Expression quantitative trait loci analysis in breast cancer tumor and normal adjacent FFPE specimens from the Nurses Health Study. A. Quiroz-Zarate1, B. J. Harshfield2, N. Knoblauch3, S. Christe3, R. Hu2, D. J. Hunter2,4, A. H. Beck3, R. M. Tamimi2,4, J. Quackenbush1,4, A. Hazra2,4, U19/GAME-ON DRIVE Consortium 1) Center for Computational Biology, Dana Farber Cancer Institute, Boston, MA; 2) Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA; 3) Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA; 4) Harvard School of Public Health, Boston, MA.
Genome-wide association studies (GWAS) of breast cancer have identified 70 risk alleles. To identify regulatory variants we conducted expression quantitative trait loci (eQTL) analysis in clinically applicable formalin-fixed paraffin embedded (FFPE) breast tissue to identify potential regulatory effects of those risk alleles. We identified 450 invasive breast cancer cases in the Nurses Health Study (NHS) diagnosed from 1990-2004 with GWAS data and sufficient RNA for expression profiling in paired breast tumor and normal adjacent breast tissue. RNA was extracted using the Qiagen AllPrep kit, amplified using the NuGen Ovation FFPE WTA kit, and profiled using the Affymetrix Human Transcriptome Array (HTA 3.0v1). The HTA includes 6,892,960 features for measuring gene expression, alternative splicing, coding SNPs, and noncoding transcripts. After filtering, we had data from 45,560 probes sets representing over 17,000 genes. 70 risk alleles were included in the linear regression model as dosages and analyzed to identify associations with gene expression. Analyses were conducted separately for tumor and normal adjacent samples and performed for breast cancer as a single disease as well as for five intrinsic molecular subtypes. We further evaluated the stepwise effect of batch-to-batch variation, patients age at diagnosis, and year of diagnosis and did not observe an effect of age or year of diagnosis at a genome-wide level. We identified SNPs associated with expression levels using a genome-wide model adjusted for batch-to-batch variation in tumor and normal tissue (stratified analysis). Finally, we performed an innovative functional QTL (fQTL) analysis to gain additional functional insight into genetic variants important in breast cancer. In the fQTL analysis we tested for the association between SNPs and the expression of gene functional classes and pathways, evaluating the hypothesis that SNPs may also be associated with regulation of processes in addition to individual genes. Here we will present significant association with the goal of understanding functional changes in genetically-regulated cellular processes that occur during the development and progression of breast cancer. Our data show that susceptibility eQTLs can be identified in archival samples. Identification of gene transcripts in FFPE tissue that are affected by breast cancer loci is critical in understanding the mechanism by which these variants affect risk and mediate disease processes.
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