Abstract A1-56: Applying TCGA data for breast cancer diagnostics and pathway analysis

Abstract

Abstract The main objective of our project is to obtain a comprehensive insight into oncogenic signaling in order to develop novel diagnostic tools for molecular subtypes of breast cancer (BRCA). We have provided about 100 BRCA samples along with full pathology and clinical annotations to the TCGA program. These cancer samples have been extensively characterized using all available genomics platforms and the obtained molecular data contributed to the molecular characterization of BRCA. This integrated effort identified four major molecular subtypes of breast cancer that can be identified across five profiling platform including genomics, transcriptomics and proteomics (Nature, 490 (7418):61-70). Building upon these datasets we aim to translate these genomics profiles into diagnostic tools that can be used in every day medical practice. In the first step, we have generated tissue microarrays (TMAs) from FFPE BRCA samples obtained from the patients enrolled into the TCGA project. Next, the TMA slides were used for immunohistochemistry (IHC) using about 100 antibodies specific for major cancer markers, including oncogenic kinases frequently activated or overexpressed in BRCA. The obtained IHC results have been scored and verified independently for each marker by two board-certified pathologists. To identify markers that correlate with or are specific to the molecular subtypes of BRCA, the TMA/IHC read- outs from each tumor sample were correlated with all TCGA genomic data. In addition, we integrate the pathology and genomics results with clinical data obtained from the TCGA-enrolled patients, including up to 4 years follow-up. Finally, we apply system biology tools to integrate the genomic data from TCGA with the proteomic analysis to understand causalities between changes in DNA, transcriptome and signal transduction pathways. Our long-term goal is to identify novel diagnostic biomarkers that will precisely identify each molecular subtype of BRCA and which may be predictive of patient response to therapy thus paving the way for novel personalized therapies for cancer. Note: This abstract was not presented at the conference. Citation Format: Jannik Andersen, Parantu Shah, Matthew Ibbs, Violetta Filas, Konstanty Korski, Andrzej Marszalek, Yuanqing Wu, Edward Chang, Carlo Toniatti, Giulio Draetta, Maciej Wiznerowicz. Applying TCGA data for breast cancer diagnostics and pathway analysis. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-56.