Polygenetic risk scores do not add predictive power to clinical models for response to anti-TNFα therapy in inflammatory bowel disease

Naomi Karmi; Amber Bangma; Lieke M Spekhorst; Hendrik M van Dullemen; Marijn C Visschedijk; Gerard Dijkstra; Rinse K Weersma; Michiel D Voskuil; Eleonora A M Festen

doi:10.1371/journal.pone.0256860

. 2021 Sep 17;16(9):e0256860. doi: 10.1371/journal.pone.0256860

Polygenetic risk scores do not add predictive power to clinical models for response to anti-TNFα therapy in inflammatory bowel disease

Naomi Karmi ^1,², Amber Bangma ^1,², Lieke M Spekhorst ¹, Hendrik M van Dullemen ¹, Marijn C Visschedijk ¹, Gerard Dijkstra ¹, Rinse K Weersma ¹, Michiel D Voskuil ^1,², Eleonora A M Festen ^1,^2,^*

Editor: Cinzia Ciccacci³

¹Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

²Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

³Unicamillus, Saint Camillus International University of Health Sciences, ITALY

Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: G.D. received an unrestricted research grant from Takeda, and received speaker fees from Pfizer and Janssen Pharmaceuticals. R.K.W. acted as consultant for Takeda, received unrestricted research grants from Takeda, Johnson and Johnson, Tramedico and Ferring and received speaker fees from MSD, Abbvie and Janssen Pharmaceuticals. E.A.M.F. received an unrestricted research grant from Takeda. The remaining authors disclose no conflicts. Our competing interests do not alter our adherence to PLOS ONE policies on sharing data and materials.

^✉

* E-mail: e.a.m.festen@umcg.nl

Roles

Naomi Karmi: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Amber Bangma: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Lieke M Spekhorst: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Resources, Validation, Writing – review & editing

Hendrik M van Dullemen: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Supervision, Validation, Writing – review & editing

Marijn C Visschedijk: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Validation, Writing – review & editing

Gerard Dijkstra: Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Validation, Writing – review & editing

Rinse K Weersma: Conceptualization, Data curation, Formal analysis, Funding acquisition, Resources, Supervision, Validation, Writing – review & editing

Michiel D Voskuil: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Eleonora A M Festen: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Cinzia Ciccacci: Editor

PMCID: PMC8448323 PMID: 34534227

Abstract

Background

Anti-tumour necrosis factor alpha (TNFα) therapy is widely used in the management of Crohn’s disease (CD) and ulcerative colitis (UC). However, up to a third of patients do not respond to induction therapy and another third of patients lose response over time. To aid patient stratification, polygenetic risk scores have been identified as predictors of response to anti-TNFα therapy. We aimed to replicate the association between polygenetic risk scores and response to anti-TNFα therapy in an independent cohort of patients, to establish its clinical validity.

Materials and methods

Primary non-response, primary response, durable response and loss of response to anti-TNFα therapy was retrospectively assessed for each patient using stringent definitions. Genome wide genotyping was performed and previously described polygenetic risk scores for primary non-response and durable response were calculated. We compared polygenetic risk scores between patients with primary response and primary non-response, and between patients with durable response and loss of response, using separate analyses for CD and UC.

Results

Out of 334 patients with CD, 15 (4%) patients met criteria for primary non-response, 221 (66%) for primary response, 115 (34%) for durable response and 35 (10%) for loss of response. Out of 112 patients with UC, 12 (11%) met criteria for primary non-response, 68 (61%) for primary response, 19 (17%) for durable response and 20 (18%) for loss of response. No significant differences in polygenetic risk scores were found between primary non-responders and primary responders, and between durable responders and loss of responders.

Conclusions

We could not replicate the previously reported association between polygenetic risk scores and response to anti-TNFα therapy in an independent cohort of patients with CD or UC. Currently, there is insufficient evidence to use polygenetic risk scores to predict response to anti-TNFα therapy in patients with IBD.

Introduction

Inflammatory bowel disease (IBD), consisting of Crohn’s disease (CD) and ulcerative colitis (UC), is a chronic inflammatory disease of the gastrointestinal tract. Although the exact pathogenesis of IBD remains unknown, IBD is thought to be caused by an exaggerated immune response to microbes in the gut in genetically susceptible individuals [1, 2]. Therapy is aimed at inducing and maintaining remission and limiting inflammation-driven damage to the intestinal mucosa.

Management of IBD was revolutionized by the introduction of anti-tumour necrosis factor alpha (anti-TNFα) therapy, initially consisting of infliximab and adalimumab [3]. Unfortunately, response rates of anti-TNFα therapy are low. Up to 30% of all patients do not respond to induction with anti-TNFα therapy (primary non-response [PNR]) and 23–46% of patients lose response over time (loss of response [LOR]) [4]. Furthermore, anti-TNFα therapy can be complicated by adverse events and is a major cost-driver in the management of IBD [5]. Given the low response rates, possible side effects, and the high costs associated with anti-TNFα therapy, there is a need for better prediction of response to anti-TNFα therapy in patients with IBD.

