Gobburu, Jogarao

ABSTRACT Enzalutamide and abiraterone are hormonal treatments that improve survival in metastatic castration‐resistant prostate cancer. Identifying genetic variants associated with the clearance of these drugs may aid in improved dosing and outcomes. We performed genetic association studies of enzalutamide and abiraterone oral clearance in the Alliance A031201 clinical trial. Genome‐wide genotyping was performed with the primary analysis limited to European‐descent participants. Pharmacogene metabolic phenotypes were estimated using PyPGx and Stargazer. Associations of metabolic activity groups for CYP3A4 , CYP3A5 , CYP2C19 and SLCO1B1 with enzalutamide clearance ( N = 706) and CYP3A4 , SLCO2B1 and UGT1A4 with abiraterone clearance ( N = 323) were tested by linear regression. Targeted SNP associations were assessed for abiraterone clearance at loci proximal to major metabolizing genes. Full genome‐wide association studies were performed for both sets of clearance values. No significant associations were identified between metabolic phenotypes and enzalutamide or abiraterone oral clearance SNPs in the SULT2A1 5′ flanking region were significantly associated with lower abiraterone clearance, (rs296373, minor allele frequency = 0.15, β = −0.457, p = 3.2E‐06). Liver protein and liver and adrenal gland gene expression QTL databases indicated significantly lower SULT2A1 expression patterns for individuals carrying associated alleles, likely explaining the lower abiraterone oral clearance. CYP2C8*3 was associated with higher enzalutamide clearance ( p = 0.012), but this was not significant after correction for multiple testing. This study is the first to identify the genetic association of SULT2A1, known to be involved in the metabolism of steroids in the liver and adrenal glands, with abiraterone clearance. Genetic variation in SULT2A1 may be useful to inform personalized dosing of abiraterone. ClinicalTrials.gov Identifier: NCT01949337

AbstractChemotherapy‐induced peripheral neuropathy (henceforth, neuropathy) is often dose limiting and is generally managed by empirical dose modifications. We aimed to (1) identify an early time point that is predictive of future neuropathy using a patient‐reported outcome and (2) propose a dose‐adjustment algorithm based on simulated data to manage neuropathy. In previous work, a dose‐neuropathy model was developed using dosing and patient‐reported outcome data from Cancer and Leukemia Group B 40502 (Alliance), a randomized phase III trial of paclitaxel, nanoparticle albumin–bound paclitaxel or ixabepilone as first‐line chemotherapy for locally recurrent or metastatic breast cancer. In the current work, an early time point that is predictive of the future severity of neuropathy was identified based on predictive accuracy of the model. Using the early data and model parameters, simulations were conducted to propose a dose‐adjustment algorithm for the prospective management of neuropathy in individual patients. The end of the first 3 cycles (12 weeks) was identified as the early time point based on a predictive accuracy of 75% for the neuropathy score after 6 cycles. For paclitaxel, nanoparticle albumin–bound paclitaxel, and ixabepilone, simulations with the proposed dose‐adjustment algorithm resulted in 61%, 48%, and 35% fewer patients, respectively, with neuropathy score ≥8 after 6 cycles compared to no dose adjustment. We conclude that early patient‐reported outcome data on neuropathy can be used to guide dose adjustments in individual patients that reduce the severity of future neuropathy. Prospective validation of this approach should be undertaken in future studies.