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

Genome Medicine

Chayaporn Suphavilai¹  Ankur Sharma¹  Ramanuj DasGupta¹  Shumei Chia¹  Aanchal Mongia²  Lorna Tu³  Niranjan Nagarajan⁴  Rafael Peres Da Silva⁵ 

[1] Genome Institute of Singapore, A*STAR, Singapore, Singapore;Genome Institute of Singapore, A*STAR, Singapore, Singapore;Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, Delhi, India;Genome Institute of Singapore, A*STAR, Singapore, Singapore;Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada;Genome Institute of Singapore, A*STAR, Singapore, Singapore;Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada;Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore;Genome Institute of Singapore, A*STAR, Singapore, Singapore;School of Computing, National University of Singapore, Singapore, Singapore;

关键词: Drug response prediction;    Single-cell RNA-seq;    Tumor heterogeneity;    Recommender system;    Combinatorial therapy;   

DOI  :  10.1186/s13073-021-01000-y

来源: Springer

【 摘 要 】

While understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc’s monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc.

【 授权许可】

CC BY