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. Author manuscript; available in PMC: 2015 Feb 1.
Published in final edited form as: Int J Cancer. 2013 Aug 9;134(3):629–644. doi: 10.1002/ijc.28377

Angiogenesis genes, dietary oxidative balance, and breast cancer risk and progression: The Breast Cancer Health Disparities Study

Martha L Slattery 1, Esther M John 2, Gabriela Torres-Mejia 3, Abbie Lundgreen 1, Juan Pablo Lewinger 4, Mariana Stern 4, Lisa Hines 5, Kathy B Baumgartner 6, Anna R Giuliano 7, Roger K Wolff 1
PMCID: PMC3830596  NIHMSID: NIHMS506759  PMID: 23832257

Abstract

Angiogenesis is essential for tumor development and progression. Genetic variation in angiogenesis-related genes may influence breast carcinogenesis. We evaluated dietary factors associated with oxidative balance, DDIT4 (1 SNP), FLT1 (35 SNPs), HIF1A (4 SNPs), KDR (19 SNPs), MPO (1 SNP), NOS2A (15 SNPs), TEK (40 SNPs), and VEGFA (8 SNPs) and breast cancer risk among Hispanic (2111 cases, 2597 controls) and non-Hispanic white (NHW) (1481 cases, 1586 controls) women in the Breast Cancer Health Disparities Study. Adaptive Rank Truncated Product (ARTP) analysis was used to determine gene and pathway significance with breast cancer. TEK was associated with breast cancer overall (PARTP = 0.03) and with breast cancer survival (PARTP = 0.01). KDR was of borderline significance overall (PARTP = 0.07), although significantly associated with breast cancer in both low and intermediate Native American (NA) ancestry groups (PARTP = 0.02) and ER+/PR- tumor phenotype (PARTP = 0.008). Both VEGFA and NOS2A were associated with ER−/PR− tumor phenotype (PARTP = 0.01 and PARTP = 0.04 respectively). FLT1 was associated with breast cancer survival among those with low NA ancestry (PARTP = 0.009). With respect to diet, having a higher dietary oxidative balance score (DOBS) was significantly associated with lower breast cancer risk (OR 0.74 95% CI 0.64–0.84), with the strongest associations observed for women with the highest NA ancestry (OR 0.44 95 %CI 0.30–0.65). We observed few interactions between DOBS and angiogenesis-related genes. Our data suggest that dietary factors and genetic variation in angiogenesis-related genes contribute to breast cancer carcinogenesis.

Keywords: Breast Cancer, FLT1, KDR, NOS2A, TEK, VEGFA, diet, antioxidants, survival, Hispanic

Introduction

Angiogenesis, or the development of new blood vessels, is essential for cancer progression by allowing tumor cells oxygen and nutrients needed for growth [1, 2]. Vascular endothelial growth factor A (VEGFA) and its receptors are major mediators of tumor angiogenesis [3]. As pro-angiogenic growth factors, VEGFA and its tyrosine kinase receptors, VEGFR-1 (alias FLT1) and VEGFR-2 (alias KDR), promote angiogenesis, vascular permeability, cell migration and gene expression and have been the target of anti-cancer therapy [1]. VEGF when released by various cells at the site of inflammation induces angiogenesis [4]. It is believed that VEGF signaling in angiogenesis is mainly mediated through KDR which stimulates endothelial cell survival, cell proliferation, migration and invasion, and capillary-like tube formation [5]. FLT1 is thought to modulate binding of KDR and VEGF. Endothelial tyrosine kinase (TEK) also known as TIE2, is involved in angiogenesis in conjunction with growth factors angiopoietin 1 and 2 [6]. Studies have linked TEK expression to breast cancer metastasis and bone metastasis in particular [7, 8].

Inflammation is closely linked to angiogenesis and a hallmark feature of tumorigenesis as inflammatory cells that infiltrate tissue can stimulate angiogenesis. One mechanism for this is the induction of nitric oxide synthase (NOS2) by inflammatory cytokines and hypoxia. NOS2 produces large amounts of nitric oxide which can increase apoptosis and inhibit carcinogenesis or promote carcinogenesis by increasing angiogenesis [9]. Hypoxia also can induce hypoxia-inducible factor-1A (HIFIA), which is a transcription factor involved in the regulation of the tumor microenvironment [10]. HIFIA has been linked to aggressive tumor phenotypes by promoting angiogenesis and tumor metastasis and invasion and is modulated by ROS in response to oxidative stress [11]. DNA Damage-Inducible transcript 4 (DDIT4 alias REDD1), is a HIF1A responsive protein that is induced by adverse environmental conditions and enhances oxidative stress-dependent cell death. It has been shown to be a negative feedback regulator of HIF1A that influences HIF1A expression and suppresses tumorigenesis [12]. Myeloperoxidase (MPO) generates reactive oxidant species as part of its function in innate host defense mechanisms that can lead to damage of normal tissue and contribute to inflammatory injury. Polymorphisms in MPO have been implicated in risk of lung and prostate cancers [13].

In this study we examined the role of genetic variation in a network of genes that play key roles in angiogenesis and related inflammatory processes in breast cancer risk. Specifically, we investigated associations between genetic variation in VEGFA, FLT1, KDR, TEK, DDIT4, HIF1A, MPO, and NOS2A genes with risk of developing breast cancer in an admixed population of non-Hispanic white (NHW) and U.S. Hispanic and Mexican women. We evaluated associations with ER and PR tumor phenotype and survival as well as the interactive effects with dietary factors that have pro and anti-oxidative properties that could modify the effects of these genes. These included alcohol, polyunsaturated fat, beta carotene, alpha tocopherol (vitamin E), vitamin C, dietary fiber, and folic acid. We created a dietary oxidative balance score (DOBS) as previously described to estimate the dietary oxidative load derived from these nutrients [14]. We focused on main effects of genetic and dietary factors as well as their interactive effects to determine how these factors work together to alter risk of breast cancer risk.

