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. 2020 Sep 15;11(37):3420–3431. doi: 10.18632/oncotarget.27707

SNPs in the interleukin-12 signaling pathway are associated with breast cancer risk in Puerto Rican women

Angel Núñez-Marrero 1, Nelly Arroyo 1, Lenin Godoy 1, Mohammad Zillur Rahman 1, Jaime L Matta 1, Julie Dutil 1,
PMCID: PMC7500104  PMID: 32973967

Abstract

Interleukin-12 (IL-12) is a proinflammatory cytokine that links innate and adaptive immune responses against tumor cells. Single Nucleotide Polymorphisms (SNPs) in IL-12 genes have been associated with cancer risk. However, limited studies have assessed the role of IL-12 in breast cancer (BC) risk comprehensively, and these were done in European and Asian populations. Here, we evaluated the association of the IL-12 signaling pathway and BC risk in Puerto Rican women. A genetic association study was completed with 461 BC cases and 463 non-BC controls. By logistic regression, IL-12 signaling SNPs were associated with an increased BC risk, including rs2243123 (IL12A), rs3761041, rs401502 and rs404733 (IL12RB1), rs7849191 (JAK2), rs280500 (TYK2) and rs4274624 (STAT4). Conversely, other SNPs were associated with reduced BC risk including rs438421 (IL12RB1), rs6693065 (IL12RB2), rs10974947, and rs2274471 (JAK2), rs10168266 and rs925847 (STAT4), and rs2069718 (IFNG). Analyses based in hormone receptors such as estrogen (ER) and progesterone (PR) receptors also revealed protective (for SNPs rs3212227-IL12B; rs3024896 and rs3821236-STAT4) and predisposing (for rs2069705-IFNG SNP) BC associations. Haplotype analysis showed a decreased BC risk for IL12B and STAT4 SNPs, whereas increased risk for IL12RB1 SNPs. This study suggests a role of the IL-12 signaling axis and BC risk. SNPs in this pathway may alter IL-12 induced anti-tumor responses and modulate BC predisposition in a population-specific context. Functional studies will be necessary to confirm these findings, which potentially may benefit IL-12 related immunotherapeutic approaches towards BC.

Keywords: breast cancer, interleukin-12 (IL-12), IL-12 signaling, genetic association, single nucleotide polymorphisms

INTRODUCTION

BC is one of the leading causes of cancer-related deaths among women and constitutes a significant public health burden worldwide [14]. BC is highly heterogeneous both pathologically and molecularly with genetics playing an important role for the diverse outcomes this cancer has across populations [5, 6]. The relevant roles of the immune system in BC have been progressively unveiled [710]. Therefore, immune systems’ related approaches to BC diagnosis or treatment have gained more attention.

Cytokines are the major cell-signaling proteins of both B and T cells (e. g. interferons, interleukins, chemokines). In addition, cytokines are the key modulators of immune system responses against cancer cells. Many studies have directed their efforts to elucidate the role of these proteins in cancer [11]. Interleukin-12 (IL-12) is an important proinflammatory cytokine for the development of effective anti-tumor immune responses [12, 13]. The main anti-tumor actions of IL-12 are promoted by a specific signaling pathway that includes STAT4 and interferon gamma (IFNG) [1416]. Despite abundant IL-12 cancer-related research, the roles of IL-12 in the BC carcinogenesis process remains unclear.

Single Nucleotide Polymorphisms (SNP) in the IL-12 genes, IL12A and IL12B, have been reported to modulate cancer risk across different populations and cancer types [1720]. Specific SNPs can impair the effectiveness of the IL-12 molecule and the overall anti-tumor capacity of IL-12 related immune cells. However, the main focus of most studies has been the IL-12 molecule itself and have not assessed IL-12 comprehensively, in the context of its main signaling pathway through the STAT4 axis. Minimal published information is available for the relationship of IL-12 SNPs and BC risk. The only reported study which found an association of the rs3212227 SNP on the IL12B gene and BC risk was conducted in an European population [21]. This is important given that the relationship between SNPs in immune-related genes and cancer risk varies across populations [22].

Meta-analyses have shown that the cancer risk contribution of IL-12 SNPs varies in the context of the populations’ genetic background [2325]. Research has tried to address how these differences modulate cancer risk. Yet, the vast majority of these genetic studies have been conducted in populations of predominantly European or Asian genetic background [24]. The Puerto Rican (PUR) population is genetically admixed, with a distinctive combination of European, African and Indigenous American, ancestral contributions [26]. Founder effects have also been reported in Puerto Rico, including in BRCA2 in the context of the hereditary breast and ovarian cancer syndrome [27]. The interplay of this diverse genetic makeup, in conjunction with the intrinsic biological heterogeneity of BC, has a relevant impact in the immune system’s biology and its role in cancer.

In this study, we assessed the association of the IL-12 signaling SNPs and BC risk in Puerto Rican women for the first time. Our main goal was to test whether SNPs in candidate genes of the IL-12 signaling pathway were associated with BC risk in this unique population. We hypothesized that SNPs in genes pertaining to this signaling pathway were associated with BC risk in PUR women.

RESULTS

Description of the study population

The study population consisted of 461 BC cases and 463 non-cancer controls after quality control analysis. The description of demographics, hormonal and pregnancy history risk factors for the study population is summarized on Table 1. The BC cases were older than the controls (p < 0.0001), had less education (p < 0.0001) and were more likely to be single or widowed than the controls (p < 0.0001). Several reproductive BC risk factors were also compared between the two groups. Women with BC were had a smaller number of full-term pregnancies and less children than the controls (p < 0.0001). Other reproductive factor such as breastfeeding duration was also significantly different between the cases and the controls, with the controls having longer breast-feeding periods than the cases (p = 0.002). These significantly different variables among cases and controls were considered confounders and further adjusted in the logistic regression analyses. The distribution of the genetic ancestry (European, African and Indigenous American admixture) in cases and non-cancer controls is depicted in Supplementary Figure 1. The tumor characteristics for women with BC is presented in Supplementary Table 1.

Table 1. Characteristics of the breast cancer cases and non-breast cancer controls.

Characteristics Cases Controls p value
Age
n (mean ± SD) 463 (51 ± 12.52) 458 (56.23 ± 5.45) < 0.0001
Civil status < 0.0001
Single n (%) 88 (19.0) 96 (22.0)
Married n (%) 302 (66.0) 233 (54.0)
Widowed n (%) 18 (4.0) 56 (13.0)
Divorced n (%) 52 (11.0) 48 (11.0)
Educational level < 0.0001
8th grade or less n (%) 8 (1.8) 32 (7.8)
9–12 grade n (%) 132 (28.7) 140 (34.5)
Associate degree n (%) 73 (15.9) 72 (17.7)
Bachelor degree or above n (%) 246 (53.6) 162 (40.0)
Occupation < 0.0001
Housewife n(%) 109 (23.5) 175 (38.5)
Works n (%) 263 (57.0) 209 (46.0)
Retired n (%) 90 (19.5) 71 (15.5)
Smoking habits 0.186
Smokers n (%) 42 (9.1) 55 (12.1)
Non-smokers n(%) 417 (90.9) 401 (87.9)
Alcohol Consumption 0.192
Has consumed n (%) 82 (17.9) 66 (14.5)
Never consumed n(%) 376 (82.1) 389 (85.5)
ERT 0.0004
Has used n (%) 124 (27.6) 76 (17.5)
Has never used n (%) 325 (72.4) 359 (82.5)
Number of pregnancies n(mean ± SD) 462 (2 ± 1.79) 451 (3 ± 2.53) < 0.0001
Number of children n(mean ± SD) 462 (1.9 ± 1.36) 454 (2.4 ± 1.81) < 0.0001
Has breastfed 0.372
Yes n (%) 248 (53.9) 218 (50.9)
No n (%) 212 (46.1) 212 (49.3)
Breastfed duration (months) 0.002
n (mean ± SD) 422 (9.2 ± 23.57) 403 (18.69 ± 57.03)

SD: standard deviation; ERT: Estrogen Replacement Therapy.

