Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2015 Aug 8.
Published in final edited form as: Breast Cancer Res Treat. 2014 Aug 8;147(1):145–158. doi: 10.1007/s10549-014-3071-y

Genetic Variation in the JAK/STAT/SOCS signaling pathway influences breast cancer-specific mortality through interaction with cigarette smoking and use of aspirin/NSAIDs: The Breast Cancer Health Disparities Study

Martha L Slattery 1, Abbie Lundgreen 1, Lisa M Hines 2, Gabriela Torres-Mejia 3, Roger K Wolff 1, Mariana C Stern 4, Esther M John 5
PMCID: PMC4167366  NIHMSID: NIHMS619904  PMID: 25104439

Abstract

Purpose

The Janus kinase (JAK)/signal transducer and activator of transcription (STAT)-signaling pathway is involved in immune function and cell growth; genetic variation in this pathway could influence breast cancer risk.

Methods

We examined 12 genes in the JAK/STAT/SOCS-signaling pathway with breast cancer risk and mortality in an admixed population of Hispanic (2111 cases, 2597 controls) and non-Hispanic white (1481 cases, 1585 controls) women. Associations were assessed by Indigenous American (IA) ancestry.

Results

After adjustment for multiple comparisons, JAK1 (3 of 10 SNPs) and JAK2 (4 of 11 SNPs) interacted with body mass index (BMI) among pre-menopausal women, while STAT3 (4 of 5 SNPs) interacted significantly with BMI among post-menopausal women to alter breast cancer risk. STAT6 rs3024979 and TYK2 rs280519 altered breast cancer-specific mortality among all women. Associations with breast cancer-specific mortality differed by IA ancestry; SOCS1 rs193779, STAT3 rs1026916, and STAT4 rs11685878 associations were limited to women with low IA ancestry and associations with JAK1 rs2780890, rs2254002, and rs310245 and STAT1 rs11887698 were observed among women with high IA ancestry. JAK2 (5 of 11 SNPs), SOCS2 (1of 3 SNPs), and STAT4 (2 of 20 SNPs) interacted with cigarette smoking status to alter breast-cancer specific mortality. SOCS2 (1 of 3 SNPs) and all STAT3, STAT5A, and STAT5B SNPs significantly interacted with use of aspirin/NSAIDs to alter breast cancer-specific mortality.

Conclusions

Genetic variation in the JAK/STAT/SOCS pathway was associated with breast cancer-specific mortality. The proportion of SNPs within a gene that significantly interacted with lifestyle factors lends support for the observed associations.

Keywords: Breast Cancer, Breast cancer-specific mortality, JAK/STAT/SOCS, polymorphisms, BMI, cigarette smoking, aspirin/NSAIDs

The Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway is involved in immune function and cell growth and differentiation[1, 2]. The JAK family consists of four non-receptor protein tyrosine kinases, JAK1, JAK2, JAK3, and TYK2. Of these, JAK1, JAK2, and TYK2 are expressed ubiquitously in mammals [3]. Once activated by cytokines, JAKs serve as docking sites for signaling molecules such as STATs. Activated STATs translocate from the cytoplasm to the nucleus where they increase the transcription rate of several genes. STAT1 and STAT2 were first identified as contributing to activation of genes involved in immune response [4]. STAT5 was first described in the mammary gland and has considerable specificity for mammary gland development [5]. Altogether, seven STATS have been identified in mammalian cells, STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6 [6]. STAT signaling has been shown to be important for mammary cell survival and tumorigenesis [6]. It has also been suggested that expression of STATs are associated with unique breast cancer subtypes defined by estrogen receptor (ER) and progesterone receptor (PR) status [7].

Cytokines up-regulate suppressors of cytokine signaling (SOCS) that inhibit the activity of JAKs and STATs [8]. Thus, research targeting an understanding of the JAK/STAT/SOCS signaling pathway often has involved the interaction between JAK/STAT/SOCS with cytokines. STAT1 and STAT2 were first identified from work involving downstream events of receptor binding of interferon γ (IFNγ) on transcriptional activation of genes involved in immune response[4]. Pro-inflammatory cytokines, such as IL-6 have been shown to up-regulate STAT proteins [4, 9, 10]. Both JAK1 and JAK2 are important for cytokines through use of the shared receptor subunits; IL-6 is an important pro-inflammatory cytokine that uses these receptors since they are essential for cytokine signaling [11]. JAK2 is essential for hormone-like cytokine signaling, including prolactin signaling [11].

This study builds on our previous work that has evaluated breast cancer associations with genetic variants in cytokines among women with diverse genetic ancestry. We have shown that breast cancer risk and mortality as well as risk associated with IL6 and other cytokine SNPs differ by Indigenous American (IA) ancestry [12, 13]. Thus, it is reasonable to hypothesize that breast cancer associations with genetic variation in JAK/STAT/SOCS genes may also vary by IA ancestry. Given previous work that suggest these genes may have unique ER/PR associations, we evaluated associations by ER/PR tumor subtype. Additionally, we evaluated the association of these genes with survival since one of their functions is to promote cell differentiation and metastases [14]. Since diet and lifestyle factors that are associated with inflammation may modify associations with these genes, we evaluate interaction of these genes with a dietary oxidative balance score, body mass index (BMI), cigarette smoking status, and use of aspirin and non-steroidal anti-inflammatory drugs (NSAIDs).

Methods

The Breast Cancer Health Disparities Study includes participants from three population-based case-control studies [15], the 4-Corners Breast Cancer Study (4-CBCS) [16], the Mexico Breast Cancer Study (MBCS)[17], and the San Francisco Bay Area Breast Cancer Study (SFBCS) [18, 19], who completed an in-person interview and who had a blood or mouthwash sample available for DNA extraction. Information on exposures was collected up to the referent year, defined as the calendar year before diagnosis for cases or before selection into the study for controls. 4-CBCS participants were between 25 and 79 years; MBCS participants were between 28 and 74 years; and SFBCS participants were between 35 to 79 years. All participants signed informed written consent prior to participation; the Institutional Review Board for Human Subjects at each institution approved the study.

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. Pre-menopausal women were those who reported still having periods during the referent year. Post-menopausal women were those who reported either a natural menopause or if they reported taking hormone therapy (HT) and were still having periods or were at or above the 95th percentile of age for those who reported having a natural menopause (i.e., ≥ 12 months since their last period). Women in 4-CBCS and SFBCS were asked to self-identify their race/ethnicity and were classified as non-Hispanic white (NHW), Hispanic, Native American (NA) or a combination of these groups. Women in MBCS were not asked their race or ethnicity and were combined with U.S. Hispanics/NAs in the analyses.

Lifestyle variables included BMI calculated as self-reported weight (kg) during the referent year divided by measured height squared (m2) and categorized as normal (<25 kg/m2), overweight (25–29.9 kg/m2), or obese (≥30 kg/m2). Regular cigarette smoking was evaluated as current, former, or never, where regular was defined as having smoked one or more cigarettes for six months or longer in 4-CBCS and SFBCS (data available for a subset of subjects only) or having smoked 100 or more cigarettes in MCBCS. A dietary oxidative balance score (DOBS) that included nutrients with anti- or pro-oxidative properties was used [20]. Dietary information was collected via a computerized validated diet history questionnaire in 4-CBCS [12, 21], a 104-item semi-quantitative Food Frequency Questionnaire (FFQ) in MBCS [22], and a modified version of the Block Food Frequency Questionnaire in SFBCS [23]. Alcohol consumption was based on long-term use; consumption during the referent year was used for a subset of SFBCS women without information on long-term use. Regular use of aspirin or NSAIDS defined as three or more times a week for at least one month was available for the 4-CBCS only. A history of diabetes was defined as ever being told by a health care provider that you had diabetes or high blood sugar (not available for all SFBCS participants).

