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Int J Biochem Cell Biol. 2013 Nov;45(11):2574-9. doi: 10.1016/j.biocel.2013.08.014. Epub 2013 Sep 1.

Dissecting cancer heterogeneity--an unsupervised classification approach.

Author information

1
Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK. Electronic address: xin.wang@cruk.cam.ac.uk.

Abstract

Gene-expression-based classification studies have changed the way cancer is traditionally perceived. It is becoming increasingly clear that many cancer types are in fact not single diseases but rather consist of multiple molecular distinct subtypes. In this review, we discuss unsupervised classification studies of common malignancies during the recent years. We found that the bioinformatic workflow of many of these studies follows a common main stream, although different statistical tools may be preferred from case to case. Here we summarize the employed methods, with a special focus on consensus clustering and classification. For each critical step of the bioinformatic analysis, we explain the biological relevance and implications of the technical principles. We think that a better understanding of these ever more frequently used methods to study cancer heterogeneity by the biomedical community is relevant as these type of studies will have an important impact on patient stratification and cancer subtype-specific drug development in the future.

KEYWORDS:

Cancer subtypes; Consensus clustering; Gene expression; Personalized medicine; Stratified medicine

PMID:
24004832
DOI:
10.1016/j.biocel.2013.08.014
[Indexed for MEDLINE]

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