Purpose: The IL6/STAT3 axis promotes inflammation, angiogenesis, and cancer. The effect of genetic variants within this pathway on benefit from antiangiogenic cancer therapy is unknown. We tested whether SNPs in genes involved in IL6/STAT3 signaling can predict efficacy of bevacizumab-based chemotherapy in metastatic colorectal cancer (mCRC) patients.

Experimental Design: Associations between potentially functional IL6 (rs2069837 and rs1800795) and STAT3 (rs744166 and rs4796793) SNPs and clinical outcomes [progression-free survival (PFS), overall survival, and tumor response rate] were evaluated in mCRC patients receiving first-line FOLFIRI plus bevacizumab in two randomized phase III trials: TRIBE (n = 223, training cohort) and FIRE-3 (n = 288, validation cohort). Patients receiving FOLFIRI plus cetuximab in FIRE-3 (n = 264) served as a control cohort. The interaction between genotype and primary tumor location with clinical outcomes was examined. Genomic DNA isolated from whole blood or tumor tissue was analyzed by PCR-based direct sequencing.

Results: Patients with an IL6 rs2069837 G allele treated with FOLFIRI plus bevacizumab had an inferior PFS than those with the A/A genotype in TRIBE [9.4 vs. 11.1 months; HR = 1.53; 95% confidence interval (CI), 1.12–2.10; P = 0.004] and FIRE-3 (8.8 vs. 10.9 months; HR = 1.40; 95% CI, 1.06–1.85; P = 0.015). These associations were confirmed in multivariable analyses and were not seen in the control cohort. In subgroup analysis, the effect of IL6 rs2069837 on PFS was present only in patients with left-sided cancers, but the test for interaction was not significant.

Conclusions:IL6 rs2069837 genotype is a clinically relevant prognostic factor in mCRC patients treated with first-line bevacizumab-based chemotherapy. Clin Cancer Res; 22(13); 3218–26. ©2016 AACR.

Translational Relevance

The IL6/STAT3 signaling pathway promotes inflammation, angiogenesis, and cancer. There are currently no validated markers of benefit from antiangiogenic cancer therapy, and understanding the prognostic impact of genetic variants within the IL6/STAT3 axis has the potential to inform therapeutic decisions. We analyzed associations between IL6 and STAT3 polymorphisms and outcomes in metastatic colorectal cancer (mCRC) patients who underwent targeted and cytotoxic therapy in two randomized phase III studies. Our data show for the first time that IL6 rs2069837 genotype is significantly associated with progression-free survival in mCRC patients treated with first-line bevacizumab-based chemotherapy, but not in those receiving cetuximab-based treatment. Furthermore, our results suggest that the effect of IL6 polymorphisms on outcomes may depend on primary tumor location. Therefore, IL6 genetic variants may serve as a promising biomarker in patients with mCRC treated with bevacizumab-based chemotherapy.

IL6 is a pleiotropic, angiogenic cytokine with far-reaching effects across vascular disease, immunity, and cancer (1, 2). As initiators of pathologic inflammation, IL6 and its downstream effector, STAT3, promote colorectal cancer development (3, 4), invasion, and metastasis (5–7) by imposing genetic alterations in tumor cells (3, 8) and facilitating immune tolerance within the microenvironment (6, 9, 10). In colitis-associated cancer in vivo models, IL6 enhances tumor cell proliferation and protects normal and malignant intestinal epithelial cells from apoptosis in a STAT3-dependent fashion.

Another critical consequence of IL6/STAT3 signaling is angiogenesis. Under hypoxic states, IL6 promotes HIF-1α and STAT3 transcription, which stimulates VEGF expression, blood vessel formation, and tumor growth (11–13). In addition to tumor cells, stromal fibroblasts are an important source of IL6 and provide a fertile environment for angiogenesis to occur (14). Evidence from tumor xenografts suggests that IL6 may trigger defective angiogenesis with reduced pericyte coverage, independent of VEGF signaling (15), and may therefore serve as a resistance mechanism to antiangiogenic therapy.

Previous data suggest that IL6/STAT3 SNPs may have prognostic utility in colorectal cancer. Although the evidence has been mixed (16–21), IL6 SNPs have been shown to correlate with serum and intratumoral IL6 levels (22–24), as well as the susceptibility (16–19, 21, 23, 25) and survival (20) of multiple cancers, including colorectal cancer. Similarly, STAT3 common genetic variants have been associated with cancer risk (26). However, the potential of IL6/STAT3 SNPs to predict efficacy of antiangiogenic therapy in colorectal cancer has not been tested.

The primary objective of this study was to investigate the predictive and prognostic impact of functionally significant IL6 and STAT3 polymorphisms in metastatic colorectal cancer (mCRC) patients receiving cytotoxic and antiangiogenic therapy. We determined associations between IL6 (rs2069837 and rs1800795) and STAT3 (rs744166 and rs4796793) SNPs and outcomes in a phase III mCRC trial of first-line bevacizumab-based chemotherapy. We then validated our findings in an independent patient cohort from another large phase III study and examined associations in a control cohort of patients receiving cetuximab-based treatment. In addition, we assessed the influence of primary tumor site on the observed associations to identify potentially clinically relevant profiles.

Study design and patient population

A total of 775 patients were included in this study. Patients treated with first-line FOLFIRI plus bevacizumab in the randomized, open-label, phase III TRIBE trial (24) served as the training cohort, whereas patients treated with first-line FOLFIRI plus bevacizumab in the randomized, open-label, phase III FIRE-3 trial (27) served as the validation cohort and patients treated with first-line FOLFIRI plus cetuximab in FIRE-3 served as the control cohort. Patients without sufficient blood for analysis were excluded. Analysis of the effects of IL6 (rs2069837, rs1800795) and STAT3 (rs744166, rs4796793) SNPs on clinical outcomes was conducted in 223 mCRC patients treated with first-line FOLFIRI plus bevacizumab in TRIBE (24) with sufficient blood for analysis (87% of 256 enrolled patients). Relevant SNPs significantly associated with outcomes were then examined in the validation and control cohorts. The validation cohort consisted of 288 patients with sufficient tissue (98% enrolled of 295 patients) treated with first-line FOLFIRI plus bevacizumab in FIRE-3 (27). The control set consisted of 264 patients with sufficient tissue (89% of 297 enrolled patients) treated with first-line FOLFIRI plus cetuximab in FIRE-3.

