Identifying therapeutic drug targets using bidirectional effect genes.

Prioritizing genes for translation to therapeutics for common diseases has been challenging. Here, we propose an approach to identify drug targets with high probability of success by focusing on genes with both gain of function (GoF) and loss of function (LoF) mutations associated with opposing effects on phenotype (Bidirectional Effect Selected Targets, BEST). We find 98 BEST genes for a variety of indications. Drugs targeting those genes are 3.8-fold more likely to be approved than non-BEST genes. We focus on five genes (IGF1R, NPPC, NPR2, FGFR3, and SHOX) with evidence for bidirectional effects on stature. Rare protein-altering variants in those genes result in significantly increased risk for idiopathic short stature (ISS) (OR = 2.75, p = 3.99 × 10-8). Finally, using functional experiments, we demonstrate that adding an exogenous CNP analog (encoded by NPPC) rescues the phenotype, thus validating its potential as a therapeutic treatment for ISS. Our results show the value of looking for bidirectional effects to identify and validate drug targets.

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.

Precision medicine, genomics and drug discovery.

The hope for precision medicine has long been on the drug discovery horizon, well before the Human Genome Project gave it promise at the turn of the 21st century. In oncology, the concept has finally been realized and is now firmly embedded in ongoing drug discovery programs, and with many recent therapies involving some level of patient/disease stratification, including some highly personalized treatments. In addition, several drugs for rare diseases have been recently approved or are in late-stage clinical development, and new delivery modalities in cell and gene therapy and oligonucleotide approaches are yielding exciting new medicines for rare diseases of unmet need. For common complex diseases, however, the GWAS-driven advances in annotation of the genetic architecture over the past decade have not led to a concomitant shift in refined treatments. Similarly, attempts to disentangle treatment responders from non-responders via genetic predictors in pharmacogenetics studies have not met their anticipated success. It is possible that common diseases are simply lagging behind due to the inherent time lag with drug discovery, but it is also possible that their inherent multifactorial nature and their etiological and clinical heterogeneity will prove more resistant to refined treatment paradigms. The emergence of population-based resources in electronic health records, coupled with the rapid expansion of mobile devices and digital health may help to refine the measurement of phenotypic outcomes to match the exquisite detail emerging at the molecular level.

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

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

The support of human genetic evidence for approved drug indications.

Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect how drug targets and indications are selected. However, there is little guidance about how much weight should be given to genetic evidence in making these key decisions. To answer this question, we investigated how well the current archive of genetic evidence predicts drug mechanisms. We found that, among well-studied indications, the proportion of drug mechanisms with direct genetic support increases significantly across the drug development pipeline, from 2.0% at the preclinical stage to 8.2% among mechanisms for approved drugs, and varies dramatically among disease areas. We estimate that selecting genetically supported targets could double the success rate in clinical development. Therefore, using the growing wealth of human genetic data to select the best targets and indications should have a measurable impact on the successful development of new drugs.

Identification of a variant in KDR associated with serum VEGFR2 and pharmacodynamics of Pazopanib.

PURPOSE: VEGF receptor (VEGFR) kinases are important drug targets in oncology that affect function of systemic endothelial cells. To discover genetic markers that affect VEGFR inhibitor pharmacodynamics, we performed a genome-wide association study of serum soluble vascular VEGFR2 concentrations [sVEGFR2], a pharmacodynamic biomarker for VEGFR2 inhibitors. EXPERIMENTAL DESIGN: We conducted a genome-wide association study (GWAS) of [sVEGFR2] in 736 healthy Old Order Amish volunteers. Gene variants identified from the GWAS were genotyped serially in a cohort of 128 patients with advanced solid tumor with baseline [sVEGFR2] measurements, and in 121 patients with renal carcinoma with [sVEGFR2] measured before and during pazopanib therapy. RESULTS: rs34231037 (C482R) in KDR, the gene encoding sVEGFR2 was found to be highly associated with [sVEGFR2], explaining 23% of the variance (P = 2.7 × 10(-37)). Association of rs34231037 with [sVEGFR2] was replicated in 128 patients with cancer with comparable effect size (P = 0.025). Furthermore, rs34231037 was a significant predictor of changes in [sVEGFR2] in response to pazopanib (P = 0.01). CONCLUSION: Our findings suggest that genome-wide analysis of phenotypes in healthy populations can expedite identification of candidate pharmacogenetic markers. Genotyping for germline variants in KDR may have clinical utility in identifying patients with cancer with unusual sensitivity to effects of VEGFR2 kinase inhibitors.

Prospective validation of HLA-DRB1*07:01 allele carriage as a predictive risk factor for lapatinib-induced liver injury.

