Abstract
Background
Growth factors (GF) stimulate cell proliferation through binding to cell membrane receptors and are thought to be involved in cancer risk and survival.
Methods
We examined how genetic variation in epidermal growth factor (EGF), neuregulin 2 (NRG2), ERBB2 (HER2/neu), fibroblast growth factors 1 and 2 (FGF1 and FGF2) and its receptor 2 (FGFR2), and platelet derived growth factor B (PDGFB) independently and collectively influence breast cancer risk and survival. We analyzed data from the Breast Cancer Health Disparities Study which includes Hispanic (2111 cases, 2597 controls) and non-Hispanic white (NHW) (1481 cases, 1586 controls) women. Adaptive Rank Truncated Product (ARTP) analysis was conducted to determine gene significance. Odds ratios (OR) and 95% confidence intervals were obtained from conditional logistic regression models to estimate breast cancer risk and Cox Proportional Hazard models were used to estimate hazard ratios (HR) of dying from breast cancer. We assessed Native American (NA) ancestry using 104 Ancestry Informative Markers.
Results
We observed few significant associations with breast cancer risk overall or by menopausal status other than for FGFR2 rs2981582. This SNP was significantly associated with ER+/PR+ (OR 1.66 95% CI 1.37, 2.00) and ER+/PR- (OR 1.54 95% CI 1.03, 2.31) tumors. Multiple SNPs in FGF1, FGF2, and NRG2 significantly interacted with multiple SNPs in EGFR, ERBB2, FGFR2, and PDGFB, suggesting that breast cancer risk is dependent on the collective effects of genetic variants in other GFs. Both FGF1 and ERBB2 significantly influenced overall survival, especially among women with low levels of NA ancestry (PARTP = 0.007 and 0.003, respectively).
Conclusions
Our findings suggest that genetic variants in growth factors signaling appear to influence breast cancer risk through their combined effects. Genetic variation in ERBB2 and FGF1 appear to be associated with survival after diagnosis with breast cancer.
Keywords: Breast Cancer, FGF1, FGFR2, ERBB2, PDGFB, Survival, Hispanic, ER/PR
Introduction
Growth factors are polypeptides that stimulate cell proliferation through binding to cell membrane receptors and are thought to play an important role in the carcinogenic process [1]. Genes that encode growth factors and their receptors may be a significant subset of regulatory genes that when altered confer disease risk and influence survival. Genetic variants in several growth factor genes, such as transforming growth factor β, insulin-like growth factors (IGF), and vascular endothelial growth factors (VEGF) have been studied for their association with breast cancer [2-5]. Moreover, fibroblast growth factor receptor 2 (FGFR2) has been associated with breast cancer risk through genome wide associations studies (GWAS) exploration and subsequent replication studies [6-11].
Fibroblast growth factors (FGF1 and FGF2) are also known as heparin-binding growth factors. Fibroblasts are involved in angiogenesis, and are responsible for maintenance of extracellular matrix, regulation of epithelial cell differentiation, and regulation of inflammatory response [12]. Fibroblasts in the tumor microenvironment have been associated with tumor progression [12]. FGF1 is one of the main ligands for FGFR2. FGF2 has been associated with regulation of tumor angiogenesis and metastasis, and is positively correlated with epidermal growth factor (EGF) and IGF [13].
EGF and its receptor (EGFR or ERBB1) have been extensively examined with cancer risk and breast cancer specifically [14, 15]. EGFR overexpression has been correlated with loss of estrogen receptor (ER) and with poor survival [16]. While our previous work with EGFR has shown few genetic variants associated with breast cancer risk, it has been proposed that EGFR may work with other genes to modify breast cancer progression [16]. Polymorphisms of EGF have been examined less frequently with some studies showing associations with EGF plasma levels, but not with breast cancer risk [17]. Her2 (Neu or ERBB2) is structurally similar to the EGFR and interacts with EGFR at the protein level [18]. Her2 expression has been extensively studied with breast cancer prognosis [19]; however, much less is known about genetic variants that might influence breast cancer risk or survival, although studies suggest minimal risk with rs1136201 [20]. Neuregulins (NRG) are growth and differentiation factors related to EGF; the ERBB family of tyrosine kinase transmembrane receptors are neuregulin receptors.
Platelet derived growth factor B (PDGF) has been shown to be a stimulator of FGF [21] and VEGF [22], leading to the conclusion that PDGF expression by tumor cells promotes angiogenesis. While it is thought that mutagenicity of one growth factor is influenced by the presence of other growth factors that collectively affect cell proliferation rates [1], PDGF has been cited as a potent mitogen that in some cells is sufficient to induce cell division in the absence of other growth factors.
In this study we examined genetic variation in seven growth-factor signaling genes, FGF1, FGF2, FGFR2, NRG2, EGF, ERBB2, and PDGFB in relation to breast cancer risk and survival. We utilized data from a multi-center study of breast cancer in a population of non-Hispanic white (NHW) and Hispanic women living in the United States and Mexico. We utilize 104 Ancestry Informative Markers (AIMs) to characterize the population as to their Native American (NA) ancestry since we hypothesize that differences in breast cancer risk and survival are influenced by level of NA ancestry. We evaluated associations by ER and progesterone receptor (PR), menopausal status, and family history of breast cancer.
Methods
A case-control study design is used using data from the Breast Cancer Health Disparities Study that includes participants from three population-based case-control studies [23], the 4-Corners Breast Cancer Study (4-CBCS) [24], the Mexico Breast Cancer Study (MBCS), and the San Francisco Bay Area Breast Cancer Study (SFBCS) [25, 26] who completed an in-person interview and who had a blood or mouthwash sample available for DNA extraction. In the 4-CBCS, participants were between 25 and 79 years; participants from the MBCS were between 28 and 74 years; the SFBCS included women aged 35 to 79 years. All participants signed informed written consent prior to participation and each study was approved by the Institutional Review Board for Human Subjects at each institution.
Data Harmonization
Data were harmonized across all study centers and questionnaires as previously described [23]. Women were classified as either pre-menopausal or post-menopausal based on responses to questions on menstrual history. Women who reported still having periods during the referent year (defined as the year before diagnosis for cases or before selection into the study for controls) were classified as pre-menopausal. Women were classified as post-menopausal if they reported either a natural menopause or if they reported taking hormone therapy (HT) and were still having periods or were at or above the 95th percentile of age for those who reported having a natural menopause (i.e., ≥ 12 months since their last period). Women were categorized as having a positive family history of breast cancer if they reported having a first-degree relative with breast cancer.
Genetic Data
DNA was extracted from either whole blood (n=7287) or mouthwash (n=634) samples. Whole Genome Amplification (WGA) was applied to the mouthwash-derived DNA samples prior to genotyping. A tagSNP approach was used to characterize variation across candidate genes. TagSNPs were selected using the following parameters: 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. Additionally, 104 Ancestry Informative Markers (AIMs) were used to distinguish European and NA ancestry in the study population [23]. 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 blinded internal replicates representing 1.6% of the sample set. The duplicate concordance rate was 99.996% as determined by 193, 297 matching genotypes among sample pairs. In the current analysis we evaluated tagSNPs for EGF (1 SNP), ERBB2 (3 SNPs), FGF1 (21 SNPs), FGF2 (16SNPs), FGFR2 (1 candidate SNP), NRG2 (22 SNPs) and PDGFB (9 SNPs). A description of these genes and SNPs is shown in online Supplement 1.
