Abstract
The Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway is involved in immune function and cell growth. We evaluated the association between genetic variation in JAK1 (10 SNPs), JAK2 (9 SNPs), TYK2 (5 SNPs), SOCS1 (2 SNPs), SOCS2 (2 SNPs), STAT1 (16 SNPs), STAT2 (2 SNPs), STAT3 (6 SNPs), STAT4 (21 SNPs), STAT5A (2 SNPs), STAT5B (3 SNPs), STAT6 (4 SNPs) with risk of colorectal cancer. We used data from population-based case-control studies (colon cancer n=1555 cases, 1956 controls; rectal cancer n=754 cases, 959 controls). JAK2, SOCS2, STAT1, STAT3, STAT5A, STAT5B, and STAT6 were associated with colon cancer; STAT3, STAT4, STAT6, and TYK2 were associated with rectal cancer. Given the biological role of the JAK/STAT-signaling pathway and cytokines, we evaluated interaction with IFNG, TNF, and IL6; numerous statistically significant associations after adjustment for multiple comparisons were observed. The following statistically significant interactions were observed: TYK2 with aspirin/NSAID use; STAT1, STAT4, and TYK2 with estrogen status; and JAK2, STAT2, STAT4, STAT5A, STAT5B, and STAT6 with smoking status and colon cancer risk; JAK2, STAT6, and TYK2 with aspirin/NSAID use; JAK1 with estrogen status; STAT2 with cigarette smoking and rectal cancer. JAK2, SOCS1, STAT3, STAT5, and TYK2 were associated with colon cancer survival (HRR of 3.3 95% CI 2.01, 5.42 for high mutational load). JAK2, SOCS1, STAT1, STAT4, and TYK2 were associated with rectal cancer survival (HRR 2.80 95 %CI 1.63, 4.80). These data support the importance of the JAK/STAT-signaling pathway in colorectal cancer and suggest targets for intervention.
Keywords: JAK, STAT, SOCS, colon cancer, rectal cancer, estrogen, NSAIDs, cigarette smoking
Introduction
The Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling pathway is involved in immune function and cell growth and differentiation1, 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, while JAK3 is expressed mainly in hematopoietic cells3. 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 response4. Five additional STATs have been identified: STAT3, STAT4, STAT5A, STAT5B, and STAT6. Cytokines, as part of a feedback loop, up-regulate suppressors of cytokine signaling (SOCS) that inhibit the activity of JAKs and STATs5. Several studies have implicated components of the JAK/STAT/SOCS-signaling pathway in colorectal adenomas and cancer, 1, 2, 6 which is biologically plausible given that the gut contains the largest collection of lymphoid tissue in the body4.
Research focused on understanding the JAK/STAT/SOCS-signaling pathway often has involved their interaction and relationship with cytokines. STAT1 and STAT2 were first identified from work involving downstream events of receptor binding of IFNγ on transcriptional activation of genes involved in immune response4. Pro-inflammatory cytokines, such as TNFα, IL-6, and INFγ have been shown to up-regulate STAT proteins4, 7, 8. Both JAK1 and JAK2 are important for cytokines through use of the shared receptor subunits, γ chain (γc) and gp130; IFNs and IL-6 are two important pro-inflammatory cytokines that use these receptors that are essential for cytokine signaling9. JAK2 is essential for hormone-like cytokine signaling, including prolactin signaling9. Thus, the JAK/STAT/SOCS-signaling pathway is an important regulator of the ultimate cellular response to cytokines.
The influence of genetic variation in the JAK/STAT/SOCS-signaling pathway on colon and rectal cancer risk is unknown. It is biologically plausible that JAK/STAT/SOCS-signaling pathway risk would be associated with genetic variation in cytokine genes such as TNF and its receptors, IFNG (IFNγ) and its receptors, and IL6 which are important cytokines associated with inflammatory processes, aspirin/NSAIDs that influence inflammation, cigarette smoking that can influence inflammation through oxidative stress, and estrogen which has many biological functions including anti-inflammatory properties. In this study we evaluate genetic variation in the JAK/STAT/SOCS-signaling pathway and assess if that variation is associated with key cytokine and inflammation-related factors and risk of developing colon and rectal cancer. Because the JAK/STAT/SOCS-signaling pathway influences cell growth, we also evaluate if genetic variation in this pathway is associated with survival after diagnosis with colon and rectal cancer.
Methods
Data for the study come from a population-based case-control study of colon cancer (cases n=1,555; controls n=1,956) and rectal cancer (cases n=754; controls n=959) The colon cancer study case identification was between October1, 1991 and September 30, 1994 and included people living in the Twin Cities Metropolitan Area, Kaiser Permanente Medical Care Program of Northern California (KPMCP) and a seven-county area of Utah 10. The rectal study used identical data collection methods as the colon study, it included population-based cases with cancer of the rectosigmoid junction or rectum who were identified between May 1997 and May 2001 in Utah and KPMCP 11. Eligible cases were between 30 and 79 years old at time of diagnosis with adenocarcinoma, English speaking, mentally competent to complete the interview, had no previous history of CRC, and no known (as indicated on the pathology report) familial adenomatous polyposis, ulcerative colitis, or Crohn’s disease. Controls were matched to cases by sex and by 5-year age groups. At KPMCP, controls were randomly selected from membership lists. In Utah, controls 65 years and older were randomly selected from the Health Care Financing Administration lists and controls younger than 65 years were randomly selected from driver’s license lists. Controls were selected from driver’s license and state-identification lists in Minnesota. Study details have been previously reported 10, 11.
Interview Data Collection
Data were collected by trained and certified interviewers using laptop computers. All interviews were audio-taped and reviewed for quality control purposes 12. The referent period for the study was two years prior to diagnosis for cases and prior to selection for controls. Detailed information was collected on diet, physical activity, medical history, and cigarette smoking history, regular use of aspirin and non-steroidal anti-inflammatory drugs, use of hormone replacement therapy, menopausal history, and body size.
Tumor Registry Data
Tumor registry data were obtained to determine disease stage at diagnosis and months of survival after diagnosis. Disease stage was categorized centrally by one pathologist in Utah using the sixth edition of the American Joint Committee on Cancer (AJCC) staging criteria. Local tumor registries also provided information on patient follow-up including vital status, cause of death, and contributing cause of death. Follow-up was obtained for all study participants for at least five years and was terminated for the Colon Cancer Study in 2000 and for the Rectal Cancer Study in 2007.
