Prospective Study Open Access
Copyright ©The Author(s) 2017. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jul 21, 2017; 23(27): 4958-4967
Published online Jul 21, 2017. doi: 10.3748/wjg.v23.i27.4958
Genetic polymorphisms predict response to anti-tumor necrosis factor treatment in Crohn’s disease
Uri Netz, Jane Victoria Carter, Maurice Robert Eichenberger, Susan Galandiuk, Price Institute of Surgical Research, the Hiram C. Polk Jr. MD Department of Surgery, University of Louisville School of Medicine, Louisville, KY 40202, United States
Uri Netz, Department of Surgery A, Soroka University Medical Center, Beer Sheva 84101, Israel
Uri Netz, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
Gerald Wayne Dryden, Department of Medicine, Division of Gastroenterology, Hepatology and Nutrition University of Louisville School of Medicine Louisville, KY 40202, United States
Jianmin Pan, Shesh Nath Rai, Department of Bioinformatics and Biostatistics, University of Louisville School of Public Health and Information Sciences, Louisville, KY 40202, United States
Author contributions: Netz U, Eichenberger MR and Galandiuk S designed the research; Netz U, Carter JV, Eichenberger MR, Dryden GW and Galandiuk S collected the data; Netz U, Carter JV and Eichenberger MR performed the genetic labwork; Netz U, Carter JV, Eichenberger MR, Pan J, Rai SN and Galandiuk S analyzed and interpreted the data; all authors drafted the manuscript and approved the final version.
Institutional review board statement: The study was reviewed and approved by the University of Louisville Institutional Review Board.
Informed consent statement: All participants signed an informed consent statement as specified by the University of Louisville Institutional Review Board - Human Subjects Protection Program Office - No 97.0361.
Conflict-of-interest statement: All authors declare no conflict of interest.
Data sharing statement: The dataset is available from the corresponding author at [s0gala01@louisville.edu]. Consent for data sharing was not obtained but the presented data are anonymized and risk of identification is low.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Susan Galandiuk, MD, FACG, AGAF, Price Institute of Surgical Research, the Hiram C. Polk Jr. MD Department of Surgery, University of Louisville School of Medicine, 550 South Jackson Street, Louisville, KY 40202, United States. s0gala01@louisville.edu
Telephone: +1-502-8524568 Fax: +1-502-8528915
Received: February 7, 2017
Peer-review started: February 9, 2017
First decision: March 16, 2017
Revised: April 5, 2017
Accepted: May 4, 2017
Article in press: May 4, 2017
Published online: July 21, 2017
Processing time: 163 Days and 4.4 Hours

Abstract
AIM

To investigate genetic factors that might help define which Crohn’s disease (CD) patients are likely to benefit from anti-tumor necrosis factor (TNF) therapy.

METHODS

This was a prospective cohort study. Patients were recruited from a university digestive disease practice database. We included CD patients who received anti-TNF therapy, had available medical records (with information on treatment duration and efficacy) and who consented to participation. Patients with allergic reactions were excluded. Patients were grouped as ever-responders or non-responders. Genomic DNA was extracted from peripheral blood, and 7 single nucleotide polymorphisms (SNPs) were assessed. The main outcome measure (following exposure to the drug) was response to therapy. The patient genotypes were assessed as the predictors of outcome. Possible confounders and effect modifiers included age, gender, race, and socioeconomic status disease, as well as disease characteristics (such as Montreal criteria).

RESULTS

121 patients were included. Twenty-one were non-responders, and 100 were ever-responders. Fas ligand SNP (rs763110) genotype frequencies, TNF gene -308 SNP (rs1800629) genotype frequencies, and their combination, were significantly different between groups on multivariable analysis controlling for Montreal disease behavior and perianal disease. The odds of a patient with a Fas ligand CC genotype being a non-responder were four-fold higher as compared to a TC or TT genotype (P = 0.009, OR = 4.30, 95%CI: 1.45-12.80). The presence of the A (minor) TNF gene -308 allele correlated with three-fold higher odds of being a non-responder (P = 0.049, OR = 2.88, 95%CI: 1.01-8.22). Patients with the combination of the Fas ligand CC genotype and the TNF -308 A allele had nearly five-fold higher odds of being a non-responder (P = 0.015, OR = 4.76, 95%CI: 1.35-16.77). No difference was seen for the remaining SNPs.

CONCLUSION

The Fas-ligand SNP and TNF gene -308 SNP are associated with anti-TNF treatment response in CD and may help select patients likely to benefit from therapy.

