The Clinical Utility of Pharmacogenomics Testing in Assessing Tyrosine Kinase Inhibitor Therapy, Intolerance and Responses in Patients with Chronic Myelogenous Leukemia

Background: Chronic myelogenous leukemia (CML) is a BCR-ABL1 driven myeloid neoplasm, with excellent response rates to tyrosine kinase inhibitors (TKI). TKI such as imatinib (IM), dasatinib (DAS), nilotinib (NIL), bosutinib (BOS), and ponatinib (PON) currently US FDA approved, have dramatically changed survival outcomes. That being said, two main challenges exist; suboptimal responses and TKI intolerance. The reason why some patients tolerate/respond to TKI better than others is unknown, but is potentially explainable based on drug metabolism. The field of pharmacogenomics (PGX) is rapidly evolving, with commercial panel's rapidly assessing metabolic pathways such as cytochrome P450 (CYP), UGTA1A, p-glycoprotein/ABCB1. We carried out this study using a 22 gene-PGX panel to assess potential causes of TKI related intolerance and suboptimal responses in compliant patients. Methods: Twenty-nine Mayo Clinic patients with CML were prospectively recruited after informed consent. Buccal swabs were utilized for PGX testing to assess variations/polymorphisms involving CYP3A4/5, 1A2, 2C9, UGT1A1, amongst others (www.Oneome.com). Three groups of patients were recruited, those with i) TKI intolerance, ii) suboptimal responses by ELN criteria, iii) and newly diagnosed cases, to assess if PGX testing could explain TKI intolerance or resistance, or alter choice of therapy in newly diagnosed cases. A pharmacology review was obtained to interpret reports in all cases. Results: 29 patients with CML were prospectively enrolled, median age 64 years, 15 (52%) male. At last follow up (median 21 months), 3 (10%) deaths and no disease progressions were documented. 82% received first line IM, whereas 18% received first line DAS. Major metabolic pathways assessed for IM included, CYP3A4 and 5, while minor pathways included 2C19, 2C9 and 2D6. Majority (83%) of IM patients had normal major pathways with 17% being intermediate-normal. With regards to the minor pathways, 25% were rapid metabolizers for 2C19, whereas 29% were intermediate and 4% poor metabolizers for 2C9 and 50% and 16% were intermediate and poor metabolizers for 2D6. In all IM treated patients PGX testing did not completely explain intolerance or resistance and did not change clinical decision making. Front line DAS was used in 18% and the major pathway assessed was CYP 3A4, with 80% being normal and 20% being intermediate metabolizers. Once again PGX testing did not completely explain intolerance/resistance and no therapeutic changes were based on PGX analysis. Seventeen (59%) patients received second line TKI therapy (50% each for intolerance and resistance), with DAS (58%) and NIL (29%), being the two most common. Once again for DAS, 90% had normal 3A4 metabolism while 10% were intermediate and for NIL 80% were normal and 20% intermediate. Only one of 2 NIL treated patients with UGTA1A polymorphisms developed indirect hyperbilirubinemia (grade 1). In the second line setting, PGX testing did not completely explain or alter treatment decisions. BOS was used as a third line agent in 3 (>10%) patients, 2 who remained refractory and one with intolerance, and all three were normal CYP3A4 metabolizers. None of these patients developed diarrhea a common side effect of BOS. PON was used in one patient with intermediate CYP3A4 metabolism as a 5th line agent and the patient continues to have refractory disease with no thrombotic events. In all cases, no changes were made to concomitant medications based on PGX testing. Conclusion: In conclusion, while PGX testing has rapidly evolved and become commercially available, in the context of TKI therapy for CML, while it offers useful insights on minor metabolic derangements and side effects like NIL induced hyperbilirubinemia, in our study, currently limited by a small sample size, it did not completely explain TKI intolerance or resistance. Recruitment of additional patients (n=100) and correlations with plasma drug levels are currently ongoing. Table. Table. Disclosures Al-Kali: Novartis: Research Funding. Stewart:Amgen Inc., Celgene, Roche, Seattle Genetics: Research Funding; Amgen Inc., BMS, Celgene, Takeda, Roche, Seattle Genetics, Janssen, Ono: Consultancy.