Citations

Abstract It is believed that multiple genetic variants with small individual effects contribute to the risk of alcohol dependence. Such polygenic effects are difficult to detect in genome-wide association studies that test for association of the phenotype with each single nucleotide polymorphism (SNP) individually. To overcome this challenge, gene-set analysis (GSA) methods that jointly test for the effects of pre-defined groups of genes have been proposed. Rather than testing for association between the phenotype and individual SNPs, these analyses evaluate the global evidence of association with a set of related genes enabling the identification of cellular or molecular pathways or biological processes that play a role in development of the disease. It is hoped that by aggregating the evidence of association for all available SNPs in a group of related genes, these approaches will have enhanced power to detect genetic associations with complex traits. We performed GSA using data from a genome-wide study of 1165 alcohol-dependent cases and 1379 controls from the Study of Addiction: Genetics and Environment (SAGE), for all 200 pathways listed in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Results demonstrated a potential role of the ‘synthesis and degradation of ketone bodies’ pathway. Our results also support the potential involvement of the ‘neuroactive ligand–receptor interaction’ pathway, which has previously been implicated in addictive disorders. These findings demonstrate the utility of GSA in the study of complex disease, and suggest specific directions for further research into the genetic architecture of alcohol dependence.

With new technologies, multiple types of genomic data are commonly collected on a single set of samples. However, standard analysis methods concentrate on a single data type at a time and ignore the relationships between genes, proteins, and biochemical reactions that give rise to complex phenotypes. In this paper, we propose a novel integrative model to incorporate multiple types of genomic data into an association analysis with a complex phenotype. The method combines path analysis and stochastic search variable selection into a Bayesian hierarchical model that simultaneously identifies both direct and indirect genomic effects on the phenotype. Results from a simulation study and application of the Bayesian model to a pharmacogenomic study of the drug gemcitabine demonstrate greater sensitivity to detect genomic effects in some simulation scenarios, when compared to the standard single data type analysis. Further research is required to extend and modify this integrative modeling framework to increase computational efficiency to best capitalize on the wealth of information available across multiple genomic data types. Genet. Epidemiol. 36:352–359, 2012. © 2012 Wiley Periodicals, Inc.

Abstract Background: Genome-wide association studies (GWAS) for epithelial ovarian cancer (EOC), the most lethal gynecologic malignancy, have identified novel susceptibility loci. GWAS for survival after EOC have had more limited success. The association of each single-nucleotide polymorphism (SNP) individually may not be well suited to detect small effects of multiple SNPs, such as those operating within the same biologic pathway. Gene set analysis (GSA) overcomes this limitation by assessing overall evidence for association of a phenotype with all measured variation in a set of genes. Methods: To determine gene sets associated with EOC overall survival, we conducted GSA using data from two large GWAS (N cases = 2,813, N deaths = 1,116), with a novel Principal Component-Gamma GSA method. Analysis was completed for all cases and then separately for high-grade serous histologic subtype. Results: Analysis of the high-grade serous subjects resulted in 43 gene sets with P < 0.005 (1.7%); of these, 21 gene sets had P < 0.10 in both GWAS, including intracellular signaling pathway (P = 7.3 × 10−5) and macrolide binding (P = 6.2 × 10−4) gene sets. The top gene sets in analysis of all cases were meiotic mismatch repair (P = 6.3 × 10−4) and macrolide binding (P = 1.0 × 10−3). Of 18 gene sets with P < 0.005 (0.7%), eight had P < 0.10 in both GWAS. Conclusion: This research detected novel gene sets associated with EOC survival. Impact: Novel gene sets associated with EOC survival might lead to new insights and avenues for development of novel therapies for EOC and pharmacogenomic studies. Cancer Epidemiol Biomarkers Prev; 21(3); 529–36. ©2012 AACR.

