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. Author manuscript; available in PMC: 2016 Jan 28.
Published in final edited form as: Nutr Cancer. 2015 Jan 28;67(2):292–304. doi: 10.1080/01635581.2015.990568

MAPK genes interact with diet and lifestyle factors to alter risk of breast cancer: The Breast Cancer Health Disparities Study

Martha L Slattery 1, Abbie Lundgreen 1, Esther M John 2, Gabriela Torres-Mejia 3, Lisa Hines 4, Anna R Giuliano 5, Kathy B Baumgartner 6, Mariana C Stern 7, Roger K Wolff 1
PMCID: PMC4450722  NIHMSID: NIHMS685971  PMID: 25629224

Abstract

Mitogen-activated protein kinases (MAPK) are integration points for multiple biochemical signals. We evaluated 13 MAPK genes with breast cancer risk and determined if diet and lifestyle factors mediated risk. Data from three population-based case-control studies conducted in Southwestern United States, California, and Mexico included 4183 controls and 3592 cases. Percent Indigenous American (IA) ancestry was determined from 104 Ancestry Informative Markers. The adaptive rank truncated product (ARTP) was used to determine the significance of each gene and the pathway with breast cancer risk, by menopausal status, genetic ancestry level, and ER/PR strata.

MAP3K9 was associated with breast cancer overall (PARTP=0.02) with strongest association among women with the highest IA ancestry (PARTP=0.04). Several SNPs in MAP3K9 were associated with ER+/PR+ tumors and interacted with dietary oxidative balance score (DOBS), dietary folate, body mass index (BMI), alcohol consumption, cigarette smoking, and a history of diabetes. DUSP4 and MAPK8 interacted with calories to alter breast cancer risk; MAPK1 interacted with DOBS, dietary fiber, folate and BMI; MAP3K2 interacted with dietary fat; and MAPK14 interacted with dietary folate and BMI. The patterns of association across diet and lifestyle factors with similar biological properties for the same SNPs within genes provide support for associations.

Keywords: Breast Cancer, Indigenous Ancestry, MAPK, MAP3K9, diet, diabetes, body size, polymorphisms

Mitogen-activated protein kinases (MAPK) act as integration points for multiple biochemical signals and are involved in a variety of cellular processes, including cell proliferation, differentiation, transcription regulation and development [1]. By phosphorylating transcription factors, kinases and other enzymes, they influence gene expression, metabolism, cell division, morphology, and survival. Each MAPK pathway is a three-tiered cascade that includes a MAP kinase kinase kinase (MAP3K, MEKK, or MKKK), Map kinase kinase (MAP2K, MEK, or MKK), and the MAP kinase (MAPK). MAPKs are attenuated by dual specificity MAPK phosphatases (MKPs or DUSP). Three of the major MAPK pathways are extracellular regulated kinases (ERK), c-Jun-N-terminal kinases (JNKs) sometimes called stress-activated protein kinases (SAPK), and p38 [2]. Deregulation of the MAPK pathways has been associated with a variety of diseases such as cancer and type-2 diabetes and with inflammation [3–5].

MAPK pathways are activated by various environmental stimuli, cytokines, and hormones. ERK1 and ERK2 are activated by stimuli such as growth factors and cytokines [1]. The JNK pathway is involved in regulating responses to stress, inflammation, and apoptosis and are activated by radiation, environmental stresses, and growth factors. The JNK pathway has been shown to be involved in the development of obesity and type 2 diabetes [3,4]. The p38 MAPKs have been linked to autoimmunity in humans and are activated by chemical stresses, hormones, cytokines including IL-1 and TNF, and oxidative stress [1,2]. MAPK mediate several signaling pathways associated with cancer, including IL1, IκBK, NFκB, PPARγ, TNFα, and TGFβ, and BMP [6–10].

Dietary factors likely affect many of these pathways through their antioxidant and pro-oxidant properties as well as possibly influencing growth factors and insulin through energy-contributing nutrients [11]. Lifestyle factors, including body size, cigarette smoking, alcohol, and diabetes may also affect the MAPK signaling pathway through their association with inflammation, oxidative stress, and insulin. Body size has been associated with breast cancer with most studies showing an inverse association with pre-menopausal women and a slight increased risk among post-menopausal women [12–15]; in Latina women obesity has been shown to be inversely associated with both pre- and post-menopausal [16,17]. Cigarette smoking has been inconsistently associated with breast cancer risk [18,19], while alcohol has been shown to slightly increase risk in most populations [20–23]. Few studies have evaluated diabetes robustly with breast cancer risk, although it has been hypothesized that insulin resistance influences breast cancer risk [24–27]. Associations with dietary intake varies and studies have suggested differences in effect for several nutrients among Latina women [20,28].

In this study we evaluated the association between genetic variation in key MAPK genes and the risk of breast cancer in a genetically admixed population living in the Southwestern United States, California, and Mexico. We investigated associations between the MAPK genes and the risk of breast cancer was modified by potential activators of the pathway such as dietary factors, body mass index (BMI), alcohol intake, cigarette smoking status, and having been diagnosed with diabetes. We hypothesize that MAPK genes are associated with breast cancer and that these associations are modified by diet and lifestyle factors as well as by IA ancestry and ER/PR tumor status.

Methods

The Breast Cancer Health Disparities Study includes participants from three population-based case-control studies, the 4-Corners Breast Cancer Study (4-CBCS), the Mexico Breast Cancer Study (MBCS), and the San Francisco Bay Area Breast Cancer Study (SFBCS) who completed an in-person interview and who had a blood or mouthwash sample available for DNA extraction [17,29–31]. All participants signed informed written consent prior to participation and each study was approved by the Institutional Review Board for Human Subjects.

4 Corner’s Breast Cancer Study

Participants were NHW, Hispanic, or Native American women living in non-reservation areas in the states of Arizona, Colorado, New Mexico, or Utah at the time of diagnosis or selection [17]. Eligible female breast cancer cases were between 25 and 79 years of age with a histological confirmed diagnosis of in situ (n=337) or invasive cancer (n=1466) (ICDO sites C50.0-C50.6 and C50.8-C50.9) between October 1999 and May 2004. Controls were selected from the target populations and were frequency matched to cases on the expected ethnicity and 5-year age distribution. In Arizona and Colorado controls under age 65 years were randomly selected from a commercial mailing list; in New Mexico and Utah they were randomly selected from driver’s license lists. In all states, women 65 years and older were randomly selected from Center for Medicare Services lists. Women were screened by telephone for eligibility and self-identified their race/ethnicity prior to study enrollment. Of cases contacted, 852 Hispanic, 22 American Indian, and 1683 NHW women participated. Of controls contacted, 913 Hispanic, 23 American Indian, and 1669 NHW women participated. Blood was collected and DNA extracted for 76% of participants in Arizona, 71% of participants in Colorado, 75% of participants in New Mexico, and 94% of participants in Utah.