Tumor Characteristics and Survival
Information on survival, differentiation, and ER/PR tumor status were not available for cases from Mexico and therefore assessment of these variables is limited to data obtained from the 4-CBCS and SFBCS. Cancer registries in Utah, Colorado, Arizona, New Mexico, and California provided information on stage at diagnosis, months of survival after diagnosis, cause of death, and ER and PR status. Surveillance Epidemiology and End Results (SEER) summary disease stage was based on three codes of local, regional, and distant.
Statistical Methods
Genetic ancestry estimation
The program STRUCTURE was used to compute individual ancestry for each study participant assuming two founding populations [27, 28]. A three-founding population model was assessed but did not fit the population structure. Participants were classified by level of percent NA ancestry. Assessment across categories of ancestry was done using cut-points, 0-28%, 29-70%, and 71-100%, based on the distribution of genetic ancestry in the control population with the goal of creating distinct ancestry groups with sufficient power to assess breast cancer risk and survival.
SNP Associations
Genes and SNPs were assessed for their association with breast cancer risk by strata of genetic ancestry and menopausal status in the whole population and by ER/PR status for the 4-CBCS and the SFBCS. All statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). Conditional logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for breast cancer risk associated with SNPs, adjusting for study as a categorical variable and age, genetic ancestry, body mass index (BMI, kg/m2) in the reference year and parity as continuous variables. Since we observed no differences in association by in situ and invasive for the 4-CBCS, we include all women in the analysis of breast cancer risk. Associations with SNPs were assessed assuming a co-dominant model. Based on the initial assessment, SNPs which appeared to have a dominant or recessive mode of inheritance were evaluated with those inheritance models in subsequent analyses. For stratified analyses, tests for interactions were calculated using a Wald one degree of freedom (1-df) test; adjustments for multiple comparisons within the gene used the step-down Bonferroni correction, taking into account the correlated nature of the data using the SNP spectral decomposition method proposed by Nyholt [29] and modified by Li and Ji [30]. We present findings that were statistically significant in the tables. Data were available for 7775 participants; of these 1996 women had ER/PR status and tumor characteristic data available.
Survival Analysis
Survival months were calculated based on month and year of diagnosis and month and year of death or date of last contact by SEER registry; all cancer registry updates were through the spring of 2012. Associations between SNPs and risk of dying of breast cancer among primary invasive cases were evaluated using Cox Proportional Hazards models to obtain multivariate hazard ratios (HR) and 95% CI by admixture strata. Since survival data were not available for the MBCS study site, the upper two admixture strata were combined. Individuals were censored when they died of causes other than breast cancer or were lost to follow-up. Models were adjusted for age, study, genetic ancestry, BMI during referent year, parity, and SEER summary stage. Interactions between genetic variants and genetic ancestry with survival were assessed using p values from 1-df Wald chi-square tests.
ARTP analysis
We used the adaptive rank truncated product (ARTP) method that utilizes a highly efficient permutation algorithm to determine the significance of association of each gene and of all genes combined with breast cancer risk overall, by menopausal status, by genetic ancestry, and by ER/PR strata. The gene p values were generated using the ARTP package in R, permuting outcome status 10,000 times while adjusting for age, reference year BMI, and genetic ancestry [31,32]. We also controlled for SEER summary stage when estimating the ARTP for survival. We report both pathway and gene p values (PARTP). The original R program was modified to incorporate Cox Proportional Hazard modeling that permuted both vital status and survival months to estimate gene and pathway associations; p values for survival analysis were based on likelihood ratio tests.
Results
The majority of breast cancer cases were Hispanic (62.1%), under 60 years of age (61.5%), and post-menopausal (66.5%) (Table 1). Among U.S. cases, most tumors were ER+/PR+ (68.2%). ER-/PR- tumors accounted for 18.4% of NHW and 23.4% of Hispanic cases. The majority of women who self-reported being NHW were estimated as having low NA ancestry (99.5% of controls), whereas U.S. women who self-reported being Hispanic were divided between those with intermediate NA ancestry (64.9% of controls) and high NA ancestry (24.4% of controls). Few cases were diagnosed at a distant disease stage and the majority of cases had ductal or lobular histology.
Table 1. Description of study population by self-reported ethnicity.
| Non-Hispanic White | U. S. Hispanic or Mexican | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Controls | Cases | Controls | Cases | |||||||
| N | % | N | % | N | % | N | % | |||
| Total | 1586 | 37.9 | 1481 | 41.2 | 2597 | 62.1 | 2111 | 58.8 | ||
| Study Site1 | ||||||||||
| 4CBCS | 1322 | 83.4 | 1227 | 82.8 | 723 | 27.8 | 597 | 28.3 | ||
| MCBS | 0 | 0.0 | 0 | 0.0 | 994 | 38.3 | 816 | 38.7 | ||
| SFBCS | 264 | 16.6 | 254 | 17.2 | 880 | 33.9 | 698 | 33.1 | ||
| Age (years) | ||||||||||
| <40 | 116 | 7.3 | 89 | 6.0 | 311 | 12.0 | 200 | 9.5 | ||
| 40-49 | 408 | 25.7 | 409 | 27.6 | 831 | 32.0 | 713 | 33.8 | ||
| 50-59 | 409 | 25.8 | 413 | 27.9 | 756 | 29.1 | 617 | 29.2 | ||
| 60-69 | 350 | 22.1 | 361 | 24.4 | 526 | 20.3 | 430 | 20.4 | ||
| >70 | 303 | 19.1 | 209 | 14.1 | 173 | 6.7 | 151 | 7.2 | ||
| Mean | 56.6 | 56 | 52.3 | 52.7 | ||||||
| Menopausal Status | ||||||||||
| Pre-menopausal | 494 | 31.5 | 489 | 33.5 | 1027 | 40.7 | 836 | 40.9 | ||
| Post-menopausal | 1076 | 68.5 | 970 | 66.5 | 1499 | 59.3 | 1210 | 59.1 | ||
| Family history of breast cancer in first-degree relative | ||||||||||
| No | 1289 | 84.5 | 1122 | 77.5 | 2326 | 91.8 | 1818 | 87.8 | ||
| Yes | 237 | 15.5 | 326 | 22.5 | 208 | 8.2 | 252 | 12.2 | ||
| Estimated Native American Ancestry | ||||||||||
| Low (0 - 28%) | 1578 | 99.5 | 1472 | 99.4 | 278 | 10.7 | 275 | 13.0 | ||
| Intermediate (29 - 70%) | 7 | 0.4 | 7 | 0.5 | 1686 | 64.9 | 1393 | 66.0 | ||
| High (71 - 100%) | 1 | 0.1 | 2 | 0.1 | 633 | 24.4 | 443 | 21.0 | ||
| ER/PR Status2 | ||||||||||
| ER+/PR+ | NA | 695 | 68.2 | NA | 605 | 61.9 | ||||
| ER+/PR- | NA | 121 | 11.9 | NA | 115 | 11.8 | ||||
| ER-/PR+ | NA | 15 | 1.5 | NA | 28 | 2.9 | ||||
| ER-/PR- | NA | 188 | 18.4 | NA | 229 | 23.4 | ||||
| Vital Status2,3 | ||||||||||
| Deceased | NA | 202 | 17.1 | NA | 202 | 17.5 | ||||
| Alive | NA | 982 | 82.9 | NA | 950 | 82.5 | ||||
| Cause of Death2,3 | ||||||||||
| Breast Cancer | NA | 102 | 50.5 | NA | 115 | 56.9 | ||||
| Other | NA | 100 | 49.5 | NA | 87 | 43.1 | ||||
| SEER Summary Stage2,3 | ||||||||||
| Local | NA | 829 | 71.1 | NA | 648 | 59.6 | ||||
| Regional | NA | 322 | 27.6 | NA | 430 | 39.6 | ||||
| Distant | NA | 15 | 1.3 | NA | 9 | 0.8 | ||||
| Tumor Grade2,3 | ||||||||||
| I - Well Differentiated | NA | 267 | 22.6 | NA | 191 | 16.6 | ||||
| II - Moderately Differentiated | NA | 463 | 39.1 | NA | 434 | 37.7 | ||||
| III - Poorly Differentiated | NA | 336 | 28.4 | NA | 394 | 34.2 | ||||
| IV - Undifferentiated/Anaplastic | NA | 18 | 1.5 | NA | 24 | 2.1 | ||||
| Not Determined | NA | 100 | 8.4 | NA | 109 | 9.5 | ||||
| Histology2,3 | ||||||||||
| Ductal | NA | 866 | 73.1 | NA | 891 | 77.3 | ||||
| Lobular | NA | 88 | 7.4 | NA | 67 | 5.8 | ||||
| Mixed Ductal/Lobular | NA | 108 | 9.1 | NA | 79 | 6.9 | ||||
| Mucinous | NA | 24 | 2.0 | NA | 28 | 2.4 | ||||
| Inflammatory | NA | 7 | 0.6 | NA | 5 | 0.4 | ||||
| Tubular | NA | 20 | 1.7 | NA | 11 | 1.0 | ||||
| Medullary | NA | 14 | 1.2 | NA | 16 | 1.4 | ||||
| Other/Mixed types | NA | 57 | 4.8 | NA | 55 | 4.9 | ||||
4CBCS= 4 Corners Breast Cancer Study; MBCS = Mexico Breast Cancer Study; SFBCS = San Francisco Bay Area Breast Cancer Study
Information unavailable for the Mexico study site.
