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International Journal of Molecular Epidemiology and Genetics logoLink to International Journal of Molecular Epidemiology and Genetics
. 2013 Nov 28;4(4):235–249.

Epidermal growth factor receptor (EGFR) polymorphisms and breast cancer among Hispanic and non-Hispanic white women: the Breast Cancer Health Disparities Study

Avonne E Connor 1, Richard N Baumgartner 1, Kathy B Baumgartner 1, Christina M Pinkston 1, Esther M John 2,3, Gabriela Torres-Mejía 4, Lisa M Hines 5, Anna R Giuliano 6, Roger K Wolff 7, Martha L Slattery 7
PMCID: PMC3852643  PMID: 24319539

Abstract

The epidermal growth factor receptor (EGFR), a member of the ErbB family of receptor tyrosine kinases, functions in cellular processes essential to the development of cancer. Overexpression of EGFR in primary breast tumors has been linked with poor prognosis. We investigated the associations between 34 EGFR tagging SNPs and breast cancer risk and breast cancer-specific mortality in 4,703 Hispanic and 3,030 non-Hispanic white women from the Breast Cancer Health Disparities Study. We evaluated associations with risk of breast cancer defined by estrogen/progesterone receptor (ER/PR) tumor phenotype. Only one association remained statistically significant after adjusting for multiple comparisons. Rs2075112GA/AA was associated with reduced risk for ER-/PR+ tumor phenotype (odds ratio (OR), 0.34; 95% confidence interval (CI) 0.18-0.63, p adj=0.01). All additional results were significant prior to adjustment for multiple comparisons. Two of the EGFR polymorphisms were associated with breast cancer risk in the overall study population (rs11770531TT: OR, 0.56, 95% CI 0.37-0.84; and rs2293348AA: OR, 1.20, 95% CI 1.04-1.38) and two polymorphisms were associated with risk among Hispanics: rs6954351AA: OR, 2.50, 95% CI 1.32-4.76; and rs845558GA/AA: OR, 1.15, 95% CI 1.01-1.30. With regard to breast cancer-specific mortality, we found positive associations with rs6978771TT hazard ratio (HR), 1.68; 95% CI 1.11-2.56; rs9642391CC HR, 1.64; 95% CI 1.04-2.58; rs4947979AG/GG HR, 1.36; 95% CI 1.03-1.79; and rs845552GG HR, 1.62; 95% CI 1.05-2.49. Our findings provide additional insight for the role of EGFR in breast cancer development and prognosis. Further research is needed to elucidate EGFR’s contribution to ethnic disparities in breast cancer.

Keywords: Breast cancer, Hispanic, epidermal growth factor receptor, polymorphisms, tumor phenotype

Introduction

The epidermal growth factor receptor (EGFR) is a member of the ErbB family of receptor tyrosine kinases, and is expressed in epithelial and mesenchymal tissues and tissues of neuronal origin. This gene plays an important role in the processes of the normal cell, which includes differentiation, proliferation, and development [1]. EGFR also functions with various cellular processes essential to the development of cancer, including cell division, angiogenesis, migration, and inhibition of apoptosis [2]. There are six known direct binding ligands for EGFR, which include EGF, transforming growth factor, amphiregulin, betacellulin, epiregulin, and heparin-binding EGF [3]. Receptor dimerization is initiated by ligand binding, and subsequently activates signaling pathways by the triggering of the cytosolic kinase domain of the receptor tyrosine kinase, leading to cross-autophosphorylation of the receptors [3]. These EGFR-signaling pathways, such as the pathway that leads to suppression of apoptosis through phosphatidylinositol 3-kinase and subsequent Akt activation, have been recognized to be supportive of the development and progression of cancer [3].

The role of EGFR in breast cancer etiology is of considerable interest [4]. Overexpression of EGFR in primary breast tumors has been linked with poor prognosis [5] and 30-52% of triple negative (estrogen receptor negative (ER-), progesterone receptor negative (PR-), Her2/neu negative) breast cancer overexpress EGFR [6]. Mutations in EGFR also have been documented in triple negative breast cancer [7]. A recent study conducted by Jacot and colleagues identified the possibility of geographic and ethnic variations in the frequency of these specific EGFR mutations [8]. Single nucleotide polymorphisms (SNPs) account for the majority of human genetic variation [9] and some research has shown that EGFR SNPs may regulate protein expression [10] and could potentially change gene expression [11,12]. No epidemiological studies to date have examined the direct relationships between EGFR polymorphisms and risk of breast cancer by tumor phenotypes, or considered these associations among women with Hispanic ethnicity, as Hispanic women with breast cancer more frequently have ER- or triple negative tumors than non-Hispanic whites [13,14]. Furthermore, the functional significance for many of the genetic variants in the EGFR gene and the potential for interethnic differences of these SNPs have yet to be completely illuminated [15]. Additionally, there have not been any epidemiological studies that examined the direct relationship between EGFR polymorphisms and breast cancer mortality.

Several recent studies have investigated the relationship between EGFR polymorphisms and breast cancer risk [2,9,12,16]. One study that examined the effect of rs11568315 found that women with the SS (short/short) genotypes were almost two times more likely (OR, 1.86; 95% CI, 1.02-4.67) to develop breast cancer compared to women with the LL (long/long) genotypes. Additionally, these women were over three times more likely (OR, 3.36; 95% CI, 1.04-10.91) to develop breast cancer before the age of 55 years [2]. Kallel et al. found no association with rs11543848; however, the homozygous GG genotype was more prevalent among breast cancer cases with lymph node metastasis and high grade tumors [16]. Choura et al. did not find significant associations between rs17337451 or rs17290699 with breast cancer risk; however, their results demonstrated that the T allele of rs1140476 was associated with increased breast cancer risk [9].

