Purpose: Cumulating evidence indicates that germline variants in the Wnt, Notch, and Hedgehog pathways are involved in colon carcinoma progression and metastasis. We investigated germline polymorphisms in a comprehensive panel of Wnt, Notch, and Hedgehog pathway genes to predict time to recurrence (TTR) and overall survival in patients with stage II and III colon carcinoma.

Experimental Design: A total of 742 consecutively collected patients with stage II and III colon carcinoma were included in this retrospective study. Genomic DNA was analyzed for 18 germline polymorphisms in Wnt, Notch, and Hedgehog pathway genes (SFRP, DKK 2 and 3, AXIN2, APC, MYC, TCF7L2, NOTCH2, and GLI1) by TaqMan 5′-exonuclease assays.

Results: In univariate analysis, the homozygous mutant variant of GLI1 rs2228226 G>C was significantly associated with decreased TTR in a recessive genetic model after adjustment for multiple testing [HR = 2.35; confidence interval (95% CI), 1.48–3.74; P < 0.001] and remained significant in multivariate analysis including clinical stage, lymphovascular-, vascular-, and perineural-invasion (HR = 2.43; CI 95%, 1.52–3.87; P < 0.001). In subanalyses, the association was limited to patients with surgery alone (HR = 3.21; CI 95%, 1.59–6.49; P = 0.001), in contrast with patients with adjuvant chemotherapy (HR = 0.82; CI 95%, 0.35–1.95; P = 0.657). When the subgroup of patients with “high-risk” GLI1 rs2228226 C/C genotype was analyzed, no benefit of adjuvant 5-fluorouracil–based chemotherapy could be found.

Conclusion: This is the first study identifying GLI1 rs2228226 G>C as an independent prognostic marker in patients with stage II and III colon carcinoma. Prospective studies are warranted to validate our findings. Clin Cancer Res; 20(6); 1687–97. ©2014 AACR.

Translational Relevance

Germline variants in cancer stem cells (CSC) may have an important role in tumor recurrence despite adjuvant chemotherapy. In the present study, we investigated germline polymorphisms in a comprehensive panel of genes in the Wnt, Notch, and Hedgehog pathways that have been previously investigated for their biologic function and/or associated with CSCs and cancer risk or clinical outcome to predict tumor recurrence in patients with stage II and III colon carcinoma. These common DNA-sequence variations may alter the gene function and/or activity, including transcription, translation, or splicing, thereby causing interindividual differences in relation to tumor recurrence capacity. Our study provides the first evidence that GLI1 rs2228226 G>C may predict early tumor recurrence in patients with stage II and III colon carcinoma.

Colorectal carcinoma is the third cause of cancer-related deaths in the United States and the second cause of cancer mortality in Europe (1, 2). Across all stages, approximately 30% of patients with colon carcinoma develop synchronous or metachronous metastases (3). The 5-year survival rate of patients with colon carcinoma with metastatic disease is less than 10% (4).

In the absence of adjuvant chemotherapy, approximately 50% of patients with colon carcinoma with resectable disease are cured by surgery alone, whereas 50% relapse. Using adjuvant chemotherapy following surgery rescues approximately 15% of patients from the relapsing group. In current practice, the majority of these patients with colon carcinoma receive adjuvant treatment unnecessarily, either because they were cured by surgery alone or because they will relapse despite adjuvant treatment. It is therefore essential to identify patients who will benefit from adjuvant therapy, sparing other needless toxicity and the financial burden of chemotherapy that will not work (5–7). Tumor recurrence after curative surgery remains a major obstacle for improving overall cancer survival, which may be, in part, due to the existence of cancer stem cells (CSC). Current therapies target populations of rapidly growing and differentiated tumor cells, but have shown to lack activity against CSCs (8, 9). CSCs therefore may have an important role in tumor recurrence despite adjuvant chemotherapy (9, 10). There is strong evidence that the embryonic signaling pathways Wnt, Notch, and Hedgehog operate in CSCs and drive tumor progression, metastasis, and chemoresistance (11–18).

There is substantial germline genetic variability within the genes of the Wnt, Notch, and Hedgehog pathways, including multiple single-nucleotide polymorphisms (SNPs). These common DNA-sequence variations may alter the gene function and/or activity, including transcription, translation, or splicing, thereby causing interindividual differences in relation to tumor recurrence capacity and chemoresistance (19–29). Furthermore, common gene variants may also predict chemoresistance and toxicity to 5-fluorouracil (5-FU) and/or oxaliplatin as recently shown for the thymidylate synthetase, 5 methyltetrahydrofolate-homocysteine methyltransferase reductase, multidrug resistance protein 2, dihydropyrimidine dehydrogenase, and the X-ray repair cross-complementing protein 1 genes (30–35).

In the present study, we investigated 18 germline polymorphisms in a comprehensive panel of genes in the Wnt, Notch, and Hedgehog pathways that have been previously investigated for their biologic function and/or associated with cancer risk or clinical outcome to predict tumor recurrence in patients with stage II and III colon carcinoma. This study was conducted adhering to the reporting recommendations for prognostic tumor marker studies (36, 37).

Eligible patients

Between 1995 and 2011, 742 patients with histopathologically confirmed stage II and III colon carcinoma were consecutively recruited at the Division of Clinical Oncology, Department of Medicine, Medical University of Graz (Graz, Austria). Tissue samples from 522 patients were available for current genetic analyses. Tissue samples were provided by the Biobank of the Medical University of Graz, the Department of Pathology of the General Hospital Graz West and the Department of Pathology of the General Hospital Leoben (Leoben, Austria). Patients treated with adjuvant chemotherapy received 5-FU–based regimens. All patients were included in the colon carcinoma surveillance program of the Division of Clinical Oncology of the Medical University of Graz, providing history and physical examination and carcinoembryonic antigen determination every 3 months for 3 years and every 6 months at years 4 and 5 after surgery, colonoscopy at year 1 and thereafter every 3 to 5 years, and chest X-ray and abdominal ultrasound or CT scans of chest and abdomen every 6 months for the first 5 years and in 12 months interval in years 6 to 10 after diagnosis. Patient data were collected retrospectively through chart review. This study has been approved by the Institutional Review Board of the Medical University of Graz. All participants were Caucasians.

Candidate gene polymorphisms

Common and putatively functional Wnt, Notch, and Hedgehog gene polymorphisms were selected using stringent and predefined selection criteria: (i) minor allele frequency (MAF) ≥10% in Caucasians (based on the population genetics section in the Ensembl Genome Browser), (ii) polymorphism that could alter the function of the gene in a biologically relevant manner [either published data or predicted function using Functional-Single-Nucleotide-Polymorphism (F-SNP) database; refs. 38, 39], and (iii) published clinical associations (e.g., cancer risk and/or clinical outcome or chemoresistance). As it was not possible to select all Wnt, Notch, and Hedgehog pathway gene variants matching these criteria for study power reasons, we focused on the most promising genes and polymorphisms. Table 1 summarizes the genes and polymorphisms investigated in our study cohort, including location and function/clinical association.

Table 1.