Among primary invasive breast cancer cases.
When we considered all tagSNPs in all genes together, we observed a statistically significant association between the pathway and breast cancer risk (PARTP for pathway = 0.0009). When considering the overall association between each of the genes and breast cancer risk we observed that only FGFR2, PDGFB, and NRG2 had significant PARTP gene p values (PARTP = 0.0001, 0.045, and 0.034, respectively) based on one significant candidate SNP in FGFR2 (rs2981582), two tagSNPs in PDGFB (rs9622978 and rs4821877), and four tagSNPs for NRG2 (rs6895139, rs265155, rs1800954, and rs2436389) (Table 2). We observed no meaningful differences in associations with breast cancer risk by genetic admixture (data not shown in table) and few by menopausal status (Table 3). Associations with seven independent SNPs were significantly different by menopausal status; however of these, only ERBB2 had a significant PARTP gene of 0.03 among post-menopausal women. Two SNPs in FGF1 (rs4912868 and rs4912876), and one in NRG2 (rs2436389) were associated with breast cancer risk among pre-menopausal women, although the PARTPs for these genes were not statistically significant and the magnitude of associations with these SNPs was modest. Four SNPs, FGF1 rs9324889, FGF2 rs308379 and rs308382, and NRG2 rs265155 showed significant interaction with family history of breast cancer prior to adjustment for multiple comparisons, however after adjustment none of these associations remained statistically significant (see Online Supplemental Data Table 2).
Table 2. Associations between growth factor related genes and risk of breast cancer: all women combined.
| Controls | Cases | ||||||
|---|---|---|---|---|---|---|---|
|
|
|||||||
| N | N | OR1 | (95% CI) | PARTP Gene | PARTP Pathway | ||
| FGFR2 (rs2981582) | 0.0001 | 0.001 | |||||
| CC | 1491 | 1103 | 1.00 | ||||
| CT | 2009 | 1749 | 1.18 | (1.06, 1.30) | |||
| TT | 638 | 708 | 1.50 | (1.31, 1.71) | |||
| PDGFB (rs9622978) | 0.045 | ||||||
| GG | 1612 | 1418 | 1.00 | ||||
| GT | 1903 | 1653 | 0.97 | (0.88, 1.07) | |||
| TT | 629 | 489 | 0.85 | (0.74, 0.98) | |||
| PDGFB (rs4821877) | |||||||
| TT | 1084 | 820 | 1.00 | ||||
| TC | 2008 | 1728 | 1.11 | (1.00, 1.25) | |||
| CC | 968 | 914 | 1.20 | (1.05, 1.36) | |||
| NRG2 (rs6895139) | 0.034 | ||||||
| GG/GA | 4124 | 3557 | 1.00 | ||||
| AA | 25 | 10 | 0.46 | (0.22, 0.96) | |||
| NRG2 (rs265155) | |||||||
| GG/GA | 4010 | 3414 | 1.00 | ||||
| AA | 138 | 152 | 1.29 | (1.02, 1.63) | |||
| NRG2 (rs1800954) | |||||||
| TT/TC | 3834 | 3227 | 1.00 | ||||
| CC | 62 | 29 | 0.53 | (0.34, 0.84) | |||
| NRG2 (rs2436389) | |||||||
| TT | 2248 | 1782 | 1.00 | ||||
| TG/GG | 1901 | 1786 | 1.12 | (1.02, 1.23) | |||
Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusted for age, study, BMI during the referent year, parity and genetic admixture; table includes only SNPs with statistically significant findings
Table 3. Associations between growth factor-related genes and breast cancer risk stratified by menopausal status.
