Abstract 5280: A web-based platform for exploring ‘omics and clinical data from healthy breast tissues

Abstract

Abstract Integrating ‘omics, epidemiology and clinical data from healthy patients and their biologic samples is important for identifying the determinants of early carcinogenesis. In a cross-sectional study of 249 women with no history of cancer and who underwent reduction mammoplasty, whole transcriptome (i.e., gene expression) and epigenome (i.e., methylation and microRNA) data were collected from dissected breast tissues. An extensive epidemiologic questionnaire ascertained data on breast cancer risk factors. Subjects with benign lesions considered at risk for future cancer were excluded. In order to manage, analyze, and share these data efficiently, we organized the data into a relational database to support a series of web-based tools for analyzing and visualizing the multidimensional data in an intuitive graphical user interface (GUI). The current platform improves upon existing web resources that integrate ‘omics and clinical data by including novel features that allow for greater flexibility of bioinformatics analyses. It also focuses on the ‘omics as an outcome, rather, e.g., what epidemiology risk factor or particular gene is associated with downstream effects across the ‘omics. Such features include: 1) a plot function that can visualize up to 4 dimension data chosen by a user from the 3 types of genetic profiles and more than 100 clinical and biomedical variables; 2) a differentiation visualization tool based on the ‘omics data of 2 groups of samples; and 3) miRNA target gene network generated by the correlations between gene expression and miRNA expression of our patient samples and validated miRNA target gene database. The GUI facilitates the discovery of research scientists by reducing complex biological and clinical data into easily understandable views. Even without the expertise of computational biology, researchers can integrate and analyze the high dimensional data that they are interested conveniently on our user-friendly designed platform. Citation Format: Xingyan Kuang, Catalin Marian, Cenny Taslim, Min-Ae Song, Daniel Weng, Jo L. Freudenheim, Theodore Brasky, Kun Huang, Kevin Coombes, Peter G. Shields. A web-based platform for exploring ‘omics and clinical data from healthy breast tissues. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5280.