Methods

The Breast Cancer Health Disparities Study includes participants from three population-based case-control studies, the 4-Corner’s Breast Cancer Study, the Mexico Breast Cancer Study, and the San Francisco Bay Area Breast Cancer Study [15] who completed an in-person interview and who had a blood or mouthwash sample available for DNA extraction. In the 4- Corner’s Breast Cancer Study, participants were 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[16]. Participants from the Mexico Breast Cancer Study were between 28 and 74 years of age. 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 and frequency matched to cases based on 5-year age distribution, membership in health care institution, and place of residence. The San Francisco Bay Area Breast Cancer Study included 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 (RDD) and frequency-matched to cases based on the expected race/ethnicity and 5-year age distribution [17, 18]. 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 [15]. 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. Center-specific definitions were used to define post-menopausal women. 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. This age at menopause was site specific by ethnicity: 58 for NHW and 56 for Hispanic women from the 4-Corner’s Breast Cancer Study; 54 for the Mexico Breast Cancer Study; and 55 for NHW and 56 for Hispanic women from the San Francisco Bay Area Breast Cancer Study. Dietary data were collected using detailed food frequency questionnaires or diet histories in all centers; the referent period was the year prior to diagnosis for both 4-Corner’s Breast Cancer Study and the San Francisco Bay Area Breast Cancer Study, while in Mexico it was for a typical week in the year prior to diagnosis or initial symptoms.

Genetic Data

DNA was extracted from either whole blood or mouthwash samples; 7287 blood-derived and 634 mouthwash-derived samples were available. 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 Ancestral Informative Markers (AIMs) were used to distinguish European and Native American ancestry in the study population [15]. 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 DDIT4 (1 SNP), FLT1 (35 SNPs), HIF1A (4 SNPs), KDR (19 SNPs), MPO (1 SNP), NOS2A (15 SNPs), TEK (40 SNPs), and VEGFA (8 SNPs). A description of these genes and SNPs is shown in online Supplement 1.

Tumor Characteristics and Survival

Survival information and ER/PR tumor information were not available for cases from Mexico and therefore assessment of these variables is limited to data obtained from the 4-Corner’s Breast Cancer Study and the San Francisco Bay Area Breast Cancer Study. 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 estrogen receptor (ER) and progesterone receptor (PR) status. Information on ER and PR status of tumors was available for 1019 (69%) NHW and 977 (75%) Hispanic cases. Surveillance Epidemiology and End Results (SEER) summary disease stage was available for breast cancer cases from the U.S. Staging is based on three codes of local, regional, and distant, where distant corresponds to AJCC stage 4, local is predominately AJCC stage 1 with some stage 2, and regional contains AJCC stage 2 and 3.

Statistical Methods

Genetic ancestry estimation

The program STRUCTURE was used to compute individual ancestry for each study participant assuming two founding populations [19, 20]. A three-founding population model was assessed but did not fit the population structure with the same level of repeatability and correlation among runs as the two-founding population model. Participants were classified by level of percent Native American (NA) ancestry. Assessment across categories of ancestry was done using cut-points based on the distribution of genetic ancestry in the control population with the goal of creating distinct ancestry groups that had sufficient power to assess associations. Three strata, 0–28%, 29 to 70%, and 71 to 100%, were used to evaluate associations by level of NA ancestry. Genetic ancestry was used as a continuous variable when included in the models to adjust for possible confounding.

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 San Francisco Bay Area and 4-Corners studies. All statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). Logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for breast cancer risk associated with SNPs, adjusting for age, study center, genetic ancestry, reference year BMI, and parity. 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 analysis test for interactions were calculated using a Wald 1 df test; adjustments for multiple comparisons within the gene used the step-down Bonferroni correction (i.e., Holm method) taking into account the correlated nature of the data using the SNP spectral decomposition method proposed by Nyholt [21] and modified by Li and Ji [22].

Dietary Analysis

Given the hypothesized pathway we evaluated nutrients with anti- or pro-oxidative balance properties. A dietary oxidative balance score (DOBS) was created based on each individual’s ranking of each nutrient included in the score. Anti-oxidants included were vitamin C, vitamin E, beta carotene (data for beta carotene was not available for Mexico), folic acid, and dietary fiber; alcohol was treated as a pro-oxidant. To account for the different number of foods queried on the diet questionnaires used for each study, nutrients were evaluated as nutrient per 1000 calories and quartiles of intake and the DOBS were based on study-specific distributions; additional adjustment for calories did not alter findings. Long-term alcohol consumption was classified into three levels: the top 25th percentile of consumption, all other drinkers, and non-drinkers. In creating the DOBS, participants were assigned values of zero for low levels (first quartile) of exposure to anti-oxidants or high exposure to pro-oxidants (fourth quartile), one for intermediate levels (second and third quartiles) of exposure, and two for high levels (fourth quartile) of exposure to anti-oxidants and low exposure (first quartile) to pro-oxidants. We report ORs and 95% CI for each component part of the DOBS as well as associations for the overall summary score. DOBS trend p values and p values for interaction between the DOBS and SNPs were based on one degree of freedom (1-df) Wald chi-square test statistics as noted above.

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 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 Mexico study site, the upper two admixture strata were combined to evaluate survival by ancestry groups. Individuals were censored when they died of causes other than breast cancer or were lost to follow-up. In addition to the minimal adjustments for age, study center, genetic ancestry, referent year BMI, and parity, models were also adjusted for SEER summary stage to estimate the HR. 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 the angiogenesis pathway with breast cancer overall, by admixture, 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 admixture [23, 24]. We also controlled for SEER summary stage when estimating the ARTP for breast cancer 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.

Results

The majority of breast cancer cases were Hispanic, under 60 years of age, and post-menopausal (Table 1). Among U.S. cases, most tumors were ER+/PR+. 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), while U.S. women who self-reported being Hispanic where divided between those with intermediate NA ancestry (64.9% of controls) and high NA ancestry (24.4% of controls). Intake of alcohol was very low in the study population and significantly lower among NHW and Hispanic controls than cases. Among women who self-reported being Hispanic or were from Mexico, median levels of all nutrients, except for vitamin C, were significantly different between cases and controls; no significant associations were observed for individual nutrients for NHW women.

Table 1.