The use of genotype data to predict response to anti-TNFα therapy is increasingly studied, but its clinical utility has not yet been established [6]. Two previous studies have identified sets of genetic variants associated with response to anti-TNFα therapy in patients with CD or UC. These genetic variants were incorporated into separate polygenetic risk scores (PRS) for CD and UC. A model combining clinical data and PRS showed a more accurate prediction of PNR and durable response (DR) to anti-TNFα therapy than a clinical-only model [7, 8]. To validate these PRS, the current study aims to replicate the association of PRS with response to anti-TNFα therapy in an independent cohort of patients with CD and UC.

Materials and methods

Study population

Patients were included as part of the 1000IBD cohort. The 1000IBD cohort is a prospective cohort of patients with IBD undergoing treatment in the University Medical Center Groningen, the Netherlands [9]. Patients with CD or UC treated with anti-TNFα therapy (infliximab and/or adalimumab) between October 1999 and May 2020 were included in this study. Each patient had been diagnosed with CD or UC by their gastroenterologist using endoscopic, histological, or radiological data, or a combination of these. Written consent was obtained from all patients, and the study was approved by the medical ethical board of the University Medical Center Groningen (PSI-UMCG [IRB no 08/279]).

Case criteria

Each patient was classified as either case or control for both PNR and DR. Response was based upon the physician global assessment using a combination of clinical, radiologic, endoscopic and laboratory data. Response criteria for definite, probable and possible cases were defined in line with previous studies [7, 8] (S1 File). Patients received anti-TNFα therapy with standard induction dosing of infliximab at week 0, 2, and 6 and for adalimumab at week 0 and 2.

Definite PNR was defined as non-response after a period of up to 16 weeks after starting anti-TNFα therapy accompanied by an alteration of therapeutic approach (addition or escalation of corticosteroids, switch to a different agent or surgery). Possible PNR cases required matching of definite case criteria but allowed continuation of anti-TNFα therapy after 16 weeks, despite no clear signs of response. Patients with a primary response, which was evaluated after a minimum of three infusions between 12 and 16 weeks after start of treatment, were included as controls for PNR. Only definite PNR and controls, from here on referred to as primary responders, were included in subsequent analyses.

Definite DR was defined as maintenance of response to anti-TNFα therapy until latest follow-up, for at least 24 months after initiation of anti-TNFα therapy. Probable DR cases were defined as maintenance of response to anti-TNFα therapy for at least 24 months after initiation, but patients were included if available data suggested LOR after the 24-month time point. Patients who ceased treatment prior to the 24-month time point due to LOR or due to adverse events related to LOR (such as immunogenicity) were included as controls for DR. Patients that ceased treatment prior to the 24-month time point due to adverse events unrelated to LOR (such as non-IBD related infections) were excluded from analyses to assess DR. Only definite DR and controls, from here on referred to as patients with LOR, were included in subsequent analyses.

Data collection

Information was collected on age, age at diagnosis, sex, duration of disease at initiation of anti-TNFα therapy, type of anti-TNFα therapy, and concomitant use of an immunomodulator (azathioprine, 6-mercaptopurine or methotrexate). All patients were genotyped using the Infinium Global Screening Array (Illumina, San Diego, CA, USA). After extensive quality control (S2 File) and pre-phasing with the Eagle2 algorithm, genotype data were imputed to the Haplotype Reference Consortium reference panel using the Michigan Imputation server [10]. After post-imputation quality control, 12,130,010 genetic variants with a minor allele frequency > 0.1% remained. To limit bias from population stratification, only patients clustering with genetic data from non-Finnish European individuals were included, using the 1KG European dataset as the external reference panel [11].

Statistical analysis

Genetic variants incorporated in previously published PRS [7, 8] were selected from the imputed genetic data based on landmark IBD genotype-phenotype studies that identified IBD susceptibility loci [12–14]. In total, we identified all out of 50 previously described variants (S1–S4 Tables). Separate analyses were performed for CD and UC, and for PNR and DR, creating four distinct analyses. Separate weighted polygenetic risk scores for PNR and DR were calculated as the cumulative sum of the product of the log-odds ratio and allele burden for each of the risk genetic variants, also called single nucleotide polymorphisms (SNPs). Clinical characteristics were presented as means or medians and standard deviations or interquartile ranges for continuous variables, and as numbers and percentages for categorical variables. Cases and controls for PNR and DR were first compared using univariate analysis. For continuous variables, an unpaired t-test for normally distributed variables and a Wilcoxon’s Rank Sum test for non-normally distributed variables was used. For categorical variables, a chi-square test was used. Significant variables (P < 0.05) in univariate analyses were included in subsequent multivariate analyses. All statistical analyses were performed using R 3.5.1 (R Foundations for Statistical Computing, Vienna, Austria). We performed a power calculation by selecting SNPs within a range of p-values from the reference CD study and calculated how much power we had to detect these SNPs in our present study.

Results

Crohn’s disease

Predictors of primary non-response

We identified 334 patients with CD who had received anti-TNFα therapy. Of these patients, we identified 15 (4%) patients with PNR and 221 (66%) as primary responders. Remaining patients did not meet the stringent case-control criteria. No significant differences between patients with PNR and primary responders in age, age at diagnosis, disease duration, sex, type of anti-TNFα therapy and concomitant therapy were found upon univariate analyses (Table 1). Furthermore, PRS were similar between patients with PNR and primary responders (0.77 [1.2] vs 1.21 [1.9]; P = 0.1955) (Fig 1A).

