Genome-driven integrated classification of breast cancer validated in over 7,500 samples

H Raza Ali¹²⁴, Oscar M Rueda¹, Suet-Feung Chin¹, Christina Curtis⁵, Mark J Dunning¹,Samuel AJR Aparicio⁶ and Carlos Caldas¹³⁴^*

*Corresponding author: Carlos Caldas carlos.caldas@cruk.cam.ac.uk

Author Affiliations

¹Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK

²Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK

³Department of Oncology, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0QQ, UK

⁴Cambridge Experimental Cancer Medicine Centre and NIHR Cambridge Biomedical, Research Centre, Cambridge University Hospitals NHS, Hills Road, Cambridge, CB2 0QQ, UK

⁵Keck School of Medicine, University of Southern California, California, 90033, CA, USA

⁶Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, V5Z 1L3, Canada

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Genome Biology 2014, 15:431 doi:10.1186/s13059-014-0431-1

The electronic version of this article is the complete one and can be found online at:http://genomebiology.com/2014/15/8/431

Received:	22 July 2014
Accepted:	1 August 2014
Published:	28 August 2014

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Abstract

Background

IntClust is a classification of breast cancer comprising 10 subtypes based on molecular drivers identified through the integration of genomic and transcriptomic data from 1,000 breast tumors and validated in a further 1,000. We present a reliable method for subtyping breast tumors into the IntClust subtypes based on gene expression and demonstrate the clinical and biological validity of the IntClust classification.