We hypothesized that the EGFR gene would be associated with risk of breast cancer and breast cancer-specific mortality in our sample of Hispanic and non-Hispanic white (NHW) women from the Breast Cancer Health Disparities Study. We also evaluated the association between EGFR polymorphisms and breast cancer risk according to ER/PR tumor phenotypes and investigated effect modification by self-reported ethnicity, percentage of Native American ancestry, and menopausal status.

Materials and methods

The Breast Cancer Health Disparities Study (BCHDS) includes participants from three population-based case-control studies: the 4-Corner’s Breast Cancer Study (4-CBCS), the San Francisco Bay Area Breast Cancer Study (SFBCS), and the Mexico Breast Cancer Study (MBCS) [17]. All participants signed informed written consent prior to participation, completed an interview, and had a blood or mouth sample available for DNA extraction; the study was approved by the Institutional Review Board for Human Subjects at each institution.

The 4-CBCS participants were Hispanic and NHW women between 25 and 79 years of age with a histological confirmed diagnosis of in situ (n=341) or invasive (n=1492) cancer between October 1999 and May 2004; controls were selected from the target populations of cases living in Arizona, Colorado, New Mexico, and Utah and were frequency matched to cases on ethnicity and 5-year age distribution [18]. Participants from the MBCS were Hispanic and between 28 and 74 years of age, living in one of three states, Monterrey, Veracruz and Mexico City, for the past five years. Eligible cases in Mexico were women diagnosed with either a new histologically confirmed in situ or invasive breast cancer between January 2004 and December 2007 at 12 participating hospitals from three main health care systems; controls were randomly selected from the catchment area of the 12 participating hospitals using a probabilistic multi-stage design [19]. The SFBCS included Hispanic and NHW women aged 35 to 79 years from the San Francisco Bay Area diagnosed with a first primary histologically confirmed invasive breast cancer between April 1995 and April 2002; controls were identified by random-digit dialing and frequency-matched to cases based on the expected race/ethnicity and 5-year age distribution [20,21].

Data harmonization

Interview data were harmonized across the three studies [17]. The present analyses considered adjusting for body mass index (BMI, kg/m2) calculated as self-reported weight during the referent year (or more distantly recalled weight if referent year weight was not available or measured weight if neither were available) divided by measured height squared, parity (number of live births and stillborn pregnancies), self-reported ethnicity in the U.S. studies (all women in Mexico were considered Hispanic), and highest level of education. The referent year was defined as the calendar year prior to diagnosis for cases or selection into the study for controls.

Genetic data

DNA was extracted from either whole blood (n=7286) or mouthwash (n=637) samples. Whole Genome Amplification (WGA) was applied to the mouthwash-derived samples prior to genotyping. A tagSNP approach was used to characterize variation across candidate genes. TagSNPs were selected as follows: linkage disequilibrium (LD) blocks were defined using a Caucasian LD map and an r2=0.8; minor allele frequency (MAF) >0.1; range=-1500 bps from the initiation codon to +1500 bps from the termination codon; and 1 SNP/LD bin. A total of 104 Ancestral Informative Markers (AIMs) was used to distinguish European and Native American ancestry in the study population [17]. All markers were genotyped using a multiplexed bead array assay format based on GoldenGate chemistry (Illumina, San Diego, California). A genotyping call rate of 99.93% was attained (99.65% for WGA samples). We included 132 internal replicates that were blinded representing 1.6% of the sample set. The duplicate concordance rate was 99.996% as determined by 193,297 matching genotypes among sample pairs [17].

In the current analysis, we examined 34 EGFR polymorphisms: rs6978771, rs72352, rs11487218, rs12535536, rs10225877, rs917880, rs12718945, rs11977660, rs2075112, rs6954351, rs6944906, rs2330951, rs17586365, rs759160, rs1344307, rs4947979, rs9642391, rs11770531, rs4947971, rs3752651, rs12671550, rs9692301, rs1558544, rs845552, rs1140475, rs845558, rs6593205, rs2472520, rs17151957, rs2293348, rs6970262, rs2280653, rs884419, and rs763317. Table 1 describes the EGFR polymorphisms in detail, including the minor allele frequencies (MAF) and adjusted Hardy-Weinberg equilibrium (HWE) p values. Online supplement 1 describes the LD between all 34 EGFR polymorphisms by self-reported ethnicity.

Table 1.