Patients in the training, validation, and control cohorts had histologically confirmed stage IV colorectal adenocarcinoma, with measurable disease per RECIST 1.0 and no prior treatment for metastatic disease or exposure to irinotecan, bevacizumab, or cetuximab. Standard inclusion and exclusion criteria were applied. Prior adjuvant oxaliplatin treatment was allowed in both TRIBE and FIRE-3 if more than 12 months had elapsed between the end of treatment and the date of study enrollment. Patients received bevacizumab (5 mg/kg) or cetuximab (400 mg/kg), followed by irinotecan (180 mg/m2) administered with folinic acid (leucovorin; 200 mg/m2), 5-fluorouracil (5-FU; 400 mg/m2) bolus infusion, and 5-FU (2,400 mg/m2) as a 48-hour continuous infusion. The regimen was repeated at two-week intervals. In the TRIBE study, patients received 12 cycles of FOLFIRI plus bevacizumab, followed by fluorouracil and bevacizumab maintenance therapy. Treatment was administered in all patients until the time of disease progression, intolerable toxicities, or patient withdrawal. Responses were measured by intravenous contrast-enhanced CT scans every 8 weeks according to RECIST v1.0.

Study protocols were approved by the Institutional Review Boards of each participating center and conducted in accordance with the Declaration of Helsinki and Good Clinical Practice Guidelines. All patients signed informed consent for the analysis of molecular correlates.

Candidate polymorphisms

Genes were chosen according to the previously published literature and databases. Polymorphisms were chosen for investigation if the minor allele frequency exceeded 10% in Caucasians according to the ENSEMBL database (http://www.ensembl.org/index.html) and if the functional or predicted functional relevance of gene transcription or protein expression was described previously. Functional significance was predicted based on information provided by the National Institute of Environmental Health Science SNP Function Prediction, Queen's University F-SNP, and the location of the SNP in the protein-coding region of the gene (http://snpinfo.niehs.nih.gov/snpinfo/snptag.htm).

Genotyping

Genomic DNA was extracted from the peripheral whole blood of patients in the training cohort and from formalin-fixed paraffin-embedded (FFPE) tissues of patients from the validation and control cohorts using the QIAamp Kit (Qiagen) according to the manufacturer's instructions (www.qiagen.com). PCR-based direct DNA sequence analysis using ABI 3100A Capillary Genetic Analyzer and Sequencing Scanner v1.0 (Applied Biosystems) was performed for genotyping the SNPs. The extracted DNA was amplified using the primer sets shown in Supplementary Table S1 and analyzed by PCR-based direct DNA sequencing.

For quality control purposes, a random selection of 10% of the samples was reexamined for each polymorphism, and the genotype concordance rate was 100%. The investigator analyzed the sequencing data using the ABI Sequencing Scanner v1.0 (Applied Biosystems, Life Technologies) and was blinded to the clinical dataset.

Statistical analysis

The primary outcome measure was progression-free survival (PFS), defined as time from randomization to first documented disease progression or death from any cause. If progression or death was not observed, PFS was censored on the day of the last CT scan. Secondary endpoints were response rate (RR) and overall survival (OS). Patients were dichotomized into responders (including complete or partial response) and nonresponders (including stable or progressive disease) as defined by RECIST. OS was defined as the period from randomization to the date of death or censored on the date of last contact if alive. Allelic distribution of polymorphisms by ethnicity was tested for deviation from Hardy–Weinberg equilibrium (HWE) using the exact test.

The differences between baseline characteristics for the three cohorts were compared by using the χ2 test or the Kruskal–Wallis test whenever appropriate. Associations between SNPs and PFS, OS, and RR were examined using Kaplan–Meier curves, log-rank test, and Fisher exact test, respectively. A Cox proportional hazards regression model with stratification factors was fitted to reevaluate the association between SNPs and PFS and OS, considering imbalances in the distributions of baseline characteristics among the cohorts. Codominant, dominant, and additive models for each SNP were analyzed. For the additive model, a trend test was calculated, assigning a linear score to each genotype (0 for homozygous common allele, 1 for heterozygous common allele, and 2 for homozygous rare allele). The baseline demographic and clinical characteristics that remained significantly associated with endpoints in the multivariable analysis (P < 0.10) were included in the final model. These findings were then evaluated in the independent validation and control cohorts. Subgroup analyses by tumor location were also investigated. Interaction terms of SNPs and tumor location were included, and likelihood ratio tests were performed in the multivariable Cox proportional hazards regression models. Case-wide deletion for missing polymorphisms was applied in univariate and multivariable analysis.

The training cohort consisted of 223 patients (171 PFS events); therefore, there was 80% power to detect an association between a SNP and PFS with a minimum HR from 1.54 to 2.07 using a two-sided 0.05-level log-rank test. We assumed that the minor allele frequency varied from 0.05 to 0.4, and the dominant model was used. In the validation cohort, there was greater than 88% power using the same test to detect the same HRs with the same allele frequencies under the dominant model (n = 288, 245 PFS events). In the control cohort (n = 264, 229 PFS events), there was 95% power to demonstrate no significant association between a SNP and PFS, with a HR equal to 1.00 under the alternative hypothesis against the null hypothesis (HR, 1.54) for a SNP with a minor allele frequency of 0.3, using a dominant model and a one-sided 0.05-level test.

SAS 9.4 (SAS Institute) was used to perform all analyses. All tests were two sided at a significance level of 0.05.

Patient and tumor characteristics

Clinicopathologic characteristics for the training, validation, and control cohorts are presented in Table 1. The median follow-up was 4.1 years in the training cohort, 3.4 years in the validation cohort, and 3.4 years in the control cohort. The median PFS and OS in each cohort were as follows: 8.2 months and 26.1 months (TRIBE training cohort), 10.1 months and 24.2 months (FIRE-3 validation cohort), and 9.6 months and 28.0 months (FIRE-3 control cohort).

Table 1.