TagSNP Selection and Genotyping
TagSNPs were selected using the following parameters: 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. All markers were genotyped using a multiplexed bead-array assay format based on GoldenGate chemistry (Illumina, San Diego, California). A genotyping call rate of 99.85% was attained. Blinded internal replicates represented 4.4% of the sample set; the duplicate concordance rate was 100%. Individuals with missing genotype data were not included in the analysis for that specific marker. We evaluated associations with candidate genes, including JAK1 (10 SNPs), JAK2 (9 SNPs), TYK2 (5 SNPs), SOCS1 (2 SNPs), SOCS2 (2 SNPs), STAT1 (16 SNPs), STAT2 (2 SNPs), STAT3 (6 SNPs), STAT4 (21 SNPs), STAT5A (2 SNPs), STAT5B (3 SNPs), STAT6 (4 SNPs). Table 1 details SNPs associated with colon or rectal cancer, either by independent associations or through interactions; online Supplement 1 contains information about all SNPs included on the platform.
Table 1.
Description of genes and respective tagSNPs associated with colon and rectal cancer.
| Symbol | Chromosome | Alias | SNP | Major/Minor Allele | MAF1 | FDR HWE2 |
|---|---|---|---|---|---|---|
| JAK1 | 1p32.3-p31.3 | JAK1A | rs310211 | A /G | 0.31 | 1.00 |
| JAK1B | rs2256298 | C /T | 0.25 | 1.00 | ||
| rs3790541 | C /T | 0.11 | 1.00 | |||
| rs310199 | T /C | 0.29 | 1.00 | |||
| rs310198 | C /T | 0.11 | 1.00 | |||
| JAK2 | 9p24 | rs1887429 | G /T | 0.27 | 1.00 | |
| rs2274471 | T /C | 0.24 | 0.96 | |||
| rs7043371 | A /T | 0.50 | 1.00 | |||
| rs10974947 | G /A | 0.25 | 0.68 | |||
| rs3780379 | G /A | 0.19 | 0.62 | |||
| rs3780381 | A /C | 0.28 | 0.95 | |||
| rs10815160 | T /G | 0.24 | 0.87 | |||
| SOCS1 | 16p13.13 | CIS, CISH, JAB, SOCS-1 | rs4780355 | T /C | 0.30 | 0.96 |
| SSI-1, SSI1, TIP3 | rs193779 | G /A | 0.25 | 0.85 | ||
| SOCS2 | 12q | CIS2, Cish2, SOCS-2 | rs768775 | T /C | 0.19 | 0.68 |
| SSI-2, SSI2, STATI2 | rs3816997 | T /G | 0.14 | 1.00 | ||
| STAT1 | 2q32.2 | DKFZp686B04100 | rs3771300 | A /C | 0.49 | 1.00 |
| ISGF-3 | rs16824035 | C /T | 0.16 | 1.00 | ||
| STAT91 | rs4327257 | A /C | 0.14 | 1.00 | ||
| rs2280233 | A /G | 0.47 | 1.00 | |||
| rs2280232 | T /G | 0.26 | 1.00 | |||
| rs7562024 | C /T | 0.40 | 1.00 | |||
| rs10199181 | A /T | 0.38 | 1.00 | |||
| rs10208033 | T /C | 0.42 | 1.00 | |||
| STAT2 | 12q13.2 | ISGF-3, P113, STAT113, MGC59816 | rs2229363 | G /T | 0.01 | <.000001 |
| STAT3 | 17q21.31 | APRF | rs1053005 | A /G | 0.19 | 0.94 |
| FLJ20882 | rs2293152 | G /C | 0.40 | 0.98 | ||
| MGC16063 | rs6503695 | T /C | 0.34 | 0.96 | ||
| rs12949918 | T /C | 0.41 | 0.87 | |||
| rs1026916 | G /A | 0.35 | 0.97 | |||
| STAT4 | 2q32.2-q32.3 | rs4853540 | G /T | 0.21 | 0.96 | |
| rs3024904 | A /T | 0.11 | 1.00 | |||
| rs3024861 | T /A | 0.24 | 0.99 | |||
| rs10168266 | C /T | 0.19 | 1.00 | |||
| rs6752770 | A /G | 0.28 | 0.95 | |||
| rs11685878 | C /T | 0.40 | 0.95 | |||
| rs12327969 | G /C | 0.21 | 0.96 | |||
| STAT5A | 17q11.2 | MGF | rs7217728 | T /C | 0.28 | 0.03 |
| STAT5 | rs12601982 | A /G | 0.17 | 0.75 | ||
| STAT5B | 17q11.2 | STAT5 | rs9900213 | G /T | 0.16 | 1.00 |
| rs6503691 | C /T | 0.10 | 0.99 | |||
| rs7218653 | A /G | 0.29 | 0.74 | |||
| STAT6 | 12q13 | D12S1644 | rs3024979 | T /A | 0.11 | 1.00 |
| IL-4-STAT | rs324015 | G /A | 0.25 | 0.97 | ||
| STAT6B | rs3024974 | C /T | 0.10 | 1.00 | ||
| STAT6C | rs324011 | C /T | 0.39 | 0.90 | ||
| TYK2 | 19p13.2 | JTK1 | rs280519 | G /A | 0.48 | 0.98 |
| rs280521 | G /A | 0.14 | 1.00 | |||
| rs280523 | G /A | 0.07 | 0.87 | |||
| rs280500 | A /G | 0.15 | 1.00 |
Minor Allele Frequency (MAF) based on controls for non-Hispanic white population.
FDR (HWE) = False Discovery Rate adjusted p value for Hardy Weinberg Equilibrium test; HWE uses NHW controls.
Statistical Methods
Statistical analyses were performed using SAS® version 9.2 (SAS Institute, Cary, NC). We report odds ratios (ORs) and 95% confidence intervals (95%CIs) assessed from multiple logistic regression models adjusting for age, center, race/ethnicity, and sex. To summarize risk associated with multiple variants across the pathway we created a summary polygenic score that was based on all at-risk genotypes for colon and rectal cancer. The score for each SNP was based on the inheritance model and its associated risk. For the co-dominant or additive model a score of zero, one, or two was assigned which directly as correlated to the number of high-risk alleles; scores of zero or two were assigned for the dominant and recessive models. After assigning a score for each SNP previously identified as being significant, the scores were summed across SNPs to generate an individual polygenic summary score. Individuals missing SNP data were dropped from the analysis. The continuous score variable was redefined as a categorical variable based on the frequency distribution within the study population.
Analysis for interaction was based on tagSNPs within each gene. We tested interaction with targeted genes including tumor necrosis factor and its receptors (TNF, TNFRSF1A, TNFRSF1B), interferon gamma and its receptors (IFNG, IFNGR1, IFNGR2), and IL6 which we hypothesized would modify the effect of candidate genes given the importance of cytokines in regulating the pathway. Lifestyle variables were selected because of their biological plausibility for involvement in this candidate pathway. In these analyses we focused on interaction between estrogen status (defined as currently using hormone replacement if post-menopausal or being pre/peri menopausal), cigarette smoking status, and use of aspirin/NSAIDs. These factors were targeted because of their influence on estrogen, inflammation, and oxidative stress. P values for interaction were determined using a likelihood-ratio test comparing a full model that included an ordinal interaction term with a reduced model without an interaction term.
