Ryan Abo

Genetic variation predicting cisplatin cytotoxicity associated with overall survival in lung cancer patients receiving platinum-based chemotherapy ,

Purpose: Inherited variability in the prognosis of lung cancer patients treated with platinum-based chemotherapy has been widely investigated. However, the overall contribution of genetic variation to platinum response is not well established. To identify novel candidate single nucleotide polymorphisms (SNP)/genes, we carried out a genome-wide association study (GWAS) for cisplatin cytotoxicity by using lymphoblastoid cell lines (LCL), followed by an association study of selected SNPs from the GWAS with overall survival (OS) in lung cancer patients. Experimental Design: A GWAS for cisplatin was conducted with 283 ethnically diverse LCLs. A total of 168 top SNPs were genotyped in 222 small cell lung cancer (SCLC) and 961 non-SCLC (NSCLC) patients treated with platinum-based therapy. Association of the SNPs with OS was determined by using the Cox regression model. Selected candidate genes were functionally validated by siRNA knockdown in human lung cancer cells. Results: Among 157 successfully genotyped SNPs, 9 and 10 SNPs were top SNPs associated with OS for patients with NSCLC and SCLC, respectively, although they were not significant after adjusting for multiple testing. Fifteen genes, including 7 located within 200 kb up or downstream of the 4 top SNPs and 8 genes for which expression was correlated with 3 SNPs in LCLs were selected for siRNA screening. Knockdown of DAPK3 and METTL6, for which expression levels were correlated with the rs11169748 and rs2440915 SNPs, significantly decreased cisplatin sensitivity in lung cancer cells. Conclusions: This series of clinical and complementary laboratory-based functional studies identified several candidate genes/SNPs that might help predict treatment outcomes for platinum-based therapy of lung cancer. Clin Cancer Res; 17(17); 5801–11. ©2011 AACR.

FKBP5 genetic variation: association with selective serotonin reuptake inhibitor treatment outcomes in major depressive disorder

Objectives FKBP51 (51 kDa immunophilin) acts as a modulator of the glucocorticoid receptor and a negative regulator of the Akt pathway. Genetic variation in FKBP5 plays a role in antidepressant response. The aim of this study was to comprehensively assess the role of genetic variation in FKBP5, identified by both Sanger and Next Generation DNA resequencing, as well as genome-wide single nucleotide polymorphisms (SNPs) associated with FKBP5 expression in the response to the selective serotonin reuptake inhibitor (SSRI) treatment of major depressive disorder. Methods We identified 657 SNPs in FKBP5 by Next Generation sequencing of 96 DNA samples from white patients, and 149 SNPs were selected for the genotyping together with 235 SNPs that were trans-associated with variation in FKBP5 expression in lymphoblastoid cells. A total of 529 DNA samples from the Mayo Clinic PGRN-SSRI Pharmacogenomic trial for which genome-wide SNPs had already been obtained were genotyped for these 384 SNPs, and associations with treatment outcomes were determined. The most significant SNPs were genotyped using 96 DNA samples from white non-Hispanic patients of the NIMH-supported Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study to attempt replication, followed by functional genomic studies. Results Genotype–phenotype association analysis indicated that rs352428 was associated with both 8-week treatment response in the Mayo study (odds ratio=0.49; P=0.003) and 6-week response in the STAR*D replication study (odds ratio=0.74; P=0.05). The electrophoresis mobility shift assay and the reporter gene assay confirmed the possible role of this SNP in transcription regulation. Conclusion This comprehensive FKBP5 sequence study provides insight into the role of common genetic polymorphisms that might influence SSRI treatment outcomes in major depressive disorder patients.

Merging pharmacometabolomics with pharmacogenomics using '1000 Genomes' single-nucleotide polymorphism imputation: Selective serotonin reuptake inhibitor response pharmacogenomics

Objective We set out to test the hypothesis that pharmacometabolomic data could be efficiently merged with pharmacogenomic data by single-nucleotide polymorphism (SNP) imputation of metabolomic-derived pathway data on a ‘scaffolding’ of genome-wide association (GWAS) SNP data to broaden and accelerate ‘pharmacometabolomics-informed pharmacogenomic’ studies by eliminating the need for initial genotyping and by making broader SNP association testing possible. Methods We previously genotyped 131 tag SNPs for six genes encoding enzymes in the glycine synthesis and degradation pathway using DNA from 529 depressed patients treated with citalopram/escitalopram to pursue a glycine metabolomics ‘signal’ associated with selective serotonine reuptake inhibitor response. We identified a significant SNP in the glycine dehydrogenase gene. Subsequently, GWAS SNP data were generated for the same patients. In this study, we compared SNP imputation within 200 kb of these same six genes with the results of the previous tag SNP strategy as a rapid strategy for merging pharmacometabolomic and pharmacogenomic data. Results Imputed genotype data provided greater coverage and higher resolution than did tag SNP genotyping, with a higher average genotype concordance between genotyped and imputed SNP data for ‘1000 Genomes’ (96.4%) than HapMap 2 (93.2%) imputation. Many low P-value SNPs with novel locations within genes were observed for imputed compared with tag SNPs, thus altering the focus for subsequent functional genomic studies. Conclusion These results indicate that the use of GWAS data to impute SNPs for genes in pathways identified by other ‘omics’ approaches makes it possible to rapidly and cost efficiently identify SNP markers to ‘broaden’ and accelerate pharmacogenomic studies.