Baseline patient and tumor characteristics

Training cohort (TRIBE)Validation cohort (FIRE-3)Control cohort (FIRE-3)
(n = 223)(n = 288)(n = 264)
n (%)n (%)n (%)Pa
Gender    0.53 
 Male 136 (61) 189 (66) 171 (65)  
 Female 87 (39) 99 (34) 93 (35)  
Age (years)     
 Median (range) 60 (29–75) 65 (31–76) 65 (38–79) <0.001 
 ≤65 160 (72) 151 (52) 140 (53) <0.001 
 >65 63 (28) 137 (48) 124 (47)  
Performance status    <0.001 
 ECOG 0 183 (82) 159 (55) 135 (51)  
 ECOG ≥1 39 (17) 129 (45) 129 (49)  
 Unknownb 1 (1)    
Primary tumor site    0.038 
 Right-sided colon 57 (26) 63 (22) 38 (14)  
 Left-sided colon 151 (68) 173 (60) 174 (66)  
 Unknownb 15 (7) 52 (18) 52 (20)  
Liver only metastases    0.94 
 Yes 71 (32) 95 (33) 88 (33)  
 No 152 (68) 193 (67) 176 (67)  
Number of metastases    0.97 
 <2 95 (43) 105 (36) 92 (35)  
 ≥2 128 (57) 137 (48) 125 (47)  
 Unknownb  46 (16) 47 (18)  
Time to metastasis    0.15 
 Synchronous 184 (83) 183 (64) 163 (62)  
 Metachronous 39 (17) 58 (20) 52 (20)  
 Unknownb  46 (16) 47 (18)  
Primary tumor resected    <0.001 
 Yes 142 (64) 249 (86) 219 (83)  
 No 81 (36) 39 (14) 44 (17)  
Adjuvant chemotherapy    0.027 
 Yes 27 (12) 53 (18) 56 (21)  
 No 196 (88) 235 (82) 207 (79)  
High LDH    0.66 
 Yes 104 (47) 71 (25) 64 (24)  
 No 87 (39) 70 (24) 64 (24)  
 Unknownb 32 (14) 147 (51) 136 (52)  
KRAS status    <0.001 
 Wild type 95 (43) 242 (84) 217 (82)  
 Mutant 90 (40) 46 (16) 47 (18)  
 Unknownb 38 (17)    
Training cohort (TRIBE)Validation cohort (FIRE-3)Control cohort (FIRE-3)
(n = 223)(n = 288)(n = 264)
n (%)n (%)n (%)Pa
Gender    0.53 
 Male 136 (61) 189 (66) 171 (65)  
 Female 87 (39) 99 (34) 93 (35)  
Age (years)     
 Median (range) 60 (29–75) 65 (31–76) 65 (38–79) <0.001 
 ≤65 160 (72) 151 (52) 140 (53) <0.001 
 >65 63 (28) 137 (48) 124 (47)  
Performance status    <0.001 
 ECOG 0 183 (82) 159 (55) 135 (51)  
 ECOG ≥1 39 (17) 129 (45) 129 (49)  
 Unknownb 1 (1)    
Primary tumor site    0.038 
 Right-sided colon 57 (26) 63 (22) 38 (14)  
 Left-sided colon 151 (68) 173 (60) 174 (66)  
 Unknownb 15 (7) 52 (18) 52 (20)  
Liver only metastases    0.94 
 Yes 71 (32) 95 (33) 88 (33)  
 No 152 (68) 193 (67) 176 (67)  
Number of metastases    0.97 
 <2 95 (43) 105 (36) 92 (35)  
 ≥2 128 (57) 137 (48) 125 (47)  
 Unknownb  46 (16) 47 (18)  
Time to metastasis    0.15 
 Synchronous 184 (83) 183 (64) 163 (62)  
 Metachronous 39 (17) 58 (20) 52 (20)  
 Unknownb  46 (16) 47 (18)  
Primary tumor resected    <0.001 
 Yes 142 (64) 249 (86) 219 (83)  
 No 81 (36) 39 (14) 44 (17)  
Adjuvant chemotherapy    0.027 
 Yes 27 (12) 53 (18) 56 (21)  
 No 196 (88) 235 (82) 207 (79)  
High LDH    0.66 
 Yes 104 (47) 71 (25) 64 (24)  
 No 87 (39) 70 (24) 64 (24)  
 Unknownb 32 (14) 147 (51) 136 (52)  
KRAS status    <0.001 
 Wild type 95 (43) 242 (84) 217 (82)  
 Mutant 90 (40) 46 (16) 47 (18)  
 Unknownb 38 (17)    

Abbreviation: LDH, lactate dehydrogenase.

aBased on χ2 test or Kruskal–Wallis test whenever appropriate.

bNot included in the χ2 test.

Associations between baseline characteristics and clinical outcomes were examined using the log-rank test in univariate analysis. In the training cohort, Eastern Cooperative Oncology Group (ECOG) performance status, primary tumor resection, and BRAF status were significantly associated with PFS and OS. In addition, age, primary tumor site, number of metastases, time to metastasis, and adjuvant chemotherapy were also significantly correlated with OS (Supplementary Table S1). In the validation cohort, ECOG performance status was significantly associated with PFS and OS. In addition, primary tumor site, liver only disease, number of metastases, and primary tumor resection were also significantly associated with OS (Supplementary Table S2). In the control cohort, primary tumor site was significantly associated with PFS and OS. In addition, sex was significantly correlated with PFS; lung metastasis, liver metastasis, number of metastases, primary tumor resection, time to metastasis, and high LDH and KRAS status were also significantly associated with OS (Supplementary Table S3).

Clinical outcomes by IL6 and STAT3 genetic variants in patients receiving FOLFIRI with bevacizumab: TRIBE and FIRE-3

All genotype frequencies for candidate polymorphisms analyzed were within HWE (P > 0.05) for each ethnic/race group in each cohort.

Genotyping for the IL6 rs2069837 candidate SNPs was successful in 223 patients and 270 (94%) patients in the training and validation cohorts, respectively. In 18 patients of the validation cohort, genotyping was not successful due to limited quantity or quality of extracted genomic DNA.

Associations between IL6 rs2069837 and outcomes were analyzed (Table 2). Among evaluable patients in the training (n = 223) and validation (n = 270) cohorts, 58.3% and 32.2% carried a G allele in IL6 rs2069837, respectively.