Table 1. Comparison of characteristics of patients with Crohn’s disease exposed to anti-tumour necrosis factor alpha therapy with primary non-response and primary response.

	Primary non-responders (n = 15)	Primary responders (n = 221)	P-value
Age, median (IQR) (in years)	45 (11)	44 (14)	0.7873
Age at diagnosis, median (IQR) (in years)	25 (12)	24 (13)	0.5602
Disease duration, median (IQR) (in years)	3 (6)	4 (9)	0.3394
Sex			0.1618
• Male, No. (%)	3 (20)	84 (38)
• Female, No. (%)	12 (80)	137 (62)
First anti-TNFα therapy, No. (%)			0.3815
• Adalimumab	2 (20)	51 (23)
• Infliximab	13 (80)	170 (77)
Combination immunosuppression (azathioprine, 6-mercaptopurine, methotrexate), No. (%)	7 (47)	123 (56)	0.4982
Weighted PRS for PNR in CD, mean (SD)	0.77 (1.2)	1.21 (1.9)	0.1955

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Abbreviations: IQR, inter-quartile range; No., number; anti-TNFα, anti-tumour necrosis factor alpha; PRS, polygenetic risk score; PNR, primary non-response; CD, Crohn’s disease; SD, standard deviation.

Fig 1 — A Boxplot representing the distribution of polygenetic risk score for primary non-response in patients with Crohn’s disease with primary non-response and primary response; B Boxplot representing the distribution of polygenetic risk score for durable response in patients with Crohn’s disease with durable response and loss of response; C Boxplot representing the distribution of polygenetic risk score for primary non-response in patients with ulcerative colitis with primary non-response and primary response; D Boxplot representing the distribution of polygenetic risk score for durable response in patients with ulcerative colitis with durable response and loss of response.

Predictors of durable response

Of the 334 patients with CD who had received anti-TNFα therapy, 115 (34%) were classified as patients with DR and 35 (10%) as patients with LOR. Remaining patients did not meet stringent case-control criteria. There were no significant differences between patients with DR and patients with LOR in age, age at diagnosis, disease duration, sex and type of anti-TNFα therapy (Table 2). The use of concomitant therapy was independently predictive of DR in patients with CD (OR 3.01 [95% CI 0.40–2.74]; P = 0.0062). PRS were similar between patients with DR and patients with LOR (0.58 [1.9] vs 0.76 [1.7]; P = 0.4666) (Fig 1B).

Table 2. Comparison of characteristics of patients with Crohn’s disease exposed to anti-tumour necrosis factor alpha therapy with durable response and loss of response.

	Durable response (n = 115)	Loss of responders (n = 35)	P-value
Age, median (IQR) (in years)	41 (20)	45 (17)	0.5648
Age at diagnosis, median (IQR) (in years)	24 (12)	25 (11)	0.6397
Disease duration, median (IQR) (in years)	3 (9)	4 (15)	0.4781
Sex			0.1894
• Male, No. (%)	47 (41)	10 (29)
• Female, No. (%)	68 (59)	25 (71)
First anti-TNFα therapy, No. (%)			0.2750
• Adalimumab	20 (17)	9 (26)
• Infliximab	95 (83)	26 (74)
Combination immunosuppression (azathioprine, 6-mercaptopurine, methotrexate), No. (%)	73 (63)	13 (37)	0.0058
Weighted PRS for DR in CD, mean (SD)	0.58 (1.9)	0.76 (1.7)	0.4666^a

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Abbreviations: IQR, inter-quartile range; No., number; anti-TNFα, anti-tumour necrosis factor alpha; PRS, polygenetic risk score; DR, durable response; CD, Crohn’s disease; SD, standard deviation.

^aP-value from multivariate analysis including the use of a concomitant immunomodulator.

Ulcerative colitis

Predictors of primary non-response

We identified 112 patients with UC who had received anti-TNFα therapy. Of these patients, we identified 12 (11%) patients with PNR and 68 (61%) as primary responders. Remaining patients did not meet stringent case-control criteria. There were no significant differences between non-responders and primary responders in age, age at diagnosis, disease duration, sex, type of anti-TNFα therapy and concomitant therapy (Table 3). PRS were similar between patients with PNR and primary responders (-0.01 [IQR 1.5] vs 0.03 [IQR 1.2], P = 0.7464) (Fig 1C).

Table 3. Comparison of characteristics of patients with ulcerative colitis exposed to anti-tumour necrosis factor alpha therapy with primary non-response and primary response.

	Primary non-responders (n = 12)	Primary responders (n = 68)	P-value
Age, mean (SD) (in years)	44 (12)	49 (15)	0.3717
Age at diagnosis, median (IQR) (in years)	29 (23)	30 (24)	0.8510
Disease duration, median (IQR) (in years)	3 (5)	5 (9)	0.0711
Sex			0.7298
• Male, No. (%)	5 (42)	32 (47)
• Female, No. (%)	7 (58)	36 (53)
First anti-TNFα therapy, No. (%)			0.8410
• Adalimumab	2 (17)	13 (19)
• Infliximab	10 (83)	55 (81)
Combination immunosuppression (azathioprine, 6-mercaptopurine, methotrexate), No. (%)	5 (42)	30 (44)	0.8746
Weighted PRS for PNR in UC, median (IQR)	-0.01 (1.5)	0.03 (1.2)	0.7464

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Abbreviations: SD, standard deviation; IQR, inter-quartile range; No., number; anti-TNFα, anti-tumour necrosis factor alpha; PRS, polygenetic risk score; PNR, primary non-response; UC, ulcerative colitis.