| Pre Menopause | Post Menopause | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
|
|||||||||||
| Controls | Cases | Controls | Cases | |||||||||
|
|
|
|
|
|||||||||
| N | N | OR1 | (95% CI) | PARTP gene2 | N | N | OR | (95% CI) | PARTP gene2 | Interaction p-value | ||
|
|
|
|
||||||||||
| EGF (rs4444903) | ||||||||||||
| AA | 412 | 341 | 1.00 | 0.12 | 689 | 644 | 1.00 | 0.13 | 0.05 | |||
| AG | 724 | 637 | 1.12 | (0.94, 1.35) | 1268 | 1051 | 0.91 | (0.79, 1.04) | ||||
| GG | 376 | 339 | 1.19 | (0.96, 1.47) | 595 | 467 | 0.88 | (0.74, 1.04) | ||||
| ERBB2 (rs1810132) | ||||||||||||
| TT | 630 | 543 | 1.00 | 0.50 | 1001 | 930 | 1.00 | 0.03 | 0.05 | |||
| TC | 689 | 609 | 1.04 | (0.89, 1.22) | 1185 | 979 | 0.90 | (0.79, 1.02) | ||||
| CC | 194 | 166 | 1.05 | (0.83, 1.34) | 363 | 255 | 0.79 | (0.65, 0.95) | ||||
| FGF1 (rs4912868) | ||||||||||||
| TT | 589 | 588 | 1.00 | 0.22 | 1113 | 924 | 1.00 | 0.51 | 0.02 | |||
| TC | 720 | 566 | 0.81 | (0.69, 0.95) | 1118 | 967 | 1.07 | (0.94, 1.21) | ||||
| CC | 204 | 163 | 0.83 | (0.66, 1.06) | 321 | 272 | 1.07 | (0.89, 1.29) | ||||
| FGF1 (rs4912876) | ||||||||||||
| AA | 749 | 615 | 1.00 | 1167 | 994 | 1.00 | 0.04 | |||||
| AG | 639 | 558 | 1.04 | (0.89, 1.21) | 1119 | 959 | 0.98 | (0.87, 1.11) | ||||
| GG | 125 | 145 | 1.33 | (1.02, 1.74) | 266 | 211 | 0.89 | (0.73, 1.09) | ||||
| NRG2 (rs4912894) | 0.29 | 0.07 | ||||||||||
| TT | 534 | 431 | 1.00 | 791 | 702 | 1.00 | 0.04 | |||||
| TC | 695 | 608 | 1.03 | (0.87, 1.23) | 1214 | 1018 | 0.90 | (0.79, 1.04) | ||||
| CC | 248 | 245 | 1.11 | (0.88, 1.39) | 486 | 400 | 0.84 | (0.71, 1.00) | ||||
| NRG2 (rs11167875) | ||||||||||||
| CC | 507 | 410 | 1.00 | 759 | 688 | 1.00 | 0.01 | |||||
| CT | 728 | 635 | 1.06 | (0.90, 1.26) | 1281 | 1069 | 0.87 | (0.76, 1.00) | ||||
| TT | 278 | 273 | 1.16 | (0.93, 1.44) | 512 | 407 | 0.82 | (0.69, 0.97) | ||||
| NRG2 (rs2436389) | ||||||||||||
| TT | 877 | 674 | 1.00 | 1318 | 1073 | 1.00 | 0.04 | |||||
| TG/GG | 635 | 644 | 1.25 | (1.06, 1.46) | 1234 | 1090 | 1.03 | (0.91, 1.16) | ||||
Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusted for age, study, BMI during referent years, parity and genetic admixture; table includes only SNPs with statistically significant findings
Pathway PARTP is 0.028 for pre-menopause and 0.001 for post-menopausal women
When the data were analyzed within ER/PR status, associations with several SNPs were significantly different at the 0.05 level (Table 4). However, the genes for the most part were not considered significant by the PARTP as contributing to breast cancer risk within these strata. FGFR2 was statistically significantly associated with breast cancer risk only among those with ER+ tumors. The associations of FGF1 and PDGFB with ER-/PR- tumors were of borderline significance (PARTP = 0.07 and 0.08, respectively), with three FGF1 SNPs significantly increasing risk of breast cancer and one PDGFB SNP associated with decreasing risk.
Table 4. Associations between growth factor-related genes and ER and PR tumor status.
| Control | ER+/PR+ | ER+/PR- | ER-/PR+ | ER-/PR- | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
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| N | N | OR1 | (95% CI) | PARTP2 | N | OR | (95% CI) | PARTP | N | OR | (95% CI) | PARTP | N | OR | (95% CI) | PARTP | |
| EGF (rs4444903) | |||||||||||||||||
| AA | 926 | 393 | 1.00 | 0.72 | 86 | 1.00 | 0.01 | 6 | 1.00 | 0.04 | 119 | 1.00 | 0.24 | ||||
| AG | 1561 | 637 | 0.97 | (0.84, 1.13) | 98 | 0.66 | (0.49, 0.90) | 27 | 2.61 | (1.07, 6.38) | 225 | 1.11 | (0.87, 1.41) | ||||
| GG | 678 | 268 | 0.97 | (0.80, 1.17) | 50 | 0.78 | (0.54, 1.13) | 9 | 1.93 | (0.67, 5.54) | 70 | 0.79 | (0.57, 1.08) | ||||
| FGF1 (rs34001) | 0.66 | 0.57 | 0.76 | 0.07 | |||||||||||||
| GG | 1118 | 430 | 1.00 | 84 | 1.00 | 14 | 1.00 | 121 | 1.00 | ||||||||
| GT | 1507 | 636 | 1.09 | (0.94, 1.26) | 108 | 0.95 | (0.70, 1.28) | 23 | 1.24 | (0.63, 2.42) | 212 | 1.31 | (1.04, 1.67) | ||||
| TT | 540 | 230 | 1.07 | (0.88, 1.29) | 43 | 1.02 | (0.69, 1.50) | 6 | 0.91 | (0.35, 2.39) | 82 | 1.43 | (1.06, 1.93) | ||||
| FGF1 (rs152524) | |||||||||||||||||
| AA | 1284 | 497 | 1.00 | 91 | 1.00 | 17 | 1.00 | 147 | 1.00 | ||||||||
| AG | 1428 | 603 | 1.05 | (0.91, 1.21) | 106 | 1.01 | (0.75, 1.36) | 21 | 1.19 | (0.61, 2.29) | 198 | 1.26 | (1.00, 1.59) | ||||
| GG | 454 | 198 | 1.04 | (0.85, 1.27) | 38 | 1.11 | (0.74, 1.67) | 5 | 0.92 | (0.33, 2.59) | 70 | 1.44 | (1.05, 1.97) | ||||
| FGF1 (rs34021) | |||||||||||||||||
| GG | 1530 | 579 | 1.00 | 113 | 1.00 | 19 | 1.00 | 176 | 1.00 | ||||||||
| GA | 1329 | 597 | 1.16 | (1.01, 1.33) | 97 | 0.98 | (0.74, 1.30) | 21 | 1.30 | (0.69, 2.44) | 196 | 1.29 | (1.03, 1.60) | ||||
| AA | 307 | 121 | 1.01 | (0.80, 1.28) | 25 | 1.10 | (0.70, 1.73) | 3 | 0.84 | (0.24, 2.87) | 43 | 1.25 | (0.87, 1.78) | ||||
| FGF2 (rs11938826) | 0.12 | 0.55 | 0.51 | 0.89 | |||||||||||||
| CC | 1988 | 833 | 1.00 | 148 | 1.00 | 28 | 1.00 | 244 | 1.00 | ||||||||
| CG | 1006 | 423 | 1.04 | (0.90, 1.20) | 74 | 1.02 | (0.76, 1.37) | 12 | 0.79 | (0.40, 1.57) | 150 | 1.18 | (0.95, 1.47) | ||||