A breast cancer risk haplotype in the caspase-8 gene

Recent large-scale studies have been successful in identifying common, low-penetrance variants associated with common cancers. One such variant in the caspase-8 (CASP8) gene, D302H (rs1045485), has been confirmed to be associated with breast cancer risk, although the functional effect of this polymorphism (if any) is not yet clear. In order to further map the CASP8 gene with respect to breast cancer susceptibility, we performed extensive haplotype analyses using single nucleotide polymorphisms (SNP) chosen to tag all common variations in the gene (tSNP). We used a staged study design based on 3,200 breast cancer and 3,324 control subjects from the United Kingdom, Utah, and Germany. Using a haplotype-mining algorithm in the UK cohort, we identified a four-SNP haplotype that was significantly associated with breast cancer and that was superior to any other single or multi-locus combination (P=8.0 x 10(-5)), with a per allele odds ratio and 95% confidence interval of 1.30 (1.12-1.49). The result remained significant after adjustment for the multiple testing inherent in mining techniques (false discovery rate, q=0.044). As expected, this haplotype includes the D302H locus. Multicenter analyses on a subset of the tSNPs yielded consistent results. This risk haplotype is likely to carry one or more underlying breast cancer susceptibility alleles, making it an excellent candidate for resequencing in homozygous individuals. An understanding of the mode of action of these alleles will aid risk assessment and may lead to the identification of novel treatment targets in breast cancer.

Gemcitabine metabolic pathway genetic polymorphisms and response in patients with non-small cell lung cancer.

Background and objective Gemcitabine is widely used to treat non-small cell lung cancer (NSCLC). The aim of this study was to assess the pharmacogenomic effects of the entire gemcitabine metabolic pathway, we genotyped single nucleotide polymorphisms (SNPs) within the 17 pathway genes using DNA samples from patients with NSCLC treated with gemcitabine to determine the effect of genetic variants within gemcitabine pathway genes on overall survival (OS) of patients with NSCLC after treatment of gemcitabine. Methods Eight of the 17 pathway genes were resequenced with DNA samples from Coriell lymphoblastoid cell lines (LCLs) using Sanger sequencing for all exons, exon–intron junctions, and 5′-, 3′-UTRs. A total of 107 tagging SNPs were selected on the basis of the resequencing data for the eight genes and on HapMap data for the remaining nine genes, followed by successful genotyping of 394 NSCLC patient DNA samples. Association of SNPs/haplotypes with OS was performed using the Cox regression model, followed by functional studies performed with LCLs and NSCLC cell lines. Results Five SNPs in four genes (CDA, NT5C2, RRM1, and SLC29A1) showed associations with OS of those patients with NSCLC, as well as nine haplotypes in four genes (RRM1, RRM2, SLC28A3, and SLC29A1) with a P value of less than 0.05. Genotype imputation using the LCLs was performed for a region of 200 kb surrounding those SNPs, followed by association studies with gemcitabine cytotoxicity. Functional studies demonstrated that downregulation of SLC29A1, NT5C2, and RRM1 in NSCLC cell lines altered cell susceptibility to gemcitabine. Conclusion These studies help in identifying biomarkers to predict gemcitabine response in NSCLC, a step toward the individualized chemotherapy of lung cancer.

Evaluation of genetic risk scores for lipid levels using genome-wide markers in the Framingham Heart Study

BackgroundMultiple single-nucleotide polymorphisms have been associated with low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels. In this paper, we evaluate a weighted and an unweighted approach for estimating the combined effect of multiple markers (using genotypes and haplotypes) on lipid levels for a given individual.MethodsUsing data from the Framingham Heart Study SHARe genome-wide association study, we tested genome-wide genotypes and haplotypes for association with lipid levels and constructed genetic risk scores (GRS) based on multiple markers that were weighted according to their estimated effects on LDL-C, HDL-C, and TG. These scores (GRS-LDL, GRS-HDL, and GRS-TG) were then evaluated for associations with LDL-C, HDL-C, and TG, and compared with results of an unweighted method based on risk-allele counts. For comparability of metrics, GRS variables were divided into quartiles.ResultsGRS-LDL quartiles were associated with LDL-C levels (p = 2.1 × 10-24), GRS-HDL quartiles with HDL-C (p = 5.9 × 10-22), and GRS-TG quartiles with TG (p = 5.4 × 10-25). In comparison, these p-values were considerably lower than those for the associations of the unweighted GRS quartiles for LDL-C (p = 3.6 × 10-7), HDL-C (p = 6.4 × 10-16), and TG (p = 4.1 × 10-10).ConclusionGRS variables were highly predictive of LDL-C, HDL-C, and TG measurements, especially when weighted based on each markers individual association with those intermediate risk phenotypes. The allele-count GRS approach that does not weight the GRS by individual marker associations was considerably less predictive of lipid and lipoprotein measures when the same genetic markers were utilized, suggesting that substantially more risk-associated genetic marker information is encapsulated by the weighted GRS variables.