Survival-months were calculated based on month and year of diagnosis and month and year of death or date of last contact. Associations between SNPs and risk of dying of colorectal cancer were evaluated using Cox proportional hazards models to obtain multivariate hazard rate ratios (HRRs) and 95% confidence intervals. We adjusted for age at diagnosis, study center, race, sex, tumor molecular phenotype, and AJCC stage to estimate HRRs.
Adjusted multiple-comparison p values, taking into account tagSNPs within the gene, were estimated using the methods of Conneely and Boehnke 13 via R version 2.12.0 (R Foundation for Statistical Computing, Vienna, Austria). Wald p values (1 df) from the main effect models and interaction p values based on likelihood-ratio tests were used in the calculation of multiple comparisons. We consider a pACT of <0.20 as potentially important given the underlying candidate pathway approach of this study and the need to consider both type 1 and type 2 errors. We believe that findings at this level would merit replication, especially when evaluating interactions.
Results
Evaluation of the associations with SNPs in genes in the JAK/STAT signaling pathway showed more significant associations for colon cancer than for rectal cancer (Table 2). JAK2 (4 SNPs), SOCS2 (1 SNP), STAT1 (2 SNPs), STAT3 (2 SNPs), STAT5A (1 SNP), STAT5B (2 SNPs), and STAT6 (1 SNP) were significantly associated with colon cancer. After adjustment for multiple testing, all but five SNPs remained significant at the 0.10 level and all but one had a pACT value of <0.20. Only four SNPs in four separate genes, STAT3, STAT4, STAT6, and TYK2, were associated with rectal cancer. The adjusted p values for STAT3, STAT6, and TYK2 were 0.0552, 0.0623, and 0.1255 respectively. Assessment of mutational load from having multiple at-risk alleles showed only minimal increased risk for colon cancer. However, for rectal cancer having all four at-risk alleles (score of 8) versus none (score of 0–2) was associated with an almost four-fold increased risk (OR 3.90 95 %CI 2.02, 7.52), which was considerably greater than the combined independent risk.
Table 2.
Associations between JAK/STAT/SOCS-signaling pathway and colon and rectal cancer
| Colon Cancer | Controls | Cases | Wald p value | pACT | ||
|---|---|---|---|---|---|---|
|
| ||||||
| N | N | OR1 | (95% CI) | |||
| JAK2 (rs10815160) | 0.0332 | 0.1718 | ||||
| TT/TG | 1840 | 1431 | 1.00 | |||
| GG | 115 | 123 | 1.34 | (1.02, 1.76) | ||
| JAK2 (rs10974947) | 0.0403 | 0.1805 | ||||
| GG/GA | 1863 | 1457 | 1.00 | |||
| AA | 93 | 97 | 1.36 | (1.01, 1.82) | ||
| JAK2 (rs1887429) | 0.0294 | 0.1704 | ||||
| GG | 1019 | 866 | 1.00 | |||
| GT/TT | 921 | 686 | 0.86 | (0.75, 0.99) | ||
| JAK2 (rs3780379) | 0.0066 | 0.0479 | ||||
| GG/GA | 1905 | 1489 | 1.00 | |||
| AA | 51 | 65 | 1.68 | (1.16, 2.44) | ||
| SOCS2 (rs768775) | 0.0196 | 0.0384 | ||||
| TT | 1303 | 983 | 1.00 | |||
| TC/CC | 652 | 572 | 1.18 | (1.03, 1.36) | ||
| STAT1 (rs2280232) | 0.0116 | 0.1124 | ||||
| TT | 1056 | 900 | 1.00 | |||
| TG | 763 | 563 | 0.86 | (0.75, 0.99) | ||
| GG | 136 | 91 | 0.77 | (0.58, 1.02) | ||
| STAT1 (rs4327257) | 0.0319 | 0.2489 | ||||
| AA | 1462 | 1213 | 1.00 | |||
| AC/CC | 494 | 341 | 0.84 | (0.72, 0.99) | ||
| STAT3 (rs12949918) | 0.0170 | 0.0651 | ||||
| TT | 690 | 492 | 1.00 | |||
| TC/CC | 1266 | 1063 | 1.19 | (1.03, 1.37) | ||
| STAT3 (rs6503695) | 0.0032 | 0.0148 | ||||
| TT | 862 | 614 | 1.00 | |||
| TC | 871 | 735 | 1.19 | (1.03, 1.37) | ||
| CC | 223 | 206 | 1.32 | (1.07, 1.64) | ||
| STAT5A (rs7217728) | 0.0035 | 0.0065 | ||||
| TT | 974 | 699 | 1.00 | |||
| TC | 710 | 614 | 1.20 | (1.04, 1.39) | ||
| CC | 182 | 174 | 1.31 | (1.04, 1.66) | ||
| STAT5B (rs6503691) | 0.0068 | 0.0187 | ||||
| CC | 1555 | 1172 | 1.00 | |||
| CT | 365 | 336 | 1.20 | (1.01, 1.42) | ||
| TT | 36 | 47 | 1.59 | (1.00, 2.53) | ||
| STAT5B (rs7218653) | 0.0092 | 0.0181 | ||||
| AA | 998 | 727 | 1.00 | |||
| AG | 780 | 658 | 1.15 | (1.00, 1.33) | ||
| GG | 178 | 170 | 1.30 | (1.03, 1.64) | ||
| STAT6 (rs324015) | 0.0201 | 0.0751 | ||||
| GG | 1118 | 946 | 1.00 | |||
| GA/AA | 838 | 609 | 0.85 | (0.74, 0.97) | ||
| Summary Score2 | ||||||
| (0 – 5) | 271 | 157 | 1.00 | |||
| (6 – 7) | 292 | 203 | 1.19 | (0.91, 1.55) | ||
| (8 – 9) | 436 | 292 | 1.17 | (0.91, 1.50) | ||
| (10 – 11) | 426 | 341 | 1.37 | (1.08, 1.75) | ||
| (12 – 13) | 288 | 286 | 1.71 | (1.32, 2.21) | ||
| (14 – 23) | 243 | 276 | 2.00 | (1.54, 2.61) | ||
| P Trend | <.0001 | |||||
| Rectal Cancer | ||||||
| STAT3 (rs2293152) | 0.0138 | 0.0552 | ||||
| GG/GC | 791 | 655 | 1.00 | |||
| CC | 168 | 99 | 0.71 | (0.54, 0.93) | ||
| STAT4 (rs3024861) | 0.0416 | 0.4679 | ||||
| TT/TA | 857 | 690 | 1.00 | |||
| AA | 102 | 64 | 0.70 | (0.49, 0.99) | ||
| STAT6 (rs3024979) | 0.0163 | 0.0623 | ||||
| TT | 772 | 643 | 1.00 | |||
| TA/AA | 187 | 111 | 0.73 | (0.56, 0.94) | ||
| TYK2 (rs280500) | 0.0294 | 0.1255 | ||||
| AA | 708 | 523 | 1.00 | |||
| AG/GG | 250 | 231 | 1.27 | (1.02, 1.57) | ||
| Summary Score | ||||||
| (0 – 2) | 48 | 13 | 1.00 | |||
| (4 – 4) | 262 | 167 | 2.45 | (1.28, 4.67) | ||
| (6 – 6) | 496 | 423 | 3.27 | (1.74, 6.14) | ||
| (8 – 8) | 153 | 151 | 3.90 | (2.02, 7.52) | ||
| P Trend | <.0001 | |||||
Odds Ratios (OR) and 95% Confidence Intervals (CI) adjusted for age, center, race, and sex.