Description of Study Population by Ethnicity

Non-Hispanic White U. S. Hispanic or Mexican
Controls Cases p
value		 Controls Cases p value
N % N % N % N %
Total 1586 37.9 1481 41.2 2597 62.1 2111 58.8
Study Site
  4-Corner’s 1322 83.4 1227 82.8 NA1 723 27.8 597 28.3 NA
  Mexico 0 0 0 0 994 38.3 816 38.7
  San Francisco Bay Area 264 16.6 254 17.2 880 33.9 698 33.1
Age (years) NA NA
  <40 116 7.3 89 6 311 12 200 9.5
  40–49 408 25.7 409 27.6 831 32 713 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 NA NA
  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
Estimated Native American Ancestry NA NA
  Low (0 – 28%) 1578 99.5 1472 99.4 278 10.7 275 13
  Intermediate (29 – 70%) 7 0.4 7 0.5 1686 64.9 1393 66
  High (71 – 100%) 1 0.1 2 0.1 633 24.4 443 21
ER/PR Status2 NA NA
  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
SEER Summary Stage2,3 NA NA
  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
Vital Status2,3 NA NA
  Deceased NA 202 17.1 NA 202 17.5
  Alive NA 982 82.9 NA 950 82.5
Cause of Death2,3 NA NA
  Breast Cancer NA 102 50.5 NA 115 56.9
  Other NA 100 49.5 NA 87 43.1
Alcohol Intake
  None 792 50.74 691 46.9 0.03 2011 78.07 1545 73.43 <.01
  Any 769 49.26 784 53.2 565 21.93 559 26.57
Daily Anti-oxidant Intake/1000 kcal) Median Median
Vitamin C (mg) 75.96 78.05 0.54 80.91 81.8 0.85
Vitamin E (mg) 4.63 4.6 0.56 4.85 4.74 <.01
Beta Carotene (mcg) 2290.08 2266.01 0.73 1997.54 1838.54 <.01
Dietary Folate (mcg) 187.32 187.93 0.37 204.26 193.49 <.01
Dietary Fiber (g) 10.72 10.82 0.99 12.86 12.47 <.01
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1

p values not applicable

2

Information unavailable for the Mexico study site.

3

Among primary invasive breast cancer cases.

Associations between genes and breast cancer risk overall and by admixture group showed that several genes in the pathway were statistically significantly associated as determined by ARTP (SNPs that showed statistical significant for KDR, NOS2A, TEK are shown in Table 2), whereas other genes, such as FLT1 had several significant SNPs that did not maintain statistical significant using ARTP (Online Supplement Table 2). When considering all women together, TEK was associated with breast cancer risk (PARTP= 0.03) while KDR was of borderline significance (PARTP = 0.07). When stratified by NA ancestry, KDR was significantly associated with breast cancer risk among women in the low and middle NA ancestry groups (PARTP = 0.02 and 0.02 respectively), this reflects the strong association observed for rs12498529 and modest associations with both rs2219471 and rs1531290. Both NOS2A and KDR were associated with breast cancer risk in the middle NA ancestry group (PARTP = 0.04 and 0.02 respectively); KDR rs12498529 remained statistically different between admixture groups after adjustment for multiple comparisons (Padj = 0.03). The significant gene associations as determined by ARTP reflect both the numbers of SNPs associated within a gene as well as the strength of the SNP associations. For KDR, these include rs12498529, rs203465, and rs1531290; for NOS2A, these include rs7406657 and rs2297516. No genes were significantly associated in the highest NA ancestry group as determined by ARTP. The overall pathway PARTP was 0.25. Associations did not differ by menopausal status.

Table 2.

Associations between angiogenesis-related genes and breast cancer risk, by Native American ancestry