PURPOSE: Liver injury is a serious adverse event leading to permanent discontinuation of lapatinib in affected patients. This study aimed to validate previously associated major histocompatibility complex (MHC) variants as predictors of risk of liver injury by using a large, randomized, placebo-controlled trial of lapatinib in human epidermal growth factor receptor 2-positive, early-stage breast cancer (Tykerb Evaluation After Chemotherapy [TEACH]: Lapatinib Versus Placebo In Women With Early-Stage Breast Cancer). PATIENTS AND METHODS: The frequency of ALT elevation cases was compared among four MHC variants in 1,194 patients randomly assigned to lapatinib. Cumulative ALT elevation time courses during treatment were also compared between carriers and noncarriers of specified MHC variants. RESULTS: In lapatinib-treated patients, there was a significant difference in ALT case incidence between HLA carriers and noncarriers. The highly correlated alleles HLA-DRB1*07:01 and HLA-DQA1*02:01 (study frequency, 22.4%) were associated with ALT elevation (odds ratio, 14) between cases (n = 37) and controls (n = 1,071). These associations strengthened at higher ALT elevation thresholds and in Hy's Law cases. In lapatinib-treated patients, the overall risk for National Cancer Institute-Common Terminology Criteria for Adverse Events grade 3 ALT elevation (> 5× upper limit of normal) was 2.1%; HLA allele carriers had an increased risk of 7.7%; in noncarriers, risk was reduced to 0.5%, comparable to ALT elevation for all patients receiving placebo. The increase in ALT case incidence in the lapatinib arm showed no evidence of plateau during 1 year of lapatinib treatment. CONCLUSION: These results validate HLA-DRB1*07:01 allele carriage as a predictor of increased risk of lapatinib-induced liver injury and implicate an immune pathology. The HLA association could support clinical management of patients experiencing hepatotoxicity during lapatinib treatment.

An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people.

Rare genetic variants contribute to complex disease risk; however, the abundance of rare variants in human populations remains unknown. We explored this spectrum of variation by sequencing 202 genes encoding drug targets in 14,002 individuals. We find rare variants are abundant (1 every 17 bases) and geographically localized, so that even with large sample sizes, rare variant catalogs will be largely incomplete. We used the observed patterns of variation to estimate population growth parameters, the proportion of variants in a given frequency class that are putatively deleterious, and mutation rates for each gene. We conclude that because of rapid population growth and weak purifying selection, human populations harbor an abundance of rare variants, many of which are deleterious and have relevance to understanding disease risk.

Deep resequencing unveils genetic architecture of ADIPOQ and identifies a novel low-frequency variant strongly associated with adiponectin variation.

Increased adiponectin levels have been shown to be associated with a lower risk of type 2 diabetes. To understand the relations between genetic variation at the adiponectin-encoding gene, ADIPOQ, and adiponectin levels, and subsequently its role in disease, we conducted a deep resequencing experiment of ADIPOQ in 14,002 subjects, including 12,514 Europeans, 594 African Americans, and 567 Indian Asians. We identified 296 single nucleotide polymorphisms (SNPs), including 30 amino acid changes, and carried out association analyses in a subset of 3,665 subjects from two independent studies. We confirmed multiple genome-wide association study findings and identified a novel association between a low-frequency SNP (rs17366653) and adiponectin levels (P = 2.2E-17). We show that seven SNPs exert independent effects on adiponectin levels. Together, they explained 6% of adiponectin variation in our samples. We subsequently assessed association between these SNPs and type 2 diabetes in the Genetics of Diabetes Audit and Research in Tayside Scotland (GO-DARTS) study, comprised of 5,145 case and 6,374 control subjects. No evidence of association with type 2 diabetes was found, but we were also unable to exclude the possibility of substantial effects (e.g., odds ratio 95% CI for rs7366653 [0.91-1.58]). Further investigation by large-scale and well-powered Mendelian randomization studies is warranted.

Deep sequencing of the LRRK2 gene in 14,002 individuals reveals evidence of purifying selection and independent origin of the p.Arg1628Pro mutation in Europe.

Genetic variation in LRRK2 predisposes to Parkinson disease (PD), which underpins its development as a therapeutic target. Here, we aimed to identify novel genotype-phenotype associations that might support developing LRRK2 therapies for other conditions. We sequenced the 51 exons of LRRK2 in cases comprising 12 common diseases (n = 9,582), and in 4,420 population controls. We identified 739 single-nucleotide variants, 62% of which were observed in only one person, including 316 novel exonic variants. We found evidence of purifying selection for the LRRK2 gene and a trend suggesting that this is more pronounced in the central (ROC-COR-kinase) core protein domains of LRRK2 than the flanking domains. Population genetic analyses revealed that LRRK2 is not especially polymorphic or differentiated in comparison to 201 other drug target genes. Among Europeans, we identified 17 carriers (0.13%) of pathogenic LRRK2 mutations that were not significantly enriched within any disease or in those reporting a family history of PD. Analysis of pathogenic mutations within Europe reveals that the p.Arg1628Pro (c4883G>C) mutation arose independently in Europe and Asia. Taken together, these findings demonstrate how targeted deep sequencing can help to reveal fundamental characteristics of clinically important loci.

