The molecular basis of breast cancer pathological phenotypes

• Type of Material: Article
• Authors: Yujing J Heng, Beth Israel Deaconess Cancer Center; Susan C Lester, Harvard Medical School; Gary MK Tse, Chinese University of Hong Kong; Rachel E Factor, University of Utah; Kimberly H Allison, Stanford University; Laura C Collins, Beth Israel Deaconess Cancer Center; Yunn-Yi Chen, University of California; Kristin C Jensen, Stanford University; Nicole B Johnson, Beth Israel Deaconess Cancer Center; Jong Cheol Jeong, Beth Israel Deaconess Cancer Center; Rahi Punjabi, Beth Israel Deaconess Cancer Center; Sandra J Shin, Weill Cornell Medical College; Kamaljeet Singh, Brown University; Gregor Krings, University of California San Francisco; David A Eberhard, University of North Carolina; Puay Hoon Tan, Singapore General Hospital; Konstanty Korski, Greater Poland Cancer Centre; Frederic M Waldman, University of California San Francisco; David Andrew Gutman, Emory University; Melinda Sanders, Vanderbilt University; Jorge S Reis-Filho, Memorial Sloan Kettering Cancer Cente; Sydney R Flanagan, Beth Israel Deaconess Cancer Center; Deena MA Gendoo, University Health Network; Gregory M Chen, University Health Network; Benjamin Haibe-Kains, University Health Network; Giovanni Ciriello, University of Lausanne; Katherine A Hoadley, University of North Carolina; Charles M Perou, University of North Carolina; Andrew H Beck, Beth Israel Deaconess Cancer Center
• Language: English
• Date: 2017-02-01
• Publisher: Wiley
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
• Final Published Version (URL): http://dx.doi.org/10.1002/path.4847
• Title of Journal or Parent Work: Journal of Pathology
• ISSN: 0022-3417
• Volume: 241
• Issue: 3
• Start Page: 375
• End Page: 391
• Grant/Funding Information: Funding for this project was provided by the Klarman Family Foundation (AHB), the National Cancer Institute of the National Institutes of Health (SPORE grant P50CA168504; AHB), and the National Library of Medicine of the National Institutes of Health Career Development Award (Number K22LM011931; AHB).
• Keywords: FIBROTIC FOCUS; TUMOR-INFILTRATING LYMPHOCYTES; genomics; BASAL PHENOTYPE; PAM50; CTLA-4 BLOCKADE; TCGA; Pathology; FATTY-ACID OXIDATION; bioinformatics; CARCINOMA IN-SITU; PROGNOSTIC-FACTORS; Life Sciences & Biomedicine; INVASIVE DUCTAL CARCINOMA; epithelial tubule formation; Science & Technology; GENE-EXPRESSION PROFILES; HISTOLOGICAL GRADE; histological grade; Oncology; mRNA
• Research Categories: Health Sciences, Oncology; Health Sciences, Pathology

Abstract

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 C ancer 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.