All 0 – 28% Native American Ancestry 29 – 70% Native American Ancestry 71 – 100% Native American Ancestry
Controls Cases OR1 (95% CI) Controls Cases OR (95% CI) Controls Cases OR (95% CI) Controls Cases OR (95% CI)
KDR PARTP 0.07 0.02 0.02 0.88
 (rs2219471)
  AA/AG 3998 3462 1.00 1731 1663 1.00 1644 1367 1.00 623 432 1.00
  GG 150 107 0.77 (0.60, 1.00) 110 79 0.75 (0.56, 1.01) 34 23 0.81 (0.47, 1.39) 6 5 1.05 (0.31, 3.56)
 (rs12498529)2
  AA 2752 2300 1.00 1217 1068 1.00 1096 910 1.00 439 322 1.00
  AT 1242 1140 1.09 (0.99, 1.20) 562 592 1.21 (1.05, 1.40) 505 444 1.04 (0.88, 1.21) 175 104 0.80 (0.60, 1.07)
  TT 154 126 0.97 (0.76, 1.24) 63 81 1.50 (1.07, 2.11) 76 34 0.51 (0.33, 0.77) 15 11 1.03 (0.46, 2.32)
 (rs7692791)
  CC 1225 1051 1.00 392 387 1.00 543 474 1.00 290 190 1.00
  CT 2012 1745 0.97 (0.87, 1.08) 902 855 0.96 (0.81, 1.14) 830 696 0.92 (0.79, 1.09) 280 194 1.05 (0.81, 1.37)
  TT 911 772 0.90 (0.79, 1.03) 547 500 0.92 (0.76, 1.11) 305 219 0.80 (0.64, 0.99) 59 53 1.30 (0.85, 1.98)
 (rs2034965)
  GG/G
  A 3867 3360 1.00 1723 1635 1.00 1557 1315 1.00 587 410 1.00
  AA 283 208 0.84 (0.70, 1.01) 119 106 0.94 (0.71, 1.23) 122 75 0.73 (0.54, 0.99) 42 27 0.92 (0.55, 1.54)
 (rs1531290)
  AA 1667 1487 1.00 470 491 1.00 778 700 1.00 419 296 1.00
  AG 1847 1545 0.87 (0.79, 0.96) 940 864 0.88 (0.75, 1.03) 720 560 0.86 (0.74, 1.00) 187 121 0.85 (0.64, 1.12)
  GG 634 533 0.83 (0.72, 0.95) 431 385 0.85 (0.70, 1.03) 180 128 0.75 (0.58, 0.97) 23 20 1.01 (0.53, 1.92)
 (rs12502008)
  GG 1176 1063 1.00 717 711 1.00 393 298 1.00 66 54 1.00
  GT 1978 1640 0.96 (0.87, 1.07) 881 802 0.92 (0.80, 1.07) 821 659 1.05 (0.87, 1.27) 276 179 0.87 (0.57, 1.32)
  TT 996 863 1.07 (0.94, 1.22) 244 227 0.94 (0.76, 1.16) 465 432 1.25 (1.02, 1.53) 287 204 0.98 (0.65, 1.49)
NOS2A PARTP 0.25 0.86 0.04 0.66
 (rs7406657)
  GG 2080 1790 1.00 1033 988 1.00 802 618 1.00 245 184 1.00
  GC 1717 1468 1.02 (0.93, 1.13) 685 652 1.00 (0.87, 1.14) 728 611 1.09 (0.94, 1.27) 304 205 0.93 (0.71, 1.22)
  CC 351 308 1.06 (0.90, 1.25) 124 100 0.83 (0.63, 1.10) 148 160 1.42 (1.11, 1.83) 79 48 0.77 (0.51, 1.17)
 (rs9906835)
  AA 1291 1110 1.00 628 591 1.00 515 397 1.00 148 122 1.00
  AG 2097 1742 0.98 (0.89, 1.09) 921 852 0.98 (0.85, 1.14) 829 672 1.06 (0.89, 1.25) 347 218 0.79 (0.59, 1.07)
  GG 760 714 1.12 (0.98, 1.28) 293 296 1.06 (0.87, 1.29) 334 321 1.25 (1.02, 1.54) 133 97 0.88 (0.62, 1.27)
 (rs2297516)
  AA 1462 1209 1.00 648 607 1.00 609 452 1.00 205 150 1.00
  AC 2046 1734 1.03 (0.93, 1.14) 914 842 0.98 (0.85, 1.13) 807 677 1.13 (0.96, 1.33) 325 215 0.90 (0.69, 1.19)
  CC 642 624 1.18 (1.03, 1.35) 280 291 1.10 (0.90, 1.34) 263 261 1.34 (1.08, 1.66) 99 72 0.96 (0.66, 1.40)
 (rs944725)
  CC 1497 1233 1.00 648 626 1.00 600 448 1.00 249 159 1.00
  CT 1958 1703 1.06 (0.96, 1.17) 892 820 0.95 (0.82, 1.10) 787 676 1.14 (0.97, 1.34) 279 207 1.15 (0.88, 1.52)
  TT 694 633 1.12 (0.98, 1.27) 301 296 1.02 (0.84, 1.24) 292 266 1.24 (1.01, 1.53) 101 71 1.06 (0.73, 1.53)
TEK PARTP 0.03 0.14 0.18 0.12
 (rs17834811)
  TT 2239 2020 1.00 892 898 1.00 950 831 1.00 397 291 1.00
  TG 1654 1325 0.86 (0.78, 0.95) 808 697 0.85 (0.74, 0.98) 641 491 0.84 (0.72, 0.98) 205 137 0.89 (0.68, 1.16)
  GG 257 223 0.92 (0.76, 1.11) 142 146 1.01 (0.79, 1.30) 88 68 0.90 (0.64, 1.25) 27 9 0.45 (0.21, 0.99)
 (rs7042119)
  CC 2747 2212 1.00 1054 933 1.00 1155 900 1.00 538 379 1.00
  CT/TT 1403 1357 1.15 (1.04, 1.27) 788 809 1.16 (1.01, 1.32) 524 490 1.17 (1.00, 1.36) 91 58 0.78 (0.54, 1.13)
 (rs10967753)
  TT 1337 1119 1.00 465 433 1.00 610 473 1.00 262 213 1.00
  TC/CC 2811 2449 1.00 (0.91, 1.10) 1377 1308 1.01 (0.87, 1.18) 1068 917 1.09 (0.93, 1.27) 366 224 0.73 (0.57, 0.94)
 (rs7047856)
  AA 1872 1728 1.00 797 841 1.00 785 671 1.00 290 216 1.00
  AG/GG 2278 1841 0.87 (0.80, 0.95) 1045 901 0.82 (0.72, 0.94) 894 719 0.93 (0.80, 1.07) 339 221 0.88 (0.68, 1.12)
 (rs581724)
  AA 1089 974 1.00 350 347 1.00 488 453 1.00 251 174 1.00
  AC/CC 3060 2595 0.90 (0.81, 1.00) 1492 1395 0.94 (0.80, 1.11) 1190 937 0.83 (0.71, 0.97) 378 263 0.95 (0.74, 1.23)
 (rs3780317)
  GG 3072 2719 1.00 1299 1274 1.00 1272 1095 1.00 501 350 1.00
  GA/AA 1077 850 0.87 (0.78, 0.96) 543 468 0.88 (0.76, 1.02) 406 295 0.82 (0.69, 0.97) 128 87 0.97 (0.71, 1.33)
 (rs3737188)
  AA 2763 2449 1.00 1111 1112 1.00 1190 1002 1.00 462 335 1.00
  AG 1238 999 0.89 (0.80, 0.98) 640 547 0.86 (0.75, 0.99) 448 353 0.92 (0.78, 1.08) 150 99 0.93 (0.69, 1.25)
  GG 149 120 0.88 (0.68, 1.12) 91 82 0.92 (0.68, 1.26) 41 35 1.02 (0.64, 1.63) 17 3 0.24 (0.07, 0.85)
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1

Adjusted for age, study center, BMI in reference year, parity, and genetic admixture

2

SNP association significantly different at the 0.05 level or less across admixture groups.

Four genes were associated with various ER/PR tumor sub-groups as determined by ARTP (Table 3). KDR was associated with ER+/PR- tumors with seven SNPs having significant associations with this tumor type (PARTP = 0.0008). NOS2A was associated with ER−/PR− tumors (2 SNPs) as was VEGFA (3 SNPs) (PARTP = 0.04 and 0.01 respectively). TEK was associated with ER+/PR+ tumors (PARTP = 0.048) having 4 SNPs significantly associated. TEK was of borderline significance (PARTP = 0.06 with ER−/PR+ tumors). Several SNPs in FLT1 were associated with specific tumor phenotype, however the gene p value from ARTP was >0.05 for all tumor phenotypes (associations shown in online Supplemental Table 3).

Table 3.

