Refining the impact of genetic evidence on clinical success.

The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.

Future prospects for human genetics and genomics in drug discovery.

Evidence from human genetics supporting the therapeutic hypothesis increases the likelihood that a drug will succeed in clinical trials. Rare and common disease genetics yield a wide array of alleles with a range of effect sizes that can proxy for the effect of a drug in disease. Recent advances in large scale population collections and whole genome sequencing approaches have provided a rich resource of human genetic evidence to support drug target selection. As the range of phenotypes profiled increases and ever more alleles are discovered across world-wide populations, these approaches will increasingly influence multiple stages across the lifespan of a drug discovery programme.

Using human genetics to improve safety assessment of therapeutics.

Human genetics research has discovered thousands of proteins associated with complex and rare diseases. Genome-wide association studies (GWAS) and studies of Mendelian disease have resulted in an increased understanding of the role of gene function and regulation in human conditions. Although the application of human genetics has been explored primarily as a method to identify potential drug targets and support their relevance to disease in humans, there is increasing interest in using genetic data to identify potential safety liabilities of modulating a given target. Human genetic variants can be used as a model to anticipate the effect of lifelong modulation of therapeutic targets and identify the potential risk for on-target adverse events. This approach is particularly useful for non-clinical safety evaluation of novel therapeutics that lack pharmacologically relevant animal models and can contribute to the intrinsic safety profile of a drug target. This Review illustrates applications of human genetics to safety studies during drug discovery and development, including assessing the potential for on- and off-target associated adverse events, carcinogenicity risk assessment, and guiding translational safety study designs and monitoring strategies. A summary of available human genetic resources and recommended best practices is provided. The challenges and future perspectives of translating human genetic information to identify risks for potential drug effects in preclinical and clinical development are discussed.

Beta-lactam-induced immediate hypersensitivity reactions: A genome-wide association study of a deeply phenotyped cohort.

BACKGROUND: ß-lactam antibiotics are associated with a variety of immune-mediated or hypersensitivity reactions, including immediate (type I) reactions mediated by antigen-specific IgE. OBJECTIVE: We sought to identify genetic predisposing factors for immediate reactions to ß-lactam antibiotics. METHODS: Patients with a clinical history of immediate hypersensitivity reactions to either penicillins or cephalosporins, which were immunologically confirmed, were recruited from allergy clinics. A genome-wide association study was conducted on 662 patients (the discovery cohort) with a diagnosis of immediate hypersensitivity and the main finding was replicated in a cohort of 98 Spanish cases, recruited using the same diagnostic criteria as the discovery cohort. RESULTS: Genome-wide association study identified rs71542416 within the Class II HLA region as the top hit (P = 2 × 10-14); this was in linkage disequilibrium with HLA-DRB1∗10:01 (odds ratio, 2.93; P = 5.4 × 10-7) and HLA-DQA1∗01:05 (odds ratio, 2.93, P = 5.4 × 10-7). Haplotype analysis identified that HLA-DRB1∗10:01 was a risk factor even without the HLA-DQA1∗01:05 allele. The association with HLA-DRB1∗10:01 was replicated in another cohort, with the meta-analysis of the discovery and replication cohorts showing that HLA-DRB1∗10:01 increased the risk of immediate hypersensitivity at a genome-wide level (odds ratio, 2.96; P = 4.1 × 10-9). No association with HLA-DRB1∗10:01 was identified in 268 patients with delayed hypersensitivity reactions to ß-lactams. CONCLUSIONS: HLA-DRB1∗10:01 predisposed to immediate hypersensitivity reactions to penicillins. Further work to identify other predisposing HLA and non-HLA loci is required.

Exome sequencing and characterization of 49,960 individuals in the UK Biobank.

The UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world1. Here we describe the release of exome-sequence data for the first 49,960 study participants, revealing approximately 4 million coding variants (of which around 98.6% have a frequency of less than 1%). The data include 198,269 autosomal predicted loss-of-function (LOF) variants, a more than 14-fold increase compared to the imputed sequence. Nearly all genes (more than 97%) had at least one carrier with a LOF variant, and most genes (more than 69%) had at least ten carriers with a LOF variant. We illustrate the power of characterizing LOF variants in this population through association analyses across 1,730 phenotypes. In addition to replicating established associations, we found novel LOF variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical importance, and show that 2% of this population has a medically actionable variant. Furthermore, we characterize the penetrance of cancer in carriers of pathogenic BRCA1 and BRCA2 variants. Exome sequences from the first 49,960 participants highlight the promise of genome sequencing in large population-based studies and are now accessible to the scientific community.

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases.

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here we estimated the effects of 1,002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium is widespread in naïve phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes ( https://www.epigraphdb.org/pqtl/ ). Evaluation of data from historic drug development programs showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of this approach in identifying and prioritizing potential therapeutic targets.

Shared Genetic Risk Factors Across Carbamazepine-Induced Hypersensitivity Reactions.

Carbamazepine (CBZ) causes life-threating T-cell-mediated hypersensitivity reactions, including serious cutaneous adverse reactions (SCARs) and drug-induced liver injury (CBZ-DILI). In order to evaluate shared or phenotype-specific genetic predisposing factors for CBZ hypersensitivity reactions, we performed a meta-analysis of two genomewide association studies (GWAS) on a total of 43 well-phenotyped Northern and Southern European CBZ-SCAR cases and 10,701 population controls and a GWAS on 12 CBZ-DILI cases and 8,438 ethnically matched population controls. HLA-A*31:01 was identified as the strongest genetic predisposing factor for both CBZ-SCAR (odds ratio (OR) = 8.0; 95% CI 4.10-15.80; P = 1.2 × 10-9 ) and CBZ-DILI (OR = 7.3; 95% CI 2.47-23.67; P = 0.0004) in European populations. The association with HLA-A*31:01 in patients with SCAR was mainly driven by hypersensitivity syndrome (OR = 12.9; P = 2.1 × 10-9 ) rather than by Stevens-Johnson syndrome/toxic epidermal necrolysis cases, which showed an association with HLA-B*57:01. We also identified a novel risk locus mapping to ALK only for CBZ-SCAR cases, which needs replication in additional cohorts and functional evaluation.

Predicting clinically promising therapeutic hypotheses using tensor factorization.

BACKGROUND: Determining which target to pursue is a challenging and error-prone first step in developing a therapeutic treatment for a disease, where missteps are potentially very costly given the long-time frames and high expenses of drug development. With current informatics technology and machine learning algorithms, it is now possible to computationally discover therapeutic hypotheses by predicting clinically promising drug targets based on the evidence associating drug targets with disease indications. We have collected this evidence from Open Targets and additional databases that covers 17 sources of evidence for target-indication association and represented the data as a tensor of 21,437 × 2211 × 17. RESULTS: As a proof-of-concept, we identified examples of successes and failures of target-indication pairs in clinical trials across 875 targets and 574 disease indications to build a gold-standard data set of 6140 known clinical outcomes. We designed and executed three benchmarking strategies to examine the performance of multiple machine learning models: Logistic Regression, LASSO, Random Forest, Tensor Factorization and Gradient Boosting Machine. With 10-fold cross-validation, tensor factorization achieved AUROC = 0.82 ± 0.02 and AUPRC = 0.71 ± 0.03. Across multiple validation schemes, this was comparable or better than other methods. CONCLUSION: In this work, we benchmarked a machine learning technique called tensor factorization for the problem of predicting clinical outcomes of therapeutic hypotheses. Results have shown that this method can achieve equal or better prediction performance compared with a variety of baseline models. We demonstrate one application of the method to predict outcomes of trials on novel indications of approved drug targets. This work can be expanded to targets and indications that have never been clinically tested and proposing novel target-indication hypotheses. Our proposed biologically-motivated cross-validation schemes provide insight into the robustness of the prediction performance. This has significant implications for all future methods that try to address this seminal problem in drug discovery.

A Missense Variant in PTPN22 is a Risk Factor for Drug-induced Liver Injury.