Table 2.

Association between IL6 rs2069837 with clinical outcomes

Tumor responsePFSOS
NCR + PRSD + PDPaMedian, months (95% CI)HR (95% CI)bPaHR (95% CI)cPaMedian, months (95% CI)HR (95% CI)bPaHR (95% CI)cPa
Training cohort    0.11   0.001  0.017   0.34  0.70 
 A/A 93 57 (63%) 34 (37%)  11.1 (9.4–12.6) 1 (reference)  1 (reference)  26.8 (23.9–35.8) 1 (reference)  1 (reference)  
 A/G 116 58 (52%) 53 (48%)  9.2 (8.0–9.7) 1.68 (1.22–2.33)  1.67 (1.14–2.46)  22.5 (19.4–27.9) 1.24 (0.91–1.69)  1.17 (0.81–1.70)  
 G/G 14 11 (79%) 3 (21%)  12.7 (8.2–14.2) 0.81 (0.41–1.62)  0.73 (0.29–1.84)  31.9 (16.3–44.1) 0.97 (0.52–1.83)  1.05 (0.45–2.44)  
    0.33   0.004  0.033   0.22  0.43 
 A/A 93 57 (63%) 34 (37%)  11.1 (9.4–12.6) 1 (reference)  1 (reference)  26.8 (23.9–35.8) 1 (reference)  1 (reference)  
 Any G 130 69 (55%) 56 (45%)  9.4 (8.6–10.3) 1.53 (1.12–2.10)  1.50 (1.03–2.17)  22.9 (19.8–27.9) 1.21 (0.89–1.63)  1.16 (0.81–1.66)  
Additive model: HR per G      1.21 (0.96–1.52) 0.10 1.20 (0.90–1.59) 0.21  1.10 (0.87–1.40) 0.42 1.10 (0.82–1.48) 0.51 
Validation cohort    0.040   0.024  0.072   0.15  0.59 
 A/A 183 113 (67%) 56 (33%)  10.9 (9.9–12.4) 1 (reference)  1 (reference)  26.1 (23.1–28.4) 1 (reference)  1 (reference)  
 A/G 58 25 (47%) 28 (53%)  7.8 (6.5–9.3) 1.54 (1.12–2.11)  1.47 (1.06–2.03)  22.7 (14.7–25.9) 1.21 (0.85–1.73)  1.11 (0.78–1.59)  
 G/G 29 16 (62%) 10 (38%)  10.2 (8.2–12.7) 1.17 (0.75–1.82)  1.12 (0.71–1.77)  19.5 (15.4–29.1) 1.51 (0.96–2.38)  1.26 (0.78–2.03)  
    0.026   0.015  0.047   0.084  0.36 
 A/A 183 113 (67%) 56 (33%)  10.9 (9.9–12.4) 1 (reference)  1 (reference)  26.1 (23.1–28.4) 1 (reference)  1 (reference)  
 Any G 87 41 (52%) 38 (48%)  8.8 (7.2–10.2) 1.40 (1.06–1.85)  1.34 (1.00–1.79)  22.1 (16.7–25.9) 1.30 (0.96–1.76)  1.16 (0.85–1.58)  
Additive model: HR per G      0.18 (0.98–1.42) 0.076 1.15 (0.94–1.39) 0.17  1.23 (1.00–1.51) 0.053 1.12 (0.90–1.39) 0.31 
Control cohort    0.042   0.22  0.24   0.66  0.43 
 A/A 179 108 (70%) 46 (30%)  9.5 (8.3–10.4) 1 (reference)  1 (reference)  28.7 (23.5–36.4) 1 (reference)  1 (reference)  
 A/G 71 43 (70%) 18 (30%)  9.7 (7.9–12.2) 1.04 (0.78–1.39)  0.98 (0.73–1.31)  28.5 (20.3–38.3) 1.09 (0.77–1.55)  1.10 (0.77–1.56)  
 G/G 14 4 (33%) 8 (67%)  6.0 (1.9–10.9) 1.64 (0.93–2.91)  1.61 (0.91–2.85)  20.2 (10.0–36.6) 1.33 (0.67–2.63)  1.56 (0.78–3.13)  
    0.36   0.46  0.77   0.48  0.41 
 A/A 179 108 (70%) 46 (30%)  9.5 (8.3–10.4) 1 (reference)  1 (reference)  28.7 (23.5–36.4) 1 (reference)  1 (reference)  
 Any G 85 47 (64%) 26 (36%)  9.6 (7.8–10.9) 1.11 (0.84–1.45)  1.04 (0.79–1.37)  25.2 (19.9–37.0) 1.13 (0.81–1.57)  1.15 (0.82–1.61)  
Additive model: HR per G      1.15 (0.92–1.44) 0.23 1.10 (0.88–1.39) 0.41  1.12 (0.86–1.47) 0.39 1.17 (0.89–1.54) 0.26 
Tumor responsePFSOS
NCR + PRSD + PDPaMedian, months (95% CI)HR (95% CI)bPaHR (95% CI)cPaMedian, months (95% CI)HR (95% CI)bPaHR (95% CI)cPa
Training cohort    0.11   0.001  0.017   0.34  0.70 
 A/A 93 57 (63%) 34 (37%)  11.1 (9.4–12.6) 1 (reference)  1 (reference)  26.8 (23.9–35.8) 1 (reference)  1 (reference)  
 A/G 116 58 (52%) 53 (48%)  9.2 (8.0–9.7) 1.68 (1.22–2.33)  1.67 (1.14–2.46)  22.5 (19.4–27.9) 1.24 (0.91–1.69)  1.17 (0.81–1.70)  
 G/G 14 11 (79%) 3 (21%)  12.7 (8.2–14.2) 0.81 (0.41–1.62)  0.73 (0.29–1.84)  31.9 (16.3–44.1) 0.97 (0.52–1.83)  1.05 (0.45–2.44)  
    0.33   0.004  0.033   0.22  0.43 
 A/A 93 57 (63%) 34 (37%)  11.1 (9.4–12.6) 1 (reference)  1 (reference)  26.8 (23.9–35.8) 1 (reference)  1 (reference)  
 Any G 130 69 (55%) 56 (45%)  9.4 (8.6–10.3) 1.53 (1.12–2.10)  1.50 (1.03–2.17)  22.9 (19.8–27.9) 1.21 (0.89–1.63)  1.16 (0.81–1.66)  
Additive model: HR per G      1.21 (0.96–1.52) 0.10 1.20 (0.90–1.59) 0.21  1.10 (0.87–1.40) 0.42 1.10 (0.82–1.48) 0.51 
Validation cohort    0.040   0.024  0.072   0.15  0.59 
 A/A 183 113 (67%) 56 (33%)  10.9 (9.9–12.4) 1 (reference)  1 (reference)  26.1 (23.1–28.4) 1 (reference)  1 (reference)  
 A/G 58 25 (47%) 28 (53%)  7.8 (6.5–9.3) 1.54 (1.12–2.11)  1.47 (1.06–2.03)  22.7 (14.7–25.9) 1.21 (0.85–1.73)  1.11 (0.78–1.59)  
 G/G 29 16 (62%) 10 (38%)  10.2 (8.2–12.7) 1.17 (0.75–1.82)  1.12 (0.71–1.77)  19.5 (15.4–29.1) 1.51 (0.96–2.38)  1.26 (0.78–2.03)  
    0.026   0.015  0.047   0.084  0.36 
 A/A 183 113 (67%) 56 (33%)  10.9 (9.9–12.4) 1 (reference)  1 (reference)  26.1 (23.1–28.4) 1 (reference)  1 (reference)  
 Any G 87 41 (52%) 38 (48%)  8.8 (7.2–10.2) 1.40 (1.06–1.85)  1.34 (1.00–1.79)  22.1 (16.7–25.9) 1.30 (0.96–1.76)  1.16 (0.85–1.58)  
Additive model: HR per G      0.18 (0.98–1.42) 0.076 1.15 (0.94–1.39) 0.17  1.23 (1.00–1.51) 0.053 1.12 (0.90–1.39) 0.31 
Control cohort    0.042   0.22  0.24   0.66  0.43 
 A/A 179 108 (70%) 46 (30%)  9.5 (8.3–10.4) 1 (reference)  1 (reference)  28.7 (23.5–36.4) 1 (reference)  1 (reference)  
 A/G 71 43 (70%) 18 (30%)  9.7 (7.9–12.2) 1.04 (0.78–1.39)  0.98 (0.73–1.31)  28.5 (20.3–38.3) 1.09 (0.77–1.55)  1.10 (0.77–1.56)  
 G/G 14 4 (33%) 8 (67%)  6.0 (1.9–10.9) 1.64 (0.93–2.91)  1.61 (0.91–2.85)  20.2 (10.0–36.6) 1.33 (0.67–2.63)  1.56 (0.78–3.13)  
    0.36   0.46  0.77   0.48  0.41 
 A/A 179 108 (70%) 46 (30%)  9.5 (8.3–10.4) 1 (reference)  1 (reference)  28.7 (23.5–36.4) 1 (reference)  1 (reference)  
 Any G 85 47 (64%) 26 (36%)  9.6 (7.8–10.9) 1.11 (0.84–1.45)  1.04 (0.79–1.37)  25.2 (19.9–37.0) 1.13 (0.81–1.57)  1.15 (0.82–1.61)  
Additive model: HR per G      1.15 (0.92–1.44) 0.23 1.10 (0.88–1.39) 0.41  1.12 (0.86–1.47) 0.39 1.17 (0.89–1.54) 0.26 