Analyzed Wnt/Notch/Hedgehog pathway genes and polymorphisms

PathwayGeneFunctionrs-numberBase exchangeLocationAssociation
WNT SFRP Soluble Wnt receptor rs1802073 C>A Nonsynonymous Rectal cancer risk (26) 
WNT SFRP Soluble Wnt receptor rs288326 G>A Nonsynonymous CRC risk (21, 22) 
WNT SFRP Soluble Wnt receptor rs7775 C>G Nonsynonymous CRC risk (21, 22) 
WNT DKK2 Inhibits Wnt by binding to LRP5/6 rs17037102 G>A Nonsynonymous RCC outcome (20) 
WNT DKK3 Inhibits Wnt by binding to LRP5/6 rs3206824 A>G Nonsynonymous RCC risk (20) 
WNT DKK3 Inhibits Wnt by binding to LRP5/6 rs1472189 C>T 3 UTR RCC outcome (20) 
WNT DKK3 Inhibits Wnt by binding to LRP5/6 rs7396187 C>G Intron RCC risk (20) 
WNT AXIN2 Suppressor rs11079571 A>G Intron BC risk (23) 
WNT AXIN2 Suppressor rs4791171 A>G Intron BC risk (23) 
WNT AXIN2 Suppressor rs3923086 T>G Intron BC risk (23) 
WNT AXIN2 Suppressor rs3923087 A>G Intron BC risk (23) 
WNT APC Suppressor rs454886 T>C Intron BC risk (23) 
WNT AXIN2 Suppressor rs2240308 G>A Nonsynonymous NSCLC risk (24) 
WNT MYC Wnt enhancer rs6983267 G>T 8q24, noncoding, near MYC CRC risk (27) 
WNT TCF7L2 Transcription factor activator rs12255372 G>T Intron CRC risk (25) 
WNT TCF7L2 Transcription factor activator rs7903146 C>T Intron CRC risk (28) 
NOTCH NOTCH2 NOTCH receptor rs11249433 T>C 1p11.2, within noncoding gene BC (29) 
HEDGEHOG GLI1 Transcriptional activator rs2228226 G>C Nonsynonymous IBD risk (47) 
PathwayGeneFunctionrs-numberBase exchangeLocationAssociation
WNT SFRP Soluble Wnt receptor rs1802073 C>A Nonsynonymous Rectal cancer risk (26) 
WNT SFRP Soluble Wnt receptor rs288326 G>A Nonsynonymous CRC risk (21, 22) 
WNT SFRP Soluble Wnt receptor rs7775 C>G Nonsynonymous CRC risk (21, 22) 
WNT DKK2 Inhibits Wnt by binding to LRP5/6 rs17037102 G>A Nonsynonymous RCC outcome (20) 
WNT DKK3 Inhibits Wnt by binding to LRP5/6 rs3206824 A>G Nonsynonymous RCC risk (20) 
WNT DKK3 Inhibits Wnt by binding to LRP5/6 rs1472189 C>T 3 UTR RCC outcome (20) 
WNT DKK3 Inhibits Wnt by binding to LRP5/6 rs7396187 C>G Intron RCC risk (20) 
WNT AXIN2 Suppressor rs11079571 A>G Intron BC risk (23) 
WNT AXIN2 Suppressor rs4791171 A>G Intron BC risk (23) 
WNT AXIN2 Suppressor rs3923086 T>G Intron BC risk (23) 
WNT AXIN2 Suppressor rs3923087 A>G Intron BC risk (23) 
WNT APC Suppressor rs454886 T>C Intron BC risk (23) 
WNT AXIN2 Suppressor rs2240308 G>A Nonsynonymous NSCLC risk (24) 
WNT MYC Wnt enhancer rs6983267 G>T 8q24, noncoding, near MYC CRC risk (27) 
WNT TCF7L2 Transcription factor activator rs12255372 G>T Intron CRC risk (25) 
WNT TCF7L2 Transcription factor activator rs7903146 C>T Intron CRC risk (28) 
NOTCH NOTCH2 NOTCH receptor rs11249433 T>C 1p11.2, within noncoding gene BC (29) 
HEDGEHOG GLI1 Transcriptional activator rs2228226 G>C Nonsynonymous IBD risk (47) 

Abbreviations: CRC, colorectal cancer; RCC, renal cell cancer; BC, breast cancer; NSCLC, non–small cell lung cancer; IBD, inflammatory bowel disease; NA, not available.

Isolation of genomic DNA and determination of single-nucleotide polymorphisms

Genomic DNA was extracted from paraffin-embedded normal tissue adjacent to the tumor samples to obtain germline DNA. DNA isolation was performed by use of the QIAamp DNA Mini Kit (Qiagen) and according to the manufacturer's instructions. Genotypes were centrally determined by 5′-exonuclease assay (TaqMan) at the Medical University of Graz. Primer and probe sets were designed and manufactured using Applied Biosystems “Assay-by-Design” custom service (Applera). General TaqMan reaction conditions were according to the manufacturer of the assays. As a control for consistency of genotyping methods, determination of genotypes was repeated in at least 96 samples. The rules of good laboratory and clinical practice were observed. The investigator analyzing the germline polymorphisms was blinded to the clinical dataset.

Immunohistochemistry

Immunohistochemistry was performed on a Ventana XT immunostainer using UltraView DAB as the detection kit and CC1 32 minutes as heat-induced epitope retrieval. The primary antibody was incubated for 32 minutes each: anti-interleukin (IL)-17 antibody ab 9565/Abcam in a dilution of 1:40, anti-IL23 antibody ab115759/Abcam in a dilution of 1:50, and anti-GLI1 (H-300) sc-20687 Santa Cruz Biotechnology, Inc. in a dilution of 1:30. The tumor center of the stained slides was captured with a ×20 objective on an Eclipse 80i microscope with Digital sight DS-Fi1 digital camera and NIS-Elements D Version 3.21.04 software, Nikon with same correction for brightness and white balance for all images. On the basis of the images, staining intensity in the tumor cells was visually semiquantified and classified by low, moderate, and high expression.

Statistical analysis

The endpoint of the study was time to recurrence (TTR). TTR was calculated from the date of diagnosis of colon cancer to the date of the first observation of tumor recurrence. TTR was censored at the time of death or at the last follow-up if the patient remained tumor recurrence free at that time. The statistical power to detect or exclude effects for the SNPs we investigated depended on MAF and SNP effect size. For the variant with the lowest MAF, SFRP rs7775, the present study had a power of 0.98, 0.89, or 0.70 to detect or exclude a HR of 2.0, 1.7, or 1.5 for recurrence. The statistical power increased with higher MAF and/or higher HRs. The secondary endpoint was overall survival (OS). OS was defined as the time from date of diagnosis of colon cancer to death from any cause. Allelic distribution of the polymorphisms was tested for deviation from Hardy–Weinberg equilibrium using HW Diagnostics-Version 1.beta (Fox Chase Cancer Center, Philadelphia, PA). The distribution of polymorphisms across baseline demographic, clinical, and pathologic characteristics was examined using Fisher exact test. The association of clinicopathological features and polymorphisms with TTR and OS was analyzed using Kaplan–Meier curves and log-rank test. In the multivariate Cox regression analyses, the models were adjusted for significant clinicopathological features from univariate analysis of TTR and OS. The true mode of inheritance of all polymorphisms tested has not been established yet and we evaluated a codominant, dominant, or recessive genetic model where appropriate. The significance threshold for an overall type I error rate of 0.05 was set at P < 0.003 based on a conservative Bonferroni correction for multiple comparison. The interactions between polymorphisms and adjuvant chemotherapy on TTR were tested by comparing likelihood ratio statistics between the baseline and nested Cox proportional hazards models that include the multiplicative product term. Case-wise deletion for missing polymorphisms was used in univariate and multivariate analyses. The association between the GLI1 rs2228226 genotypes and GLI1, IL-17, and IL-21 expression in tumor was examined using χ2 test. All analyses have been performed using the SPSS 21.0 statistical software package (SPSS Inc.).