BACKGROUND & AIMS: We performed genetic analyses of a multiethnic cohort of patients with idiosyncratic drug-induced liver injury (DILI) to identify variants associated with susceptibility. METHODS: We performed a genome-wide association study of 2048 individuals with DILI (cases) and 12,429 individuals without (controls). Our analysis included subjects of European (1806 cases and 10,397 controls), African American (133 cases and 1,314 controls), and Hispanic (109 cases and 718 controls) ancestry. We analyzed DNA from 113 Icelandic cases and 239,304 controls to validate our findings. RESULTS: We associated idiosyncratic DILI with rs2476601, a nonsynonymous polymorphism that encodes a substitution of tryptophan with arginine in the protein tyrosine phosphatase, nonreceptor type 22 gene (PTPN22) (odds ratio [OR] 1.44; 95% confidence interval [CI] 1.28-1.62; P = 1.2 × 10-9 and replicated the finding in the validation set (OR 1.48; 95% CI 1.09-1.99; P = .01). The minor allele frequency showed the same effect size (OR > 1) among ethnic groups. The strongest association was with amoxicillin and clavulanate-associated DILI in persons of European ancestry (OR 1.62; 95% CI 1.32-1.98; P = 4.0 × 10-6; allele frequency = 13.3%), but the polymorphism was associated with DILI of other causes (OR 1.37; 95% CI 1.21-1.56; P = 1.5 × 10-6; allele frequency = 11.5%). Among amoxicillin- and clavulanate-associated cases of European ancestry, rs2476601 doubled the risk for DILI among those with the HLA risk alleles A*02:01 and DRB1*15:01. CONCLUSIONS: In a genome-wide association study, we identified rs2476601 in PTPN22 as a non-HLA variant that associates with risk of liver injury caused by multiple drugs and validated our finding in a separate cohort. This variant has been associated with increased risk of autoimmune diseases, providing support for the concept that alterations in immune regulation contribute to idiosyncratic DILI.

Drug-Induced Liver Injury due to Flucloxacillin: Relevance of Multiple Human Leukocyte Antigen Alleles.

Some patients prescribed flucloxacillin (~ 0.01%) develop drug-induced liver injury (DILI). HLA-B*57:01 is an established genetic risk factor for flucloxacillin DILI. To consolidate this finding, identify additional genetic factors, and assess relevance of risk factors for flucloxacillin DILI in relation to DILI due to other penicillins, we performed a genomewide association study involving 197 flucloxacillin DILI cases and 6,835 controls. We imputed single-nucleotide polymorphism and human leukocyte antigen (HLA) genotypes. HLA-B*57:01 was the major risk factor (allelic odds ratio (OR) = 36.62; P = 2.67 × 10-97 ). HLA-B*57:03 also showed an association (OR = 79.21; P = 1.2 × 10-6 ). Within the HLA-B protein sequence, imputation showed valine97 , common to HLA-B*57:01 and HLA-B*57:03, had the largest effect (OR = 38.1; P = 9.7 × 10-97 ). We found no HLA-B*57 association with DILI due to other isoxazolyl penicillins (n = 6) or amoxicillin (n = 15) and no significant non-HLA signals for any penicillin-related DILI.

Phenome-wide association study using research participants' self-reported data provides insight into the Th17 and IL-17 pathway.

A phenome-wide association study of variants in genes in the Th17 and IL-17 pathway was performed using self-reported phenotypes and genetic data from 521,000 research participants of 23andMe. Results replicated known associations with similar effect sizes for autoimmune traits illustrating self-reported traits can be a surrogate for clinically assessed conditions. Novel associations controlling for a false discovery rate of 5% included the association of the variant encoding p.Ile684Ser in TYK2 with increased risk of tonsillectomy, strep throat occurrences and teen acne, the variant encoding p.Arg381Gln in IL23R with a decrease in dandruff frequency, the variant encoding p.Asp10Asn in TRAF3IP2 with risk of male-pattern balding, and the RORC regulatory variant (rs4845604) with protection from allergies. This approach enabled rapid assessment of association with a wide variety of traits and investigation of traits with limited reported associations to overlay meaningful phenotypic context on the range of conditions being considered for drugs targeting this pathway.