Associations between angiogenesis genes and breast cancer defined by ER and PR tumor status

Controls ER + / PR + ER + / PR − ER − / PR + ER − / PR −4
N N OR1 (95% CI) N OR (95% CI) N OR (95% CI) N OR (95% CI)
KDR PARTP 0.19 0.0008 0.31 0.20
 (rs2219471)2 AA 2064 837 1.00 129 1.00 27 1.00 271 1.00
AG/GG 1100 461 1.00 (0.87, 1.15) 106 1.55 (1.18, 2.03) 16 1.20 (0.64, 2.28) 144 1.03 (0.82, 1.28)
 (rs7692791)2 CC 836 351 1.00 51 1.00 8 1.00 130 1.00
CT 1536 632 0.96 (0.82, 1.12) 119 1.28 (0.91, 1.80) 25 1.82 (0.81, 4.09) 198 0.84 (0.66, 1.07)
TT 792 315 0.89 (0.74, 1.07) 65 1.32 (0.89, 1.94) 10 1.41 (0.54, 3.65) 86 0.69 (0.52, 0.93)
 (rs12498529) AA 2077 819 1.00 134 1.00 27 1.00 265 1.00
AT/TT 1087 478 1.12 (0.98, 1.28) 100 1.43 (1.09, 1.87) 16 1.14 (0.61, 2.13) 149 1.08 (0.87, 1.34)
 (rs17709898) AA 1619 673 1.00 104 1.00 23 1.00 216 1.00
AG/GG 1547 625 0.93 (0.82, 1.07) 131 1.32 (1.01, 1.74) 20 0.98 (0.52, 1.82) 199 0.99 (0.80, 1.22)
 (rs10020464) CC 1596 638 1.00 101 1.00 16 1.00 215 1.00
CT/TT 1568 660 1.05 (0.92, 1.19) 133 1.34 (1.02, 1.75) 27 1.74 (0.93, 3.25) 198 0.94 (0.77, 1.16)
 (rs6837735) CC 2069 829 1.00 136 1.00 27 1.00 264 1.00
CT/TT 1097 469 1.08 (0.95, 1.24) 99 1.40 (1.07, 1.84) 16 1.08 (0.58, 2.02) 151 1.07 (0.86, 1.32)
 (rs2034965)2 GG 1762 708 1.00 107 1.00 25 1.00 243 1.00
GA/AA 1404 590 1.04 (0.91, 1.18) 127 1.48 (1.13, 1.94) 18 0.87 (0.47, 1.60) 172 0.87 (0.71, 1.08)
 (rs1531290)2 AA 1083 470 1.00 103 1.00 19 1.00 152 1.00
AG/GG 2081 826 0.86 (0.75, 0.99) 131 0.63 (0.47, 0.83) 24 0.68 (0.36, 1.27) 263 0.91 (0.73, 1.14)
NOS2A PARTP 0.72 0.48 0.93 0.04
 (rs8072199)2 CC 1332 534 1.00 101 1.00 18 1.00 206 1.00
CT 1392 585 1.00 (0.86, 1.15) 107 0.98 (0.73, 1.31) 22 1.19 (0.63, 2.27) 165 0.75 (0.60, 0.94)
TT 442 179 0.94 (0.76, 1.16) 27 0.78 (0.49, 1.22) 3 0.54 (0.16, 1.91) 44 0.64 (0.45, 0.91)
 (rs3729508) GG/GA 2708 1105 1.00 195 1.00 35 1.00 372 1.00
AA 457 193 1.00 (0.83, 1.21) 40 1.19 (0.83, 1.70) 8 1.38 (0.63, 3.02) 43 0.69 (0.49, 0.96)
 (rs3729508) CC 1248 507 1.00 96 1.00 17 1.00 138 1.00
CT/TT 1911 787 1.06 (0.93, 1.21) 137 0.97 (0.73, 1.27) 25 0.92 (0.49, 1.72) 276 1.29 (1.04, 1.61)
TEK PARTP 0.05 0.58 0.06 0.52
 (rs4242698)2 AA/AC 2781 1169 1.00 221 1.00 35 1.00 360 1.00
CC 385 128 0.82 (0.66, 1.01) 14 0.47 (0.27, 0.81) 8 1.66 (0.76, 3.62) 54 1.10 (0.81, 1.49)
 (rs586441)2 AA/AG 3123 1267 1.00 230 1.00 41 1.00 410 1.00
GG 43 31 1.83 (1.14, 2.93) 5 1.64 (0.64, 4.20) 2 3.90 (0.91, 16.78) 5 0.93 (0.37, 2.38)
 (rs7042119)2 CC 1966 741 1.00 137 1.00 23 1.00 243 1.00
CT/TT 1200 557 1.20 (1.05, 1.37) 98 1.15 (0.87, 1.51) 20 1.53 (0.83, 2.83) 172 1.18 (0.96, 1.46)
 (rs7047856)2 AA 1402 634 1.00 124 1.00 23 1.00 180 1.00
AG/GG 1764 664 0.83 (0.73, 0.95) 111 0.71 (0.54, 0.93) 20 0.70 (0.38, 1.28) 235 1.05 (0.85, 1.29)
 (rs3780317)2 GG/GA 3105 1265 1.00 230 1.00 38 1.00 409 1.00
AA 60 33 1.40 (0.91, 2.15) 5 1.21 (0.48, 3.05) 5 7.72 (2.89, 20.64) 6 0.79 (0.34, 1.85)
VEGFA PARTP 0.11 0.64 0.70 0.01
 (rs25648)2 CC 2178 854 1.00 169 1.00 27 1.00 307 1.00
CT 874 381 1.11 (0.96, 1.28) 55 0.80 (0.59, 1.10) 14 1.32 (0.69, 2.53) 97 0.79 (0.62, 1.01)
TT 90 48 1.44 (1.00, 2.07) 6 0.93 (0.40, 2.16) 2 1.93 (0.45, 8.27) 4 0.33 (0.12, 0.90)
 (rs833070)2 GG 920 339 1.00 65 1.00 9 1.00 151 1.00
GA 1579 650 1.10 (0.94, 1.29) 120 1.06 (0.77, 1.45) 24 1.67 (0.77, 3.62) 198 0.79 (0.63, 0.99)
AA 666 308 1.22 (1.01, 1.47) 50 1.04 (0.71, 1.53) 10 1.70 (0.68, 4.23) 66 0.62 (0.46, 0.85)
 (rs3025010)2,3 TT 1279 503 1.00 94 1.00 17 1.00 195 1.00
TC 1440 619 1.09 (0.95, 1.26) 106 0.99 (0.74, 1.32) 19 1.03 (0.53, 1.99) 180 0.83 (0.67, 1.04)
CC 446 176 1.02 (0.83, 1.25) 35 1.09 (0.73, 1.64) 7 1.20 (0.49, 2.93) 40 0.59 (0.41, 0.84)
Open in a new tab
1

Odds ratios (OR) and 95% confidence intervals (CI) adjusted for age, study center, BMI in reference year, parity, and genetic admixture (continuous).

2

SNP association significantly different at the 0.05 level or less across ER/PR groups.

