Schaid, Daniel J.

Abstract Purpose: Tamoxifen and raloxifene, are primary prevention strategies for women at high risk of breast cancer (Visvanathan, 2013). Recent advances in genetic studies of breast cancer risk have identified common susceptibility loci that explain 14% of familial risk for breast cancer in the general population (Michailidou, 2013). However, it is not known if these loci are risk factors for breast cancer among high-risk women treated with SERMs for breast cancer prevention. We hypothesized that the large risk reduction associated with SERMs, coupled with the fact that several breast cancer loci correlate with family history, may limit the contribution of these common genetic loci to breast cancer in this high risk population. We present the first report to evaluate 75 established breast cancer susceptibility loci, in the context of a polygenic risk score (PRS), as a risk factor for breast cancer among high risk women taking raloxifene and tamoxifen for breast cancer prevention. Methods: We conducted a matched case-control study of 594 cases (i.e., participants who developed breast cancer while on SERM therapy) and 1,171 matched controls selected from the 33,000 participants enrolled in the NSABP P-1 and P-2 breast cancer prevention trials. Genotypes of 75 single nucleotide polymorphisms (SNPs) were available from a genome-wide association study conducted at the RIKEN Center for Genomic Medicine. We formed a quantitative PRS from reported per-SNP odds ratios (OR) for the 75 susceptibility loci. Conditional logistic regression was used to examine the PRS as a risk factor for breast cancer and to assess whether the PRS and breast cancer association differed by treatment type, family history, or other clinical characteristics. Analyses also examined associations of PRS with invasive vs. in situ cancer and ER-positive vs. ER-negative cancer. Results: The PRS ranged from 3.98 to 7.74, and a one unit change in PRS was associated with a 42% increase in breast cancer (OR=1.42; 95% CI: 1.18-1.70; P = 0.0002). There was evidence of a stronger association of PRS with breast cancer among women with no first-degree family history (OR=1.62, 95% CI: 1.18-2.21) compared to those with a positive family history (OR=1.32, 95% CI: 1.06-1.66) (Pintx<0.05). The PRS also appeared a stronger risk factor for ER-positive (OR=1.59, 95% CI: 1.25-2.02, P < 0.0002) vs. ER-negative (OR=1.05, 95% CI: 0.68-1.62, P=0.84) breast cancer, although differences did not reach statistical significance (Pintx=0.10). PRS and breast cancer associations were similar across tamoxifen and raloxifene treatments, age at trial entry, 5-year predicted Gail model risk, hysterectomy status, BMI, presence of atypical hyperplasia and invasive vs. in situ cancer. Conclusion: A polygenic risk score composed of 75 loci was a risk factor for ER-positive breast cancer, especially in the absence of a first-degree family history of breast cancer. Further, the PRS associations with breast cancer were similar for women taking tamoxifen or raloxifene for prevention. These data suggest that common genetic variation adds information on risk of ER-positive breast cancer in a high-risk population receiving SERMs. Citation Format: Celine M Vachon, Daniel J Schaid, James N Ingle, Matthew P Goetz, Donald L Wickerham, Michiaki Kubo, Erin E Carlson, Soonmyung Paik, Norman Wolmark, Yusuke Nakamura, Liewei Wang, Richard M Weinshilboum, Fergus J Couch. The contribution of common genetic variation to breast cancer risk among women receiving tamoxifen or raloxifene within the National Surgical Adjuvant Breast and Bowel Project (NSABP) P-1 and P-2 trials [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-10-03.

Abstract Family-based study design is commonly used in genetic research. It has many ideal features, including being robust to population stratification (PS). With the advance of high-throughput technologies and ever-decreasing genotyping cost, it has become common for family studies to examine a large number of variants for their associations with disease phenotypes. The yield from the analysis of these family-based genetic data can be enhanced by adopting computationally efficient and powerful statistical methods. We propose a general framework of a family-based U-statistic, referred to as family-U, for family-based association studies. Unlike existing parametric-based methods, the proposed method makes no assumption of the underlying disease models and can be applied to various phenotypes (e.g., binary and quantitative phenotypes) and pedigree structures (e.g., nuclear families and extended pedigrees). By using only within-family information, it can offer robust protection against PS. In the absence of PS, it can also utilize additional information (i.e., between-family information) for power improvement. Through simulations, we demonstrated that family-U attained higher power over a commonly used method, family-based association tests, under various disease scenarios. We further illustrated the new method with an application to large-scale family data from the Framingham Heart Study. By utilizing additional information (i.e., between-family information), family-U confirmed a previous association of CHRNA5 with nicotine dependence.

Whole exome sequencing (WES) provides an unprecedented opportunity to identify the potential aetiological role of rare functional variants in human complex diseases. Large-scale collaborations have generated germline WES data on patients with a number of diseases, especially cancer, but less often on healthy controls under the same sequencing procedures. These data can be a valuable resource for identifying new disease susceptibility loci if study designs are appropriately applied. This review describes suggested strategies and technical considerations when focusing on case-only study designs that use WES data in complex disease scenarios. These include variant filtering based on frequency and functionality, gene prioritisation, interrogation of different data types and targeted sequencing validation. We propose that if case-only WES designs were applied in an appropriate manner, new susceptibility genes containing rare variants for human complex diseases can be detected.