Key Words: Anti-tumor necrosis factor, Fas ligand, Antibody, Response, Crohn’s disease, Single nucleotide polymorphisms, Genotype, Tumor necrosis factor gene

Core tip: Predicting the subset of patients who do not respond to anti-tumor necrosis factor (TNF) treatment is important clinically and economically. Patients with Crohn’s disease who received anti-TNF therapy were grouped as ever-responders or non-responders. Genomic DNA was extracted from peripheral blood, and 7 single nucleotide polymorphisms (SNPs) were assessed. 121 patients were included. Twenty-one were non-responders, and 100 were ever-responders. Fas ligand SNP (rs763110) genotype frequencies, TNF gene -308 SNP (rs1800629) genotype frequencies, and their combination, were significantly different between groups on multivariable analysis and may help select patients likely to benefit from anti-TNF therapy.



INTRODUCTION

Crohn’s disease (CD) is a transmural chronic inflammatory disease that can affect any part of the alimentary tract, but which often involves the distal ileum.

Anti-tumor necrosis factor-α (anti-TNF-α) monoclonal antibodies are generally used for inducing and maintaining remission and can be used alone or in combination with other drugs[1]. The most common drugs in this group, for CD, are infliximab (chimeric murine - human IgG1 monoclonal antibody targeting TNF-α), adalimumab (fully humanized IgG1 anti-TNF-α monoclonal antibody), and certolizumab pegol (a humanized monoclonal Fab′ fragment with a high binding affinity for TNF-α)[2,3].

Although the majority of patients benefit from anti-TNF treatment, approximately one-third of patients treated with an induction dose of anti-TNF do not improve clinically, termed primary non-response[3]. An additional significant population who initially respond to treatment eventually lose responsiveness, termed a secondary non-response.

Identifying patients who will fail treatment with anti-TNF agents is of significant importance both from a clinical and economic perspective. Anti-TNF drugs have been associated with an increased risk of opportunistic infections, melanoma, and lymphoma[4-6]. Anti-TNF treatment is also very expensive, with 2013 annual per patient costs for adalimumab and infliximab at approximately $25000 and $24000 respectively[7,8].

Factors associated with the success of anti-TNF treatment include shorter disease duration, inflammatory (as opposed to fibrostenotic) disease phenotype, isolated colonic disease, young age, non-smoking status, as well as a serum high C-reactive protein that returns to normal after initiation of treatment[9-11]. Non-response can be due to multiple factors such as an alternative non-TNF mediated pathway of inflammation, due to a differential role of TNF in certain stages of disease and/or due to the presence or development of anti-drug antibodies. Additionally, individual differences in drug bioavailability and pharmacokinetics can be factors associated with non-response[9].

A possible conduit to predict response to anti-TNF therapy could be through genetic testing. Several genes have been implicated in the pathogenesis of CD, including NOD2 and ATG16L1[12,13]. There is, however, limited data on the ability to predict anti-TNF treatment response in CD based upon genetic data. Some genes have been investigated without success[14]. Our aim was to investigate genetic factors that might help define which CD patients are likely to benefit from anti-TNF therapy and permit efficient and cost-effective treatment. We hypothesized that specific single nucleotide polymorphism (SNP) genotypes are associated with anti-TNF treatment response in patients with CD. We chose to examine a series of SNPs within genes that have been linked either with CD and/or with anti-TNF treatment response in order to determine whether these could aid in predicting response to anti-TNF treatment in CD patients.

MATERIALS AND METHODS

This study complies with the STROBE guidelines and the extension for genetic association studies[15].

Patient recruitment and data collection

This is a prospective cohort study approved by the University of Louisville Institutional Review Board. All patients signed a written informed consent. Consecutive patients with a diagnosis of CD were identified from a large prospectively maintained genetic database, from a large University digestive disease practice, encompassing the period 1/1998 to 4/2016. Inclusion criteria were CD patients who had received anti-TNF therapy, and whose medical records were available, with information about receipt of anti-TNF therapy, its duration, efficacy, and cessation where applicable. Included patients received appropriate drug doses and had a follow-up of at least 12 mo following treatment initiation[16,17]. Patients were excluded if anti-TNF treatment was stopped due to side-effects, local and/or systemic allergy, or if it was impossible to distinguish from the medical records whether the drug worked.

Additional data collected from the medical records included gender, race, socioeconomic status (patient’s

zip code of residence was used to obtain median household income based on United States census data from the American Community Survey 2014 - 5 year estimates)[18], surgical history, and clinical state of the disease according to the Montreal classification for CD, including age at diagnosis, location, disease behavior, and the presence or absence of perianal disease[19].