Genetic Data

DNA was extracted from either whole blood (n=7287) or mouthwash (n=634) samples. Whole genome amplification (WGA) was applied to the mouthwash-derived DNA samples prior to genotyping. TagSNPs were selected to characterize the genetic variation using the following parameters: linkage disequilibrium (LD) blocks were defined using a Caucasian LD map and an r2=0.8; minor allele frequency (MAF) >0.1; range= −1500 bps from the initiation codon to +1500 bps from the termination codon; and 1 SNP/LD bin. Additionally, 104 Ancestry Informative Markers (AIMs) were used to distinguish European and Indigenous American (IA) ancestry [15]. All markers were genotyped using a multiplexed bead array assay format based on GoldenGate chemistry (Illumina, San Diego, California. In the current analysis we evaluated tagSNPs for JAK1 (10 SNPs), JAK2 (11 SNPs), SOCS1 (2 SNPs), SOCS2 (3 SNPs), STAT1 (15 SNPs), STAT2 (2 SNPs), STAT3 (5 SNPs), STAT4 (20 SNPs), STAT5A (2 SNPs), STAT5B (3 SNPs), STAT6 (6 SNPs), TYK2 (4 SNPs). Online Supplement 1 provides a description of these genes and SNPs; online supplement 2 describes LD structure of these genes.

Tumor Characteristics and Survival

Data on estrogen receptor (ER) and progesterone receptor (PR) tumor status and survival were available for cases from 4-CBCS and SFBCS only. Cancer registries in Utah, Colorado, Arizona, New Mexico, and California provided information on stage at diagnosis, months of survival after diagnosis, cause of death, and ER and PR status. Surveillance Epidemiology and End Results (SEER) disease stage was categorized as local, regional, or distant.

Statistical Methods

Genetic ancestry estimation

The program STRUCTURE was used to estimate individual ancestry for each study participant assuming two founding populations [24, 25]. A three-founding population model was assessed but did not fit the population structure. Participants were classified by level of percent IA ancestry (≤28%, >28–70%, and >70%), based on the distribution of genetic ancestry in the control population [15].

SNP Associations

Genes and SNPs were assessed for their association with breast cancer risk overall, by strata of IA ancestry, and by menopausal status in the whole population and by ER/PR status for the 4-CBCS and SFBCS. All statistical analyses were performed using SAS version 9.4 (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 study, BMI in the referent year, and parity as categorical variables and age (five-year categories) and genetic ancestry as continuous variables. Associations with SNPs were assessed assuming a co-dominant model. Based on the initial assessment, SNPs that appeared to have a dominant or recessive mode of inheritance were evaluated with those inheritance models in subsequent analyses. For stratified analyses, the p value was based on the Wald chi-square test comparing the homozygote rare to the homozygote common when presenting the co-dominant model. The multinomial p value reported for ER/PR status using the glogit link in the logistic procedure compares unique associations by tumor phenotype. Adjustments for multiple comparisons within the gene used the step-down Bonferroni correction, taking into account the degree of correlation of the SNPs within genes using the SNP spectral decomposition method proposed by Nyholt [26] and modified by Li and Ji [27]. An unadjusted p value of <0.05 was considered statistically significant; results are presented for those where the multiple comparison adjusted p value was <0.15 and are noted as being marginally associated.

Interactions

We assessed gene by environment interactions for lifestyle factors that could influence candidate genes given their potential involvement in inflammation, including BMI (separately for pre- and post-menopausal women given differences in risk associated with BMI by menopausal status), smoking (current, former, or never smokers), dietary oxidative balance score (DOBS), and regular use of aspirin/NSAID (for 4-CBCS participants only). DOBS was based on each individual’s intake of anti-oxidants (vitamin C, vitamin E, beta carotene (data for beta carotene were not available for MBCS), folic acid, and dietary fiber) and pro-oxidants (alcohol). Nutrients were evaluated per 1000 calories and the DOBS was based on study-specific distributions given the different dietary questionnaires used. Alcohol consumption was classified into three levels: the top 25th percentile of consumption, all other drinkers, and non-drinkers. The DOBS ranges from low levels (first quartile) of exposure to anti-oxidants or high exposure to pro-oxidants (fourth quartile) to high levels of anti-oxidants (fourth quartile) and low exposure to pro-oxidants (non-drinkers). Tests for interactions were evaluated using Wald one degree of freedom (1-df) chi-square tests.

Survival Analysis

Survival months were calculated based on month and year of diagnosis and month and year of death or last contact. Survival updates were received in the winter of 2013 that included complete survival surveillance through December of 2012. Associations between SNPs and breast cancer-specific mortality among cases with a first primary invasive breast cancer were evaluated using Cox proportional hazards models to obtain multivariate hazard ratios (HR) and 95% confidence CI. Individuals were censored when they died of causes other than breast cancer or were lost to follow-up. We present Wald p values for all women and by ancestry strata based on the comparison between the homozygote rare and common genotype when presenting the co-dominant inheritance model using models adjusted for age, study center, genetic ancestry, and SEER stage. Since survival data were not available for MBCS, the upper two ancestry strata were combined to evaluate survival by genetic ancestry. Interactions between genetic variants and genetic ancestry, BMI, cigarette smoking, DOBS, and aspirin/NSAID use with survival were assessed using p values from 1-df Wald chi-square tests.

Results

The majority of women were U.S. Hispanic or Mexican and were slightly younger than U.S. NHW women (Table 1). U.S. Hispanic women were more likely to have ER−/PR− tumors than NHW women. Approximately 20% of women had died, with 47.6% of deaths being from breast cancer among NHW and 55.9% of deaths among U.S. Hispanic women.

Table 1.

Description of study population by self-reported race/ethnicity

U.S. non-Hispanic White U. S. Hispanic/Native American or Mexican
Controls Cases Controls Cases
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 82.8 723 27.8 597 28.3
 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)
 <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
 60–69 349 22 361 24.4 526 20.3 430 20.4
 >70 303 19.1 209 14.1 173 6.7 151 7.2
 Mean 56.6 56 52.3 52.7
Menopausal Status
 Pre-menopausal 494 31.5 489 33.5 1027 40.7 836 40.9
 Post-menopausal 1075 68.5 970 66.5 1499 59.3 1210 59.1
Estimated Percent Indigenous American Ancestry
 0–28 1577 99.5 1472 99.4 278 10.7 275 13
 29–70 7 0.4 7 0.5 1686 64.9 1393 66
 71–100 1 0.1 2 0.1 633 24.4 443 21
ER/PR Status2
 ER+/PR+ NA1 695 68.2 NA1 605 61.9
 ER+/PR− NA1 121 11.9 NA1 115 11.8
 ER−/PR+ NA1 15 1.5 NA1 28 2.9
 ER−/PR− NA1 188 18.4 NA1 229 23.4
SEER Summary Stage2,3
 Local NA1 830 70.9 NA1 650 59.6
 Regional NA1 325 27.8 NA1 432 39.6
 Distant NA1 15 1.3 NA1 9 0.8
Vital Status2,3
 Deceased NA1 254 21.4 NA1 229 19.8
 Alive NA1 935 78.6 NA1 929 80.2
Cause of Death2,3
 Breast Cancer NA1 121 47.6 NA1 128 55.9
 Other NA1 133 52.4 NA1 101 44.1
Smoking Status4
 Never 794 60.3 688 56.1 1616 72.1 1298 70.1
 Former 360 27.3 386 31.5 347 15.5 322 17.4
 Current 163 12.4 152 12.4 278 12.4 231 12.5
BMI (kg/m2)
 <25 699 44.4 678 45.9 453 17.6 492 23.5
 25–29.9 465 29.5 433 29.3 951 36.9 768 36.7
 >30 412 26.1 367 24.8 1172 45.5 832 39.8
NSAID use5
 No 708 53.7 670 54.7 446 61.7 395 66.2
 Yes 610 46.3 554 45.3 277 38.3 202 33.8
Dietary Oxidative Balance Score6[mean (SD)]
 4-CBCS 6.3 (2.7) 6.3 (2.6) 6.7 (2.5) 6.5 (2.6)
 MCBCS NA1 NA1 5.9 (2.0) 5.7 (2.0)
 SFBCS 5.6 (2.6) 5.7 (2.6) 6.9 (2.5) 6.1 (2.5)
Open in a new tab
1

Data not applicable (NA)

2

Data unavailable from Mexico Breast Cancer Study (MBCS)

3

Includes first primary invasive breast cancer cases from the 4-Corners Breast Cancer Study (4-CBCS) and San Francisco Bay Area Breast Cancer Study (SFBCS)

4

Data unavailable from women using questionnaire’s one and two from SFBCS

5

Data only available for the 4-CBCS

6

Dietary Oxidative Balance Score (DOBS) includes alcohol (pro-oxidant), vitamin C, vitamin E, beta carotene (data not available for MCBCS), folic acid, and dietary fiber (anti-oxidants).