The baseline characteristics of the 742 patients included in this analysis are summarized in Table 2. A total of 231 patients received infusional 5-FU monotherapy (bolus of 5-FU (450 mg/m2)-leucovorin (20 mg/m2) day (d)1-d5 or bolus of 5-FU (500 mg/m2)-leucovorin (500 mg/m2) weekly for 6 consecutive weeks), 110 patients capecitabine monotherapy (capecitabine (2500 mg/m2) d1-d14), 108 patients FOLFOX (oxaliplatin (85 mg/m2) d1, leucovorin (200 mg/m2) d1 and d2, bolus of 5-FU (400 mg/m2) d1 and d2 and 5-FU (600 mg) d1 and d2), 16 patients XELOX [(oxaliplatin (130 mg/m2) d1 and capecitabine (2000 mg/m2) d1-d14], and the treatment regimen of 18 patients was unknown. The median age at time of diagnosis was 64 years (range 27–95 years), with a median follow-up time of 64.8 months (range 1–199 months). The median TTR was 54.5 months (range 1–199 months) and the median OS was 64.8 months (range 1–199 months). The genotyping quality control provided a genotype concordance of 100%. Genotyping was successful in at least 91% of patients for each polymorphism analyzed, with the exception of DKK3 rs7396187 (86.8%). In failed cases, genotyping was not successful because of limited quantity and/or quality of extracted genomic DNA. The genotype frequencies for all polymorphisms were within the probability limits of Hardy–Weinberg equilibrium.

Table 2.

Clinicopathological characteristics and TTR and OS in univariate analysis

TTROS
ParameterN%HR (95% CI)PHR (95% CI)P
Gender 
 Male 416 56.1 1 (reference) 0.750 1 (reference) 0.401 
 Female 326 43.9 0.96 (0.74–1.24) — 0.89 (0.67–1.17) — 
Tumor location 
 Left 274 36.9 1 (reference) 0.168 1 (reference) 0.025 
 Right 468 63.1 0.83 (0.64–1.08) — 0.73 (0.55–0.96) — 
Tumor size 
 T1 12 1.6 1 (reference) — 1 (reference) — 
 T2 32 4.3 0.97 (0.10–9.35) <0.001 1.33 (0.16–11.43) <0.001 
 T3 536 72.2 3.27 (0.46–23.36) — 2.26 (0.32–16.15) — 
 T4 162 21.8 6.58 (0.91–47.35) — 5.56 (0.77–40.05) — 
Lymph node involvement 
 N0 298 40.2 1 (reference) — 1 (reference) — 
 N1 276 37.2 1.44 (1.03–2.02) <0.001 1.33 (0.93–1.88) <0.001 
 N2 167 22.5 3.86 (2.80–5.32) — 3.12 (2.23–4.36) — 
 Unknown 0.1 — — — — 
Tumor grade 
 G1 37 1 (reference) — 1 (reference) — 
 G2 480 64.7 1.16 (0.57–2.37) 0.337 0.77 (0.37–1.57) 0.032 
 G3 224 30.2 1.40 (0.68–2.91) — 1.12 (0.54–2.33) — 
 Unknown 0.1 — — — — 
Lymphovascular invasion 
 No 532 71.7 1 (reference) 0.001 1 (reference) 0.009 
 Yes 210 28.3 1.58 (1.21–2.06) — 1.47 (1.10–1.96) — 
Vascular invasion 
 No 665 89.6 1 (reference) <0.001 1 (reference) 0.001 
 Yes 77 10.4 2.40 (1.73–3.32) — 2.06 (1.43–2.96) — 
Perineural invasion 
 No 721 97.2 1 (reference) <0.001 1 (reference) 0.006 
 Yes 21 2.8 3.64 (2.12–6.26) — 2.45 (1.29–4.62) — 
Clinical stage 
 II 295 39.8 1 (reference) <0.001 1 (reference) <0.001 
 III 446 60.1 2.26 (1.69–3.03) — 1.92 (1.42–2.58) — 
 Unknown 0.1 — — — — 
Adjuvant chemotherapy 
 No 256 34.5 1 (reference) 0.239 1 (reference) 0.300 
 Yes 483 65.1 1.18 (0.90–1.56) — 0.86 (0.65–1.14) — 
 Unknown 0.4 — — — — 
TTROS
ParameterN%HR (95% CI)PHR (95% CI)P
Gender 
 Male 416 56.1 1 (reference) 0.750 1 (reference) 0.401 
 Female 326 43.9 0.96 (0.74–1.24) — 0.89 (0.67–1.17) — 
Tumor location 
 Left 274 36.9 1 (reference) 0.168 1 (reference) 0.025 
 Right 468 63.1 0.83 (0.64–1.08) — 0.73 (0.55–0.96) — 
Tumor size 
 T1 12 1.6 1 (reference) — 1 (reference) — 
 T2 32 4.3 0.97 (0.10–9.35) <0.001 1.33 (0.16–11.43) <0.001 
 T3 536 72.2 3.27 (0.46–23.36) — 2.26 (0.32–16.15) — 
 T4 162 21.8 6.58 (0.91–47.35) — 5.56 (0.77–40.05) — 
Lymph node involvement 
 N0 298 40.2 1 (reference) — 1 (reference) — 
 N1 276 37.2 1.44 (1.03–2.02) <0.001 1.33 (0.93–1.88) <0.001 
 N2 167 22.5 3.86 (2.80–5.32) — 3.12 (2.23–4.36) — 
 Unknown 0.1 — — — — 
Tumor grade 
 G1 37 1 (reference) — 1 (reference) — 
 G2 480 64.7 1.16 (0.57–2.37) 0.337 0.77 (0.37–1.57) 0.032 
 G3 224 30.2 1.40 (0.68–2.91) — 1.12 (0.54–2.33) — 
 Unknown 0.1 — — — — 
Lymphovascular invasion 
 No 532 71.7 1 (reference) 0.001 1 (reference) 0.009 
 Yes 210 28.3 1.58 (1.21–2.06) — 1.47 (1.10–1.96) — 
Vascular invasion 
 No 665 89.6 1 (reference) <0.001 1 (reference) 0.001 
 Yes 77 10.4 2.40 (1.73–3.32) — 2.06 (1.43–2.96) — 
Perineural invasion 
 No 721 97.2 1 (reference) <0.001 1 (reference) 0.006 
 Yes 21 2.8 3.64 (2.12–6.26) — 2.45 (1.29–4.62) — 
Clinical stage 
 II 295 39.8 1 (reference) <0.001 1 (reference) <0.001 
 III 446 60.1 2.26 (1.69–3.03) — 1.92 (1.42–2.58) — 
 Unknown 0.1 — — — — 
Adjuvant chemotherapy 
 No 256 34.5 1 (reference) 0.239 1 (reference) 0.300 
 Yes 483 65.1 1.18 (0.90–1.56) — 0.86 (0.65–1.14) — 
 Unknown 0.4 — — — — 

In our study cohort, we found a significant association between tumor size, lymph node involvement, lymphovascular-, vascular-, and perineural-invasion, and clinical stage with TTR and OS. In addition, tumor location and histopathological grade were significantly associated with OS (Table 2). When the polymorphisms were correlated with the clinicopathological features, we found a significant association between APC rs454886 G>A and tumor size (P = 0.001) and vascular invasion (P = 0.001), observing larger tumors and increased vascular invasion in patients carrying the wild-type. Furthermore, patients with colon cancer with NOTCH2 rs11249433 T>C wild-type showed significantly increased lymphovascular invasion (P = 0.001). No association was found between the other tested polymorphisms and clinicopathological features (data not shown).