Summary Score is based on all SNPs showing independent effects
Given the biological role of the JAK/STAT signaling pathway and its involvement with IFNG, TNF, and other cytokines, we evaluated interaction with IFNG and its receptors, TNF and its receptors, and IL6. We observed numerous statistically significant associations; those with pACT values of <0.20 are shown in Table 3 while those with unadjusted p values of <0.05 but adjusted p values of >0.20 are available in the online Supplement 2. We observed more associations with colon cancer than with rectal cancer. For colon cancer, we observed significant interaction and adjusted pACTs of <0.2 between IFNG and STAT4, JAK1, JAK2, and SOCS1; between IFNGR1 and STAT6 and TYK2; IFNGR2 and STAT1, STAT5B, SOCS2, SOCS1, and STAT4. TNF interacted significantly with JAK2, STAT1, STAT6, JAK1, and STAT4; TNFRSF1A and STAT3, STAT6, STAT5A, STAT5B, TYK2, and JAK1. IL6 interacted with STAT5B, JAK1, JAK2, STAT3, STAT6, and STAT4. For rectal cancer we observed that IFNG interacted with JAK2, IFNGR1 interacted with STAT5B and SOCS2, and IFNGR2 interacted with JAK1, JAK2, STAT3, and STAT5A. TNF interacted with TYK2 and TNFRSF1A interacted with JAK2, SOCS2, SOCS1, and STAT4. IL6 interacted with JAK2, STAT1, STAT4 and with TYK2. For both colon and rectal cancer several SNPs within each gene interacted with the targeted pathway genes, i.e. IFNG, TNF, and IL6.
Table 3.
Associations between JAK, SOCS, STAT, TYK2 genes and IFNG, TNF, and IL6
|
|
|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| JAK, SOCS, STAT, TYK2 | SNP (Model)1 | INFG, TNF, IL6 | SNP (Model)1 | Wild type2 Variant | Variant3 Wild type | Variant4 Variant | Interaction P Value | p_ACT | |||
| OR | 95% CI | OR | (95% CI) | OR | 95% CI | ||||||
| COLON | |||||||||||
| STAT4 | rs4853540 A) | IFNG | rs2069718 (A) | 0.74 | (0.58,0.95) | 0.52 | (0.30,0.90) | 1.64 | (0.87,3.08) | 0.0029 | 0.0900 |
| JAK1 | rs3790541 (D) | rs2069718 (D) | 0.99 | (0.85,1.17) | 1.43 | (1.10,1.88) | 0.89 | (0.71,1.11) | 0.0055 | 0.0701 | |
| rs2069727 (A) | 0.98 | (0.79,1.22) | 0.85 | (0.63,1.14) | 1.68 | (1.21,2.34) | 0.0031 | 0.0453 | |||
| JAK2 | rs1887429 (D) | rs2069727 (D) | 1.26 | (1.03,1.53) | 1.10 | (0.87,1.41) | 0.97 | (0.79,1.18) | 0.0146 | 0.1670 | |
| SOCS1 | rs193779 (D) | 1.22 | (1.01,1.48) | 1.11 | (0.87,1.42) | 0.96 | (0.78,1.17) | 0.0197 | 0.0744 | ||
| STAT6 | rs3024979 (D) | IFNGR1 | rs3799488 (D) | 1.08 | (0.90,1.29) | 1.09 | (0.90,1.32) | 0.69 | (0.49,0.96) | 0.0100 | 0.0942 |
| rs9376267 (D) | 0.97 | (0.83,1.13) | 1.18 | (0.94,1.47) | 0.69 | (0.53,0.90) | 0.0050 | 0.0526 | |||
| TYK2 | rs280523 (D) | 0.83 | (0.71,0.96) | 0.71 | (0.54,0.93) | 0.96 | (0.71,1.29) | 0.0175 | 0.1855 | ||
| STAT1 | rs4327257 (D) | IFNGR2 | rs1532 (A) | 1.17 | (0.88,1.55) | 1.03 | (0.82,1.28) | 0.47 | (0.27,0.84) | 0.0024 | 0.0876 |
| STAT5B | rs9900213 (D) | rs2834211 (D) | 1.21 | (1.00,1.48) | 1.21 | (1.03,1.42) | 0.92 | (0.69,1.22) | 0.0100 | 0.0798 | |
| SOCS2 | rs768775 (D) | rs2834215 (D) | 1.19 | (0.98,1.43) | 1.66 | (1.27,2.17) | 1.23 | (1.00,1.51) | 0.0031 | 0.0208 | |
| SOCS1 | rs4780355 (D) | rs9808753 (D) | 0.76 | (0.61,0.95) | 0.89 | (0.76,1.04) | 0.99 | (0.80,1.23) | 0.0150 | 0.0814 | |
| STAT4 | rs6752770 (D) | 0.73 | (0.59,0.92) | 0.90 | (0.77,1.05) | 1.04 | (0.84,1.29) | 0.0037 | 0.1578 | ||
| JAK2 | rs2274471 (D) | TNF | rs1799964 (D) | 1.30 | (1.09,1.56) | 0.99 | (0.83,1.18) | 0.92 | (0.75,1.12) | 0.0182 | 0.1381 |
| STAT1 | rs3771300 (D) | 0.72 | (0.55,0.95) | 0.87 | (0.71,1.05) | 1.14 | (0.93,1.40) | 0.0002 | 0.0037 | ||
| STAT6 | rs3024979 (D) | 1.02 | (0.88,1.19) | 0.79 | (0.64,0.99) | 1.36 | (1.04,1.77) | 0.0041 | 0.0216 | ||