Predictors of durable response

Of the 112 patients with UC that received anti-TNFα therapy, 19 (17%) were classified as patients with DR and 20 (18%) as patients with LOR. Remaining patients did not meet stringent case-control criteria. There were no significant differences between patients with DR and patients with LOR in age at diagnosis, disease duration, sex and type of anti-TNFα therapy (Table 4). A lower age and the use of concomitant immunosuppressive therapy were predictive of DR in univariate analyses (P = 0.0407 and P = 0.0379, respectively), but these associations were lost in multivariate analyses (P = 0.1780 and P = 0.1710, respectively). PRS were similar in patients with DR and patients with LOR (-0.04 [1.5] vs -0.19 [1.4]; P = 0.8840) (Fig 1D).

Table 4. Comparison of characteristics of patients with ulcerative colitis exposed to anti-tumour necrosis factor alpha therapy with durable response and loss of response.

	Durable response (n = 19)	Loss of responders (n = 20)	P-value
Age, mean (SD) (in years)	44 (15)	54 (16)	0.0407
Age at diagnosis, mean (SD) (in years)	27 (16)	34 (16)	0.2654
Disease duration, median (IQR) (in years)	5 (6)	11 (14)	0.2087
Sex			0.1481
• Male, No. (%)	7 (37)	12 (60)
• Female, No. (%)	12 (63)	8 (40)
First anti-TNFα therapy, No. (%)			0.2193
• Adalimumab	6 (32)	3 (15)
• Infliximab	13 (68)	17 (85)
Combination immunosuppression (azathioprine, 6-mercaptopurine, methotrexate), No. (%)	12 (63)	6 (30)	0.0379
Weighted PRS for DR in UC, mean (SD)	-0.04 (1.5)	-0.19 (1.4)	0.8840^a

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Abbreviations: SD, standard deviation; IQR, inter-quartile range; No., number; anti-TNFα, anti-tumour necrosis factor alpha; PRS, polygenetic risk score; DR, durable response; UC, ulcerative colitis.

^aP-value from multivariate analysis including age and the use of a concomitant immunomodulator.

Discussion

This present study aimed to replicate previously described PRS for PNR and DR to anti-TNFα therapy in patients with IBD. Using an independent cohort of patients with CD and UC, we could not replicate the association of PRS with response to anti-TNFα therapy. Our findings show that there is currently insufficient scientific basis for the use of PRS as an addition to clinical models to predict response to anti-TNFα therapy in patients with IBD.

To compare our data to previously published PRS, we used similar methodology to generate the PRS. However, our methodology had stricter definitions of cases and weighted PRS were calculated for CD and UC. Therefore, the methodology used may explain why we could not identify any predictive power of PRS with regard to response to anti-TNFα therapy. PRS in the original studies were based on genetic variants associated with PNR and DR in patients with CD and UC. Known IBD risk genetic variants were incorporated if they were associated with PNR or DR with P < 0.05. Additionally, non-IBD risk genetic variants were selected from Immunochip loci if they were associated with PNR or DR with a P < 1 × 10⁻⁴ for CD and P < 1 × 10⁻⁶ for UC [7, 8]. For UC, genetic variants were weighted by the sum of log-odds ratio and allele burden to create PRS, whereas for CD, selected genetic variants were combined into unweighted PRS. Since genetic variants may have different effect sizes, the use of a weighted PRS is preferred over non-weighted PRS. Therefore, in this present study, genetic variants in both risk scores for CD and UC were weighted by the sum of known log-odds ratio and allele burden [7, 8]. The fact that both our scores were weighted, could add to the differences found in response associations compared to the prior studies. Furthermore, we believe that future studies exploring PRS to predict response to anti-TNFα therapy could benefit from much stricter p-value thresholds. Using these stricter p-value thresholds, larger and independent cohorts of patients should limit false-positive findings. A combined predictive model including PRS and clinical data has been previously associated with response to anti-TNFα therapy [7, 8]. These prior studies have focused on creating PRS to predict response to anti-TNFα therapy for CD and UC separately, using IBD specific and non-specific risk alleles. However, one could wonder why almost no overlap in genetic variants was observed between the scores for CD and UC, while the mechanism of response to anti-TNFα are expected to be similar in both IBD phenotypes. In addition, thresholds for significance used in the discussed studies were relatively lenient (e.g. P < 0.05). This lack of overlap suggests that the selected genetic variants for the PRS may be false positives rather than a reflection of true biologic signals. Future studies should focus on creating international collaborations to achieve larger sample sizes, enabling the use of more stringent significance thresholds and reproducibility.