Few associations were observed between SNPs in our candidate genes and breast cancer risk (Table 2). STAT5B rs6503691 and TYK2 rs280519 were associated with reduced risk among women with high IA ancestry (ORCT/TT = 0.63 95% CI 0.41, 0.97, Phet=0.39 and ORAG/GG = 0.75 95% CI = 0.58, 0.96, Phet=0.46) and STAT6 rs3024974 (ORCT/TT = 1.15 95 %CI 1.02, 1.29, Phet=0.71) was associated with increased breast cancer risk overall. More associations were observed for specific tumor phenotype, with JAK2 rs1536800 being associated with ER−/PR− tumors and STAT3 (4 SNPs) and STAT5A (2 SNPs) and STAT5B (1 SNP) associated with ER−/PR+ tumors. These differences were statistically significant for JAK2 rs1536800 (Phet=0.03), STAT3 rs8069645 (Phet=0.03), STAT5A 7217728 (Phet=0.04), and STAT5B rs7218653 (Phet=0.02). No significant differences in association were detected by menopausal status (data not shown).

Table 2.

Associations between JAK/STAT genes and risk of breast cancer by genetic ancestry and Estrogen and Progesterone Receptor Tumor Phenotype.
Overall ≤28% IA Ancestry >28–70% IA Ancestry >70% IA Ancestry
Controls Cases OR2 (95% CI) Controls Cases OR (95% CI) Controls Cases OR (95% CI) Controls Cases OR (95% CI)
STAT5B (rs6503691)
 CC 3378 2897 1.00 1473 1383 1.00 1350 1115 1.00 555 399 1.00
 CT/TT 779 672 0.98 (0.87, 1.10) 374 359 1.01 (0.86, 1.19) 331 275 1.01 (0.84, 1.21) 74 38 0.63 (0.41, 0.97)
 P-value (raw; adjusted) 0.713, 1.000 0.906, 0.906 0.938, 1.000 0.035, 0.069
STAT6 (rs3024974)
 CC 3435 2868 1.00 1507 1394 1.00 1384 1097 1.00 544 377 1.00
 CT/TT 720 698 1.15 (1.02, 1.29) 340 346 1.10 (0.93, 1.30) 295 292 1.25 (1.04, 1.50) 85 60 1.01 (0.70, 1.46)
 P-value (raw; adjusted) 0.018, 0.072 0.268, 1.000 0.017, 0.068 0.953, 1.000
TYK2 (rs280519)
 AA 1499 1265 1.00 476 439 1.00 681 562 1.00 342 264 1.00
 AG/GG 2651 2289 0.96 (0.87, 1.06) 1371 1295 1.03 (0.89, 1.20) 995 821 0.98 (0.84, 1.13) 285 173 0.75 (0.58, 0.96)
 P-value (raw; adjusted) 0.416, 1.000 0.704, 1.000 0.766, 1.000 0.025, 0.093
ER+/PR+ ER+/PR− ER−/PR+ ER−/PR−
JAK2 (rs1536800)
 CC 1798 764 1.00 122 1.00 26 1.00 263 1.00
 CT/TT 1374 533 0.92 (0.81, 1.05) 113 1.22 (0.93, 1.59) 17 0.85 (0.46, 1.57) 152 0.75 (0.61, 0.93)
 P-value (raw; adjusted) 0.225, 1.000 0.150, 0.899 0.595, 1.000 0.009, 0.053
STAT3 (rs8069645)1
 AA 1872 756 1.00 133 1.00 18 1.00 253 1.00
 AG 1119 468 1.00 (0.87, 1.15) 94 1.16 (0.88, 1.53) 19 1.91 (0.99, 3.68) 144 0.96 (0.77, 1.20)
 GG 181 74 0.94 (0.70, 1.25) 8 0.58 (0.28, 1.21) 6 3.79 (1.46, 9.84) 18 0.74 (0.44, 1.22)
 P-value (raw; adjusted) 0.785, 0.959 0.831, 1.000 0.004, 0.010 0.324, 0.882
STAT3 (rs6503695)
 TT 1662 672 1.00 117 1.00 17 1.00 222 1.00
 TC 1256 519 0.98 (0.85, 1.12) 106 1.17 (0.88, 1.55) 19 1.63 (0.83, 3.19) 164 0.99 (0.79, 1.23)
 CC 255 106 0.93 (0.73, 1.19) 12 0.62 (0.33, 1.14) 7 3.10 (1.23, 7.81) 28 0.83 (0.54, 1.26)
 P-value (raw; adjusted) 0.564, 0.959 0.687, 1.000 0.016, 0.029 0.507, 0.882
STAT3 (rs12949918)
 TT 1428 578 1.00 109 1.00 13 1.00 197 1.00
 TC 1371 573 0.98 (0.85, 1.13) 97 0.90 (0.67, 1.21) 23 2.07 (1.03, 4.19) 171 0.92 (0.73, 1.15)
 CC 372 147 0.88 (0.71, 1.10) 29 0.95 (0.61, 1.47) 7 2.47 (0.94, 6.47) 46 0.91 (0.64, 1.29)
 P-value (raw; adjusted) 0.344, 0.959 0.631, 1.000 0.030, 0.030 0.454, 0.882
STAT3 (rs1026916)
 GG 1550 636 1.00 119 1.00 14 1.00 215 1.00
 GA/AA 1622 662 0.96 (0.84, 1.09) 116 0.91 (0.69, 1.19) 29 2.13 (1.11, 4.09) 200 0.90 (0.73, 1.11)
 P-value (raw; adjusted) 0.522, 0.959 0.488, 1.000 0.024, 0.029 0.317, 0.882
STAT5A (rs7217728)1
 TT 1836 740 1.00 130 1.00 16 1.00 247 1.00
 TC/CC 1335 557 0.99 (0.87, 1.13) 105 1.09 (0.83, 1.43) 27 2.5 (1.32, 4.72) 168 0.94 (0.76, 1.17)
 P-value (raw; adjusted) 0.920, 0.920 0.533, 0.835 0.005, 0.007 0.600, 0.600
STAT5A (rs12601982)
 AA 2364 945 1.00 174 1.00 26 1.00 320 1.00
 AG/GG 806 353 1.06 (0.91, 1.22) 61 1.00 (0.74, 1.36) 17 2.03 (1.08, 3.80) 95 0.88 (0.69, 1.12)
 P-value (raw; adjusted) 0.479, 0.750 0.980, 0.980 0.027, 0.027 0.294, 0.461
STAT5B (rs7218653)1
 AA 1849 745 1.00 132 1.00 15 1.00 237 1.00
 AG/GG 1323 553 1.00 (0.87, 1.14) 103 1.07 (0.81, 1.40) 28 2.85 (1.49, 5.43) 178 1.07 (0.86, 1.32)
 P-value (raw; adjusted) 0.946, 0.958 0.631, 0.631 0.002, 0.003 0.541, 0.541
Open in a new tab
1

p values for difference by ER/PR group after adjustment for multiple comparisons were 0.07, 0.05, and 0.04 respectively

2

Odds ratios (OR) and 95% confidence Intervals (CI) adjusted for age, study center, BMI during referent year, parity, and genetic ancestry. Risk estimates are shown in the table if one or more of the adjusted p values for multiple comparisons is <0.15.