Association of the IL-12 signaling SNPs with BC in Puerto Rican women

From 115 SNPs tagging the IL-12 signaling axis, 12 were removed because of call rate < 90% (n = 4), poor clustering (n = 3), not in Hardy Weinberg equilibrium (n = 4) or were monoallelic in the studied population (n = 1). Consequently, a total of 103 SNPs remained and were used for further analysis. The list of SNPs tested and relevant information about genomic position is summarized in Supplementary Table 2.

Association between each SNP and BC risk was conducted under four genetic models: allelic, dominant, recessive and additive. A total of 19 SNPs for which the association reached statistical significance (p < 0.05) under at least one of the models were further studied (Supplementary Table 3). Also, 3 additional SNPs that were not significant in the overall analysis reached statistical significance after stratifying by estrogen and or progesterone receptor status. All associations were also modeled using the ancestry proportions as covariates (fraction of an individual’s genome from European, African and Indigenous American origin), but did not impact the observed associations (data not shown).

The IL12A gene SNP rs2243123 T allele was associated with increased BC risk under the dominant genetic model for the crude (OR 1.35, 95% CI: 1.04–1.75, P = 0.02) and for the age and educational level adjusted models (OR 1.34, 95% CI: 1.02–1.77, P = 0.04). IL12RB1 gene resulted with several SNPs associated with BC risk (Supplementary Table 4). SNP rs438421 was significantly associated with BC risk under the allelic model, with A allele having a protective association (OR 0.81, 95% CI: 0.67–0.98, P = 0.03). This rs438421 SNP was also associated with decreased BC risk under the dominant model (crude OR 0.81, 95% CI:0.67–0.98; Age, educational level, ERT and breastfeeding duration adjusted model: OR 0.71, 95% CI: 0.48–1.14, P = 0.03). Similarly, this rs438421 SNP also resulted in significant risk reduction with BC under the additive model (crude OR 0.81, 95% CI:0.67–0.98, P = 0.03; adjusted OR 0.78, 95% CI: 0.54–0.91, P = 0.03). For the IL12RB1 gene SNPs rs3761041 and rs401502 were also associated with increased BC risk under the recessive genetic model (Supplementary Table 4). Lastly for this IL12RB1 gene, the rs404733 SNP was associated with increased BC risk under the allelic (adjusted OR 1.26, 95% CI: 1.03–1.54, P = 0.02) and additive (adjusted OR 1.24, 95% CI: 1.02–1.51, P = 0.03).

SNP rs6693065 on the IL-12 receptor gene IL12RB2 was associated with reduced BC risk under the dominant model (OR 0.74, 95% CI: 0.58–0.96, P = 0.03).

JAK2 gene SNP rs2274471 (OR 0.78, 95% CI: 0.63–0.96, P = 0.02), was found to have a protective association under the allelic, dominant, additive and recessive models (Supplementary Table 4). This is a non-receptor tyrosine kinase implicated in signaling by members of the type II cytokine receptor family. From these the most significant associations were for the additive (adjusted OR 0.70, 95% CI:0.54–0.91, P = 0.007) and recessive (adjusted OR 0.39, 95% CI: 0.19–0.76, P = 0.007) models. Another JAK2 SNP; rs10974947 was associated with a protective effect under the dominant model (adjusted OR 0.73, 95% CI: 0.55–0.97, P = 0.01). In contrast, SNP rs7849191 (JAK2) resulted in an increased BC risk under the dominant model (adjusted OR 1.49, 95% CI: 1.05–2.13, P = 0.02). The other tyrosine kinase IL-12 signaling gene TYK2 was also significantly associated with BC risk, with SNP rs280500 increasing BC risk under the allelic (crude OR 1.36, 95% CI: 1.03–1.79, P = 0.03), additive (crude OR 1.35, 95% CI: 1.03–1.77, P = 0.02) and recessive (crude OR 2.10, 95% CI: 1.06–4.39, P = 0.04) models (Table 2). STAT4 SNPs were also associated with BC in the study population. The rs4274624 in the STAT4 gene resulted in decreased risk under the allelic (crude OR 0.80, 95% CI: 0.64–0.99, P = 0.04), dominant (crude OR 0.74, 95% CI: 0.57–0.96, P = 0.02) and the additive (crude OR 0.78, 95% CI: 0.62–0.99, P = 0.04) genetic models. SNP rs10168266 (STAT4) was associated with reduced BC risk under the crude and adjusted dominant model (Table 2). The rs925847 SNP on the STAT4 gene had also a protective association under the additive model (crude OR 0.81, 95% CI:0.67–0.99, P = 0.04). SNP rs7599504 (STAT4) was associated with an increased BC risk under the recessive genetic model (Supplementary Table 4).

Table 2. Stratification analysis for ER positive and PR positive/negative tumors associations between selected IL-12 signaling SNPs and BC risk in Puerto Rican women (n = 224).

Gene SNP Genetic model P value unstratified crude Crude OR stratified (95% CI) Pvalue Adjusted OR stratified1 (95% CI) Pvalue Adjusted OR stratified2 (95% CI) P value
IL12B rs3212227 Dominant 0.09 0.69 (0.50–0.73) 0.02 0.70 (0.50–0.98) 0.04 0.67 (0.46–0.97) 0.03
Additive 0.32 0.74 (0.57–0.96) 0.02 0.76 (0.58–0.99) 0.04 0.75 (0.56–1.01) 0.06
IL12RB1 rs438421 Dominant 0.04 0.66 (0.48–0.92) 0.01 0.65 (0.46–0.92) 0.01 0.62 (0.42–0.91) 0.01
Additive 0.03 0.76 (0.60–0.96) 0.02 0.76 (0.59–0.96) 0.02 0.75 (0.57–0.98) 0.03
IL12RB2 rs6693065 Dominant 0.04 0.66 (0.47–0.91) 0.01 0.67 (0.47–0.95) 0.02 0.63 (0.43–0.91) 0.01
Additive 0.07 0.54 (0.42–0.68) 0.19 0.84 (0.65–1.08) 0.17 0.74 (0.56–0.98) 0.04
JAK2 rs10974947 Dominant 0.03 0.67 (0.48–0.93) 0.02 0.68 (0.48–0.96) 0.03 0.66 (0.45–0.97) 0.04
Additive 0.07 0.73 (0.56–0.96) 0.03 0.75 (0.56–1.00) 0.05 0.72 (0.52–0.99) 0.05
rs2274471 Dominant 0.03 0.65 (0.46–0.90) 0.01 0.66 (0.46–0.93) 0.02 0.69 (0.47–1.00) 0.05
Additive 0.02 0.68 (0.52–0.90) 0.007 0.66 (0.49–0.88) 0.005 0.65 (0.47–0.89) 0.009
Recessive 0.16 0.54 (0.25–1.07) 0.09 0.35 (0.13–0.80) 0.02 0.25 (0.07–0.65) 0.01
TYK2 rs280500 Recessive 0.04 2.70 (1.26–5.99) 0.01 2.78 (1.19–6.52) 0.02 3.00 (1.20–7.62) 0.02
IFNG rs2069718 Dominant 0.12 0.73 (0.51–1.04) 0.08 0.65 (0.44–0.94) 0.02 0.58 (0.39–0.88) 0.01
Additive 0.04 0.76 (0.60–0.96) 0.02 0.70 (0.54–0.89) 0.004 0.68 (0.52–0.89) 0.005
Recessive 0.08 0.65 (0.43–0.98) 0.04 0.59 90.37–0.90) 0.02 0.62 (0.35–0.99) 0.05

OR: Odds ratio; CI: confidence interval; ER: Estrogen receptor; PR: progesterone receptor; neg: negative. 1Adjusted for Age and Educational level; 2Adjusted for Age, Educational level, ERT and Breastfeeding duration.

