Abstract A136: Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles

Abstract

Abstract Single-cell sequencing techniques have greatly advanced our current understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies and therapeutic responses. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key step in development of such approaches centers around accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method detects shared expression patterns between the two data sources and utilizes such information to project cellular drug response. This method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0); this holds when using single-cell RNA-seq from both cell line and in vivo models. More importantly, we examine our method’s utility with three distinct prospective tests, and in each our predicted results are accurate and mirrored biological expectations. In the first two tests, we investigated predicting drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. In both, our results showed high consistency with results from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify drugs predicted effective on the resistant line, and validated the predictions with in vitro experiments. Together, scIDUC quickly and directly translates scRNA-seq data into meaningful cellular drug response for individual cells, displaying the potential to be used by researchers as a first-line tool for nuanced and heterogeneity-aware drug discovery. Citation Format: Weijie Zhang, Danielle Maeser, Adam Lee, Yingbo Huang, Robert F Gruener, Sampreeti Jena, Anand G Patel, R. Stephanie Huang. Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A136.