BMI was the main lifestyle factor that interacted with these genes to alter risk of breast cancer (Table 3). JAK1 (3 SNPs) and JAK2 (4 SNPs) interacted with BMI among pre-menopausal women, with the majority of the differences observed among those who were obese. Among post-menopausal women, STAT3 (4 SNPs) interacted with BMI to alter breast cancer risk, with the majority of the differences observed among those with normal BMI. Additionally, interactions were seen between STAT1 (2 SNPs) and STAT4 (1 SNP) and DOBS. We also assessed interaction between IL6 and its receptor and JAK/STAT/SOC pathway genes and observed several significant interactions although after adjustment for multiple comparisons none of the associations were statistically significant (data not shown).

Table 3.

Interaction between BMI, DOBS and JAK/STAT genes and risk of breast cancer

Controls Cases OR1 (95% CI) Controls Cases OR (95% CI) Controls Cases OR (95% CI) Interaction P (raw; adjusted)
Normal (< 25 kg/m2) Overweight (25 to <30 kg/m2) Obese (>= 30 kg/m2)
Pre-menopausal
JAK1 (rs4916005)
 TT 307 328 1.00 310 237 0.76 (0.60, 0.96) 277 241 0.89 (0.70, 1.13) 0.009, 0.054
 TC 143 151 1.01 (0.76, 1.34) 180 147 0.86 (0.65, 1.14) 201 125 0.66 (0.49, 0.88)
 CC 17 33 1.98 (1.07, 3.67) 35 31 0.97 (0.57, 1.65) 46 25 0.60 (0.35, 1.01)
JAK1 (rs310211)
 AA 188 212 1.00 165 115 0.66 (0.48, 0.90) 133 132 0.97 (0.70, 1.33) 0.009, 0.054
 AG 205 207 0.93 (0.71, 1.23) 239 199 0.81 (0.61, 1.07) 247 187 0.76 (0.56, 1.01)
 GG 74 92 1.15 (0.79, 1.66) 121 99 0.85 (0.60, 1.21) 143 71 0.50 (0.35, 0.73)
JAK1 (rs2256298)
 CC 225 247 1.00 202 143 0.69 (0.52, 0.92) 167 152 0.91 (0.68, 1.22) 0.027, 0.106
 CT 187 197 1.02 (0.77, 1.34) 229 193 0.85 (0.65, 1.13) 241 179 0.77 (0.58, 1.02)
 TT 55 68 1.19 (0.79, 1.78) 94 79 0.91 (0.63, 1.33) 116 60 0.54 (0.37, 0.79)
JAK2 (rs10974916)
 GG 228 245 1.00 260 196 0.76 (0.58, 0.99) 231 202 0.90 (0.68, 1.18) 0.018, 0.074
 GA 201 216 1.01 (0.77, 1.32) 224 183 0.83 (0.63, 1.09) 233 159 0.71 (0.53, 0.94)
 AA 38 51 1.31 (0.82, 2.07) 41 36 0.90 (0.55, 1.48) 60 30 0.51 (0.32, 0.83)
JAK2 (rs7043371)
 AA 120 148 1.00 127 106 0.74 (0.51, 1.05) 155 94 0.55 (0.39, 0.79) 0.024, 0.074
 AT 241 248 0.86 (0.64, 1.16) 280 207 0.66 (0.48, 0.89) 261 192 0.66 (0.48, 0.91)
 TT 106 116 0.88 (0.61, 1.26) 118 102 0.76 (0.53, 1.10) 108 105 0.87 (0.60, 1.26)
JAK2 (rs1536800)
 CC 264 283 1.00 295 227 0.77 (0.60, 0.99) 254 228 0.93 (0.72, 1.20) 0.001, 0.008
 CT 184 191 0.98 (0.75, 1.28) 203 166 0.83 (0.63, 1.09) 226 143 0.64 (0.49, 0.85)
 TT 19 38 1.96 (1.10, 3.50) 26 21 0.85 (0.46, 1.56) 44 20 0.47 (0.27, 0.83)
JAK2 (rs3780381)
 AA 228 256 1.00 274 199 0.70 (0.54, 0.90) 225 211 0.92 (0.70, 1.20) 0.009, 0.045
 AC 205 206 0.90 (0.69, 1.17) 216 182 0.81 (0.62, 1.07) 241 150 0.61 (0.46, 0.81)
 CC 34 50 1.36 (0.85, 2.19) 34 34 0.98 (0.58, 1.64) 58 30 0.50 (0.31, 0.82)
Post-Menopause
STAT3 (rs8069645)
 AA 376 383 1.00 545 468 0.91 (0.75, 1.10) 640 463 0.78 (0.64, 0.94) 0.025, 0.026
 AG 256 218 0.82 (0.65, 1.04) 269 256 0.96 (0.76, 1.20) 340 275 0.85 (0.68, 1.06)
 GG 38 27 0.66 (0.40, 1.11) 48 36 0.74 (0.47, 1.16) 43 38 0.90 (0.57, 1.43)
STAT3 (rs6503695)
 TT 320 329 1.00 502 430 0.90 (0.73, 1.10) 592 424 0.76 (0.62, 0.93) 0.012, 0.026
 TC 291 257 0.83 (0.66, 1.05) 297 284 0.95 (0.75, 1.19) 371 297 0.82 (0.66, 1.02)
 CC 59 42 0.64 (0.41, 0.98) 64 46 0.67 (0.44, 1.01) 60 55 0.92 (0.62, 1.38)
STAT3 (rs12949918)
 TT 271 288 1.00 435 372 0.86 (0.69, 1.07) 531 381 0.74 (0.59, 0.92) 0.009, 0.026
 TC 308 277 0.81 (0.64, 1.02) 340 316 0.89 (0.71, 1.12) 398 321 0.79 (0.63, 0.99)
 CC 91 63 0.60 (0.42, 0.86) 88 72 0.72 (0.51, 1.03) 92 74 0.78 (0.55, 1.11)
STAT3 (rs1026916)
 GG 317 337 1.00 461 390 0.85 (0.69, 1.05) 549 407 0.76 (0.62, 0.94) 0.047, 0.047
 GA/AA 353 291 0.75 (0.60, 0.94) 402 370 0.89 (0.72, 1.10) 473 369 0.78 (0.63, 0.96)
DOBS Low DOBS Intermediate DOBS High
STAT1 (rs1400657)
 AA 784 831 1.00 1781 1488 0.81 (0.72, 0.91) 784 587 0.72 (0.63, 0.84) 0.006, 0.065
 AC/CC 182 150 0.76 (0.60, 0.97) 388 306 0.75 (0.63, 0.90) 154 145 0.90 (0.70, 1.15)
STAT1 (rs3771300)
 CC 321 359 1.00 746 567 0.69 (0.57, 0.83) 344 240 0.64 (0.51, 0.80) 0.014, 0.126
 CA 450 428 0.81 (0.66, 0.99) 1044 889 0.75 (0.63, 0.89) 438 349 0.69 (0.56, 0.85)
 AA 195 195 0.82 (0.63, 1.05) 378 337 0.76 (0.62, 0.94) 153 143 0.80 (0.61, 1.06)
STAT4 (rs925847)
 CC 491 561 1.00 1204 922 0.69 (0.59, 0.80) 511 374 0.66 (0.55, 0.80) 0.003, 0.035
 CT 390 357 0.80 (0.66, 0.97) 817 739 0.81 (0.69, 0.95) 353 301 0.75 (0.62, 0.92)
 TT 81 61 0.63 (0.44, 0.90) 143 131 0.81 (0.62, 1.05) 72 57 0.72 (0.50, 1.04)
Open in a new tab
1

Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusted for age, study center, BMI during referent year (where appropriate), parity, and genetic ancestry. Risk estimates are shown in the table if the adjusted p value for multiple comparison is <0.15.