STOPGAP: a database for systematic target opportunity assessment by genetic association predictions.

SUMMARY: We developed the STOPGAP (Systematic Target OPportunity assessment by Genetic Association Predictions) database, an extensive catalog of human genetic associations mapped to effector gene candidates. STOPGAP draws on a variety of publicly available GWAS associations, linkage disequilibrium (LD) measures, functional genomic and variant annotation sources. Algorithms were developed to merge the association data, partition associations into non-overlapping LD clusters, map variants to genes and produce a variant-to-gene score used to rank the relative confidence among potential effector genes. This database can be used for a multitude of investigations into the genes and genetic mechanisms underlying inter-individual variation in human traits, as well as supporting drug discovery applications. AVAILABILITY AND IMPLEMENTATION: Shell, R, Perl and Python scripts and STOPGAP R data files (version 2.5.1 at publication) are available at https://github.com/StatGenPRD/STOPGAP . Some of the most useful STOPGAP fields can be queried through an R Shiny web application at http://stopgapwebapp.com . CONTACT: matthew.r.nelson@gsk.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide association study of nevirapine hypersensitivity in a sub-Saharan African HIV-infected population.

Background: The antiretroviral nevirapine is associated with hypersensitivity reactions in 6%-10% of patients, including hepatotoxicity, maculopapular exanthema, Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN). Objectives: To undertake a genome-wide association study (GWAS) to identify genetic predisposing factors for the different clinical phenotypes associated with nevirapine hypersensitivity. Methods: A GWAS was undertaken in a discovery cohort of 151 nevirapine-hypersensitive and 182 tolerant, HIV-infected Malawian adults. Replication of signals was determined in a cohort of 116 cases and 68 controls obtained from Malawi, Uganda and Mozambique. Interaction with ERAP genes was determined in patients positive for HLA-C*04:01 . In silico docking studies were also performed for HLA-C*04:01 . Results: Fifteen SNPs demonstrated nominal significance ( P < 1 × 10 -5 ) with one or more of the hypersensitivity phenotypes. The most promising signal was seen in SJS/TEN, where rs5010528 ( HLA-C locus) approached genome-wide significance ( P < 8.5 × 10 -8 ) and was below HLA -wide significance ( P < 2.5 × 10 -4 ) in the meta-analysis of discovery and replication cohorts [OR 4.84 (95% CI 2.71-8.61)]. rs5010528 is a strong proxy for HLA-C*04:01 carriage: in silico docking showed that two residues (33 and 123) in the B pocket were the most likely nevirapine interactors. There was no interaction between HLA-C*04:01 and ERAP1 , but there is a potential protective effect with ERAP2 [ P = 0.019, OR 0.43 (95% CI 0.21-0.87)]. Conclusions: HLA-C*04:01 predisposes to nevirapine-induced SJS/TEN in sub-Saharan Africans, but not to other hypersensitivity phenotypes. This is likely to be mediated via binding to the B pocket of the HLA-C peptide. Whether this risk is modulated by ERAP2 variants requires further study.

Association of Liver Injury From Specific Drugs, or Groups of Drugs, With Polymorphisms in HLA and Other Genes in a Genome-Wide Association Study.

