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

Summary 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&gt;0.6), as well as optimal drug combinations to target different subclonal populations (&gt;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 <jats:fig id="ufig1" position="float" fig-type="figure" orientation="portrait"><jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="389676v1_ufig1" position="float" orientation="portrait" /></jats:fig>Highlights <jats:list list-type="bullet">Large-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.

Medienart:	Preprint

Erscheinungsjahr:	2022
Erschienen:	2022

Enthalten in:	bioRxiv.org - (2022) vom: 25. Mai Zur Gesamtaufnahme - year:2022

Sprache:	Englisch

Beteiligte Personen:	Suphavilai, Chayaporn [VerfasserIn] Chia, Shumei [VerfasserIn] Sharma, Ankur [VerfasserIn] Tu, Lorna [VerfasserIn] Da Silva, Rafael Peres [VerfasserIn] Mongia, Aanchal [VerfasserIn] DasGupta, Ramanuj [VerfasserIn] Nagarajan, Niranjan [VerfasserIn]

Links:	Volltext [lizenzpflichtig] Volltext [kostenfrei]

doi:	10.1101/2020.11.23.389676

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	XBI019406908