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 Colorectal cancer (CRC) is estimated to be the third most common cancer in both men and women in the US in 2012, accounting for over 50,000 deaths. Familial CRC accounts for approximately 30% of all CRC, yet only 5% of familial CRCs can be attributed to mutations in known hereditary colon cancer (HCC) genes. Many families with a strong family history of CRC do not have mutations in known CRC genes. Finding the genes underpinning CRC in these families may improve screening for disease and allow the development of more targeted therapeutics. We completed targeted sequencing of over 2,000 genes and miRNAs in 1,500 cases of CRC from the Colon Cancer Family Registry (CCFR) in an effort to identify rare variants associated with CRC risk and to identify mutations in known CRC genes missed by conventional sequencing. Several categories of genes were included: 18 known HCC genes; 18 candidate HCC genes; 185 genes in previously reported CRC linkage peaks; 1,423 miRNAs; and 363 genes identified through whole exome sequencing of 40 affected cases in 16 families with hereditary CRC (n=2,007 total genes sequenced). Genomic DNA samples were indexed, then libraries were made using Agilent's SureSelect Targeted Enrichment kit. Samples were pooled (96 samples/pool), then each pool of samples was run on five lanes on an Illumina HiSeq2000 (average of 19.2 samples/lane). GenomeGPS, an in-house developed workflow, was used for read alignment, variant calling, and annotation. In early analyses of the first 161 cases, the mean number of mapped reads exceeded 20 million per sample and over 90% of the target region exceeded 40x coverage. A total of 26,484 SNPs were identified and passed quality filtering. Sequencing identified known mutations in MMR genes in positive control cases as well as mutations in HCC genes in previously untested cases. Analysis is currently on-going on the remaining samples and we will report results on the full set of 1500 cases at the meeting. Citation Format: Melissa S. DeRycke, Shanaka R. Gunawardena, Sumit Middha, Shannon K. McDonnell, Shaun M. Riska, Zachary C. Fogarty, Bruce W. Eckloff, Daniel J. Schaid, Ellen L. Goode, Stephen N. Thibodeau. Targeted sequencing to identify rare variants in colorectal 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 2548. doi:10.1158/1538-7445.AM2013-2548

Purpose Liver injury is a serious adverse event leading to permanent discontinuation of lapatinib in affected patients. This study aimed to validate previously associated major histocompatibility complex (MHC) variants as predictors of risk of liver injury by using a large, randomized, placebo-controlled trial of lapatinib in human epidermal growth factor receptor 2–positive, early-stage breast cancer (Tykerb Evaluation After Chemotherapy [TEACH]: Lapatinib Versus Placebo In Women With Early-Stage Breast Cancer). Patients and Methods The frequency of ALT elevation cases was compared among four MHC variants in 1,194 patients randomly assigned to lapatinib. Cumulative ALT elevation time courses during treatment were also compared between carriers and noncarriers of specified MHC variants. Results In lapatinib-treated patients, there was a significant difference in ALT case incidence between HLA carriers and noncarriers. The highly correlated alleles HLA-DRB1*07:01 and HLA-DQA1*02:01 (study frequency, 22.4%) were associated with ALT elevation (odds ratio, 14) between cases (n = 37) and controls (n = 1,071). These associations strengthened at higher ALT elevation thresholds and in Hy's Law cases. In lapatinib-treated patients, the overall risk for National Cancer Institute-Common Terminology Criteria for Adverse Events grade 3 ALT elevation (> 5× upper limit of normal) was 2.1%; HLA allele carriers had an increased risk of 7.7%; in noncarriers, risk was reduced to 0.5%, comparable to ALT elevation for all patients receiving placebo. The increase in ALT case incidence in the lapatinib arm showed no evidence of plateau during 1 year of lapatinib treatment. Conclusion These results validate HLA-DRB1*07:01 allele carriage as a predictor of increased risk of lapatinib-induced liver injury and implicate an immune pathology. The HLA association could support clinical management of patients experiencing hepatotoxicity during lapatinib treatment.

Aims Citalopram ( CT ) and escitalopram ( S ‐ CT ) are among the most widely prescribed selective serotonin reuptake inhibitors used to treat major depressive disorder ( MDD ). We applied a genome‐wide association study to identify genetic factors that contribute to variation in plasma concentrations of CT or S ‐ CT and their metabolites in MDD patients treated with CT or S ‐ CT . Methods Our genome‐wide association study was performed using samples from 435 MDD patients. Linear mixed models were used to account for within‐subject correlations of longitudinal measures of plasma drug/metabolite concentrations (4 and 8 weeks after the initiation of drug therapy), and single‐nucleotide polymorphisms ( SNPs ) were modelled as additive allelic effects. Results Genome‐wide significant associations were observed for S ‐ CT concentration with SNPs in or near the CYP2C19 gene on chromosome 10 (rs1074145, P = 4.1 × 10 −9 ) and with S ‐didesmethylcitalopram concentration for SNPs near the CYP2D6 locus on chromosome 22 (rs1065852, P = 2.0 × 10 −16 ), supporting the important role of these cytochrome P450 ( CYP ) enzymes in biotransformation of citalopram. After adjustment for the effect of CYP2C19 functional alleles, the analyses also identified novel loci that will require future replication and functional validation. Conclusions In vitro and in vivo studies have suggested that the biotransformation of CT to monodesmethylcitalopram and didesmethylcitalopram is mediated by CYP isozymes. The results of our genome‐wide association study performed in MDD patients treated with CT or S ‐ CT have confirmed those observations but also identified novel genomic loci that might play a role in variation in plasma levels of CT or its metabolites during the treatment of MDD patients with these selective serotonin reuptake inhibitors.