This study emphasizes the importance of replication when studying clinically relevant scientific findings. Before a genetic test can be implemented into clinical care, its clinical validity has to be established. Key in this is replication of the genetic association in an independent cohort [3]. In recent years, some replications of earlier studies have been unable to reproduce important scientific findings, leading to a “reproducibility crisis” [15]. This emphasizes the importance of not only publishing the original positive results, but also those that fail in replication [16, 17].

There are several strengths to this study. First, our cohort provides detailed phenotypic data in combination of genome-wide genetic data. Second, together with the detailed description of the cohort, long-term follow-up enabled stringent definitions for PNR and DR. Third, with the aim to replicate previous findings, we used similar definitions for PNR, and DR as used in the reference studies [7, 8].

However, our study has several limitations which should be taken into account when interpreting the results. The retrospective character of this study posed a challenge in case-control adjudication. Ideally, patients are prospectively followed to allow accurate testing of case-control definitions. Immunogenicity or low trough levels may cause therapeutic failure and would preferably be ruled out prior to adjudicating patients as true primary non-responder [18]. Our cohort lacks detailed data on drug trough levels and anti-drug antibodies, which could add bias to our results. Lastly, the relatively small sample size of this present study might be contributing to the lack of significance presented here. Nevertheless, we identified all out of 50 previously described variants (i.e. genotypes present in dataset with expected minor allele frequencies). This identification implicates that our sample size was large enough to comment on the association between PRS and response to anti-TNFα therapy, as identified in the reference studies [7, 8]. Furthermore, a post-hoc power calculation on the prior studies PRS showed >80% power to detect more than half of the genetic variants in the present CD study.

In conclusion, we were not able to replicate the association of PRS with response to anti-TNFα therapy for patients with IBD, demonstrating insufficient scientific evidence for the use of genetic data to successfully predict response to anti-TNFα therapy in IBD. Low response rates and high costs remain a challenge for anti-TNFα therapy, which emphasizes the need for better prediction tools of response [5, 19]. Future research based on international collaborations should focus on providing better understanding and prediction of therapies for IBD.

Supporting information

S1 File. Case and control criteria.

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S2 File. Genetic data generation.

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S3 File

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S1 Table. Single-nucleotide polymorphisms associated with primary non-response in patients with Crohn’s disease.

SNPs were selected in a prior study at p-value < 0.05 among 163 IBD risk alleles and p-value of <1 × 10–4 among the immunochip. For the weighted analysis of PRS we used the previously calculated odds ratios [1]. a = our study in CD. b = the prior study in CD. Abbreviations: SNP, single-nucleotide polymorphism; Freq., Frequency; PNR, primary non-response; PR, primary response; IBD, inflammatory bowel disease; CD, Crohn’s disease.

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S2 Table. Single-nucleotide polymorphisms associated with durable response in patients with Crohn’s disease.

SNPs were selected in a prior study at p-value < 0.05 among 163 IBD risk alleles and p-value of <1 × 10–4 among the immunochip. For the weighted analysis of PRS we used the previously calculated odds ratios [1]. a = our study in CD. b = the prior study in CD. Abbreviations: SNP, single-nucleotide polymorphism; Freq., Frequency; DR, durable response; LOR, loss of response; IBD, inflammatory bowel disease; CD, Crohn’s disease.

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S3 Table. Single-nucleotide polymorphisms associated with primary non-response in patients with ulcerative colitis.

SNPs were selected in a prior study at p-value < 0.05 among 201 IBD risk alleles and p-value of <1 × 10–6 among the immunochip. For the weighted analysis of PRS we used the previously calculated odds ratios [2]. a = our study in UC. b = the prior study in UC. Abbreviations: SNP, single-nucleotide polymorphism; Freq. Frequency; PNR, primary non-response; PR, primary response; IBD, inflammatory bowel disease; UC, ulcerative colitis.

(DOCX)

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S4 Table. Single-nucleotide polymorphisms associated with durable response in patients with ulcerative colitis.

SNPs were selected in a prior study at p-value < 0.05 among 201 IBD risk alleles and p-value of <1 × 10–6 among the immunochip. For the weighted analysis of PRS we used the previously calculated odds ratios [2]. a = our study in UC. b = the prior study in UC. Abbreviations: SNP, single-nucleotide polymorphism; Freq. Frequency; DR, durable response; LOR, loss of response; IBD, inflammatory bowel disease; UC, ulcerative colitis.

(DOCX)

Click here for additional data file.^{(16.6KB, docx)}

Acknowledgments

The authors thank all participants of the 1000IBD cohort.

Abbreviations

CD: Crohn’s disease
DR: durable response
IBD: inflammatory bowel disease
LOR: loss of response
PNR: primary non-response
PRS: polygenetic risk scores
SNP: single nucleotide polymorphism
TNFα: tumour necrosis factor alpha
UC: ulcerative colitis

Data Availability

Raw data is (in part) available at https://ega-archive.org/studies/EGAS00001002702, or upon request.

Funding Statement

R.K.W. is supported by a Diagnostics Grant from the Dutch Digestive Foundation (D16-14). www.narcis.nl Yes - played a role in: Conceptualization Data curation Formal analysis Funding acquisition Resources Supervision Validation Writing – review & editing E.A.M.F. is supported by a MLDS Career Development grant (CDG 14-04). www.mlds.nl Yes - played a role in: Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Resources Software Supervision Validation Visualization Writing – original draft Writing – review & editing.

