Abo, Ryan

Abstract BACKGROUND Metformin, a widely used anti-diabetic drug, is being considered as a highly promising agent for treatment and prevention of many types of cancer, including breast cancer. Although the exact mechanism of action of metformin is unknown, it is thought that metformin is an AMPK activator. To better individualize metformin therapy and explore additional underlying molecular mechanism associated with metformin action, we conducted a metformin pharmacogenomic study using 266 genomic data-enriched lymphoblastoid cell lines (LCLs). METHODS Genome-wide microarray data were generated for all LCLs, including 1.3 million SNPs, 485K DNA methylation probes and 54K mRNA expression probe sets. Metformin cytotoxicity assay was performed using MTS assay. Genome-wide association (GWA) analyses and integrated analyses using all the genomic data and metformin cytotoxicity (IC50 value) were performed to identify top candidate genes for functional validation in breast cancer cell lines, MDA-MB 231 and Hs578T, using siRNA screening approach. Further mechanistic study was performed to investigate the role of candidates in regulation of AMPK activity. RESULTS GWA analyses in LCLs identified 198 mRNA expression probe sets and 210 DNA methylation probes associated with metformin IC50 with p-value <10-4, respectively. Analysis of 1.3 million SNPs found 12 loci (a region containing at least 1 SNP of p-value <10-5 and 1 SNP of p-value <10-3 within 50kb) that were associated with metformin IC50. Integrated SNP loci-mRNA expression-IC50 analysis indicated that the SNP loci on chromosome 16 was associated with metformin IC50 through a trans-regulation of expression of 7 genes with p-value <10-4. Integrated methylation-mRNA expression-IC50 analysis showed that 15 DNA methylation probes in 6 genes were associated with metformin IC50 through both cis- and trans-regulation of expression of 48 genes with p-value <10-4, including 4 methylation probes in 3 of those 6 genes that were cis-correlated with its own gene expression. Ingenuity pathway analysis of top 62 candidate genes identified four major pathways and 55 genes within those pathways were selected for functional validation in breast cancer cell lines. The knockdown experiment showed that down regulation of 25 genes significantly altered metformin sensitivity in MDA-MB231, and 14 of them also showed the same effect in Hs578T. Preliminary mechanistic experiment indicated that knockdown of 7 genes significantly reduced AMPK activity in MDA-MB231 and 3 of them had same effect in Hs578T. CONCLUSIONS GWAS using a genomic data-enriched LCL model system, together with functional validation in breast cancer cell lines, could help us to identify novel genetic and epigenetic biomarkers involved in metformin response and help us to better understand the mechanisms of metformin in cancer treatment. Citation Format: Nifang Niu, Xianglin Tan, Brooke L. Fridley, Daniel J. Schaid, Ryan P. Abo, Anthony Batzler, Erin E. Carlson, Gregory Jenkins, Sebastian Moran, Holger Andrea Heyn, Manel Esteller Badosa, Liewei Wang. Metformin pharmacogenomics: A genome-wide associate study to identify genetic and epigenetic biomarkers involved in metformin response. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2271. doi:10.1158/1538-7445.AM2013-2271

Abstract The FKBP5 gene encodes the FKBP51 immunophilin, a negative regulator of AKT. Variability in level of FKBP5 expression is a major factor contributing to variation in response to several classes of chemotherapeutic agents including gemcitabine, first line treatment for pancreatic cancer. Genetic variation of FKBP5 could influence its expression, function, and, ultimately, treatment response of pancreatic cancer. We set out to comprehensively study the role of genetic variation in FKBP5 identified by Next Generation DNA resequencing in response to gemcitabine treatment of pancreatic cancer by utilizing both tumor and germline DNA samples from 43 pancreatic cancer patients, including 19 paired tumor-normal samples. Genotype-phenotype association studies with overall survival (in tumor only) as well as with FKBP5 gene expression were performed using the same samples in which Next Generation DNA resequencing had been performed, followed by functional genomic studies of top selected single nucleotide polymorphisms (SNPs). In-depth resequencing identified 404 FKBP5 SNPs in both normal and tumor DNA patient samples. SNPs with the smallest p-values for association with FKBP5 survival or expression were subjected to functional genomic studies. Electromobility shift assay (EMSA) showed that the rs73748206 “T” allele altered DNA-protein binding patterns, consistent with a significantly increased reporter gene activity. The effect of rs73748206 was also confirmed by mRNA expression studies using lymphoblastoid cell lines (LCL) derived from the same patients from whom DNA used for resequencing had been obtained. This comprehensive FKBP5 resequencing study provides insight into the role of inheritance in variation in response to gemcitabine therapy of pancreatic cancer. Citation Format: Katarzyna A. Ellsworth, Bruce W. Eckloff, Liang Li, Irene Moon, Brooke L. Fridley, Greg D. Jenkins, Erin Carlson, Abra Brisbin, Ryan Abo, William Bamlet, Gloria Petersen, Eric D. Wieben, Liewei Wang. Contribution of FKBP5 genetic variation to gemcitabine treatment and survival in pancreatic cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2209. doi:10.1158/1538-7445.AM2013-2209