STAT6 rs3024979 and TYK2 rs280519 were associated with breast cancer-specific mortality overall (Table 4). However, most associations were restricted to either low or high IA ancestry group. Among women with low IA ancestry, SOCS1 rs193779, STAT3 rs1026916, and STAT4 rs11685878 were associated with breast cancer-specific mortality. Among women with high IA ancestry (JAK1 rs2780890, rs2254002, and rs310245) and STAT1 rs11887698 were associated with breast cancer-specific mortality. Additionally, JAK2 (5 SNPs), SOCS2 (1 SNP), and STAT4 (2 SNPs) interacted with cigarette smoking status to alter breast cancer-specific mortality (Table 5), with associations predominantly observed among current smokers. STAT1 rs2030171 and STAT5B rs9900213 interacted with DOBS to alter breast cancer-specific mortality (Table 5). Interactions also were seen between SOCS2 (1 SNP), STAT3 (5 SNPs), STAT5A (2 SNPs), and STAT5B (3 SNPs) and regular use of aspirin/NSAIDs. No significant interactions with BMI were observed after adjustment for multiple comparisons.

Table 4.

Associations between breast cancer-specific mortality and JAK/STAT/SOC genes by genetic ancestry

Overall ≤28% IA Ancestry >28% IA Ancestry Interaction P (raw; adjusted)
Deaths/Person Years HR (95% CI) Deaths/Person Years HR (95% CI) Deaths/Person Years HR (95% CI)
JAK1 (rs2780890) 0.031, 0.123
 AA/AG 182/17816 1.00 99/9601 1.00 83/8215 1.00
 GG 67/5942 1.19 (0.89, 1.59) 46/4600 0.96 (0.68, 1.36) 21/1342 1.84 (1.13, 3.00)
 P-value (raw; adjusted) 0.237, 0.950 0.823, 1.000 0.015, 0.058
JAK1 (rs2254002) 0.019, 0.116
 GG 91/7759 1.00 57/5631 1.00 34/2129 1.00
 GT/TT 158/15987 0.79 (0.61, 1.03) 88/8571 1.02 (0.73, 1.43) 70/7417 0.54 (0.36, 0.82)
 P-value (raw; adjusted) 0.088, 0.529 0.911, 1.000 0.004, 0.023
JAK1 (rs310245) 0.024, 0.119
 CC 90/7651 1.00 56/5499 1.00 34/2152 1.00
 CT/TT 159/16107 0.80 (0.61, 1.04) 89/8702 1.01 (0.72, 1.42) 70/7405 0.55 (0.36, 0.83)
 P-value (raw; adjusted) 0.090, 0.529 0.948, 1.000 0.005, 0.023
SOCS1 (rs193779) 0.055, 0.104
 GG 164/14841 1.00 93/8135 1.00 71/6706 1.00
 GA/AA 85/8876 0.87 (0.67, 1.14) 52/6040 0.70 (0.50, 0.99) 33/2836 1.19 (0.78, 1.81)
 P-value (raw; adjusted) 0.303, 0.570 0.045, 0.085 0.431, 0.431
STAT1 (rs11887698) 0.025, 0.232
 AA 139/12820 1.00 97/10015 1.00 42/2805 1.00
 AG 89/8187 0.91 (0.69, 1.22) 43/3677 1.17 (0.80, 1.70) 46/4509 0.65 (0.42, 0.99)
 GG 21/2741 0.62 (0.38, 1.03) 5/499 0.89 (0.36, 2.22) 16/2242 0.46 (0.25, 0.83)
 P-value (raw; adjusted) 0.063, 0.513 0.799, 0.799 0.010, 0.092
STAT3 (rs1026916) 0.006, 0.018
 GG 121/11584 1.00 51/6044 1.00 70/5540 1.00
 GA/AA 128/12174 1.04 (0.81, 1.34) 94/8157 1.44 (1.02, 2.03) 34/4017 0.67 (0.45, 1.02)
 P-value (raw; adjusted) 0.744, 1.000 0.039, 0.108 0.062, 0.157
STAT4 (rs11685878) 0.011, 0.148
 CC 104/8981 1.00 61/4679 1.00 43/4302 1.00
 CT 111/11114 0.88 (0.67, 1.16) 69/7167 0.76 (0.54, 1.07) 42/3947 1.08 (0.70, 1.65)
 TT 34/3651 0.86 (0.58, 1.27) 15/2343 0.55 (0.31, 0.97) 19/1308 1.49 (0.86, 2.58)
 P-value (raw; adjusted) 0.438, 1.000 0.039, 0.539 0.151, 1.000
STAT6 (rs3024979) 0.531, 0.733
 TT 195/19983 1.00 109/11477 1.00 86/8506 1.00
 TA/AA 53/3760 1.52 (1.12, 2.07) 35/2708 1.43 (0.97, 2.10) 18/1051 1.74 (1.04, 2.91)
 P-value (raw; adjusted) 0.008, 0.032 0.071, 0.167 0.033, 0.133
TYK2 (rs280519) 0.866, 1.000
 AA 93/7511 1.00 41/3505 1.00 52/4006 1.00
 AG 115/10927 0.97 (0.73, 1.28) 73/6753 0.99 (0.67, 1.46) 42/4174 0.91 (0.60, 1.38)
 GG 39/5187 0.67 (0.46, 0.98) 30/3863 0.69 (0.43, 1.11) 9/1324 0.57 (0.28, 1.17)
 P-value (raw; adjusted) 0.040, 0.147 0.130, 0.347 0.124, 0.455
Open in a new tab
1

Hazard Ratio (HR) and 95% Confidence Interval (CI) adjusted for age, study center, BMI during referent year, SEER summary stage, and genetic ancestry.

Risk estimates are shown in the table if one or more of the adjusted p values for multiple comparisons is <0.15.

Table 5.

Interactions between cigarette smoking, DOBS, aspirin/NSAID and JAK/STAT/SOC genes and risk of breast cancer-specific mortality