The main outcome measure (following exposure to the drug) was response to therapy. Participants were grouped as ever-responders if they had initial response to anti-TNF treatment (even if this was later lost due to antibody formation) or non-responders in accordance with the treating physician decision. The patient genotypes (see below) were assessed as the predictors of outcome. Possible confounders and effect modifiers included age, gender, race, and socioeconomic status disease, as well as disease characteristics (such as Montreal criteria).

DNA extraction

Peripheral blood was collected by venipuncture (after written informed consent) in EDTA-vacutainers (BD, Franklin Lakes, NJ, United States) and stored at 4 °C until further use.

Genomic DNA was extracted from blood samples using the illustra GenomiPhi V2 DNA Amplification Kit (GE Healthcare, Pittsburgh, PA, United States) using the manufacturer's protocol[20]. Briefly, the blood was initially diluted with PBS buffer. Blood was then lysed: 1 μL of diluted blood was lysed with 1 μL of cell lysis solution (400 mmol/L KOH, 10 mmol/L EDTA, 100 mmol/L DTT), followed by the addition of 1 μL of neutralization buffer (400 mmol/L HCl, 600 mmol/L Tris-HCl, pH 7.5). Whole genome amplification was then performed: 17 μL of master mix [7 μL sample buffer, 9 μL reaction buffer, and 1 μL enzyme mix from the illustra GenomiPhi V2 DNA Amplification Kit (GE Healthcare, Pittsburgh, PA, United States)] was added to each sample for a total reaction volume of 20 μL. Amplification was performed according to the following program: 30 °C for two hours, followed by 65 °C for 10 min, then cooled to 4 °C. Following whole genome amplification, DNA concentration was determined using NanoDrop® 2000 spectrophotometry. The samples were diluted and stored at -20 °C until analysis.

SNP genotyping

SNPs selection: A PubMed literature search was conducted using the keywords “tumor necrosis factor-alpha”, “anti-TNF”, “infliximab”, “adalimumab”, “polymorphism”, “Crohn’s disease”, “response”, “biomarker” using Boolean operators (AND), (OR), (NOT). Results were narrowed down to original studies investigating SNPs including frequency of alleles and genotypes for different groups. We included SNPs that had demonstrated association with CD or anti-TNF treatment, those that had biological relevance, and those that had an expected minor allele frequency ≥ 5%. Both new genetic associations and previously described efforts were investigated. SNPs were excluded if they had been extensively investigated and if there was no prognostic value for the combination of CD and anti-TNF treatment response. As a result of this search, the following seven SNPs within 5 genes were selected for study and assessed in each patient’s DNA sample: ATG16L1 (rs10210302, T300A rs2241880), Fas ligand (-843 rs763110), IBD5 (rs2522057), FCGR 3A (rs396991), and TNF (-308 rs1800629, -238 rs361525).

SNP assessment was performed using TaqMan® predesigned genotyping assays (Life Technologies®, Carlsbad CA)[21]. The TaqMan® genotyping assays were diluted to a 20× working stock solution with 1× TE buffer (10 mmol/L Tris-HCl, 1 mmol/L EDTA, pH 8.0, in DNase-free, sterile-filtered water) and stored at -20 °C, as recommended by the manufacturer.

MicroAmpR Fast Optical 96-well reaction plates (Applied Biosystems, Foster City, CA, United States) were used. Six microliters of master mix was used for each assay (5.5 μL of TaqMan® Universal Master Mix II, no UNG [Applied Biosystems™] together with 0.5 μL of 20 × working assay [TaqMan® predesigned Genotyping Assays]). Five microliters of DNA (4.5 ng/μL) was added to the plate. PCR reactions were performed using a Step-One Plus® RT-PCR System (Life Technologies®, Carlsbad, CA, United States) and the following program: 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s, and then 60 °C for 1 min. Analysis was performed using Step-One Plus® software v2.1 (applied Biosystems, Foster City, CA, United States). Each genotype was independently assigned by two investigators. In cases of disagreement, assignment was reached by consensus. All laboratory work and genotyping was done at the Price Institute of Surgical Research, Louisville Kentucky, United States.

