Parallel Characterization of Genetic Variants in Chemotherapy-Induced Cardiotoxicity Using iPSCs

项目总结

Project Summary Cardiotoxicity of cancer treatments can lead to severe heart failure in cancer survivors or discontinuation of cancer treatments. Currently, it is challenging to evaluate who is at the risk of developing cardiotoxicity prior to cancer treatments and prevent the adverse side effects. Genome-wide association studies (GWASs) have shown that genetic predispositions are one of the key determinants of risk susceptibility to chemotherapy- induced cardiotoxicity. Consistently, the susceptibility to anti-cancer agents can be recapitulated by patient- specific induced pluripotent stem cell (iPSC)-derived cardiomyocytes (iPSC-CMs), which reflect the donor patients’ genetic makeup. The goal of this project is to identify genes and genetic variants that affect the susceptibility to anti-cancer agents for developing cardio-protective therapies and risk assessment systems. Although this proposal focuses on doxorubicin, which is one of the most commonly-used anti-cancer agents, the approach described here is expandable to any other cancer treatment-induced cardiotoxicities such as those by tyrosine kinase inhibitors and radiation therapy. First, I plan to identify genes whose inhibition or activation can protect iPSC-CMs from doxorubicin using our iPSC-based CRISPR screening platform. The screened genes will be causative in doxorubicin-induced cardiotoxicity (DIC) and thus promising therapeutic targets. Second, I plan to utilize bioengineering technologies and in vivo mouse models to assess the effects of candidate therapies more accurately than simple monolayer culture systems. Since repurposing of approved drugs can accelerate the clinical application of the findings, I plan to test existing drugs that target the validated genes in these models. Finally, to develop a system to evaluate the genetic susceptibility to DIC, I plan to perform parallel characterization of many genetic variants using iPSC-based base/prime-editing screens in DIC and generate “susceptibility scores” of individual variants that are clinically implicated in DIC. The result will help with risk stratification of patients who receive chemotherapies.