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PLoS One. doi: 10.1371/journal.pone.0256860.r001

Decision Letter 0

Cinzia Ciccacci

2 Mar 2021

PONE-D-20-37588

Insufficient evidence that polygenetic risk scores can be used to predict response to anti-TNFα therapy in inflammatory bowel disease

PLOS ONE

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Reviewer #1: Yes

**********

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Reviewer #1: No

**********

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**********

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Reviewer #1: In their manuscript “Insufficient evidence that polygenetic risk scores can be used to predict response to anti-TNFα therapy in inflammatory bowel disease”, the authors aim to replicate previous findings on the predictive value of IBD genetic susceptibility loci for therapy response. In contrast to the previous work, their results demonstrate no such predictive capability, which is a (negative) finding well worth publishing to enable a balanced picture in this research area. However, their study designed has several limitations, as pointed out below, which need to be addressed and properly discussed in the manuscript.

Minor concerns:

• Line 59: the authors state high toxicity of anti-TNF therapy; however, the side effects of anti-TNF are considerably lower, relative to conventional immunotherapy with agents such as thiopurines – this has to be clarified in the text

• Throughout, the authors should state the allele frequencies of risk SNPs in their study populations

• The definition of response is not clear, as the authors state it was due to the individual clinician’s perspective – the methods will need to state precise and uniform criteria for response, otherwise it will be hard to rule out bias through non-uniform response definition

• Whenever data is presented as box plots, all data points should be shown

• Given the topic of the manuscript, the authors will need to reference all landmark studies that identified IBD susceptibility loci (e.g. Jostins et al, to only name one)

Major concerns:

• Line 64f: the authors reference two studies demonstrating a predictive value of “PRS” for therapy response – however in both of these studies, clinical parameters were incorporated into the predictive model, in addition to genetics; this has to be clarified, as the title of the manuscript suggests the use of genetic markers only

• Definition of response does not consider the anti-TNF drug levels – patients should only be considered true non-responders, if they had adequate trough drug levels but still did not respond; otherwise a therapy failure due to pharmacological aspects rather than genetics cannot be ruled out

• The # of patients is not particularly high, so probably study was underpowered to detect many of the other IBD risk SNPs; this has to be stated and discussed in the manuscript; in particular it will require altering the author’s conclusion that genetic risk loci are not predictive of therapy response in IBD – they can only comment on the ones they reliably detected

• Related to the point above, a power calculation should be presented and how this impacts on the authors’ conclusions

• To increase power, the author’s should conduct a meta-analysis (including data from Refs 7 and 8)

**********

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Reviewer #1: No

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PLoS One. 2021 Sep 17;16(9):e0256860. doi: 10.1371/journal.pone.0256860.r002

Author response to Decision Letter 0

25 May 2021

We would like to express our thanks for the reviewer’ efforts and invested time to review our manuscript. The mentions concerns were useful and really added to our manuscript. All together it resulted in a much-improved version of the manuscript.

Reviewer #1:

In their manuscript “Insufficient evidence that polygenetic risk scores can be used to predict response to anti-TNFα therapy in inflammatory bowel disease”, the authors aim to replicate previous findings on the predictive value of IBD genetic susceptibility loci for therapy response. In contrast to the previous work, their results demonstrate no such predictive capability, which is a (negative) finding well worth publishing to enable a balanced picture in this research area. However, their study designed has several limitations, as pointed out below, which need to be addressed and properly discussed in the manuscript.

Minor concerns:

Q1 Line 59: the authors state high toxicity of anti-TNF therapy; however, the side effects of anti-TNF are considerably lower, relative to conventional immunotherapy with agents such as thiopurines – this has to be clarified in the text

R1 We would like to thank the reviewer for bringing this inaccurate adjective to our attention and for allowing us to convey nuance to the text. We agree that anti-TNF� therapy is generally well-tolerated in clinical practice, although it increases the susceptibility to severe infections, possibly melanoma skin cancer, and treatment-related complications, such as lupus-like syndromes and allergic reactions. To write a truer definition of adverse events in anti-TNF� therapy we replaced ‘high toxicity’ with ‘possible side effects’ at lines 59-60.

Q2 Throughout, the authors should state the allele frequencies of risk SNPs in their study populations.

R2 Thank you for this suggestion. All allele frequencies are now available in our supplemental tables. Furthermore, we adjusted the table legends of tables S1-S4 in our supporting information to clarify the source of the frequencies, p-values and odds ratios.

Q3 The definition of response is not clear, as the authors state it was due to the individual clinician’s perspective – the methods will need to state precise and uniform criteria for response, otherwise it will be hard to rule out bias through non-uniform response definition

R3 We agree with this reviewer that the definition used to assess response is crucial for genotype-phenotype studies like the present study. For our retrospective study, we have used the 1000IBD cohort, which we described previously. In this cohort, clinical, radiological, endoscopic, laboratory data, as well as the clinician’s perspective, are prospectively collected in a uniform format. Using a combination of these data, we defined stringent definitions of response, which we present in the supporting information (S1 File. Case and control criteria).

