Breast cancer classification: Linking molecular mechanisms to disease prognosis

Atefeh Taherian-Fard, Sriganesh Srihari, Mark A. Ragan

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)


Breast cancer was traditionally perceived as a single disease; however, recent advances in gene expression and genomic profiling have revealed that breast cancer is in fact a collection of diseases exhibiting distinct anatomical features, responses to treatment and survival outcomes. Consequently, a number of schemes have been proposed for subtyping of breast cancer to bring out the biological and clinically relevant characteristics of the subtypes. Although some of these schemes capture underlying molecular differences, others predict variations in response to treatment and survival patterns. However, despite this diversity in the approaches, it is clear that molecular mechanisms drive clinical outcomes, and therefore an effective scheme should integrate molecular as well as clinical parameters to enable deeper understanding of cancer mechanisms and allow better decision making in the clinic. Here, using a large cohort of ~550 breast tumours fromThe Cancer Genome Atlas, we systematically evaluate a number of expression-based schemes including at least eight molecular pathways implicated in breast cancer and three prognostic signatures, across a variety of classification scenarios coveringmolecular characteristics, biomarker status, tumour stages and survival patterns.We observe that a careful combination of these schemes yields better classification results compared with using them individually, thus confirming that molecular mechanisms and clinical outcomes are related and that an effective scheme should therefore integrate both these parameters to enable a deeper understanding of the cancer.

Original languageEnglish
Pages (from-to)461-474
Number of pages14
JournalBriefings in Bioinformatics
Issue number3
Publication statusPublished or Issued - 2 Aug 2014


  • Biomarkers
  • Breast cancer classification
  • Clinical subtypes
  • Gene expression
  • Molecular signature

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

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