| rs324011 (D) | 1.36 | (1.09,1.71) | 1.23 | (1.03,1.46) | 1.23 | (1.01,1.49) | 0.0345 | 0.1247 | |||
| JAK1 | rs2256298 (D) | rs1800630 (D) | 1.39 | (1.14,1.69) | 1.24 | (1.06,1.46) | 1.22 | (0.98,1.51) | 0.0190 | 0.1342 | |
| STAT4 | rs11685878 (D) | 1.54 | (1.20,1.97) | 1.17 | (0.99,1.39) | 1.21 | (0.99,1.48) | 0.0118 | 0.1810 | ||
| rs12327969 (D) | 1.39 | (1.15,1.67) | 1.20 | (1.02,1.42) | 1.12 | (0.90,1.40) | 0.0093 | 0.1500 | |||
| STAT6 | rs3024979 (D) | 1.08 | (0.92,1.27) | 0.83 | (0.67,1.01) | 1.50 | (1.12,2.02) | 0.0060 | 0.0295 | ||
| STAT3 | rs12949918 (A) | TNFRSF1A | rs4149570 (A) | 0.51 | (0.35,0.73) | 1.00 | (0.73,1.38) | 1.09 | (0.72,1.66) | 0.0146 | 0.1398 |
| STAT6 | rs3024979 (D) | rs4149570 (D) | 0.96 | (0.82,1.11) | 1.29 | (0.97,1.72) | 0.79 | (0.63,0.99) | 0.0140 | 0.1340 | |
| STAT3 | rs1053005 (D) | rs4149576 (D) | 1.22 | (1.02,1.46) | 1.44 | (1.13,1.83) | 1.20 | (0.98,1.46) | 0.0115 | 0.1173 | |
| rs6503695 (D) | 1.31 | (1.05,1.63) | 1.52 | (1.20,1.92) | 1.41 | (1.15,1.75) | 0.0210 | 0.1832 | |||
| STAT5A | rs12601982 (D) | 1.24 | (1.04,1.47) | 1.54 | (1.20,1.97) | 1.20 | (0.98,1.47) | 0.0027 | 0.0141 | ||
| rs7217728 (D) | 1.27 | (1.03,1.56) | 1.48 | (1.17,1.88) | 1.40 | (1.14,1.73) | 0.0458 | 0.1621 | |||
| STAT5B | rs7218653 (D) | 1.26 | (1.03,1.55) | 1.45 | (1.15,1.83) | 1.34 | (1.09,1.63) | 0.0286 | 0.1728 | ||
| STAT6 | rs3024979 (D) | 0.98 | (0.84,1.15) | 0.69 | (0.51,0.93) | 1.10 | (0.88,1.38) | 0.0086 | 0.0897 | ||
| STAT3 | rs6503695 (A) | rs4149577 (A) | 0.66 | (0.49,0.89) | 1.04 | (0.69,1.57) | 1.31 | (0.86,1.99) | 0.0192 | 0.1711 | |
| TYK2 | rs280519 (D) | rs4149578 (D) | 0.68 | (0.49,0.96) | 0.92 | (0.78,1.09) | 1.08 | (0.85,1.36) | 0.0074 | 0.0940 | |
| JAK1 | rs2256298 (D) | rs4149584 (D) | 0.61 | (0.38,0.96) | 1.07 | (0.93,1.23) | 1.85 | (1.13,3.04) | 0.0020 | 0.0411 | |
| STAT5B | rs9900213 (D) | IL6 | rs1800796 (D) | 0.75 | (0.58,0.97) | 1.03 | (0.88,1.20) | 1.22 | (0.87,1.72) | 0.0364 | 0.1926 |
| JAK1 | rs2256298 (A) | rs1800797 (A) | 1.17 | (0.90,1.53) | 1.75 | (1.16,2.63) | 0.94 | (0.43,2.03) | 0.0040 | 0.0692 | |
| JAK2 | rs10815160 (A) | 1.10 | (0.85,1.43) | 1.81 | (1.20,2.74) | 1.05 | (0.54,2.05) | 0.0045 | 0.0739 | ||
| STAT3 | rs12949918 (A) | 0.66 | (0.46,0.94) | 1.01 | (0.74,1.38) | 1.56 | (1.01,2.40) | 0.0152 | 0.1419 | ||
| rs6503695 (A) | 0.70 | (0.51,0.96) | 1.09 | (0.77,1.54) | 1.99 | (1.17,3.38) | 0.0212 | 0.1794 | |||
| STAT6 | rs324015 (A) | 0.77 | (0.59,0.99) | 0.77 | (0.49,1.22) | 1.66 | (0.79,3.47) | 0.0219 | 0.1895 | ||
| JAK2 | rs3780379 (D) | rs1800797 (D) | 1.10 | (0.93,1.31) | 1.26 | (1.01,1.59) | 0.85 | (0.70,1.03) | 0.0009 | 0.0175 | |
| STAT5B | rs9900213 (D) | 1.03 | (0.87,1.22) | 1.31 | (1.05,1.64) | 0.99 | (0.80,1.21) | 0.0347 | 0.1922 | ||
| STAT4 | rs10168266 (D) | rs2069827 (D) | 0.70 | (0.56,0.89) | 0.89 | (0.76,1.04) | 1.09 | (0.82,1.45) | 0.0039 | 0.1373 | |
| rs3024861 (D) | 0.67 | (0.52,0.86) | 0.91 | (0.79,1.06) | 1.05 | (0.81,1.37) | 0.0030 | 0.1131 | |||
| RECTAL | |||||||||||
| JAK2 | rs3780381 (A) | IFNG | rs2069727 (A) | 0.61 | (0.41,0.89) | 0.43 | (0.19,0.98) | 0.9 | (0.41,1.96) | 0.0124 | 0.1497 |
| STAT5B | rs9900213 (D) | IFNGR1 | rs1327474 (D) | 0.84 | (0.65,1.08) | 0.69 | (0.48,0.98) | 0.92 | (0.69,1.24) | 0.0342 | 0.1906 |
| SOCS2 | rs3816997 (D) | rs3799488 (D) | 0.98 | (0.75,1.28) | 0.75 | (0.59,0.96) | 1.23 | (0.82,1.84) | 0.0474 | 0.1905 | |
| rs768775 (D) | rs9376267 (D) | 1.02 | (0.80,1.30) | 0.93 | (0.72,1.21) | 1.49 | (1.11,2.01) | 0.0285 | 0.1290 | ||
| JAK1 | rs310198 (D) | IFNGR2 | rs1532 (D) | 1.02 | (0.81,1.27) | 1.15 | (0.84,1.57) | 0.64 | (0.46,0.91) | 0.0100 | 0.1725 |