Mexico Breast Cancer Study

Participants were between 28 and 74 years of age, living in one of three states, Monterrey, Veracruz and Mexico City, for the past five years as previously described [32]. Eligible cases 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 in Mexico, IMSS, ISSTE, and SS. In situ and invasive cancers were not distinguished in the study database. Controls were randomly selected from the catchment area of the 12 participating hospitals using a probabilistic multi-stage design. A total of 1000 cases and 1074 controls were recruited, and blood was collected and DNA extracted from 85% and 96% of women respectively.

San Francisco Bay Area Breast Cancer Study

Participants were Hispanic, African American, and NHW women aged 35 to 79 from the San Francisco Bay Area diagnosed with a first primary histologically confirmed invasive breast cancer between 1995 and 2002; controls were identified by random-digit dialing (RDD) [30,31]. This analysis was limited to women who participated in the biospecimen component of the parent study that was initiated in 1999 [33]. Eligible cases were Hispanic women diagnosed between April 1997 and April 2002 and a 10% random sample of NHW women diagnosed between April 1997 and April 1999. RDD controls were frequency-matched to cases based on race/ethnicity and the expected 5-year age distribution of cases. Women participated in a telephone screening interview that assessed study eligibility and self-identified race/ethnicity. DNA was available for 93% of cases and 92% of controls interviewed, including 1105 cases (793 Hispanics, 312 NHW) and 1318 controls (998 Hispanics, 320 NHW).

Data Harmonization

Data were harmonized across all study centers and questionnaires as previously described [29]. 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 calendar 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 and 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).

Lifestyle variables included body mass index (BMI) calculated as self-reported weight (kg) during the referent year divided by measured height squared (m2). Parity was defined as the number of total pregnancies. Cigarette smoking was evaluated as ever versus never having smoked cigarettes on a regular basis or more than 100 cigarettes. Those classified as having a history of diabetes reported being told by a doctor or health professional that they had diabetes or high blood sugar. A dietary oxidative balance score (DOBS) that included nutrients with anti- or pro-oxidative balance properties was developed as previously reported [34]. Dietary information was collected via a computerized validated diet history questionnaire for the 4-CBCS [35,36], a 104-item semi-quantitative Food Frequency Questionnaire (FFQ) in Mexico City [37], and the Block Food Frequency Questionnaire in SFBCS [38]. The food frequency questionnaire used in the 4-CBCS queries consumption of foods in major categories and if that is yes, then more detail about specific foods are obtained. For instance, a question would ask “Do you eat eggs?” If the response is yes, then details of types of eggs and related frequency and amount for each type were obtained. The FFQ asked a list of food items and participants provide information for each food item in the list. Given differences in food questionnaires, categories of consumption were study specific.

Genetic Data

DNA was derived from either whole blood or mouthwash samples obtained from study participants. A total of 7286 blood-derived and 637 mouthwash-derived samples were studied. 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. For genes where a functional SNP was identified, that SNP was included in the platform. Additionally, 104 Ancestral Informative Markers (AIMs) were used to distinguish European and Native American ancestry in the study population [29]. All markers were genotyped using a multiplexed bead array assay format based on GoldenGate chemistry (Illumina, San Diego, California). A genotyping call rate of 99.93% was attained (99.65% for WGA samples). We included 132 internal replicates that were blinded representing 1.6% of the sample set. The duplicate concordance rate was 99.996% as determined by 193,297 matching genotypes among sample pairs. In the current analyses we evaluated DUSP4 (6 SNPs), DUSP6 (1 SNP), MAP2K1 (6 SNPs), MAP3K1 (7 SNPs), MAP3K2 (3 SNPs), MAP3K3 (2 SNPs), MAP3K7 (6 SNPs), MAP3K9 (19 SNPs), MAPK1 (6 SNPs), MAPK3 (1 SNP), MAPK8 (4 SNPs), MAPK12 (2 SNPs), and MAPK14 (9 SNPs). Genes and SNPs are described in online Supplements 1 and 2.

Tumor Characteristics

Data for ER/PR tumor status were available from local tumor registries for cases from the 4-CBCS and the SFBCS for 1019 (69%) non-Hispanic white (NHW) and 977 (75%) Hispanic/Native American (NA) women.

Statistical Methods

The program STRUCTURE was used to compute individual ancestry for each study participant assuming two founding populations [39,40]. A two-founding population model was used. Assessment across categories of ancestry was done using cut-points based on the distribution of genetic ancestry in the control population. Three strata, ≤28%, >28–70%, and >70%, were used to evaluate associations by level of Indigenous American (IA) ancestry. Cut-points were chosen to maximize power within the three ancestry groups while maintaining the ability to discriminate unique ancestry groups.

P values are based on chi-square tests when comparing number of cases to controls by categorical variables and on Wilcoxon Rank Sum tests when measuring differences in median values. Genes and SNPs were assessed for their association with breast cancer risk by strata of menopausal status and genetic ancestry in the whole population and by ER/PR status for the SFBCS and 4-CBCS. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC) unless otherwise noted. Logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for breast cancer risk associated with SNPs, adjusting for five-year age categories (continuous), study center, genetic ancestry (continuous), BMI during referent year (continuous), and parity (continuous). Age and study center were matching variables and therefore adjusted in the analysis. BMI and parity were included as adjustment variables given their association with breast cancer and possible association with genes being examined.The generalized logit link function was used when estimating risk by ER/PR status. 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.

We used the adaptive rank truncated product (ARTP) method that is based on a highly efficient permutation algorithm to determine the significance of association of each gene and of the pathway with breast cancer overall, by menopausal status, by genetic ancestry level, 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, BMI during referent year, and genetic ancestry [41,42]. We report both pathway and gene p values (PARTP) as an indicator of the importance of the gene and the overall pathway with breast cancer risk.

We examined if the association between SNPs and risk of breast cancer was different by menopausal status, ER and PR tumor status, level of IA ancestry, and diet and lifestyle factors. Diet and lifestyle factors were selected based on their potential to modify factors associated with oxidative stress, inflammation, growth factors, and/or insulin and categorized to test for interactions. For stratified analyses, tests for interactions were calculated using a Wald 1-degree of freedom (1-df) chi-square tests; overall SNP associations with breast cancer by ER/PR status are estimated using p values from 4-df Wald tests. Adjustments for multiple comparisons for stratified analyses 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 and modified by Li and Ji [43,44]. We report both the unadjusted and adjusted p values for interactions between genes and diet and lifestyle factors.