An 18-kDa translocator protein (TSPO) polymorphism explains differences in binding affinity of the PET radioligand PBR28.

[(11)C]PBR28 binds the 18-kDa Translocator Protein (TSPO) and is used in positron emission tomography (PET) to detect microglial activation. However, quantitative interpretations of signal are confounded by large interindividual variability in binding affinity, which displays a trimodal distribution compatible with a codominant genetic trait. Here, we tested directly for an underlying genetic mechanism to explain this. Binding affinity of PBR28 was measured in platelets isolated from 41 human subjects and tested for association with polymorphisms in TSPO and genes encoding other proteins in the TSPO complex. Complete agreement was observed between the TSPO Ala147Thr genotype and PBR28 binding affinity phenotype (P value=3.1 × 10(-13)). The TSPO Ala147Thr polymorphism predicts PBR28 binding affinity in human platelets. As all second-generation TSPO PET radioligands tested hitherto display a trimodal distribution in binding affinity analogous to PBR28, testing for this polymorphism may allow quantitative interpretation of TSPO PET studies with these radioligands.

The use of genome-wide eQTL associations in lymphoblastoid cell lines to identify novel genetic pathways involved in complex traits.

The integrated analysis of genotypic and expression data for association with complex traits could identify novel genetic pathways involved in complex traits. We profiled 19,573 expression probes in Epstein-Barr virus-transformed lymphoblastoid cell lines (LCLs) from 299 twins and correlated these with 44 quantitative traits (QTs). For 939 expressed probes correlating with more than one QT, we investigated the presence of eQTL associations in three datasets of 57 CEU HapMap founders and 86 unrelated twins. Genome-wide association analysis of these probes with 2.2 m SNPs revealed 131 potential eQTLs (1,989 eQTL SNPs) overlapping between the HapMap datasets, five of which were in cis (58 eQTL SNPs). We then tested 535 SNPs tagging the eQTL SNPs, for association with the relevant QT in 2,905 twins. We identified nine potential SNP-QT associations (P<0.01) but none significantly replicated in five large consortia of 1,097-16,129 subjects. We also failed to replicate previous reported eQTL associations with body mass index, plasma low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides levels derived from lymphocytes, adipose and liver tissue. Our results and additional power calculations suggest that proponents may have been overoptimistic in the power of LCLs in eQTL approaches to elucidate regulatory genetic effects on complex traits using the small datasets generated to date. Nevertheless, larger tissue-specific expression data sets relevant to specific traits are becoming available, and should enable the adoption of similar integrated analyses in the near future.

Pazopanib efficacy in renal cell carcinoma: evidence for predictive genetic markers in angiogenesis-related and exposure-related genes.

PURPOSE: Pazopanib, an oral angiogenesis inhibitor, is approved for the treatment of advanced renal cell carcinoma (RCC). Response to pazopanib monotherapy varies between patients, and no validated biomarkers predictive of treatment outcome have been identified. We tested the hypothesis that this variability is partially dependent on germline genetic variants that may affect pazopanib exposure or angiogenesis pathways. PATIENTS AND METHODS: Twenty-seven functional polymorphisms within 13 genes were evaluated in 397 patients with RCC. Genetic association with progression-free survival (PFS) and objective response rate (RR) was analyzed using the Cox proportional hazards model and proportional odds model, respectively. RESULTS: Three polymorphisms in IL8 and HIF1A and five polymorphisms in HIF1A, NR1I2, and VEGFA showed nominally significant association (P ≤ .05) with PFS and RR, respectively. Compared with the wild-type AA genotype (median PFS, 48 weeks), the IL8 2767TT variant genotype showed inferior PFS (27 weeks, P = .009). The HIF1A 1790AG genotype was associated with inferior PFS and reduced RR, compared with the wild-type GG genotype (median PFS, 20 v 44 weeks; P = .03; RR, 30% v 43%, P = .02). Reductions in RR were detected for the NR1I2 -25385TT genotype, compared with the wild-type CC genotype (37% v 50%, P = .03), and for the VEGFA -1498CC genotype compared with the TT genotypes (33% v 51%). CONCLUSION: Germline variants in angiogenesis- and exposure-related genes may predict treatment response to pazopanib monotherapy in patients with RCC. If validated, these markers may explain why certain patients fail antiangiogenesis therapy and they may support the use of alternative strategies to circumvent this issue.

