Pharmacogenomics for Prediction of Cardiovascular Toxicity: Landscape of Emerging Data in Breast Cancer Therapies

Simple Summary With the increasing number of breast cancer survivors and with the progress in survival of patients with metastatic breast cancer, consideration of survivorship issues becomes vital. Cardiotoxicity has been an important aspect in the management of early or metastatic breast cancer, not least due to the wide use of anthracyclines and, in HER2-positive breast cancer, anti-HER2 agents. Current baseline assessment includes clinical, such as history of cardiovascular comorbidities and lifestyle factors, and biochemical markers. Further biomarkers for tailored risk assessment and management remain an unmet need. Pharmacogenomics, an emerging field investigating how individual genetic variations, alone or in combination with polygenic risk scores, can impact drug metabolism and efficacy, and could be a complement. We hereby present a comprehensive review of the literature and the current landscape on the role of pharmacogenomics in cardiotoxicity prediction and its potential to become an additional biomarker in personalized risk assessment algorithms. Pharmacogenomics is an emerging field in oncology, one that could provide valuable input on identifying patients with inherent risk of toxicity, thus allowing for treatment tailoring and personalization on the basis of the clinical and genetic characteristics of a patient. Cardiotoxicity is a well-known side effect of anthracyclines and anti-HER2 agents, although at a much lower incidence for the latter. Data on single-nucleotide polymorphisms related to cardiotoxicity are emerging but are still scarce, mostly being of retrospective character and heterogeneous. A literature review was performed, aiming to describe current knowledge in pharmacogenomics and prediction of cardiotoxicity related to breast cancer systemic therapies and radiotherapies. Most available data regard genes encoding various enzymes related to anthracycline metabolism and HER2 polymorphisms. The available data are presented, together with the challenges and open questions in the field.