Integrated analyses of growth differentiation factor-15 concentration and cardiometabolic diseases in humans.

Growth differentiation factor-15 (GDF15) is a stress response cytokine that is elevated in several cardiometabolic diseases and has attracted interest as a potential therapeutic target. To further explore the association of GDF15 with human disease, we conducted a broad study into the phenotypic and genetic correlates of GDF15 concentration in up to 14,099 individuals. Assessment of 772 traits across 6610 participants in FINRISK identified associations of GDF15 concentration with a range of phenotypes including all-cause mortality, cardiometabolic disease, respiratory diseases and psychiatric disorders, as well as inflammatory markers. A meta-analysis of genome-wide association studies (GWAS) of GDF15 concentration across three different assay platforms (n=14,099) confirmed significant heterogeneity due to a common missense variant (rs1058587; p.H202D) in GDF15, potentially due to epitope-binding artefacts. After conditioning on rs1058587, statistical fine mapping identified four independent putative causal signals at the locus. Mendelian randomisation (MR) analysis found evidence of a causal relationship between GDF15 concentration and high-density lipoprotein (HDL) but not body mass index (BMI). Using reverse MR, we identified a potential causal association of BMI on GDF15 (IVW pFDR = 0.0040). Taken together, our data derived from human population cohorts do not support a role for moderately elevated GDF15 concentrations as a causal factor in human cardiometabolic disease but support its role as a biomarker of metabolic stress.

Multiomic Characterization of High-Grade Serous Ovarian Carcinoma Enables High-Resolution Patient Stratification.

PURPOSE: High-grade serous ovarian carcinoma (HGSOC) is the most common ovarian cancer type; most patients experience disease recurrence that accumulates chemoresistance, leading to treatment failure. Genomic and transcriptomic features have been associated with differential outcome and treatment response. However, the relationship between events at the gene sequence, copy number, and gene-expression levels remains poorly defined. EXPERIMENTAL DESIGN: We perform multiomic characterization of a large HGSOC cohort (n = 362) with detailed clinical annotation to interrogate the relationship between patient subgroups defined by specific molecular events. RESULTS: BRCA2-mutant (BRCA2m) and EMSY-overexpressing cases demonstrated prolonged survival [multivariable hazard ratios (HR) 0.40 and 0.51] and significantly higher first- and second-line chemotherapy response rate. CCNE1-gained (CCNE1g) cases demonstrated underrepresentation of FIGO stage IV cases, with shorter survival but no significant difference in treatment response. We demonstrate marked overlap between the TCGA- and Tothill-derived subtypes. IMR/C2 cases displayed higher BRCA1/2m frequency (25.5%, 32.5%) and significantly greater immune cell infiltration, whereas PRO/C5 cases had the highest CCNE1g rate (23.9%, 22.2%) and were uniformly low in immune cell infiltration. The survival benefit for cases with aberrations in homologous recombination repair (HRR) genes was apparent across all transcriptomic subtypes (HR range, 0.48-0.68). There was significant co-occurrence of RB loss and HRR gene aberrations; RB loss was further associated with favorable survival within HRR-aberrant cases (multivariable HR, 0.50). CONCLUSIONS: These data paint a high-resolution picture of the molecular landscape in HGSOC, better defining patients who may benefit most from specific molecular therapeutics and highlighting those for whom novel treatment strategies are needed to improve outcomes.

Rare variant contribution to human disease in 281,104 UK Biobank exomes.

Genome-wide association studies have uncovered thousands of common variants associated with human disease, but the contribution of rare variants to common disease remains relatively unexplored. The UK Biobank contains detailed phenotypic data linked to medical records for approximately 500,000 participants, offering an unprecedented opportunity to evaluate the effect of rare variation on a broad collection of traits1,2. Here we study the relationships between rare protein-coding variants and 17,361 binary and 1,419 quantitative phenotypes using exome sequencing data from 269,171 UK Biobank participants of European ancestry. Gene-based collapsing analyses revealed 1,703 statistically significant gene-phenotype associations for binary traits, with a median odds ratio of 12.4. Furthermore, 83% of these associations were undetectable via single-variant association tests, emphasizing the power of gene-based collapsing analysis in the setting of high allelic heterogeneity. Gene-phenotype associations were also significantly enriched for loss-of-function-mediated traits and approved drug targets. Finally, we performed ancestry-specific and pan-ancestry collapsing analyses using exome sequencing data from 11,933 UK Biobank participants of African, East Asian or South Asian ancestry. Our results highlight a significant contribution of rare variants to common disease. Summary statistics are publicly available through an interactive portal ( http://azphewas.com/ ).