Results

The majority of women were post-menopausal at the time of diagnosis (Table 1). Almost all women (over 99%) who self-identified as being NHW had low levels of IA ancestry (≤28%), while those who self-identified as being Hispanic or Native American or who lived in Mexico, had a range of IA levels, although the majority had intermediate and high IA ancestry (>28%). Almost 50% of NHW women reported never drinking alcohol, compared to 78% of Hispanic/IA controls and 73% of Hispanic/NA cases reported never drinking alcohol. Among Hispanic/NA women, total calories was significantly higher among cases than controls and dietary fiber, folate, vitamin E, and beta carotene were significantly lower among cases than controls.

Table 1.

Description of Study Population by self-reported Race/Ethnicity

U.S. NHW U. S. Hispanic/Native American or from Mexico
Controls Cases p value Controls Cases p value
N % N % N % N %
Total 1585 37.9 1481 41.2 2597 62.1 2111 58.8
Study Site
  4-CBCS 1321 83.3 1227 82.8 NA1 723 27.8 597 28.3 NA
  MBCS 0 0 0 0 994 38.3 816 38.7
  SFBCS 264 16.7 254 17.2 880 33.9 698 33.1
Age (years) NA NA
  <40 116 7.3 89 6.0 311 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
  60–69 349 22.0 361 24.4 526 20.3 430 20.4
  ≥70 303 19.1 209 14.1 173 6.7 151 7.2
  Mean 56.6 56.0 52.3 52.7
Menopausal Status NA NA
  Pre-menopausal 494 31.5 489 33.5 1027 40.7 836 40.9
  Post-menopausal 1075 68.5 970 66.5 1499 59.3 1210 59.1
Estimated Indigenous American
Ancestry		 NA NA
  Low (≤28%) 1577 99.5 1472 99.4 278 10.7 275 13.0
  Intermediate (>28 – 70%) 7 0.4 7 0.5 1686 64.9 1393 66.0
  High (>70%) 1 0.1 2 0.1 633 24.4 443 21.0
ER/PR Status2
  ER+/PR+ 695 68.2 NA 605 61.9 NA
  ER+/PR− 121 11.9 115 11.8
  ER−/PR+ 15 1.5 28 2.9
  ER−/PR− 188 18.4 229 23.4
Alcohol Intake3
  None 807 50.9 695 46.9 0.03 2025 78.0 1550 73.4 <.01
  Any 778 49.1 786 53.1 572 22.0 561 26.6
Cigarette Smoking
  Never 765 58.1 662 54.0 0.04 1627 71.9 1297 69.8 0.14
  Ever 552 41.9 564 46.0 635 28.1 561 30.2
Parity
  Nulliparous 248 15.7 261 17.6 <.01 181 7.0 229 10.8 <.01
  1 to 2 638 40.3 646 43.6 790 30.5 786 37.2
  3 to 4 529 33.4 462 31.2 997 38.4 738 35.0
  ≥5 167 10.6 111 7.5 626 24.1 358 17.0
History of Diabetes or High Blood Sugar
  No 1299 91.4 1222 92.4 0.36 1945 83.4 1573 83.4 0.97
  Yes 122 8.6 101 7.6 386 16.6 313 16.6
Median Median Median Median
BMI (kg/m2) 25.8 25.7 0.76 29.4 28.5 <.01
Dietary Intake (per 1000 kcal)
Calories (kcal) 1911.3 1947.4 0.12 2009.3 2168.1 <.01
Total Fat (g) 38.8 38.5 0.2 36.7 36.7 0.46
Fiber (g)3 10.7 10.8 0.99 12.9 12.5 <.01
Calcium (mg) 461.0 458.7 0.65 422.2 413.4 0.07
Folate (mcg)3 187.3 187.9 0.37 204.3 193.5 <.01
Vitamin C (mg)3 76.0 78.1 0.54 80.9 81.8 0.85
Vitamin E (mg)3 4.6 4.6 0.56 4.9 4.7 <.01
Beta Carotene2,3 (mcg) 2290.1 2266.0 0.73 1997.5 1838.5 <.01
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1

p values not applicable (NA)

2

Information unavailable for the MBCS.

3

Included in the dietary oxidative balance score (DOBS)

The overall pathway was not statistically significant overall or for any admixture group. Only MAP3K9 was significantly associated with breast cancer risk overall (PARTP=0.02) and for women with the highest level of IA ancestry (PARTP=0.04) (Table 2). Several MAP3K9 SNPs were significantly associated with breast cancer among all women and/or by strata of IA ancestry (rs11628333, rs10483834, rs11622989, rs12883244, rs11158881, rs4902855, rs10143031, and rs11624934). MAP3K3 rs3785574 and MAPK8 rs10508901 associations with breast cancer were significantly different across ancestry group although neither of the genes was statistically significant by the ARTP p value. We did not observe differences in breast cancer associations by menopausal status.

Table 2.

