Abstract
IntroductionEarly 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.MethodsBlood 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.ResultsA 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.ConclusionsWe have identified a gene signature in whole blood that classifies breast cancer patients and healthy women with good accuracy supporting our previous findings.
Highlights
Detection of breast cancer is key to successful treatment and patient survival
The 738 probe list predicted cases and controls with an estimated accuracy of 79.5% based on leave-one-out cross validation (LOO-CV) with a sensitivity of 80.6% and specificity of 78.3%
When plotting the sensitivity versus 1-specificity in a receiver operating characteristics (ROC) curve (Figure 1b), we observe a good separation of the two groups with an area under curve (AUC) of 0.88
Summary
Detection of breast cancer is key to successful treatment and patient survival. To reduce breast cancer mortality, early detection and appropriate treatment play a key role [3]. The fiveyear survival rate for stage I breast cancer in Norway in the period 1998 to 2002 was 95%, and 16.8% for stage IV metastatic breast cancer [2]. This emphasizes the importance of early detection so that treatment can be initiated as early as possible during tumor development. MRI is expensive, and the high false positive rate, limited resources and lack of universally accepted imaging guidelines restrict the use of MRI in a screening setting. The need for improved methods to accurately detect breast cancer at an early stage is highly desirable
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