Dataset Information

Phenome-wide Association Study Relating Pretreatment Laboratory Parameters With Human Genetic Variants in AIDS Clinical Trials Group Protocols.

ABSTRACT: Background. ?Phenome-Wide Association Studies (PheWAS) identify genetic associations across multiple phenotypes. Clinical trials offer opportunities for PheWAS to identify pharmacogenomic associations. We describe the first PheWAS to use genome-wide genotypic data and to utilize human immunodeficiency virus (HIV) clinical trials data. As proof-of-concept, we focused on baseline laboratory phenotypes from antiretroviral therapy-naive individuals. Methods. ?Data from 4 AIDS Clinical Trials Group (ACTG) studies were split into 2 datasets: Dataset I (1181 individuals from protocol A5202) and Dataset II (1366 from protocols A5095, ACTG 384, and A5142). Final analyses involved 2547 individuals and 5 954 294 imputed polymorphisms. We calculated comprehensive associations between these polymorphisms and 27 baseline laboratory phenotypes. Results. ?A total of 10 584 (0.17%) polymorphisms had associations with P < .01 in both datasets and with the same direction of association. Twenty polymorphisms replicated associations with identical or related phenotypes reported in the Catalog of Published Genome-Wide Association Studies, including several not previously reported in HIV-positive cohorts. We also identified several possibly novel associations. Conclusions. ?These analyses define PheWAS properties and principles with baseline laboratory data from HIV clinical trials. This approach may be useful for evaluating on-treatment HIV clinical trials data for associations with various clinical phenotypes.

SUBMITTER: Moore CB

PROVIDER: S-EPMC4396430 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Phenome-wide Association Study Relating Pretreatment Laboratory Parameters With Human Genetic Variants in AIDS Clinical Trials Group Protocols.

Moore Carrie B CB Verma Anurag A Pendergrass Sarah S Verma Shefali S SS Johnson Daniel H DH Daar Eric S ES Gulick Roy M RM Haubrich Richard R Robbins Gregory K GK Ritchie Marylyn D MD Haas David W DW

Open forum infectious diseases 20150109 1

Background. Phenome-Wide Association Studies (PheWAS) identify genetic associations across multiple phenotypes. Clinical trials offer opportunities for PheWAS to identify pharmacogenomic associations. We describe the first PheWAS to use genome-wide genotypic data and to utilize human immunodeficiency virus (HIV) clinical trials data. As proof-of-concept, we focused on baseline laboratory phenotypes from antiretroviral therapy-naive individuals. Methods. Data from 4 AIDS Clinical Trials Group ( ...[more]

PMID: 25884002

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Project description:Association studies have shown and continue to show a substantial amount of success in identifying links between multiple single nucleotide polymorphisms (SNPs) and phenotypes. These studies are also believed to provide insights toward identification of new drug targets and therapies. Albeit of all the success, challenges still remain for applying and prioritizing these associations based on available biological knowledge. Along with single variant association analysis, genetic interactions also play an important role in uncovering the etiology and progression of complex traits. For gene-gene interaction analysis, selection of the variants to test for associations still poses a challenge in identifying epistatic interactions among the large list of variants available in high-throughput, genome-wide datasets. Therefore in this study, we propose a pipeline to identify interactions among genetic variants that are associated with multiple phenotypes by prioritizing previously published results from main effect association analysis (genome-wide and phenome-wide association analysis) based on a-priori biological knowledge in AIDS Clinical Trials Group (ACTG) data. We approached the prioritization and filtration of variants by using the results of a previously published single variant PheWAS and then utilizing biological information from the Roadmap Epigenome project. We removed variants in low functional activity regions based on chromatin states annotation and then conducted an exhaustive pairwise interaction search using linear regression analysis. We performed this analysis in two independent pre-treatment clinical trial datasets from ACTG to allow for both discovery and replication. Using a regression framework, we observed 50,798 associations that replicate at p-value 0.01 for 26 phenotypes, among which 2,176 associations for 212 unique SNPs for fasting blood glucose phenotype reach Bonferroni significance and an additional 9,970 interactions for high-density lipoprotein (HDL) phenotype and fasting blood glucose (total of 12,146 associations) reach FDR significance. We conclude that this method of prioritizing variants to look for epistatic interactions can be used extensively for generating hypotheses for genomewide and phenome-wide interaction analyses. This original Phenome-wide Interaction study (PheWIS) can be applied further to patients enrolled in randomized clinical trials to establish the relationship between patient's response to a particular drug therapy and non-linear combination of variants that might be affecting the outcome.