Description of EGFR polymorphisms

non-Hispanic Whites Hispanics
EGFR SNPs Coordinate Region Major/Minor Allele1 Major allele freq. Minor allele freq. FDR adjusted HWE p value Major allele freq. Minor allele freq. FDR adjusted HWE p value Proportion Missing
rs6978771 55140296 INTRON C/T 0.75 0.25 0.62 0.61 0.39 0.95 0.00023753
rs11487218 55141540 INTRON T/C 0.66 0.35 0.93 0.80 0.20 0.08 0.000475059
rs10225877 55150822 INTRON A/T 0.81 0.19 0.96 0.86 0.14 0.30 0.00023753
rs12718945 55192963 INTRON G/T 0.55 0.45 0.98 0.67 0.33 0.05 0.000950119
rs2075112 55219611 INTRON G/A 0.58 0.42 0.93 0.59 0.41 0.62 0
rs6944906 55251953 INTRON A/G 0.59 0.41 0.96 0.56 0.44 0.98 0.00023753
rs17586365 55140786 INTRON G/A 0.86 0.14 0.96 0.90 0.10 0.93 0
rs1344307 55137888 INTRON A/G 0.80 0.20 0.96 0.89 0.11 0.59 0
rs9642391 55245364 INTRON G/C 0.72 0.28 0.84 0.65 0.35 0.83 0
rs4947971 55160995 INTRON C/T 0.69 0.31 1.00 0.76 0.24 0.70 0
rs12671550 55173675 INTRON C/G 0.69 0.31 0.93 0.60 0.40 0.20 0.00023753
rs1558544 55228053 INTRON T/A 0.72 0.28 0.96 0.85 0.15 0.72 0.00023753
rs1140475 55266417 CODING C/T 0.88 0.12 0.96 0.89 0.11 0.86 0
rs6593205 55168692 INTRON G/A 0.60 0.40 0.98 0.63 0.37 0.61 0.00023753
rs17151957 55200512 INTRON G/A 0.77 0.23 0.89 0.68 0.32 0.48 0.00023753
rs6970262 55259763 INTRON G/A 0.62 0.39 0.92 0.72 0.28 0.56 0
rs884419 55276280 INTERGENIC C/T 0.90 0.10 0.96 0.86 0.14 0.50 0.00023753
rs763317 55095197 INTRON G/A 0.52 0.48 0.96 0.72 0.28 0.35 0
rs723527 55134872 INTRON G/A 0.59 0.41 0.86 0.67 0.33 0.10 0.004038005
rs12535536 55154381 INTRON A/G 0.70 0.30 0.96 0.85 0.15 0.91 0.00023753
rs917880 55162011 INTRON C/T 0.53 0.47 0.96 0.58 0.42 0.88 0.00023753
rs11977660 55162336 INTRON T/C 0.53 0.47 0.98 0.53 0.47 0.29 0
rs6954351 55171190 INTRON G/A 0.86 0.14 0.86 0.92 0.08 0.67 0.000712589
rs2330951 55174342 INTRON A/C 0.75 0.25 0.66 0.75 0.26 0.23 0
rs759160 55181442 INTRON A/G 0.76 0.24 0.93 0.83 0.17 0.41 0.00023753
rs4947979 55195625 INTRON A/G 0.79 0.21 0.86 0.85 0.15 0.95 0.00023753
rs11770531 55220905 INTRON C/T 0.86 0.14 0.52 0.91 0.09 0.30 0
rs3752651 55229543 INTRON T/C 0.80 0.20 0.96 0.85 0.15 0.44 0.00023753
rs9692301 55243754 INTRON A/G 0.69 0.31 0.98 0.64 0.36 0.60 0.000475059
rs845552 55245507 INTRON A/G 0.52 0.48 0.96 0.54 0.46 0.52 0.059144893
rs845558 55247588 INTRON G/A 0.58 0.42 0.94 0.57 0.43 0.61 0.012589074
rs2472520 55265940 INTRON C/G 0.57 0.44 1.00 0.54 0.46 0.66 0
rs2293348 55266757 INTRON G/A 0.69 0.31 0.93 0.63 0.37 0.19 0.003562945
rs2280653 55276094 INTERGENIC T/C 0.84 0.16 0.96 0.81 0.19 0.68 0.00023753
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1

Major/minor allele reported for NHW population; minor allele frequency and Hardy-Weinberg Equilibrium (HWE) based on control population.

Tumor characteristics and survival data

Information on ER and PR status was obtained from the cancer registries in New Mexico, Utah, Colorado, Arizona, and California for 979 (68%) NHW cases and 958 (75%) Hispanic cases. These data were not available for the MBCS.

Survival status was available for the New Mexico, Utah, Colorado, Arizona, and California study centers. Each center’s respective cancer registry provided information on date of death or last follow-up (month and year). Survival (in months) was calculated as the difference between diagnosis date and date of death or last follow-up. The cause of death was classified as breast cancer if either the primary or contributing cause of death noted on the death certificate was breast cancer. Survival data were not available for the MBCS.

Statistical methods

STRUCTURE was used to compute individual ancestry assuming two founding populations [22,23] and each study participant was classified by level of percent Native American ancestry. The following strata for percentage of genetic ancestry were created using cut-points based on the distribution of genetic ancestry in the control population: 0-28%, 29-70%, and 71-100%. The groups were categorized in this manner to ensure sufficient power to assess associations. When used as an adjusting variable to assess confounding, genetic ancestry was modeled as a continuous variable. Descriptive statistics were calculated for all covariates and t-tests and chi-square tests were used to assess differences between groups. The homozygous wildtypes for each polymorphism were used as the referent categories. Using co-dominant models, genotype associations for all EGFR SNPs were estimated as odds ratios (ORs) with 95% confidence intervals (CIs) by unconditional logistic regression with adjustments for age and study center. Based on initial assessment of the co-dominant associations, dominant and recessive models were also examined. Potential confounders included BMI, menopausal status, menopausal hormone therapy (HT) use, physical activity, caloric intake per day, and smoking status (ever or never). These covariates were included in multivariable models if their univariate P values were ≤0.20 and if they changed the point estimate for the main effects of the EGFR genotypes by ≥10% for SNPs that were found to be statistically significant prior to multiple comparisons [24]. However, there was no evidence of confounding and the models were adjusted for age, study site, and percentage of Native American ancestry. Interactions between EGFR variants with ethnicity, genetic ancestry, and menopausal status were assessed using the likelihood-ratio test comparing the model including an interaction term with a reduced model without the term. For survival analyses, hazard ratios (HR) and 95% CIs were derived using multivariable Cox proportional hazard models and were adjusted for SEER disease stage at diagnosis, age, genetic ancestry, and study center. Stratified analyses were also conducted for survival analyses to determine if there was evidence of effect modification by genetic ancestry or by ethnicity.