Interferon gamma is the main downstream effector of the IL-12 signaling. SNP rs2069718 on this gene was found to be associated with BC risk reduction under the allelic, dominant, additive and recessive genetic models (Supplementary Table 4). From these, the allelic (adjusted OR 0.75, 95% CI: 061–0.91, P = 0.004) and additive (adjusted OR 0.74, 95% CI: 0.60–0.91, P = 0.004) models resulted in the most significant associations. TBX21 gene SNP rs2158079 was associated with increased BC risk under the recessive model (Crude OR 2.30, 95% CI: 1.06–3.56, P = 0.04). Finally, PIAS2 SNPs rs10502878 and rs9304337 were associated with reduced BC risk (Supplementary Table 4). The rs2156049 SNP on PIAS2 gene was associated with increased BC risk. The overall results for the association analysis for all of the 103 SNPs is presented in Supplementary Table 3.

Stratifying analysis by Estrogen and Progesterone receptor status

BC GWAS studies have uncovered separate susceptibility loci for risk according to tumor characteristics, especially ER and PR status [2833]. A stratified analysis focusing on the expression of the ER and PR was completed to verify for associations of the IL-12 signaling SNPs and BC risk. From this analysis, new associations emerged. The IL12B rs3212227 SNP was found to be associated with a reduced BC risk in a subset of luminal A and B BC tumors (ER positive and PR positive or negative) under the dominant and additive models (Table 2). In addition, a stronger association was observed for SNPs rs438421 (IL12RB1), rs6693065 (IL12RB2), rs10974947 and rs2274471 (JAK2), rs280500 (TYK2) and rs2069718 (IFNG) after stratifying for luminal tumors (Table 2). Similarly, when assessing the association for the ER negative and PR negative tumors subset, a stronger association was observed for the rs401502 (IL12RB1) under the recessive model (Table 3). In addition, SNPs such as 3024896 and rs3821236 (STAT4) were associated with a decreased BC risk in the receptor negative tumors (Table 4). Also, the rs2069705 SNP in the IFNG gene was significantly associated with an increased BC risk only for the receptor negative tumors subset (Table 3).

Table 3. Stratification analysis for ER negative and PR negative associations between selected IL-12 signaling SNPs and BC risk in Puerto Rican women (n = 80).

Gene SNP Genetic model P value unstratified Crude OR stratified (95% CI) P value Adjusted OR stratified1(95% CI) P value Adjusted OR stratified2(95% CI) P value
IL12RB1 rs401502 Recessive 0.009 2.92 (1.08–7.19) 0.02 3.54 (1.28–9.04) 0.01 4.14 (1.35–11.82) 0.009
STAT4 rs3024896 Dominant 0.07 0.58 (0.37–0.99) 0.05 0.58 (0.32–1.00) 0.06 0.47 (0.22–0.92) 0.04
Additive 0.08 0.59 (0.35–0.95) 0.04 0.59 (0.34–0.97 0.05 0.46 (0.23–0.86) 0.02
rs3821236 Dominant 0.08 0.59 (0.36–0.95) 0.03 0.59 (0.55–0.97) 0.04 0.62 (0.34–1.11) 0.11
IFNG rs2069705 Additive 0.30 1.34 (0.95–1.90) 0.09 1.45 (1.01–2.09) 0.04 1.48 (0.97–2.26) 0.07
Recessive 0.29 1.79 (0.97–3.18) 0.05 2.15 (1.15–3.91) 0.01 2.35 (1.14–4.66) 0.02

OR: Odds ratio; CI: confidence interval; ER: Estrogen receptor; PR: progesterone receptor; neg: negative. 1Adjusted for Age and Educational level; 2Adjusted for Age, Educational level, ERT and Breastfeeding duration.

Table 4. Haplotype association with BC risk of selected IL-12 signaling gene SNPs.

Gene
IL12B rs3212227 rs2546892 rs2569254 rs3181216 rs730691 Frequency OR (95% CI); pvalue
 1 T G C A T 0.27 Ref.
 2 G G C A T 0.13 0.62 (0.42–0.91); 0.01
IL12RB1 rs401502 rs3761041 rs404733 rs438421 Frequency OR (95% CI); pvalue
 1 C C A A 0.29 Ref.
 2 C C T G 0.25 1.36 (1.04–1.78); 0.03
 3 C T A G 0.02 2.97 (1.04–8.51); 0.04
STAT4 rs10168266 rs4274624 rs925847 Frequency OR (95% CI); pvalue
 1 C C T 0.49 Ref.
 2 T T C 0.24 0.57 (0.39–0.84); 0.004

OR: Odds ratio; CI: confidence interval.

Haplotype analysis was also performed for all candidate genes of the IL-12 signaling pathway. The IL12B gene a BC risk reduction was evident for the rs3212227G-rs2546892G-rs2569254C-rs3181216A-rs730691T genotypes when compared to the TGCAT genotype (OR 0.62, 95% CI: 0.42–0.91, P = 0.01) as seen in Table 4. In addition, the haplotype estimation for specific STAT4 gene SNPs rs10168266T-rs4274624T-rs925847C genotypes resulted in a protective association when compared to the CCT genotype (OR 0.57, 95% CI:0.39–0.84, P = 0.004). On the other hand, the haplotype analysis for the IL12RB1 SNP rs401502C-rs3761041C-rs404733T-rs438421G showed an increased BC risk when compared to the CCAA genotype (OR 1.36, 95% CI:1.04–1.78, P = 0.03). Similarly, the rs401502C-rs3761041T-rs404733A-rs438421G haplotype resulted in an increased BC risk when compared to the CCAA genotype (OR 2.97, 95% CI: 1.04–8.51, p = 0.04; Table 4).

DISCUSSION

IL-12 is a strong proinflammatory cytokine that induces a signaling pathway in which STAT4 and IFNG are key for anti-tumor responses [12, 34, 35]. Immune cells that are closely related to IL-12 signaling, predominantly T cells, can infiltrate breast tumors and induce effective anti-tumor actions [36, 37]. No comprehensive analyses focusing on the role of genetic variation in IL-12 signaling genes in relation to BC risk exists. Furthermore, the limited available studies have been completed within European or Asian populations, which do not fully encompass the genetic diversity that characterizes human populations. In this study we report for the first-time association between SNPs in IL-12 signaling genes and BC risk in Puerto Rican women. We have identified associations of SNPs in several core genes of the IL-12 signaling axis and BC risk in Puerto Rican women, including some that have been reported in other populations, such as IL12A, IL12B, IL12RB1, TYK2, JAK2 and STAT4. In the era of precision medicine and immunotherapy, having specific information on how inherited genetics modulates immune responses towards cancer is especially desirable.