BACKGROUND & AIMS: We performed a genome-wide association study (GWAS) to identify genetic risk factors for drug-induced liver injury (DILI) from licensed drugs without previously reported genetic risk factors. METHODS: We performed a GWAS of 862 persons with DILI and 10,588 population-matched controls. The first set of cases was recruited before May 2009 in Europe (n = 137) and the United States (n = 274). The second set of cases were identified from May 2009 through May 2013 from international collaborative studies performed in Europe, the United States, and South America. For the GWAS, we included only cases with patients of European ancestry associated with a particular drug (but not flucloxacillin or amoxicillin-clavulanate). We used DNA samples from all subjects to analyze HLA genes and single nucleotide polymorphisms. After the discovery analysis was concluded, we validated our findings using data from 283 European patients with diagnosis of DILI associated with various drugs. RESULTS: We associated DILI with rs114577328 (a proxy for A*33:01 a HLA class I allele; odds ratio [OR], 2.7; 95% confidence interval [CI], 1.9-3.8; P = 2.4 × 10-8) and with rs72631567 on chromosome 2 (OR, 2.0; 95% CI, 1.6-2.5; P = 9.7 × 10-9). The association with A*33:01 was mediated by large effects for terbinafine-, fenofibrate-, and ticlopidine-related DILI. The variant on chromosome 2 was associated with DILI from a variety of drugs. Further phenotypic analysis indicated that the association between DILI and A*33:01 was significant genome wide for cholestatic and mixed DILI, but not for hepatocellular DILI; the polymorphism on chromosome 2 was associated with cholestatic and mixed DILI as well as hepatocellular DILI. We identified an association between rs28521457 (within the lipopolysaccharide-responsive vesicle trafficking, beach and anchor containing gene) and only hepatocellular DILI (OR, 2.1; 95% CI, 1.6-2.7; P = 4.8 × 10-9). We did not associate any specific drug classes with genetic polymorphisms, except for statin-associated DILI, which was associated with rs116561224 on chromosome 18 (OR, 5.4; 95% CI, 3.0-9.5; P = 7.1 × 10-9). We validated the association between A*33:01 terbinafine- and sertraline-induced DILI. We could not validate the association between DILI and rs72631567, rs28521457, or rs116561224. CONCLUSIONS: In a GWAS of persons of European descent with DILI, we associated HLA-A*33:01 with DILI due to terbinafine and possibly fenofibrate and ticlopidine. We identified polymorphisms that appear to be associated with DILI from statins, as well as 2 non-drug-specific risk factors.

Characterization of ADME gene variation in 21 populations by exome sequencing.

OBJECTIVE: Proteins involving absorption, distribution, metabolism, and excretion (ADME) play a critical role in drug pharmacokinetics. The type and frequency of genetic variation in the ADME genes differ among populations. The aim of this study was to systematically investigate common and rare ADME coding variation in diverse ethnic populations by exome sequencing. MATERIALS AND METHODS: Data derived from commercial exome capture arrays and next-generation sequencing were used to characterize coding variation in 298 ADME genes in 251 Northeast Asians and 1181 individuals from the 1000 Genomes Project. RESULTS: Approximately 75% of the ADME coding sequence was captured at high quality across the joint samples harboring more than 8000 variants, with 49% of individuals carrying at least one 'knockout' allele. ADME genes carried 50% more nonsynonymous variation than non-ADME genes (P=8.2×10) and showed significantly greater levels of population differentiation (P=7.6×10). Out of the 2135 variants identified that were predicted to be deleterious, 633 were not on commercially available ADME or general-purpose genotyping arrays. Forty deleterious variants within important ADME genes, with frequencies of at least 2% in at least one population, were identified as candidates for future pharmacogenetic studies. CONCLUSION: Exome sequencing was effective in accurately genotyping most ADME variants important for pharmacogenetic research, in addition to identifying rare or potentially de novo coding variants that may be clinically meaningful. Furthermore, as a class, ADME genes are more variable and less sensitive to purifying selection than non-ADME genes.

HLA-DRB1*16: 01-DQB1*05: 02 is a novel genetic risk factor for flupirtine-induced liver injury.