| JAK2 | rs7043371 (A) | rs2834211 (D) | 2.18 | (1.39,3.41) | 1.32 | (0.97,1.79) | 1.09 | (0.68,1.74) | 0.0031 | 0.0668 | |
| STAT3 | rs1026916 (A) | rs2834215 (A) | 1.65 | (1.07,2.53) | 1.7 | (0.98,2.96) | 0.91 | (0.51,1.63) | 0.0054 | 0.0683 | |
| STAT5A | rs12601982 (D) | rs2834215 (D) | 1.22 | (0.94,1.58) | 1.53 | (1.03,2.29) | 1.08 | (0.81,1.46) | 0.0229 | 0.1098 | |
| TYK2 | rs280500 (D) | TNF | rs1800630 (D) | 1.39 | (1.08,1.78) | 1.48 | (1.15,1.91) | 1.22 | (0.86,1.73) | 0.0261 | 0.1443 |
| JAK2 | rs10815160 (D) | TNFRSF1A | rs4149570 (D) | 1.37 | (1.05,1.78) | 1.39 | (1.02,1.91) | 1.08 | (0.81,1.42) | 0.0048 | 0.0847 |
| SOCS2 | rs3816997 (D) | rs4149576 (D) | 1.28 | (1.01,1.63) | 1.17 | (0.83,1.64) | 0.87 | (0.65,1.18) | 0.0150 | 0.0696 | |
| rs4149578 (D) | 0.9 | (0.67,1.20) | 0.73 | (0.57,0.93) | 1.28 | (0.85,1.94) | 0.0114 | 0.0587 | |||
| STAT4 | rs3024904 (D) | 1.34 | (1.02,1.76) | 1.27 | (0.97,1.65) | 0.73 | (0.45,1.16) | 0.0035 | 0.1397 | ||
| SOCS1 | rs193779 (D) | 1.39 | (1.02,1.91) | 1.36 | (1.09,1.70) | 1.06 | (0.74,1.52) | 0.0181 | 0.0792 | ||
| rs4780355 (D) | 0.66 | (0.44,0.99) | 0.88 | (0.71,1.09) | 1.29 | (0.94,1.77) | 0.0018 | 0.0102 | |||
| JAK2 | rs3780379 (D) | IL6 | rs1800797 (D) | 1.36 | (1.07,1.74) | 1.46 | (1.07,2.01) | 1.14 | (0.85,1.54) | 0.0088 | 0.1332 |
| STAT4 | rs6752770 (A) | rs2069840 (A) | 0.58 | (0.37,0.91) | 0.55 | (0.31,0.95) | 1.24 | (0.53,2.89) | 0.0044 | 0.1564 | |
| STAT1 | rs16824035 (D) | rs2069840 (D) | 0.71 | (0.56,0.89) | 0.71 | (0.51,0.99) | 1.01 | (0.76,1.34) | 0.0015 | 0.0475 | |
| rs4327257 (D) | 0.74 | (0.59,0.92) | 0.7 | (0.48,1.01) | 1.06 | (0.78,1.44) | 0.0023 | 0.0694 | |||
| TYK2 | rs280519 (D) | 0.57 | (0.39,0.83) | 0.7 | (0.51,0.97) | 0.71 | (0.52,0.97) | 0.0097 | 0.1123 | ||
Models: A - additive or co-dominant; D - dominant
Compares wild type (WT) JAK/STAT/SOCS SNP and variant from additive or co-dominant model or heterozygote/variant if dominant model for pathway SNP relative to both WT
Compares variant from additive or co-dominant model or heterozygote/variant if dominant model for JAK/STAT/SOCS SNP and wild type (WT) pathway SNP relative to both WT
Compares variant from additive or co-dominant model or heterozygote/variant if dominant model for both JAK/STAT/SOCS and pathway SNPs relative to both WT
Assessment of interaction between genes in the JAK/STAT-signaling pathway and use of aspirin/NSAIDs, estrogen status, and cigarette smoking showed several significant interactions (Table 4). For colon cancer, TYK2 interacted significantly with aspirin/NSAID use; STAT1, STAT4, and TYK2 interacted with estrogen status; and JAK2, STAT2, STAT4. STAT5A, STAT5B, and STAT6 interacted with smoking status. Several significant associations also were detected with rectal cancer. JAK2, STAT6, and TYK2 interacted significantly with aspirin/NSAID use; JAK1 interacted with estrogen status and STAT2 with cigarette smoking. Of potential importance, is the observation that five STAT1 SNPs interacted with estrogen status for colon cancer and four JAK1 SNPs interacted with estrogen status for rectal cancer. Also, three JAK2 SNPs interacted with smoking status. The associations involving the variant genotype with either estrogen or NSAID use resulted in reduced risk of colon cancer below that observed for the variant without the lifestyle exposure, which is also true for NSAID use and rectal cancer. For colon cancer, having the variant genotype in the presence of smoking typically increased risk beyond that observed for being a smoker and not having the variant or having the variant and not smoking cigarettes.
Table 4.