| GG | 169 | 41 | 0.63 | (0.44, 0.89) | 13 | 1.16 | (0.64, 2.10) | 3 | 1.06 | (0.31, 3.56) | 21 | 0.94 | (0.58, 1.51) | ||||
| FGF2 (rs1960669) | |||||||||||||||||
| GG | 2329 | 877 | 1.00 | 159 | 1.00 | 30 | 1.00 | 282 | 1.00 | ||||||||
| GT/TT | 575 | 271 | 1.21 | (1.03, 1.43) | 41 | 1.01 | (0.70, 1.45) | 7 | 1.01 | (0.43, 2.35) | 61 | 0.86 | (0.64, 1.15) | ||||
| FGFR2 (rs2981582) | 0.0001 | 0.02 | 0.09 | 0.56 | |||||||||||||
| CC | 1123 | 386 | 1.00 | 65 | 1.00 | 11 | 1.00 | 160 | 1.00 | ||||||||
| CT | 1552 | 632 | 1.19 | (1.02, 1.38) | 127 | 1.41 | (1.04, 1.92) | 22 | 1.42 | (0.68, 2.94) | 186 | 0.83 | (0.67, 1.04) | ||||
| TT | 483 | 274 | 1.66 | (1.37, 2.00) | 43 | 1.54 | (1.03, 2.31) | 10 | 2.10 | (0.89, 5.00) | 67 | 0.96 | (0.71, 1.30) | ||||
| PDGFB (rs9622978) | |||||||||||||||||
| GG/GT | 2633 | 1093 | 1.00 | 0.08 | 202 | 1.00 | 0.49 | 38 | 1.00 | 0.90 | 367 | 1.00 | 0.08 | ||||
| TT | 528 | 198 | 0.89 | (0.74, 1.06) | 32 | 0.78 | (0.53, 1.15) | 5 | 0.69 | (0.27, 1.76) | 47 | 0.65 | (0.47, 0.90) | ||||
| PDGFB (rs5750781) | |||||||||||||||||
| CC | 1839 | 784 | 1.00 | 123 | 1.00 | 27 | 1.00 | 249 | 1.00 | ||||||||
| CA/AA | 1325 | 512 | 0.87 | (0.76, 0.99) | 111 | 1.24 | (0.94, 1.63) | 16 | 0.90 | (0.47, 1.70) | 166 | 0.95 | (0.77, 1.18) | ||||
| PDGFB (rs2857402) | |||||||||||||||||
| CC | 1865 | 793 | 1.00 | 127 | 1.00 | 28 | 1.00 | 256 | 1.00 | ||||||||
| CG/GG | 1295 | 503 | 0.87 | (0.76, 1.00) | 106 | 1.18 | (0.90, 1.56) | 15 | 0.84 | (0.44, 1.61) | 158 | 0.91 | (0.74, 1.14) | ||||
| NRG2 (rs2431384) | 0.13 | 0.98 | 0.39 | 0.39 | |||||||||||||
| AA | 2309 | 969 | 1.00 | 175 | 1.00 | 34 | 1.00 | 302 | 1.00 | ||||||||
| AG | 789 | 303 | 0.91 | (0.78, 1.06) | 52 | 0.86 | (0.62, 1.19) | 9 | 0.75 | (0.36, 1.58) | 110 | 1.06 | (0.84, 1.33) | ||||
| GG | 62 | 26 | 0.98 | (0.61, 1.56) | 6 | 1.25 | (0.53, 2.95) | 0 | 2 | 0.23 | (0.06, 0.95) | ||||||
| NRG2 (rs1800954) | |||||||||||||||||
| TT/TC | 2860 | 1135 | 1.00 | 196 | 1.00 | 36 | 1.00 | 341 | 1.00 | ||||||||
| CC | 52 | 11 | 0.51 | (0.26, 0.97) | 4 | 1.08 | (0.38, 3.03) | 1 | 1.62 | (0.22, 12.17 | 4 | 0.64 | (0.23, 1.79) | ||||
| NRG2 (rs2436389) | |||||||||||||||||
| TT | 1536 | 612 | 1.00 | 106 | 1.00 | 18 | 1.00 | 182 | 1.00 | ||||||||
| TG/GG | 1629 | 685 | 1.01 | (0.89, 1.16) | 129 | 1.13 | (0.86, 1.49) | 25 | 1.47 | (0.78, 2.76) | 233 | 1.26 | (1.02, 1.56) | ||||
Odds Ratios (OR) and 95% Confidence Intervals (CI) are adjusted for age, study, BMI during referent year, parity and genetic admixture form U.S. Studies. Table includes only SNPs with statistically significant findings
PARTP values in table are for gene; the overall pathway p value for ER+/PR+ tumors was 0.001; for ER+/PR- tumors was 0.06; for ER-/PR+ was 0.29, and for ER-/PR- was 0.33.
We examined the interaction between growth factor-related genes to determine whether the combined effect was different from the independent gene effects. We observed several significant interactions between ERBB2 and FGF1 and EGFR and NRG2, and between FGFR2 rs2981582 and FGF2 (rs7700205, rs17408757, rs1960669, and rs6534365) and EGFR (rs17586365 and rs6954351) (Table 5). In all instances, having both variant genotypes was associated with a greater increase in risk than having either variant genotype alone. PDGFB (rs9622978 and rs2247128) also interacted significantly with FGF1 (rs250092 and rs4912868), and PDGFB rs6001512 interacted significantly with FGF2 rs308435. Except for the interaction between PDGFB rs9622978 and FGF1 where the homozygote common genotype of PDGFB and the homozygote variant of FGF1 were associated with a significantly reduced risk compared to other genotype combinations, having the two variant genotypes had the greatest influence on risk.NRG2 interacted with EGFR (12 NRG2 SNPs interacting with 10 EGFR SNPs), FGF1 (three SNPs), FGF2 (two SNPs), and PDGFB (1 SNP).
Table 5. Interaction between genes related to growth factors.
| Gene 1 | Gene 2 | wt 1/variant 2 OR (95% CI)1 | variant 1/wt 2 OR (95% CI) | variant 1/variant 2 OR (95% CI) | p interaction |
|---|---|---|---|---|---|
| EGFR | |||||
| (rs6944906) | FGF1 (rs152528) | 0.76 (0.59,0.99) | 0.82 (0.71,0.95) | 0.785 (0.70,1.05) | 0.02 |
| FGF2 (rs1960669) | 0.83 (0.67,1.03) | 0.86 (0.77,0.96) | 1.05 (0.89,1.24) | <0.01 | |
| (rs1558544) | FGF2 (rs308395) | 0.85 (0.65,1.10) | 0.67 (0.50,0.89) | 0.83 (0.30,2.31) | <0.01 |
| FGF2 (rs167428) | 0.79 (0.63,0.98) | 0.66 (0.47,0.91) | 1.21 (0.62,2.38) | 0.01 | |
| FGF2 (rs11837725) | 0.84 (0.66,1.08) | 0.62 (0.46, 0.83) | 0.70 (0.19,2.49) | 0.02 | |
| FGF2 (rs308441) | 0.72 (0.56,0.93) | 0.70 (0.52,0.94) | 1.50 (0.61,3.71) | <0.01 | |
| (rs6593205) | FGF2 (rs1476214) | 1.05 (0.83,1.33) | 0.67 (0.54,0.83) | 1.27 (0.88,1.83) | 0.03 |
| FGF2 (rs3789138) | 0.93 (0.75,1.14) | 0.68 (0.53,0.87) | 1.11 (0.82,1.51) | 0.03 | |
| FGF2 (rs3804158) | 0.96 (0.78,1.18) | 0.66 (0.51,0.86) | 1.16 (0.87,1.57) | 0.02 | |
| (rs17151957) | EGF (rs4444903) | 0.90 (0.75,1.08) | 0.61 (0.43,0.86) | 0.97 (0.69,1.36) | 0.03 |
| (rs6970262) | FGF1 (rs4912876) | 0.86 (0.68,1.09) | 0.87 (0.69,1.10) | 1.60 (1.00,2.54) | 0.01 |