3

Similar associations for rs2146323 (r2 values range from 0.88 to 0.94 across admixture groups).

4

pathway PARTP= ER+/PR+ = 0.44; ER+/PR- = 0.01; ER−/PR+ = 0.52; ER−/PR− = 0.06.

Given the biological plausibility that dietary factors with pro- and anti-oxidant properties could modify breast cancer risk associated with angiogenesis-related genes, we evaluated dietary factors that have recognized pro- or anti-oxidant properties. Several of these factors were statistically significantly associated with breast cancer overall and by admixture groups (Table 4). These include alcohol, vitamin E, beta carotene, folic acid, dietary fiber, and the summary DOBS. For the most part, associations were strongest among women with the highest level of NA ancestry. For instance, highest level of alcohol intake was only associated with an increased risk among women with the highest NA ancestry, while vitamin E, folic acid, and the dietary oxidative balance score were more associated with decreased risk (DOBS interaction p value = 0.001). No association was observed for vitamin C for all groups.

Table 4.

Dietary factors associated with oxidative balance and breast cancer risk, by Native American ancestry

All 0% – 28% Native American Ancestry 29% – 70% Native American Ancestry 71% – 100% Native American Ancestry
Controls Cases OR1 (95% CI) Controls Cases OR (95% CI) Controls Cases OR (95% CI) Controls C cases OR (95% CI)
Alcohol2
  None 2780 2216 1.00 940 838 1.00 1276 1007 1.00 564 371 1.00
  Low/Moderate 998 963 1.06 (0.95, 1.19) 644 631 1.06 (0.91, 1.23) 305 292 1.07 (0.89, 1.29) 49 40 1.24 (0.79, 1.96)
  High 334 377 1.21 (1.03, 1.43) 236 267 1.21 (0.98, 1.48) 82 85 1.22 (0.88, 1.68) 16 25 2.32 (1.21, 4.47)
Vitamin C per 1000 Cal
  Low 1015 881 1.00 457 430 1.00 427 367 1.00 131 84 1.00
  Moderate 2028 1696 0.95 (0.85, 1.06) 915 883 1.04 (0.88, 1.22) 814 624 0.87 (0.73, 1.04) 299 189 0.98 (0.70, 1.37)
  High 1025 931 1.02 (0.89, 1.16) 467 425 0.96 (0.79, 1.16) 407 368 1.03 (0.84, 1.26) 151 138 1.35 (0.93, 1.95)
Vitamin E per 1000 Cal2
  Low 1015 991 1.00 441 424 1.00 412 406 1.00 162 161 1.00
  Moderate 2040 1700 0.84 (0.75, 0.94) 907 867 0.99 (0.84, 1.17) 847 641 0.76 (0.64, 0.91) 286 192 0.68 (0.51, 0.91)
  High 1013 822 0.79 (0.70, 0.90) 491 447 0.92 (0.77, 1.11) 389 315 0.79 (0.64, 0.97) 133 60 0.42 (0.29, 0.62)
Beta-Carotene per 1000 Cal
  Low 797 725 1.00 430 392 1.00 343 309 1.00 24 24 1.00
  Moderate 1578 1381 0.95 (0.84, 1.08) 904 874 1.08 (0.91, 1.27) 604 467 0.84 (0.69, 1.03) 70 40 0.51 (0.24, 1.07)
  High 790 658 0.89 (0.77, 1.03) 496 450 1.01 (0.84, 1.23) 267 191 0.77 (0.60, 0.98) 27 17 0.63 (0.26, 1.56)
Folic Acid per 1000 Cal2
  Low 1011 997 1.00 551 529 1.00 348 360 1.00 112 108 1.00
  Moderate 2037 1764 0.90 (0.80, 1.00) 894 859 1.01 (0.87, 1.18) 830 682 0.82 (0.68, 0.98) 313 223 0.76 (0.55, 1.05)
  High 1019 750 0.77 (0.67, 0.88) 394 350 0.93 (0.77, 1.13) 470 319 0.69 (0.56, 0.85) 155 81 0.53 (0.36, 0.79)
Dietary Fiber per 1000 Cal
  Low 1013 997 1.00 563 577 1.00 341 330 1.00 109 90 1.00
  Moderate 2031 1718 0.89 (0.79, 0.99) 891 826 0.93 (0.80, 1.08) 839 686 0.88 (0.73, 1.06) 301 206 0.84 (0.60, 1.19)
  High 1024 797 0.82 (0.72, 0.94) 385 335 0.87 (0.72, 1.06) 468 346 0.81 (0.65, 1.00) 171 116 0.81 (0.55, 1.19)
Dietary Oxidative Balance Score2
  Quartile 1 960 984 1.00 477 490 1.00 355 371 1.00 128 123 1.00
  Quartile 2 946 863 0.91 (0.80, 1.04) 466 456 0.98 (0.82, 1.17) 368 328 0.91 (0.73, 1.12) 112 79 0.73 (0.49, 1.07)
  Quartile 3 1142 925 0.82 (0.72, 0.93) 456 432 0.94 (0.78, 1.13) 494 353 0.72 (0.58, 0.88) 192 140 0.73 (0.52, 1.03)
  Quartile 4 970 714 0.74 (0.64, 0.84) 412 350 0.85 (0.70, 1.03) 411 299 0.73 (0.59, 0.90) 147 65 0.44 (0.30, 0.65)
  Trend P <.0001 0.10 <0.01 <0.01
Open in a new tab
1

Odds ratios (OR) and 95% confidence intervals (CI) adjusted for age, study center, BMI in reference year, parity, and genetic admixture (continuous). Low = bottom quartile; Moderate = middle two quartiles, High = upper quartile

2

Associations were significantly different at the <0.05 level by ancestry group

Significant interaction was observed between the DOBS and the following SNPs: FLT1 rs7987649; KDR rs1531289; TEK rs669102, rs12350649, rs17834811, rs7047856, and rs581724; and VEGFA rs3025033, although after adjustment for multiple comparisons only the VEGFA rs3025033 remained statistically significant (Padj=0.03) (Table 5). The protective association observed for having a high DOBS was observed for all genotypes, however, the magnitude of that association differed by genotypes, and in some instances such as TEK rs17834811, rs7047856, and rs581724 there was no additional reduction in risk beyond that observed for the homozygote variant genotype group.

Table 5.