Women were classified as either premenopausal or postmenopausal based on self-reported responses to questions on menstrual history. Women who reported menstruation during the referent year were classified as premenopausal. The classification for postmenopausal women was established by using criteria provided by each individual study. If women were taking (HT) and still having periods and were at or above the 95th percentile of age for ethnicity of those who reported having a natural menopause among their study site, they were classified as postmenopausal. This age was 58 for NHW and 56 for Hispanics in the 4-CBCS, age 54 in the MBCS, and 55 for NHW and 56 for Hispanics in the SFBCS.

Multinomial logistic regression models were constructed to evaluate the associations between EGFR genotypes and breast cancer risk by ER/PR status [25,26]. Results were adjusted for multiple comparisons taking into account tagSNPs within the gene using the step-down Bonferroni correction (i.e., Holm’s method) based on the effective number of independent SNPs as determined using the SNP spectral decomposition method proposed by Nyholt [27] and modified by Li and Ji [28]. The interaction p values, based on 1-df likelihood-ratio tests, were adjusted using the step-down Bonferroni correction or the Holm’s test [29]. We considered an adjusted p value of 0.10 or less as potentially important for main effects and a Holm’s p value of 0.15 or less for interactions. All data analyses were performed using SAS version 9.3 (SAS Institute, Cary NC).

Results

The distributions of the demographic and major risk factors for breast cancer in the Breast Cancer Health Disparities Study have been previously reported [17,30]. A total of 7,733 breast cancer cases and controls were included in analyses that evaluated breast cancer risk; 1,943 cases were available for analyses by ER/PR tumor status. Table 2 describes the distribution of selected variables of importance to the present analysis.

Table 2.

Characteristics of study population, stratified by ethnicity and case-control status, the Breast Cancer Health Disparities Study (n=7,733)

Non-Hispanic Whites (n=3,030) Hispanics (n=4,703)
Cases Controls Cases Controls
No. % No. % No. % No. % p valuea
Total Subjects 1431 1599 2093 2610
Study Site
    4-CBCS 1177 82.3 1335 83.5 579 27.7 736 28.2 <0.001
    MBCS - - 816 39.0 994 38.1
    SFBCS 254 17.8 264 16.5 698 33.4 880 33.7
Age (years)
    <40 87 6.1 117 7.3 198 9.5 313 12.0 <0.001
    40-49 401 28.0 409 25.6 708 33.8 834 32.0
    50-59 403 28.2 410 25.6 614 29.3 758 29.0
    60-69 340 23.8 356 22.3 425 20.3 530 20.3
    70+ 200 14.0 307 19.2 148 7.1 175 6.7
Percentage of Native American Ancestry
    ≤0.28 1420 99.2 1591 99.5 276 13.2 280 10.7 <0.001
    0.28-0.70 7 0.5 7 0.4 1373 65.6 1697 65.0
    >0.70 4 0.3 1 0.1 444 21.2 633 24.3
Menopausal Status
    Premenopausal 475 34.1 494 31.5 831 41.2 1027 40.7 <0.001
    Postmenopausal 919 66.9 1075 68.5 1186 58.8 1499 59.3
Estrogen/Progesterone Receptor Status
    ER+/PR+ 674 68.5 --- 594 62.1 --- 0.002
    ER+/PR- 116 11.8 --- 114 11.9 ---
    ER-/PR+ 15 1.5 --- 28 2.8 ---
    ER-/PR- 179 18.3 --- 223 23.2 ---
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Missing information: Menopausal status n=227; eligible cases missing estrogen receptor status n=765.

a

Ethnic group comparison, regardless of case-control status.

p values from chi-square tests.

Table 3 describes the significant associations (p<0.05), prior to adjustment for multiple comparisons, between the EGFR polymorphisms and breast cancer overall and by ethnicity. Two of the polymorphisms were associated with risk (rs11770531TT: OR, 0.56; 95% CI 0.37-0.84, Wald p=0.01; and rs2293348AA: OR, 1.20; 95% CI 1.04-1.38, p trend=0.12). Among Hispanic women, the AG/GG genotypes versus the AA genotype of rs6944906 were associated with decreased risk (OR, 0.87; 95% CI 0.77-0.99) and positive associations were found for rs6954351 AA genotype (OR, 2.49; 95% CI 1.31-4.72, Wald p=0.01), the rs845558 GA/AA genotypes (OR, 1.15; 95% CI 1.01-1.30, Wald p=0.03), the CC genotype of rs3752651 (OR, 1.51; 95% CI 1.03-2.20, Wald p=0.03), and the AA genotype of rs2293348 (OR, 1.24; 95% CI 1.03-1.48, p trend=0.19). Among NHW women, there was an inverse association with the TT genotype of rs11770531 (OR, 0.53; 95% CI 0.31-0.90, Wald p=0.02). Overall, none of the associations between the EGFR SNPs and breast cancer risk remained statistically significant after adjustment for multiple comparisons (Table 3). In analyses stratified by percentage of Native American ancestry (data not shown), decreased breast cancer risk was associated with the TT genotype vs. the CC/TT genotypes of rs11770531 among women with 0-28% Native American ancestry (OR, 0.54; 95% CI 0.32-0.89 Wald p=0.02) and with the AG/GG genotypes of rs6944906 among women with 71-100% ancestry (OR, 0.74; 95% CI 0.57-0.97, Wald p=0.03), after adjusting for age and study site. These results were no longer significant after adjustment for multiple comparisons.

Table 3.