| (rs723527) | FGF2 (rs308441) | 0.69 (0.48,0.99) | 0.90 (0.76,1.07) | 1.12 (0.72,1.75) | 0.02 |
| (rs3752651) | FGF1 (rs34019) | 0.86 (0.76,0.97) | 0.96 (0.72,1.29) | 2.60 (1.30,5.17) | <0.01 |
| ERBB2 | |||||
| (rs1810132) | FGF1 (rs34016) | 0.79 (0.55,1.13) | 0.77 (0.64,0.93) | 1.70 (0.87,3.34) | 0.01 |
| (rs1136201) | EGFR (rs11770531) | 0.71 (0.44,1.15) | 1.04 (0.94,1.15) | 0.25 (0.10,0.62) | 0.04 |
| FGFR2 | |||||
| (rs2981582) | FGF2 (rs7700205) | 1.33 (1.14,1.55) | 0.80 (0.66,0.97) | 1.85 (1.44,2.38) | <0.01 |
| FGF2 (rs17408757) | 1.40 (1.21,1.62) | 0.87 (0.71,1.07) | 1.79 (1.36,2.34) | 0.03 | |
| FGF2 (rs1960669) | 1.36 (1.17,1.58) | 0.87 (0.70,1.09) | 1.97 (1.47,2.63) | 0.01 | |
| FGF2 (rs6534365) | 1.24 (1.03,1.50) | 1.03 (0.77,1.38) | 1.65 (1.12,2.43) | 0.03 | |
| EGFR (rs17586365) | 0.84 (0.44,1.61) | 1.40 (1.21,1.63) | 7.77 (2.24,26.93) | 0.03 | |
| (rs6954351) | 1.27 (0.61,2.62) | 1.63 (1.40,1.89) | 2.22 (0.85,5.78) | 0.02 | |
| PDGFB | |||||
| (rs9622978) | FGF1 (250092) | 0.95 (0.82,1.09) | 1.03 (0.91,1.17) | 0.59 (0.44,0.79) | <0.01 |
| FGF1 (rs4912868) | 0.70 (0.57,0.86) | 0.91 (0.78,1.07) | 1.16 (0.82,1.62) | <0.01 | |
| (rs2247128) | FGF1 (rs4912868) | 0.87 (0.66,1.13) | 0.88 (0.73,1.07) | 1.97 (1.07,3.63) | 0.04 |
| (rs6001512) | FGF2 (rs308435) | 1.01 (0.85,1.20) | 1.02 (0.91,1.14) | 1.47 (1.13,1.93) | 0.03 |
| NRG2 | |||||
| (rs265159) | EGFR (rs2280653) | 0.65 (0.45,0.92) | 1.03 (0.82,1.30) | 1.85 (0.71,4.81) | <0.01 |
| FGF1 (rs1609763) | 1.02 (0.83,1.25) | 1.08 (0.89,1.33) | 1.72 (1.01,2.95) | 0.04 | |
| (rs3863190) | EGFR (rs9642391) | 0.93 (0.78,1.11) | 0.83 (0.51,1.35) | 8.47 (1.91,37.54) | 0.02 |
| (rs2330951) | 1.00 (0.81,1.25) | 1.59 (1.03,2.46) | 0.68 (0.22,2.10) | 0.02 | |
| (rs2280653) | 0.95 (0.71,1.27) | 1.73 (1.14,2.62) | 0.44 (0.05,4.29) | 0.05 | |
| FGF1 (rs34019) | 0.95 (0.84,1.09) | 1.52 (1.05,2.21) | 0.49 (0.21,1.14) | <0.01 | |
| (rs265155) | EGFR (rs12671550) | 1.00 (0.87,1.16) | 1.73 (1.20,2.50) | 0.74 (0.36,1.51) | 0.02 |
| (rs2916092) | EGFR (rs11770531) | 0.23 (0.12,0.46) | 0.97 (0.82,1.14) | 0.98 (0.24,3.94) | <0.01 |
| (rs4912894) | PDGFB (rs4821877) | 1.32 (1.05,1.66) | 1.07 (0.81,1.41) | 0.92 (0.71,1.21) | 0.05 |
| (rs1800954) | EGFR (rs172718945) | 1.07 (0.97,1.18) | 1.03 (0.48,2.23) | 0.42 (0.24,0.73) | 0.04 |
| (rs17151957) | 0.96 (0.80,1.15) | 0.34 (0.18,0.66) | 1.33 (0.18,9.57) | 0.04 | |
| rs(1422187) | EGFR (rs4947979) | 1.60 (1.13,2.26) | 1.17 (0.89,1.54) | 0.67 (0.26,1.73) | 0.01 |
| (rs197197) | FGF2 (rs11938826) | 0.65 (0.44,0.96) | 0.86 (0.72,1.01) | 0.77 (0.52,1.16) | 0.01 |
| (rs11746363) | EGFR (rs917880) | 0.83 (0.70,0.98) | 0.68 (0.45,1.02) | 0.91 (0.53,1.57) | 0.05 |
| (rs13173983) | EGFR (rs2280653) | 0.79 (0.56,1.11) | 0.72 (0.56.0.93) | 0.50 (0.13,1.93) | 0.02 |
| FGF1 (rs152528) | 1.08 (0.88,1.31) | 0.97 (0.69,1.35) | 0.55 (0.33,0.92) | 0.05 | |
| (rs6580353) | EGFR (rs11770531) | 0.81 (0.46,1.42) | 1.03 (0.86,1.22) | 0.10 (0.01,0.76) | 0.02 |
| FGF2 (rs11938826) | 0.72 (0.53,0.97) | 0.90 (0.72,1.12) | 1.61 (0.88,2.96) | 0.03 | |
| (rs2436389) | EGFR (rs11487218) | 1.14 (0.88,1.48) | 1.25 (1.10,1.42) | 0.97 (0.77,1.23) | 0.01 |
| (rs10225877) | 1.42 (0.96,2.11) | 1.26 (1.13,1.41) | 0.96 (0.6,1.41) | <0.01 | |
| (rs6944906) | 0.85 (0.72,0.97) | 0.98 (0.84,1.15) | 1.02 (0.89,1.17) | 0.04 | |
Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusted for age, study, BMI during the referent year, parity and genetic admixture; table includes only statistically significant interactions
None of the growth factor-related genes seemed to influence breast cancer-specific mortality, with the exception of ERRB2 that showed marginal associations within groups defined by genetic admixture (Table 6). However, some of these genes showed associations with all-cause mortality. Many of these associations differed by genetic admixture. Specifically, ERBB2 rs1810132 was associated with increased the risk of both all-cause mortality (PARTP =0.005) and breast cancer-specific mortality (PARTP =0.06) among women with low NA ancestry, but was associated with decreased mortality risk among women with higher NA ancestry. Also, having a variant allele of ERBB2 rs4252596 was associated with significantly reduced mortality risk among women with low NA ancestry, but did not influence risk among those with higher NA ancestry (heterogeneity p <0.001 for all-cause mortality and p = 0.003 for breast cancer-specific mortality). FGF1 was associated with all-cause mortality (PARTP =0.04), with different associations by level of NA ancestry (heterogeneity p = 0.03). FGF1 rs1596776 was associated with significantly increased risk of all-cause mortality among those with greater NA ancestry; FGF1 rs17099156 was associated with increased risk of all-cause mortality among those with low NA ancestry; and FGF1 rs152524 was associated with increased risk of breast cancer-specific mortality among those with high NA ancestry. The gene PARTP value for FGF1 for all-cause mortality was 0.007 and the pathway PARTP was 0.005 for women with low NA ancestry.