The associations between all polymorphisms tested and TTR and OS are provided in Table 3. GLI1 rs2228226 G>C, AXIN2 rs3923086 T>G, and AXIN2 rs4791171 A>G showed an association with TTR in a codominant model (P < 0.05; Table 3). In multiple testing, only GLI1 rs2228226 G>C using a recessive genetic model remained significant for TTR in univariate analysis [HR = 2.35; 95% confidence interval (CI), 1.48–3.74; P < 0.001]. Patients harboring the homozygous mutant variant (C/C) had a median TTR of 52.2 months, in contrast with patients carrying the G/G or G/C genotype with a median TTR of 121.8 months (Fig. 1). In OS analyses, no statistically significant association between the polymorphisms and OS could be found (Table 3). In the multivariate analysis including clinical stage (because clinical stage derives from tumor size and lymph node involvement, which all have been significant in univariate analysis, only clinical stage was incorporated in the multivariate model), lymphovascular-, vascular-, and perineural-invasion, the homozygous mutant variant of GLI1 rs2228226 G>C remained significantly associated with decreased TTR (HR = 2.43; 95% CI, 1.52–3.87; P < 0.001).

Figure 1.

Association between GLI1 rs2228226 G>C and TTR in all patients with colon carcinoma.

Figure 1.

Association between GLI1 rs2228226 G>C and TTR in all patients with colon carcinoma.

Close modal
Table 3.

Association between the polymorphisms and TTR and OS in univariate analysis

TTROS
PolymorphismNHR (95% CI)PHR (95% CI)P
SFRP rs1802073 
 C/C 217 — — — — 
 C/A 231 0.98 (0.78–1.23) 0.858 0.93 (0.72–1.19) 0.568 
 A/A 63 — — — — 
SFRP rs288326 
 G/G — — — — 
 G/A 103 1.04 (0.74–1.48) 0.809 0.78 (0.55–1.10) 0.156 
 A/A 363 — — — — 
SFRP rs7775 
 C/C — — — — 
 C/G 64 1.01 (0.66–1.54) 0.967 1.04 (0.65–1.68) 0.866 
 G/G 436 — — — — 
DKK2 rs17037102 
 G/G 408 — — — — 
 G/A 88 0.83 (0.56–1.23) 0.351 0.89 (0.58–1.37) 0.586 
 A/A — — — — 
DKK2 rs3206824 
 A/A 288 — — — — 
 A/G 188 1.01 (0.79–1.30) 0.922 1.05 (0.80–1.37) 0.745 
 G/G 34 — — — — 
DKK2 rs1472189 
 C/C 241 — — — — 
 C/T 220 0.90 (0.71–1.15) 0.405 1.11 (0.85–1.44) 0.438 
 T/T 50 — — — — 
DKK2 rs7396187 
 C/C 14 — — — — 
 C/G 156 1.19 (0.87–1.63) 0.285 0.91 (0.66–1.26) 0.575 
 G/G 283 — — — — 
AXIN2 rs11079571 
 A/A 22 — 0.171 — 0.645 
 A/G 189 1.23 (0.92–1.64) — 1.08 (0.79–1.48) — 
 G/G 283 — — — — 
AXIN2 rs4791171 
 A/A 205 — — — — 
 A/G 251 0.72 (0.56–0.93) 0.012 0.80 (0.60–1.05) 0.108 
 G/G 52 — — — — 
AXIN2 rs3923086 
 T/T 107 — — — — 
 T/G 261 1.31 (1.05–1.65) 0.019 1.19 (0.93–1.52) 0.160 
 G/G 142 — — — — 
AXIN2 rs3923087 
 A/A 255 — — — — 
 A/G 210 0.78 (0.60–1.02) 0.073 0.85 (0.63–1.14) 0.270 
 G/G 32 — — — — 
APC rs454886 
 T/T 267 — — — — 
 T/C 201 0.76 (0.60–1.01) 0.054 0.79 (0.58–1.07) 0.121 
 C/C 30 — — — — 
AXIN2 rs2240308 
 G/G 114 — — — — 
 G/A 267 0.80 (0.64–1.00) 0.054 0.90 (0.71–1.15) 0.407 
 A/A 129 — — — — 
Near MYC rs6983267 
 G/G 112 — — — — 
 G/T 252 0.85 (0.66–1.08) 0.189 0.95 (0.73–1.24) 0.719 
 T/T 112 — — — — 
TCF7L2 rs12255372 
 G/G 252 — — — — 
 G/T 199 1.09 (0.85–1.40) 0.485 0.10 (0.76–1.31) 0.974 
 T/T 40 — — — — 
TCF7L2 rs7903146 
 C/C 248 — — — — 
 C/T 205 1.06 (0.83–1.36) 0.649 0.93 (0.71–1.22) 0.601 
 T/T 43 — — — — 
NOTCH2 rs11249433 
 T/T 176 — — — — 
 T/C 244 0.96 (0.77–1.19) 0.682 1.05 (0.83–1.33) 0.696 
 C/C 85 — — — — 
GLI1 rs2228226 
 G/G 249 — — — — 
 G/C 209 1.36 (1.06–1.74) 0.015 1.22 (0.93–1.61) 0.156 
 C/C 40 — — — — 
TTROS
PolymorphismNHR (95% CI)PHR (95% CI)P
SFRP rs1802073 
 C/C 217 — — — — 
 C/A 231 0.98 (0.78–1.23) 0.858 0.93 (0.72–1.19) 0.568 
 A/A 63 — — — — 
SFRP rs288326 
 G/G — — — — 
 G/A 103 1.04 (0.74–1.48) 0.809 0.78 (0.55–1.10) 0.156 
 A/A 363 — — — — 
SFRP rs7775 
 C/C — — — — 
 C/G 64 1.01 (0.66–1.54) 0.967 1.04 (0.65–1.68) 0.866 
 G/G 436 — — — — 
DKK2 rs17037102 
 G/G 408 — — — — 
 G/A 88 0.83 (0.56–1.23) 0.351 0.89 (0.58–1.37) 0.586 
 A/A — — — — 
DKK2 rs3206824 
 A/A 288 — — — — 
 A/G 188 1.01 (0.79–1.30) 0.922 1.05 (0.80–1.37) 0.745 
 G/G 34 — — — — 
DKK2 rs1472189 
 C/C 241 — — — — 
 C/T 220 0.90 (0.71–1.15) 0.405 1.11 (0.85–1.44) 0.438 
 T/T 50 — — — — 
DKK2 rs7396187 
 C/C 14 — — — — 
 C/G 156 1.19 (0.87–1.63) 0.285 0.91 (0.66–1.26) 0.575 
 G/G 283 — — — — 
AXIN2 rs11079571 
 A/A 22 — 0.171 — 0.645 
 A/G 189 1.23 (0.92–1.64) — 1.08 (0.79–1.48) — 
 G/G 283 — — — — 
AXIN2 rs4791171 
 A/A 205 — — — — 
 A/G 251 0.72 (0.56–0.93) 0.012 0.80 (0.60–1.05) 0.108 
 G/G 52 — — — — 
AXIN2 rs3923086 
 T/T 107 — — — — 
 T/G 261 1.31 (1.05–1.65) 0.019 1.19 (0.93–1.52) 0.160 
 G/G 142 — — — — 
AXIN2 rs3923087 
 A/A 255 — — — — 
 A/G 210 0.78 (0.60–1.02) 0.073 0.85 (0.63–1.14) 0.270 
 G/G 32 — — — — 
APC rs454886 
 T/T 267 — — — — 
 T/C 201 0.76 (0.60–1.01) 0.054 0.79 (0.58–1.07) 0.121 
 C/C 30 — — — — 
AXIN2 rs2240308 
 G/G 114 — — — — 
 G/A 267 0.80 (0.64–1.00) 0.054 0.90 (0.71–1.15) 0.407 
 A/A 129 — — — — 
Near MYC rs6983267 
 G/G 112 — — — — 
 G/T 252 0.85 (0.66–1.08) 0.189 0.95 (0.73–1.24) 0.719 
 T/T 112 — — — — 
TCF7L2 rs12255372 
 G/G 252 — — — — 
 G/T 199 1.09 (0.85–1.40) 0.485 0.10 (0.76–1.31) 0.974 
 T/T 40 — — — — 
TCF7L2 rs7903146 
 C/C 248 — — — — 
 C/T 205 1.06 (0.83–1.36) 0.649 0.93 (0.71–1.22) 0.601 
 T/T 43 — — — — 
NOTCH2 rs11249433 
 T/T 176 — — — — 
 T/C 244 0.96 (0.77–1.19) 0.682 1.05 (0.83–1.33) 0.696 
 C/C 85 — — — — 
GLI1 rs2228226 
 G/G 249 — — — — 
 G/C 209 1.36 (1.06–1.74) 0.015 1.22 (0.93–1.61) 0.156 
 C/C 40 — — — — 

