1. Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures
- Author
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Shumei Chia, Da Silva Rp, Ramanuj DasGupta, Niranjan Nagarajan, Tu L, Chayaporn Suphavilai, Aanchal Mongia, and Ankur Sharma
- Subjects
Tumor heterogeneity ,Combinatorial therapy ,In silico ,Cell ,Clone (cell biology) ,Method ,Computational biology ,QH426-470 ,Biology ,Transcriptome ,Drug response prediction ,Neoplasms ,Genetics ,medicine ,Drug response ,Humans ,Precision Medicine ,Recommender system ,Molecular Biology ,Genetics (clinical) ,Single-cell RNA-seq ,Sequence Analysis, RNA ,Tumor biology ,Gene Expression Profiling ,Clone Cells ,Drug repositioning ,medicine.anatomical_structure ,Precision oncology ,Molecular Medicine ,Medicine ,Single-Cell Analysis ,Software - Abstract
SummaryWhile 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 abstractHighlightsLarge-scale analysis to establish the impact of transcriptomic heterogeneity within tumors on clinical outcomesCalibrated recommender system for drug response prediction based on single-cell RNA-seq data (CaDRReS-Sc)Prediction of drug response in patient-derived cell lines with transcriptomic heterogeneityIn silico identification of drug combinations that work based on clonal vulnerabilities
- Published
- 2020
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