Statistical analysis

Descriptive and analytical statistics were performed using SAS version 9.4 statistical software[22]. Genotype frequencies, demographic, and disease characteristics were compared using a χ2 test (or Fisher’s exact test for 2 × 2 tables). Socioeconomic status was calculated according to the national percentile of the patient’s median household income divided into quartiles (0-25, 26-50, 51-75 and 76-100) and compared using a χ2 test. Comparison of continuous variables was performed using a two-sample t-test or ANOVA. In order to explore for the presence of bias in the cohort, a group of contemporary subjects who did not receive anti-TNF treatment were compared with patients included in this study. Following this, characteristics between ever-responders and non-responders in the study group were then compared[23].

Hardy-Weinberg equilibrium was determined for each SNP (Table 1). Univariable logistic regression was modeled for the probability of anti-TNF treatment failure for each covariate. Multivariable logistic regression models were used for separate SNPs and covariates exhibiting a trend towards a significant difference (P < 0.15)[24]. Final models included OR and 95%CI. A P-value of < 0.05 was considered statistically significant.

Table 1 Hardy weinberg equilibrium n (%).
Genotype distribution
Hardy Weinberg equilibrium
SNPGenotypeFrequencyExpected frequency (n)P value1
ATG16L1 rs10210302CC25 (21.0)24NS
TC57 (47.9)59
TT37 (31.1)36
ATG16L1 rs2241880AA27 (22.5)26NS
GA57 (47.5)60
GG36 (30.0)35
Fas Ligand rs763110CC42 (36.5)41NS
TC54 (47.0)55
TT19 (16.5)18
IBD5 rs2522057CC35 (29.2)290.045
GC49 (40.8)60
GG36 (30.0)31
FCGR 3A rs396991AA70 (58.3)620.0005
AC33 (27.5)48
CC17 (14.2)9
TNF gene (-308) rs1800629AA4 (3.4)3NS
GA31 (26.1)33
GG84 (70.6)83
TNF gene (-238) rs361525AA2 (1.7)1NS
GA15 (12.5)17
GG103 (85.8)102

The statistical methods of this study were reviewed by Pan J and Rai SN.

RESULTS
Patient demographics

Table 1 shows a flow diagram of patient selection; 121 patients were selected for study. Of these, 21 (17.4%) patients were primary non-responders to anti-TNF treatment and 100 (82.6%) patients were ever-responders to anti-TNF treatment. A quarter of these initial ever-responders (25/100) lost response at a later time and were termed secondary non-responders. The patient population was predominantly Caucasian (92.6%), with a higher proportion of women (58.7%) (Table 2). With regards to clinical parameters, 90/121 (74.4%) patients were diagnosed between the ages of 17 and 40 years of age (Montreal A2). Most CD patients, 74/121 (61.2%), had combined ileocolonic disease (Montreal L3), whereas 15/121 (12.4%) had isolated ileal disease (Montreal L1), and 32/121 (26.4%) had only colonic disease (Montreal L2). Only 3/121 (2.5%) patients had upper GI (Montreal L4) involvement, all of whom were responders. Montreal L4 disease was analyzed separately from L1-3, due to the fact that, according to the Montreal classification, it is not mutually exclusive and can be added to any of the other locations when concomitant upper GI disease is present[19]. The population was fairly evenly distributed with respect to disease behavior with 36/121 (30%) patients having non-stricturing, non-penetrating disease (Montreal B1), 45/121 (37%) patients having stricturing disease (Montreal B2), and 40/121 (33%) patients having penetrating disease (Montreal B3). In addition, 37 of 121 (31%) patients had perianal disease (Montreal P designation). Table 2 shows the clinical and demographic data of the participants, as well as these data for the non-responder and ever-responder groups. None of the clinical or demographic characteristics were significantly different between these 2 groups.

Table 2 Clinical and demographic patient characteristics n (%).
VariablesTotalAnti-TNF treatment
P value
Non-respondersEver responders
Total121 (100)21 (17)100 (83)NA
Patient demographics
Gender
Female71 (59)15 (71)56 (56)NS
Male50 (41)6 (29)44 (44)
Race
Caucasian112 (93)21 (100)91 (91)NS
African American9 (7)0 (0.0)9 (9)
Socioeconomic status1
1st Quartile24 (20)4 (19)20 (20)NS
2nd Quartile40 (33)6 (29)34 (34)
3rd Quartile29 (24)3 (14)26 (26)
4th Quartile28 (23)8 (38)20 (20)
Montreal classification
Age of onset (A)
A1 - below 16 years old14 (12)1 (5)13 (13)NS
A2 - between 17 and 40 years old90 (74)17 (81)73 (73)
A3 - above 40 years old17 (14)3 (14)14 (14)
Location (L)
L1 - ileal15 (12)1 (5)14 (14)NS
L2 - colonic32 (26)5 (24)27 (27)
L3 - ileocolonic74 (61)15 (71)59 (59)
Location (L4) upper
No upper GI disease118 (98)21 (100)97 (97)NS
L4 - upper GI disease3 (2)0 (0)3 (3)
Behavior (B)
B1 - non-stricturing, non-penetrating36 (30)4 (19)32 (32)0.1
B2 - stricturing45 (37)12 (57)33 (33)
B3 - penetrating40 (33)5 (24)35 (35)
Behavior (p) perianal disease
No perianal disease84 (69)18 (86)66 (66)0.08
p - perianal disease present37 (31)3 (14)34 (34)
Anti-TNF treatment type
Drugs received
Infliximab46 (38)10 (48)36 (36)NS
Adalimumab45 (38)5 (24)40 (40)
Infliximab and Adalimumab229 (24)6 (29)23 (23)
Certolizumab pegol505
Presence of bias