In interaction analysis, there was a significant association between GLI1 rs2228226 G>C and adjuvant chemotherapy with TTR (P < 0.05). When only patients with surgery alone were analyzed, we found a highly significant association between GLI1 rs2228226 G>C and TTR (HR = 3.21; 95% CI, 1.59–6.49; P < 0.001). Patients harboring the homozygous mutant variant showed a median TTR of 49.9 months, whereas patients harboring the G/G or G/C genotype had a median TTR of 123.6 months (Fig. 2). In multivariate analysis including clinical stage, lymphovascular-, vascular-, and perineural-invasion, we observed a statistical trend toward decreased TTR in patients carrying the homozygous mutant variant (HR = 2.35; 95% CI, 1.13–4.850; P = 0.022). In patients with adjuvant chemotherapy, we found no significant association between GLI1 rs2228226 G>C and TTR (HR = 1.99; 95% CI, 1.06–3.72; P = 0.031). In this subgroup, patients harboring the C/C genotype had a median TTR of 52.5 months, in contrast with patients carrying the G/G or G/C genotype with a median TTR of 119.1 months (Fig. 3).

Figure 2.

Association between GLI1 rs2228226 G>C and TTR in patients with colon carcinoma with surgery alone.

Figure 2.

Association between GLI1 rs2228226 G>C and TTR in patients with colon carcinoma with surgery alone.

Close modal
Figure 3.

Association between GLI1 rs2228226 G>C and TTR in patients with colon carcinoma with adjuvant chemotherapy.

Figure 3.

Association between GLI1 rs2228226 G>C and TTR in patients with colon carcinoma with adjuvant chemotherapy.

Close modal

To evaluate whether “high-risk” patients based on the GLI1 rs2228226 G>C polymorphism (40 patients) benefit from adjuvant chemotherapy compared with surgery alone, we performed a Kaplan–Meier analysis and log-rank test for this subgroup. According to the treatment regimen [surgery alone (19 patients) vs. surgery plus adjuvant chemotherapy (21 patients; 8 patients received 5-FU monotherapy, 7 patient capecitabine, and 6 patients FOLFOX], no significant difference in TTR was identified in this high-risk subgroup (HR = 0.82; 95% CI, 0.35–1.95; P = 0.657; Fig. 4).

Figure 4.

Association in patients with colon carcinoma homozygous mutant (C/C) for GLI1 rs2228226 G>C (40 patients) between adjuvant chemotherapy (21 patients) or surgery alone (19 patients) and TTR.

Figure 4.

Association in patients with colon carcinoma homozygous mutant (C/C) for GLI1 rs2228226 G>C (40 patients) between adjuvant chemotherapy (21 patients) or surgery alone (19 patients) and TTR.

Close modal

When we correlated the GLI1 rs2228226 genotypes using the recessive genetic model with GLI1, IL-17, and IL-21 expression in tumor in a subset of patients (n = 27 for wild-type and heterozygous mutant and n = 12 for homozygous mutant), we found no significant association (P = 0.697, P = 0.338, P = 0.596, respectively).

It is becoming increasingly apparent that disease progression and chemoresistance are driven by a multitude of signaling networks and the analysis of a single marker may fail to predict clinical outcome and treatment efficacy with a high degree of accuracy and reproducibility. Therefore, it is critical to adopt and implement a pathway-based approach. In the present study, we investigated germline polymorphisms in a comprehensive panel of the Wnt, Notch, and Hedgehog pathway genes to predict tumor recurrence in patients with stage II and III colon carcinoma. Our results indicate that GLI1 rs2228226 G>C may be an independent prognostic marker. Our findings further suggest that patients harboring the homozygous mutant variant do not benefit from adjuvant 5-FU–based chemotherapy.

The exact molecular mechanisms involved in how the GLI1 rs2228226 G>C polymorphism exerts effect on colon carcinoma outcome are not clarified yet. Nonsynonymous polymorphisms result in amino acid changes and thus may affect the protein function (40). We used the F-SNP database to predict the functional effects of the analyzed polymorphisms. F-SNP gathers computationally predicted functional information about polymorphisms, particularly aiming to facilitate identification of disease-related polymorphisms in association studies (38, 39). When used for GLI1 rs2228226 G>C, F-SNP predicted changes in splicing regulation and posttranslation, thus supporting the effects seen in our study. In a recent study, however, Páez and colleagues investigated the association of GLI1 rs2228226 with TTR in 234 patients with stage III and high-risk stage II patients, all treated with adjuvant 5-FU–based chemotherapy, but found no clinical effect (41). Moreover, in genome-wide association studies, GLI1 rs2228226 has not been identified as a prognostic or predictive marker in colorectal cancer (42–44).

The Hedgehog signaling pathway induces expression of the gene SNAIL1, a transcription repressor of E-cadherin. Its transcriptional upregulation is directly mediated by the transcription factor GLI1 (17). Patched 1 (PTCH1), a membrane protein, functions as a tumor suppressor and normally inhibits the membrane protein Smoothened (SMO) from activating GLI1. The binding of one of the three Hedgehog ligands (Sonic, Indian, or Desert) to PTCH1 abrogates its repressive effects on SMO allowing the translocation of GLI1 to the nucleus where it induces the expression of multiple target genes (45, 46). The Hedgehog signal transduction pathway regulates many processes of development and tissue homeostasis and is dysregulated in malignancies and inflammatory diseases of the gastrointestinal tract (47–50).