We agree that defining response is preferably done prospectively and based on validated scores, calprotectin, CRP and/or endoscopy. The aim of this study, however, was to replicate previously identified genotype-phenotype interactions. These previous studies are also retrospective in nature, using physician’s Global Assessment as a measure of response. Therefore, we defined response in a similar fashion to these previous studies. We discuss this issue of response definition in the discussion section of our manuscript at lines 290-292.

Q4 Whenever data is presented as box plots, all data points should be shown

R4 We agree with the reviewer that Figure 1, “Boxplot representing a distribution of polygenetic risk scores for response in patients with inflammatory bowel disease”, should be altered. We have added all data points to the box plots.

Q5 Given the topic of the manuscript, the authors will need to reference all landmark studies that identified IBD susceptibility loci (e.g. Jostins et al, to only name one)

R5 We thank the reviewer for this suggestion, and have added the following landmark IBD genotype-phenotype studies to our manuscript (lines 121-123):

- de Lange KM, Moutsianas L, Lee JC, et al. Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease. Nat Genet. 2017;49(2):256‐261.

- Liu JZ, van Sommeren S, Huang H, et al; International IBD Genetics Consortium (IIBDGC). Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat Genet. 2015;47:979–86.

- Jostins L, Ripke S, Weersma RK, et al; International IBD Genetics Consortium (IIBDGC). Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature. 2012;491:119–24.

Major concerns:

Q6 Line 64f: the authors reference two studies demonstrating a predictive value of “PRS” for therapy response – however in both of these studies, clinical parameters were incorporated into the predictive model, in addition to genetics; this has to be clarified, as the title of the manuscript suggests the use of genetic markers only

R6 We acknowledge that both prior studies incorporated genetics as an addition to clinical covariates into a predictive model of response to anti-TNF� therapy in patients with IBD. Both prior studies compared the performance of a combined clinical-genetic model with clinical-only and genetic-only models. We now discuss this in our introduction (lines 65-68) and discussion (lines 266-268). In our present study, we used univariate analyses to identify clinical parameters predictive of response. Our area under the receiver operating characteristics (AUROC) curve models demonstrate that a combined clinical-genetic model predicts durable response in patients with CD better than a genetic-only model (AUROC 0.66 vs. 0.55, P= 0.0479). However, the incorporated PRS of these models was non-significant in univariate analysis. Moreover, in patients with UC a combined clinical-genetic model did not predict durable response better than a genetic-only model.

With our aim to replicate previous findings, we initially formulated the title of our manuscript in a similar fashion to the studies referenced. However, we agree with this reviewer that this formulation may not be accurate. We have changed the title of our manuscript to ‘Polygenetic risk scores do not add predictive power to clinical models for response to anti-TNF� therapy in inflammatory bowel disease”.

Q7 Definition of response does not consider the anti-TNF drug levels – patients should only be considered true non-responders, if they had adequate trough drug levels but still did not respond; otherwise a therapy failure due to pharmacological aspects rather than genetics cannot be ruled out

R7 We agree with the reviewer that multiple aspects may contribute to therapy failure. Indeed, immunogenicity or low trough levels may cause therapeutic failure in induction remission of severe disease or fistula-disease and then should be ruled out prior to adjudicating patients as ‘true non-responders’. Unfortunately, data regarding trough levels and anti-drug antibodies are only common practice since 2017. Therefore, data on trough levels and anti-drug antibodies are only available for a small proportion of our cohort. To increase patient numbers, and thereby power, we included all patients from our cohort that matched our inclusion criteria, despite the lack of data on trough levels or anti-drug antibodies. In fact, the CD reference study we aimed to validate also included patients without these data. Moreover, the UC reference study showed no associations between response and infliximab trough level in a small subgroup. However, we agree that genetic variants may contribute to therapeutic failure due to immunogenicity (Sazonovs A. et al, 2020). These signals may remain undetected in our present study, which could add bias to our results. We discuss this in our revised discussion at lines 292-295.

Q8 The # of patients is not particularly high, so probably study was underpowered to detect many of the other IBD risk SNPs; this has to be stated and discussed in the manuscript; in particular it will require altering the author’s conclusion that genetic risk loci are not predictive of therapy response in IBD – they can only comment on the ones they reliably detected

R8 We agree with this reviewer’s observation that our study is limited by its relatively small size. Indeed, using a cohort of limited sample size limits our power to detect genotype-phenotype associations of genetic variants with lower allele frequencies or lower effect sizes.

However, adding a power calculation, as suggested in R9, will show the level of power we have to detect at least half of the previously selected SNPs. This study aimed to validate previously described associations between polygenetic risk scores and response to anti-TNF� therapy. We identified all out of 50 previously described variants and incorporated these into separate PRS for CD and UC. This implicates that our sample size was large enough to comment on the association between PRS and response to anti-TNF� therapy, as identified in the reference studies. However, we agree that genotype-phenotype associations of genetic variants outside this selection may remain undetected.

While UC and CD have a relatively shared genetic background, it is surprising that the genetic variants in previously identified PRS for CD and UC, respectively, do not overlap. This lack of overlap suggests that the selected genetic variants for the PRS may be false positives rather than a reflection of true biologic signals. We discuss this in our revised manuscript on lines 273-275 and 296-300.