Interaction between cigarette smoking, estrogen status, and NSAID use with JAK, STAT, TYK and cancer risk
|
|
|||||||||
|---|---|---|---|---|---|---|---|---|---|
| Gene | SNP (Model)1 | Variant2 | Wildtype | Interaction P Value | p_ACT | ||||
| OR | (95% CI) | OR | (95% CI) | OR | (95% CI) | ||||
| Colon Cancer | No Recent Aspirin/NSAID Use | Recent Aspirin/NSAID Use | |||||||
|
|
|||||||||
| TYK2 | rs280521 (D) | 1.00 | (0.82, 1.21) | 0.49 | (0.38, 0.64) | 0.72 | (0.61, 0.84) | 0.0266 | 0.1146 |
|
|
|||||||||
| No Recent Estrogen | Estrogen Use | ||||||||
|
|
|||||||||
| STAT1 | rs10199181 (R) | 1.19 | (0.85, 1.67) | 0.37 | (0.23, 0.62) | 0.65 | (0.50, 0.85) | 0.0171 | 0.1406 |
| rs10208033 (A) | 1.35 | (0.93, 1.96) | 0.39 | (0.23, 0.65) | 0.66 | (0.44, 0.98) | 0.0405 | 0.2628 | |
| rs2280233 (A) | 0.78 | (0.54, 1.11) | 0.58 | (0.37, 0.90) | 0.37 | (0.25, 0.56) | 0.0138 | 0.1239 | |
| rs3771300 (A) | 0.70 | (0.49, 1.00) | 0.58 | (0.37, 0.89) | 0.37 | (0.24, 0.57) | 0.0081 | 0.0805 | |
| rs7562024 (A) | 1.44 | (0.97, 2.12) | 0.48 | (0.29, 0.78) | 0.71 | (0.48, 1.04) | 0.0322 | 0.2250 | |
| STAT4 | rs6572770 (A) | 1.33 | (0.83, 2.11) | 0.40 | (0.20, 0.79) | 0.70 | (0.51, 0.98) | 0.0345 | 0.3578 |
| TYK2 | rs280519 (R) | 1.50 | (1.12, 2.01) | 0.59 | (0.39, 0.89) | 0.66 | (0.50, 0.87) | 0.0403 | 0.1705 |
|
|
|||||||||
| Non Smoker | Recent Smoker | ||||||||
|
|
|||||||||
| JAK2 | rs10815160 (R) | 1.17 | (0.87, 1.58) | 3.15 | (1.60, 6.22) | 1.11 | (0.93, 1.32) | 0.0166 | 0.0869 |
| rs1887429 (D) | 0.79 | (0.68, 0.92) | 1.16 | (0.90, 1.48) | 0.94 | (0.75, 1.19) | 0.0131 | 0.0873 | |
| rs7043371 (A) | 1.21 | (0.98, 1.49) | 1.12 | (0.80, 1.57) | 1.67 | (1.19, 2.34) | 0.015 | 0.0895 | |
| STAT2 | rs2229363 (D) | 1.20 | (0.74, 1.95) | 0.39 | (0.14, 1.07) | 1.20 | (1.01, 1.43) | 0.0156 | 0.0302 |
| STAT4 | rs4853540 (A) | 1.21 | (0.86, 1.68) | 0.79 | (0.42, 1.48) | 1.33 | (1.07, 1.66) | 0.0304 | 0.3310 |
| STAT5A | rs7217728 (A) | 1.15 | (0.89, 1.49) | 2.39 | (1.44, 3.96) | 0.98 | (0.76, 1.26) | 0.0098 | 0.0177 |
| STAT5B | rs7218563 (A) | 1.14 | (0.88, 1.47) | 2.30 | (1.39, 3.83) | 0.97 | (0.76, 1.25) | 0.0151 | 0.0398 |
| STAT6 | rs3024974 (D) | 0.88 | (0.72, 1.06) | 1.48 | (1.02, 2.15) | 1.07 | (0.89, 1.30) | 0.0465 | 0.1709 |
|
|
|||||||||
| Rectal Cancer | No Recent Aspirin/NSAID Use | Recent Aspirin/NSAID Use | |||||||
|
|
|||||||||
| JAK2 | rs10815160 (A) | 1.52 | (0.92, 2.51) | 0.49 | (0.26, 0.91) | 0.82 | (0.63, 1.07) | 0.019 | 0.1149 |
| STAT6 | rs324011 (D) | 1.48 | (1.14, 1.92) | 0.86 | (0.65, 1.14) | 0.94 | (0.68, 1.30) | 0.0207 | 0.0782 |
| TYK2 | rs280521 (D) | 1.40 | (1.05, 1.85) | 0.62 | (0.44, 0.88) | 0.82 | (0.65, 1.03) | 0.0092 | 0.0418 |
|
|
|||||||||
| No Recent Estrogen | Estrogen Use | ||||||||
|
|
|||||||||
| JAK1 | rs2256298 (A) | 0.38 | (0.14, 1.05) | 0.94 | (0.44, 2.02) | 0.55 | (0.35, 0.85) | 0.0427 | 0.1931 |
| rs310198 (D) | 0.69 | (0.40, 1.19) | 0.81 | (0.49, 1.34) | 0.54 | (0.37, 0.79) | 0.0276 | 0.1402 | |
| rs310199 (A) | 0.75 | (0.34, 1.62) | 1.01 | (0.50, 2.06) | 0.44 | (0.27, 0.71) | 0.0136 | 0.0837 | |
| rs310211 (A) | 0.32 | (0.12, 0.80) | 0.84 | (0.42, 1.69) | 0.51 | (0.32, 0.82) | 0.0138 | 0.0790 | |
|
|
|||||||||
| Non Smoker | Recent Smoker | ||||||||
|
|
|||||||||
| STAT2 | rs2229363 (D) | 1.74 | (0.86, 3.54) | 0.23 | (0.03, 1.92) | 1.36 | (1.05, 1.76) | 0.016 | 0.0309 |
Models: A - additive or co-dominant; D - dominant
Heterozygote/variant genotype if dominant model; variant if recessive or additive (or co-dominant); all comparisons are made to non-user/smoker and wildtype genotype
Several SNPs were associated with survival after diagnosis for both colon and rectal cancer (Table 5). For colon cancer, JAK2 (5 SNPs), SOCS1 (1 SNP), STAT3 (3 SNPs), STAT5 (1 SNP), and TYK2 (2 SNPs) were associated with survival. For rectal cancer, JAK2 (1 SNP), SOCS1 (1 SNP), STAT1 (4 SNPs), STAT4 (2 SNPs), and TYK2 (1 SNP) were associated with survival. In Table 5, we summarize the combined effect of these at-risk SNPs in relation to survival. For both colon and rectal cancer the hazard of dying increases with mutational load after adjusting for disease stage and molecular phenotype of the tumor. For colon cancer the estimate of risk of dying is HRR of 3.3 (95% CI 2.01, 5.42) for the highest category of mutational load, while for rectal cancer it is 2.80 (95 % CI 1.63, 4.80).
Table 5.
HRR from pathway SNPs associated with survival after diagnosis with colon or rectal cancer
| Summary Score | Death/Person Years | HRR1 | (95% CI) |
|---|---|---|---|
|
|
|||
| ColonCancer2 | |||
|
|
|||
| (0–7) | 31/1304 | 1.00 | |
| (8–9) | 36/985 | 2.41 | (1.47, 3.94) |
| (10–11) | 42/1487 | 1.70 | (1.06, 2.73) |
| (12–13) | 65/1530 | 2.49 | (1.60, 3.87) |
| (14–15) | 52/1212 | 2.63 | (1.67, 4.16) |
| (16–18) | 47/1061 | 2.57 | (1.61, 4.10) |
| (19–24) | 36/570 | 3.30 | (2.01, 5.42) |
| P Trend | <.0001 | ||
|
|
|||
| Rectal Cancer3 | |||
|
|
|||
| (0–4) | 19/729 | 1.00 | |
| (5–7) | 19/695 | 1.29 | (0.67, 2.49) |
| (8–10) | 33/842 | 1.97 | (1.11, 3.49) |
| (11–13) | 47/1074 | 1.62 | (0.94, 2.80) |
| (14–18) | 53/951 | 2.80 | (1.63, 4.80) |
| P Trend | <0.0001 | ||
Hazard Rate Ratios (HRR) and 95% Confidence Intervals (CI) adjusted for age, center, race, sex, AJCC stage, and tumor molecular phenotype
SNPs in colon cancer summary score: JAK2 rs10815160(A), rs10974947(D), rs1887429(A), rs3780379(A), and rs7043371(R), SOCS1 rs4780355(D), STAT3 rs1053005(D), rs2293152(R), and rs8069645(D), STAT5A rs12601982(D), TYK2 rs280521(D) and rs280523(D)
SNPs in rectal cancer summary score: JAK2 rs1536800(R), SOCS1 rs193779(D), STAT1 rs10199181(D), rs1547550(D), rs2280234(D) and rs7562024(A), STAT4 rs10168266(A) and rs16833260(D), TYK2 rs280519(D)
Discussion
Genetic variation in the JAK/STAT/SOCS-signaling pathway appears to be associated with both colon and rectal cancer risk. We observed associations with several SNPs for development of both colon and rectal cancer as well as with survival after diagnosis. The impact of the genetic variation in this signaling pathway goes beyond that observed for main effects and encompasses additional risk associated with interaction of genetic and lifestyle factors.