Deaths/Person Years HR1 (95% CI) Deaths/Person Years HR (95% CI) Deaths/Person Years HR (95% CI) Interaction P (raw; adjusted)
Never Smoker Former Smoker Current Smoker
JAK2 (rs2274471) 0.008, 0.024
 TT 73/6760 1.00 20/2545 0.73 (0.44, 1.20) 11/1074 0.99 (0.52, 1.87)
 TC/CC 37/4214 0.84 (0.56, 1.25) 14/2043 0.73 (0.41, 1.30) 24/1015 2.26 (1.41, 3.61)
JAK2 (rs7043371) 0.024, 0.047
 AA 21/3033 1.00 9/1192 0.96 (0.44, 2.10) 15/526 3.11 (1.59, 6.11)
 AT/TT 90/7935 1.57 (0.98, 2.54) 25/3396 1.14 (0.64, 2.05) 20/1563 2.05 (1.11, 3.79)
JAK2 (rs10974947) <.001, 0.003
 GG 76/6702 1.00 17/2522 0.63 (0.37, 1.07) 11/1166 0.86 (0.45, 1.62)
 GA/AA 35/4275 0.75 (0.50, 1.13) 17/2065 0.78 (0.46, 1.34) 24/922 2.43 (1.52, 3.90)
JAK2 (rs3780379) 0.002, 0.008
 GG 83/7477 1.00 22/2910 0.73 (0.45, 1.17) 14/1333 0.99 (0.56, 1.75)
 GA/AA 28/3491 0.78 (0.51, 1.19) 12/1677 0.70 (0.38, 1.30) 21/756 2.65 (1.62, 4.32)
JAK2 (rs10815160) 0.002, 0.008
 TT 64/5649 1.00 18/2343 0.70 (0.41, 1.18) 8/1055 0.76 (0.36, 1.59)
 TG/GG 47/5328 0.78 (0.54, 1.14) 16/2244 0.67 (0.39, 1.17) 27/1034 2.16 (1.37, 3.42)
SOCS2 (rs3816997) 0.028, 0.057
 TT 73/7405 1.00 23/3073 0.80 (0.50, 1.28) 29/1335 2.27 (1.47, 3.50)
 TG/GG 38/3571 1.09 (0.73, 1.62) 11/1514 0.77 (0.41, 1.45) 6/754 0.83 (0.36, 1.91)
STAT4 (rs4853546) 0.010, 0.137
 GG 62/4918 1.00 13/1960 0.51 (0.28, 0.94) 12/962 1.06 (0.57, 1.98)
 GA/AA 49/6059 0.64 (0.44, 0.93) 21/2627 0.69 (0.42, 1.14) 23/1127 1.58 (0.97, 2.57)
STAT4 (rs1031508) 0.010, 0.137
 CC 67/5811 1.00 14/2500 0.46 (0.26, 0.82) 15/1157 1.20 (0.68, 2.10)
 CT/TT 44/5166 0.73 (0.50, 1.07) 20/2087 0.97 (0.58, 1.60) 20/932 1.78 (1.07, 2.96)
DOBS Low DOBS Intermediate DOBS High
STAT1 (rs2030171) 0.008, 0.069
 GG 35/2447 1.00 48/3891 0.82 (0.53, 1.28) 12/1583 0.53 (0.27, 1.03)
 GA 31/2873 0.70 (0.43, 1.13) 62/5774 0.70 (0.46, 1.07) 16/2273 0.52 (0.28, 0.94)
 AA 8/1315 0.32 (0.15, 0.71) 25/2541 0.66 (0.39, 1.13) 11/948 0.87 (0.43, 1.75)
STAT5B (rs9900213) 0.021, 0.041
 GG 54/4998 1.00 100/8944 1.10 (0.79, 1.54) 35/3605 1.09 (0.71, 1.67)
 GT/TT 20/1680 1.32 (0.79, 2.23) 36/3299 1.14 (0.74, 1.74) 4/1206 0.34 (0.12, 0.93)
Non-Regular Aspirin/NSAID Users Regular Aspirin/NSAID Users
SOCS2 (rs3816997) 0.042, 0.083
 TT 59/5410 1.00 40/3790 0.99 (0.66, 1.48)
 TG/GG 28/2283 1.16 (0.73, 1.83) 8/1619 0.46 (0.22, 0.96)
STAT3 (rs1053005) <.001, <.001
 AA 66/5127 1.00 22/3604 0.45 (0.28, 0.74)
 AG/GG 21/2566 0.57 (0.35, 0.94) 26/1793 1.12 (0.71, 1.79)
STAT3 (rs8069645) 0.014, 0.014
 AA 53/4328 1.00 20/3009 0.51 (0.30, 0.86)
 AG/GG 34/3365 0.78 (0.50, 1.20) 28/2400 0.99 (0.62, 1.58)
STAT3 (rs6503695) 0.006, 0.006
 TT 50/3938 1.00 15/2471 0.44 (0.24, 0.79)
 TC/CC 37/3755 0.71 (0.46, 1.10) 33/2937 0.90 (0.57, 1.41)
STAT3 (rs12949918) 0.003, 0.005
 TT 44/3243 1.00 13/2202 0.40 (0.21, 0.75)
 TC 36/3653 0.75 (0.48, 1.17) 24/2397 0.78 (0.47, 1.30)
 CC 7/796 0.48 (0.21, 1.10) 11/810 0.99 (0.50, 1.95)
STAT3 (rs1026916) 0.006, 0.006
 GG 44/3549 1 14/2605 0.42 (0.23, 0.77)
 GA/AA 43/4144 0.84 (0.55, 1.29) 34/2803 1.04 (0.65, 1.65)
STAT5A (rs7217728) <.001, <.001
 TT 66/4310 1.00 13/2709 0.28 (0.15, 0.52)
 TC/CC 21/3383 0.37 (0.22, 0.60) 35/2699 0.87 (0.57, 1.33)
STAT5A (rs12601982) <.001, <.001
 AA 70/5470 1.00 25/3805 0.49 (0.30, 0.77)
 AG/GG 17/2223 0.51 (0.30, 0.88) 23/1604 1.12 (0.69, 1.81)
STAT5B (rs9900213) 0.050, 0.050
 GG 70/5514 1.00 31/3886 0.64 (0.42, 0.98)
 GT/TT 17/2179 0.66 (0.39, 1.13) 17/1522 0.94 (0.55, 1.63)
STAT5B (rs6503691) 0.002, 0.002
 CC 78/5966 1.00 33/4215 0.59 (0.39, 0.89)
 CT/TT 9/1727 0.42 (0.21, 0.84) 15/1193 1.11 (0.64, 1.94)
STAT5B (rs7218653) <.001, <.001
 AA 63/4307 1.00 13/2691 0.30 (0.16, 0.54)
 AG/GG 24/3386 0.43 (0.27, 0.70) 35/2718 0.90 (0.59, 1.37)
Open in a new tab
1

Hazard ratio (HR) and 95% confidence intervals (CI) adjusted for age, study center, BMI during referent year (where appropriate), genetic ancestry, and SEER stage.

Risk estimates are shown in the table if the adjusted p value for multiple comparison is <0.15.

Discussion

Among genes within the JAK/STAT/SOC-signaling pathway, we observed several associations between SNPs and breast cancer mortality, and a few significant associations with risk of breast cancer, irrespective of genetic ancestry or ER/PR tumor subtypes. However, lifestyle factors that influence inflammation interacted with these genes to alter breast cancer risk and mortality. Specifically, BMI and DOBS interacted with these genes to alter breast cancer risk, while cigarette smoking and aspirin/NSAID use interacted with them to influence breast cancer-specific mortality. The proportion of SNPs within a gene that significantly interacted with DOBS and lifestyle factors was considerably greater than by chance, lending support for the observed associations.

Although it is reasonable to genes could help explain differences in breast cancer incidence rates when comparing populations with high vs. low IA ancestry, our results provide little support for that hypothesis. Others have suggested that this pathway has unique associations with tumor phenotype and estrogen [7, 28–30], but we found minimal support for differences in associations by ER/PR subtype. The majority of associations were with ER−/PR+ tumors, and although statistically significant after adjustment for multiple comparisons, there were few individuals with that phenotype and therefore estimates of association were imprecise even though they were statistically significant.

Of interest are the consistent associations observed for the interaction of BMI with JAK1 (3 out of 10 SNPs) and JAK2 (4 out of 11 SNPs) for pre-menopausal breast cancer risk and with STAT3 (4 of 5 SNPs) for post-menopausal breast cancer risk. The number of SNPs within genes that were associated with breast cancer risk was greater than one would expect by chance. One explanation for the interactions between BMI and JAK1, JAK2, and STAT3 could be the strong correlation between leptin and BMI. Although leptin is mainly produced by white adipocytes, it is also produced by mammary epithelium. The leptin receptor is a class I cytokine receptor that acts through JAK and STATs and the JAK/STAT pathway is one of the main signaling cascades activated by leptin [31]. Additionally, STAT3 specifically has been shown to influence energy homeostasis [32, 33]. Activation of the JAK/STAT pathway also can promote tumor growth and induce inflammation as well as regulate other genes that control cell proliferation, differentiation, tumor development, and cell survival.