Abbreviations: CR, complete response; PD, progressive disease; PR, partial response; SD, stable disease.

aP value was based on Fisher exact test for tumor response.

bP value was based on log-rank test for PFS and OS in the univariate analysis and Wald test for PFS and OS in the multivariable Cox regression model adjusted for age, performance status, primary tumor site, primary tumor resection, BRAF mutation status, and adjuvant chemotherapy in the training cohort.

cAdjusted for sex, performance status, liver only disease, and primary tumor resection in the validation cohort and control cohort.

In the TRIBE training cohort, patients carrying an IL6 rs2069837 G allele had an inferior median PFS of 9.4 months compared with those with the A/A genotype, who had a median PFS of 11.1 months [HR, 1.53; 95% confidence interval (CI), 1.12–2.10; P = 0.004; Table 2; Fig. 1A]. This association remained significant in multivariable analysis (HR, 1.50; adjusted P = 0.033; Table 2). Additive models showed no evidence of significant effect for each G allele. There was no evidence for an association between IL6 rs2069837 genotype and RR or any significant relationships between IL6 rs1800795 and STAT3 (rs744166, rs4796793) genotype and outcomes in the training cohort (Supplementary Table S4).

Figure 1.

Comparison of clinical outcomes by IL6 gene polymorphism in two cohorts. Kaplan–Meier cumulative PFS probability curves stratified by IL6 rs2069837 genotype in the training cohort (A) and validation cohort (B).

Figure 1.

Comparison of clinical outcomes by IL6 gene polymorphism in two cohorts. Kaplan–Meier cumulative PFS probability curves stratified by IL6 rs2069837 genotype in the training cohort (A) and validation cohort (B).

Close modal

In the FIRE-3 validation cohort, IL6 rs2069837 was significantly associated with PFS in univariate analysis. Patients harboring any G allele had a shorter median PFS (8.8 months) compared with those with an A/A genotype (10.9 months; HR, 1.40; 95% CI, 1.06–1.85; P = 0.015; Table 2; Fig. 1B). In multivariable analysis, IL6 rs2069837 genotype remained significantly associated with PFS (HR, 1.34; adjusted P = 0.047). In addition, those with an A/A genotype had a significantly higher tumor response rate compared with those with any G allele (67% vs. 52%, Fisher exact P = 0.026).

There was no evidence for an association between IL6 rs2069837 and OS in either the training or validation cohorts.

Clinical outcomes by IL6 genetic variants in patients receiving FOLFIRI with cetuximab: FIRE-3

In the FIRE-3 control cohort, genotyping for the IL6 rs2069837 was successful in 264 patients (89%). Genotyping was not successful in 33 patients because of a limited quantity and quality of extracted genomic DNA. Patients with the G/G genotype had a significantly lower RR (33%) than those with the A/G or A/A variants (70% for both, Fisher exact P = 0.042; Table 3). There was no evidence for an association between the IL6 rs2069837 genotype with PFS or OS in univariate or multivariate analyses (Table 2).

