Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

Abstract

While understanding heterogeneity in molecular signatures across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Single-cell RNA-seq (scRNA-seq) technologies have facilitated investigations into the role of intra-tumor transcriptomic heterogeneity (ITTH) in tumor biology and evolution, but their application to in silico models of drug response has not been explored. Based on large-scale analysis of cancer omics datasets, we highlight the utility of ITTH for predicting clinical outcomes. We then show that heterogeneous gene expression signatures obtained from scRNA-seq data can be accurately analyzed (80%) in a recommender system framework (CaDRReS-Sc) for in silico drug response prediction. Patient-derived cell lines capturing transcriptomic heterogeneity from primary and metastatic tumors were used as in vitro proxies for validating monotherapy predictions (Pearson r>0.6), as well as optimal drug combinations to target different subclonal populations (>10% improvement). Applying CaDRReS-Sc to the increasing number of publicly available tumor scRNA-seq datasets can serve as an in silico screen for further in vitro and in vivo drug repurposing studies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=191 SRC="FIGDIR/small/389676v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1aa5795org.highwire.dtl.DTLVardef@5c2323org.highwire.dtl.DTLVardef@106e074org.highwire.dtl.DTLVardef@7a0834_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LILarge-scale analysis to establish the impact of transcriptomic heterogeneity within tumors on clinical outcomes C_LIO_LICalibrated recommender system for drug response prediction based on single-cell RNA-seq data (CaDRReS-Sc) C_LIO_LIPrediction of drug response in patient-derived cell lines with transcriptomic heterogeneity C_LIO_LIIn silico identification of drug combinations that work based on clonal vulnerabilities C_LI