Development of a Dose-Adjusted Polygenic Risk Model for Anthracycline-Induced Cardiotoxicity

Association between genetic variants of transmembrane transporters and susceptibility to anthracycline-induced cardiotoxicity: Current understanding and existing evidence

Anthracyclines remain the cornerstone of numerous chemotherapeutic protocols, with beneficial effects against haematological malignancies and solid tumours. Unfortunately, the clinical usefulness of anthracyclines is compromised by the development of cardiotoxic side effects, leading to dose limitations or treatment discontinuation. There is no absolute linear correlation between the incidence of cardiotoxicity and the threshold dose, suggesting that genetic factors may modify the association between anthracyclines and cardiotoxicity risk. And the majority of single nucleotide polymorphisms (SNPs) associated with anthracycline pharmacogenomics were identified in the ATP-binding cassette (ABC) and solute carrier (SLC) transporters, generating increasing interest in the pharmacogenetic implications of their genetic variations for anthracycline-induced cardiotoxicity (AIC). This review focuses on the influence of SLC and ABC polymorphisms on AIC and highlights the prospects and clinical significance of pharmacogenetics for individualised preventive approaches.

Anthracycline Toxicity: Light at the End of the Tunnel?

Anthracycline-induced cardiotoxicity (AIC) is a serious and common side effect of anthracycline therapy. Identification of genes and genetic variants associated with AIC risk has clinical potential as a cardiotoxicity predictive tool and to allow the development of personalized therapies. In this review, we provide an overview of the function of known AIC genes identified by association studies and categorize them based on their mechanistic implication in AIC. We also discuss the importance of functional validation of AIC-associated variants in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) to advance the implementation of genetic predictive biomarkers. Finally, we review how patient-specific hiPSC-CMs can be used to identify novel patient-relevant functional targets and for the discovery of cardioprotectant drugs to prevent AIC. Implementation of functional validation and use of hiPSC-CMs for drug discovery will identify the next generation of highly effective and personalized cardioprotectants and accelerate the inclusion of approved AIC biomarkers into clinical practice.