Evaluation of genetic variation in this pathway with risk of colon and rectal cancer has not previously been reported to our knowledge, however genetic associations between JAK2, TYK2, and STAT3 have been reported with Crohn’s disease and ulcerative colitis.14 The JAK/STAT/SOCS-signaling pathway plays a critical role in immune response and regulation of inflammation given its essential affiliation with cytokine signaling. Additionally, components of the pathway, such as STAT3, have been shown to promote uncontrolled cell growth and survival through dysregulation of gene expression involved in apoptosis, cell-cycle regulation, and angiogenesis.15 JAK1, JAK2, and STAT3 have been associated with colorectal cancer progression2. Thus, our observation that mutational load associated with the pathway influences survival is consistent with previous reports of biological effects of this pathway.
While the pathway appeared to be associated with both colon and rectal cancer, the magnitude of the association identified with each independent SNP was generally weak for both colon and rectal cancer. Key differences between colon and rectal cancer were observed. First, the number of SNPs and genes associated with colon cancer was greater than that observed for rectal cancer. However, evaluation of mutational load derived from these SNPs and the corresponding associations implied that for colon cancer the composite effect of having multiple variant alleles was only marginally greater than the risk associated with the individual SNPs themselves, while for rectal cancer, having all high-risk alleles, resulted in considerably greater risk than would be expected from addition of the independent risk. Likewise, differences also were observed in the SNPs associated with colon and rectal cancer. It is unclear why these differences exist. It could stem from the relative importance of different biological mechanisms for colon and rectal cancer, despite the overlap of importance for the pathway for both cancers, including genes targeted by various STATs. While we acknowledge that these differences could stem from chance findings, many associations remained significant after adjusting for multiple comparisons. These findings are supported by other reports showing differences in both genetic and lifestyle factors for colon and rectal cancer 11, 16–19. We have reported that miRNA expression profile of normal tissue from colon and rectal cancer are different20, further supporting the hypothesis that colon and rectal cancer represent two distinct diseases.
This pathway was associated with several key lifestyle factors, including aspirin/NSAID use, cigarette smoking and estrogen status. These lifestyle factors were targeted because of their association with inflammation which appears to be a critical modulator of colon and rectal cancer risk21. The role of aspirin and NSAID use in colon and rectal cancer risk is well documented 22–25 and has been hypothesized as stemming from the anti-inflammatory properties of these drugs. Cigarette smoking has been associated with increased nitric oxide (NO) synthesis by activating nitric oxide synthase (NOS2) and inflammation;26–28 NO has been shown to contribute to chronic inflammation 29. Estrogens could be operating via an inflammation-related mechanism given their influence on the NFκB pathway.30, 31 Estrogens also have been shown to activate STAT4.32 Additionally, JAK2 is essential for hormone-like cytokines such as prolactin9; estrogens are key regulators of prolactin. Thus, the observation that estrogen status interacts with genes in the JAK/STAT/SOCS-signaling pathway has a biological basis.
TNF, IFNG, and IL6 also were hypothesized to interact with JAK/STAT/SOCS-signaling pathway genes. The association between JAKs and cytokine signaling was identified when mutant JAK cell lines were shown to lack responsiveness to interferon while adding TYK2 restored IFN signaling9. Since then, both JAK1 and JAK2 have been shown to be important for cytokines such as TNF, IFN, and IL6 9. STAT1 and STAT2 also were originally discovered as mediators of IFN signaling8. JAK1 and JAK2 have been shown to be associated with IFNγ receptors subunits33. SOCS interacts directly with the JAK/STAT pathway and has been shown to suppress cellular response to various cytokines including IL6 and IFNγ34. Thus, we targeted cytokines thought to be operating in the pathway. We observed numerous statistically significant interactions between genes and SNPs in the JAK/STAT/SOCS signaling pathway and these targeted cytokines. Of these interactions, 10 had an adjusted p value of <0.05 and another 19 had adjusted p values of <0.10, many more had adjusted p values of 0.20 or less. Taken together, our data support the importance of the pathway and that genetic variation in this pathway is associated with colon and rectal cancer both for the independent effects, but also for their effect modification of cytokine genes.
Major strengths of our study were the hypothesis-driven approach, the large and extensive data set that includes information on genetic, lifestyle, and survival data, and our ability to examine colon and rectal cancer separately. While we believe that the data we present is both thorough and informative, we acknowledge that limitations exist. For instance, while we have detected associations we have minimal information on the functionality of the SNPs evaluated. Additional lab-based experiments are needed to determine functionality. Through our analysis of the JAK/STAT/SOCS-signaling pathway, we have made many comparisons. Although we have provided pACT values to take into account multiple comparisons, chance findings may exist and therefore replication of these findings is critical. A hazard of multiple testing adjustments is the increased likelihood of rejecting a finding that is true. Thus, we believe that adjusted p values of <0.20, especially for interactions, merit replication in other large sample sets to validate these findings.
In summary, these data support the hypothesis that the JAK/STAT/SOCS signaling pathway is associated with colon and rectal cancer because of their independent effects on risk as well as from the modifying effect they have on lifestyle and genetic factors. We hypothesized that this pathway is central to development of colon and rectal cancer because of its role in regulation of inflammation. We also provide data which suggest that this pathway is importantly related to survival after cancer diagnosis.
Supplementary Material
Acknowledgments
This study was funded by NCI grants CA48998 and CA61757. This 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 Northern California Cancer Registry, and the Sacramento Tumor Registry. 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. We would like to acknowledge the contributions of Dr. Bette Caan, Donna Schaffer, and Judy Morse at the Kaiser Permanente Medical Care Program in Oakland, California; Jennifer Herrick, Sandra Edwards, Roger Edwards, and Leslie Palmer at the University of Utah; and Drs. Kristin Anderson and John Potter at the University of Minnesota for data management and collection.
Footnotes
No authors have any conflict of interest to report.
References
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