Abstract Integrative genomics has the potential to uncover relevant loci, as clinical outcome and response to chemotherapies are most likely not due to a single gene (or data type) but rather a complex relationship involving genetic variation, mRNA, DNA methylation, and copy number variation. In addition to this complexity, many complex phenotypes are thought to be controlled by the interplay of multiple genes within the same molecular pathway or gene set (GS). To address these two challenges, we propose an integrative gene set analysis approach and apply this strategy to a cisplatin (CDDP) pharmacogenomics study involving lymphoblastoid cell lines for which genome-wide SNP and mRNA expression data was collected. Application of the integrative GS analysis implicated the role of the RNA binding and cytoskeletal part GSs. The genes LMNB1 and CENPF, within the cytoskeletal part GS, were functionally validated with siRNA knockdown experiments, where the knockdown of LMNB1 and CENPF resulted in CDDP resistance in multiple cancer cell lines. This study demonstrates the utility of an integrative GS analysis strategy for detecting novel genes associated with response to cancer therapies, moving closer to tailored therapy decisions for cancer patients.

Abstract Variation in drug response results from a combination of factors that include differences in gender, ethnicity, and environment, as well as genetic variation that may result in differences in mRNA and protein expression. This article presents two integrative analytic approaches that make use of both genome-wide SNP and mRNA expression data available on the same set of subjects: a step-wise integrative approach and a comprehensive analysis using sparse canonical correlation analysis (SCCA). In addition to applying standard SCCA, we present a novel modification of SCCA which allows different weighting for the various pair-wise relationships in the SCCA. These integrative approaches are illustrated with both simulated data and data from a pharmacogenomic study of the drug gemcitabine. Results from these analyses found little overlap in terms of genes detected, possibly detecting different biological mechanisms. In addition, we found the proposed weighted SCCA to outperform its unweighted counterpart in detecting associations between the genomic features and phenotype. Further research is needed to develop and assess new integrative methods for pharmacogenomic studies, as these types of analyses may uncover novel insights into the relationship between genomic variation and drug response.

Abstract Background: Multiple genome-wide and candidate gene association studies have been conducted in search of common risk variants for breast cancer. Recent large meta analyses, consolidating evidence from these studies, have been consistent in highlighting the caspase-8 (CASP8) gene as important in this regard. To define a risk haplotype and map the CASP8 gene region with respect to underlying susceptibility variant/s, we screened four genes in the CASP8 region on 2q33-q34 for breast cancer risk. Methods: Two independent data sets from the United Kingdom and the United States, including 3,888 breast cancer cases and controls, were genotyped for 45 tagging single nucleotide polymorphisms (tSNP) in the expanded CASP8 region. SNP and haplotype association tests were carried out using Monte Carlo-based methods. Results: We identified a three-SNP haplotype across rs3834129, rs6723097, and rs3817578 that was significantly associated with breast cancer (P < 5 × 10−6), with a dominant risk ratio and 95% CI of 1.28 (1.21–1.35) and frequency of 0.29 in controls. Evidence for this risk haplotype was extremely consistent across the two study sites and also consistent with previous data. Conclusion: This three-SNP risk haplotype represents the best characterization so far of the chromosome upon which the susceptibility variant resides. Impact: Characterization of the risk haplotype provides a strong foundation for resequencing efforts to identify the underlying risk variant, which may prove useful for individual-level risk prediction, and provide novel insights into breast carcinogenesis. Cancer Epidemiol Biomarkers Prev; 21(1); 176–81. ©2011 AACR.

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.