Associations between significant MAPK genes and breast cancer risk for all women and by level of Indigenous American Ancestry
All ≤28%Indigenous Ancestry > 28 – 70% Indigenous Ancestry >70%Indigenous Ancestry Interaction P-value
Cn Cs OR1 (95% CI) PARTP Cn Cs OR (95% CI) PARTP Cn Cs OR (95% CI) PARTP Cn Cs OR (95%CI) PARTP (raw; adjusted)
JNK/ERK
MAP3K3 (rs3785574) 0.96 0.17 0.26 0.58 0.012,0.023
  AA 1794 1555 1.00 880 798 1.00 692 583 1.00 222 174 1.00
  AG 1884 1607 1.00 (0.91, 1.10) 803 765 1.06 (0.92, 1.21) 773 647 1.00 (0.85, 1.16) 308 195 0.81 (0.62, 1.07)
  GG 472 407 1.03 (0.88, 1.19) 159 179 1.26 (1.00, 1.60) 214 160 0.88 (0.69, 1.11) 99 68 0.86 (0.59, 1.26)
JNK
MAP3K9 (rs11628333) 0.02 0.51 0.06 0.04 0.018,0.087
  TT/TC 3501 3080 1.00 1594 1505 1.00 1421 1210 1.00 486 365 1.00
  CC 648 488 0.86 (0.76, 0.98) 248 236 1.00 (0.83, 1.21) 257 180 0.83 (0.67, 1.02) 143 72 0.68 (0.49, 0.93)
MAP3K9 (rs10483834) 0.002,0.015
  AA 2873 2372 1.00 1079 1038 1.00 1238 966 1.00 556 368 1.00
  AG/GG 1277 1197 1.09 (0.98, 1.20) 763 704 0.96 (0.84, 1.10) 441 424 1.23 (1.05, 1.45) 73 69 1.34 (0.93, 1.93)
MAP3K9 (rs11622989) 0.013,0.087
  CC 1199 920 1.00 487 453 1.00 496 350 1.00 216 117 1.00
  CT/TT 2949 2649 1.16 (1.05, 1.29) 1353 1289 1.02 (0.88, 1.19) 1183 1040 1.25 (1.06, 1.47) 413 320 1.43 (1.09, 1.88)
MAP3K9 (rs12883244) 0.014,0.087
  CC 1088 830 1.00 422 399 1.00 459 315 1.00 207 116 1.00
  CT/TT 3062 2739 1.16 (1.04, 1.29) 1420 1343 1.00 (0.85, 1.17) 1220 1075 1.27 (1.07, 1.50) 422 321 1.36 (1.03, 1.79)
MAP3K9 (rs11158881) 0.079,0.155
  TT 2164 1814 1.00 1052 995 1.00 846 662 1.00 266 157 1.00
  TC/CC 1985 1752 1.08 (0.99, 1.19) 790 744 0.99 (0.87, 1.13) 832 728 1.12 (0.97, 1.30) 363 280 1.33 (1.03, 1.73)
MAP3K9 (rs4902855) <.001,0.008
  CC 1367 1092 1.00 578 554 1.00 555 407 1.00 234 131 1.00
  CT 2011 1817 1.13 (1.02, 1.25) 892 882 1.03 (0.89, 1.20) 821 716 1.17 (0.99, 1.38) 298 219 1.31 (0.99, 1.74)
  TT 772 660 1.07 (0.94, 1.22) 372 306 0.86 (0.71, 1.04) 303 267 1.21 (0.98, 1.50) 97 87 1.58 (1.09, 2.28)
MAP3K9 (rs10143031) 0.032,0.114
  CC 1114 1022 1.00 511 489 1.00 452 407 1.00 151 126 1.00
  CT 2087 1790 0.94 (0.84, 1.04) 914 866 1.00 (0.85, 1.17) 861 697 0.89 (0.75, 1.06) 312 227 0.87 (0.65, 1.17)
  TT 949 756 0.87 (0.76, 0.99) 417 387 0.98 (0.81, 1.18) 366 285 0.86 (0.69, 1.05) 166 84 0.62 (0.43, 0.89)
MAP3K9 (rs11624934) 0.078,0.155
  AA/AG 3582 3174 1.00 1638 1565 1.00 1448 1236 1.00 496 373 1.00
  GG 568 394 0.80 (0.69, 0.91) 204 176 0.89 (0.72, 1.11) 231 154 0.79 (0.63, 0.98) 133 64 0.65 (0.47, 0.92)
MAPK8 (rs10508901) 0.39 0.11 0.45 0.07 0.003,0.006
  CC 2395 2043 1.00 824 830 1.00 1079 901 1.00 492 312 1.00
  CA/AA 1753 1525 0.96 (0.87, 1.05) 1018 911 0.90 (0.79, 1.03) 598 489 0.95 (0.81, 1.10) 137 125 1.41 (1.05, 1.87)
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1

Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusted for age, study center, BMI during referent year, parity, and genetic ancestry. Cn is controls and Cs is cases.

Significant differences in breast cancer risk were identified by ER/PR tumor status (Table 3).). The pathway PARTP was of borderline significance for ER−/PR− tumors (PARTP=0.06). MAP3K3 was significantly associated with ER−/PR− tumors (PARTP=0.002) and MAP3K3 rs3785574 was significantly associated with these tumors (OR 1.74, 95% CI 1.26, 2.39). MAP3K9 was significantly associated with ER+/PR+ tumors (PARTP=0.01) based on significant associations with several SNPs (rs11622989, rs17176971, rs12883244, rs4902855, and rs11624934). MAPK3 was significantly associated with ER+/PR− tumors (PARTP=0.048) with rs7698 being inversely associated with breast cancer risk (OR 0.65, 95% CI 0.43, 0.99).

Table 3.

Associations between significant MAPK genes and ER and PR tumor status1

Cn ER+ /PR+ ER+ / PR− ER− / PR+ ER− / PR− Multinomial
p-value
(raw; adjusted)
N N OR2 (95% CI) PARTP N OR (95% CI) PARTP N OR (95% CI) PARTP N OR (95% CI) PARTP
JNK/ERK
MAP3K3 (rs3785574) 0.96 0.93 0.76 0.002 0.011,0.019
  AA 1418 597 1.00 106 1.00 21 1.00 156 1.00
  AG 1424 553 0.94 (0.82, 1.08) 113 1.09 (0.82, 1.43) 19 0.89 (0.48, 1.67) 196 1.25 (1.00, 1.57)
  GG 324 148 1.14 (0.91, 1.42) 16 0.71 (0.41, 1.22) 3 0.58 (0.17, 1.97) 63 1.74 (1.26, 2.39)
JNK/p38
MAP3K7 (rs150117) 0.36 0.35 0.26 0.09 0.011,0.056
  AA 1461 583 1.00 121 1.00 16 1.00 217 1.00
  AT 1380 572 1.04 (0.90, 1.19) 87 0.76 (0.57, 1.01) 18 1.21 (0.61, 2.39) 168 0.83 (0.67, 1.03)
  TT 323 143 1.09 (0.87, 1.35) 27 0.98 (0.63, 1.52) 9 2.71 (1.18, 6.22) 30 0.65 (0.43, 0.96)
JNK
MAP3K9 (rs11622989) 0.01 0.45 0.79 0.36 0.027,0.209
  CC 884 306 1.00 63 1.00 9 1.00 98 1.00
  CT/TT 2280 992 1.24 (1.07, 1.44) 172 1.04 (0.77, 1.41) 34 1.50 (0.72, 3.15) 317 1.27 (1.00, 1.61)
MAP3K9 (rs17176971) 0.082,0.463
  GG 2054 901 1.00 155 1.00 30 1.00 281 1.00
  GA/AA 1112 396 0.82 (0.72, 0.95) 80 0.98 (0.74, 1.30) 13 0.79 (0.41, 1.52) 134 0.88 (0.71, 1.09)
MAP3K9 (rs12883244) 0.003,0.029
  CC 789 260 1.00 65 1.00 8 1.00 86 1.00
  CT/TT 2377 1038 1.30 (1.11, 1.52) 170 0.84 (0.62, 1.13) 35 1.51 (0.70, 3.28) 329 1.29 (1.01, 1.66)
MAP3K9 (rs4902855) 0.018,0.172
  CC 1030 372 1.00 88 1.00 11 1.00 123 1.00
  CT/TT 2136 926 1.18 (1.03, 1.36) 147 0.78 (0.59, 1.03) 32 1.45 (0.73, 2.90) 292 1.17 (0.93, 1.46)
MAP3K9 (rs11624934) 0.052,0.342
  AA 1366 610 1.00 95 1.00 20 1.00 170 1.00
  AG 1413 564 0.91 (0.79, 1.04) 110 1.13 (0.85, 1.51) 18 0.87 (0.46, 1.65) 213 1.21 (0.98, 1.51)
  GG 387 123 0.73 (0.58, 0.91) 30 1.15 (0.75, 1.76) 5 0.83 (0.31, 2.25) 32 0.65 (0.44, 0.97)
ERK
MAPK3 (rs7698) 0.85 0.048 0.77 0.88 0.373,0.373
  CC 2660 1087 1.00 209 1.00 37 1.00 348 1.00
  CT/TT 502 210 1.01 (0.85, 1.21) 26 0.65 (0.43, 0.99) 6 0.87 (0.36, 2.07) 67 1.02 (0.77, 1.35)
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1