AbstractGene‐set analyses have been widely used in gene expression studies, and some of the developed methods have been extended to genome wide association studies (GWAS). Yet, complications due to linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs), and variable numbers of SNPs per gene and genes per gene‐set, have plagued current approaches, often leading to ad hoc “fixes.” To overcome some of the current limitations, we developed a general approach to scan GWAS SNP data for both gene‐level and gene‐set analyses, building on score statistics for generalized linear models, and taking advantage of the directed acyclic graph structure of the gene ontology when creating gene‐sets. However, other types of gene‐set structures can be used, such as the popular Kyoto Encyclopedia of Genes and Genomes (KEGG). Our approach combines SNPs into genes, and genes into gene‐sets, but assures that positive and negative effects of genes on a trait do not cancel. To control for multiple testing of many gene‐sets, we use an efficient computational strategy that accounts for LD and provides accurate step‐down adjustedP‐values for each gene‐set. Application of our methods to two different GWAS provide guidance on the potential strengths and weaknesses of our proposed gene‐set analyses.

Abstract 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.

Abstract Background Taxane, a class of common anticancer agent, is widely used to treat many cancers. Among the taxanes, paclitaxel and docetaxel are the most commonly used agents. The mechanism of action for taxane drugs is through stabilizing microtubules by enhancing tubulin assembly and against depolymerization, thereby disrupting the dynamics of the microtubule. Large inter-individual variation has been observed in response to taxane treatment. Therefore, in the present study, we conducted a genome-wide association (GWA) study to identify genetic polymorphisms that might contribute to variation in response to taxanes drugs using the “Human Variation Panel” lymphoblastoid cell line (LCL) model system that consists of 287 ethnically defined cell lines. Method Genome-wide SNP and basal gene expression data were obtained for 287 LCLs using Illumina HumanHap 550K, 510S BeadChips, Affymetix SNP Array 6.0 and Affymetrix U133 plus 2.0 GeneChip. Cytotoxicity assay was also performed in the same cell lines for both paclitaxel and docetaxel. Candidate genes were selected based on an integrated analysis of genome-wide SNPs, expression array data and IC50 values (a cytotoxicity phenotype indicating the drug concentration required for 50% inhibition of maximal cell growth) for paclitaxel and docetaxel. SiRNA screening was performed to functionally validate those top candidates, followed by MTS assay in a breast cancer cell line, MDA-MB231. Results Of the 54,000 probe sets considered, 390 probe sets, representing 272 unique annotated genes, were found to be associated with docetaxel cytotoxicity (IC50 value) with p value <10−3, while 176 probe sets, representing 124 unique genes, were associated with paclitaxel IC50. Of these probe sets, 50 probe sets were associated with IC50 for both drugs. Analysis of 1.3 million SNPs identified 11 loci, defined as a region containing at least 1 SNP of p value <10−5 and 1 SNP of p value <10−4 within 50kb, associated with docetaxel IC50, while 7 loci were associated with paclitaxel IC50. One locus was found to be associated with both drugs. Previous studies suggested that gene expression is regulated in a cell-type specific manner. Therefore, based on our analysis and biological implication, we selected 16 top candidate genes that were expressed in our LCLs to perform functional validation studies using siRNA, followed by MTS assay in a breast cancer cell line, MDA-MB231. Functional studies showed that knockdown of SLC44A1 and HSPA5 significantly desensitized the cancer cell to both taxanes treatment. Conclusion GWAS using LCL model system could provide potential pharmacogenomic candidates for future clinical translational studies. Functional validation in tumor cell lines may help determine the genomic mechanism contributing to the inter-individual variation of taxane response. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2599.