Table 3.

Association between IL6 rs2069837 and clinical outcomes stratified by primary tumor location

Tumor responsePFSOS
NCR + PRSD + PDPaMedian, months (95% CI)HR (95% CI)bPaHR (95% CI)cPaMedian, months (95% CI)HR (95% CI)bPaHR (95% CI)cPa
Left-sided               
Training cohort    0.19   0.031  0.058   0.88  0.87 
 A/A 65 39 (61%) 25 (39%)  11.1 (9.4–12.7) 1 (reference)  1 (reference)  33.1 (24.8–39.7) 1 (reference)  1 (reference)  
 A/G 76 38 (51%) 36 (49%)  9.6 (8.3–10.8) 1.57 (1.05–2.36)  1.68 (1.05–2.69)  27.9 (20.5–34.4) 1.10 (0.75–1.62)  1.13 (0.72–1.78)  
 G/G 10 8 (80%) 2 (20%)  12.7 (4.5–24.0) 0.86 (0.39–1.89)  0.74 (0.26–2.09)  34.5 (5.2–55.5+) 1.02 (0.48–2.15)  1.02 (0.40–2.61)  
    0.50   0.050  0.088   0.65  0.62 
 A/A 65 39 (61%) 25 (39%)  11.1 (9.4–12.7) 1 (reference)  1 (reference)  33.1 (24.8–39.7) 1 (reference)  1 (reference)  
 Any G 86 46 (55%) 38 (45%)  9.7 (8.6–11.0) 1.43 (0.97–2.12)  1.48 (0.94–2.32)  28.3 (21.1–34.4) 1.09 (0.75–1.58)  1.12 (0.72–1.73)  
Additive model: HR per G      1.20 (0.75–1.93) 0.45 1.17 (0.84–1.63) 0.36  1.05 (0.78–1.42) 0.74 1.06 (0.75–1.51) 0.73 
Validation cohort    0.30   0.13  0.23   0.14  0.75 
 A/A 122 76 (68%) 35 (32%)  11.7 (9.9–13.0) 1 (reference)  1 (reference)  27.5 (24.8–31.9) 1 (reference)  1 (reference)  
 A/G 28 13 (52%) 12 (48%)  10.1 (6.5–12.2) 1.53 (0.97–2.44)  1.47 (0.92–2.34)  30.3 (16.7–42.5) 1.01 (0.59–1.73)  0.95 (0.55–1.63)  
 G/G 14 9 (64%) 5 (36%)  10.2 (7.4–12.9) 1.35 (0.75–2.43)  1.32 (0.72–2.43)  18.9 (15.1–29.6) 1.81 (0.98–3.35)  1.27 (0.64–2.51)  
    0.18   0.047  0.090   0.32  0.85 
 A/A 122 76 (68%) 35 (32%)  11.7 (9.9–13.0) 1 (reference)  1 (reference)  27.5 (24.8–31.9) 1 (reference)  1 (reference)  
 Any G 42 22 (56%) 17 (44%)  10.2 (7.4–12.0) 1.46 (0.99–2.16)  1.41 (0.95–2.11)  21.3 (17.3–34.6) 1.25 (0.80–1.93)  1.04 (0.66–1.65)  
Additive model: HR per G      1.24 (0.96–1.60) 0.093 1.22 (0.93–1.59) 0.15  1.26 (0.94–1.69) 0.12 1.08 (0.79–1.48) 0.64 
Right-sided               
Training cohort    0.80   0.14  0.43   0.073  0.19 
 A/A 22 14 (67%) 7 (33%)  9.3 (6.7–13.4) 1 (reference)  1 (reference)  24.3 (12.5–33.9) 1 (reference)  1 (reference)  
 A/G 32 16 (55%) 13 (45%)  8.8 (6.2–9.9) 1.53 (0.83–2.81)  1.38 (0.63–3.03)  18.3 (14.1–20.3) 1.68 (0.92–3.08)  1.69 (0.78–3.69)  
 G/G 2 (67%) 1 (33%)            
Additive model: HR per G      1.29 (0.74–2.24) 0.38 1.11 (0.61–2.00) 0.73  1.80 (0.99–3.30) 0.056 1.55 (0.82–2.93) 0.17 
Validation cohort    0.15   0.22  0.28   0.35  0.20 
 A/A 29 19 (70%) 8 (30%)  10.5 (9.0–13.0) 1 (reference)  1 (reference)  21.0 (16.5–24.2) 1 (reference)  1 (reference)  
 A/G 19 7 (41%) 10 (59%)  6.6 (5.7–8.8) 1.40 (0.78–2.52)  1.41 (0.76–2.62)  15.9 (9.3–23.7) 1.33 (0.72–2.46)  1.55 (0.79–3.02)  
 G/G 10 5 (71%) 2 (29%)            
Additive model: HR per G      1.16 (0.78–1.72) 0.47 1.14 (0.75–1.74) 0.54  1.26 (0.74–1.71) 0.58 1.19 (0.76–1.86) 0.45 
Tumor responsePFSOS
NCR + PRSD + PDPaMedian, months (95% CI)HR (95% CI)bPaHR (95% CI)cPaMedian, months (95% CI)HR (95% CI)bPaHR (95% CI)cPa
Left-sided               
Training cohort    0.19   0.031  0.058   0.88  0.87 
 A/A 65 39 (61%) 25 (39%)  11.1 (9.4–12.7) 1 (reference)  1 (reference)  33.1 (24.8–39.7) 1 (reference)  1 (reference)  
 A/G 76 38 (51%) 36 (49%)  9.6 (8.3–10.8) 1.57 (1.05–2.36)  1.68 (1.05–2.69)  27.9 (20.5–34.4) 1.10 (0.75–1.62)  1.13 (0.72–1.78)  
 G/G 10 8 (80%) 2 (20%)  12.7 (4.5–24.0) 0.86 (0.39–1.89)  0.74 (0.26–2.09)  34.5 (5.2–55.5+) 1.02 (0.48–2.15)  1.02 (0.40–2.61)  
    0.50   0.050  0.088   0.65  0.62 
 A/A 65 39 (61%) 25 (39%)  11.1 (9.4–12.7) 1 (reference)  1 (reference)  33.1 (24.8–39.7) 1 (reference)  1 (reference)  
 Any G 86 46 (55%) 38 (45%)  9.7 (8.6–11.0) 1.43 (0.97–2.12)  1.48 (0.94–2.32)  28.3 (21.1–34.4) 1.09 (0.75–1.58)  1.12 (0.72–1.73)  
Additive model: HR per G      1.20 (0.75–1.93) 0.45 1.17 (0.84–1.63) 0.36  1.05 (0.78–1.42) 0.74 1.06 (0.75–1.51) 0.73 
Validation cohort    0.30   0.13  0.23   0.14  0.75 
 A/A 122 76 (68%) 35 (32%)  11.7 (9.9–13.0) 1 (reference)  1 (reference)  27.5 (24.8–31.9) 1 (reference)  1 (reference)  
 A/G 28 13 (52%) 12 (48%)  10.1 (6.5–12.2) 1.53 (0.97–2.44)  1.47 (0.92–2.34)  30.3 (16.7–42.5) 1.01 (0.59–1.73)  0.95 (0.55–1.63)  
 G/G 14 9 (64%) 5 (36%)  10.2 (7.4–12.9) 1.35 (0.75–2.43)  1.32 (0.72–2.43)  18.9 (15.1–29.6) 1.81 (0.98–3.35)  1.27 (0.64–2.51)  
    0.18   0.047  0.090   0.32  0.85 
 A/A 122 76 (68%) 35 (32%)  11.7 (9.9–13.0) 1 (reference)  1 (reference)  27.5 (24.8–31.9) 1 (reference)  1 (reference)  
 Any G 42 22 (56%) 17 (44%)  10.2 (7.4–12.0) 1.46 (0.99–2.16)  1.41 (0.95–2.11)  21.3 (17.3–34.6) 1.25 (0.80–1.93)  1.04 (0.66–1.65)  
Additive model: HR per G      1.24 (0.96–1.60) 0.093 1.22 (0.93–1.59) 0.15  1.26 (0.94–1.69) 0.12 1.08 (0.79–1.48) 0.64 
Right-sided               
Training cohort    0.80   0.14  0.43   0.073  0.19 
 A/A 22 14 (67%) 7 (33%)  9.3 (6.7–13.4) 1 (reference)  1 (reference)  24.3 (12.5–33.9) 1 (reference)  1 (reference)  
 A/G 32 16 (55%) 13 (45%)  8.8 (6.2–9.9) 1.53 (0.83–2.81)  1.38 (0.63–3.03)  18.3 (14.1–20.3) 1.68 (0.92–3.08)  1.69 (0.78–3.69)  
 G/G 2 (67%) 1 (33%)            
Additive model: HR per G      1.29 (0.74–2.24) 0.38 1.11 (0.61–2.00) 0.73  1.80 (0.99–3.30) 0.056 1.55 (0.82–2.93) 0.17 
Validation cohort    0.15   0.22  0.28   0.35  0.20 
 A/A 29 19 (70%) 8 (30%)  10.5 (9.0–13.0) 1 (reference)  1 (reference)  21.0 (16.5–24.2) 1 (reference)  1 (reference)  
 A/G 19 7 (41%) 10 (59%)  6.6 (5.7–8.8) 1.40 (0.78–2.52)  1.41 (0.76–2.62)  15.9 (9.3–23.7) 1.33 (0.72–2.46)  1.55 (0.79–3.02)  
 G/G 10 5 (71%) 2 (29%)            
Additive model: HR per G      1.16 (0.78–1.72) 0.47 1.14 (0.75–1.74) 0.54  1.26 (0.74–1.71) 0.58 1.19 (0.76–1.86) 0.45 