Includes participants from 4-CBCS and SFBCS only.

2

Odds Ratios (OR) and 95% Confidence Intervals adjusted for age, study center, BMI during referent year, parity and genetic ancestry. The pathway PARTP was of borderline significance for ER−/PR− tumors (PARTP=0.06)

Assessment of dietary factors that could modify associations between MAPK genes and breast cancer risk showed several significant interactions (Table 4). DUSP4 (1 SNP), MAP3K7 (1 SNP), MAP3K9 (2 SNPs), MAPK14 (1 SNP), and MAPK1 (3 SNPs) interacted with the DOBS. High DOBS reduced breast cancer risk for those with the homozygote common genotype. DUPS4 (3 SNPs), MAP3K1 (1 SNP), MAPK8 (2 SNPs), and MAPK3 (1 SNP) interacted with total caloric intake; fewer calories generally reduced breast cancer risk among women with the homozygote rare alleles. MAP3K2 (2 SNPs) interacted with dietary fat; a high fat diet and having the CC genotype of rs12613413 increased breast cancer risk, while a high fat diet decreased breast cancer risk among women with the TT genotype of rs6732279. MAPK8 (1 SNP) and MAPK1 (4 SNPs) interacted with dietary fiber; high intake of dietary fiber generally reducing breast cancer risk among women with the homozygote common genotype. Dietary folate interacted with DUSP4 (1 SNP), MAP3K7 (1 SNP), MAP3K9 (4 SNPs), MAPK8 (1 SNP), MAPK14 (2 SNPs), and MAPK1 (3 SNPs); reduced breast cancer risk was observed for the homozygote common genotype in the presence of high folate..

Table 4.

