Research

High-resolution aCGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer

Suet F Chin^1* , Andrew E Teschendorff^1,2* , John C Marioni^2,4* , Yanzhong Wang¹ , Nuno L Barbosa-Morais² , Natalie P Thorne^2,4 , Jose L Costa⁷ , Sarah E Pinder⁶ , Mark A van de Wiel^8,9 , Andrew R Green⁵ , Ian O Ellis⁵ , Peggy L Porter¹⁰ , Simon Tavaré^2,4 , James D Brenton³ , Bauke Ylstra⁷ and Carlos Caldas^1,6

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

²  Computational Biology Group, Cancer Research UK Cambridge Research Institute and Department of Oncology University of Cambridge, Li Ka-Shing Centre, Robinson Way, Cambridge CB2 0RE, UK

³  Functional Genomics of Drug Resistance, Cancer Research UK Cambridge Research Institute and Department of Oncology University of Cambridge, Li Ka-Shing Centre, Robinson Way, Cambridge CB2 0RE, UK

⁴  Computational Biology Group, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Centre for Mathematical Sciences, Wilberforce Road, Cambridge CB3 0WA, UK

⁵  Histopathology, Nottingham City Hospital NHS Trust and University of Nottingham, Nottingham NG5 1PB, UK

⁶  Cambridge Breast Unit, Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge, UK

⁷  Department of Pathology, VU University Medical Center, PO Box 7057, 1007MB Amsterdam, The Netherlands

⁸  Department of Biostatistics, VU University Medical Center, PO Box 7057, 1007MB Amsterdam, The Netherlands

⁹  Department of Mathematics, Vrije Universiteit, Amsterdam, Netherlands

¹⁰  Division of Human Biology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA

author email corresponding author email* Contributed equally

Genome Biology 2007, 8:R215doi:10.1186/gb-2007-8-10-r215

Published:	7 October 2007

Subject areas: Bioinformatics, Cancer, Genome studies

Abstract

Background

The characterization of copy number alteration patterns in breast cancer requires high-resolution genome-wide profiling of a large panel of tumor specimens. To date, most genome-wide array comparative genomic hybridization studies have used tumor panels of relatively large tumor size and high Nottingham Prognostic Index (NPI) that are not as representative of breast cancer demographics.

Results

We performed an oligo-array-based high-resolution analysis of copy number alterations in 171 primary breast tumors of relatively small size and low NPI, which was therefore more representative of breast cancer demographics. Hierarchical clustering over the common regions of alteration identified a novel subtype of high-grade estrogen receptor (ER)-negative breast cancer, characterized by a low genomic instability index. We were able to validate the existence of this genomic subtype in one external breast cancer cohort. Using matched array expression data we also identified the genomic regions showing the strongest coordinate expression changes ('hotspots'). We show that several of these hotspots are located in the phosphatome, kinome and chromatinome, and harbor members of the 122-breast cancer CAN-list. Furthermore, we identify frequently amplified hotspots on 8q22.3 (EDD1, WDSOF1), 8q24.11-13 (THRAP6, DCC1, SQLE, SPG8) and 11q14.1 (NDUFC2, ALG8, USP35) associated with significantly worse prognosis. Amplification of any of these regions identified 37 samples with significantly worse overall survival (hazard ratio (HR) = 2.3 (1.3-1.4) p = 0.003) and time to distant metastasis (HR = 2.6 (1.4-5.1) p = 0.004) independently of NPI.

Conclusion

We present strong evidence for the existence of a novel subtype of high-grade ER-negative tumors that is characterized by a low genomic instability index. We also provide a genome-wide list of common copy number alteration regions in breast cancer that show strong coordinate aberrant expression, and further identify novel frequently amplified regions that correlate with poor prognosis. Many of the genes associated with these regions represent likely novel oncogenes or tumor suppressors.