# 125 Expression profiling of peripheral blood cells for early detection of breast cancer J. Aarøe 1 • T. Lindahl 2 • S. Sæbø 3 • P. Skaane 4 • S. Myhre 1 • T. Reiersen 1 • A. Lönneborg 2 • A-L. Børresen-Dale 1 • P. Sharma 2 1 Department of Genetics, The Norwegian Radium Hospital, N-0310 Oslo, Norway. 2 Dia. Genic ASA, Oslo, Norway. 3 IKBM, University of Life Sciences, 1432 Ås, Norway. 4 Department of Surgery, Ullevål University Hospital, Oslo, Norway. Introduction Results and Discussion Early detection of breast cancer is a key to successful treatment and patient survival. The existing methods to detect breast cancer in asymptomatic patients have limitations, and there is a need to develop more accurate and convenient methods. In a recently published study (Sharma et al. 2005), we demonstrate the potential use of gene expression profiling in peripheral blood cells (PBC) for early detection of breast cancer. However, the study was based on limited sample size and the use of in-house manufactured macroarrays. The aim of the present study was to investigate whether the findings reported earlier could be reproduced using a larger sample size and a commercially available microarray platform. We identified a set of 58 genes that correctly predicted the diagnostic class in 75% ± 7% of the samples, including several cases of early stage cancers (stage 0 and stage I). In addition to some gene families reported earlier such as ribosomal genes, the identified predictive genes also included some novel gene families. Pathway analysis identified a number of pathways based on the identified genes. These pathways are currently being investigated more thoroughly to reveal possible tumor-blood interactions. Materials and methods Whole blood were collected in PAX tubes from 64 females diagnosed with breast cancer and 76 females with no reported sign of the disease. Total RNA was extracted and gene expression analyses were conducted using high density oligonucleotide arrays (Agilent Technologies) containing 22. 000 probes. Expression data were analyzed using several statistical approaches: Partial Least Square Regression (PLSR) was used for model building while a novel approach combining double and triple cross validation (CV) was used to identify stable and relevant predictive genes and estimate their prediction efficiency. A number of studies have reported overoptimistic accuracy levels due to improper validation where selection bias has not been taken into account (Ambroise and Mc. Lachlan, 2002). In order to avoid such selection bias and to obtain unbiased estimates of accuracy, a trippel CV approach was required, since the gene selection procedure was based on an inner double CV routine (Figure 1). Based on the selected predictors, pathway analysis was conducted (Pathway. Assist, Ariadne Genomics). In this study, factors known to significantly affect data quality such as manufacturing lot, hybridization and labeling time were randomized as we expected less noise when using commercial arrays and protocols. Though, the results show that diagnostic information relating to early stage breast cancer can still be mined from PBC, it is important that any test intended for breast cancer diagnosis has high prediction accuracy. We have recently conducted a pilot study using Applied Biosystems 44 K microarrays following a design that allows efficient normaliztion of the facors affecting data quality. The results show a significant improvement in the prediction accuracy. We are now reanalyzing 130 blood samples using ABI microarrays in a carefully designed experiment. Conclusions • Larger study supports our previous finding that breast cancer affects gene expression patterns in PBC during early stages of disease development. • A blood-based gene expression test can potentially be developed for early detection of breast cancer. Ongoing and future studies • Validate the relevancy of identified predictive genes by Taq. Man RT-PCR. Repeated for all 140 samples Test samples Classification Cross-validation Model selection Variable selection using class info on training samples Model selection Cross-validation Full variable set Variable selection using class info on all samples Training samples Model 1 Training samples Model 2 Full variable set Model 3 All samples Model 4 Proper validation Model 139 Improper validation Select stable and common genes across segments • Develop a simple, objective and accurate gene expression based diagnostic tool for early detection of breast cancer. References Test samples Classification • Try to understand the underlying biology causing the gene expression changes in blood cells of breast cancer patients. Sample 140 Jackknife significant genes 1. Sharma et al. (2005). Breast Cancer Research 7(5): R 634 -644. 2. Ambroise C, Mc. Lachlan GJ (2002). Proc. Natl. Acad. Sci. USA, 99: 6562 -6566. Acknowledgement Figure 1 The present study was supported by The National Programme for Research in Functional Genomics in Norway (FUGE) in the Research Council of Norway The 97 th AACR Annual Meeting, Washington DC, USA, 1 -5 April 2006
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