Laboratory-based studies showed that host immune genes could influence the prognosis of non-small-cell lung cancer (NSCLC). Therefore, genetic polymorphisms in host immune genes may serve as predictors for NSCLC clinical outcome. To test the hypothesis that functional single nucleotide polymorphisms (SNPs) in host immune genes are associated with the prognosis of NSCLC, we systematically performed a genotyping analysis for a total of 178 SNPs from 52 immune genes in a prospective case cohort of 568 NSCLC patients. Among the 178 SNPs, 24 were significantly associated with NSCLC prognosis in different genetic models and four of them were remained in the final predictive model after multivariate stepwise Cox regression, including IL-5R rs11713419 (5′-untranslated region, 5′-UTR) (P = 0.001), IL23R rs6682925 (5′-flanking region, 5′-FR) (P = 0.017), TLR1 rs5743551 (5′-FR) (P = 0.02) and TLR3 rs3775291 (Leu412Phe) (P = 0.01). We then put the above four SNPs together, and found that the risk of death was significantly increased by 124% (HR = 2.24, 95% CI: 1.33–3.75) for the patients carrying “1” unfavorable locus and by 175% (HR = 2.75, 95% CI: 1.67–4.51) for those carrying “2-4” unfavorable loci. The risk score model and time-dependent ROC analyses further support the four SNPs and clinical risk score model. The area under curve (AUC) at year 5 increased from 0.484 to 0.831 after combining the four SNPs risk score with clinical risk score. These findings indicate that potentially functional polymorphisms in immune genes may serve as prognostic markers of clinical outcome of NSCLC.
There are continuous improvements to our understanding of the molecular connections of immune response and inflammation with cancer development and progression. A regulated adaptive immune response is antitumorigenic, whereas an unrestrained innate or inappropriate adaptive response may lead to chronic inflammation and a protumorigenic environment.1
Cytokines and their receptors play an essential role in regulation of the host immunity and could act as potential autocrine or paracrine factors for cancer initiation and/or progression. The expression of cytokines and their respective receptors appear to influence cell proliferation, differentiation, and movement of both tumor and stromal cells, regulate communications between tumor and stroma, and interactions between tumor and the extracellular matrix.2 Toll-like receptors (TLRs) have emerged as a key component of the innate immune system that recognizes a wide variety of pathogen-associated molecular patterns, as well as some host molecules.3–5 Malfunction or improper regulation of TLRs may lead to an unbalanced ratio of pro- to anti-inflammatory cytokines in the host, contributing to the onset and progression of cancers.
Immunologic function is in part influenced by host genetics, and germ line genetic variations in cytokines and related immune genes have been associated with risk of developing cancer and overall survival after cancer diagnosis. Previous studies were mostly focused on hematological neoplasms, especially lymphomas. For example, genetic polymorphisms in the tumor necrosis factor (TNF) and interleukin (IL) -10 loci were reported to influence clinical outcome of non-Hodgkin's lymphoma.6, 7 Host immune gene polymorphisms also predict survival in diffuse large B-cell lymphoma patients,8, 9 follicular lymphoma patients10 and also chronic lymphocytic leukemia patients.11 For solid tumors, relative limited evidence suggested that genetic variation in pro-inflammatory cytokines (IL-1β, IL-1α and IL-6) were associated with the aggressive forms, survival and relapse prediction of breast carcinoma,12 whereas IL-4/IL-4R polymorphisms were associated with prognosis of renal cell carcinoma.13, 14
In this study, we did an exploratory study with 178 single nucleotide polymorphisms (SNPs) from 52 immune genes and 568 NSCLC patients to test the hypothesis that inherited variation in cytokines, TLRs and related immune genes impact non-small-cell lung cancer (NSCLC) survival.
NSCLC: non-small-cell lung cancer; CI: confidence interval; UTR: untranslated region; FR: flanking region; MAF: minor allele frequency; HR: hazard ratio; SNPs: single nucleotide polymorphisms; ROC: receiver-operator characteristic; AUC: area under curve; MST: median survival time; TLRs: Toll-like receptors; TNF: tumor necrosis factor; IL: interleukin
Material and Methods
The study patients were reported recently.15 In brief, all the patients were newly diagnosed and histopathologically confirmed incident NSCLC cases, without prior history of other cancers or previous chemo- or radio- therapy. Since July 2003, patients were prospectively recruited from the Cancer Hospital of Jiangsu Province and the First Affiliated Hospital of Nanjing Medical University, Nanjing, China. The last follow-up date was July 2009 and the median survival time (MST) for the 568 patients was 24.8 months. Among these subjects, 257(45.25%) patients were flagged as censored data, who were lost to follow-up (0.53%) or alive till the last follow-up (44.72%). This study was approved by the Institutional Review Boards of Nanjing Medical University.