Abbreviations: CR, complete response; PD, progressive disease; PR, partial response; SD, stable disease.

aP value was based on Fisher exact test for tumor response.

bP value was based on log-rank test for PFS and OS in the univariate analysis and Wald test for PFS and OS in the multivariable Cox regression model adjusted for age, performance status, primary tumor site, primary tumor resection, BRAF mutation status, and adjuvant chemotherapy in the training cohort.

cAdjusted for sex, performance status, liver only disease, and primary tumor resection in the validation cohort and control cohort.

Effect of IL6 genetic variants on clinical outcomes by primary tumor location in patients receiving FOLFIRI with bevacizumab: TRIBE and FIRE-3

In a prior study, we showed that the impact of genetic variants involved in pericyte maturation on outcomes was influenced by tumor subsite (28). Here, we explored whether associations between IL6 rs2069837 and outcomes were affected by primary tumor location.

In patients with left-sided colon cancer, IL6 rs2069837 genotype was significantly associated with PFS in the training and validation cohorts. In the TRIBE training cohort, patients with any G allele had a significantly inferior PFS (9.7 months) compared with those with an A/A genotype in univariate analysis (11.1 months; HR, 1.43; 95% CI, 0.97–2.12; P = 0.050; Table 3). Similarly, in FIRE-3 patients receiving bevacizumab-based therapy, patients with any G allele had significantly shorter PFS (10.2 months) compared with those with an A/A genotype in univariate analysis (11.7 months; HR, 1.46; 95% CI, 0.99–2.16; P = 0.047; Table 3). In multivariable analysis, there was a trend towards inferior PFS among IL6 rs2069837 G allele carriers in both cohorts, but this did not reach statistical significance (adjusted P = 0.088 and 0.090 in the training and validation cohorts, respectively; Table 3).

There was no evidence for an association between IL6 rs2069837 genotype and outcomes in patients with right-sided tumors in either the training or validation cohorts. However, the interaction term of IL6 rs2069837 and tumor location showed no significance in both cohorts.

IL6/STAT3 signaling is crucial for angiogenesis (29, 30). As activators of VEGF expression and tumor vessel development, IL6 and STAT3 may serve as important potential biomarkers of antiangiogenic therapy, yet the clinical relevance of IL6/STAT3 genetic variants has previously not been reported. Here, we examined the predictive and prognostic impact of IL6 and STAT3 SNPs in patients receiving cytotoxic and targeted agents. To our knowledge, we are the first to show that IL6 rs2069837 genotype is significantly associated with PFS in mCRC patients receiving first-line chemotherapy with bevacizumab.