Beyond the IL-12 genes, SNPs distributed along the IL-12 signaling pathway resulted significantly associated with BC risk in this study-population. From these, the most notable associations were observed for the SNPs in the IL12 receptor genes, IL12RB1 (rs401502, rs404733 and rs438421) and IL12RB2 (rs6693065), the tyrosine kinases genes TYK2 (rs280500) and JAK2 (rs2274471), STAT4 (rs10168266, rs4274624 and rs925847) and IFNG (rs2069718). In addition, the SNPs in genes IL12B (rs3212227), STAT4 (rs3024896 and rs3821236) and IFNG (rs2069705) were also noticeably associated with BC risk when stratifying by hormone receptor status. Haplotype analysis also contributed to identifying associations in IL12B, Il12RB1 and STAT4.

Previously, IL-12-related genetic associations studies have centered their focus specifically in the heterodimeric IL-12 molecule (encoded by the IL12A and IL12B genes). Several meta-analyses have summarized the associations of these IL-12 genes and cancer risk [23, 24, 38]. For the IL12A gene, the SNPs rs568048 has been one of the most published [20, 38]. This SNP was not found to be associated with BC risk in our study population. However, we found an association of the IL12A rs2243123 SNP and BC risk, which to our knowledge is the first report for BC. Ter-Minassian and colleagues reported an association of IL12A with neuroendocrine cancer [39]. The IL12B gene, which codes for the IL12 p40 subunit has been the most studied IL-12 related-gene in cancer genetic association analyses. A high number of studies have evaluated the relationship of the rs3212227 SNP in IL12B with cancer risk. Cervical, hepatic, colorectal, gastric and head and neck cancers are the most common cancers associated with the IL12B rs3212227 SNP [23]. The direction of rs3212227 association has been shown to be inconsistent, indicating that SNP it is not likely to be the causative variant underlying this association. There was one report of the association of IL12B SNP rs3212227 and BC risk in the Croatian population [21]. Recent meta-analyses identified an increased risk of this rs3212227 with several cancers for populations of European ancestry [23] In contrast, the rs3212227 SNP has been mostly associated with reduced cancer risk, including BC, among populations of Asian ancestry [23]. In our population we only found a significant association of the rs3212227 SNP and BC risk only after stratifying the cases by hormonal receptor status. Causative variants in IL12 may either through altered levels of the protein or structural may result in an attenuated IL-12 signal, given that IL12p40 (IL12B) plays the major role in the IL-12 signaling cascade initiation [40, 41]. Because of their central role in the activation of the IL-12 pathway, the IL12 genes have received the majority of attention. However, a more in-depth information of the contribution of several SNPs in a pathway-context has been proved to be effective to assess cancer predisposition in other studies [42].

The IL12RB1 and IL12RB2 genes code for the functional IL-12 receptor which increases its expression upon IL-12 binding, in NK cells, cytotoxic T cells and Th1 helper T cells [15, 43]. For the first time, we report a genetic association of the IL-12 receptor subunits-genes and BC risk in the Puerto Rico population. Previous studies have found associations of IL12RB1 and IL12RB2 gene-SNPs with cervical, vulvar and head and neck cancers [25, 44]. In the current study, the association of IL12RB1 rs401502 and BC was more pronounced in hormonal receptor negative tumors. In addition, rs438421 was significantly associated with lower BC risk under all genetic models assessed. Currently, the molecular mechanisms underlying these associations remain unknown, and it cannot be excluded that the observed associations are the results of other variants located in close proximity. It is possible that certain variants increase risk through a loss-of-function effect, resulting in reduced IL-12 signal transduction in related immune cells. In contrast, protective variants may trigger an enhanced IL-12R activity thereby promoting IFNG production predominantly in cytotoxic T cells. We also report the association of IL12RB2 rs6693065 with reduced BC risk in the Puerto Rico population. The IL12 beta 2 subunit of the IL-12R, plays a central role in the signaling pathway leading to the development of anti-tumor Th1 T cells [45, 46]. SNPs in the IL12RB2 gene associated with BC reduction may enhance IL-12 signaling pathway in cytotoxic T cells and promote Th1 cell differentiation, both of which promote anti-tumor actions at tumor sites.

Further downstream in the IL-12 signaling pathway, the Janus kinases TYK2 and JAK2 target STAT4 for further activation. Here we found that TYK2 rs280500 was significantly associated with increased BC risk, independently of BC subtype. This SNP has previously been found to be associated with colorectal cancer [47], and the risk for several autoimmune diseases such as colitis [48]. Other studies have linked the contribution of variants in TYK2 with cancers, including BC [49, 50]. Interestingly, the role of TYK2 is not restricted to the IL-12 signaling cascade as this gene is known to crosstalk with other immune and cellular proliferation pathways [51]. Therefore, this association may result from functional variants in TYK2 that either promote a reduced function and consequently a less effective signaling of anti-tumor cytokines such as IL12, or increase the tyrosine kinase activation of molecules that are related to proliferation and sustained proinflammatory responses. The JAK2 kinase is also involved in other cytokines signaling besides the IL-12. SNPs in the JAK2 gene have been associated with cancer risk, such as cervical, gastric, and prostate cancers [5254]. In this study, the rs2274471 SNP in JAK2 was also associated with BC risk in Puerto Rican women. Slattery et al., 2013, reported a protective association of rs2274471 with colon-rectal cancer risk [47]. Although this SNP is novel for BC risk, other studies have also found associations between rs2274471 and cancer risks, including prostate and colorectal cancers [47, 55]. JAK2 SNPs related to protective association with BC may promote a more efficient signaling pathway and the eventual activation of STAT4. This in turn may enhance IFNG secretion and the anti-tumor actions that this molecule promotes.

STAT4 is the main IL-12 message-transducer molecule of the IL-12 pathway, and STAT4 gene variants have been implicated with an increased risk of several cancers, including stomach, colon, lung and breast [50, 56]. This gene has also been associated with autoimmune and inflammatory diseases such as lupus erythematous, rheumatoid arthritis, psoriasis, colitis among others [57, 58]. In this study we found a significant association of the rs4274624 STAT4 SNP and BC risk. This is the first published study finding a protective BC risk association for this SNP. Previously, this SNP was reported to be associated with higher hepatitis B-related cancer risk [59]. We also report a significant association of the IFNG gene SNP rs2069718 and BC in our cohort, which has been previously associated with autoimmune disease risk [60]. Specific SNPs in STAT4 and IFNG could be key for the promotion of the IL-12 pathway anti-tumor actions in NK, macrophages and T cells.

While this study presents the first comprehensive analysis on the role of inherited variants in the genes of the IL-12 signaling axis and BC risk, it does present some limitations. First, the cases and non-cancer controls have not been matched and came from different clinical settings. While such factors can be accounted for in the regression analysis, this method is not as robust as working from a matched set. For instance, we observed that the associations of rs404733 in the IL12RB1 gene SNP was only significant after correcting for education level, which is used as a measure of socio-economic status. There was an imbalance in the education levels between controls and cases, which may be a consequence of recruitment sites. Controls, are identified in family medicine and internal medicine clinics, where patients with chronic diseases would be followed up. The IL12 cytokine family have been implicated in cardiovascular diseases including atherosclerosis, stroke and myocardial infarction [61]. Type II diabetes progression has also been linked to dysregulation of the IL12/STAT4 axis [62]. It is therefore possible that the association between genetic variants in IL12RB1 and breast cancer be confounded by education level as a result of an increased prevalence of chronic diseases in the control group, due to recruitment strategies. Second, given that there was not strict correction for multiple comparisons, the limited samples size of our cohort and magnitude of P values observed, our result will need to be validated in other cohorts of similar ancestral composition or further functional work will be needed to validate these associations.