Increasing evidence supports the involvement of inflammation in cancer progression and metastasis (51, 52). Hedgehog signaling plays a crucial role in the inflammatory response because Sonic Hedgehog is critical for T-lymphocyte development, adult human CD4+ T-cell activation, and myeloid cell maturation (53–56). Recently, Lees and colleagues demonstrated an overall downregulation of Hedgehog signaling pathway activity, including GLI1 and PTCH, in colonic inflammation in humans. Furthermore, they identified the GLI rs2228226 G>C polymorphism as functionally deficient in activating GLI-responsive transcription in vitro, showing a 50% less efficient transcriptional activity compared with the wild-type (57). GLI1 rs2228226 G>C encodes a change from glutamine to glutamic acid, causing a significant charge change in a conserved region adjacent to the known transactivation region of GLI1, that may directly modify transactivation activity and/or affect protein stabilization (58). In addition, Lees and colleagues showed in an established mouse model of colitis that animals carrying the mutant allele of GLI1 rs2228226 G>C develop severe intestinal inflammation, indicating that tolerance to inflammatory stimuli requires a fully functional Hedgehog signal transduction network (51). The most highly expressed cytokine in mice harboring the mutant allele of GLI1 rs2228226 G>C in their study was IL-23, a molecule that promotes the differentiation of T-helper IL-17–producing (TH17) cells that are involved in inflammation processes, including inflammatory bowel disease (57). IL-23 is also known as a procarcinogenic cytokine, which is mainly produced by tumor-associated macrophages in the tumor microenvironment, via direct transcriptional activation of the IL-23/p19 gene (59). In the study by Lees and colleagues, also IL-17, a cytokine closely associated with IL-23, was markedly upregulated in animals harboring the GLI1 rs2228226 G>C mutant variant (57). Grivennikov and colleagues investigated mechanisms responsible for tumor-elicited inflammation in a mouse model of colorectal carcinogenesis, which, like human colorectal carcinoma, also exhibited upregulation of IL-23 and IL-17 (59, 60). They found that IL-23 signaling promotes tumor growth and progression, and the development of tumoral IL-17 response, resulting in an additional aggravation of disease progression (60). Efforts to target pathogenic Hedgehog signaling have steadily progressed from the laboratory to the clinic, and the recent approval of vismodegib for patients with advanced basal cell carcinoma represents an important milestone (61–66). However, in a recent phase II study, vismodegib did not add to the efficacy of standard first-line treatment for metastatic colorectal cancer (67).

In our study cohort, we found a statistically significant association between GLI1 rs2228226 G>C and TTR, showing a decreased TTR in patients carrying the homozygous mutant genotype. Hence, we hypothesize that patients with colon carcinoma harboring the functionally deficient homozygous mutant variant, which is associated with upregulation of IL-23 and IL-17, are more likely to develop a recurrent disease, due to a supportive inflammatory microenvironment for tumor growth. However, we could not experimentally underline this biologic function because the GLI1 rs2228226 genotypes were not significantly associated with GLI1, IL-17, and IL-21 expression in tumor in a subset of our patient cohort. Our results further suggest that patients harboring the homozygous mutant variant of GLI1 rs2228226 G>C do not benefit from adjuvant 5-FU–based chemotherapy. We also found a significant association between APC rs454886 G/G and larger tumor size and increased vascular invasion, furthermore, patients with NOTCH2 rs11249433 TT showed an increased lymphovascular invasion. F-SNP predicted changes in the transcriptional regulation for the intronic APC rs454886 G>A. No prediction could be provided for the intergenic located NOTCH2 rs11249433 T>C by the software (38, 39). Because the biologic function of these SNPs is unknown, these associations remain to be elucidated.

The strength of the present study is the large sample size and the long follow-up period. However, because of the retrospective study design, a selection bias cannot be fully excluded. The subgroup of “high-risk” GLI1 rs2228226 C/C included overall only 40 patients and only 21 patients treated with various chemotherapy regimens. Therefore, it is currently unknown whether this association is truly significant and/or whether only patients with monotherapy or both, mono- and combination therapy do not benefit in this “high-risk” subgroup. Another limitation is the lack of the microsatellite instability (MSI) status in our study cohort; hence the evaluation of GLI1 rs2228226 G>C in comparison with MSI was not feasible. Finally, the method of preservation of the tissue samples was performed by three different institutions over a number of years, undermining the consistency of sample preparation.

In conclusion, this study provides the first evidence that GLI1 rs2228226 G>C may predict early tumor recurrence in patients with stage II and III colon carcinoma. Prospective studies are warranted to validate our findings.

No potential conflicts of interest were disclosed.

Conception and design: J. Szkandera, M. Pichler, G. Absenger, H. Samonigg, T. Winder, A. Gerger

Development of methodology: M. Asslaber, W. Renner, A. Gerger

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J. Szkandera, G. Absenger, M. Stotz, M. Weissmueller, M. Asslaber, S. Lax, A. Gerger

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J. Szkandera, M. Pichler, G. Absenger, M. Stotz, H. Samonigg, M. Asslaber, T. Winder, W. Renner

Writing, review, and/or revision of the manuscript: J. Szkandera, M. Pichler, G. Absenger, M. Stotz, M. Weissmueller, H. Samonigg, M. Asslaber, S. Lax, T. Winder, W. Renner, A. Gerger

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): G. Absenger, M. Asslaber, G. Leitner, W. Renner, A. Gerger

Study supervision: H. Samonigg, A. Gerger

This work was supported by funds of the Oesterreichische Nationalbank (Anniversary Fund, project number: 14258).

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.