Table 6. Associations between variants in growth factor-related genes and survival.
| All Women | 0 - 28% NA Ancestry | 29 - 100% NA Ancestry | Interaction p-value | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Deaths/Person Year | HR1 | (95% CI) | PARTP2 | Deaths/Person Year | HR | (95% CI) | PARTP | Deaths/Person Year | HR | (95% CI) | PARTP | raw | |
| All-Cause Mortality | |||||||||||||
| ERBB2 (rs1810132) | 0.25 | 0.003 | 0.12 | ||||||||||
| TT | 175/8834 | 1.00 | 102/5833 | 1.00 | 73/3001 | 1.00 | 0.0007 | ||||||
| TC | 178/9266 | 0.99 | (0.80, 1.22) | 108/5114 | 1.25 | (0.95, 1.64) | 70/4152 | 0.70 | (0.50, 0.97) | ||||
| CC | 51/2373 | 1.13 | (0.83, 1.55) | 33/1159 | 1.74 | (1.17, 2.58) | 18/1214 | 0.67 | (0.40, 1.12) | ||||
| ERBB2 (rs4252596) | |||||||||||||
| CC | 348/16896 | 1.00 | 204/9248 | 1.00 | 144/7647 | 1.00 | 0.03 | ||||||
| CA/AA | 56/3568 | 0.78 | (0.59, 1.04) | 39/2848 | 0.64 | (0.45, 0.90) | 17/720 | 1.24 | (0.75, 2.06) | ||||
| FGF1 (rs17099156) | 0.04 | 0.007 | 0.18 | ||||||||||
| GG | 257/12989 | 1.00 | 151/7881 | 1.00 | 106/5108 | 1.00 | 0.01 | ||||||
| GA | 121/6676 | 0.92 | (0.74, 1.14) | 73/3856 | 1.04 | (0.78, 1.37) | 48/2820 | 0.79 | (0.56, 1.12) | ||||
| AA | 26/796 | 1.68 | (1.12, 2.52) | 19/369 | 2.75 | (1.70, 4.46) | 7/426 | 0.75 | (0.35, 1.63) | ||||
| FGF1 (rs6893408) | |||||||||||||
| GG | 293/14658 | 1.00 | 174/8766 | 1.00 | 119/5892 | 1.00 | 0.17 | ||||||
| GA | 97/5350 | 0.95 | (0.75, 1.19) | 57/3107 | 0.94 | (0.70, 1.27) | 40/2243 | 0.95 | (0.66, 1.36) | ||||
| AA | 14/466 | 1.56 | (0.91, 2.66) | 12/233 | 2.67 | (1.48, 4.82) | 2/232 | 0.45 | (0.11, 1.81) | ||||
| FGF1 (rs6580256) | |||||||||||||
| CC | 288/14391 | 1.00 | 195/9172 | 1.00 | 93/5219 | 1.00 | 0.02 | ||||||
| CT/TT | 116/6082 | 0.91 | (0.73, 1.14) | 48/2934 | 0.71 | (0.52, 0.98) | 68/3148 | 1.21 | (0.88, 1.65) | ||||
| FGF1 (rs152524) | |||||||||||||
| AA | 146/8104 | 1.00 | 72/3801 | 1.00 | 74/4303 | 1.00 | 0.17 | ||||||
| AG | 192/9391 | 1.22 | (0.98, 1.51) | 126/6022 | 1.14 | (0.85, 1.52) | 66/3369 | 1.28 | (0.91, 1.79) | ||||
| GG | 66/2977 | 1.31 | (0.98, 1.77) | 45/2283 | 1.11 | (0.77, 1.62) | 21/695 | 1.81 | (1.10, 2.97) | ||||
| FGF1 (rs6884797) | |||||||||||||
| CC | 287/15814 | 1.00 | 171/9265 | 1.00 | 116/6549 | 1.00 | 0.45 | ||||||
| CA/AA | 116/4654 | 1.40 | (1.13, 1.74) | 71/2836 | 1.33 | (1.01, 1.75) | 45/1818 | 1.57 | (1.11, 2.23) | ||||
| NRG2 (rs6895139) | 0.30 | 0.59 | 0.76 | ||||||||||
| GG | 358/17636 | 1.00 | 214/10478 | 1.00 | 144/7158 | 1.00 | 0.56 | ||||||
| GA/AA | 45/2824 | 0.73 | (0.54, 1.00) | 29/1628 | 0.77 | (0.52, 1.15) | 16/1196 | 0.66 | (0.39, 1.10) | ||||
| NRG2 (rs11745110) | |||||||||||||
| GG | 269/14486 | 1.00 | 181/9124 | 1.00 | 88/5362 | 1.00 | 0.21 | ||||||
| GA/AA | 77/3263 | 1.28 | (1.00, 1.66) | 51/2190 | 1.15 | (0.84, 1.57) | 26/1074 | 1.64 | (1.05, 2.55) | ||||
| NRG2 (rs1422187) | |||||||||||||
| TT | 257/12768 | 1.00 | 152/7434 | 1.00 | 105/5334 | 1.00 | 0.91 | ||||||
| TC | 133/6633 | 1.01 | (0.82, 1.25) | 82/3904 | 1.04 | (0.79, 1.36) | 51/2729 | 0.94 | (0.67, 1.32) | ||||
| CC | 13/1068 | 0.54 | (0.31, 0.95) | 9/768 | 0.51 | (0.26, 1.00) | 4/300 | 0.60 | (0.22, 1.65) | ||||
| Breast Cancer Mortality | |||||||||||||
| ERBB2 (rs1810132) | 0.53 | 0.053 | 0.06 | 0.003 | |||||||||
| TT | 101/8834 | 1.00 | (0.68, 1.21) | 54/5833 | 1.00 | 47/3001 | 1.00 | ||||||
| TC | 91/9266 | 0.91 | (0.66, 1.60) | 56/5114 | 1.26 | (0.87, 1.84) | 35/4152 | 0.58 | (0.37, 0.90) | ||||
| CC | 25/2373 | 1.03 | 15/1159 | 1.73 | (0.97, 3.09) | 10/1214 | 0.58 | (0.29, 1.15) | |||||
| ERBB2 (rs4252596) | |||||||||||||
| CC | 186/16896 | 1.00 | (0.55, 1.19) | 104/9248 | 1.00 | 82/7647 | 1.00 | 0.08 | |||||
| CA/AA | 31/3568 | 0.81 | 21/2848 | 0.65 | (0.40, 1.04) | 10/720 | 1.35 | (0.69, 2.61) | |||||
| FGF1 (rs152524) | 0.15 | 0.14 | 0.42 | ||||||||||
| AA | 80/8104 | 1.00 | 39/3801 | 1.00 | 41/4303 | 1.00 | 0.03 | ||||||
| AG | 100/9391 | 1.21 | (0.90, 1.64) | 64/6022 | 1.05 | (0.70, 1.57) | 36/3369 | 1.32 | (0.84, 2.08) | ||||
| GG | 37/2977 | 1.35 | (0.90, 2.02) | 22/2283 | 0.93 | (0.55, 1.56) | 15/695 | 2.36 | (1.27, 4.37) | ||||
| FGF1 (rs6884797) | |||||||||||||
| CC | 148/15814 | 1.00 | 82/9265 | 1.00 | 66/6549 | 1.00 | 0.54 | ||||||
| CA/AA | 69/4654 | 1.58 | (1.18, 2.10) | 43/2836 | 1.72 | (1.19, 2.49) | 26/1818 | 1.46 | (0.92, 2.30) | ||||
| FGF1 (rs6893408) | |||||||||||||
| GG | 159/14658 | 1.00 | 92/8766 | 1.00 | 67/5892 | 1.00 | 0.82 | ||||||
| GA | 49/5350 | 0.89 | (0.65, 1.23) | 26/3107 | 0.80 | (0.52, 1.24) | 23/2243 | 1.02 | (0.63, 1.65) | ||||
| AA | 9/466 | 2.01 | (1.03, 3.95) | 7/233 | 2.69 | (1.24, 5.84) | 2/232 | 1.01 | (0.25, 4.16) | ||||
| NRG2 (rs11738832) | 0.34 | 0.48 | 0.98 | ||||||||||
| AA | 73/5637 | 1.00 | 36/2729 | 1.00 | 37/2908 | 1.00 | 0.27 | ||||||
| AG | 103/10007 | 0.82 | (0.60, 1.10) | 64/5890 | 0.92 | (0.61, 1.39) | 39/4117 | 0.71 | (0.45, 1.12) | ||||
| GG | 40/4799 | 0.66 | (0.44, 0.97) | 24/3457 | 0.53 | (0.32, 0.90) | 16/1342 | 0.98 | (0.54, 1.77) | ||||
| NRG2 (rs1422187) | |||||||||||||
| TT | 136/12768 | 1.00 | 75/7434 | 1.00 | 61/5334 | 1.00 | 0.73 | ||||||
| TC | 76/6633 | 1.09 | (0.82, 1.45) | 48/3904 | 1.20 | (0.83, 1.73) | 28/2729 | 0.92 | (0.59, 1.44) | ||||
| CC | 5/1068 | 0.41 | (0.17, 1.00) | 2/768 | 0.22 | (0.05, 0.88) | 3/300 | 0.92 | (0.28, 2.97) | ||||
Hazard Ratios (HR) and 95% Confidence Intervals (CI) adjusted for age, study, SEER summary Stage, and genetic admixture; data limited to U.S. studies; table includes only statistically significant findings,
Pathway PARTPs for all-cause mortality were 0.25 for all participants, 0.005 for 0-25% NA ancestry and 0.61 for 29-100% NA ancestry; for breast cancer mortality these values were 0.62, 0.39, and 0.46 respectively
Discussion
In this study we studied seven genes involved in growth factor regulation that may be relevant for breast cancer development, taking into account menopausal and ER/PR status among Hispanic and NHW women stratified by level of NA ancestry. FGFR2 and PDGFB were associated with breast cancer risk overall, although associations were generally modest. ERBB2 was significantly associated with breast cancer risk among post-menopausal women only. Although no unique associations were observed by NA ancestry group, multiple associations were restricted to specific tumor subtypes. FGFR2 was only significantly associated with breast cancer risk among those who had ER+ tumors, whereas FGF1 was of border line significance for ER-/PR- tumors. Genetic variants in both ERBB2 and FGF1 were significantly associated with all-cause mortality as well as breast cancer-specific mortality among women with low NA ancestry.