Abstract Background Radiation therapy is used to treat half of the all cancer patients. Response to radiation therapy varies widely, in terms of both response and toxicity related events. Previous studies suggested almost 80% of the variation in normal tissue response to radiation treatment was likely to be due to genetic factors. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers that might help predict radiation response using a the “Human Variation Panel” lymphoblastoid cell lines (LCLs) that consists 287 ethnically defined cell lines. Method Basal gene expression level, 1.3 million genome-wide SNP data were obtained for 287 human LCLs using Affymetrix U133 plus 2.0 GeneChip, Illumina HumanHap 550K, 510S BeadChips and Affymetix SNP Array 6.0. Radiation cytotoxicity assay was also performed to obtain the area under radiation dose response curve (AUC), a summary measure for cytotoxicity for the same cell lines. Functional validation of candidate genes, selected based on the integrated analysis of SNP, expression and AUC data, was performed in multiple cancer cell lines using specific siRNAs followed by MTS and colony formation assays. Results 27 loci, containing at least 2 SNPs of p values <10−4 within 50kb were associated with radiation AUC. 270 expression probe sets were associated with radiation AUC (p< 10−3). The integrated analysis of 27 loci, 54,000 basal expression probe sets and radiation AUC identified 50 unique SNPs associated with AUC were also associated with gene expression of 39 unique annotated genes. In addition, these 39 genes were associated with radiation AUC with p values <10−3. Based on the GWAS and integrated analysis of SNP, expression array and radiation AUC data, 22 genes were selected for functional validation studies using siRNA knock down followed by MTS and colony formation assays in multiple tumor cell lines. C13orf34 (Bora), MAD2L1, PLK4, TPD52 significantly altered the radiation sensitivity in at least 2 cancer cell lines for both MTS and colony formation assays (p<0.05). Knock down of DEPDC1B, a gene containing 2 SNPs at the 3′-end that was significantly related with Bora gene expression level (p<10−4) significantly desensitized cancer cells to radiation treatment. Both microarray data and QRT-PCR analysis indicated that expression levels for those two genes were significantly correlated. Conclusion Our results using the lymphoblastoid cell lines might help identify novel biomarkers that contribute to variation in response to radiation therapy and help understand the mechanisms underlying the association. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3602.

Abstract Background: The third-generation aromatase inhibitors (AIs) are established adjuvant therapy for postmenopausal women with early stage breast cancer. Musculoskeletal adverse events (MS AE) have become increasingly recognized as an issue for many women receiving AIs resulting in decreased quality of life and, in some instances, discontinuation of the AI. MA.27 is an ongoing randomized clinical trial comparing anastrozole with exemestane that accrued 7576 patients (pts), including 6827 from North America, about 79% of whom provided DNA and consent for genetic testing. A review of withdrawals related to adverse events revealed that the most common cause was MS AE. The hypothesis for our study was that a genome-wide association (GWA) case control study would identify single nucleotide polymorphisms (SNPs) associated with MS AE.Methods: Cases were defined as pts with grade 3 or 4 MS AE (NCI CTCAE v3.0) or who went off treatment for any grade MS AE within the first 2 years and were matched to 2 controls based on treatment arm, length of follow-up, prior chemotherapy, age and prior celecoxib (at least 3 months prior to MS AE). The study was restricted to the 94% of pts self-identified as Caucasian. Genotyping was performed with the Illumina Human610-Quad. Genotype quality control prior to analysis required call rates ≥98%. Eigenstrat analyses were performed to control for population stratification.Results: The GWA study included 293 cases and 586 controls with 6 pts (0.7%) (4 cases, 2 controls) excluded for call rates <98%. 551,358 SNPs were used in the analyses after removing 29,439 for a minor allele frequency <1% and 82 with Hardy Weinberg Equilibrium p-values <10-6. Eigenvectors did not impact the results when used as covariates. Four SNPs with the lowest p-values (3.3-8.2 x 10-6, Armitage test), 3 in high LD (r2>.8) on chromosome (ch) 14 (rs7158782, rs7159713, rs2369049) and 1 on the X ch (rs6637820) were identified. The gene closest (4000-7000 bp) to the 3 SNPs on ch 14 was T-Cell Leukemia 1A (TCL1A), encoding a protein known to augment AKT kinase activity. Expression array data from lymphoblastoid cell lines from 300 subjects of 3 ethnicities revealed that all 3 SNPs were associated with decreased TCL1A expression after adjusting for ethnicity using 2 probe sets (p=0.006 to 0.03). Electromobility shift assay (EMSA) showed that 2 of these SNPs (rs7158782, rs7159713) displayed a shift and the former SNP showed substantially less binding for variant than wild type. The gene closest to the X ch SNP was Immunoglobulin Superfamily 1 (IGSF1), but EMSA did not show a shift for this SNP.Conclusions: MS AEs represent a major impediment to optimal use of AIs in women with breast cancer and this GWA study identified SNPs on ch 14 that provide a focus for further research to identify pts at risk for, and means to ameliorate, this adverse event.(Supported in part by NIH grants U01GM61388, U01GM63173, P50CA116201 and U10CA77202) Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 15.