Interactions between MAPK genes and dietary intake

Genotype (GT)1 GT1/High Diet GT2/Low Diet GT2/High Diet Interaction P-value
Pathway 1 (common) 2 (rare) OR2 (95% CI) OR (95% CI) OR (95% CI) raw, adjusted
Dietary Oxidative Balance Score (DOBS)
DUSP4 rs2056025 DUSP TT TG/GG 0.85 (0.73, 1.00) 1.13 (0.92, 1.39) 0.73 (0.59, 0.90) 0.013, 0.053
MAP3K7 rs379912 JNK/p38 AA AG/GG 0.74 (0.64, 0.85) 0.84 (0.66, 1.07) 0.93 (0.72, 1.21) 0.045, 0.223
MAP3K9 rs10483834 JNK AA GG 0.73 (0.62, 0.86) 0.72 (0.42, 1.23) 0.79 (0.44, 1.41) 0.032, 0.308
MAP3K9 rs17766621 JNK TT CC 0.72 (0.59, 0.86) 0.62 (0.45, 0.86) 0.94 (0.66, 1.34) 0.012, 0.122
MAPK14 rs7761118 p38 GG GA/AA 0.76 (0.65, 0.87) 0.87 (0.68, 1.12) 0.87 (0.66, 1.14) 0.041, 0.290
MAPK1 rs2298432 ERK CC CA/AA 0.67 (0.55, 0.81) 0.89 (0.74, 1.07) 0.83 (0.68, 1.00) 0.018, 0.070
MAPK1 rs9610375 ERK GG TT 0.94 (0.75, 1.17) 1.07 (0.82, 1.39) 0.66 (0.50, 0.87) 0.048, 0.102
MAPK1 rs8136867 ERK AA GG 0.67 (0.53, 0.85) 0.81 (0.63, 1.06) 0.84 (0.63, 1.11) 0.034, 0.102
Calories
DUSP4 rs12540995 DUSP CC TT 1.45 (1.18, 1.79) 0.65 (0.48, 0.87) 1.82 (1.39, 2.36) 0.012, 0.049
DUSP4 rs3824133 DUSP AA GG 1.47 (1.20, 1.81) 0.58 (0.42, 0.81) 1.83 (1.38, 2.42) 0.013, 0.049
DUSP4 rs567436 DUSP AA TT 1.56 (1.27, 1.91) 0.67 (0.50, 0.90) 1.99 (1.53, 2.59) 0.031, 0.061
MAP3K1 rs33323 JNK/ERK CC GG 1.89 (1.50, 2.40) 1.06 (0.81, 1.38) 1.40 (1.07, 1.82) 0.030, 0.149
MAPK8 rs10857565 JNK GG AA 1.76 (1.50,2.07) 0.86 (0.50, 1.49) 1.41 (0.87, 2.27) 0.029, 0.029
MAPK8 rs10508901 JNK CC AA 1.85 (1.56, 2.20) 1.10 (0.76, 1.57) 1.41 (0.97, 2.05) 0.007, 0.014
MAPK3 rs7698 ERK CC CT/TT 1.45 (1.26, 1.68) 0.74 (0.56, 0.97) 1.82 (1.41, 2.35) 0.005, 0.005
MAP3K2 rs12613413 JNK/ERK TT CC 0.91 (0.78, 1.06) 0.99 (0.60, 1.63) 1.71 (1.00, 2.94) 0.035, 0.091
Fat
MAP3K2 rs6732279 JNK/ERK TT GG 0.75 (0.59, 0.95) 0.82
 (0.63, 1.05) 0.86 (0.67, 1.10) 0.047, 0.091
Fiber
MAPK8 rs10508901 JNK CC AA 0.73 (0.62, 0.87) 0.81 (0.58, 1.14) 0.88 (0.58, 1.33) 0.046, 0.093
MAPK1 rs2298432 ERK CC AA 0.71 (0.59, 0.86) 0.90 (0.66, 1.22) 1.02 (0.73, 1.42) 0.017, 0.034
MAPK1 rs743409 ERK CC TT 0.71 (0.57, 0.88) 0.88 (0.67, 1.14) 0.97 (0.73, 1.28) 0.027, 0.034
MAPK1 rs9610375 ERK GG TT 1.04 (0.83, 1.30) 1.08 (0.84, 1.40) 0.67 (0.51, 0.90) 0.005, 0.019
MAPK1 rs8136867 ERK AA GG 0.70 (0.55, 0.87) 0.81 (0.62, 1.04) 0.94 (0.72, 1.23) 0.009, 0.027
Calcium
MAPK1 rs8136867 ERK AA GG 0.75 (0.60, 0.95) 0.83 (0.64, 1.08) 0.98 (0.75, 1.28) 0.018, 0.071
Folate
DUSP4 rs2056025 DUSP TT GG 0.85 (0.73, 0.99) 1.27 (0.66, 2.43) 1.11 (0.58, 2.10) 0.035, 0.139
MAP3K7 rs379912 JNK/p38 AA GG 0.72 (0.63, 0.84) 0.74 (0.31, 1.79) 0.88 (0.38, 2.03) 0.038, 0.191
MAP3K9 rs8011507 JNK AA GG 0.71 (0.61, 0.83) 0.33 (0.17, 0.67) 1.06 (0.45, 2.47) 0.009, 0.100
MAP3K9 rs11622989 JNK CC TT 0.98 (0.76, 1.26) 1.43 (1.11, 1.85) 0.92 (0.71, 1.20) 0.021, 0.183
MAP3K9 rs12883244 JNK CC TT 1.00 (0.77, 1.30) 1.37 (1.06, 1.77) 0.93 (0.72, 1.22) 0.043, 0.285
MAP3K9 rs17766621 JNK TT CC 0.69 (0.57, 0.83) 0.69 (0.51, 0.93) 0.68 (0.48, 0.96) 0.050, 0.285
MAPK8 rs10508901 JNK CC AA 0.66 (0.55, 0.79) 0.78 (0.56, 1.08) 0.91 (0.63, 1.33) 0.006, 0.012
MAPK14 rs7761118 p38 GG AA 0.72 (0.63, 0.83) 0.88 (0.28, 2.75) 2.14 (0.41, 11.13) 0.016, 0.113
MAPK14 rs3730327 p38 AA GG 0.73 (0.63, 0.84) 0.90 (0.31, 2.60) 2.15 (0.41, 11.17) 0.030, 0.182
MAPK1 rs2298432 ERK CC AA 0.64 (0.53, 0.77) 0.87 (0.63, 1.19) 0.85 (0.61, 1.20) 0.009, 0.036
MAPK1 rs743409 ERK CC TT 0.68 (0.55, 0.85) 0.88 (0.67, 1.15) 0.91 (0.68, 1.21) 0.043, 0.087
MAPK1 rs8136867 ERK AA GG 0.68 (0.54, 0.86) 0.80 (0.62, 1.04) 0.92 (0.69, 1.21) 0.016, 0.049
Open in a new tab
1

Referent group is genotype group 1 (GT1) or homozygote common genotype and low dietary intake; Genotype 2 group designated as GT2 represents homozygote rare genotype or in some instances the dominant model as indicated.

2

Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusted for age, genetic ancestry, study center, BMI (kg/m2) during referent year, and parity.

When evaluating the interaction of MAPK genes and BMI we saw different sets of genes interacting with BMI to alter risk of pre-menopausal breast cancer versus post-menopausal breast cancer (Table 5). Among pre-menopausal breast cancer cases DUSP4 (1 SNP), MAP3K9 (5 SNPs), MAPK8 (1 SNP), and MAPK1 (2 SNPs) interacted with BMI, while among post-menopausal women BMI interacted with MAPK1 (1 SNP), MAP3K3 (1 SNP), MAP3K9 (1 SNP), and MAPK14 (2 SNPs). MAP3K9 rs11622989 and rs12883244 interacted with both alcohol intake and cigarette smoking. High alcohol intake was associated with increased risk among women with the homozygote common genotype of DUSP4 rs474824; cigarette smoking most strongly increased risk among women with the homozygote rare genotype of MAPK14 rs13196204. A history of diabetes interacted with 11 MAP3K9 SNPs to alter breast cancer risk. A history of diabetes was associated with increased risk of breast cancer among those with the homozygote rare genotype for rs11844774, rs11622989, rs12883244, rs1115881, rs4902855, and rs17108548. For MAP3K9 rs11625206, rs11628333, rs10143031, rs8022269, and rs11624934, a history of diabetes in conjunction with the homozygote common allele genotype were associated with increased breast cancer risk.

Table 5.

Interactions between MAPK genes and BMI, alcohol intake, cigarette smoking, and self-reported history of diabetes

