Consistency in large pharmacogenomic studies.
Ontology highlight
ABSTRACT:
SUBMITTER: Geeleher P
PROVIDER: S-EPMC6290674 | biostudies-literature | 2016 Nov
REPOSITORIES: biostudies-literature
Publications
Consistency in large pharmacogenomic studies.
Geeleher Paul P Gamazon Eric R ER Seoighe Cathal C Cox Nancy J NJ Huang R Stephanie RS
Nature 20161101 7631
Similar Datasets
Project description:Two large-scale pharmacogenomic studies were published recently in this journal. Genomic data are well correlated between studies; however, the measured drug response data are highly discordant. Although the source of inconsistencies remains uncertain, it has potential implications for using these outcome measures to assess gene-drug associations or select potential anticancer drugs on the basis of their reported results.
| S-EPMC4237165 | biostudies-literature
Project description:In 2013, we published a comparative analysis of mutation and gene expression profiles and drug sensitivity measurements for 15 drugs characterized in the 471 cancer cell lines screened in the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). While we found good concordance in gene expression profiles, there was substantial inconsistency in the drug responses reported by the GDSC and CCLE projects. We received extensive feedback on the comparisons that we performed. This feedback, along with the release of new data, prompted us to revisit our initial analysis. We present a new analysis using these expanded data, where we address the most significant suggestions for improvements on our published analysis - that targeted therapies and broad cytotoxic drugs should have been treated differently in assessing consistency, that consistency of both molecular profiles and drug sensitivity measurements should be compared across cell lines, and that the software analysis tools provided should have been easier to run, particularly as the GDSC and CCLE released additional data. Our re-analysis supports our previous finding that gene expression data are significantly more consistent than drug sensitivity measurements. Using new statistics to assess data consistency allowed identification of two broad effect drugs and three targeted drugs with moderate to good consistency in drug sensitivity data between GDSC and CCLE. For three other targeted drugs, there were not enough sensitive cell lines to assess the consistency of the pharmacological profiles. We found evidence of inconsistencies in pharmacological phenotypes for the remaining eight drugs. Overall, our findings suggest that the drug sensitivity data in GDSC and CCLE continue to present challenges for robust biomarker discovery. This re-analysis provides additional support for the argument that experimental standardization and validation of pharmacogenomic response will be necessary to advance the broad use of large pharmacogenomic screens.
| S-EPMC5580432 | biostudies-literature
Project description:Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs.
| S-EPMC5630037 | biostudies-literature
Project description:In a common pharmacogenomic scenario, outcome measures are compared for treated and untreated subjects across genotype-defined subgroups. The key question is whether treatment benefit (or harm) is particularly strong in certain subgroups, and therefore the statistical analysis focuses on the interaction between treatment and genotype. However, genome-wide analysis in such scenarios requires careful statistical thought as, in addition to the usual problems of multiple testing, the marker-defined sample sizes, and therefore power, vary across the individual genotypes being evaluated. The variability in power means that the usual practice of using a common P-value threshold across tests has difficulties. The reason is that the use of a fixed threshold, with variable power, implies that the costs of type I and type II errors vary across tests in a manner that is implicit rather than dictated by the analyst. In this paper we discuss this problem and describe an easily implementable solution based on Bayes factors. We pay particular attention to the specification of priors, which is not a straightforward task. The methods are illustrated using data from a randomized controlled clinical trial in which homocysteine levels are compared in individuals receiving low and high doses of folate supplements and across marker subgroups. The method we describe is implemented in the R computing environment with code available from http://faculty.washington.edu/jonno/cv.html.
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Project description: Not available
| S-EPMC3893087 | biostudies-literature
Project description:The consistency of in vitro drug sensitivity data is of key importance for cancer pharmacogenomics. Previous attempts to correlate drug sensitivities from the large pharmacogenomics databases, such as the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC), have produced discordant results. We developed a new drug sensitivity metric, the area under the dose response curve adjusted for the range of tested drug concentrations, which allows integration of heterogeneous drug sensitivity data from the CCLE, the GDSC, and the Cancer Therapeutics Response Portal (CTRP). We show that there is moderate to good agreement of drug sensitivity data for many targeted therapies, particularly kinase inhibitors. The results of this largest cancer cell line drug sensitivity data analysis to date are accessible through the online portal, which serves as a platform for high power pharmacogenomics analysis.