When comparing the characteristics of the patients who received anti-TNF treatment and included in the study (n = 121) with those who did not receive anti-TNF treatment (n = 152) in order to ascertain the presence of bias, no difference was found in 3 of the 4 variables examined: gender (P = 0.27), race (P = 0.95), or socioeconomic status (P = 0.23). The patients included in the study who received anti-TNF treatment were, however, younger (41.6 years old, 95%CI: 39.2-44.0) than those that did not receive anti-TNF treatment (49.4 years old, 95%CI: 47.1-51.7) (P < 0.001).

We assessed 7 different SNPs associated with 5 different genes and observed less than 5% technical failure rate in all assays. Table 3 shows the SNPs tested as well as their genotype and allele distribution. Comparison of genotypes between ever responders and non-responders (Table 4) identified a significant difference in the Fas ligand SNP rs763110 genotypes (P = 0.042). Patients with a CC genotype (as compared to those with a TC or TT genotype) were more likely to be non-responders to anti-TNF treatment, (P = 0.016; OR = 0.31, 95%CI: 0.11-0.83). Genotypes of another SNP, such as -308 (rs1800629), within the TNF gene demonstrated a trend towards correlation with response to anti-TNF treatment (P = 0.088) when grouping genotypes AA and GA compared to genotype GG (P = 0.093, OR = 2.29, 95%CI: 0.85-6.17).

Table 3 Single nucleotide polymorphisms tested with genotype and allele distribution for entire patients group n (%).
Genotype distribution
Allele distribution
SNPLocation relative to gene (Chromosome number)Nucleotide changeAssay successfulGenotypeFrequencyAlleleFrequency
ATG16L1 rs102103022 kb upstream (2)C/T119 (98)CC25 (21.0)Total238
TC57 (47.9)C107 (45.0)
TT37 (31.1)T131 (55.0)
ATG16L1 rs2241880Thr300Ala (2)A/G120 (99)AA27 (22.5)Total240
GA57 (47.5)A111 (46.3)
GG36 (30.0)G129 (53.7)
FAS ligand rs763110-843 (1)C/T115 (95)CC42 (36.5)Total230
TC54 (47.0)C138 (60.0)
TT19 (16.5)T92 (40.0)
IBD5 rs2522057Intergenic region (5)C/G120 (99)CC35 (29.2)Total240
GC49 (40.8)C119 (49.6)
GG36 (30.0)G121 (50.4)
FCGR 3A rs396991Phe175Val (1)C/G120 (99)AA70 (58.3)Total240
AC33 (27.5)A173 (72.1)
CC17 (14.2)C67 (27.9)
TNF gene (-308) rs1800629promotor region (6)A/G119 (98)AA4 (3.4)Total238
GA31 (26.1)A39 (19.6)
GG84 (70.6)G199 (80.4)
TNF gene (-238) rs361525promotor region (6)A/G120 (99)AA2 (1.7)Total240
GA15 (12.5)A19 (7.9)
GG103 (85.8)G221 (92.1)
Table 4 Single nucleotide polymorphism genotypes according to anti-tumor necrosis factor treatment response.
SNPGenotypeAnti-TNF treatment response
P value1
Non-respondersResponders
ATG16L1 rs10210302CC5 (25.0)20 (20.2)NS
TC9 (45.0)48 (48.5)
TT6 (30.0)31 (31.3)
ATG16L1 rs2241880AA5 (23.8)22 (22.2)NS
GA10 (47.6)47 (47.5)
GG6 (28.6)30 (30.3)
FAS Ligand rs763110CC12 (60.0)30 (31.6)0.042
TC5 (25.0)49 (51.6)
TT3 (15.0)16 (16.8)
FAS Ligand rs763110 (grouped)CC12 (60.0)30 (31.6)0.016 OR = 3.23, 95%CI: 1.20-8.78
TC + TT8 (40.0)65 (68.4)
IBD5 rs2522057CC6 (28.6)29 (29.3)NS
GC9 (42.9)40 (40.4)
GG6 (28.6)30 (30.3)
FCGR 3A rs396991AA11 (52.4)59 (59.6)NS
AC6 (28.6)27 (27.3)
CC4 (19.0)13 (13.1)
TNF gene (-308) rs1800629AA2 (10.0)2 (2.0)0.088
GA7 (35.0)24 (24.2)
GG11 (55.0)73 (73.7)
TNF gene (-308) rs1800629 (grouped)AA + GA9 (45.0)26 (26.3)0.093 OR = 2.29, 95%CI: 0.85-6.17
GG11 (55.0)73 (73.7)
TNF gene (-238) rs361525AA0 (0.0)2 (2.0)NS
GA2 (10.0)13 (13.0)
GG18 (90.0)85 (85.0)