Interaction between angiogenesis-related genes and dietary oxidative balance score

Dietary Oxidative Balance Score (DOBS)
Quartile 1 Quartile 2 Quartile 3 Quartile 4 Interaction
Controls Cases OR1 (95% CI) Controls Cases OR (95% CI) Controls Cases OR (95% CI) Controls Cases OR (95% CI) P-value
FLT1 (rs7987649)
  AA 381 381 1.00 375 341 0.95 (0.77, 1.17) 432 385 0.93 (0.76, 1.14) 447 276 0.64 (0.52, 0.78) 0.03
  AG 416 415 1.01 (0.82, 1.23) 409 350 0.89 (0.72, 1.09) 512 371 0.75 (0.62, 0.91) 361 285 0.81 (0.65, 1.00)
  GG 110 104 0.95 (0.70, 1.29) 105 82 0.81 (0.58, 1.12) 119 100 0.88 (0.65, 1.19) 92 92 1.03 (0.74, 1.42)
KDR (rs1531289)
  GG 530 480 1.00 511 441 0.99 (0.83, 1.19) 607 483 0.92 (0.77, 1.09) 522 373 0.80 (0.67, 0.97) 0.04
  GA 364 423 1.27 (1.06, 1.54) 370 363 1.08 (0.89, 1.31) 437 366 0.94 (0.78, 1.14) 361 294 0.92 (0.75, 1.12)
  AA 65 81 1.37 (0.96, 1.95) 65 59 1.01 (0.69, 1.47) 97 75 0.87 (0.63, 1.21) 86 47 0.61 (0.41, 0.89)
TEK (rs669102)
  GG 293 261 1.00 272 255 1.07 (0.84, 1.36) 327 232 0.84 (0.66, 1.06) 270 224 0.96 (0.75, 1.23) 0.02
  GA 469 502 1.24 (1.01, 1.53) 464 409 1.06 (0.85, 1.31) 556 465 1.00 (0.81, 1.23) 469 350 0.89 (0.71, 1.10)
  AA 198 221 1.36 (1.05, 1.75) 210 198 1.17 (0.90, 1.52) 259 228 1.10 (0.86, 1.41) 231 140 0.75 (0.57, 0.98)
TEK (rs12350649)
  AA 598 575 1.00 603 529 0.93 (0.79, 1.10) 676 532 0.85 (0.72, 1.00) 566 446 0.84 (0.71, 1.00) 0.01
  AT 305 338 1.23 (1.01, 1.49) 276 272 1.13 (0.92, 1.39) 380 323 0.97 (0.80, 1.18) 336 229 0.77 (0.62, 0.95)
  TT 53 66 1.45 (0.98, 2.13) 64 58 1.11 (0.76, 1.62) 82 66 0.99 (0.70, 1.41) 65 36 0.68 (0.44, 1.04)
TEK (rs17834811)
  TT 506 578 1.00 506 478 0.85 (0.71, 1.01) 599 527 0.8 (0.67, 0.94) 543 384 0.63 (0.53, 0.76) 0.01
  TG 388 345 0.75 (0.62, 0.91) 374 333 0.78 (0.64, 0.94) 477 353 0.65 (0.54, 0.78) 373 268 0.62 (0.51, 0.76)
  GG 66 61 0.76 (0.52, 1.10) 66 52 0.68 (0.46, 1.00) 66 44 0.58 (0.39, 0.87) 54 62 0.98 (0.66, 1.44)
TEK (rs7047856)
  AA 416 493 1.00 412 423 0.89 (0.73, 1.07) 520 452 0.75 (0.63, 0.91) 459 324 0.61 (0.50, 0.74) 0.005
  AG 437 394 0.75 (0.62, 0.91) 437 363 0.71 (0.59, 0.86) 507 389 0.67 (0.55, 0.81) 424 308 0.62 (0.51, 0.76)
  GG 107 97 0.75 (0.55, 1.02) 97 77 0.69 (0.49, 0.95) 115 84 0.63 (0.46, 0.87) 87 82 0.81 (0.58, 1.12)
TEK (rs581724)
  AA 245 281 1.00 248 237 0.86 (0.67, 1.11) 295 250 0.77 (0.61, 0.99) 249 178 0.65 (0.50, 0.84) 0.04
  AC 471 477 0.86 (0.69, 1.06) 434 415 0.82 (0.66, 1.03) 577 451 0.68 (0.55, 0.85) 512 358 0.60 (0.48, 0.75)
  CC 243 226 0.76 (0.59, 0.98) 264 211 0.67 (0.52, 0.87) 270 224 0.70 (0.55, 0.90) 209 178 0.72 (0.55, 0.94)
VEGFA (rs3025033)
  AA 584 588 1.00 569 537 0.96 (0.81, 1.14) 625 556 0.91 (0.77, 1.07) 553 453 0.83 (0.70, 0.98) 0.005
  AG 325 327 1.03 (0.85, 1.25) 318 284 0.94 (0.77, 1.15) 427 312 0.78 (0.65, 0.95) 349 226 0.68 (0.56, 0.84)
  GG 50 68 1.46 (0.99, 2.16) 59 42 0.78 (0.52, 1.19) 88 54 0.70 (0.48, 1.00) 68 34 0.58 (0.38, 0.90)
Open in a new tab
1

Odds ratios (OR) and 95% confidence intervals (CI) adjusted for age, study center, BMI in reference year, parity and genetic admixture (continuous).

Angiogenesis genes also were associated with survival (Figure 1), however only KDR (PARTP = 0.04), and TEK (PARTP = 0.02) showed statistically significant p values for the association as estimated by ARTP and FLT1 was of borderline significance (PARTP = 0.052). FLT1 also was significantly associated with breast cancer survival among those women with the lowest level of NA ancestry (PARTP = 0.009) with the pathway p value among this group being 0.09. As shown in Figure 1, both of these genes had several SNPs that were associated with survival overall and within specific ancestry groups. The overall pathway PARTP for survival was 0.06. Only DDIT4 was of borderline significance among those with NA ancestry over 28% (PARTP = 0.07).

Figure 1.

