (p = 1.2 × 10−46). We validated these findings using independent cell line screening data and pathway analysis. Additionally, co-administration of AZD-1775 with standard-of-care paclitaxel was able to inhibit tumor growth (p < 0.05) and increase survival (p < 0.01) in a xenograft mouse model of TNBC. (4) Conclusions: Overall, this study provides a framework to turn any cancer transcriptomic dataset into a dataset for drug discovery. Using this framework, one can quickly generate meaningful drug discovery hypotheses for a cancer population of interest.