The vast majority of participants were Caucasian. Only 9 patients were African American, all of whom were ever-responders. Analyzing the Caucasians separately as a sensitivity analysis achieved similar results for the grouped Fas ligand SNP (P = 0.029) and for the grouped -308 TNF gene SNP (P = 0.049). No significant difference was observed for the remaining SNPs studied: ATG16L1 (rs10210302, T300A rs2241880), IBD5 (rs2522057), FCGR 3A (rs396991), and TNF (-238 rs361525).

Results of the univariable comparisons are shown in Table 5. In univariable analyses, the Fas ligand SNP (rs763110) demonstrated a difference between ever-responders and non-responders with borderline significance (P = 0.058) and significance when grouping TC and TT genotypes together (P = 0.020). The comparison of -308 SNP (rs1800629) genotypes between ever-responders and non-responders (P = 0.130) became more different when grouping AA and GA genotypes together (P = 0.099). Univariate variables with P < 0.15 were included in the multivariable analysis for the comparison between anti-TNF treatment ever-responders and non-responders. Both Montreal disease behavior (P = 0.125) and perianal disease classification (P = 0.086) were included in the multivariable analysis.

Table 5 Univariable logistic regression data regarding factors associated with anti-tumor necrosis factor treatment failure.
CovariateP value
Demographic variables
GenderNS
RaceNS
Montreal Classification
Age of onset (A) MontrealNS
Location (L) MontrealNS
Location (L4) upper GI MontrealNS
Behavior (B) Montreal0.131
Behavior (p) perianal Montreal0.091
SNP variables
ATG16L1 rs10210302NS
ATG16L1 rs2241880NS
FAS Ligand rs7631100.0571
FAS Ligand rs763110 (TC + TT grouped)0.021
IBD5 rs2522057NS
FCGR 3A rs396991NS
TNF gene (-308) rs18006290.131
TNF gene (-308) rs1800629 (AA + GA grouped)0.0991
TNF gene(-238) rs361525NS

Logistic multivariable regression models were developed for the Fas ligand (rs763110) SNP, the TNF gene -308A/G (rs1800629) SNP, and their combination. The multivariable logistic regression models included genotype data for each of these two SNPs (with genotypes grouped as described above), the Montreal disease behavior classification, and the Montreal perianal disease classification (Table 6). The Fas ligand SNP (rs763110) CC genotype was predictive of non-response, as compared to the TC and TT genotypes (P = 0.009, OR = 4.30, 95%CI: 1.45-12.80). In the -308 TNF gene (rs1800629) SNP multivariable model, the AA and GA genotypes were significantly predictive of non-response as compared to the GG genotype (P = 0.049, OR = 2.88, 95%CI: 1.01-8.22). Patients with the combination of the Fas ligand (rs763110) CC genotype and presence of the TNF -308 A allele (genotypes AA or GA as opposed to GG) had nearly five-fold higher odds of being non-responders (P = 0.015, OR = 4.76, 95%CI: 1.35-16.77). This occurred in 16 (13%) of our patients. Montreal disease behavior and the presence of perianal disease were not found to be predictive in any of the multivariable models.