Genotype (GT)1 GT1/High Lifestyle GT2/Low lifestyle GT2/High Lifestyle Interaction P-value
Pathway 1 (common) 2 (rare) OR2 (95% CI) OR (95% CI) OR (95% CI) raw, adjusted
Pre-Menopausal Women BMI (<25 kg/m2 vs. ≥30 kg/m2)
DUSP4 rs474824 DUSP CC TT 0.48 (0.33,0.69) 0.70 (0.48,1.01) 0.62 (0.41,0.93) 0.032,0.128
MAP3K9 rs11625206 JNK CC TT 0.93 (0.70,1.23) 1.59 (1.03,2.43) 0.76 (0.48,1.19) 0.019,0.181
MAP3K9 rs10143031 JNK CC TT 0.96 (0.67,1.37) 1.44 (1.00,2.07) 0.79 (0.54,1.15) 0.033,0.248
MAP3K9 rs17766621 JNK TT CC 0.68 (0.52,0.89) 0.55 (0.35,0.86) 1.04 (0.59,1.83) 0.046,0.299
MAP3K9 rs8022269 JNK GG AA 0.99 (0.71,1.38) 1.50 (1.04,2.17) 0.76 (0.51,1.14) 0.014,0.153
MAP3K9 rs11624934 JNK AA GG 0.87 (0.65,1.16) 1.39 (0.92,2.12) 0.62 (0.40,0.95) 0.045,0.299
MAPK8 rs10857565 JNK GG AA 0.67 (0.53,0.84) 0.36 (0.17,0.75) 0.66 (0.24,1.81) 0.033,0.065
MAPK1 rs2298432 ERK CC AA 0.58 (0.44,0.76) 0.69 (0.44,1.07) 0.70 (0.43,1.15) 0.012,0.049
MAPK1 rs743409 ERK CC TT 0.60 (0.44,0.81) 0.72 (0.49,1.05) 0.85 (0.56,1.28) 0.022,0.049
Post-Menopausal Women BMI (<25 kg/m2 vs. ≥30 kg/m2)
MAP3K1 rs33330 JNK/ERK GG AA 0.81 (0.66,0.99) 0.74 (0.51,1.08) 1.23 (0.83,1.81) 0.035,0.173
MAP3K3 rs3785574 JNK/ERK AA GG 1.06 (0.85,1.32) 1.40 (0.97,2.03) 0.76 (0.54,1.06) 0.006,0.010
MAP3K9 rs1034769 JNK TT TG/GG 0.98 (0.83,1.16) 1.23 (0.94,1.61) 0.78 (0.61,0.99) 0.009,0.098
MAPK14 rs3804454 p38 AA CC 0.79 (0.66,0.94) 1.01 (0.59,1.72) 1.19 (0.66,2.16) 0.018,0.125
MAPK14 rs17714205 p38 CC CT/TT 0.83 (0.70,0.98) 0.92 (0.70,1.22) 1.13 (0.87,1.46) 0.030,0.183
Alcohol Intake (none vs. >75% of drinkers)3
DUSP4 rs474824 DUSP CC TT 1.65 (1.19, 2.29) 1.07 (0.90, 1.27) 1.05 (0.78, 1.41) 0.014, 0.055
MAP3K1 rs33330 JNK/ERK GG AA 1.36 (1.09, 1.71) 1.31 (1.02, 1.69) 1.19 (0.75, 1.88) 0.044, 0.218
MAP3K7 rs150117 JNK/ERK AA TT 1.30 (1.03, 1.65) 1.22 (1.00, 1.49) 1.36 (0.88, 2.11) 0.049, 0.243
MAP3K9 rs11622989 JNK CC TT 1.35 (0.99, 1.85) 1.28 (1.09, 1.50) 1.25 (0.90, 1.75) 0.033, 0.320
MAP3K9 rs12883244 JNK CC TT 1.43 (1.03, 1.98) 1.25 (1.07, 1.47) 1.24 (0.90, 1.72) 0.022, 0.236
Cigarette Smoking (Never vs. Ever)4
MAP3K9 rs11622989 JNK CC TT 1.21 (0.99, 1.47) 1.29 (1.09, 1.54) 1.05 (0.84, 1.30) 0.011, 0.111
MAP3K9 rs12883244 JNK CC TT 1.22 (0.99, 1.50) 1.23 (1.03, 1.46) 1.07 (0.87, 1.33) 0.028, 0.237
MAP3K9 rs11624934 JNK AA GG 0.95 (0.81, 1.11) 0.74 (0.61, 0.89) 0.95 (0.73, 1.24) 0.027, 0.237
MAPK12 rs2272857 p38 GG AA 1.23 (1.07, 1.42) 1.26 (0.97, 1.64) 1.33 (0.93, 1.91) 0.043, 0.085
MAPK14 rs13196204 p38 TT GG 1.02 (0.91, 1.15) 0.88 (0.57, 1.35) 1.82 (1.04, 3.19) 0.036, 0.255
History of Diabetes (No vs. Yes)5
MAP3K9 rs11625206 JNK CC TT 1.40 (1.12,1.74) 1.01 (0.85,1.20) 0.71 (0.47,1.07) 0.001,0.011
MAP3K9 rs11844774 JNK TT CC 0.95 (0.73,1.23) 1.02 (0.88,1.18) 1.57 (1.16,2.13) 0.026,0.057
MAP3K9 rs11628333 JNK TT CC 1.46 (1.15,1.85) 0.94 (0.80,1.11) 0.90 (0.63,1.29) 0.009,0.057
MAP3K9 rs11622989 JNK CC TT 0.92 (0.70,1.21) 1.05 (0.91,1.22) 1.62 (1.19,2.21) 0.017,0.057
MAP3K9 rs12883244 JNK CC TT 0.89 (0.67,1.17) 1.00 (0.86,1.16) 1.67 (1.24,2.26) 0.003,0.020
MAP3K9 rs1115881 JNK TT CC 0.94 (0.77,1.15) 0.97 (0.80,1.18) 1.85 (1.18,2.90) 0.010,0.057
MAP3K9 rs4902855 JNK CC TT 0.93 (0.72,1.19) 0.96 (0.82,1.11) 1.62 (1.17,2.24) 0.009,0.057
MAP3K9 rs10143031 JNK CC TT 1.37 (1.05,1.79) 0.93 (0.80,1.08) 0.81 (0.60,1.10) 0.028,0.057
MAP3K9 rs17108548 JNK TT CC 0.95 (0.79,1.15) 0.91 (0.71,1.15) 1.73 (1.03,2.91) 0.015,0.057
MAP3K9 rs8022269 JNK GG AA 1.52 (1.17,1.96) 1.04 (0.89,1.20) 0.85 (0.61,1.18) 0.002,0.017
MAP3K9 rs11624934 JNK AA GG 1.46 (1.16,1.84) 0.88 (0.74,1.04) 0.75 (0.51,1.10) 0.002,0.020
MAPK1 rs9610470 ERK TT CC 1.20 (1.01,1.42) 1.14 (0.86,1.50) 0.69 (0.32,1.45) 0.019,0.074
Open in a new tab
1

Referent group is the common genotype group 1 (GT1) and low risk exposure (i.e. BMI <25kg/m2, no alcohol intake, never smoker, no history of diabetes); GT2 is genotype 2 group that represents homozygote rare genotype or in some instances the dominant model as indicated.

2

Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusted for age, genetic ancestry, study center, BMI during referent year, and parity.

3

To determine top quarter of drinkers we used the following study specific cut-points: 4-CBCS 10.45 g/day, MBCS 4.11 g/day, and SFBCS 10.86 g/day.

4

Smoking information is missing from 5 women from the 4-CBCS and was not collected for 1095 women from the SFBCS.

5

Diabetes information is missing from 72 women from the 4-CBCS, 152 women from the MBCS, and was not collected for 584 women from the SFBCS.