We tried to include all common (minor allele frequency, MAF > 0.05 in Asians) SNPs with potentially functional significance in candidate immune related genes, especially cytokines and their receptors and TLRs (Supporting Information Table 1). The SNPs located at 5′ flaking regions (5′FR), 5′ untranslated regions (5′UTRs), coding regions with amino acid changes, or 3′ UTRs according to NCBI dbSNPs (last search date: Feb., 2008) were selected. Intron SNPs demonstrated to be of biological significance or associated with gene expression and/or cancer risk/survival according to the literature review were also included. As a result, 193 SNPs were selected for genotyping and the details were shown in Supporting Information Table 1.
Fifteen SNPs were excluded because of chip design. Genotyping was performed by using Illumina Golden Gate platform. The information on genotyping assay conditions, primers and probes is available upon request. Quality control for genotyping was described previously.15 To further control the genotyping quality by the Illumina platform, we use the TaqMan (Applied Biosystems, CA) assay to genotype a randomly selected SNP (IL23R rs6682925) in all 568 samples and the results were 100% consistent. As a result, 178 SNPs were genotyped, while 21 SNPs with genotyping call rate < 90% and/or deviated from Hardy-Weinberg equilibrium (threshold 0.01) were removed from the final analyses.
Survival time was calculated from the date of NSCLC diagnosis to the date of patients dead or the last time of follow-up. Log-rank test was used to compare the survival time in different subgroups categorized by patient characteristics, clinical features and genotypes. Analytic strategy for variable selection followed four steps (Supporting Information Fig. 1). Risk score model was constructed with a linear combination of the SNP genotypes or clinical factors weighted by the regression coefficient. We analyzed the association of the risk score with overall survival using Cox model and time-dependent receiver-operator characteristic (ROC) curves for censored data and calculated Area under curve (AUC) of the ROC curves.16 We also evaluated the performances of different scores by plotting [t, AUC (t)] for different values of follow up time t. The heterogeneity between subgroups was assessed with the Chi-square-based Q test. All the statistical analyses were carried out by Statistical Analysis System software (version 9.1.3; SAS Institute, Cary, NC), STATA statistical software (version 10.1; StataCorp, College Station, TX) and R software (version 2.10.1; The R Foundation for Statistical Computing).
Patients' Characteristics and Clinical Features were described previously.15 The details of 178 selected SNPs were summarized in Supporting Information Table 1. Log-rank test was used to test the association of each SNP with NSCLC survival in different models, and 24 SNPs located at 15 different immune related genes were significantly associated with lung cancer survival: [rs20417 (5′FR) in COX2; rs1332190 (5′-FR) in IFNA1; rs9808753 (Gln64Arg) in IFNGR2; rs1800794 (5′-FR), rs1800587 (5′-UTR), rs17561 (Ala114Ser) and rs1304037 (3′-UTR) in IL1A; rs11713419 (5′-UTR) in IL5R; rs4073 (5′-FR) and rs2227306 (IVS1-204) in IL-8; rs1126579 (3′-UTR) in IL8RB; rs9610 (3′-UTR) in IL10RA; rs17875486 (5′FR) in IL16; rs6682925 (5′-FR) and rs1884444 (Gln3His) in IL23R; rs5743551 (5′-FR) and rs4833095 (Ser248Asn) in TLR1; rs3775291 (Leu412Phe) in TLR3; rs5744174 (Phe616Leu) in TLR5; rs5743808 (Ile120Thr) in TLR6; rs10004195 (5′FR), rs11466649 (Ala163Ser), rs11096957 (His241Asn) and rs11096955 (Leu369Ile) in TLR10] (Supporting Information Table 1). Multivariate Cox regression model, adjusting for smoking status, histology, stage, surgical operation and radio- or chemo-therapy status, was further used to select the optimal SNPs, ten SNPs in nine related genes were survival at this step (Supporting Information Table 2). However, when we used multivariate stepwise Cox regression analysis, only four SNPs (IL5R rs11713419, P = 0.001; TLR3 rs3775291, P = 0.010; TLR1 rs5743551, P = 0.020 and IL23R rs6682925, P = 0.017) were remained in the final predictive model together with stage and surgical operation (Table 1).