Basic statistical analysis in genetic case-control studies.

This protocol describes how to perform basic statistical analysis in a population-based genetic association case-control study. The steps described involve the (i) appropriate selection of measures of association and relevance of disease models; (ii) appropriate selection of tests of association; (iii) visualization and interpretation of results; (iv) consideration of appropriate methods to control for multiple testing; and (v) replication strategies. Assuming no previous experience with software such as PLINK, R or Haploview, we describe how to use these popular tools for handling single-nucleotide polymorphism data in order to carry out tests of association and visualize and interpret results. This protocol assumes that data quality assessment and control has been performed, as described in a previous protocol, so that samples and markers deemed to have the potential to introduce bias to the study have been identified and removed. Study design, marker selection and quality control of case-control studies have also been discussed in earlier protocols. The protocol should take ~1 h to complete.

HLA-DQA1*02:01 is a major risk factor for lapatinib-induced hepatotoxicity in women with advanced breast cancer.

PURPOSE: Hepatobiliary adverse events (AEs) have been observed in a small proportion of patients with metastatic breast cancer (MBC) treated with lapatinib. This study sought to identify gene variants associated with lapatinib-induced ALT elevation and hepatobiliary AEs. PATIENTS AND METHODS: A two-stage pharmacogenetic investigation of ALT elevation was conducted in lapatinib-treated patients with MBC. Exploratory marker identification evaluated classical HLA alleles, candidate genes, and genome-wide screening in 37 cases with ALT greater than 3 times the upper limit of normal (ULN) and 286 controls with ALT ≤ 1× ULN, selected from 901 lapatinib-treated patients in 12 trials. Markers achieving prespecified association thresholds were progressed to an independent confirmatory data set of 24 ALT cases and 155 controls selected from a subsequent trial of 374 lapatinib-treated patients. RESULTS: Of 58 variants associated with ALT elevation in the exploratory data set, four exceeded the prespecified significance threshold in the confirmatory analysis. These variants reside in the same MHC genomic locus and include HLA-DQA1*02:01. In the confirmatory study, DQA1*02:01 allele carriage was present in 71% of ALT cases and in 21% of controls (P < .001; odds ratio, 9.0; 95% CI, 3.2 to 27.4). As a predictor of liver safety risk in ALT cases versus noncases, DQA1*02:01 had negative and positive predictive values of 0.97 (95% CI, 0.95 to 0.99) and 0.17 (95% CI 0.10 to 0.26), respectively. CONCLUSION: These results support a role for immune mechanisms in lapatinib-induced hepatotoxicity. Further work is required to determine whether testing for DQA1*02:01 allele carriage is clinically useful in managing liver safety risk during lapatinib treatment.

Association of the hemochromatosis gene with pazopanib-induced transaminase elevation in renal cell carcinoma.

BACKGROUND & AIMS: Pazopanib has demonstrated clinical benefit in patients with advanced renal cell carcinoma (RCC) and is generally well tolerated. However, transaminase elevations have commonly been observed. This 2-stage study sought to identify genetic determinants of alanine transaminase (ALT) elevations in pazopanib-treated white patients with RCC. METHODS: Data from two separate clinical studies were used to examine the association of genetic polymorphisms with maximum on-treatment ALT levels. RESULTS: Of 6852 polymorphisms in 282 candidate genes examined in an exploratory dataset of 115 patients, 92 polymorphisms in 40 genes were significantly associated with ALT elevation (p<0.01). Two markers (rs2858996 and rs707889) in the HFE gene, which are not yet known to be associated with hemochromatosis, showed evidence for replication. Because of multiple comparisons, there was a 12% likelihood the replication occurred by chance. These two markers demonstrated strong linkage disequilibrium (r(2)=0.99). In the combined dataset, median (25-75th percentile) maximum ALT values were 1.2 (0.7-1.9), 1.1 (0.8-2.5), and 5.4 (1.9-7.6)×ULN for rs2858996 GG (n=148), GT (n=82), and TT (n=1 2) genotypes, respectively. All 12 TT patients had a maximum ALT>ULN, and 8 (67%) had ALT≥3×ULN. The odds ratio (95% CI) for ALT≥3×ULN for TT genotype was 39.7 (2.2-703.7) compared with other genotypes. As a predictor of ALT≥3×ULN, the TT genotype had a negative predictive value of 0.83 and positive predictive value of 0.67. No TT patients developed liver failure. CONCLUSIONS: The rs2858996/rs707889 polymorphisms in the HFE gene may be associated with reversible ALT elevation in pazo-panib-treated patients with RCC.