Figure 1

Open in a new tab

Hazard ratios and 95% confidence bounds of angiogenesis-related genes associated with breast cancer survival

A=Additive model, D=Dominant Model, R= Recessive Model; p values are for ARTP

Discussion

Angiogenesis-related genes were associated with both breast cancer development and progression in this population of NHW and Hispanic/Mexican women. Some associations appeared stronger for specific tumor phenotype and others appeared to interact with dietary factors associated with oxidative balance. Of the genes assessed, TEK appeared to influence breast cancer the most, as seen by its association with breast cancer risk and survival. KDR, NOS2A, and VEGFA were associated with breast cancer for specific tumor phenotypes, while FLT1 was associated with survival among women who were primarily NHW. We did not observe differences in association by menopausal status and most associations were strongest in groups that did not include high NA ancestry.

Angiogenesis is an essential component of the carcinogenic process. Increased vascularization allows tumors to obtain the necessary nutrients and oxygen needed for growth and invasion. As such, angiogenesis-related genes are potentially important in regulating breast cancer development and progression. Studies have evaluated angiogenesis genes with mixed results. VEGFA has been the focus of much research because of its well-documented role in angiogenesis and its potential as a treatment modality for cancer patients. Several polymorphisms have been associated with breast cancer. The Cancer Prevention Study II cohort examined three polymorphisms and found an association with invasive breast cancer for −2578 (rs699947) and −1154 (rs1570360) [25]. The −2578 polymorphism also was associated with increased breast cancer risk in a study of African American women by Schneider [26] but not in one by Langsenlehner [27] or Jin [28]. The +936 (rs3025039) was not associated with breast cancer risk in a study conducted by Oliveira [29], Balasubramanian [30], Langsenlehner [27], although Krippl [31], Rodrigues [32] , and Kataoka [33] saw an inverse association with the TT genotype. We did not observe a significant association with this polymorphism. Likewise, we did not observe a significant association for VEGFA rs25648 similar to what has reported by Langsenlehner [27]; Balasubramanian [30] only observed a significant association with survival. We only observed an association between VEGFA rs25648 and ER−/PR− tumors but not with breast cancer survival. Beeghly-Fadiel and colleagues in their study of Chinese women saw an increased risk with VEGFA rs833070 and FLT1 rs9551471; we did not see an increased risk with either of these polymorphisms. We also did not observe an association between breast cancer survival and VEGFA.

Our findings suggest that VEGFA receptors may play a more important role in breast cancer carcinogenesis than VEGFA itself. KDR, a type 2 receptor, is primarily responsible for VEGF signaling in the angiogenesis process; VEGFA has been shown to induce tumor cell proliferation via activation of KDR [3]. Studies also have shown that drugs that inhibit VEGF signaling reduce phosphorylated VEGFR2 expression in patients with inflammatory breast cancer [34]. KDR was significantly associated with breast cancer for all groups except the highest NA ancestry group. It also was significantly associated with ER+/PR- tumors and was of borderline significant (PARTP= 0.07) for overall survival. The VEGFA type 1 receptor, FLT1, was significantly associated with survival among those with low NA ancestry. The role of FLT1 in angiogenesis is less well defined [3] although several studies have shown that FLT1 stimulates tumor growth [2]. Our data suggest that FLT1 may be a tumor promoter, enhancing metastasis, given our observed association with survival.

TEK appeared to have the greatest overall impact on breast cancer in this population. It was associated with breast cancer risk overall as well as risk of dying from breast cancer after diagnosis. The strongest associations were for ER+/PR+ tumors, which represent the majority of breast cancer tumors. TEK regulates angiogenic growth factors, is a receptor for angiopoietin-1 and 2 (Ang1 and Ang2), and has been have linked to breast cancer metastasis including bone metastasis [7, 8]. The angiopoietin/TEK pathway is critical to the developing vasculature and vessel stabilization [35]. Recent studies also have shown the importance of TEK expression in distinct tie expressing monocytes, or TEMs, that play a key role in tumor promotion and angiogenesis [36, 37]. These TEMs cluster in hypoxic areas of solid tumors and migrate in response to angiopoietin-2, which modulate TEK-dependent signaling and regulates apoptosis [38].

Of the genes that could influence angiogenesis through their role in hypoxia and oxidative stress, only NOS2A was associated with breast cancer risk and only among women with intermediate NA ancestry and those with ER−/PR− tumors. Nitric oxide can affect cancer through many ways. It can increase apoptosis and inhibit carcinogenesis or promote carcinogenesis through increasing angiogenesis [9]. While we had hypothesized that NOS2A and HIF1A would interact with dietary antioxidants to alter breast cancer risk, as has been shown in other cancers [14], we did not observe the same level of association in this study.

However, dietary pro- and anti-oxidants were associated with breast cancer risk, especially among those with greater NA ancestry. While many nutrients had a strong association with breast cancer and the overall DOBS showed that those who consumed a diet that was high in anti-oxidants and low in pro-oxidants had a reduced risk of breast cancer, the influence of genetic factors on these associations was minimal. This suggests that oxidative processes are important and that dietary intake may play an important and robust role in this process despite genetic variation in related pathways.

The Breast Cancer Health Disparities Study has strengths, in that it is the largest collection of breast cancer cases of Hispanic and Mexican women reported to date. Additionally, we utilized information on genetic admixture to more accurately define NA ancestry and capture heterogeneity among Hispanics. All three contributing data sites collected extensive diet and lifestyle data, allowing us to utilize harmonized data to assess main effects as well as interaction with genes. While we were able to evaluate tumor phenotype and survival in the U.S. studies, we did not have those data available from Mexico. Thus, data on ER and PR tumor status and survival do not have the range of NA ancestry that is included in the main effect risk estimates and dietary associations. A smaller sample to evaluate these associations can also contribute to less power, which could limit our ability to detect some associations, especially for rarer variants. Additionally we lack complete treatment data which prohibits us from evaluating associations with these genes stratified by type of treatment, which could be informative. Studies that focus on determining functionality of SNPs within these genes could importantly add to this work.

In summary, our data suggest that angiogenesis-related genes are important in both breast cancer risk and survival. Genetic variation in the tyrosine kinase receptors, TEK, KDR, and FLT1 appear to have the most effect on disease risk and survival and thus these genes may be candidates for drug therapy targets. While dietary antioxidants were associated with breast cancer risk, the genes evaluated had little modifying effect on observed associations with diet. The findings from this study support the importance of these genes in breast cancer carcinogenesis and should be replicated in other population-based studies.

Supplementary Material

Supp Table S1-S2

Acknowledgments

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). We would also like to acknowledge the contributions of the following individuals to the study: Sandra Edwards for data harmonization oversight; Jennifer Herrick for data management and data harmonization; 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.

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