Abstract 2627: Molecular analyses of histopathologic morphologic features in breast cancer

Abstract

Abstract Traditional histopathologic analysis of breast cancer phenotypes has played a central role in the diagnosis, prognosis and clinical management of breast cancer. This study integrated molecular data with breast cancer histopathologic annotations to elucidate the molecular basis of these common morphologic features. We constructed a large, comprehensive histopathologic database of 850 invasive breast cancer cases from The Cancer Genome Atlas (TCGA). We integrated the consensus assessments of 11 morphologic features (nuclear pleomorphism, mitotic count, epithelial tubule formation, inflammation, DCIS, LCIS, lymphovascular invasion, necrosis, fibrotic focus, apocrine features and proportion of epithelium in invasive portion by area) with TCGA's genomic, transcriptomic and proteomic data. Using this highly annotated dataset, we identified molecular profiles associated with morphologic features, constructed Omics-based multivariate models to predict morphologic features and provided insights into their molecular etiology. The association of morphologic features’ signatures with survival in ER-positive and ER-negative breast cancer was assessed using six independent datasets. All data are publicly accessible at http://pathology.ai/tcga_breast. Morphologic features were associated with PAM50 subtypes, PAM50 proliferation scores, genomic alterations and gene expression (p<0.05). Clustering of morphologic features and genomic alterations produced two clusters of morphologic features and their separate were driven by TP53, CDH1 and PIK3CA mutations and chr12p13.3, ch8q24.21 and chr3q26.3 amplifications. The clustering of morphologic features and gene sets/pathways also produced two clusters of morphologic features characterized by “proliferation” or “inflammation”. The transcriptomic signatures of nuclear pleomorphism and epithelial tubule formation were independently prognostic in ER-positive breast cancer. No signatures were prognostic in ER-negative. Our detailed morphologic data enrich and complement TCGA's existing molecular data, increase our understanding of the molecular basis of breast cancer pathologic phenotypes, can facilitate the refinement of breast cancer classification, and enhance our understanding of breast cancer biology. Citation Format: Yu Jing Jan Heng, TCGA Breast Cancer Expert Pathology Committee, Jong Cheol Jeong, Deena M.A Gendoo, Benjamin Haibe-Kains, Giovanni Ciriello, Katherine A. Hoadley, Charles M. Perou, Andrew H. Beck. Molecular analyses of histopathologic morphologic features in breast cancer. [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 2627.