IL6 maps to chromosome 7p21 and encodes a phosphorylated, glycoprotein with 212 amino acids, consisting of four introns and five exons, spanning approximately 4.9 kb of the genomic DNA (31). Binding of IL6 to the signal transducer, gp130, along with either membrane-bound or soluble IL6 receptor, leads to IL6 classical or trans-signaling, respectively. Trans-signaling is implicated in T-cell activation, stromal tissue inflammation as well as cardiovascular disorders, inflammatory bowel disease, and colitis-associated cancers (1). Knockout IL6 mice models demonstrate deficiencies in wound healing (32) and impaired recovery from colitis (33). Clinically, elevated serum IL6 has been associated with inferior survival in colorectal cancer (34), and in a phase II study of rectal cancer patients treated with neoadjuvant bevacizumab and chemoradiation, change in plasma IL6 correlated with degree of tumor regression (35). Data regarding STAT3 have been less conclusive, with studies supporting both positive (36–38) and negative (39, 40) prognostic roles for intratumoral STAT3 expression.

Evidence relating IL6 SNPs to colorectal cancer outcomes has been inconsistent. Although most studies have shown a decreased colorectal cancer risk with the IL6 rs1800795 C allele (18, 19, 21), particularly in those taking NSAIDs (15, 18), Landi and colleagues reported an increased susceptibility (17), and others have been unable to demonstrate any significant association (16, 20). A study by Wilkening and colleagues showed a heterozygote survival advantage over those with the IL6 rs1800795 CC genotype (20). Our study was unable to demonstrate any significant association between this SNP and outcomes. IL6 rs2069837 is purported to be at a transcription factor–binding site and as a tag SNP may exert functional effects through linked polymorphisms at other loci. There are no reports linking IL6 rs2069837 with cancer survival, but the G allele has been associated with increased susceptibility to cervical (23) and liver (25) cancers, in addition to increased IL6 tumor protein expression (23). In our study, patients with a G allele had a significantly shorter median PFS compared with those with the A/A variant. Although we were not able to measure IL6 protein expression, the IL6 rs2069837 G allele may lead to increased IL6 expression and serve as a surrogate for resistance towards anti-VEGF therapy in mCRC patients.

Notably, our data showed that the association between IL6 rs2069837 and outcomes may differ by primary tumor location. We previously demonstrated that genetic variants in angiogenesis pathways, specifically pericyte maturation, variably affect outcomes in mCRC by tumor subsite (28). In this study, the effect of IL6 rs2069837 on PFS remained significant in patients with distal but not proximal tumors. However, the test for interaction between IL6 genotype and primary tumor site was not significant, and these findings warrant further exploration. The mechanisms remain to be elucidated, although prior studies have suggested a prominent role IL6 plays in colitis-associated cancers, which are predominantly left sided (41). On the other hand, Dejea and colleagues (42) demonstrated that bacterial biofilms induce IL6 expression and STAT3 activation and were particularly associated with the development of right-sided colorectal cancers. Taken together, this suggests distinct environmental and inherited mechanisms for IL6-mediated carcinogenesis in right- and left-sided colorectal cancers, respectively.

In addition to carcinogenesis, IL6 contributes to hemostasis (43, 44) and vasculopathy (45, 46), and IL6 genetic variants have been implicated in atherosclerosis (43, 47), hypertension (47), and cancer-associated thromboembolism (48). Accordingly, we explored whether IL6 SNPs could predict bevacizumab toxicity (i.e., hypertension, bleeding, and thromboembolic events) but found no significant associations in our cohort (data not shown).

Our study is limited by its retrospective design and sample size. Importantly, we were not able to correlate IL6 polymorphisms with serum or intratumoral IL6 expression levels, which may lend insight in mechanisms of bevacizumab resistance. We were also not able to determine interactions between microsatellite instability (8, 49), NSAID use, or inflammatory bowel disease, all of which may be affect the influence of IL6 polymorphisms on outcomes. Functional studies are certainly needed to determine the significance of each examined genetic variant. Whether the observed associations can be generalized to patients receiving oxaliplatin-containing regimens should also be tested.

Comprehensive molecular profiling has advanced the understanding of several cancers, but the potential of patient genetic profiling to influence therapeutic decisions in mCRC has yet to be realized. Here, we present the first evidence that IL6 common genetic variants are associated with PFS in mCRC patients treated with first-line bevacizumab-based chemotherapy. Our findings further suggest that the effect of IL6 rs2069837 may rely upon primary tumor site. If confirmed in larger prospective studies, IL6 genotype may serve as a promising predictive and prognostic biomarker in mCRC patients receiving bevacizumab-based chemotherapy.

F. Loupakis is a consultant/advisory board member for Roche. H.-J. Lenz reports receiving other commercial research support from and is a consultant/advisory board member for Roche. No potential conflicts of interest were disclosed by the other authors.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NCI or the NIH.

Conception and design: S. Matsusaka, H.-J. Lenz

Development of methodology: S. Matsusaka, S. Cao, W. Zhang, S. Okazaki, H.-J. Lenz

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): S. Matsusaka, S. Stintzing, F. Loupakis, H.-J. Lenz

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): S. Matsusaka, D.L. Hanna, S. Cao, W. Zhang, D. Yang, Y. Ning, H.-J. Lenz

Writing, review, and/or revision of the manuscript: S. Matsusaka, D.L. Hanna, S. Cao, W. Zhang, D. Yang, Y. Sunakawa, M.D. Berger, S. Stintzing, F. Loupakis, H.-J. Lenz

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): Y. Sunakawa, Y. Miyamato, A. Parekh, S. Stintzing, F. Loupakis, H.-J. Lenz

Study supervision: H.-J. Lenz

The project described was supported in part by award number P30CA014089 from the NCI. This project was also supported in part by the Wunderglo Project and Care to Cure Research Fund. S. Matsusaka is a recipient of Takashi Tsuruo Memorial Fund. M.D. Berger received a grant from the Swiss Cancer League (BIL KLS-3334-02-2014).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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