Gene Expression Profiling of Peripheral Blood Cells for Early Detection of Breast Cancer

Jørgen Aarøe; Torbjørn Lindahl; Vanessa Dumeaux; Solve Sæbø; Derek Tobin; Nina Hagen; Per Skaane; Anders Lönneborg; Praveen Sharma; Anne-Lise Børresen-Dale

Disclosures

Breast Cancer Res. 2010;12(1):R7

Abstract and Introduction

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.

Introduction

Cancer of the breast is the most common cancer among women worldwide with an estimated 1,300,000 new cases and 465,000 deaths annually.^[1] In Norway, the age-adjusted incidence rate for breast cancer has more than doubled from 36.7 per 100,000 in the period 1953 to 1957 to 75.6 per 100,000 in the period 2003 to 2007.^[2] To reduce breast cancer mortality, early detection and appropriate treatment play a key role.^[3] The five-year 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. Mammographic screening, physical examination and self examination are the main modalities for breast cancer detection today, but only mammography screening has been shown to reduce mortality. When a tumor is detectable in the breast, either by palpation or mammography, the tumor might have been present for several years and have had the ability to spread to distant organs. The growth rate of breast tumors varies considerably between subjects.^[4] Some tumors grow so rapidly that they escape a biannual screening program and hence show clinical symptoms before detection by mammography. In addition, mammographic sensitivity is significantly reduced in women with dense breast tissue, often seen in pre-menopausal women or those receiving menopausal hormone therapy.^[5] Due to the low sensitivity of mammography in women with dense breast tissue, other imaging modalities have been introduced in breast cancer screening including ultrasonography and magnetic resonance imaging (MRI). However, ultrasound is very operator-dependent, time-consuming, and is associated with many false positive results. 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.

Previous studies have found that use of peripheral blood cells for transcriptome analysis is valuable to assess disease-associated^[6–10] and drug-response related gene signatures.^[11] We have previously demonstrated the potential use of gene expression profiling of peripheral blood cells for early detection of breast cancer.^[12] Blood samples are easily available, minimally invasive and can be collected at low cost making them an attractive alternative modality for diagnostic purposes. The rationale for using blood as a clinical sample is that breast cancer triggers a response in circulating blood cells, leading to a traceable change in the whole blood gene expression signature. In this study we aimed to refine our previous findings^[12] with a different sample set, using a larger sample size and a commercially available microarray platform.

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Cite this: Gene Expression Profiling of Peripheral Blood Cells for Early Detection of Breast Cancer - Medscape - Jan 15, 2010.

Table 1. Clinical characteristics of the subjects included in the study (n = 127)
Diagnosis	Number of samples
Total Breast Cancer	67
Pure DCIS	10
Histological grade I	1
Histological grade II	2
Histological grade III	7
Invasive Ductal Carcinoma (IDC)	49
Histological grade I	11
Histological grade II	17
Histological grade III	16
Histological grade Unknown	5
Invasive Lobular Carcinoma (ILC)	4
Histological grade I	2
Histological grade II	2
Histological grade III	0
Other invasive	4
Invasive Tubular Carcinoma (ITC)	2
Medullary Carcinoma	1
Other/mixed cases	1
Total Non-malignant	63*
Benign changes	12
Fibroadenoma	1
Fibroadenoma and haematoma	1
Cyst	6
Unspecified findings	4
No mammographic findings	42
Controls	9
Breast feeding	1
Pregnant	3
Menstrual cycle (2 subjects)	5
Total samples	130*

* Data from biological replicates were merged leaving 127 assays for analyses.
DCIS = ductal carcinoma in situ.

Table 2. ER and PR status among the 67 breast cancers samples
Status	Number of samples
ER+/PR+	36
ER-/PR-	7
ER+/PR-	7
ER-/PR+	1
Unknown	16

ER = estrogen receptor; PR = progesterone receptor

Table 3. Subject demographics
Demographic information	Breast Cancer N = 67	Healthy N = 60
Age
Mean	58	56
Min	38	37
Max	82	70
Not registered	9	10
Menopausal status
Pre-menopausal	14	15
Post Menopausal	37	29
Unknown	16	16
Menopausal hormone therapy
Yes	13	13
No	54	57

Table 4. Functional enrichment of genes expressed higher in blood of breast cancer patients compared to healthy subjects
Biological process	Count	%	P-value	Genes	Fold enrichment	FDR
GO:0006412 Translation	20	8.55%	0.0037	RPL26L1, LOC440587, RPS29, RPL37A, RPL11, UBA52, RPS3A, EEF1G, TRSPAP1, RPL36A, RPL24, RPL17, RPL14, RPL15, RPL4, RPL6, RPS25, ETF1, AARSD1, RPL12,	2.0	6.6
GO:0042742 Defense response to bacterium	5	2.14%	0.0064	DEFA3, LTF, CAMP, PPBP, S100A12,	6.5	11.3
GO:0044249 Cellular biosynthetic process	27	11.54%	0.0112	LOC440587, RPL26L1, ATP5E, UBA52, RPL11, RPL14, RPL4, ATP6V0B, RPS25, RPS29, RPL37A, RPS3A, ATP5L, EEF1G, TRSPAP1, RPL24, RNPEPL1, RPL36A, RPL17, GUK1, RPL15, PRODH, MTHFS, RPL6, ETF1, AARSD1, RPL12,	1.6	18.9
GO:0009605 Response to external stimulus	16	6.84%	0.0115	DEFA3, TIRAP, S100A12, CDKN2D, NMI, CXCR3, STAT3, RALBP1, CLU, PF4, AIF1, PPBP, C8B, CMTM5, ANXA1, GP1BB,	2.0	19.4

FDR = false discovery rate

Table 5. Functional enrichment of genes expressed lower in blood of breast cancer patients compared to healthy subjects
Biological process	Count	%	P-Value	Genes	Fold Enrichment	FDR
GO:0044255 Cellular lipid metabolic process	20	7.69%	0.0008	C10orf33, MBTPS1, PMVK, OSBPL7, SULT1A2, PEMT, LASS6, CMAS, SYK, PLAA, SULT1A4, INSIG1, IDI1, FDPS, HEXA, PECI, CYP2J2, ACAA1, SULT1A1, GRN,	2.3	1.6
GO:0008202 Steroid metabolic process	9	3.46%	0.0022	INSIG1, IDI1, MBTPS1, FDPS, PMVK, OSBPL7, SULT1A2, SULT1A1, SULT1A4,	3.8	4.0
GO:0006629 Lipid metabolic process	21	8.08%	0.0027	C10orf33, MBTPS1, ACAT2, PMVK, OSBPL7, SULT1A2, PEMT, LASS6, CMAS, SYK, PLAA, SULT1A4, INSIG1, IDI1, FDPS, HEXA, PECI, CYP2J2, ACAA1, SULT1A1, GRN,	2.1	4.9
GO:0006584 Catecholamine metabolic process	4	1.54%	0.0057	SULT1A2, HDC, SULT1A1, SULT1A4,	10.3	10.1
GO:0018958 Phenol metabolic process	4	1.54%	0.0057	SULT1A2, HDC, SULT1A1, SULT1A4,	10.3	10.1

FDR = false discovery rate

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