In conclusion, this study supports a role for the IL-12 axis in the genetic predisposition of BC. It is possible that inherited variations that result in modulation of the IL-12 pathway, either through structural or regulatory changes participate in shaping the anti-tumor immune responses that are promoted by this cytokine. Functional genetic studies will be required to elucidate the mechanism through which these variants impact BC risk. Understanding the genetic basis to cancer risk, including in understudied populations, is a key element in identifying individuals or populations at higher cancer risk that require increased surveillance. This work may also contribute to promote IL-12-related targeted immunotherapeutic approaches.

MATERIALS AND METHODS

Ethics statement

The methodology of this study was conducted following the principles included in the Helsinki Declaration.

Study population

The Ponce Health Sciences University Institutional Review Board approved this study (IRB # 070918-JD and #130207-JM). Each participant signed an Informed Consent form, providing permission to collect a blood sample and to review their pathology reports. All participants completed a 7-page epidemiological questionnaire requesting demographic variables. Participants in this study were selected from a BC cohort recruited from 2006 to 2012 as described in Matta et al. 2012 [63]. Out of these, the final study-population consisted of 924 Puerto Rican women (461 cases and 463 controls). All participants were 21 years or older. Cases were recruited through Oncologic hospitals and private physician offices (oncologists and surgeons) and were recently diagnosed (prior to initiation of any form of therapy such as chemotherapy, radiotherapy, surgery) with a histopathologically confirmed breast carcinoma. Only cases with primary and metastatic breast carcinoma tumors, rather than secondary to other type of cancer, are included. The pathology report from each patient is obtained to know the tumor grade, tumor size, presence of axillary’s lymph node metastasis, and other clinically relevant information. The cases were also screened for BRCA1/BRCA2 mutations in order to exclude those who were BRCA1/2-positive. The non-cancer Controls were recruited from individuals visiting gynecological and primary care medical offices for their routine mammography. All controls had a negative mammography within the last 6 months and a clinical breast examination during that period. Any potential participants (cases or non-cancer controls) with known autoimmune conditions was excluded from the current study. All the qualified participants completed a questionnaire including demographics information, hormonal and pregnancy history as well as life-style related questions. The participants also provided a venous blood sample for DNA extraction.

Tag SNP selection and genotyping

The tag SNPs in the IL-12 signaling pathway genes of interest were selected by a bioinformatics approach. We used the University of California Santa Cruz Genome Browser (UCSC) to obtain the candidate-gene position [64]. Information on the IL-12 signaling candidate-gene SNPs for the Puerto Rican-population was obtained from the 1000 Genomes Browser [65]. The tag SNPs were selected using Haploview [66]. Tag SNPs were selected with the following criteria: (1) SNPs located in one of the eleven genes comprising the IL-12 signaling (Supplementary Table 4); (2) each SNP had a MAF ≥ 0.1 in the Puerto Rican population; (3) each SNP had a r2 ≥ 0.8. A total of 115 tag SNPs in the eleven IL-12 signaling were selected for genotyping. High quality extracted DNA was only included for the genotyping assay. Multiplexed SNP Mass EXTENDED assay was designed using Sequenom MassArray Assay Design 3.0 Software. Sequenom MassARRAY RS1000 was used for genotyping [67]. A set of 106 single nucleotide polymorphisms (SNPs) that can discriminate indigenous American, African, and European ancestry was used to estimate the proportion of genetic ancestry in cases and non-cancer controls was genotype on the same platform. This panel has been described previously [68].

Genotyping analysis quality control

SNPs-tests with call rates lower than 90% were excluded. All final analyzed tSNPs were in Hardy Weinberg Equilibrium (HWE). Individual samples with genotyping calling rates lower than 70% were excluded from the final analysis. Concordance of duplicated samples was assessed with Plink and these were also eliminated from the final analysis [69]. Global ancestry proportions were estimated from AIMs using ADMIXTURE under a supervised model at k = 3. Reference populations consisted of 42 Europeans (Coriell’s North American Caucasian panel), 37 West Africans (non-admixed Africans living in London, United Kingdom, and South Carolina), and 30 indigenous Americans (15 Mayans and 15 Nahuas) [70].

Statistical analysis

The genotype-phenotype association was evaluated using the dominant, allelic, additive and recessive genetic models [71]. Frequency distributions differences were calculated by Pearson’s chi-square test. R version 3.2.1 implemented in R Studio was used for the statistical analyses [72]. Haplotype analysis was done for the dominant model using SNPStats software [73]. Associations between the candidate-gene SNPs and BC risk were estimated by obtaining the odds ratio (OR) and the 95% confidence interval by logistic regression. In the analysis we corrected for BC confounders. A p-value < 0.05 was considered statistically significant based on a two-tailed test.

SUPPLEMENTARY MATERIALS

ACKNOWLEDGMENTS

The authors acknowledge the patients and participants of the study, Wanda Vargas and Marta Báez for their valuable contributions.

Abbreviations

BC

Breast cancer

SNP

Single Nucleotide Polymorphism

IFNG

Interferon gamma

PUR

Puerto Rican

OR

Odds ratio

CI

Confidence interval

HEW

Hardy-Weiberg equilibrium

Author contributions

Conception and design: ANM and JD. JM and JD recruited the study participants. ANM, NA and LG performed the laboratory work. Data acquisition QC and analysis: ANM, MZR and JD completed the analysis. Original draft: ANM. Manuscript revision: JM and JD. All co-authors have agreed with the final manuscript.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

FUNDING

This work was supported by grants 1SC1A182845 (National Institute of Health (NIH), National Institute of General Medical Sciences (NIGMS)), the Ponce Health Sciences University-Moffitt Cancer Center PACHE Partnership U54 CA163071 (NIH, National Cancer Institute (NCI)), with the technical support of the Ponce Research Institute Molecular and Genomics Core (M.A.G.I.C.) (NIH- National Institute on Minority Health and Health Disparities (NIMHD) MD007579). Recruitment of participants was also supported by grants S06GM008239-20, 9SC1CA182846 and 5SC1CA157250. Angel Núñez-Marrero received a NIH-NIGMS fellowship (R25GM082406).

