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Open Access Highly Accessed Research article

Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules

Andrew E Teschendorff18*, Sergio Gomez2, Alex Arenas234, Dorraya El-Ashry5, Marcus Schmidt6, Mathias Gehrmann7 and Carlos Caldas1

Author Affiliations

1 Breast Cancer Functional Genomics Laboratory, Cancer Research UK Cambridge Research Institute and Department of Oncology University of Cambridge, Li Ka-Shing Centre, Robinson Way, Cambridge CB2 0RE, UK

2 Departament d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain

3 Institute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza 50009, Spain

4 Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

5 Sylvester Comprehensive Cancer Center and Braman Family Breast Cancer Institute, University of Miami Miller School of Medicine, Miami FL 33136, USA

6 Department of Obstetrics and Gynecology, Medical School, Johannes Gutenberg University, Mainz 55131, Germany

7 Siemens Medical Solutions Diagnostics GmbH, Cologne 50829, Germany

8 Medical Genomics Group, Paul O'Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK

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BMC Cancer 2010, 10:604  doi:10.1186/1471-2407-10-604

Published: 4 November 2010



Elucidating the activation pattern of molecular pathways across a given tumour type is a key challenge necessary for understanding the heterogeneity in clinical response and for developing novel more effective therapies. Gene expression signatures of molecular pathway activation derived from perturbation experiments in model systems as well as structural models of molecular interactions ("model signatures") constitute an important resource for estimating corresponding activation levels in tumours. However, relatively few strategies for estimating pathway activity from such model signatures exist and only few studies have used activation patterns of pathways to refine molecular classifications of cancer.


Here we propose a novel network-based method for estimating pathway activation in tumours from model signatures. We find that although the pathway networks inferred from cancer expression data are highly consistent with the prior information contained in the model signatures, that they also exhibit a highly modular structure and that estimation of pathway activity is dependent on this modular structure. We apply our methodology to a panel of 438 estrogen receptor negative (ER-) and 785 estrogen receptor positive (ER+) breast cancers to infer activation patterns of important cancer related molecular pathways.


We show that in ER negative basal and HER2+ breast cancer, gene expression modules reflecting T-cell helper-1 (Th1) and T-cell helper-2 (Th2) mediated immune responses play antagonistic roles as major risk factors for distant metastasis. Using Boolean interaction Cox-regression models to identify non-linear pathway combinations associated with clinical outcome, we show that simultaneous high activation of Th1 and low activation of a TGF-beta pathway module defines a subtype of particularly good prognosis and that this classification provides a better prognostic model than those based on the individual pathways. In ER+ breast cancer, we find that simultaneous high MYC and RAS activity confers significantly worse prognosis than either high MYC or high RAS activity alone. We further validate these novel prognostic classifications in independent sets of 173 ER- and 567 ER+ breast cancers.


We have proposed a novel method for pathway activity estimation in tumours and have shown that pathway modules antagonize or synergize to delineate novel prognostic subtypes. Specifically, our results suggest that simultaneous modulation of T-helper differentiation and TGF-beta pathways may improve clinical outcome of hormone insensitive breast cancers over treatments that target only one of these pathways.