Purpose We performed a case-control genome-wide association study (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with musculoskeletal adverse events (MS-AEs) in women treated with aromatase inhibitors (AIs) for early breast cancer. Patients and Methods A nested case-control design was used to select patients enrolled onto the MA.27 phase III trial comparing anastrozole with exemestane. Cases were matched to two controls and were defined as patients with grade 3 or 4 MS-AEs (according to the National Cancer Institute's Common Terminology Criteria for Adverse Events v3.0) or those who discontinued treatment for any grade of MS-AE within the first 2 years. Genotyping was performed with the Illumina Human610-Quad BeadChip. Results The GWAS included 293 cases and 585 controls. A total of 551,358 SNPs were analyzed, followed by imputation and fine mapping of a region of interest on chromosome 14. Four SNPs on chromosome 14 had the lowest P values (2.23E-06 to 6.67E-07). T-cell leukemia 1A (TCL1A) was the gene closest (926-7000 bp) to the four SNPs. Functional genomic studies revealed that one of these SNPs (rs11849538) created an estrogen response element and that TCL1A expression was estrogen dependent, was associated with the variant SNP genotypes in estradiol-treated lymphoblastoid cells transfected with estrogen receptor alpha and was directly related to interleukin 17 receptor A (IL17RA) expression. Conclusion This GWAS identified SNPs associated with MS-AEs in women treated with AIs and with a gene (TCL1A) which, in turn, was related to a cytokine (IL17). These findings provide a focus for further research to identify patients at risk for MS-AEs and to explore the mechanisms for these adverse events.

Abstract Aromatase (CYP19) is a critical enzyme in estrogen biosynthesis and aromatase inhibitors (AI) are employed widely for endocrine therapy in postmenopausal women with breast cancer. We hypothesized that single nucleotide polymorphisms (SNPs) in the CYP19 gene may alter the effectiveness of AI therapy in the neoadjuvant setting. Genomic DNA was obtained for sequencing from 52 women pre-AI and post-AI treatment in this setting. Additionally, genomic DNA obtained from 82 samples of breast cancer and 19 samples of normal breast tissue was subjected to resequencing. No differences in CYP19 sequence were observed between tumor and germ-line DNA in the same patient. A total of 48 SNPs were identified including 4 novel SNPs when compared with previous resequencing data. For genotype-phenotype association studies, we determined the levels of aromatase activity, estrone, estradiol, and tumor size in patients pre-AI and post-AI treatment. We defined two tightly linked SNPs (rs6493497 and rs7176005 in the 5'-flanking region of CYP19 exon 1.1) that were significantly associated with a greater change in aromatase activity after AI treatment. In a follow-up study of 200 women with early-stage breast cancer who were treated with adjuvant anastrozole, these same two SNPs were also associated with higher plasma estradiol levels in patients pre-AI and post-AI treatment. Electrophoretic mobility shift and reporter gene assays confirmed likely functional effects of these two SNPs on transcription of CYP19. Our findings indicate that two common genetic polymorphisms in the aromatase gene CYP19 vary the response of breast cancer patients to aromatase inhibitors. Cancer Res; 70(1); 319–28