REFERENCES

  • 1. The Global Cancer Observatory. Cancer fact sheet. World Heal Organ. 2018. https://gco.iarc.fr/today/data/factsheets/cancers/39-All-cancers-fact-sheet.pdf. [Google Scholar]
  • 2. Fitzmaurice C, Allen C, Barber RM, Barregard L, Bhutta ZA, Brenner H, Dicker DJ, Chimed-Orchir O, Dandona R, Dandona L, Fleming T, Forouzanfar MH, Hancock J, et al. , and Global Burden of Disease Cancer Collaboration. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-years for 32 Cancer Groups, 1990 to 2015: A Systematic Analysis for the Global Burden of Disease Study. JAMA Oncol. 2017; 3:524–48. 10.1001/jamaoncol.2016.5688. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Brawley OW. Health disparities in breast cancer. Obstet Gynecol Clin North Am. 2013; 40:513–23. 10.1016/j.ogc.2013.06.001. [DOI] [PubMed] [Google Scholar]
  • 4. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018; 68:394–424. 10.3322/caac.21492. [DOI] [PubMed] [Google Scholar]
  • 5. Dai X, Li T, Bai Z, Yang Y, Liu X, Zhan J, Shi B. Breast cancer intrinsic subtype classification, clinical use and future trends. Am J Cancer Res. 2015; 5:2929–43. [PMC free article] [PubMed] [Google Scholar]
  • 6. Shiovitz S, Korde LA. Genetics of breast cancer: a topic in evolution. Ann Oncol. 2015; 26:1291–99. 10.1093/annonc/mdv022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Palucka AK, Coussens LM. The Basis of Oncoimmunology. Cell. 2016; 164:1233–47. 10.1016/j.cell.2016.01.049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Law AM, Lim E, Ormandy CJ, Gallego-Ortega D. The innate and adaptive infiltrating immune systems as targets for breast cancer immunotherapy. Endocr Relat Cancer. 2017; 24:R123–44. 10.1530/ERC-16-0404. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Dahlberg CI, Sarhan D, Chrobok M, Duru AD, Alici E. Natural killer cell-based therapies targeting cancer: possible strategies to gain and sustain anti-tumor activity. Front Immunol. 2015; 6:605. 10.3389/fimmu.2015.00605. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Migali C, Milano M, Trapani D, Criscitiello C, Esposito A, Locatelli M, Minchella I, Curigliano G. Strategies to modulate the immune system in breast cancer: checkpoint inhibitors and beyond. Ther Adv Med Oncol. 2016; 8:360–74. 10.1177/1758834016658423. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Dranoff G. Cytokines in cancer pathogenesis and cancer therapy. Nat Rev Cancer. 2004; 4:11–22. 10.1038/nrc1252. [DOI] [PubMed] [Google Scholar]
  • 12. Tugues S, Burkhard SH, Ohs I, Vrohlings M, Nussbaum K, Vom Berg J, Kulig P, Becher B. New insights into IL-12-mediated tumor suppression. Cell Death Differ. 2015; 22:237–46. 10.1038/cdd.2014.134. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Yan J, Smyth MJ, Teng MW. Interleukin (IL)-12 and IL-23 and Their Conflicting Roles in Cancer. Cold Spring Harb Perspect Biol. 2018; 10:a028530. 10.1101/cshperspect.a028530. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Del Vecchio M, Bajetta E, Canova S, Lotze MT, Wesa A, Parmiani G, Anichini A. Interleukin-12: biological properties and clinical application. Clin Cancer Res. 2007; 13:4677–85. 10.1158/1078-0432.CCR-07-0776. [DOI] [PubMed] [Google Scholar]
  • 15. Vignali DA, Kuchroo VK. IL-12 family cytokines: immunological playmakers. Nat Immunol. 2012; 13:722–28. 10.1038/ni.2366. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Overacre-Delgoffe AE, Chikina M, Dadey RE, Yano H, Brunazzi EA, Shayan G, Horne W, Moskovitz JM, Kolls JK, Sander C, Shuai Y, Normolle DP, Kirkwood JM, et al. Interferon-γ Drives Treg Fragility to Promote Anti-tumor Immunity. Cell. 2017; 169:1130–1141.e11. 10.1016/j.cell.2017.05.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Chen X, Han S, Wang S, Zhou X, Zhang M, Dong J, Shi X, Qian N, Wang X, Wei Q, Shen H, Hu Z. Interactions of IL-12A and IL-12B polymorphisms on the risk of cervical cancer in Chinese women. Clin Cancer Res. 2009; 15:400–05. 10.1158/1078-0432.CCR-08-1829. [DOI] [PubMed] [Google Scholar]
  • 18. Han SS, Cho EY, Lee TS, Kim JW, Park NH, Song YS, Kim JG, Lee HP, Kang SB. Interleukin-12 p40 gene (IL12B) polymorphisms and the risk of cervical cancer in Korean women. Eur J Obstet Gynecol Reprod Biol. 2008; 140:71–75. 10.1016/j.ejogrb.2008.02.007. [DOI] [PubMed] [Google Scholar]
  • 19. Ebadi N, Jahed M, Mivehchi M, Majidizadeh T, Asgary M, Hosseini SA. Interleukin-12 and interleukin-6 gene polymorphisms and risk of bladder cancer in the Iranian population. Asian Pac J Cancer Prev. 2014; 15:7869–73. 10.7314/APJCP.2014.15.18.7869. [DOI] [PubMed] [Google Scholar]
  • 20. Yuzhalin AE, Kutikhin AG. Interleukin-12: clinical usage and molecular markers of cancer susceptibility. Growth Factors. 2012; 30:176–91. 10.3109/08977194.2012.678843. [DOI] [PubMed] [Google Scholar]
  • 21. Kaarvatn MH, Vrbanec J, Kulic A, Knezevic J, Petricevic B, Balen S, Vrbanec D, Dembic Z. Single nucleotide polymorphism in the interleukin 12B gene is associated with risk for breast cancer development. Scand J Immunol. 2012; 76:329–35. 10.1111/j.1365-3083.2012.02736.x. [DOI] [PubMed] [Google Scholar]
  • 22. Quan L, Gong Z, Yao S, Bandera EV, Zirpoli G, Hwang H, Roberts M, Ciupak G, Davis W, Sucheston L, Pawlish K, Bovbjerg DH, Jandorf L, et al. Cytokine and cytokine receptor genes of the adaptive immune response are differentially associated with breast cancer risk in American women of African and European ancestry. Int J Cancer. 2014; 134:1408–21. 10.1002/ijc.28458. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Zheng Y, Wang M, Tian T, Liu K, Liu X, Zhai Y, Lin S, Yang P, Li S, Dai Z, Lu J. Role of interleukin-12 gene polymorphisms in the onset risk of cancer: a meta-analysis. Oncotarget. 2017; 8:29795–807. 10.18632/oncotarget.16080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Shi X, Jia Y, Xie X, Li S. Single-nucleotide polymorphisms of the IL-12 gene lead to a higher cancer risk: a meta-analysis based on 22,670 subjects. Genes Genet Syst. 2018; 92:173–87. 10.1266/ggs.16-00024. [DOI] [PubMed] [Google Scholar]
  • 25. Zhou L, Yao F, Luan H, Wang Y, Dong X, Zhou W, Wang Q. Functional polymorphisms in the interleukin-12 gene contribute to cancer risk: evidence from a meta-analysis of 18 case-control studies. Gene. 2012; 510:71–77. 10.1016/j.gene.2012.08.019. [DOI] [PubMed] [Google Scholar]