A novel automated SARS-CoV-2 saliva PCR test protects a global asymptomatic workforce.

Regular PCR testing of nasopharyngeal swabs from symptomatic individuals for SARS-CoV-2 virus has become the established method by which health services are managing the COVID-19 pandemic. Businesses such as AstraZeneca have also prioritised voluntary asymptomatic testing to keep workplaces safe and maintain supply of essential medicines to patients. We describe the development of an internal automated SARS-CoV-2 testing programme including the transformative introduction of saliva as an alternative sample type.

Structural Variants at the BRCA1/2 Loci are a Common Source of Homologous Repair Deficiency in High-grade Serous Ovarian Carcinoma.

PURPOSE: The abundance and effects of structural variation at BRCA1/2 in tumors are not well understood. In particular, the impact of these events on homologous recombination repair deficiency (HRD) has yet to be demonstrated. EXPERIMENTAL DESIGN: Exploiting a large collection of whole-genome sequencing data from high-grade serous ovarian carcinoma (N = 205) together with matched RNA sequencing for the majority of tumors (N = 150), we have comprehensively characterized mutation and expression at BRCA1/2. RESULTS: In addition to the known spectrum of short somatic mutations (SSM), we discovered that multi-megabase structural variants (SV) were a frequent, unappreciated source of BRCA1/2 disruption in these tumors, and we found a genome-wide enrichment for large deletions at the BRCA1/2 loci across the cohort. These SVs independently affected a substantial proportion of patients (16%) in addition to those affected by SSMs (24%), conferring HRD and impacting patient survival. We also detail compound deficiencies involving SSMs and SVs at both loci, demonstrating that the strongest risk of HRD emerges from combined SVs at both BRCA1 and BRCA2 in the absence of SSMs. Furthermore, these SVs are abundant and disruptive in other cancer types. CONCLUSIONS: These results extend our understanding of the mutational landscape underlying HRD, increase the number of patients predicted to benefit from therapies exploiting HRD, and suggest there is currently untapped potential in SV detection for patient stratification.

Exome-Based Rare-Variant Analyses in CKD.

BACKGROUND: Studies have identified many common genetic associations that influence renal function and all-cause CKD, but these explain only a small fraction of variance in these traits. The contribution of rare variants has not been systematically examined. METHODS: We performed exome sequencing of 3150 individuals, who collectively encompassed diverse CKD subtypes, and 9563 controls. To detect causal genes and evaluate the contribution of rare variants we used collapsing analysis, in which we compared the proportion of cases and controls carrying rare variants per gene. RESULTS: The analyses captured five established monogenic causes of CKD: variants in PKD1, PKD2, and COL4A5 achieved study-wide significance, and we observed suggestive case enrichment for COL4A4 and COL4A3. Beyond known disease-associated genes, collapsing analyses incorporating regional variant intolerance identified suggestive dominant signals in CPT2 and several other candidate genes. Biallelic mutations in CPT2 cause carnitine palmitoyltransferase II deficiency, sometimes associated with rhabdomyolysis and acute renal injury. Genetic modifier analysis among cases with APOL1 risk genotypes identified a suggestive signal in AHDC1, implicated in Xia-Gibbs syndrome, which involves intellectual disability and other features. On the basis of the observed distribution of rare variants, we estimate that a two- to three-fold larger cohort would provide 80% power to implicate new genes for all-cause CKD. CONCLUSIONS: This study demonstrates that rare-variant collapsing analyses can validate known genes and identify candidate genes and modifiers for kidney disease. In so doing, these findings provide a motivation for larger-scale investigation of rare-variant risk contributions across major clinical CKD categories.

