Mechanistic Insight to Predict and Prevent Nilotinib-Induced Artery Disease - PROJECT SUMMARY The BCR-ABL1 tyrosine kinase inhibitor (TKI) nilotinib (Tasigna, Novartis) is the most commonly prescribed treatment for chronic myelogenous leukemia (CML). The 10-year analysis of the ENESTnd clinical trial (2021) showed that nilotinib is more effective than its closest competitor imatinib, with 97.3% of patients achieving freedom from progression to acute/blast phase. Despite this success in effectively curing this once fatal blood cancer, the ENESTnd trial has also clarified the well-known vascular side-effects of nilotinib, demonstrating that 23.5% of patients experienced some form of artery disease, which in some cases is severe enough to require amputation. This nilotinib-induced artery disease (NIAD) occurs even in patients with no pre-existing cardiovascular risk factors. Currently, no tools exist to elucidate the mechanisms of NIAD or predict which patients are predisposed to this adverse effect. Susceptible patients are therefore only identified after they have developed irreversible complications. We hypothesize that an individual’s predisposition to NIAD is due to variance in their endothelial cell (EC) response to kinase inhibition by nilotinib. Our recent publication has already eliminated vascular smooth muscle cells as part of this NIAD phenotype. We have also identified that it is not on-target ABL1 kinase inhibition that is responsible for the EC phenotype (Pinheiro et al., 2024). We have previously demonstrated that human induced pluripotent stem cells (hiPSCs) are a uniquely powerful tool with which to discover the genomic basis of adverse drug reactions. In this study, we shall first recruit 100 nilotinib- treated patients, 50 with artery disease or reduced ankle brachial index (ABI) and 50 without. We will then generate hiPSC from all 100 patients and complete whole genome sequencing. Each hiPSC line will be differentiated to ECs and their response to nilotinib will be functionally characterized using RNA-seq and a battery of six phenotypic and biochemical assays we have already developed and validated. This work will confirm the in vitro patient-specific predisposition to NIAD and confirm its genomic nature. The dose-response changes in gene expression after nilotinib exposure will then be correlated with the patients’ whole genome data to identify differential expression quantitative trait loci (deQTL). We will recruit a 200-patient DNA-only replication cohort and complete SNP array genotyping to corroborate findings. Novel NIAD-associated SNPs discovered will then be corrected/introduced using CRISPR-based genome editing to functionally validate their role in NIAD. This validated variant data will identify candidate mechanisms and pathways that we will then pharmacologically target to assess suitability in preventing NIAD. This study will provide 1, functionally validated predictive SNPs that can be used to inform clinicians on the most appropriate CML therapy, 2, mechanistic understanding on the mechanisms of NIAD, and 3, candidate protective adjuvant therapeutics discovered in a human model suitable for clinical trial. Together, these tools will advance the use of precision medicine in oncology and allow for proactive prevention of NIAD.
