Abstract A08: Carpe D.I.E.M: A Data Integration Expectation Map of multi-`omics data in breast cancer disparities

Abstract

Abstract Advances in high-throughput technologies and the availability of multi-`omics data present the opportunity for more holistic understandings of biologic regulation in breast cancer disparities. The complexity and heterogeneity of breast cancer requires the development of equally complex breast cancer models with multiple layers of biologic information. This however, requires the integration of biologic, computational, and statistical domains. Currently, nonetheless, there exist major gaps in the availability and knowledge among the three domains. Typically, biologists experience problems with processing and analyzing biologic data, and therefore seek data scientists for more customized analysis. In contrast, some data scientists lack a thorough understanding of biologic regulation and the complex interactions of various systems giving rise to varying breast cancer phenotypes. This generally results in less comprehensive analysis and an overall narrow understanding of breast cancer disparities. The various attributes giving rise to breast cancer disparities in African Americans can only be thoroughly understood when various levels of `omic interactions are considered as a whole. Thus, developing the most comprehensive biologic models of breast cancer disparities must consider the multiple appropriate layers of genomic, epigenomic, transcriptomic, proteomic, and metabolomic regulation, as well as the potential role environmental and social factors play at each `omic level. Historically, diverse data types have been considered independently while combinations of two or more data types have been utilized less frequently. Singular analysis of independent `omic contributions in breast cancer often neglect the intricate interactions among the distinct levels giving rise to complex disease traits. Although environmental and social factors have a major role in breast cancer disparities, breast cancer can also result from mutual alterations in assorted pathways and biologic processes, including gene mutations, epigenetic changes, and modifications in gene regulation. Therefore, the various phenotypes in diverse subtypes of breast cancer as well as the nature of disparities in incidence and mortality rates represent a major example of the need for integrated biologic models for breast cancer analysis. These integrative approaches to breast cancer disparities can consequently result in an increased ability to facilitate successful and personalized selection of novel targets for treatment and drug development. In this study, we present the Data Integration Expectation Map (D.I.E.M), in which we explore the scientific value of integrating various `omic data combinations to reveal mechanisms of biologic regulation in breast cancer disparities. Several journal articles have described the integration of one or more `omic data types in various disease traits; however, this study is unique in addressing the large-scale information acquired from combining two or more types of data and the diverse combinations of data, compatible for further analysis. The goal of D.I.E.M is to convey the potential for integration of genomic, epigenomic, transcriptomic, proteomic, and metabolomic data for improving our understanding of the nature of breast cancer heterogeneity and disparities. In doing so, this map will address the gaps in knowledge among biologic, computational, and statistical domains. D.I.E.M will also reveal the expected outcomes for each `omic data type and the various combinations that may or may not divulge a holistic view into complex breast cancer phenotypes. With that, we expect to gain a greater understanding of physiologic processes contributing to breast cancer heterogeneity and disparities as well as the role each `omic interaction plays in screening, diagnosis, and prognosis of breast cancer. Citation Format: Tia A. Hudson, ClarLynda Williams-DeVane. Carpe D.I.E.M: A Data Integration Expectation Map of multi-`omics data in breast cancer disparities [abstract]. In: Proceedings of the Tenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2017 Sep 25-28; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2018;27(7 Suppl):Abstract nr A08.