title: Dissecting cancer heterogeneity--an unsupervised classification approach.
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.
authors.affiliation: Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK. Electronic address: xin.wang@cruk.cam.ac.uk.
authors.forename: Xin
authors.initials: X
authors.lastname: Wang
authors.forename: Florian
authors.initials: F
authors.lastname: Markowetz
authors.forename: Felipe
authors.initials: F
authors.lastname: De Sousa E Melo
authors.forename: Jan Paul
authors.initials: JP
authors.lastname: Medema
authors.forename: Louis
authors.initials: L
authors.lastname: Vermeulen
mesh_terms: D016000:Cluster Analysis; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D018740:Genetic Heterogeneity; D006801:Humans; D009369:Neoplasms / Q000145:classification / Q000235:genetics*
keywords: Cancer subtypes; Consensus clustering; Gene expression; Personalized medicine; Stratified medicine