Table 6 Multivariable logistic regression models predicting anti-tumor necrosis factor treatment failure.
ModelVariableClassEstimateGlobal P valueOR (95%CI)
aFAS ligand rs763110 (TC + TT grouped)CC1.4600.0094.30 (1.45-12.80)
TC+TT0
Montreal behavior (B)B1-0.083NS0.92 (0.21-3.96)
B21.1583.18 (0.89-11.34)
B30
Montreal behavior (p) perianalNo0.964NS2.62 (0.68-10.09)
Yes0
bTNF gene (-308) rs1800629 (AA+GA grouped)AA+GA1.0560.0492.88 (1.01-8.22)
GG0
Montreal Behavior (B)B1-0.153NS0.86 (0.20-3.59)
B20.8262.28 (0.68-7.61)
B30
Montreal Behavior (p) perianalNo1.213NS3.36 (0.87-12.93)
Yes0
cFAS ligand (CC genotype) and TNF gene -308 (AA or GA genotype) combinedCC and AA or GA1.5600.0154.76 (1.35-16.77)
Other0
Montreal behavior (B)B1-0.022NS0.98 (0.23-4.22)
B20.9502.59 (0.73-9.17)
B30
Montreal behavior (p) perianalNo0.970NS2.64 (0.69-10.10)
Yes0
DISCUSSION

We identified two SNPs, Fas Ligand SNP (rs763110) and the TNF gene -308 (rs1800629), as being associated with CD patient response to anti-TNF treatment.

The Fas ligand SNP (rs763110) genotype frequencies were significantly different between non-responders and ever-responders (P = 0.042). This association became more significant when grouping the TC and TT genotypes as compared to the CC genotype (P = 0.016). According to our multivariable analysis, the odds of a patient with a Fas ligand CC genotype being a non-responder were four-fold higher as compared with a TC or TT genotype (P = 0.009, OR = 4.30, 95%CI: 1.45-12.80), when controlling for both Montreal disease behavior and perianal disease classification.

Abnormal regulation of apoptosis is one of the mechanisms of CD pathogenesis. Apoptosis (programmed cell death) can be induced through both extrinsic and intrinsic pathways[25]. The extrinsic pathway is controlled through plasma membrane receptors belonging to the TNF receptor superfamily that include, among others, the Fas/Fas ligand which has been implicated in inflammatory bowel disease (IBD)[26,27]. The SNP that we examined (rs763110) in the -843 position, which was located in a binding motif for the transcription factor CAAT/enhancer-binding protein β, has been implicated in carcinogenesis through the dysregulation of apoptosis[28]. Higher basal expression of Fas ligand has been significantly associated with the C allele compared with the T allele of this SNP[29]. The mechanism of action of anti-TNF drugs is complex; affecting many pathways, involving both soluble and membrane - bound TNF[30]. Currently, anti-TNF treatments have not been linked directly with Fas ligand; however, an interaction is possible either directly with the Fas ligand or indirectly by affecting the cells upon which the Fas ligand acts. Hlavaty et al[31] examined response to infliximab and the Fas ligand (rs763110) SNP and found the TT genotype to be correlated with non-response.

The TNF gene SNP was also found to be associated with response to anti-TNF treatment. Two polymorphisms: -308 (rs1800629) and -238 (rs361525), both in the promotor region have previously had conflicting data reported with respect to response to anti-TNF treatment in rheumatoid arthritis and in IBD[11,32-35]. The -308 (rs1800629) SNP has been shown previously to affect regulation of TNFα synthesis, with the minor allele (A) being a powerful transcriptional activator associated with increased TNFα production with the common allele (G)[36,37]. Our study demonstrated a possible correlation with anti-TNF treatment response with the -308 (rs1800629) SNP. Separately, -308 (rs1800629) when examined by itself demonstrated only a trend towards significance, but when combined with disease behavior and perianal disease, a significant correlation was demonstrated (P = 0.049, OR = 2.88, 95%CI: 1.01-8.22). The presence of the AA and GA genotypes (A being the minor allele) was correlated with non-response, implying a 2.88 higher odds of being a non-responder to anti-TNF treatment if the patient has an A allele. The fact that the A allele is observed fairly infrequently (19.6% in our study, and 9%-16% in others[38]) may explain the borderline statistical significance of this finding. Patients with the combination of the Fas ligand CC genotype and the TNF -308 A allele had nearly five-fold higher odds of being non-responders (P = 0.015, OR = 4.76, 95%CI: 1.35-16.77). This combination gives an additive effect compared to each SNP separately, most likely acting by different mechanisms. Considering that they are also found on different chromosomes (1 and 6), they are also in all probability inherited independently. The -238 TNF gene SNP (rs361525) did correlate with response to anti-TNF treatment. This combination was found in 16 patients in our study (13%).