Discussion

Although the overall MAPK pathway was not statistically significant, several MAPK genes were associated with breast cancer risk. MAP3K9 appeared to make the largest contribution to risk through its overall effect on breast cancer risk that was stronger with increasing level of IA. Several SNPs in MAP3K9 were associated with ER+/PR+ tumors specifically and showed interaction with DOBS, folate, obesity, alcohol intake, cigarette smoking, and a history of diabetes. In addition to MAP3K9, several other MAPK genes had multiple SNPs that jointly altered breast cancer risk with diet and lifestyle exposures. The patterns of association across diet and lifestyle factors with similar biological properties were similar for the same SNPs within genes, providing support that associations may be more than chance findings.

There is a continuum of decreasing breast cancer incidence rates across the spectrum of European to IA ancestry [29], hence our focus on differences in breast cancer risk by genetic ancestry. The admixed population of women living in the Southwestern United States, California, and Mexico included in this study allows us to examine this continuum. We observed the strongest associations for MAP3K9, the only gene with overall statistical significance, among those with the highest IA ancestry. For most SNPs we observed a continuum of risk across ancestry groups. MAP3K9 was most strongly associated among women with the highest IA ancestry and multiple SNPs in this gene interacted with a history of diabetes to alter risk of breast cancer. At a population level, rates of diabetes and metabolic syndrome are higher among Hispanic, Native American and Mexican women than among NHW women [45–47]. While assessment of interaction of diet and lifestyle factors within ancestry groups would be desirable, our power was limited to meaningfully evaluate these 3-way interactions.

Several patterns of association emerged when evaluating interactions between dietary variables and MAPK genes and breast cancer risk. For example significant interactions were observed with breast cancer risk for MAPK1 and DOBS, dietary fiber and folate; DUSP4 rs2056025 interacted with both DOBS and dietary folate; MAPK8 rs10508901 interacted with total calories, fiber, and folate intake. Additionally, directions of association for high and low risk genotype and high and low risk lifestyle group were similar for factors expected to have similar mechanisms, such as DOBS, folate, and fiber having a similar effect, but opposite of those observed for total calories. Patterns of interaction by lifestyle factors also showed consistency across SNPs. For example four of the five SNPs in MAP3K9 shown to interact with pre-menopausal obesity also interacted with a history of diabetes. The two MAP3K9 SNPs interacting with alcohol intake also interacted with cigarette smoking.

These patterns of association support the reported mechanisms of MAPK genes that include activation by stimuli such as growth factors, inflammation, cytokines, and stress [1]. The JNK pathway, which was associated with breast cancer risk in these data, is involved in regulating responses to stress, inflammation, and apoptosis and is activated by radiation, environmental stresses, and growth factors. MAP3K9 appeared to be one of the most important MAPK genes with breast cancer in our population, both in terms of independent risk and its interactive effects with diet and lifestyle factors. MAP3K9, also known as mixed-lineage kinase 1 (MLK1), is instrumental in the regulation of the JNK pathway that is associated with normal and malignant cellular growth and division [48]. Other genes that regulate the JNK and ERK pathways, including MAP3K7 and MAP3K, also showed frequent interaction with diet and lifestyle factors. It is possible that response to diet and lifestyle factors is influenced by variation in genes at the activation point of these pathways. Dietary factors that influence oxidative balance may modify the effects of genes that respond to oxidative stress and inflammation. Cigarette smoking also could influence oxidative stress and importantly influence the effects of these genes to respond to stress. It could be further hypothesized that having a homozygote variant genotype of MAP3K9 makes individuals more sensitive to the effects of obesity or diabetes resulting in activation of JNK-signaling pathway that in turn regulates cell growth, differentiation, and apoptosis and ultimately cancer risk. The JNK pathway has been shown previously to be involved in the development of obesity and type 2 diabetes [3]; our data suggest significant interaction between BMI and a history of diabetes with MAP3K9. MAP3K7 is associated with transforming-growth factor β and bone morphogenetic protein signaling, both of which have been shown to influence breast cancer risk [49–51]; MAP3K1 and MAP3K3 enhance transcription of NFκB which is a key regulator of inflammatory response and associated with numerous cancers.

Few studies have examined genetic variation in MAPK genes and risk of cancer in general or breast cancer specifically. The variant allele of MAP3K1 rs889312 has been associated with increased breast cancer in a GWAS of European women [52] and among women with ER− tumors [53], although we did not observe a significant association with this SNP. Studies that evaluated interaction between this SNP and BMI did not observe a significant association with breast cancer risk [54]. However, SNPs in MAPK genes have been shown to interact with diet and lifestyle factors to alter colon cancer risk [55,56]. A study of breast tumors by Stephens and colleagues [57], concluded that MAP3K1 may harbor an important driver mutation. Hori and colleagues showed that ERα is regulated by MAPK and breast tumors that overexpressed ERK1, JNK1, and p38 proteins had more invasive tumor growth [58]. Given the biological role of MAPK genes there is support for an association, although previous studies have not examined polymorphisms in these genes and breast cancer risk.

The study has several strengths, including a large sample of Hispanic, NHW, and Mexican women, the assessment of AIMs that allowed examination of the continuum of European to IA ancestry, and our ability to look at interactions of key diet and lifestyle factors with these genes. While the information provided is novel and insightful to the pathways being studied, other MAPK genes and other diet and lifestyle factors that we did not have data on also may contribute to breast cancer risk and further illuminate these findings. A strength is our utilization of ARTP to evaluate the overall pathway and gene associations. This statistical method weighs the importance of the gene. Unfortunately ARTP has not been modified at this time to evaluate gene*environment interactions. Additionally, although we have limited information on functionality of SNPs associated with breast cancer, identification of similar associations for multiple SNPs within genes and patterns of interaction across genes and diet and lifestyle factors provides support for observed associations. Differences in dietary patterns by level of ancestry could influence ability to detect associations for various ancestry groups.

In conclusion, our findings suggest that several MAPK genes were associated with breast cancer risk, although MAP3K9 appeared to make the largest contribution to breast cancer risk. Several MAPK genes and SNPs, especially in MAP3K9, interacted with DOBS, dietary folate and fiber, total calories, obesity, alcohol intake, cigarette smoking, and a history of diabetes. The patterns of association across diet and lifestyle factors with similar biological properties for the same SNPs within genes provide support for the associations. Our findings suggest that this pathway may be most important for those women.

Supplementary Material

supplment

Acknowledgments

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-Corners Breast Cancer Study; and Dr. Josh Galanter for assistance in selection of AIMs markers.

Funding: 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-Corners 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).

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