Associations with EGFR polymorphisms and breast cancer risk overall and interaction by ethnicity, the Breast Cancer Health Disparities Study

Cases Controls All Women Combined Non-Hispanic Whites Hispanics
EGFR SNPs Genotype N % N % OR 95% CI OR 95% CI OR 95% CI p-int padj
rs6944906
AA 1211 (46.83) 1375 (53.17) 1.00 1.00 1.00 0.10 1.00
AG/GG 2311 (44.93) 2833 (55.07) 0.93 (0.85-1.03) 1.03 (0.88-1.19) 0.87 (0.77-0.99)
P-value: Wald; p adj 0.15; 1.00 0.72; 1.00 0.03; 0.61
rs6954351
GG/GA 3460 (45.44) 4154 (54.56) 1.00 1.00 1.00 0.01 0.17
AA 59 (53.15) 52 (46.85) 1.30 (0.89-1.90) 0.86 (0.52-1.40) 2.49 (1.31-4.72)
P-value: Wald; p adj 0.17; 1.00 0.53; 1.00 0.01; 0.12
rs11770531
CC/CT 3489 (45.73) 4140 (54.27) 1.00 1.00 1.00 0.70 1.00
TT 34 (33.01) 69 (66.99) 0.56 (0.37-0.84) 0.53 (0.31-0.90) 0.61 (0.32-1.17)
P-value: Wald; p adj 0.01; 0.13 0.02; 0.45 0.13; 1.00
rs3752651
TT/TC 3406 (45.44) 4089 (54.56) 1.00 1.00 1.00 0.05 0.79
CC 117 (49.58) 119 (50.42) 1.15 (0.88-1.49) 0.90 (0.63-1.29) 1.51 (1.03-2.20)
P-value: Wald; p adj 0.30; 1.00 0.57; 1.00 0.03; 0.65
rs845558
GG 1092 (44.14) 1382 (55.86) 1.00 1.00 1.00 0.21 1.00
GA/AA 2386 (46.24) 2774 (53.76) 1.09 (0.99-1.20) 1.02 (0.87-1.18) 1.15 (1.01-1.30)
P-value: Wald; p adj 0.07; 1.00 0.84; 1.00 0.03; 0.61
rs2293348
GG 1494 (45.73) 1773 (54.27) 1.00 1.00 1.00 0.17 1.00
GA 1535 (44.38) 1924 (55.62) 0.96 (0.87-1.06) 1.06 (0.91-1.23) 0.91 (0.80-1.03)
AA 482 (49.23) 497 (50.77) 1.20 (1.04-1.38) 1.10 (0.86-1.41) 1.24 (1.03-1.48)
P-value: trend; p adj 0.12; 1.00 0.36; 1.00 0.19; 1.00
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Models adjusted for age, study site, and genetic ancestry.

Table 4 shows associations of EGFR polymorphisms associated with breast cancer risk (p <0.05), prior to adjustment for multiple comparisons, by ER/PR tumor phenotype. We found inverse associations for the GG genotype of rs12671550 and ER+/PR- tumors (OR, 0.55; 95% CI 0.32-0.94, Wald p=0.10) and for the TT genotype versus the CC/TT genotypes of rs11770531 and ER+/PR+ tumors (OR, 0.44; 95% CI 0.23-0.83; Wald p=0.01). The following SNPs were associated with ER-/PR+ tumors: rs2075112GA/AA (OR, 0.34; 95% CI 0.18-0.63, Wald p<0.001, p adj=0.01); rs12671550CG (OR, 0.42; 95% CI 0.21-0.84, Wald p=0.18); and rs2472520GG (OR, 2.55; 95% CI 1.01-6.43, Wald p=0.05). The GT/TT genotypes of rs12718945 were associated with increased risk for ER-/PR- tumors (OR, 1.28; 95% CI 1.02-1.61 Wald p=0.04). After adjusting for multiple comparisons, only the association with rs2075112 remained statistically significant.

Table 4.

Associations with EGFR polymorphisms and risk of breast cancer by tumor phenotype, the Breast Cancer Health Disparities Study

ER+/PR+ ER+/PR- ER-/PR+ ER-/PR-
EGFR SNPs N OR (95% CI) N OR (95% CI) N OR (95% CI) N OR (95% CI) P-value (P adj)
rs12718945
GG 449 1.00 71 1.00 16 1.00 121 1.00 0.15 (1.00)
GT/TT 818 0.97 (0.84, 1.11) 158 1.21 (0.90, 1.63) 27 0.97 (0.51, 1.82) 281 1.28 (1.02, 1.61)
Wald P; p adj 0.62; 1.00 0.20; 1.00 0.92; 1.00 0.04; 0.75
rs2075112
GG 423 1.00 77 1.00 26 1.00 144 1.00 0.01 (0.24)
GA/AA 845 1.05 (0.91, 1.20) 153 1.05 (0.79, 1.39) 17 0.34 (0.18, 0.63) 258 0.93 (0.75, 1.16)
P-trend; p adj 0.50; 1.00 0.76; 1.00 0.00; 0.01 0.83; 1.00
rs12671550
CC 564 1.00 105 1.00 26 1.00 178 1.00 0.18 (1.00)
CG 554 0.95 (0.83, 1.10) 109 1.00 (0.75, 1.32) 12 0.42 (0.21, 0.84) 181 0.96 (0.77, 1.20)
GG 150 0.99 (0.80, 1.23) 16 0.55 (0.32, 0.94) 5 0.64 (0.24, 1.69) 43 0.86 (0.61, 1.23)
P-trend; p adj 0.72; 1.00 0.10; 1.00 0.06; 1.00 0.44; 1.00
rs763317
GG 460 1.00 80 1.00 15 1.00 169 1.00 0.30 (1.00)
GA/AA 808 0.94 (0.82, 1.08) 150 1.03 (0.78, 1.38) 28 1.11 (0.58, 2.11) 233 0.76 (0.61, 0.95)
Wald P; p adj 0.36; 1.00 0.82; 1.00 0.76; 1.00 0.01; 0.32
rs6954351
GG 953 1.00 173 1.00 38 1.00 308 1.00 0.09 (1.00)
GA 280 1.12 (0.95, 1.31) 55 1.22 (0.89, 1.68) 5 0.51 (0.20, 1.31) 90 1.12 (0.87, 1.45)
AA 32 1.67 (1.06, 2.63) 1 0.29 (0.04, 2.15) 0 0.00 (0.00, 0.00) 4 0.68 (0.24, 1.91)
P-trend; p adj 0.02; 0.52 0.63; 1.00 0.11; 1.00 0.66; 1.00
rs11770531
CC/CT 1256 1.00 228 1.00 41 1.00 400 1.00 0.01 (0.27)
TT 11 0.44 (0.23, 0.83) 2 0.45 (0.11, 1.84) 2 2.91 (0.68, 12.47) 2 0.27 (0.06, 1.10)
Wald P; p adj 0.01; 0.26 0.26; 1.00 0.15; 1.00 0.07; 1.00
rs2472520
CC 386 1.00 77 1.00 7 1.00 114 1.00 0.32 (1.00)
CG 620 0.99 (0.86, 1.16) 104 0.84 (0.62, 1.14) 23 1.98 (0.84, 4.63) 199 1.07 (0.84, 1.36)
GG 262 1.01 (0.84, 1.22) 49 0.95 (0.65, 1.38) 13 2.55 (1.01, 6.43) 89 1.12 (0.83, 1.50)
P-trend; p adj 0.93; 1.00 0.65; 1.00 0.05; 0.99 0.46; 1.00
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Models adjusted for age, study site, and genetic ancestry.