| S-EPMC5239501 | biostudies-literature
Project description:IntroductionIncreasing clinical evidence supports the implementation of genotyping for anti-hypertensive drug dosing and selection. Despite robust evidence gleaned from clinical trials, the translation of genotype guided therapy into clinical practice faces significant challenges. Challenges to implementation include the small effect size of individual variants and the polygenetic nature of antihypertensive drug response, a lack of expert consensus on dosing guidelines even without genetic information, and proper definition of major antihypertensive drug toxicities. Balancing clinical benefit with cost, while overcoming these challenges, remains crucial.Areas coveredThis review presents the most impactful clinical trials and cohorts which continue to inform and guide future investigation. Variants were selected from among those identified in the Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR), the Genetic Epidemiology of Responses to Antihypertensives study (GERA), the Genetics of Drug Responsiveness in Essential Hypertension (GENRES) study, the SOPHIA study, the Milan Hypertension Pharmacogenomics of hydro-chlorothiazide (MIHYPHCTZ), the Campania Salute Network, the International Verapamil SR Trandolapril Study (INVEST), the Nordic Diltiazem (NORDIL) Study, GenHAT, and others.Expert commentaryThe polygenic nature of antihypertensive drug response is a major barrier to clinical implementation. Further studies examining clinical effectiveness are required to support broad-based implementation of genotype-based prescribing in medical practice.
| S-EPMC5990020 | biostudies-literature
Project description:AimsTo examine the allelic variation of three enzymes involved in 6-mercaptopurine/azathioprine (6-MP/AZA) metabolism and evaluate the influence of these polymorphisms on toxicity, haematological parameters and metabolite levels in patients with acute lymphoblastic leukaemia (ALL) or inflammatory bowel disease (IBD).MethodsClinical data and blood samples were collected from 19 ALL paediatric patients and 35 IBD patients who were receiving 6-MP/AZA therapy. All patients were screened for seven genetic polymorphisms in three enzymes involved in mercaptopurine metabolism [xanthine oxidase, inosine triphosphatase (C94-->A and IVS2+21A-->C) and thiopurine methyltransferase]. Erythrocyte and plasma metabolite concentrations were also determined. The associations between the various genotypes and myelotoxicity, haematological parameters and metabolite concentrations were determined.ResultsThiopurine methyltransferase variant alleles were associated with a preferential metabolism away from 6-methylmercaptopurine nucleotides (P = 0.008 in ALL patients, P = 0.038 in IBD patients) favouring 6-thioguanine nucleotides (6-TGNs) (P = 0.021 in ALL patients). Interestingly, carriers of inosine triphosphatase IVS2+21A-->C variants among ALL and IBD patients had significantly higher concentrations of the active cytotoxic metabolites, 6-TGNs (P = 0.008 in ALL patients, P = 0.047 in IBD patients). The study confirmed the association of thiopurine methyltransferase heterozygosity with leucopenia and neutropenia in ALL patients and reported a significant association between inosine triphosphatase IVS2+21A-->C variants with thrombocytopenia (P = 0.012). CONCLUSIONS; Pharmacogenetic polymorphisms in the 6-MP pathway may help identify patients at risk for associated toxicities and may serve as a guide for dose individualization.
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Project description:Arabinoxylan-Oligosaccharide Intake changes the microbiota and softens stool consistency without changes in gut transit and metabolic health in healthy adults
| PRJEB32919 | ENA
Project description:Pharmacogenomics (PGx) is an integral part of precision medicine and contributes to the maximization of drug efficacy and reduction of adverse drug event risk. Accurate information on PGx allele frequencies improves the implementation of PGx. Nonetheless, curating such information from published allele data is time and resource intensive. The limited number of allelic variants in most studies leads to an underestimation of certain alleles. We applied the Pharmacogenomics Clinical Annotation Tool (PharmCAT) on an integrated 200K UK Biobank genetic dataset (N = 200,044). Based on PharmCAT results, we estimated PGx frequencies (alleles, diplotypes, phenotypes, and activity scores) for 17 pharmacogenes in five biogeographic groups: European, Central/South Asian, East Asian, Afro-Caribbean, and Sub-Saharan African. PGx frequencies were distinct for each biogeographic group. Even biogeographic groups with similar proportions of phenotypes were driven by different sets of dominant PGx alleles. PharmCAT also identified "no-function" alleles that were rare or seldom tested in certain groups by previous studies, e.g., SLCO1B1∗31 in the Afro-Caribbean (3.0%) and Sub-Saharan African (3.9%) groups. Estimated PGx frequencies are disseminated via the PharmGKB (The Pharmacogenomics Knowledgebase: www.pharmgkb.org). We demonstrate that genetic biobanks such as the UK Biobank are a robust resource for estimating PGx frequencies. Improving our understanding of PGx allele and phenotype frequencies provides guidance for future PGx studies and clinical genetic test panel design, and better serves individuals from wider biogeographic backgrounds.
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