hapConstructor: automatic construction and testing of haplotypes in a Monte Carlo framework

Summary: Haplotypes carry important information that can direct investigators towards underlying susceptibility variants, and hence multiple tagging single nucleotide polymorphisms (tSNPs) are usually studied in candidate gene association studies. However, it is often unknown which SNPs should be included in haplotype analyses, or which tests should be performed for maximum power. We have developed a program, hapConstructor, which automatically builds multi-locus SNP sets to test for association in a case-control framework. The multi-SNP sets considered need not be contiguous; they are built based on significance. An important feature is that the missing data imputation is carried out based on the full data, for maximal information and consistency. HapConstructor is implemented in a Monte Carlo framework and naturally extends to allow for significance testing and false discovery rates that account for the construction process and to related individuals. HapConstructor is a useful tool for exploring multi-locus associations in candidate genes and regions. Availability: http://www-genepi.med.utah.edu/Genie Contact: ryan.abo@hsc.utah.edu

Serine hydroxymethyltransferase 1 and 2: gene sequence variation and functional genomic characterization

J. Neurochem. (2012) 120, 881–890.

Exploring multilocus associations of inflammation genes and colorectal cancer risk using hapConstructor

BackgroundIn candidate-gene association studies of single nucleotide polymorphisms (SNPs), multilocus analyses are frequently of high dimensionality when considering haplotypes or haplotype pairs (diplotypes) and differing modes of expression. Often, while candidate genes are selected based on their biological involvement in a given pathway, little is known about the functionality of SNPs to guide association studies. Investigators face the challenge of exploring multiple SNP models to elucidate which variants, independently or in combination, might be associated with a disease of interest. A data mining module, hapConstructor (freely-available in Genie software) performs systematic construction and association testing of multilocus genotype data in a Monte Carlo framework. Our objective was to assess its utility to guide statistical analyses of haplotypes within a candidate region (or combined genotypes across candidate genes) beyond that offered by a standard logistic regression approach.MethodsWe applied the hapConstructor method to a multilocus investigation of candidate genes involved in pro-inflammatory cytokine IL6 production, IKBKB, IL6, and NFKB1 (16 SNPs total) hypothesized to operate together to alter colorectal cancer risk. Data come from two U.S. multicenter studies, one of colon cancer (1,556 cases and 1,956 matched controls) and one of rectal cancer (754 cases and 959 matched controls).ResultsHapConstrcutor enabled us to identify important associations that were further analyzed in logistic regression models to simultaneously adjust for confounders. The most significant finding (nominal P = 0.0004; false discovery rate q = 0.037) was a combined genotype association across IKBKB SNP rs5029748 (1 or 2 variant alleles), IL6 rs1800797 (1 or 2 variant alleles), and NFKB1 rs4648110 (2 variant alleles) which conferred an ~80% decreased risk of colon cancer.ConclusionsStrengths of hapConstructor were: systematic identification of multiple loci within and across genes important in CRC risk; false discovery rate assessment; and efficient guidance of subsequent logistic regression analyses.

Discovery of genetic biomarkers contributing to variation in drug response of cytidine analogues using human lymphoblastoid cell lines

BackgroundTwo cytidine analogues, gemcitabine and cytosine arabinoside (AraC), are widely used in the treatment of a variety of cancers with a large individual variation in response. To identify potential genetic biomarkers associated with response to these two drugs, we used a human lymphoblastoid cell line (LCL) model system with extensive genomic data, including 1.3 million SNPs and 54,000 basal expression probesets to perform genome-wide association studies (GWAS) with gemcitabine and AraC IC50 values.ResultsWe identified 11 and 27 SNP loci significantly associated with gemcitabine and AraC IC50 values, respectively. Eleven candidate genes were functionally validated using siRNA knockdown approach in multiple cancer cell lines. We also characterized the potential mechanisms of genes by determining their influence on the activity of 10 cancer-related signaling pathways using reporter gene assays. Most SNPs regulated gene expression in a trans manner, except 7 SNPs in the PIGB gene that were significantly associated with both the expression of PIGB and gemcitabine cytotoxicity.ConclusionThese results suggest that genetic variation might contribute to drug response via either cis- or trans- regulation of gene expression. GWAS analysis followed by functional pharmacogenomics studies might help identify novel biomarkers contributing to variation in response to these two drugs and enhance our understanding of underlying mechanisms of drug action.