Table 5 shows associations of EGFR polymorphisms with risk of breast cancer-specific mortality for all invasive breast cancer cases and by ethnicity. The results displayed in this table were all significant (p<0.05) prior to adjustment for multiple comparisons. After adjusting for age, study site, SEER summary stage, and genetic ancestry, the TT genotype of rs6978771 (HR, 1.68; 95% CI 1.11-2.56, p trend=0.03), the CC genotype of rs9642391 (HR, 1.64; 95% CI 1.04-2.58, p trend=0.02), the AG/GG genotypes of rs4947979 (HR, 1.36; 95% CI 1.03-1.79, Wald p=0.03), and the GG genotype of rs845552 (HR, 1.62; 95% 1.05-2.49, p trend=0.03) were associated with increased risk of breast cancer death, whereas the GG genotype of rs1344307 (HR, 0.24; 95% CI 0.06-0.97, p trend=0.01) and the GA/AA genotypes of rs17151957 (HR, 0.69; 95% CI 0.52-0.91, Wald p=0.01) were associated with decreased risk of breast cancer death. Among Hispanics, two SNPs were associated with increased risk of breast cancer death (rs6593205AA: HR, 2.08; 95% CI 1.24-3.48, p trend=0.03; rs6944906AA/GG: HR, 1.62; 95% CI 1.05-2.48, p trend=0.03), and two SNPs were associated with decreased risk of mortality (rs17151957GA/AA: HR, 0.61; 95% CI 0.42-0.88, Wald p=0.01; and rs917880CT/TT: HR, 0.62; 95% CI 0.42-0.90, Wald p=0.01). Among NHW women, the CT/TT genotypes of rs4947971 decreased risk of breast cancer death (HR, 0.59; 95% CI 0.40-0.89, Wald p=0.01) and the GG genotype of rs845552 was associated with increased risk (HR, 1.86; 95% CI 1.02-3.37, p trend=0.04). Overall, none of the associations between the EGFR SNPs and breast cancer-specific mortality remained statistically significant after adjusting for multiple comparisons (Table 5). Interaction between EGFR polymorphisms and breast cancer death by genetic ancestry were similar to those reported by ethnicity (data not shown).

Table 5.

Associations between EGFR polymorphisms and breast cancer-specific mortality and interaction by self-reported ethnicity, the Breast Cancer Health Disparities Study