Diagnostic Utility of Exome Sequencing for Kidney Disease.

BACKGROUND: Exome sequencing is emerging as a first-line diagnostic method in some clinical disciplines, but its usefulness has yet to be examined for most constitutional disorders in adults, including chronic kidney disease, which affects more than 1 in 10 persons globally. METHODS: We conducted exome sequencing and diagnostic analysis in two cohorts totaling 3315 patients with chronic kidney disease. We assessed the diagnostic yield and, among the patients for whom detailed clinical data were available, the clinical implications of diagnostic and other medically relevant findings. RESULTS: In all, 3037 patients (91.6%) were over 21 years of age, and 1179 (35.6%) were of self-identified non-European ancestry. We detected diagnostic variants in 307 of the 3315 patients (9.3%), encompassing 66 different monogenic disorders. Of the disorders detected, 39 (59%) were found in only a single patient. Diagnostic variants were detected across all clinically defined categories, including congenital or cystic renal disease (127 of 531 patients [23.9%]) and nephropathy of unknown origin (48 of 281 patients [17.1%]). Of the 2187 patients assessed, 34 (1.6%) had genetic findings for medically actionable disorders that, although unrelated to their nephropathy, would also lead to subspecialty referral and inform renal management. CONCLUSIONS: Exome sequencing in a combined cohort of more than 3000 patients with chronic kidney disease yielded a genetic diagnosis in just under 10% of cases. (Funded by the National Institutes of Health and others.).

Impact of a five-dimensional framework on R&D productivity at AstraZeneca.

In 2011, AstraZeneca embarked on a major revision of its research and development (R&D) strategy with the aim of improving R&D productivity, which was below industry averages in 2005-2010. A cornerstone of the revised strategy was to focus decision-making on five technical determinants (the right target, right tissue, right safety, right patient and right commercial potential). In this article, we describe the progress made using this '5R framework' in the hope that our experience could be useful to other companies tackling R&D productivity issues. We focus on the evolution of our approach to target validation, hit and lead optimization, pharmacokinetic/pharmacodynamic modelling and drug safety testing, which have helped improve the quality of candidate drug nomination, as well as the development of the right culture, where 'truth seeking' is encouraged by more rigorous and quantitative decision-making. We also discuss where the approach has failed and the lessons learned. Overall, the continued evolution and application of the 5R framework are beginning to have an impact, with success rates from candidate drug nomination to phase III completion improving from 4% in 2005-2010 to 19% in 2012-2016.

Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework.

Maintaining research and development (R&D) productivity at a sustainable level is one of the main challenges currently facing the pharmaceutical industry. In this article, we discuss the results of a comprehensive longitudinal review of AstraZeneca's small-molecule drug projects from 2005 to 2010. The analysis allowed us to establish a framework based on the five most important technical determinants of project success and pipeline quality, which we describe as the five 'R's: the right target, the right patient, the right tissue, the right safety and the right commercial potential. A sixth factor - the right culture - is also crucial in encouraging effective decision-making based on these technical determinants. AstraZeneca is currently applying this framework to guide its R&D teams, and although it is too early to demonstrate whether this has improved the company's R&D productivity, we present our data and analysis here in the hope that it may assist the industry overall in addressing this key challenge.

Interstitial lung disease in gefitinib-treated Japanese patients with non-small-cell lung cancer: genome-wide analysis of genetic data.

AIM: To investigate potential relationships between SNPs and acute interstitial lung disease (ILD) events in Japanese non-small-cell lung cancer patients receiving gefitinib. MATERIALS & METHODS: Japanese non-small-cell lung cancer patients treated with gefitinib from a prospective pharmacoepidemiological cohort with a nested case-control study component ('CCS'; 52 ILD cases, 139 controls) and a retrospective study (28 ILD cases, 55 controls) were genotyped for nearly 500,000 SNPs. Associations between genotype and ILD were evaluated using Fisher's exact test and logistic regression modeling, and false discovery rate analysis was used to adjust for the large number of statistical tests. RESULTS: The CCS data provided some false discovery rate evidence that the significance of top-ranking SNPs exceeded levels expected by chance, suggesting some genuine associations. However, replication analyses using retrospective study data were not supportive and there was little evidence of strong genetic associations from a combined analysis. Adjustment of CCS analyses for clinical variables provided little additional convincing evidence. Significant gene-gene interactions between SNP pairs using CCS data were not confirmed in retrospective study replication analyses. CONCLUSION: Although it is not possible to exclude genetic influences in ILD etiology, common sequence variation is unlikely to explain a major component of ILD risk. Our top results may provide a useful hypothesis-generating starting point for further research.