Previous GWAS and replication studies have identified FGFR2 rs2981582 as being associated with breast cancer risk [6-11]. However, few studies have evaluated this gene for associations with tumor phenotype. A study conducted in China by Cen and colleagues showed that this SNP was associated with ER+ tumors only [33]. That study also suggested that the FGF1 rs250108 was associated with ER- tumors. The magnitudes of associations were similar to what we report here. We found that this FGFR2 SNP is associated with all tumor types except ER-/PR- tumors, whereas FGF1 is only associated with ER-/PR- tumors. Additionally, we show that despite associations with breast cancer risk, FGFR2 was not associated with survival after diagnosis. However, FGF1 influenced survival, especially among women with low levels of NA ancestry. While FGF1 activates FGFR2, it appears that other factors may play a contributing role in terms of breast cancer risk and survival.
ERBB2 is of interest with breast cancer risk and survival because women with HER2 negative tumors have poorer survival than those who are HER2 positive. Studies that have evaluated polymorphisms in ERBB2 have often focused on rs1136201, with a large meta-analysis of 33 case-control studies showing no effect with an OR of 1.05 [20]. Conversely, another large meta-analysis of 27 published case-controls studies suggested a modest significant risk (OR 1.10 95% CI 1.01, 1.20) with stronger associations among African women. In this study we did not observe a significant associations for this SNP overall, by menopausal status, or by level of NA ancestry. However, we observed associations with survival for two other ERBB2 SNPs (rs1810132 and rs4252596), especially among women with low levels of NA ancestry. Although associations were stronger for all-cause mortality than for breast cancer-specific mortality, given the similarities in HR estimates we believe that lack of statistical significance observed for breast cancer is due to lack of statistical power. For instance, the HRs were 0.65 (95% CI 0.40, 1.04) and 0.64 (95% CI 0.45, 0.90) for breast cancer-specific mortality and all-cause mortality respectively; we view these as comparable findings.
PDGFB was marginally associated with breast cancer risk overall (PARTP =0.049), although we observed no significant associations with survival. Two SNPs also were associated with ER+/PR+ tumors and one was associated with ER-/PR- tumors. All associations were modest and the PARTP was of borderline significance for ER-/PR- tumors (PARTP =0.08). We found no reports of association with these SNPs in other breast cancer studies. Many of the significant associations with PDGFB were from interaction with other growth factor genes.
It has been proposed that growth factors work together to exert their biological effect [1]. Given that hypothesis, we evaluated interaction between growth factor genes. Our data support this hypothesis, in that several genetic variants interacted to alter breast cancer risk. FGF1 and FGF2 illustrate this observation. FGF1 significantly interacted with EGFR, ERBB2, and PDGFB, whereas FGF2 interacted with EGFR, FGFR2, and PDGFB. In many instances multiple SNPs from the same gene showed interaction. For example, four SNPs in FGF2 interacted with FGFR2; three SNPs in FGF1 interacted with PDGFB; three SNPs in FGF1 interacted with EGFR; and eight SNPs in FGF2 interacted with EGFR. While we saw no significant associations of FGF2 with breast cancer risk overall or by menopausal status, admixture, or with survival, our data suggest that FGF2 works in conjunction with other growth factors to alter risk and may still be an important player in breast cancer carcinogenesis.
The study has many strengths including the large genetically admixed population. However, as pointed out previously, power is modest to look at breast cancer survival. This is in part because we lack survival information from MCBCS participants. We have taken a tagSNP approach to evaluate genetic variation across genes and have followed that approach by looking at the overall gene effect using ARTP statistics. Using this approach we could have missed important SNPs and associations could be chance findings. Additionally, there is little information on the functionality of these SNPs. Thus, we encourage others to replicate these findings, especially those pertaining to survival, and to conduct functionality studies that will help guide future work in this area.
In summary, our findings suggest that associations with breast cancer risk are generally modest for the growth factors evaluated. Genetic variants in growth factor signaling appear to influence breast cancer risk through their combined effects more consistently than independent influence on risk. FGFR2 consistently had the strongest association with breast cancer risk. However, genetic variation in ERBB2 and FGF1 appears to be associated with survival. These findings support the importance of considering combinatorial effects when evaluating the role of growth factors in breast cancer development and prognosis and may provide insight into treatment modalities based on an individual's genetic composition.
Supplementary Material
Supplemental Table 1. Description of growth factor-related genes
Supplemental Table 2. Associations between growth factor genes and family history of breast cancer in first-degree relatives
1Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusted for age, study center, BMI during referent year, parity and genetic ancestry
Acknowledgments
We would also like to acknowledge the contributions of the following individuals to the study: 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.
Grant Support: 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 HHSN261201000036C awarded 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).
Footnotes
Conflict of Interest: None of the authors have any conflict of interest to report.
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Associated Data
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Supplementary Materials
Supplemental Table 1. Description of growth factor-related genes
Supplemental Table 2. Associations between growth factor genes and family history of breast cancer in first-degree relatives
1Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusted for age, study center, BMI during referent year, parity and genetic ancestry