Table 1. Results of multivariate stepwise cox regression analysis on NSCLC-related survival
As shown in Table 2, the four SNPs were significantly associated with NSCLC survival after adjusted for smoking status, histology, stage, surgical operation and radio- or chemo-therapy status (recessive genetic model: hazard ratio (HR) = 6.60, 95% confidence interval (CI): 2.42–18.02 for IL5R rs11713419; dominant genetic model: HR = 1.37, 95%CI: 1.09–1.73 for TLR3 rs3775291; HR = 0.78, 95%CI: 0.62–0.97 for TLR1 rs5743551 and HR = 1.34, 95%CI: 1.05–1.70 for IL23R rs6682925). We then put the above four SNPs together to assess the combined effects and found that the more unfavorable loci the patients carried, the shorter of MST they survived, suggesting a locus-dosage effect between combined loci and survival (P for trend < 0.0001) (Table 2 and Fig. 1). In the trichotomized analysis, patients with “1” and “2-4” unfavorable loci had a MST of 27.4 and 20.9 months, respectively, whereas those with “0” unfavorable locus had a MST of 51.9 months. After adjusted for smoking status, histology, stage, surgical operation and radio- or chemo-therapy status, the risk of death was significantly increased by 124% (HR = 2.24, 95% CI: 1.33–3.75) for the patients carrying “1” unfavorable locus and by 175% (HR = 2.75, 95% CI: 1.67–4.51) for those carrying “2-4” unfavorable loci. However, in the stratified analyses, no significant differences were observed on the association between the combined effects of the SNPs and NSCLC survival by the stratification for age, gender, smoking, histology, stage, surgical operation and radio- or chemo-therapy status (Supporting Information Table 3).
Table 2. Polymorphisms in immune genes and NSCLC patients survival
To evaluate the predictive ability of the six variables: four SNPs (rs11713419, rs3775291, rs5743551 and rs6682925) and two clinical factors (stage and surgery), we conducted a time-dependent ROC analysis for censored data. The sensitivity and specificity, both of them are time-dependent, were used to measure the prognostic capacity of a survival model as measured by the area under the curve (AUC). The risk score of four SNPs was significantly associated with the survival of NSCLC in a Cox regression model (P = 3.38 × 10−7). Figure 2 illustrated that the predictive effect of the model increased with the number of risk factors, when all the six variables (stage, surgery and four SNPs) contained in the model, we obtained the best performance. On the other hand, the other six SNPs significant in the multivariate Cox proportional hazards regression analysis were added to the six variable model one by one to see if the predictive effect could be refined. As shown in Supporting Information Table 4, adding any of the six SNPs, the model performance has little improved. According to the “fewer variables and better performance” modeling principle, the final risk predictive model included the four SNPs (rs11713419, rs3775291, rs5743551 and rs6682925) and two clinical factors (stage and surgery).
In this exploratory study, we systematically evaluated the association of 178 SNPs from 52 immune related genes with NSCLC survival. We found that four SNPs (two cytokine related SNPs, IL5R rs11713419 and IL23R rs6682925, and two TLR related SNPs, TLR3 rs3775291 and TLR1 rs5743551) together with stage and surgical operation were included in the final stepwise multivariate Cox regression model for overall survival in a case cohort of 568 patients. Our results, based on prospective clinical data and a comprehensive panel of potentially functional SNPs of immune related genes, can improve the ability to predict a patient's prognosis of NSCLC, especially when only very limited studies have analyzed the clinical course of NSCLC in association with inherited genetic polymorphisms of immune-related genes.
IL23 receptor is composed of IL23R subunit and IL12Rβ1 subunit shared with IL12R.17 Although the structures and biological activities of IL23 and IL12 as well as their receptors are similar, IL12/IL12R is mainly involved in Th1-cell immune regulation18 while IL23/IL23R is specifically essential for Th17 cell-mediated inflammatory process.19 Recently, several GWA studies have independently identified rs11209026, a nonsynonymous variant (R381Q), of the IL23R gene, as a susceptibility locus associated with chronic inflammatory diseases.20–23 However, the rs11209026 polymorphism does not exist in Asians and IL23R rs6682925 (5′-FR) as identified in our study in Chinese population might be a functional variant to regulate gene expression.
TLR1 is important in regulating TLR responses.24 For example, TLR1 can associate with TLR4 to inhibit its signaling in endothelial cells,25 and TLR1 can associate with TLR2 to inhibit the TLR2-mediated response to phenol-soluble modulin.26 The SNP rs5743551 (5′FR), reported in the promoter region, alters a putative core binding site of the proto-oncogene PU.1 (AAAAGGAGAAG)27 However, SNPs IL-5R rs11713419 (5′-UTR), TLR3 rs3775291 (Leu412Phe) were rarely mentioned in previous studies.
The goal of our studies is to generate the necessary data to move toward an era in which cancer treatment is individualized on the basis of both genetic constitution conventional markers. We can see that the incorporate of genetic parameters may increase the predictive effect of conventional markers of surgery and stage. However, this is an exploratory study and the results are preliminary. Functional significance of associated SNPs was largely unknown, especially under the circumstance of NSCLC. Therefore, large well-designed studies are warranted to confirm and expand our findings.