Abstract P3-05-03: Identification of epigenetically silenced breast cancer driver genes
Authors: S Panjarian, J Madzo, C Slater, J Jelinek, X Chen, J-P Issa
Journal: Cancer Research (2019)

Abstract Breast cancer is clinically and molecularly complex disease driven by aberrant genetic and epigenetic alterations. Epigenetic alterations in particular DNA methylation changes are one of the most important events involved in breast cancer initiation and progression. Previous reports identified many aberrant DNA methylation signatures associated with molecular subtypes of breast cancer and over 100 candidate genes with promoter hypermethylation in breast cancer. However, it remains elusive which of these genes with promoter hypermethylation play “driver” role in tumorigenesis. In previous studies, the average gain of DNA methylation across all cancer samples compared to the average DNA methylation in normal samples has been the criterion to select for potential targets. However, known tumor suppressor driver genes regulated by methylation are relatively infrequently altered in target cancers. Therefore, we propose the paradoxical hypothesis that identifying hypermethylated cancer drivers require focusing on infrequent rather than frequent events. Hence, to identify these potential driver genes, we developed an algorithm with two unique properties. First, unlike previous studies we focused on targets that gained DNA methylation relatively infrequently (10-40%) and that lost expression in breast cancer. Second, using this algorithm, we distinguished cancer dependent gain of DNA methylation from age-dependent gain of methylation. To discern age dependent and independent DNA methylation changes, we generated DNA methylation sequencing data on 29 normal purified breast epithelium (age range 33-82 years old). Furthermore, to study the biological effects of the overexpression or downregulation of these genes, we generated DNA methylation sequencing data on 6 breast cancer cell lines. We also used DNA methylation and RNA expression datasets (675 cancer, 100 normal) available through the TCGA. Using our algorithm, we identified 53 genes with age independent promoter hypermethylation and loss of expression in TCGA tumor samples. To begin testing the biological effects of these driver genes, we performed canonical pathway enrichment analyses using Ingenuity Pathway Analysis software. We also investigated the mutational status of these genes and their molecular subtype enrichment. Based on these analyses, we picked 12 genes (C10orf125, RUNX3, YOD1, FXYD5, SMOC1, SLC16A5, RNLS, DKK1, PNPLA3, FZD10, RND2, and PLCB1) for further study. We stably overexpressed these potential driver genes in different breast cancer cell lines. Twelve genes out of the 12 tested, slowed cell proliferation and 9 decreased anchorage independent growth. We further validated these driver genes by knocking them out in normal human mammary epithelial cells using CRISPR/Cas9 tool. The loss of these genes, increased cell proliferation rate in normal human mammary epithelial cells compared to the control cells. In conclusion, based on our preliminary data, using bioinformatics tools as well as functional assays, we identified epigenetically altered breast cancer driver genes. Identifying and deciphering true epigenetic cancer drivers could potentially lead to the development of therapeutic drugs targeting these genes and/or targeting pathway dependence. Citation Format: Panjarian S, Madzo J, Slater C, Jelinek J, Chen X, Issa J-P. Identification of epigenetically silenced breast cancer driver genes [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-05-03.