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. 2024 Aug 14;18(8):1190-1201.
doi: 10.1093/ecco-jcc/jjad133.

Whole Blood DNA Methylation Changes Are Associated with Anti-TNF Drug Concentration in Patients with Crohn's Disease

Simeng Lin  1   2 , Eilis Hannon  3 , Mark Reppell  4 , Jeffrey F Waring  4 , Nizar Smaoui  4 , Valerie Pivorunas  4 , Heath Guay  4 , Neil Chanchlani  1   2 , Claire Bewshea  2 , Benjamin Y H Bai  5   6 , Nicholas A Kennedy  1   2 , James R Goodhand  1   2 , Jonathan Mill  3 , Tariq Ahmad  1   2
Affiliations

Whole Blood DNA Methylation Changes Are Associated with Anti-TNF Drug Concentration in Patients with Crohn's Disease

Simeng Lin et al. J Crohns Colitis. .

Abstract

Background and aims: Anti-tumour necrosis factor [TNF] treatment failure in patients with inflammatory bowel disease [IBD] is common and frequently related to low drug concentrations. In order to identify patients who may benefit from dose optimisation at the outset of anti-TNF therapy, we sought to define epigenetic biomarkers in whole blood at baseline associated with anti-TNF drug concentrations at week 14.

Methods: DNA methylation from 1104 whole blood samples from 385 patients in the Personalised Anti-TNF Therapy in Crohn's disease [PANTS] study were assessed using the Illumina EPIC Beadchip [v1.0] at baseline and weeks 14, 30, and 54. We compared DNA methylation profiles in anti-TNF-treated patients who experienced primary non-response at week 14 if they were assessed at subsequent time points and were not in remission at week 30 or 54 [infliximab n = 99, adalimumab n = 94], with patients who responded at week 14 and when assessed at subsequent time points were in remission at week 30 or 54 [infliximab n = 99, adalimumab n = 93].

Results: Overall, between baseline and week 14, we observed 4999 differentially methylated positions [DMPs] annotated to 2376 genes following anti-TNF treatment. Pathway analysis identified 108 significant gene ontology terms enriched in biological processes related to immune system processes and responses. Epigenome-wide association [EWAS] analysis identified 323 DMPs annotated to 210 genes at baseline associated with higher anti-TNF drug concentrations at Week 14. Of these, 125 DMPs demonstrated shared associations with other common traits [proportion of shared CpGs compared with DMPs] including body mass index [23.2%], followed by C-reactive protein [CRP] [11.5%], smoking [7.4%], alcohol consumption per day [7.1%], and IBD type [6.8%]. EWAS of primary non-response to anti-TNF identified 20 DMPs that were associated with both anti-TNF drug concentration and primary non-response to anti-TNF with a strong correlation of the coefficients [Spearman's rho = -0.94, p <0.001].

Conclusion: Baseline DNA methylation profiles may be used as a predictor for anti-TNF drug concentration at week 14 to identify patients who may benefit from dose optimisation at the outset of anti-TNF therapy.

Keywords: Crohn’s disease; DNA methylation; IBD; anti-TNF; epigenetics.

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Conflict of interest statement

SL reports non-financial support from Pfizer outside the submitted work. MR, JFW, VP, NS, and HG are employees of AbbVie and may own stock/options. NAK reports grants from F. Hoffmann-La Roche AG, grants from Biogen, grants from Celltrion Healthcare, grants from Galapagos NV, and non-financial support from Immundiagnostik, grants and non-financial support from AbbVie, grants and personal fees from Celltrion, personal fees and non-financial support from Janssen, personal fees from Takeda, and personal fees and non-financial support from Dr Falk, outside the submitted work. JRG reports grants from F. Hoffmann-La Roche AG, grants from Biogen, grants from Celltrion Healthcare, grants from Galapagos NV, and non-financial support from Immundiagnostik, outside the conduct of the study. TA reports grants and non-financial support from F. Hoffmann-La Roche AG, grants from Biogen, grants from Celltrion Healthcare, grants from Galapagos NV, and non-financial support from Immundiagnostik, personal fees from Biogen, grants and personal fees from Celltrion Healthcare, personal fees and non-financial support from Immundiagnostik, personal fees from Takeda, personal fees from ARENA, personal fees from Gilead, personal fees from Adcock Ingram Healthcare, personal fees from Pfizer, personal fees from Genentech, and non-financial support from Tillotts, outside the submitted work.

Figures

Figure 1.
Figure 1.
Change in derived cell proportions following treatment with anti-TNF. Predicted derived cell proportions over time estimated from the regression analysis is represented in solid blue lines, and observed cell proportions in faded lines; p-value represents the change in individual cell proportions over time. TNF, tumour necrosis factor.
Figure 2.
Figure 2.
CpG sites associated with change over time following anti-TNF treatment regardless of treatment outcome. A] Manhattan plot of CpG sites associated with change over time following anti-TNF treatment regardless of treatment outcome. The top 10 differentially methylated positions with annotations are labelled in the plot. The grey horizontal line represents the significant p-value threshold of 9 x 10-8. B] Predicted beta values of the top 10 differentially methylated positions and its change over time following anti-TNF treatment. TNF, tumour necrosis factor.
Figure 3.
Figure 3.
CpG sites at baseline associated with anti-TNF drug concentration. A] Manhattan plot of CpG sites at baseline associated with anti-TNF drug concentration at week 14. The top 10 CpG sites with their associated gene annotations are labelled in brackets. The grey horizontal line represents the significant p-value threshold of 9 x 10-8. B] Beta methylation values at baseline of the top 20 CpG sites associated with both anti-TNF drug concentration at week 14 and primary non-response. TNF, tumour necrosis factor.
Figure 4.
Figure 4.
Coefficients of DMPs associated with anti-TNF drug concentration at week 14 and primary non-response. Coefficients represent the beta values of each CpG from linear mixed effects model to each outcome. Spearman’s rho correlation of the coefficients calculated for those that were associated with both anti-TNF drug concentration and primary non-response, and the remaining that were only significant to anti-TNF drug concentration. TNF, tumour necrosis factor; PNR, primary non-response; DMP, differentially methylated positions.
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