Open Access Highly Accessed Research article

Gene expression profiling of peripheral blood cells for early detection of breast cancer

Jørgen Aarøe16, Torbjørn Lindahl2, Vanessa Dumeaux3, Solve Sæbø4, Derek Tobin2, Nina Hagen2, Per Skaane56, Anders Lönneborg2, Praveen Sharma2 and Anne-Lise Børresen-Dale16*

Author Affiliations

1 Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Montebello, Oslo, NO-0310, Norway

2 DiaGenic ASA, Grenseveien 92, Oslo, NO-0663, Norway

3 Institute of Community Medicine, University of Tromsø, Tromsø, NO-9037, Norway

4 Department of Chemistry, Biotechnology, and Food Science, Norwegian University of Life Sciences, Ås, NO-1432, Norway

5 Department of Radiology, Oslo University Hospital Ullevål, Oslo, NO-0407, Norway

6 Institute of Clinical Medicine, University of Oslo, Oslo, NO-0316, Norway

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Breast Cancer Research 2010, 12:R7  doi:10.1186/bcr2472

Published: 15 January 2010

Abstract

Introduction

Early detection of breast cancer is key to successful treatment and patient survival. We have previously reported the potential use of gene expression profiling of peripheral blood cells for early detection of breast cancer. The aim of the present study was to refine these findings using a larger sample size and a commercially available microarray platform.

Methods

Blood samples were collected from 121 females referred for diagnostic mammography following an initial suspicious screening mammogram. Diagnostic work-up revealed that 67 of these women had breast cancer while 54 had no malignant disease. Additionally, nine samples from six healthy female controls were included. Gene expression analyses were conducted using high density oligonucleotide microarrays. Partial Least Squares Regression (PLSR) was used for model building while a leave-one-out (LOO) double cross validation approach was used to identify predictors and estimate their prediction efficiency.

Results

A set of 738 probes that discriminated breast cancer and non-breast cancer samples was identified. By cross validation we achieved an estimated prediction accuracy of 79.5% with a sensitivity of 80.6% and a specificity of 78.3%. The genes deregulated in blood of breast cancer patients are related to functional processes such as defense response, translation, and various metabolic processes, such as lipid- and steroid metabolism.

Conclusions

We have identified a gene signature in whole blood that classifies breast cancer patients and healthy women with good accuracy supporting our previous findings.