The molecular basis of breast cancer pathological phenotypes

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• 作者：Yujing J Heng ; Susan C Lester ; Gary MK Tse ; Rachel E Factor ; Kimberly H Allison ; Laura C Collins ; Yunn-Yi Chen ; Kristin C Jensen ; Nicole B Johnson ; Jong Cheol Jeong ; Rahi Punjabi ; Sandra J Shin ; Kamaljeet Singh ; Gregor Krings ; David A Eberhard ; Puay Hoon Tan ; Konstanty Korski ; Frederic M Waldman ; David A Gutman ; Melinda Sanders ; Jorge S Reis-Filho ; Sydney R Flanagan ; Deena MA Gendoo ; Gregory M Chen ; Benjamin Haibe-Kains ; Giovanni Ciriello ; Katherine A Hoadley ; Charles M Perou and Andrew H Beck
• 刊名：The Journal of Pathology
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：241
• 期：3
• 页码：375-391
• 全文大小：1363K
• ISSN：1096-9896

文摘

The histopathological evaluation of morphological features in breast tumours provides prognostic information to guide therapy. Adjunct molecular analyses provide further diagnostic, prognostic and predictive information. However, there is limited knowledge of the molecular basis of morphological phenotypes in invasive breast cancer. This study integrated genomic, transcriptomic and protein data to provide a comprehensive molecular profiling of morphological features in breast cancer. Fifteen pathologists assessed 850 invasive breast cancer cases from The Cancer Genome Atlas (TCGA). Morphological features were significantly associated with genomic alteration, DNA methylation subtype, PAM50 and microRNA subtypes, proliferation scores, gene expression and/or reverse-phase protein assay subtype. Marked nuclear pleomorphism, necrosis, inflammation and a high mitotic count were associated with the basal-like subtype, and had a similar molecular basis. Omics-based signatures were constructed to predict morphological features. The association of morphology transcriptome signatures with overall survival in oestrogen receptor (ER)-positive and ER-negative breast cancer was first assessed by use of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset; signatures that remained prognostic in the METABRIC multivariate analysis were further evaluated in five additional datasets. The transcriptomic signature of poorly differentiated epithelial tubules was prognostic in ER-positive breast cancer. No signature was prognostic in ER-negative breast cancer. This study provided new insights into the molecular basis of breast cancer morphological phenotypes. The integration of morphological with molecular data has the potential to refine breast cancer classification, predict response to therapy, enhance our understanding of breast cancer biology, and improve clinical management. This work is publicly accessible at www.dx.ai/tcga_breast. Copyright