1.
Siegel
R
,
Naishadham
D
,
Jemal
A
. 
Cancer statistics, 2012
.
CA Cancer J Clin
2012
;
62
:
10
29
.
2.
Ferlay
J
,
Parkin
DM
,
Steliarova-Foucher
E
. 
Estimates of cancer incidence and mortality in Europe in 2008
.
Eur J Cancer
2010
;
46
:
765
81
.
3.
Manfredi
S
,
Bouvier
AM
,
Lepage
C
,
Hatem
C
,
Dancourt
V
,
Faivre
J
. 
Incidence and patterns of recurrence after resection for cure of colonic cancer in a well defined population
.
Br J Surg
2006
;
93
:
1115
22
.
4.
Davies
JM
,
Goldberg
RM
. 
Treatment of metastatic colorectal cancer
.
Semin Oncol
2011
;
38
:
552
60
.
5.
Tejpar
S
,
Bertagnolli
M
,
Bosman
F
,
Lenz
HJ
,
Garraway
L
,
Waldman
F
, et al
Prognostic and predictive biomarkers in resected colon cancer: current status and future perspectives for integrating genomics into biomarker discovery
.
Oncologist
2010
;
15
:
390
404
.
6.
Winder
T
,
Lenz
HJ
. 
Molecular predictive and prognostic markers in colon cancer
.
Cancer Treat Rev
2010
;
36
:
550
6
.
7.
Sinicrope
FA
,
Sargent
DJ
. 
Clinical implications of microsatellite instability in sporadic colon cancers
.
Curr Opin Oncol
2009
;
21
:
369
73
.
8.
LaBarge
MA
. 
The difficulty of targeting cancer stem cell niches
.
Clin Cancer Res
2010
;
16
:
3121
9
.
9.
Clevers
H
. 
The cancer stem cell: premises, promises and challenges
.
Nat Med
2011
;
17
:
313
9
.
10.
O'Brien
CA
,
Kreso
A
,
Jamieson
CH
. 
Cancer stem cells and self-renewal
.
Clin Cancer Res
2010
;
16
:
3113
20
.
11.
Kiesslich
T
,
Berr
F
,
Alinger
B
,
Kemmerling
R
,
Pichler
M
,
Ocker
M
, et al
Current status of therapeutic targeting of developmental signalling pathways in oncology
.
Curr Pharm Biotechnol
2012
;
13
:
2184
220
.
12.
Takahashi-Yanaga
F
,
Kahn
M
. 
Targeting Wnt signaling: can we safely eradicate cancer stem cells?
Clin Cancer Res
2010
;
16
:
3153
62
.
13.
Polakis
P
. 
Wnt signaling and cancer
.
Genes Dev
2000
;
14
:
1837
51
.
14.
Saif
MW
,
Chu
E
. 
Biology of colorectal cancer
.
Cancer J
2010
;
16
:
196
201
.
15.
Pannuti
A
,
Foreman
K
,
Rizzo
P
,
Osipo
C
,
Golde
T
,
Osborne
B
, et al
Targeting Notch to target cancer stem cells
.
Clin Cancer Res
2010
;
16
:
3141
52
.
16.
Miele
L
,
Golde
T
,
Osborne
B
. 
Notch signaling in cancer
.
Curr Mol Med
2006
;
6
:
905
18
.
17.
Merchant
AA
,
Matsui
W
. 
Targeting Hedgehog–a cancer stem cell pathway
.
Clin Cancer Res
2010
;
16
:
3130
40
.
18.
Monzo
M
,
Moreno
I
,
Artells
R
,
Ibeas
R
,
Navarro
A
,
Moreno
J
, et al
Sonic hedgehog mRNA expression by real-time quantitative PCR in normal and tumor tissues from colorectal cancer patients
.
Cancer Lett
2006
;
233
:
117
23
.
19.
Coate
L
,
Cuffe
S
,
Horgan
A
,
Hung
RJ
,
Christiani
D
,
Liu
G
. 
Germline genetic variation, cancer outcome, and pharmacogenetics
.
J Clin Oncol
2010
;
28
:
4029
37
.
20.
Hirata
H
,
Hinoda
Y
,
Nakajima
K
,
Kikuno
N
,
Yamamura
S
,
Kawakami
K
, et al
Wnt antagonist gene polymorphisms and renal cancer
.
Cancer
2009
;
115
:
4488
503
.
21.
Shanmugam
KS
,
Brenner
H
,
Hoffmeister
M
,
Chang-Claude
J
,
Burwinkel
B
. 
The functional genetic variant Arg324Gly of frizzled-related protein is associated with colorectal cancer risk
.
Carcinogenesis
2007
;
28
:
1914
7
.
22.
Berndt
SI
,
Huang
WY
,
Yeager
M
,
Weissfeld
JL
,
Chanock
SJ
,
Hayes
RB
. 
Genetic variants in frizzled-related protein (FRZB) and the risk of colorectal neoplasia
.
Cancer Causes Control
2009
;
20
:
487
90
.
23.
Wang
X
,
Goode
EL
,
Fredericksen
ZS
,
Vierkant
RA
,
Pankratz
VS
,
Liu-Mares
W
, et al
Association of genetic variation in genes implicated in the beta-catenin destruction complex with risk of breast cancer
.
Cancer Epidemiol Biomarkers Prev
2008
;
17
:
2101
8
.
24.
Kanzaki
H
,
Ouchida
M
,
Hanafusa
H
,
Yano
M
,
Suzuki
H
,
Aoe
M
, et al
Single nucleotide polymorphism of the AXIN2 gene is preferentially associated with human lung cancer risk in a Japanese population
.
Int J Mol Med
2006
;
18
:
279
84
.
25.
Hazra
A
,
Fuchs
CS
,
Chan
AT
,
Giovannucci
EL
,
Hunter
DJ
. 
Association of the TCF7L2 polymorphism with colorectal cancer and adenoma risk
.
Cancer Causes Control
2008
;
19
:
975
80
.
26.
Frank
B
,
Hoffmeister
M
,
Klopp
N
,
Illig
T
,
Chang-Claude
J
,
Brenner
H
. 
Single nucleotide polymorphisms in Wnt signaling and cell death pathway genes and susceptibility to colorectal cancer
.
Carcinogenesis
2010
;
31
:
1381
6
.
27.
Tuupanen
S
,
Turunen
M
,
Lehtonen
R
,
Hallikas
O
,
Vanharanta
S
,
Kivioja
T
, et al
The common colorectal cancer predisposition SNP rs6983267 at chromosome 8q24 confers potential to enhanced Wnt signaling
.
Nat Genet
2009
;
41
:
885
90
.
28.
Slattery
ML
,
Folsom
AR
,
Wolff
R
,
Herrick
J
,
Caan
BJ
,
Potter
JD
. 
Transcription factor 7-like 2 polymorphism and colon cancer
.
Cancer Epidemiol Biomarkers Prev
2008
;
17
:
978
82
.
29.
Fu
YP
,
Edvardsen
H
,
Kaushiva
A
,
Arhancet
JP
,
Howe
TM
,
Kohaar
I
, et al
NOTCH2 in breast cancer: association of SNP rs11249433 with gene expression in ER-positive breast tumors without TP53 mutations
.
Mol Cancer
2010
;
9
:
113
.
30.
Wang
YC
,
Xue
HP
,
Wang
ZH
,
Fang
JY
. 
An integrated analysis of the association between Ts gene polymorphisms and clinical outcome in gastric and colorectal cancer patients treated with 5-FU-based regimens
.
Mol Biol Rep
2013
;
40
:
4637
44
.
31.
Mirakhorli
M
,
Rahman
SA
,
Abdullah
S
,
Vakili
M
,
Rozafzon
R
,
Khoshzaban
A
. 
Multidrug resistance protein 2 genetic polymorphism and colorectal cancer recurrence in patients receiving adjuvant FOLFOX-4 chemotherapy
.
Mol Med Rep
2013
;
7
:
613
7
.
32.
O'Donnell
PH
,
Stark
AL
,
Gamazon
ER
,
Wheeler
HE
,
McIlwee
BE
,
Gorsic
L
, et al
I dentification of novel germline polymorphisms governing capecitabine sensitivity
.
Cancer
2012
;
118
:
4063
73
.
33.
Zhang
X
,
Sun
B
,
Lu
Z
. 
Evaluation of clinical value of single nucleotide polymorphisms of dihydropyrimidine dehydrogenase gene to predict 5-Fluorouracil toxicity in 60 colorectal cancer patients in China
.
Int J Med Sci
2013
;
10
:
894
902
.
34.
Lv
H
,
Li
Q
,
Qiu
W
,
Xiang
J
,
Wei
H
,
Liang
H
, et al
Genetic polymorphism of XRCC1 correlated with response to oxaliplatin-based chemotherapy in advanced colorectal cancer
.
