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

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Chayaporn Suphavilai ◽  
Shumei Chia ◽  
Ankur Sharma ◽  
Lorna Tu ◽  
Rafael Peres Da Silva ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Drug Response ◽  
Drug Repurposing ◽  
High Accuracy ◽  
Molecular Heterogeneity ◽  
Precision Oncology ◽  
Scale Analysis ◽  
Rna Seq ◽  
Large Scale Analysis

AbstractWhile 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.

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

2020 ◽  
Author(s):  
Chayaporn Suphavilai ◽  
Shumei Chia ◽  
Ankur Sharma ◽  
Lorna Tu ◽  
Rafael Peres Da Silva ◽  
...  
Keyword(s):  
Single Cell ◽  
Recommender System ◽  
In Silico ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Scale Analysis ◽  
Rna Seq ◽  
Large Scale Analysis

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

scholarly journals Quantitative model suggests both intrinsic and contextual features contribute to the transcript coding ability determination in cells

2021 ◽  
Author(s):  
Yu-jian Kang ◽  
Jing-Yi Li ◽  
Lan Ke ◽  
Shuai Jiang ◽  
De-chang Yang ◽  
...  
Keyword(s):  
Gene Transcription ◽  
Large Scale ◽  
Protein Translation ◽  
Quantitative Model ◽  
High Accuracy ◽  
Biological Information ◽  
Scale Analysis ◽  
Large Scale Analysis ◽  
Context Dependent ◽  
Key Steps

Gene transcription and protein translation are two key steps of the "central dogma". It is still a major challenge to quantitatively deconvolute factors contributing to the coding ability of transcripts in mammals. Here, we propose Ribosome Calculator (RiboCalc) for quantitatively modeling the coding ability of RNAs in human genome. In addition to effectively predicting the experimentally confirmed coding abundance via sequence and transcription features with high accuracy, RiboCalc provides interpretable parameters with biological information. Large-scale analysis further revealed a number of transcripts with a variety of coding ability for distinct types of cells (i.e., context-dependent coding transcripts, CDCTs), suggesting that, contrary to conventional wisdom, a transcript's coding ability should be modeled as a continuous spectrum with a context-dependent nature.

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