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Author Notes:

Correspondence to: YJ Heng and AH Beck, Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, 330 Brookline Ave, Dana 517/528, Boston MA 02115, USA. yheng@bidmc.harvard.edu and abeck2@bidmc.harvard.edu

The authors contributed in the following way: AHB, CMP: conception and design of the study; AHB, SCL, GMKT, REF, KHA, LCC, YC, KCJ, NBJ, SJS, KS, GK, DAE, PHT, KK, FMW, JSRF: provided pathology annotations and clinical input; YJH, AHB, BHK, DMAG, GMC, GC, KAH, CMP: retrieved and acquired other relevant data; YJH, AHB: analysed and interpreted the data; JCJ, SRF, RP, DMAG, BHK, GMC, DAG: provided website and/or database support; YJH, AHB, CMP, SCL, GMKT, DMAG, BHK, KCJ, MS, JSRF, PHT, SJS, DAE, REF, KAH, KK: involved in the writing, reviewing and revision of the manuscript.

The data used in this study were in whole or in part based on the data generated by the TCGA Research Network: http://cancergenome.nih.gov/.

Conflicts of interest statement CMP is an equity stock holder and Board of Director Member of BioClassifier. CMP is also listed an inventor on patent applications on the Breast PAM50 assay.

AHB is an equity stock holder and Board of Director Member of PathAI.

The remaining authors declare no competing financial interests.

Subjects:

Research Funding:

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:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Oncology
  • Pathology
  • PAM50
  • TCGA
  • bioinformatics
  • genomics
  • mRNA
  • epithelial tubule formation
  • histological grade
  • CARCINOMA IN-SITU
  • TUMOR-INFILTRATING LYMPHOCYTES
  • INVASIVE DUCTAL CARCINOMA
  • GENE-EXPRESSION PROFILES
  • FATTY-ACID OXIDATION
  • HISTOLOGICAL GRADE
  • PROGNOSTIC-FACTORS
  • BASAL PHENOTYPE
  • CTLA-4 BLOCKADE
  • FIBROTIC FOCUS

The molecular basis of breast cancer pathological phenotypes

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Journal Title:

Journal of Pathology

Volume:

Volume 241, Number 3

Publisher:

, Pages 375-391

Type of Work:

Article | Post-print: After Peer Review

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.

Copyright information:

© 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

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