Delivering on the promise of personalized healthcare.

Personalized healthcare (PHC) aims to deliver the right medicine, to the right patient, at the right dose and time, and has the potential to benefit everyone in the healthcare system. Delivering the promise of PHC depends on overcoming key challenges (e.g., identifying biomarkers or disease segmentation tools, facilitating partnerships, clinical development, regulatory engagement and market access) in an integrated fashion. Despite this, progress in PHC has been accelerating over the last few years and is now at a turning point. Flexibility from all those involved in PHC, including the pharmaceutical and diagnostic industries, health authorities and payers, will be required to ensure that the field comes to maturity and delivers its potential to improve patients' lives.

Serious carbamazepine-induced hypersensitivity reactions associated with the HSP70 gene cluster.

OBJECTIVES: The use of carbamazepine (CBZ), the most commonly prescribed antiepileptic drug, is hampered by the occurrence of severe, potentially lethal hypersensitivity reactions. The pathogenesis of hypersensitivity is not yet known, but immune mechanisms are involved. Predisposition to CBZ hypersensitivity is likely to be genetically determined, and genes within the major histocompatibility complex (MHC) have been implicated. The heat shock protein (HSP70) gene cluster is located in the MHC class III region. METHODS: Using a case-control study design, we compared 61 patients with CBZ hypersensitivity (22 with a severe reaction) to 44 patients on CBZ with no signs of hypersensitivity and 172 healthy controls. The genotyping strategy involved identification of common and rare single nucleotide polymorphisms (SNPs) within the HSP70 gene cluster by sequencing, estimation of linkage disequilibrium (LD) and haplotype structure, and thereafter, analysis of SNP/haplotype frequencies in the cases and controls. Population substructure was evaluated by genotyping of 34 microsatellites. RESULTS: Twenty-five SNPs were detected across the three HSP70 genes. Analyses revealed that alleles G, T and C at the SNPs HSPA1A +1911 C/G, HSPA1A +438 C/T and HSPA1L +2437 T/C, respectively, were associated with protection from serious hypersensitivity reactions to CBZ, with the associated alleles falling on a common haplotype. We were unable to detect the presence of population stratification in our patients and controls. CONCLUSIONS: Our data show that HSP70 gene variants are associated with serious CBZ hypersensitivity reactions, but whether this is causal or reflects LD with another gene within the MHC requires further study.

Gene mapping strategies for complex disease and drug response.

The identification of genetic variants involved in disease susceptibility and response to drugs through the use of statistical and epidemiological approaches is a potentially powerful methodology for uncovering causal relationships in human disease and its treatment. Here we introduce and compare the application of genetics in these two fields of research.:

Challenges and opportunities of pharmacogenetics in drug development.

Drugs fail the regulatory process for a variety of reasons, including issues with pharmacokinetics, safety and efficacy. One of the most exciting questions in drug development today is how far the science of pharmacogenetics, the study of the genetics of drug response, can be used to address the fundamental issues that the pharmaceutical industry is facing. In particular, the question of how far it is possible to use this emerging science to deliver the right treatment, to the right patient, at the right dose, at the right time is both the challenge and opportunity of using pharmacogenetics in drug development. This review will address these questions with several real-life examples.

Automation and validation of DNA-banking systems.