The JAK/STAT pathway is critical for cell development, cell survival, cell proliferation, and apoptosis; our results suggest genetic variation in these genes is important for breast cancer-specific mortality. We observed stronger estimates of association and more consistent associations across genes, and SNPs within those genes, with breast cancer-specific mortality than with breast cancer risk. Thus, it is possible that these genes function as tumor promoters, as has been suggested [6, 34]. The strongest and most consistent associations were observed for the interaction between cigarette smoking and aspirin/NSAID use with JAK2, STAT3, STAT5a, and STAT5b and to a lesser extent with SOSC2 (rs3816997 which interacted with both cigarette smoking and aspirin/NSAIDs use) to influence breast cancer-specific mortality. The JAK/STAT signaling pathway is activated when cytokines are bound to their receptors while SOCs suppresses the signaling. Nicotine has been shown to activate the JAK2/STAT3 pathway [35], which in this case appears to promote tumor progression depending on JAK2/STAT3 genotype.

Regular use of aspirin/NSAIDs interacted with all SNPs evaluated for STAT3 (5), STAT5A (2), and STAT5B (3) to alter breast cancer-specific mortality. STAT3 has been shown to be a promoter of tumor invasiveness and angiogenesis [6]. Activation of STAT5, which was first recognized as mammary gland factor, results in regulation of several genes involved in cell apoptosis, survival, and proliferation [14]. It has been shown that aspirin regulates apoptosis by down-regulating the IL6-STAT3 pathway [36] and that the epidermal growth factor induces COX2 through STAT5 signaling [37] thus providing biological support for our observations.

We believe that these findings are unique and have found no reference to the importance of these genes in the literature or in GWAS studies of breast cancer. Our candidate pathway approach has enabled us to identified important genes based on their biological function. Furthermore our ability to evaluate interaction with diet and lifestyle factors has enhanced our understanding of these genes and how they influence breast cancer risk and mortality.

This study has both strengths and limitations. The population represents a large genetically diverse population that includes extensive data on diet and lifestyle factors along with genetic data, ER/PR status, and vital status. However, ER/PR status and vital status were available only for the U.S. based studies. We used a tag-SNP approach to characterize genetic variation in these genes, although other SNPs could be important that were not analyzed. We adjusted for multiple comparisons within our candidate genes, although we cannot exclude the possibility of chance observations. However, the number of SNPs within genes for which we observed associations further indicates that these observations may be more than chance findings. Nevertheless, we encourage others to replicate our findings, especially those that pertain to survival, given their implication for treatment modalities as has been suggested [38].

In conclusion, our findings suggest that genetic variation in the JAK/STAT/SOCS signaling pathway is important for breast cancer-specific mortality. Of note is the consistent and stronger interaction observed between JAK2, SOCS2, STAT3 and STAT5 and cigarette smoking and use of aspirin/NSAIDs to modify breast cancer-specific mortality. Additionally JAK1, JAK2, and STAT3 interacted with BMI to modify risk of developing breast cancer. Given the potential importance of these findings on modalities such as aspirin/NSAIDS to improve survival on a subset of women, replication of these findings in other populations is needed.

Supplementary Material

10549_2014_3071_MOESM1_ESM

Supplemental Table 1. Summary of genes and tagSNPs analyzed.

Supplemental Table 2. R2 values for SNPs within genes

Acknowledgments

We would also like to acknowledge the contributions of the following individuals to the study: Sandra Edwards and Jennifer Herrick for data harmonization oversight; Erica Wolff and Michael Hoffman for laboratory support; Carolina Ortega for her assistance with data management for the Mexico Breast Cancer Study, Jocelyn Koo for data management for the San Francisco Bay Area Breast Cancer Study; Dr. Tim Byers, Dr. Kathy Baumgartner, and Dr. Anna Giuliano for their 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).

Footnotes

Conflict of Interest. The authors have no conflict of interest to report.

Financial Relationships: The authors have no financial relationships with the sponsored research other than through grant funding to MS, EJ, RW, and GTM.

Contributor Information

Abbie Lundgreen, Email: abbie.lundgreen@hsc.utah.edu.

Lisa M. Hines, Email: lhines@uccs.edu.

Gabriela Torres-Mejia, Email: México.gtorres@insp.mx.

Roger K. Wolff, Email: Roger.Wolff@hsc.utah.edu.

Mariana C. Stern, Email: marianas@usc.edu.

Esther M. John, Email: Esther.John@cpic.org.