Ethnicity
All women combined Non-Hispanic Whites Hispanics
EGFR Death Person Years HR 95% CI Death Person Years HR 95% CI Death Person Years HR 95% CI P (P adj)
rs6978771
CC 93 9976 1.00 47 5539 1.00 46 4438 1.00 0.64 (1.00)
CT 93 8516 1.11 0.83 1.49 45 3861 1.26 0.83 1.91 48 4656 0.99 0.66 1.50
TT 31 1960 1.68 1.11 2.56 10 705 1.94 0.97 3.88 21 1254 1.50 0.88 2.54
p-trend; p adj 0.03; 0.55 0.06; 1.00 0.24; 1.00
rs6944906
AA 67 6890 1.00 39 3390 1.00 28 3500 1.00 0.02 (0.50)
AG/GG 150 13554 1.15 0.86 1.54 63 6715 0.83 0.55 1.25 87 6839 1.62 1.05 2.48
Wald p; p adj 0.34; 1.00 0.37; 1.00 0.03; 0.55
rs1344307
AA 167 14545 1.00 73 6643 1.00 94 7902 1.00 0.75 (1.00)
AG 48 5253 0.78 0.56 1.08 28 3046 0.90 0.58 1.41 20 2207 0.72 0.43 1.18
GG 2 668 0.24 0.06 0.97 1 416 0.22 0.03 1.60 1 252 0.29 0.04 2.07
p-trend; p adj 0.01; 0.31 0.18; 1.00 0.08; 1.00
rs9642391
GG 90 9674 1.00 38 5015 1.00 52 4659 1.00 0.28 (1.00)
GC 102 8983 1.25 0.94 1.66 54 4354 1.53 1.00 2.34 48 4629 1.01 0.68 1.51
CC 25 1808 1.64 1.04 2.58 10 735 1.93 0.93 4.01 15 1073 1.37 0.77 2.47
p-trend; p adj 0.02; 0.45 0.02; 0.46 0.41; 1.00
rs4947971 0.02 (0.48)
CC 125 10886 1.00 64 4986 1.00 61 5900 1.00
CT/TT 92 9580 0.81 0.62 1.07 38 5119 0.59 0.40 0.89 54 4461 1.13 0.78 1.65
Wald p; p adj 0.14; 1.00 0.01; 0.27 0.52; 1.00
rs6593205
GG 85 8122 1.00 42 3729 1.00 43 4394 1.00 0.06 (1.00)
GA 94 9519 0.94 0.70 1.27 45 4712 0.85 0.55 1.30 49 4807 1.01 0.66 1.53
AA 38 2815 1.37 0.93 2.01 15 1664 0.77 0.42 1.40 23 1152 2.08 1.24 3.48
p-trend; p adj 0.25; 1.00 0.33; 1.00 0.03; 0.55
rs17151957
GG 132 11014 1.00 65 5877 1.00 67 5137 1.00 0.30 (1.00)
GA/AA 85 9451 0.69 0.52 0.91 37 4228 0.79 0.53 1.20 48 5224 0.61 0.42 0.88
Wald p; p adj 0.01; 0.18 0.27; 1.00 0.01 0.20
rs917880
CC 69 6279 1.00 20 2754 1.00 49 3525 1.00 0.01 (0.13)
CT/TT 148 14187 0.90 0.67 1.20 82 7351 1.48 0.90 2.42 66 6836 0.62 0.42 0.90
Wald p; p adj 0.47; 1.00 0.12; 1.00 0.01 0.28
rs4947979
AA 131 13572 1.00 59 6509 1.00 72 7063 1.00 0.50 (1.00)
AG/GG 85 6889 1.36 1.03 1.79 42 3591 1.23 0.82 1.84 43 3298 1.47 1.00 2.16
Wald p; p adj 0.03 0.51 0.31 1.00 0.05 0.93
rs845552
AA 38 4799 1.00 19 2509 1.00 19 2289
AG 99 9086 1.43 0.98 2.09 52 5146 1.28 0.75 2.19 47 3940
GG 47 3857 1.62 1.05 2.49 28 2172 1.86 1.02 3.37 19 1685
p-trend; p adj 0.03 0.51 0.04 0.78
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Models adjusted for age, study, admixture, SEER summary stage.

We also examined the associations between EGFR polymorphisms and risk of breast cancer death by menopausal status (data not shown). There were no significant interactions for risk of breast cancer death by menopausal status within our study population. Our data did suggest an increase in risk of breast cancer mortality among premenopausal women for the following polymorphisms: rs9642391CC HR, 2.08, 95% CI 1.08-4.02, p trend=0.06 and rs4947979AG/GG HR, 1.56, 95% CI 1.03-2.38, Wald p=0.04. The TT genotype of rs6978771 (HR, 1.74, 95% CI 1.02-2.97, p trend=0.17) and the CC genotype of rs3752651 (HR, 2.12, 95% CI 1.01-4.45, Wald p=0.05) were associated with increased risk of breast cancer mortality among postmenopausal women. The GA/AA genotypes of rs17151957 was inversely associated with breast cancer mortality among postmenopausal cases (HR, 0.58, 95% CI 0.39-0.85, Wald p=0.01). None of the results by menopausal status remained statistically significant after multiple comparisons.

Discussion

Our study is one of the first using a tag-SNP approach to examine the associations of EGFR polymorphisms with risk of breast cancer-specific mortality and risk of breast cancer by ER/PR tumor phenotype. Nonetheless, only one association from the present analysis remained statistically significant after adjusting for multiple comparisons; rs2075112 was associated with significantly reduced risk for ER-/PR+ tumor phenotype. Prior to adjustment for multiple comparisons, two EGFR SNPs were found to be associated with overall breast cancer risk. With respect to breast cancer-specific mortality, we identified associations with four EGFR SNPs (rs6978771, rs9642391, rs4947979, and rs845552); and, after stratifying by ethnicity, we found rs6944906 and rs6593205 to be uniquely associated with increased risk of breast cancer death among Hispanic women. Only rs845552 was associated with increased risk of breast cancer death among NHW women from our sample, prior to adjustment for multiple comparisons.

EGFR is known to transfer extra-cellular mitogenic signals, such as EGF and transforming growth factor-alpha (TGF-α), by activating numerous downstream signaling cascades, which involve phospholipase C-c, Ras, and phosphatidylinositol-3 kinase (PI-3K) [31]. Apoptosis usually occurs after activation of the EGFR-mediated downstream pathways [31]. However, within cancer cells, there are altered gene activities leading to uncontrolled tumor proliferation. The mechanisms behind these outcomes are thought to involve Akt, also known as protein kinase B (PKB) [31]. When Akt is activated in breast cells, it phosphorylates cell cycle regulators, such as p21Cip/WAF1, and subsequently promotes tumor survival by eradicating the cell cycle checkpoints and apoptosis [31,32]. Other research has suggested the existence of a more direct mode of the EGFR pathway which involves cellular transport of EGFR from the cell-surface to the cell nucleus, association of nuclear EGFR complex with gene promoters, and transcriptional regulation of the target genes [31]. Furthermore, evidence suggests that the EGFR pathway itself is associated with increased tumor cell proliferation and poor survival rate in women with breast cancer [31,33].