OBJECTIVE: Flupirtine is a nonopioid analgesic with regulatory approval in a number of European countries. Because of the risk of serious liver injury, its use is now limited to short-term pain management. We aimed to identify genetic risk factors for flupirtine-related drug-induced liver injury (DILI) as these are unknown. MATERIALS AND METHODS: Six flupirtine-related DILI patients from Germany were included in a genome-wide association study (GWAS) involving a further 614 European cases of DILI because of other drugs and 10,588 population controls. DILI was diagnosed by causality assessment and expert review. Human leucocyte antigen (HLA) and single nucleotide polymorphism genotypes were imputed from the GWAS data, with direct HLA typing performed on selected cases to validate HLA predictions. Four replication cases that were unavailable for the GWAS were genotyped by direct HLA typing, yielding an overall total of 10 flupirtine DILI cases. RESULTS: In the six flupirtine DILI cases included in the GWAS, we found a significant enrichment of the DRB1*16:01-DQB1*05:02 haplotype compared with the controls (minor allele frequency cases 0.25 and minor allele frequency controls 0.013; P=1.4 × 10(-5)). We estimated an odds ratio for haplotype carriers of 18.7 (95% confidence interval 2.5-140.5, P=0.002) using population-specific HLA control data. The result was replicated in four additional cases, also with a haplotype frequency of 0.25. In the combined cohort (six GWAS plus four replication cases), the haplotype was also significant (odds ratio 18.7, 95% confidence interval 4.31-81.42, P=6.7 × 10(-5)). CONCLUSION: We identified a novel HLA class II association for DILI, confirming the important contribution of HLA genotype towards the risk of DILI generally.

The genetics of drug efficacy: opportunities and challenges.

Lack of sufficient efficacy is the most common cause of attrition in late-phase drug development. It has long been envisioned that genetics could drive stratified drug development by identifying those patient subgroups that are most likely to respond. However, this vision has not been realized as only a small proportion of drugs have been found to have germline genetic predictors of efficacy with clinically meaningful effects, and so far all but one were found after drug approval. With the exception of oncology, systematic application of efficacy pharmacogenetics has not been integrated into drug discovery and development across the industry. Here, we argue for routine, early and cumulative screening for genetic predictors of efficacy, as an integrated component of clinical trial analysis. Such a strategy would identify clinically relevant predictors that may exist at the earliest possible opportunity, allow these predictors to be integrated into subsequent clinical development and provide mechanistic insights into drug disposition and patient-specific factors that influence response, therefore paving the way towards more personalized medicine.

Impact of genetically supported target selection on R&D productivity.

This corrects the article DOI: 10.1038/nrd.2016.164.

HLA-B*57:01 Confers Susceptibility to Pazopanib-Associated Liver Injury in Patients with Cancer.

PURPOSE: Pazopanib is an effective treatment for advanced renal cell carcinoma and soft-tissue sarcoma. Transaminase elevations have been commonly observed in pazopanib-treated patients. We conducted pharmacogenetic analyses to explore mechanistic insight into pazopanib-induced liver injury. EXPERIMENTAL DESIGN: The discovery analysis tested association between four-digit HLA alleles and alanine aminotransferase (ALT) elevation in pazopanib-treated patients with cancer from eight clinical trials (N = 1,188). We conducted confirmatory analysis using an independent dataset of pazopanib-treated patients from 23 additional trials (N = 1,002). Genome-wide association study (GWAS) for transaminase elevations was also conducted. RESULTS: The discovery study identified an association between HLA-B*57:01 carriage and ALT elevation [P = 5.0 × 10(-5) for maximum on-treatment ALT (MaxALT); P = 4.8 × 10(-4) for time to ALT > 3× upper limit of normal (ULN) event; P = 4.1 × 10(-5) for time to ALT > 5× ULN event] that is significant after adjustment for number of HLA alleles tested. We confirmed these associations with time to ALT elevation event (P = 8.1 × 10(-4) for ALT > 3× ULN, P = 9.8 × 10(-3) for ALT > 5× ULN) in an independent dataset. In the combined data, HLA-B*57:01 carriage was associated with ALT elevation (P = 4.3 × 10(-5) for MaxALT, P = 5.1 × 10(-6) for time to ALT > 3×ULN event, P = 5.8 × 10(-6) for time to ALT > 5× ULN event). In HLA-B*57:01 carriers and noncarriers, frequency of ALT > 3× ULN was 31% and 19%, respectively, and frequency of ALT > 5× ULN was 18% and 10%, respectively. GWAS revealed a possible borderline association, which requires further evaluation. CONCLUSIONS: These data indicate that HLA-B*57:01 carriage confers higher risk of ALT elevation in patients receiving pazopanib and provide novel insight implicating an immune-mediated mechanism for pazopanib-associated hepatotoxicity in some patients.