bioRxivGenomicsPreprint
Genetic Determinants of Cell-Type-Specific Responses to Cardiotoxic Chemotherapeutic Agents
Erik M. McIntireiD, Kenneth BarriD, Natalia M. GonzalesiD, Olivia L Allen, Jonathan E. Burnett, Yoav GiladiD
Abstract
Doxorubicin (DOX) and 5-fluorouracil (5-FU) are effective for treating a variety of cancers, yet many patients taking these drugs experience adverse cardiovascular events that are severe enough to pause treatment, often prolonging the course of chemotherapy and reducing its efficacy. Individual risk for drug-induced cardiotoxicity is likely mediated, at least in part, by genetic variants that control how the heart and vasculature respond to drug-induced perturbations. To identify loci associated with drug-induced cardiotoxicity, we developed an in vitro model that allows us to characterize the effects of chemotherapeutic drugs on a wide range of cardiac cell types, including cardiomyocytes, endothelial cells, and fibroblasts. Using single-cell RNA-seq, we profiled responses to DOX and 5-FU across 46 individuals and 14 cell types, identifying hundreds of expression quantitative trait loci that modify cardiovascular transcription in response to each drug (response eQTLs). Drug-responsive eQTL genes include genes that were previously linked to drug-induced cardiotoxicity and cardiovascular disease, such as ABCB4, HAS3, TNFRSF1A, and OGFR. Over a third of drug-responsive genes are shared between the DOX and 5-FU treatments, suggesting that genes predictive of DOX-induced cardiotoxicity may extend to 5-FU. Among the credible colocalizations we identified between GWAS signals and drug-response eQTLs, we discovered a locus associated with ischemic heart disease that is also a DOX-responsive eQTL for IL6R. IL6R was previously nominated as a druggable target for heart disease, and its binding partner IL6 is a candidate biomarker for anthracycline-induced cardiotoxicity. These findings advance the discovery of genetic variants underlying cardiotoxicity and support development of genotype-based predictors for treating cancer.
- organism: human
- assay: bulk RNA-seq
- research focus: eQTL / molecular QTL mapping
- approach: statistical model
- Citations
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