Previous studies that examined the associations between EGFR polymorphisms and breast cancer risk have produced mixed findings. Several recent studies found no association between EGFR SNPs and breast cancer risk [9,12,16]. In a two-stage breast cancer case-control study using data from the Shanghai Breast Cancer Study [15], Hong et al. assessed associations with 51 EGFR polymorphisms, using a tagSNP approach. Stage 1 included 1,062 cases and 1,069 controls; and Stage 2 included 1,932 cases and 1,857 controls. Of the 51 EGFR SNPs, we examined the following SNPs: rs9642391, rs884419, rs6978771, rs6593205, rs763317, rs917880, rs11977660, rs3752651, rs2472520, and rs2293348. The Shanghai Study found significant associations with ten SNPs in Stage 1 (rs3735064, rs845562, rs845560, rs17172434, rs7780270, rs9642391, rs11976696, rs15543848, rs7808697, and rs884419). However, in a Stage 2 analysis in an independent study population, associations with the 10 SNPs could not be confirmed, suggesting that the results detected in Stage 1 were perhaps chance findings [15]. Similarly, we found no significant associations with rs9642391 and rs884419.

As previously reported, Jami and colleagues reported that the short/short genotype, compared to the long/long genotype, of rs11568315 was associated with an almost two-fold increased risk of breast cancer overall, and a nearly three-fold increased risk among women aged <55 years [2]. Brandt et al. also examined the relationship between EGFR and breast cancer risk in young women diagnosed at age <50 years and found no association for the main effects between the polymorphic CA repeat located at the 5-regulatory sequence in the intron 1 of EGFR and breast cancer risk; however, having two long alleles (≥19 CA) was associated with a significantly increased risk of breast cancer among women with a first degree family history of breast cancer (OR, 10.4; 95% CI 1.85-58.70, p interaction=0.015) [34]. We also investigated interaction effects by menopausal status; however, we did not find significant differences in results between premenopausal and postmenopausal women.

EGFR overexpression has been found in approximately 50% of triple negative breast cancer cases [35] and Hispanic women with breast cancer compared to NHWs are more likely to have triple negative disease [13,14]. Our study is the first to investigate the associations between EGFR polymorphisms and breast cancer risk among Hispanic women. Although we were unable to assess risk for triple negative breast cancer due to incomplete data on HER2 status, our results suggest, prior to multiple comparisons, an association between one EGFR SNP and ER-/PR- tumor phenotype. This analysis, however, was limited to the two U.S. study centers, given the lack of tumor phenotype data for the MBCS.

There are other strengths and limitations to the present analysis. Our study was able to compare the associations between 34 EGFR polymorphisms and breast cancer risk by ethnicity and levels of Native American ancestry. However, given the large number of SNPs analyzed, almost all of the detected associations were no longer significant after adjustment for multiple comparisons. This adjustment may have contributed to false negative associations [36], thus replication of our findings is warranted.

We also were able to examine associations of EGFR polymorphisms with breast cancer-specific mortality. There are well documented disparities in breast cancer outcomes between Hispanic and NHW women [13,37], and we examined whether differences in associations with EGFR polymorphisms could possibly explain some of the breast cancer survival disparities. This analysis, however, was limited to the U.S. study centers and thus we were not able to evaluate the full range of Native American ancestry. A strength of the survival analyses is the length of follow-up time, approximately 10 years for the SFBCS and approximately 8 years for the 4-CBCS.

In conclusion, we observed significant associations of specific SNPs in the EGFR gene with breast cancer risk and with breast cancer-specific mortality, before adjustment for multiple comparisons. Our results also suggest that these associations may differ according to ER/PR tumor phenotype. Some of our findings also suggest that differences between Hispanic and NHW women for breast cancer risk and mortality might be influenced by specific EGFR SNPs. These findings provide additional insight for the role of EGFR in breast cancer development and prognosis. Further research is needed to elucidate the contribution of EGFR to ethnic disparities in breast cancer.

Acknowledgements

The Breast Cancer Health Disparities Study was funded by grant CA14002 from the National Cancer Institute to Dr. Slattery. The San Francisco Bay Area Breast Cancer Study was supported by grants CA63446 and CA77305 from the National Cancer Institute, grant DAMD17-96-1-6071 from the U.S. Department of Defense and grant 7PB-0068 from the California Breast Cancer Research Program. The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the National Cancer Institute’s Surveillance, Epidemiology and End Results Program under contract HHSN261201000036Cawarded to the Cancer Prevention Institute of California; and the Centers for Disease Control and Prevention’s National Program of Cancer Registries, under agreement #1U58 DP000807-01 awarded to the Public Health Institute. The 4-Corner’s Breast Cancer Study was funded by grants CA078682, CA078762, CA078552, and CA078802 from the National Cancer Institute. The research also was supported by the Utah Cancer Registry, which is funded by contract N01-PC-67000 from the National Cancer Institute, with additional support from the State of Utah Department of Health, the New Mexico Tumor Registry, and the Arizona and Colorado cancer registries, funded by the Centers for Disease Control and Prevention National Program of Cancer Registries and additional state support. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official view of the National Cancer Institute or endorsement by the State of California Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors. The Mexico Breast Cancer Study was funded by Consejo Nacional de Ciencia y Tecnología (CONACyT) (SALUD-2002-C01-7462). We would also like to acknowledge the contributions of the following individuals to the study: Jennifer Herrick and Sandra Edwards for data harmonization oversight; Erica Wolff and Michael Hoffman for laboratory support; Carolina Ortega for her assistance with data management for the Mexico Breast Cancer Study, Jocelyn Koo for data management for the San Francisco Bay Area Breast Cancer Study, Dr. Tim Byers for his contribution to the 4-Corner’s Breast Cancer Study, and Dr. Josh Galanter for assistance in selection of AIMs markers for the study.

Disclosure of conflict of interest

None.

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