  • 26. Via M, Gignoux CR, Roth LA, Fejerman L, Galanter J, Choudhry S, Toro-Labrador G, Viera-Vera J, Oleksyk TK, Beckman K, Ziv E, Risch N, Burchard EG, Martínez-Cruzado JC. History shaped the geographic distribution of genomic admixture on the island of Puerto Rico. PLoS One. 2011; 6:e16513. 10.1371/journal.pone.0016513. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Dutil J, Colon-Colon JL, Matta JL, Sutphen R, Echenique M. Identification of the prevalent BRCA1 and BRCA2 mutations in the female population of Puerto Rico. Cancer Genet. 2012; 205:242–48. 10.1016/j.cancergen.2012.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Ferreira MA, Gamazon ER, Al-Ejeh F, Aittomäki K, Andrulis IL, Anton-Culver H, Arason A, Arndt V, Aronson KJ, Arun BK, Asseryanis E, Azzollini J, Balmaña J, et al. , and EMBRACE Collaborators, and GC- HBOC Study Collaborators, and GEMO Study Collaborators, and ABCTB Investigators, and HEBON Investigators, and BCFR Investigators. Genome-wide association and transcriptome studies identify target genes and risk loci for breast cancer. Nat Commun. 2019; 10:1741. 10.1038/s41467-018-08053-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Milne RL, Kuchenbaecker KB, Michailidou K, Beesley J, Kar S, Lindström S, Hui S, Lemaçon A, Soucy P, Dennis J, Jiang X, Rostamianfar A, Finucane H, et al. , and ABCTB Investigators, and EMBRACE, and GEMO Study Collaborators, and HEBON, and kConFab/AOCS Investigators, and NBSC Collaborators. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nat Genet. 2017; 49:1767–78. 10.1038/ng.3785. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Lindström S, Thompson DJ, Paterson AD, Li J, Gierach GL, Scott C, Stone J, Douglas JA, dos-Santos-Silva I, Fernandez-Navarro P, Verghase J, Smith P, Brown J, et al. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk. Nat Commun. 2014; 5:5303. 10.1038/ncomms6303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Ghoussaini M, Fletcher O, Michailidou K, Turnbull C, Schmidt MK, Dicks E, Dennis J, Wang Q, Humphreys MK, Luccarini C, Baynes C, Conroy D, Maranian M, et al. , and Netherlands Collaborative Group on Hereditary Breast and Ovarian Cancer (HEBON), and Familial Breast Cancer Study (FBCS), and Gene Environment Interaction of Breast Cancer in Germany (GENICA) Network, and kConFab Investigators, and Australian Ovarian Cancer Study Group. Genome-wide association analysis identifies three new breast cancer susceptibility loci. Nat Genet. 2012; 44:312–18. 10.1038/ng.1049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Michailidou K, Beesley J, Lindstrom S, Canisius S, Dennis J, Lush MJ, Maranian MJ, Bolla MK, Wang Q, Shah M, Perkins BJ, Czene K, Eriksson M, et al. , and BOCS, and kConFab Investigators, and AOCS Group, and NBCS, and GENICA Network. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer. Nat Genet. 2015; 47:373–80. 10.1038/ng.3242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Turnbull C, Ahmed S, Morrison J, Pernet D, Renwick A, Maranian M, Seal S, Ghoussaini M, Hines S, Healey CS, Hughes D, Warren-Perry M, Tapper W, et al. , and Breast Cancer Susceptibility Collaboration (UK). Genome-wide association study identifies five new breast cancer susceptibility loci. Nat Genet. 2010; 42:504–07. 10.1038/ng.586. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Lasek W, Zagożdżon R, Jakobisiak M. Interleukin 12: still a promising candidate for tumor immunotherapy? Cancer Immunol Immunother. 2014; 63:419–35. 10.1007/s00262-014-1523-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Kerkar SP, Goldszmid RS, Muranski P, Chinnasamy D, Yu Z, Reger RN, Leonardi AJ, Morgan RA, Wang E, Marincola FM, Trinchieri G, Rosenberg SA, Restifo NP. IL-12 triggers a programmatic change in dysfunctional myeloid-derived cells within mouse tumors. J Clin Invest. 2011; 121:4746–4757. 10.1172/JCI58814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. DeNardo DG, Brennan DJ, Rexhepaj E, Ruffell B, Shiao SL, Madden SF, Gallagher WM, Wadhwani N, Keil SD, Junaid SA, Rugo HS, Hwang ES, Jirström K, et al. Leukocyte complexity predicts breast cancer survival and functionally regulates response to chemotherapy. Cancer Discov. 2011; 1:54–67. 10.1158/2159-8274.CD-10-0028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Savas P, Salgado R, Denkert C, Sotiriou C, Darcy PK, Smyth MJ, Loi S. Clinical relevance of host immunity in breast cancer: from TILs to the clinic. Nat Rev Clin Oncol. 2016; 13:228–41. 10.1038/nrclinonc.2015.215. [DOI] [PubMed] [Google Scholar]
  • 38. Xiao Y, Liu G, Gong L. Systematic Review and Meta-Analysis on the Association between Polymorphisms in Genes of IL-12 Signaling Pathway and Hepatocellular Carcinoma Risk. J Cancer. 2018; 9:3583–92. 10.7150/jca.26419. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Ter-Minassian M, Wang Z, Asomaning K, Wu MC, Liu CY, Paulus JK, Liu G, Bradbury PA, Zhai R, Su L, Frauenhoffer CS, Hooshmand SM, De Vivo I, et al. Genetic associations with sporadic neuroendocrine tumor risk. Carcinogenesis. 2011; 32:1216–22. 10.1093/carcin/bgr095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Klinke DJ 2nd. The ratio of P40 monomer to dimer is an important determinant of IL-12 bioactivity. J Theor Biol. 2006; 240:323–35. 10.1016/j.jtbi.2005.09.022. [DOI] [PubMed] [Google Scholar]
  • 41. Jalah R, Rosati M, Ganneru B, Pilkington GR, Valentin A, Kulkarni V, Bergamaschi C, Chowdhury B, Zhang GM, Beach RK, Alicea C, Broderick KE, Sardesai NY, et al. The p40 subunit of interleukin (IL)-12 promotes stabilization and export of the p35 subunit: implications for improved IL-12 cytokine production. J Biol Chem. 2013; 288:6763–76. 10.1074/jbc.M112.436675. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Braun R, Buetow K. Pathways of distinction analysis: a new technique for multi-SNP analysis of GWAS data. PLoS Genet. 2011; 7:e1002101. 10.1371/journal.pgen.1002101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Gee K, Guzzo C, Che Mat NF, Ma W, Kumar A. The IL-12 family of cytokines in infection, inflammation and autoimmune disorders. Inflamm Allergy Drug Targets. 2009; 8:40–52. 10.2174/187152809787582507. [DOI] [PubMed] [Google Scholar]
  • 44. Hussain SK, Madeleine MM, Johnson LG, Du Q, Galloway DA, Daling JR, Malkki M, Petersdorf EW, Schwartz SM. Nucleotide variation in IL-10 and IL-12 and their receptors and cervical and vulvar cancer risk: a hybrid case-parent triad and case-control study. Int J Cancer. 2013; 133:201–13. 10.1002/ijc.28000. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Tosolini M, Kirilovsky A, Mlecnik B, Fredriksen T, Mauger S, Bindea G, Berger A, Bruneval P, Fridman WH, Pagès F, Galon J. Clinical impact of different classes of infiltrating T cytotoxic and helper cells (Th1, th2, treg, th17) in patients with colorectal cancer. Cancer Res. 2011; 71:1263–71. 10.1158/0008-5472.CAN-10-2907. [DOI] [PubMed] [Google Scholar]
  • 46. Airoldi I, Di Carlo E, Cocco C, Sorrentino C, Fais F, Cilli M, D’Antuono T, Colombo MP, Pistoia V. Lack of Il12rb2 signaling predisposes to spontaneous autoimmunity and malignancy. Blood. 2005; 106:3846–53. 10.1182/blood-2005-05-2034. [DOI] [PubMed] [Google Scholar]
