Impact of Polymorphisms in Pharmacokinetic Genes on Tyrosine Kinase Inhibitor (TKI) Tolerability in Patients with Chronic Myeloid Leukemia (CML) or Philadelphia Chromosome Positive Acute Lymphoblastic Leukemia (Ph+ ALL)

Background: Although tyrosine kinase inhibitors (TKIs) are very effective in the treatment of CML and Ph+ ALL, the use of TKIs has been associated with many serious adverse events that can result in patient non-compliance and/or alterations in treatment, which can lead to suboptimal outcomes. The metabolic pathway of TKIs is mediated primarily by hepatic cytochrome P450s, flavin monooxygenases, and drug transporters. There are limited studies evaluating genetic determinants of interindividual variability in TKI disposition and tolerability, which could impact clinical response. Herein, we evaluated single nucleotide polymorphisms (SNPs) in candidate genes coding for metabolic enzymes and transporters that could impact TKI disposition. We hypothesize that one or more of these SNPs may affect TKI tolerability and could be used to personalize TKI dosing in future studies.

Methods: This is a retrospective-prospective, non-interventional, pharmacogenetic study of patients at Levine Cancer Institute diagnosed with CML or Ph+ ALL and taking imatinib, dasatinib, nilotinib, or bosutinib. Adult patients newly diagnosed and starting TKI (prospective) and those on longstanding TKI (retrospective) were identified for inclusion. Eligible patients underwent IRB-approved consent for buccal swab collection and clinical data extraction. Data collected included patient demographics, diagnosis, TKI start date, TKI dose, TKI-related adverse events (any grade and attribution assigned by the treating provider), and treatment interruption (e.g., dose reduction, switch to alternate TKI, or holding of treatment). A subset of patients also underwent blood collection to estimate trough and peak drug concentrations (not yet analyzed). DNA was extracted from buccal swabs using the MagMAX^TM DNA kit. A custom TaqMan assay was developed targeting SNPs in the following candidate genes: ABCB1, ABCG2, CYP2C8, CYP3A4, CYP3A5, FMO3, PPAR-alpha, POR, PXR, SLC22A1, and SLC22A4. The co-primary objectives were to evaluate the association between each SNP and 1) incidence of any grade TKI-related adverse events; and 2) treatment interruption due to adverse event(s), using univariate logistic regression. All results were adjusted for age, sex, and race.

Results: Of 67 evaluable patients, the median age was 57 (range 22-102), 36 (54%) were male, 53 (79%) were white, 7 (10%) were Black, 62 (93%) had chronic phase CML, and 5 (7%) had Ph+ ALL. Nineteen patients received imatinib (28%), 21 (31%) dasatinib, 14 (21%) nilotinib, and 13 (20%) bosutinib. Across all TKIs, 42 patients (63%) experienced any-grade TKI-related adverse event, including gastrointestinal (27), fatigue (9), hematologic (8), pruritus (7), myalgias (6), transaminitis (4), dyspnea (3), weight gain (2), headache (2), bruising (2), photosensitivity (2), elevated serum creatinine (2), and edema (2). Two SNPs were significantly associated with odds of any-grade TKI-related adverse events: rs2266780 in FMO3 (OR 5.44, 95% CI 1.64-18.08, p=0.006) and rs4253728 in PPAR-alpha (OR 0.27, 95% CI 0.08-0.97, p=0.045). None of the remaining SNPs were associated with TKI-related adverse events, and no SNPs were associated with treatment interruption.

Conclusion:

Polymorphisms in FMO3 and PPAR-alpha were associated with TKI-related adverse event risk. Along with CYP3A4/5, FMO3 is involved in the metabolism of TKIs. PPAR-alpha is a nuclear receptor protein functioning as a transcription factor and can regulate the transcription of various CYP genes. Importantly, polymorphisms in pharmacokinetic genes CYP3A4 and CYP3A5 were not associated with adverse event risk. No SNPs were associated with odds of treatment interruption. These results demonstrate the feasibility of using pharmacogenetics to identify predictors of TKI-related adverse events that can be tested in prospective trials to personalize TKI dosing. In the next phase of this project, we will correlate our pharmacogenetic findings with pharmacokinetic results, including estimated trough and peak concentrations, obtained from the same cohort.

Patel:Clarified Precision Medicine: Consultancy; Bristol Myers Squibb: Research Funding; VieCure: Consultancy. Moore:Pfizer: Consultancy; AstraZeneca: Consultancy; Janssen: Consultancy; Oncopeptides: Consultancy. Chojecki:Servier: Consultancy, Membership on an entity's Board of Directors or advisory committees; Incyte: Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees. Symanowski:Eli Lilly & Co.: Consultancy; Camarus: Consultancy; CARsgen: Consultancy; Immatics: Consultancy. Grunwald:Medtronic: Current equity holder in private company; Genetech/Roche, Incyte Corporation, Janssen: Research Funding; AbbVie, Agios/Servier, Amgen, Astellas Pharma, Blueprint Medicines, Bristol Myers Squibb, Cardinal Health, CTI BioPharma, Daiichi Sankyo, Gamida Cell, Gilead Sciences, Incyte Corporation, Invitae, Karius, Novartis, Ono Pharmaceuticals, Pfizer, ,: Consultancy; Daiichi Sankyo, Gamida Cell, Gilead Sciences, Incyte Corporation, Invitae, Karius, Novartis, Ono Pharmaceutical, Pfizer, Pharmacosmos, Premier, Sierra Oncology, Stemline Therapeutics: Consultancy.