Cancer Invest
2013
;
31
:
24
8
.
35.
Cortejoso
L
,
López-Fernández
LA
. 
Pharmacogenetic markers of toxicity for chemotherapy in colorectal cancer patients
.
Pharmacogenomics
2012
;
13
:
1173
91
.
36.
Alonzo
TA
. 
Standards for reporting prognostic tumor marker studies
.
J Clin Oncol
2005
;
23
:
9053
4
.
37.
McShane
LM
,
Altman
DG
,
Sauerbrei
W
,
Taube
SE
,
Gion
M
,
Clark
GM
. 
Statistics subcommittee of the NCI-EORTC working group on cancer diagnostics. Reporting recommendations for tumor marker prognostic studies
.
J Clin Oncol
2005
;
23
:
9067
72
.
38.
Lee
PH
,
Shatkay
H
. 
F-SNP: computationally predicted functional SNPs for disease association studies
.
Nucleic Acids Res
2008
;
36
:
D820
4
.
39.
Lee
PH
,
Shatkay
H
. 
An integrative scoring system for ranking SNPs by their potential deleterious effects
.
Bioinformatics
2009
;
25
:
1048
55
.
40.
Ng
PC
,
Henikoff
S
. 
Predicting the effects of amino acid substitutions on protein function
.
Annu Rev Genomics Hum Genet
2006
;
7
:
61
80
.
41.
Páez
D
,
Gerger
A
,
Zhang
W
,
Yang
D
,
Labonte
MJ
,
Benhaim
L
, et al
Association of common gene variants in the WNT/β-catenin pathway with colon cancer recurrence
.
Pharmacogenomics J
2013
Jul 2. [Epub ahead of print]
.
42.
Wong
SH
,
Sung
JJ
,
Chan
FK
,
To
KF
,
Ng
SS
,
Wang
XJ
, et al
Genome-wide association and sequencing studies on colorectal cancer
.
Semin Cancer Biol
2013
;
23
:
502
11
.
43.
Dai
J
,
Gu
J
,
Huang
M
,
Eng
C
,
Kopetz
ES
,
Ellis
LM
, et al
GWAS-identified colorectal cancer susceptibility loci associated with clinical outcomes
.
Carcinogenesis
2012
;
33
:
1327
31
.
44.
Fernandez-Rozadilla
C
,
Cazier
JB
,
Moreno
V
,
Crous-Bou
M
,
Guino
E
,
Duran
G
, et al
Pharmacogenomics in colorectal cancer: a genome-wide association study to predict toxicity after 5-fluorouracul or FOLFOX administration
.
Pharmacogenomics J
2013
;
13
:
209
17
.
45.
Taipale
J
,
Beachy
PA
. 
The Hedgehog and Wnt signalling pathways in cancer
.
Nature
2001
;
411
:
349
54
.
46.
Wang
Y
,
McMahon
AP
,
Allen
BL
. 
Shifting paradigms in Hedgehog signaling
.
Curr Opin Cell Biol
2007
;
19
:
159
65
.
47.
Ingham
PW
,
McMahon
AP
. 
Hedgehog signaling in animal development: paradigms and principles
.
Genes Dev
2001
;
15
:
3059
87
.
48.
Lees
C
,
Howie
S
,
Sartor
RB
,
Satsangi
J
. 
The hedgehog signalling pathway in the gastrointestinal tract: implications for development, homeostasis, and disease
.
Gastroenterology
2005
;
129
:
1696
710
.
49.
Madison
BB
,
Braunstein
K
,
Kuizon
E
,
Portman
K
,
Qiao
XT
,
Gumucio
DL
. 
Epithelial hedgehog signals pattern the intestinal crypt-villus axis
.
Development
2005
;
132
:
279
89
.
50.
Yauch
RL
,
Gould
SE
,
Scales
SJ
,
Tang
T
,
Tian
H
,
Ahn
CP
, et al
A paracrine requirement for hedgehog signalling in cancer
.
Nature
2008
;
455
:
406
10
.
51.
Coussens
LM
,
Werb
Z
. 
Inflammation and cancer
.
Nature
2002
;
420
:
860
7
.
52.
Mantovani
A
,
Allavena
P
,
Sica
A
,
Balkwill
F
. 
Cancer-related inflammation
.
Nature
2008
;
454
:
436
44
.
53.
El Andaloussi
A
,
Graves
S
,
Meng
F
,
Mandal
M
,
Mashayekhi
M
,
Aifantis
I
. 
Hedgehog signaling controls thymocyte progenitor homeostasis and differentiation in the thymus
.
Nat Immunol
2006
;
7
:
418
26
.
54.
Lowrey
JA
,
Stewart
GA
,
Lindey
S
,
Hoyne
GF
,
Dallman
MJ
,
Howie
SE
, et al
Sonic hedgehog promotes cell cycle progression in activated peripheral CD4(+) T lymphocytes
.
J Immunol
2002
;
169
:
1869
75
.
55.
Stewart
GA
,
Lowrey
JA
,
Wakelin
SJ
,
Fitch
PM
,
Lindey
S
,
Dallman
MJ
, et al
Sonic hedgehog signaling modulates activation of and cytokine production by human peripheral CD4+ T cells
.
J Immunol
2002
;
169
:
5451
7
.
56.
Varas
A
,
Hernández-López
C
,
Valencia
J
,
Mattavelli
S
,
Martínez
VG
,
Hidalgo
L
, et al
Survival and function of human thymic dendritic cells are dependent on autocrine Hedgehog signaling
.
J Leukoc Biol
2008
;
83
:
1476
83
.
57.
Lees
CW
,
Zacharias
WJ
,
Tremelling
M
,
Noble
CL
,
Nimmo
ER
,
Tenesa
A
, et al
Analysis of germline GLI1 variation implicates hedgehog signalling in the regulation of intestinal inflammatory pathways
.
PLoS Med
2008
;
5
:
e239
.
58.
Huntzicker
EG
,
Estay
IS
,
Zhen
H
,
Lokteva
LA
,
Jackson
PK
,
Oro
AE
. 
Dual degradation signals control Gli protein stability and tumor formation
.
Genes Dev
2006
;
20
:
276
81
.
59.
Kortylewski
M
,
Xin
H
,
Kujawski
M
,
Lee
H
,
Liu
Y
,
Harris
T
, et al
Regulation of the IL-23 and IL-12 balance by Stat3 signaling in the tumor microenvironment
.
Cancer Cell
2009
;
15
:
114
23
.
60.
Grivennikov
SI
,
Wang
K
,
Mucida
D
,
Stewart
CA
,
Schnabl
B
,
Jauch
D
, et al
Adenoma-linked barrier defects and microbial products drive IL-23/IL-17-mediated tumour growth
.
Nature
2012
;
491
:
254
8
.
61.
Axelson
M
,
Liu
K
,
Jiang
X
,
He
K
,
Wang
J
,
Zhao
H
, et al
U.S. Food and Drug Administration approval: vismodegib for recurrent, locally advanced, or metastatic basal cell carcinoma
.
Clin Cancer Res
2013
;
19
:
2289
93
.
62.
Tang
JY
,
Mackay-Wiggan
JM
,
Aszterbaum
M
,
Yauch
RL
,
Lindgren
J
,
Chang
K
, et al
Inhibiting the hedgehog pathway in patients with the basal-cell nevus syndrome
.
N Engl J Med
2012
;
366
:
2180
8
.
63.
Queiroz
KC
,
Spek
CA
,
Peppelenbosch
MP
. 
Targeting Hedgehog signaling and understanding refractory response to treatment with Hedgehog pathway inhibitors
.
Drug Resist Updat
2012
;
15
:
211
22
.
64.
Sahebjam
S
,
Siu
LL
,
Razak
AA
. 
The utility of hedgehog signaling pathway inhibition for cancer
.
Oncologist
2012
;
17
:
1090
9
.
65.
Onishi
H
,
Katano
M
. 
Hedgehog signaling pathway as a therapeutic target in various types of cancer
.
Cancer Sci
2011
;
102
:
1756
60
.
66.
Onishi
H
,
Morifuji
Y
,
Kai
M
,
Suyama
K
,
Iwasaki
H
,
Katano
M
. 
Hedgehog inhibitor decreases chemosensitivity to 5-fluorouracil and gemcitabine under hypoxic conditions in pancreatic cancer
.
Cancer Sci
2012
;
103
:
1272
9
.
67.
Berlin
J
,
Bendell
JC
,
Hart
LL
,
Firdaus
I
,
Gore
I
,
Hermann
RC
, et al
A randomized phase II trial of vismodegib versus placebo with FOLFOX or FOLFIRI and bevacizumab in patients with previously untreated metastatic colorectal cancer
.
Clin Cancer Res
2013
;
19
:
258
67
.