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Breast Cancer Molecular Stratification: From Intrinsic Subtypes to Integrative Clusters

  • Hege G. Russnes
  • , Ole Christian Lingjærde
  • , Anne Lise Børresen-Dale
  • , Carlos Caldas*
  • *Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

225 Scopus citations

Abstract

Breast carcinomas can be stratified into different entities based on clinical behavior, histologic features, and/or by biological properties. A classification of breast cancer should be based on underlying biology, which we know must be determined by the somatic genomic landscape of mutations. Moreover, because the latest generations of anticancer agents are founded on biological mechanisms, a detailed molecular stratification is a requirement for appropriate clinical management. Such stratification, based on genomic drivers, will be important for selecting patients for clinical trials. It will also facilitate the discovery of novel drivers, the study of tumor evolution, and the identification of mechanisms of treatment resistance. Assays for risk stratification have focused mainly on response prediction to existing treatment regimens. Molecular stratification based on gene expression profiling revealed that breast cancers could be classified in so-called intrinsic subtypes (luminal A and B, HER2-enriched, basal-like, and normal-like), which mostly corresponded to hormone receptor and HER2 status, and further stratified luminal tumors based on proliferation. The realization that a significant proportion of the gene expression landscape is determined by the somatic copy number alterations that drive expression in cis led to the newer classification of breast cancers into integrative clusters. This stratification of breast cancers into integrative clusters reveals prototypical patterns of single-nucleotide variants and is associated with distinct clinical courses and response to therapy.

Original languageEnglish
Pages (from-to)2152-2162
Number of pages11
JournalAmerican Journal of Pathology
Volume187
Issue number10
DOIs
StatePublished - Oct 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2017 American Society for Investigative Pathology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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