DNA banking is one of the central capabilities on which modern genetic research rests. The DNA-banking system plays an essential role in the flow of genetic data from patients and genetics researchers to the application of genetic research in the clinic. Until relatively recently, large collections of DNA samples were not common in human genetics. Now, collections of hundreds of thousands of samples are common in academic institutions and private companies. Automation of DNA banking can dramatically increase throughput, eliminate manual errors and improve the productivity of genetics research. An increased emphasis on pharmacogenetics and personalized medicine has highlighted the need for genetics laboratories to operate within the principles of a recognized quality system such as good laboratory practice (GLP). Automated systems are suitable for such laboratories but require a level of validation that might be unfamiliar to many genetics researchers. In this article, we use the AstraZeneca automated DNA archive and reformatting system (DART) as a case study of how such a system can be successfully developed and validated within the principles of GLP.

Rosuvastatin pharmacokinetics and pharmacogenetics in white and Asian subjects residing in the same environment.

BACKGROUND: Systemic exposure to rosuvastatin had been observed to be approximately 2-fold higher in Japanese subjects living in Japan compared with white subjects in Western Europe or the United States. The organic anion transporting polypeptide 1B1 contributes to the hepatic uptake of rosuvastatin. Polymorphisms in the SLCO1B1 gene can lead to reduced transport function in vitro (T 521>C). This study was conducted to determine whether the pharmacokinetic differences between Japanese and white subjects extended to other Asian ethnic groups and to determine whether polymorphisms in the SLCO1B1 gene contribute to any pharmacokinetic differences observed. METHODS: Rosuvastatin pharmacokinetics was studied in an open-label, parallel-group, single-oral dose (40 mg) study in 36 white, 36 Chinese, 35 Malay, and 35 Asian-Indian subjects living in Singapore, Singapore. Plasma concentrations of rosuvastatin and metabolites were determined by HPLC-mass spectrophotometry. Two SLCO1B1 polymorphisms (A 388>G and T 521>C) were genotyped. RESULTS: Ratios for rosuvastatin area under the plasma concentration-time curve from time 0 to the time of the last quantifiable concentration were 2.31, 1.91, and 1.63 and ratios for maximum plasma concentration were 2.36, 2.00, and 1.68 in Chinese, Malay, and Asian-Indian subjects, respectively, compared with white subjects. Similar increases in exposure to N-desmethyl rosuvastatin and rosuvastatin-lactone were observed. SLCO1B1 genotypes did not account for the observed pharmacokinetic differences between Asians and white subjects. CONCLUSIONS: Plasma exposure to rosuvastatin and its metabolites was significantly higher in Chinese, Malay, and Asian-Indian subjects compared with white subjects living in the same environment.

Novel technology and the development of pharmacogenetics within the pharmaceutical industry.

This article focuses on the role of pharmacogenetics (PGx) technology across the drug development pipeline. Recent technology developments in three main areas are discussed: the discovery of polymorphisms or other variants in genes of interest; genotyping technologies used in PGx research (both for candidate gene analyses and for a whole-genome association approach); and the use of genotyping in patients prior to prescription (diagnostics). Finally, the associated issues of genetic data management and analysis are addressed, and the challenges facing the pharmaceutical industry in storing, manipulating and exploiting the large and complex data sets that will be generated from emerging PGx platforms are discussed. In conclusion, it is demonstrated that, despite the failures of some technology development programs and the slow rate of progress of others, there has, in fact, been steady progress toward the implementation of PGx within the pharmaceutical industry.

Selecting cases from nuclear families for case-control association analysis.

We examine the efficiency of a number of schemes to select cases from nuclear families for case-control association analysis using the Genetic Analysis Workshop 14 simulated dataset. We show that with this simulated dataset comparing all affected siblings with unrelated controls is considerably more powerful than all of the other approaches considered. We find that the test statistic is increased by almost 3-fold compared to the next best sampling schemes of selecting all affected sibs only from families with affected parents (AF aff), one affected sib with most evidence of allele-sharing from each family (SF), and all affected sibs from families with evidence for linkage (AF L). We consider accounting for biological relatedness of samples in the association analysis to maintain the correct type I error. We also discuss the relative efficiencies of increasing the ratio of unrelated cases to controls, methods to confirm associations and issues to consider when applying our conclusions to other complex disease datasets.