Q9 Related to the point above, a power calculation should be presented and how this impacts on the authors’ conclusions

R9 To further assess the power of our study we added a power calculation as suggested by the reviewer. We selected different variants in the range of p-values from the reference CD study and calculated how much power we had to detect these variants in our present study (GAS Power Calculator - Skol et. al, 2006). Unfortunately, despite our request to the authors, we did not have access to the allele frequencies of genetic variants used in the PRS of the UC reference study. Without these data, we could not reliably perform a power calculation for our present UC study.

Below is a visual presentation of our power to detect the prior selected genetic variants in CD. Our power ranged from 93% to 100%, and for more than half of the genetic variants we had >80% power to detect them.

Q10 To increase power, the author’s should conduct a meta-analysis (including data from Refs 7 and 8)

R10 Thank you for this suggestion. We agree that a proper meta-analyse would be of great value. Unfortunately, after several requests, we could not obtain the relevant data from the reference studies.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(34KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0256860.r003

Decision Letter 1

Cinzia Ciccacci

18 Aug 2021

Polygenetic risk scores do not add predictive power to clinical models for response to anti-TNF a therapy in inflammatory bowel disease

PONE-D-20-37588R1

Dear Dr. Festen,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Cinzia Ciccacci

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

PLoS One. doi: 10.1371/journal.pone.0256860.r004

Acceptance letter

Cinzia Ciccacci

25 Aug 2021

PONE-D-20-37588R1

Polygenetic risk scores do not add predictive power to clinical models for response to anti-TNFa therapy in inflammatory bowel disease

Dear Dr. Festen:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Cinzia Ciccacci

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Case and control criteria.

(DOCX)

Click here for additional data file.^{(14.7KB, docx)}

S2 File. Genetic data generation.

(DOCX)

Click here for additional data file.^{(14.8KB, docx)}

S3 File

(DOCX)

Click here for additional data file.^{(34.2KB, docx)}

S1 Table. Single-nucleotide polymorphisms associated with primary non-response in patients with Crohn’s disease.

(DOCX)

Click here for additional data file.^{(17.2KB, docx)}

S2 Table. Single-nucleotide polymorphisms associated with durable response in patients with Crohn’s disease.

SNPs were selected in a prior study at p-value < 0.05 among 163 IBD risk alleles and p-value of <1 × 10–4 among the immunochip. For the weighted analysis of PRS we used the previously calculated odds ratios [1]. a = our study in CD. b = the prior study in CD. Abbreviations: SNP, single-nucleotide polymorphism; Freq., Frequency; DR, durable response; LOR, loss of response; IBD, inflammatory bowel disease; CD, Crohn’s disease.

(DOCX)

Click here for additional data file.^{(17.3KB, docx)}

S3 Table. Single-nucleotide polymorphisms associated with primary non-response in patients with ulcerative colitis.

(DOCX)

Click here for additional data file.^{(16.1KB, docx)}

S4 Table. Single-nucleotide polymorphisms associated with durable response in patients with ulcerative colitis.

SNPs were selected in a prior study at p-value < 0.05 among 201 IBD risk alleles and p-value of <1 × 10–6 among the immunochip. For the weighted analysis of PRS we used the previously calculated odds ratios [2]. a = our study in UC. b = the prior study in UC. Abbreviations: SNP, single-nucleotide polymorphism; Freq. Frequency; DR, durable response; LOR, loss of response; IBD, inflammatory bowel disease; UC, ulcerative colitis.

(DOCX)

Click here for additional data file.^{(16.6KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(34KB, docx)}

Data Availability Statement

Raw data is (in part) available at https://ega-archive.org/studies/EGAS00001002702, or upon request.

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PERMALINK

Polygenetic risk scores do not add predictive power to clinical models for response to anti-TNFα therapy in inflammatory bowel disease

Naomi Karmi

Amber Bangma

Lieke M Spekhorst

Hendrik M van Dullemen

Marijn C Visschedijk

Gerard Dijkstra

Rinse K Weersma

Michiel D Voskuil

Eleonora A M Festen

Roles

Abstract

Background

Materials and methods

Results

Conclusions

Introduction

Materials and methods

Study population

Case criteria

Data collection

Statistical analysis

Results

Crohn’s disease

Predictors of primary non-response

Table 1. Comparison of characteristics of patients with Crohn’s disease exposed to anti-tumour necrosis factor alpha therapy with primary non-response and primary response.

Fig 1. Boxplot representing the distribution of polygenetic risk scores for response in patients with inflammatory bowel disease.

Predictors of durable response

Table 2. Comparison of characteristics of patients with Crohn’s disease exposed to anti-tumour necrosis factor alpha therapy with durable response and loss of response.

Ulcerative colitis

Predictors of primary non-response

Table 3. Comparison of characteristics of patients with ulcerative colitis exposed to anti-tumour necrosis factor alpha therapy with primary non-response and primary response.

Predictors of durable response

Table 4. Comparison of characteristics of patients with ulcerative colitis exposed to anti-tumour necrosis factor alpha therapy with durable response and loss of response.

Discussion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Cinzia Ciccacci

Roles

Author response to Decision Letter 0

Decision Letter 1

Cinzia Ciccacci

Roles

Acceptance letter

Cinzia Ciccacci

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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