  • 47. Slattery ML, Lundgreen A, Kadlubar SA, Bondurant KL, Wolff RK. JAK/STAT/SOCS-signaling pathway and colon and rectal cancer. Mol Carcinog. 2013; 52:155–66. 10.1002/mc.21841. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48. Wang K, Zhang H, Kugathasan S, Annese V, Bradfield JP, Russell RK, Sleiman PM, Imielinski M, Glessner J, Hou C, Wilson DC, Walters T, Kim C, et al. Diverse genome-wide association studies associate the IL12/IL23 pathway with Crohn Disease. Am J Hum Genet. 2009; 84:399–405. 10.1016/j.ajhg.2009.01.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Zhang Q, Sturgill JL, Kmieciak M, Szczepanek K, Derecka M, Koebel C, Graham LJ, Dai Y, Chen S, Grant S, Cichy J, Shimoda K, Gamero A, et al. The role of Tyk2 in regulation of breast cancer growth. J Interferon Cytokine Res. 2011; 31:671–77. 10.1089/jir.2011.0023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50. Slattery ML, Lundgreen A, Hines LM, Torres-Mejia G, Wolff RK, Stern MC, John EM. 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. Breast Cancer Res Treat. 2014; 147:145–58. 10.1007/s10549-014-3071-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Übel C, Mousset S, Trufa D, Sirbu H, Finotto S. Establishing the role of tyrosine kinase 2 in cancer. OncoImmunology. 2013; 2:e22840. 10.4161/onci.22840. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Yang L, Liu D, Liang S, Guo R, Zhang Z, Xu H, Yang C, Zhu Y. Janus kinase 2 polymorphisms are associated with risk in patients with gastric cancer in a Chinese population. PLoS One. 2013; 8:e64628. 10.1371/journal.pone.0064628. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53. Kwon EM, Holt SK, Fu R, Kolb S, Williams G, Stanford JL, Ostrander EA. Androgen metabolism and JAK/STAT pathway genes and prostate cancer risk. Cancer Epidemiol. 2012; 36:347–53. 10.1016/j.canep.2012.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54. Zhang Z, Fye S, Borecki IB, Rader JS. Polymorphisms in immune mediators associate with risk of cervical cancer. Gynecol Oncol. 2014; 135:69–73. 10.1016/j.ygyno.2014.07.106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55. Zhang BY, Riska SM, Mahoney DW, Costello BA, Kohli R, Quevedo JF, Cerhan JR, Kohli M. Germline genetic variation in JAK2 as a prognostic marker in castration-resistant prostate cancer. BJU Int. 2017; 119:489–95. 10.1111/bju.13584. [DOI] [PubMed] [Google Scholar]
  • 56. Zundler S, Neurath MF. Interleukin-12: functional activities and implications for disease. Cytokine Growth Factor Rev. 2015; 26:559–68. 10.1016/j.cytogfr.2015.07.003. [DOI] [PubMed] [Google Scholar]
  • 57. Kim SW, Kim ES, Moon CM, Kim TI, Kim WH, Cheon JH. Abnormal genetic and epigenetic changes in signal transducer and activator of transcription 4 in the pathogenesis of inflammatory bowel diseases. Dig Dis Sci. 2012; 57:2600–07. 10.1007/s10620-012-2199-z. [DOI] [PubMed] [Google Scholar]
  • 58. Moon CM, Cheon JH, Kim SW, Shin DJ, Kim ES, Shin ES, Kang Y, Park JJ, Hong SP, Nam SY, Kim TI, Kim WH. Association of signal transducer and activator of transcription 4 genetic variants with extra-intestinal manifestations in inflammatory bowel disease. Life Sci. 2010; 86:661–67. 10.1016/j.lfs.2010.02.016. [DOI] [PubMed] [Google Scholar]
  • 59. Jiang X, Su K, Tao J, Fan R, Xu Y, Han H, Li L, Li MD. Association of STAT4 polymorphisms with hepatitis B virus infection and clearance in Chinese Han population. Amino Acids. 2016; 48:2589–98. 10.1007/s00726-016-2283-3. [DOI] [PubMed] [Google Scholar]
  • 60. Kim K, Cho SK, Sestak A, Namjou B, Kang C, Bae SC. Interferon-gamma gene polymorphisms associated with susceptibility to systemic lupus erythematosus. Ann Rheum Dis. 2010; 69:1247–50. 10.1136/ard.2009.117572. [DOI] [PubMed] [Google Scholar]
  • 61. van der Heijden T, Bot I, Kuiper J. The IL-12 cytokine family in cardiovascular diseases. Cytokine. 2019; 122:154188. 10.1016/j.cyto.2017.10.010. [DOI] [PubMed] [Google Scholar]
  • 62. Weaver JR, Nadler JL, Taylor-Fishwick DA. Interleukin-12 (IL-12)/STAT4 Axis Is an Important Element for β-Cell Dysfunction Induced by Inflammatory Cytokines. PLoS One. 2015; 10:e0142735. 10.1371/journal.pone.0142735. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63. Matta J, Echenique M, Negron E, Morales L, Vargas W, Gaetan FS, Lizardi ER, Torres A, Rosado JO, Bolaños G, Cruz JG, Laboy J, Barnes R, et al. The association of DNA Repair with breast cancer risk in women. A comparative observational study. BMC Cancer. 2012; 12:490. 10.1186/1471-2407-12-490. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64. Casper J, Zweig AS, Villarreal C, Tyner C, Speir ML, Rosenbloom KR, Raney BJ, Lee CM, Lee BT, Karolchik D, Hinrichs AS, Haeussler M, Guruvadoo L, et al. The UCSC Genome Browser database: 2018 update. Nucleic Acids Res. 2018; 46:D762–69. 10.1093/nar/gkx1020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65. Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA, Abecasis GR, and 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature. 2015; 526:68–74. 10.1038/nature15393. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66. Barrett JC. Haploview: Visualization and analysis of SNP genotype data. Cold Spring Harb Protoc. 2009; 2009:pdb.ip71. 10.1101/pdb.ip71. [DOI] [PubMed] [Google Scholar]
  • 67. Gabriel S, Ziaugra L, Tabbaa D. SNP genotyping using the sequenom massARRAY iPLEX Platform. Curr Protoc Hum Genet. 2009; 60:2.12.1–18. 10.1002/0471142905.hg0212s60. [DOI] [PubMed] [Google Scholar]
  • 68. Fejerman L, John EM, Huntsman S, Beckman K, Choudhry S, Perez-Stable E, Burchard EG, Ziv E. Genetic ancestry and risk of breast cancer among U.S. Latinas. Cancer Res. 2008; 68:9723–28. 10.1158/0008-5472.CAN-08-2039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81:559–75. 10.1086/519795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009; 19:1655–64. 10.1101/gr.094052.109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71. Clarke GM, Anderson CA, Pettersson FH, Cardon LR, Morris AP, Zondervan KT. Basic statistical analysis in genetic case-control studies. Nat Protoc. 2011; 6:121–33. 10.1038/nprot.2010.182. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72. Rstudio Team. RStudio: Integrated Development for R. [Online] RStudio, Inc., Boston, MA: 2016. 10.1007/978-81-322-2340-5. [DOI] [Google Scholar]
  • 73. Solé X, Guinó E, Valls J, Iniesta R, Moreno V. SNPStats: a web tool for the analysis of association studies. Bioinformatics. 2006; 22:1928–29. 10.1093/bioinformatics/btl268. [DOI] [PubMed] [Google Scholar]

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