References

  • 1.Lu Y, Zhou J, Xu C, Lin H, Xiao J, Wang Z, Yang B. JAK/STAT and PI3K/AKT pathways form a mutual transactivation loop and afford resistance to oxidative stress-induced apoptosis in cardiomyocytes. Cell Physiol Biochem. 2008;21(4):305–314. doi: 10.1159/000129389. [DOI] [PubMed] [Google Scholar]
  • 2.Xiong H, Zhang ZG, Tian XQ, Sun DF, Liang QC, Zhang YJ, Lu R, Chen YX, Fang JY. Inhibition of JAK1, 2/STAT3 signaling induces apoptosis, cell cycle arrest, and reduces tumor cell invasion in colorectal cancer cells. Neoplasia. 2008;10(3):287–297. doi: 10.1593/neo.07971. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Ghoreschi K, Laurence A, O’Shea JJ. Janus kinases in immune cell signaling. Immunol Rev. 2009;228(1):273–287. doi: 10.1111/j.1600-065X.2008.00754.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Shea-Donohue T, Fasano A, Smith A, Zhao A. Enteric pathogens and gut function: Role of cytokines and STATs. Gut Microbes. 2010;1(5):316–324. doi: 10.4161/gmic.1.5.13329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Watson CJ, Neoh K. The Stat family of transcription factors have diverse roles in mammary gland development. Semin Cell Dev Biol. 2008;19(4):401–406. doi: 10.1016/j.semcdb.2008.07.021. [DOI] [PubMed] [Google Scholar]
  • 6.Haricharan S, Li Y. STAT signaling in mammary gland differentiation, cell survival and tumorigenesis. Mol Cell Endocrinol. 2014;382(1):560–569. doi: 10.1016/j.mce.2013.03.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Furth PA. STAT signaling in different breast cancer sub-types. Mol Cell Endocrinol. 2014;382(1):612–615. doi: 10.1016/j.mce.2013.03.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Pfitzner E, Kliem S, Baus D, Litterst CM. The role of STATs in inflammation and inflammatory diseases. Curr Pharm Des. 2004;10(23):2839–2850. doi: 10.2174/1381612043383638. [DOI] [PubMed] [Google Scholar]
  • 9.Chen B, He L, Savell VH, Jenkins JJ, Parham DM. Inhibition of the interferon-gamma/signal transducers and activators of transcription (STAT) pathway by hypermethylation at a STAT-binding site in the p21WAF1 promoter region. Cancer Res. 2000;60(12):3290–3298. [PubMed] [Google Scholar]
  • 10.Bromberg JF. Activation of STAT proteins and growth control. Bioessays. 2001;23(2):161–169. doi: 10.1002/1521-1878(200102)23:2<161::AID-BIES1023>3.0.CO;2-0. [DOI] [PubMed] [Google Scholar]
  • 11.Yamaoka K, Saharinen P, Pesu M, Holt VE, 3rd, Silvennoinen O, O’Shea JJ. The Janus kinases (Jaks) Genome biology. 2004;5(12):253. doi: 10.1186/gb-2004-5-12-253. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Slattery ML, Caan BJ, Duncan D, Berry TD, Coates A, Kerber R. A computerized diet history questionnaire for epidemiologic studies. J Am Diet Assoc. 1994;94(7):761–766. doi: 10.1016/0002-8223(94)91944-5. [DOI] [PubMed] [Google Scholar]
  • 13.Slattery ML, Herrick JS, Torres-Mejia G, John EM, Giuliano AR, Hines LM, Stern MC, Baumgartner KB, Presson AP, Wolff RK. Genetic variants in interleukin genes are associated with breast cancer risk and survival in a genetically admixed population: the Breast Cancer Health Disparities Study. Carcinogenesis. 2014 doi: 10.1093/carcin/bgu078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Wei L, Laurence A, O’Shea JJ. New insights into the roles of Stat5a/b and Stat3 in T cell development and differentiation. Semin Cell Dev Biol. 2008;19(4):394–400. doi: 10.1016/j.semcdb.2008.07.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Slattery ML, John EM, Torres-Mejia G, Lundgreen A, Herrick JS, Baumgartner KB, Hines LM, Stern MC, Wolff RK. Genetic variation in genes involved in hormones, inflammation and energetic factors and breast cancer risk in an admixed population. Carcinogenesis. 2012;33(8):1512–1521. doi: 10.1093/carcin/bgs163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Slattery ML, Sweeney C, Edwards S, Herrick J, Baumgartner K, Wolff R, Murtaugh M, Baumgartner R, Giuliano A, Byers T. Body size, weight change, fat distribution and breast cancer risk in Hispanic and non-Hispanic white women. Breast Cancer Res Treat. 2007;102(1):85–101. doi: 10.1007/s10549-006-9292-y. [DOI] [PubMed] [Google Scholar]
  • 17.Angeles-Llerenas A, Ortega-Olvera C, Perez-Rodriguez E, Esparza-Cano JP, Lazcano-Ponce E, Romieu I, Torres-Mejia G. Moderate physical activity and breast cancer risk: the effect of menopausal status. Cancer Causes Control. 2010;21(4):577–586. doi: 10.1007/s10552-009-9487-8. [DOI] [PubMed] [Google Scholar]
  • 18.John EM, Horn-Ross PL, Koo J. Lifetime physical activity and breast cancer risk in a multiethnic population: the San Francisco Bay area breast cancer study. Cancer Epidemiol Biomarkers Prev. 2003;12(11 Pt 1):1143–1152. [PubMed] [Google Scholar]
  • 19.John EM, Phipps AI, Davis A, Koo J. Migration history, acculturation, and breast cancer risk in Hispanic women. Cancer Epidemiol Biomarkers Prev. 2005;14(12):2905–2913. doi: 10.1158/1055-9965.EPI-05-0483. [DOI] [PubMed] [Google Scholar]
  • 20.Slattery ML, John EM, Torres-Mejia G, Lundgreen A, Lewinger JP, Stern M, Hines L, Baumgartner KB, Giuliano AR, Wolff RK. Angiogenesis genes, dietary oxidative balance, and breast cancer risk and progression: The breast cancer health disparities study. Int J Cancer. 2013 doi: 10.1002/ijc.28377. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Murtaugh MA, Sweeney C, Giuliano AR, Herrick JS, Hines L, Byers T, Baumgartner KB, Slattery ML. Diet patterns and breast cancer risk in Hispanic and non-Hispanic white women: the Four-Corners Breast Cancer Study. Am J Clin Nutr. 2008;87(4):978–984. doi: 10.1093/ajcn/87.4.978. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Hernandez-Avila M, Romieu I, Parra S, Hernandez-Avila J, Madrigal H, Willett W. Validity and reproducibility of a food frequency questionnaire to assess dietary intake of women living in Mexico City. Salud Publica Mex. 1998;40(2):133–140. doi: 10.1590/s0036-36341998000200005. [DOI] [PubMed] [Google Scholar]
  • 23.Horn-Ross PL, John EM, Lee M, Stewart SL, Koo J, Sakoda LC, Shiau AC, Goldstein J, Davis P, Perez-Stable EJ. Phytoestrogen consumption and breast cancer risk in a multiethnic population: the Bay Area Breast Cancer Study. Am J Epidemiol. 2001;154(5):434–441. doi: 10.1093/aje/154.5.434. [DOI] [PubMed] [Google Scholar]
  • 24.Falush D, Stephens M, Pritchard JK. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics. 2003;164(4):1567–1587. doi: 10.1093/genetics/164.4.1567. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945–959. doi: 10.1093/genetics/155.2.945. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Nyholt DR. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. American journal of human genetics. 2004;74(4):765–769. doi: 10.1086/383251. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Li J, Ji L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity. 2005;95(3):221–227. doi: 10.1038/sj.hdy.6800717. [DOI] [PubMed] [Google Scholar]
  • 28.Coughlan N, Thillainadesan G, Andrews J, Isovic M, Torchia J. beta-Estradiol-dependent activation of the JAK/STAT pathway requires p/CIP and CARM1. Biochim Biophys Acta. 2013;1833(6):1463–1475. doi: 10.1016/j.bbamcr.2013.02.009. [DOI] [PubMed] [Google Scholar]
  • 29.Gupta N, Grebhardt S, Mayer D. Janus kinase 2--a novel negative regulator of estrogen receptor alpha function. Cell Signal. 2012;24(1):151–161. doi: 10.1016/j.cellsig.2011.08.016. [DOI] [PubMed] [Google Scholar]
  • 30.Hong CC, Yao S, McCann SE, Dolnick RY, Wallace PK, Gong Z, Quan L, Lee KP, Evans SS, Repasky EA, et al. Pretreatment levels of circulating Th1 and Th2 cytokines, and their ratios, are associated with ER-negative and triple negative breast cancers. Breast Cancer Res Treat. 2013;139(2):477–488. doi: 10.1007/s10549-013-2549-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Fruhbeck G. Intracellular signalling pathways activated by leptin. Biochem J. 2006;393(Pt 1):7–20. doi: 10.1042/BJ20051578. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Bates SH, Myers MG. The role of leptin-->STAT3 signaling in neuroendocrine function: an integrative perspective. Journal of molecular medicine. 2004;82(1):12–20. doi: 10.1007/s00109-003-0494-z. [DOI] [PubMed] [Google Scholar]
  • 33.Cui Y, Huang L, Elefteriou F, Yang G, Shelton JM, Giles JE, Oz OK, Pourbahrami T, Lu CY, Richardson JA, et al. Essential role of STAT3 in body weight and glucose homeostasis. Mol Cell Biol. 2004;24(1):258–269. doi: 10.1128/MCB.24.1.258-269.2004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Resemann HK, Watson CJ, Lloyd-Lewis B. The Stat3 paradox: a killer and an oncogene. Mol Cell Endocrinol. 2014;382(1):603–611. doi: 10.1016/j.mce.2013.06.029. [DOI] [PubMed] [Google Scholar]
  • 35.Arredondo J, Chernyavsky AI, Jolkovsky DL, Pinkerton KE, Grando SA. Receptor-mediated tobacco toxicity: cooperation of the Ras/Raf-1/MEK1/ERK and JAK-2/STAT-3 pathways downstream of alpha7 nicotinic receptor in oral keratinocytes. Faseb J. 2006;20(12):2093–2101. doi: 10.1096/fj.06-6191com. [DOI] [PubMed] [Google Scholar]
  • 36.Kim SR, Bae MK, Kim JY, Wee HJ, Yoo MA, Bae SK. Aspirin induces apoptosis through the blockade of IL-6-STAT3 signaling pathway in human glioblastoma A172 cells. Biochem Biophys Res Commun. 2009;387(2):342–347. doi: 10.1016/j.bbrc.2009.07.022. [DOI] [PubMed] [Google Scholar]
  • 37.Cao S, Yan Y, Zhang X, Zhang K, Liu C, Zhao G, Han J, Dong Q, Shen B, Wu A, et al. EGF stimulates cyclooxygenase-2 expression through the STAT5 signaling pathway in human lung adenocarcinoma A549 cells. Int J Oncol. 2011;39(2):383–391. doi: 10.3892/ijo.2011.1053. [DOI] [PubMed] [Google Scholar]
  • 38.Walker SR, Xiang M, Frank DA. Distinct roles of STAT3 and STAT5 in the pathogenesis and targeted therapy of breast cancer. Mol Cell Endocrinol. 2014;382(1):616–621. doi: 10.1016/j.mce.2013.03.010. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

10549_2014_3071_MOESM1_ESM

Supplemental Table 1. Summary of genes and tagSNPs analyzed.

Supplemental Table 2. R2 values for SNPs within genes

NIHMS619904-supplement-10549_2014_3071_MOESM1_ESM.docx (62.1KB, docx)

RESOURCES