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Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures Suphavilai, Chayaporn; Chia, Shumei; Sharma, Ankur; Tu, Lorna; Da Silva, Rafael P.; Mongia, Aanchal; DasGupta, Ramanuj; Nagarajan, Niranjan

Abstract

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 .

Item Metadata

Title
Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures
Creator
Suphavilai, Chayaporn; Chia, Shumei; Sharma, Ankur; Tu, Lorna; Da Silva, Rafael P.; Mongia, Aanchal; DasGupta, Ramanuj; Nagarajan, Niranjan
Publisher
BioMed Central
Date Issued
2021-12-16
Description
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 .
Subject
Drug response prediction; Single-cell RNA-seq; Tumor heterogeneity; Recommender system; Combinatorial therapy
Genre
Article
Type
Text
Language
eng
Date Available
2022-01-12
Provider
Vancouver : University of British Columbia Library
Rights
Attribution 4.0 International (CC BY 4.0)
DOI
10.14288/1.0406259
URI
http://hdl.handle.net/2429/80641
Affiliation
Science, Faculty of; Non UBC; Physics and Astronomy, Department of
Citation
Genome Medicine. 2021 Dec 16;13(1):189
Publisher DOI
10.1186/s13073-021-01000-y
Peer Review Status
Reviewed
Scholarly Level
Faculty; Other
Copyright Holder
The Author(s)
Rights URI
http://creativecommons.org/licenses/by/4.0/
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Attribution 4.0 International (CC BY 4.0)