The ATG16L1 gene has been well described in CD and has an important role in autophagy[39]. A SNP in this gene (rs2241880) has been linked to diminished autophagy, predisposing to CD[40]. Our study did not show an association with anti-TNF treatment response for this SNP, nor for another SNP on this gene (rs10210302) that had shown promise as a predictor for response to adalimumab in Slovenian CD patients[41].

Another region, the 5q31 gene cluster (IBD5), has been described to confer CD risk in some populations[42-44]. A SNP in this cluster (rs2522057) may be associated with response to infliximab in CD[45]. We were, however, unable to demonstrate such a correlation.

Antibody-dependent cell-mediated cytotoxicity is important in the mechanism of action of anti-TNF drugs. It requires leukocyte receptors for the Fc portion of IgG. A polymorphism (rs396991) in the gene encoding FCGR 3A expressed on macrophages and natural killer cells was associated with the response to rituximab in follicular non-Hodgkin’s lymphomas[46]. This polymorphism has also been associated with the response to infliximab in CD[47], but this was not able to be confirmed in our study.

Our study population included patients from a large university digestive practice. The patients included in the study and receiving anti-TNF drugs were younger than the excluded non-anti-TNF treated patients but were comparable with respect to race, gender, and socioeconomic status. Their younger age can be explained by the fact that younger patients tend to have a more severe disease[1] and would probably require anti-TNF drugs more frequently. Since patients were comparable in other aspects, we believe there was no selection bias. All CD patients were followed by gastroenterologists and surgeons, and as such, had a higher incidence of complicated CD. This may be a source of potential bias. This may also explain why we did not observe a difference with respect to likelihood of anti-TNF treatment response and disease behavior which has been observed in prior studies[9]. The clinical allocation into groups was performed by experienced clinicians (Galandiuk S, Dryden GW), who deal with this population on a daily basis. The assessment was made on the basis of the patient history, physical exam, patient follow-up, lab-work and endoscopy/pathology when clinically indicated. This study assessed non-response (no improvement whatsoever) vs ever-response. Since this was not a formal clinical trial and since only a quarter of patients had colonic disease, colonoscopy was not performed at defined intervals, but at the discretion of the treating physician. This could be a source of bias, but we believe that clinical assessments based on the above criteria are valid.

In summary, identification of patients whose anti-TNF treatment will fail is important, both from a clinical and from an economic perspective. We have identified two functional SNPs, Fas ligand (rs763110) and the TNF gene -308 (rs1800629), associated with non-response to anti-TNF treatment. Genotyping these SNPs from DNA obtained from peripheral blood may help define which CD patients are likely to benefit from anti-TNF therapy and permit efficient and cost-effective treatment by avoiding expensive therapy that is likely to fail and permitting selection of other treatments more likely to succeed.

ACKNOWLEDGMENTS

The authors would like to thank the John W and Barbara Thruston Atwood Price Trust for their support of the Price Institute of Surgical Research.

COMMENTS
Background

Anti-tumor necrosis factor (TNF) agents will not be effective in a subset of patients with Crohn’s disease (CD). Predicting the subset of patients who do not respond to anti-TNF treatment is important clinically and economically.

Research frontiers

A possible conduit to predict response to anti-TNF therapy could be through genetic testing. The authors chose to examine a series of single nucleotide polymorphisms (SNPs) within genes that have been linked either with CD and/or with anti-TNF treatment response in order to determine whether these could aid in predicting response to anti-TNF treatment in CD patients.

Innovations and breakthroughs

Two SNPs Fas ligand and TNF gene -308 were associated with response to anti-TNF treatment.

Applications

Genotyping these SNPs from DNA obtained from peripheral blood may help define which CD patients are likely to benefit from anti-TNF therapy and permit efficient and cost-effective treatment by avoiding expensive therapy that is likely to fail and permitting selection of other treatments more likely to succeed.

Terminology

Single nucleotide polymorphisms are a type of genetic variation in which a change is found in a single nucleotide at a specific position in the genome.

Peer-review

The authors investigate genetic factors that might help define which CD patients are likely to benefit from anti-TNF therapy. This is an interesting paper.

Footnotes

Manuscript source: Unsolicited manuscript

Specialty type: Gastroenterology and hepatology

Country of origin: United States

Peer-review report classification

Grade A (Excellent): 0

Grade B (Very good): 0

Grade C (Good): C, C

Grade D (Fair): 0

Grade E (Poor): 0

P- Reviewer: Chiu CC, Lakatos PL S- Editor: Ma YJ L- Editor: A E- Editor: Li D

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