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Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care?

Nature Reviews Cancer volume 7, pages 545–553 (2007)Cite this article

Abstract

The advent of microarray technology has enabled scientists to simultaneously investigate the expression of thousands of genes. Gene-expression profiling studies have provided a molecular classification of breast cancer into clinically relevant subtypes, new tools to predict disease recurrence and response to different treatments, and new insights into various oncogenic pathways and the process of metastatic progression. Here we describe the state of the art of gene-expression studies in breast cancer, and consider both their current limitations and future promises. We also discuss the potential of molecular signatures to have an impact on individual breast cancer patient management, and ultimately to accelerate the transition between empirical and tailored medicine.

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Figure 1: TRANSBIG independent validation study.
Figure 2: Comprehensive clinical decision-making algorithm.

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Acknowledgements

We would like to thank the reviewers for their constructive comments and suggestions, as well as C. Straehle for her technical assistance. All the research work at the Translational Research Unit was supported by the MEDIC foundation, Breast Cancer Research foundation (BCRF), Fonds de la Recherche Scientifique (FNRS), les Amis de l'Institut Jules Bordet, Veridex LLC (San Diego, USA) and the European Union FP6 programme.

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Authors and Affiliations

  1. Christos Sotiriou is at the Translational Research Unit, Jules Bordet Institute, 121 Boulevard de Waterloo, 1000 Brussels, Belgium.,

    Christos Sotiriou

  2. Martine J. Piccart is at the Medicine Department, Jules Bordet Institute, 121 Blvd de Waterloo, 1000 Brussels, Belgium.,

    Martine J. Piccart

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  1. Christos Sotiriou
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Correspondence to Martine J. Piccart.

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Related links

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DATABASES

National Cancer Institute

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FURTHER INFORMATION

Authors' laboratory homepage

Adjuvant!Online

Microarray Quality Control

Glossary

Bottom-up supervised approaches

The 'bottom-up' approach first identifies gene-expression profiles linked with a specific biological phenotype, and subsequently correlates these findings to survival.

Top-down supervised approaches

The 'top-down' approach generates gene-expression patterns associated with clinical outcome without any a priori biological assumptions.

Gene classifier

A statistical procedure in which individual items (that is, patients) are placed into groups (that is, low and high risk) based on quantitative information on one or more genes.

Predictive factors

Factors that correlate with response to, or benefit from, a given treatment according to specific patient or tumour characteristics.

Prognostic factors

Factors that give information on the prognosis for different subgroups of patients, describing the natural course and outcome, unrelated to different therapeutic interventions.

Supervised analysis

The method is 'supervised' or taught on a set of training data for which the outputs are already known. The algorithm attempts to match its predicted outputs to the known outputs.

Unsupervised analysis

A statistical method for microarrays that does not need additional previously derived information about the data to be analysed. The outputs are simply a description of the relationships among the samples or genes. A widely used unsupervised approach is hierarchical clustering, whereby single expression profiles are successively joined to form nodes that form a single hierarchical tree.

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Sotiriou, C., Piccart, M. Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care?. Nat Rev Cancer 7, 545–553 (2007). https://doi.org/10.1038/nrc2173

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