Project description:BackgroundPeripheral blood parameters are correlated to immune-checkpoint inhibitor efficacy in solid tumors, such as melanoma and non-small cell lung cancer. Few data are currently available on the prognostic role of these immune-inflammatory biomarkers for other solid tumors and immunotherapy combinations.Material and methodsFrom August 2014 to May 2019, 153 patients with metastatic solid tumors were enrolled in phase I clinical trials testing immunotherapy both as single agents and as combinations. Primary endpoint was to evaluate the impact of baseline blood parameters on progression-free survival (PFS) and overall survival (OS).ResultsThe most common tumor types were gastrointestinal, breast, and gynecological cancers (22.9%, 22.2%, and 15.0%, respectively). Higher lactate dehydrogenase (LDH) and derived neutrophil-to-lymphocyte ratio (dNLR) were independently associated with reduced PFS (hazard ratio [HR], 1.97; 95% confidence interval [CI], 1.30-2.99; p = .001, and HR, 2.29; 95% CI, 1.39-3.77; p = .001, respectively) and reduced OS (HR, 2.04; 95% CI, 1.26-3.28; p = .004, and HR, 2.06; 95% CI, 1.12-3.79; p = .02, respectively). In the subgroup analysis, (single agent vs. combination), patients at "good" (dNLR <3 and LDH < upper limit of normal [ULN]) and "intermediate and poor" (dNLR >3 and/or LDH > ULN) risk had higher and lower PFS, respectively (p for interaction = .002). Conversely, patients receiving monotherapy presented statistically significant difference in OS according to the risk group, whereas this effect was not observed for those treated with combinations (p for interaction = .004).ConclusionElevated LDH and dNLR are associated with poorer survival outcomes in patients treated with immunotherapy in phase I clinical trials, regardless of tumor type. These parameters represent an easy tool that might be considered as stratification factors in immunotherapy-based clinical trials.Implications for practiceIn this retrospective cohort study of 153 patients with metastatic solid tumors treated with immunotherapy in the context of phase I clinical trials, elevated baseline lactate dehydrogenase and derived neutrophil-to-lymphocyte ratio were associated with reduced survival regardless of tumor subtype. If prospectively validated, these parameters might represent low-cost and easy biomarkers that could help patient selection for early phase immunotherapy trials and be applied as a stratification factor in randomized studies testing immunotherapy agents.

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Project description:BACKGROUND:The association of inflammatory biomarkers with clinical events after antiretroviral therapy initiation is unclear. METHODS:A5202 randomized 1857 treatment-naive subjects to abacavir/lamivudine or tenofovir-DF/emtricitabine with efavirenz or atazanavir/ritonavir. Substudy A5224s measured inflammatory biomarkers on subjects with available plasma from baseline and week 24 or 96. An exploratory analysis of the association of high-sensitivity C-reactive protein, interleukin-6 (IL-6), soluble receptors of tumor necrosis factor ? (sTNF)-RI, sTNF-RII, TNF-?, soluble vascular cellular adhesion molecules (sVCAM-1), and soluble intercellular adhesion molecules (sICAM-1) with times to AIDS and to non-AIDS events used Cox proportional hazards models. RESULTS:Analysis included 244 subjects; 85% men and 48% white non-Hispanic with median age 39 years, HIV-1 RNA of 4.6 log10 copies per milliliter, and CD4 of 240 cells per microliter. Overall, 13 AIDS events (9 opportunistic infections, 3 AIDS-cancers, and 1 recurrent bacterial pneumonia) and 18 non-AIDS events (6 diabetes, 4 cancers, 3 cardiovascular, and 5 pneumonias) occurred. Higher baseline IL-6, sTNF-RI, sTNF-RII, and sICAM-1 were significantly associated with increased risk of AIDS-defining events. Adjustment for baseline HIV-1 RNA did not change results, whereas adjusting for baseline CD4 count left only sTNF-RI and sICAM-1 significantly associated with increased risk. Time-updated values of IL-6, sTNFR-I and II, and sICAM-1 were also associated with an increased risk. For non-AIDS events, only higher baseline high-sensitivity C-reactive protein was significantly associated with increased risk, whereas higher IL-6 was marginally associated with higher risk. Analyses of time-updated biomarker values showed tumor necrosis factor ? to be significantly associated with increased risk, even after adjustment for antiretroviral therapy, and CD4 count or HIV-1 RNA. CONCLUSIONS:Higher levels of several inflammatory biomarkers were independently associated with increased risk of AIDS and non-AIDS events.

Dataset Information

Phenome-wide Association Study Relating Pretreatment Laboratory Parameters With Human Genetic Variants in AIDS Clinical Trials Group Protocols.

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Phenome-wide Association Study Relating Pretreatment Laboratory Parameters With Human Genetic Variants in AIDS Clinical Trials Group Protocols.

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