Crona, Daniel James

Hypertension is a common bevacizumab‐induced toxicity. No markers are available to predict patients at risk of developing hypertension. We hypothesized that genetic risk of essential hypertension, as measured by a blood pressure polygenic risk score (PRS), would be associated with risk of severe bevacizumab‐induced hypertension. PRSs were calculated for 1,027 bevacizumab‐treated patients of European descent with cancer from four clinical trials (Alliance for Clinical Trials in Oncology (Alliance) / Cancer and Leukemia Group B (CALGB) 80303, 40503, 90401, 40502) using summary systolic blood pressure (SBP) and diastolic blood pressure (DBP) genome‐wide association results obtained from 757,601 individuals of European descent. The association between PRS and grade 3 bevacizumab‐induced hypertension (Common Toxicity Criteria for Adverse Events version 3) in each trial was performed by multivariable logistic regression. Fixed‐effect meta‐analyses odds ratios (ORs) per standard deviation (SD) of the association of PRS (quantitative) and hypertension across trials were estimated by inverse‐variance weighting. PRSs were additionally stratified into quintiles, with the bottom quintile as the referent group. The OR of the association between hypertension and each quintile vs. the referent group was determined by logistic regression. The most significant PRS (quantitative)‐hypertension association included up to 67 single‐nucleotide variants (SNPs) associated with SBP (P = 0.0077, OR per SD = 1.31, 95% confidence interval (CI), 1.07–1.60), and up to 53 SNPs associated with DBP (P = 0.0209, OR per SD = 1.27, 95% CI, 1.04–1.56). Patients in the top quintile had a higher risk of developing bevacizumab‐induced hypertension compared with patients in the bottom quintile using SNPs associated with SBP (P = 4.75 × 10−4, OR = 3.72, 95% CI, 1.84–8.16) and DBP (P = 0.076, OR = 1.83, 95% CI, 0.95–3.64). Genetic variants associated with essential hypertension, mainly SBP, increase the risk of severe bevacizumab‐induced hypertension.

3030 Background: Diarrhea, HFS, and hypertension are common toxicities of sorafenib. No markers are validated to predict patients at risk of these toxicities. This study aimed to identify genetic predictors of sorafenib-induced toxicities. Methods: A two-step, discovery-validation approach was used. The discovery set included 140 renal cell carcinoma patients from the TARGET study treated with sorafenib (400 mg twice daily) and genotyped for 1040 single-nucleotide polymorphisms (SNPs) in 56 genes. The three most statistically significant SNPs associated with grade ≥2 composite toxicity (either hypertension, diarrhea, HFS, or other skin toxicities, CTCAE v.3.0) were tested for association with grade 3 composite toxicity (either hypertension, diarrhea, or HFS, CTCAE v.4.0) in a validation set of 240 hepatocellular carcinoma patients from Alliance/CALGB 80802 treated with sorafenib (400 mg twice daily) alone or with doxorubicin. Associations between SNPs and composite toxicity was performed by logistic regression, with adjusting covariates (age, gender, race, and treatment arm, the latter two covariates for the validation set only). A meta-analysis odds ratio (OR) of each SNP-grade 3 toxicity association between the discovery and validation sets was obtained by inverse variance to point toward effects specific to a type of toxicity. Results: In the discovery set, the top three SNPs associated with grade ≥2 composite toxicity were rs12366035 (C>T, minor allele frequency, MAF 0.34) in VEGFB (p 0.0007), rs4035887 (G>A, MAF 0.49) in EPAS1 (p 0.0021), and rs4864950 (T>A, MAF 0.23) in KDR (p 0.0058). These SNPs were genotyped in the validation set and only rs4864950 in KDR was replicated. No grade 4 toxicities were reported. Similar to the discovery set (OR 2.41, 95% CI 1.29-4.51), the A allele of rs4864950 increased the risk of grade 3 composite toxicity (p 0.032, OR 2.12, 95% CI 1.70-4.27) in the validation set. Grade 3 toxicity prevalence in the discovery and validation sets were 3.6% and 7.4% diarrhea, 8.6% and 12.3% HFS, 3.6% and 8.8% hypertension, respectively. The meta-analysis of the two datasets showed that the A allele of rs4864950 increased the risk of grade 3 diarrhea (p 0.045, OR 3.09, 95% CI 1.03-9.29), grade 3 HFS (p 0.012, OR 2.57, 95% CI 1.24-5.37), but not grade 3 hypertension (p 0.207, OR 0.51, 95% CI 0.18-1.45). Conclusions: We provide the first evidence of clinical validity of a marker of sorafenib-induced diarrhea and HFS. Sorafenib inhibits VEGFR2 (coded by KDR), leading to epithelial hypoxia and causing diarrhea and HFS. Variant rs4864950 might affect the function VEGFR2, which, during VEGFR2 inhibition, increases the risk of diarrhea and HFS. This SNP is common and can be genotyped in patients before receiving sorafenib for a better risk assessment. Support: U10CA180821, U10CA180882, U24CA196171; https://acknowledgments.alliancefound.org ClinicalTrials.gov Id: NCT01015833.

AbstractPurposeThis report examines and evaluates pharmacogenomics as an emerging science as it relates to the Practice Advancement Initiative and its predecessor the Pharmacy Practice Model Initiative’s consensus statements for optimal pharmacy practice models.SummaryPharmacogenomics is one of many emerging sciences to impact medication management and delivery of patient care. Increasingly, biomarkers are included in drug labeling and can assist pharmacists with personalizing medicine to optimize patient therapies and avoid adverse effects. The 2011 ASHP Pharmacy Practice Model Summit generated a list of 147 consensus statements for optimal pharmacy practice. Of these, 1 statement explicitly describes adjustment of drug regimens based on genetic factors as an essential activity of pharmacist-provided drug regimens, and 9 other statements provide additional support for incorporation of this emerging science into all aspects of patient care provided by pharmacists. We describe 4 institutions that have made significant inroads to implementing pharmacogenomics, to provide a framework and serve as resources for other institutions initiating their own pharmacogenomics implementation journeys.ConclusionThrough prioritized efforts of the pharmacy profession and health care institutions, pharmacogenomics will be disseminated and implemented, and the goal of the Pharmacy Practice Model Initiative’s consensus statements of improving health care using patients’ genetic characteristics will be realized.