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Longitudinal peripheral blood DNA methylation profiling of endoscopic response to tofacitinib in moderate-to-severe ulcerative colitis patients

ABSTRACT: Despite proven clinical efficacy of tofacitinib in ulcerative colitis (UC), merely 31.3% of patients achieve mucosal healing at week 8. Therefore, predictive biomarkers that allow for pre-treatment selection are urgently needed. Here, we performed a longitudinal case-control study investigating whether the DNA methylome of peripheral blood (PB) was capable of prognostically predicting response to tofacitinib. We prospectively recruited moderate-to-severe adult UC patients, scheduled to start tofacitinib treatment and measured their PB DNA methylation profiles prior to treatment during baseline endoscopy (T1; N = 31), after 8 weeks (T2; N = 17) and for a subset after a median of 20 weeks (T3; N = 8) into treatment using the Illumina Infinium HumanMethylation EPIC BeadChip. After 8 weeks of treatment, patients were categorized as responders (R) or non-responders (NR) based on a centrally read endoscopic evaluation in combination with steroid-free clinical response and/or biochemical response. Samples taken at T1 were used for biomarker identification and classification analyses using stability selected gradient boosting.

PROVIDER: EGAS00001006968 | EGA |

REPOSITORIES: EGA

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Project description:The objective of this study was to identify pre-treatment colonic gene expression patterns in patients with inflammatory bowel disease (Ulcerative Colitis and colonic Crohn’s disease) that are predictive of a good versus poor response to treatment with the bioloic anti-TNFa. By identifying key pathways that are altered in responders versus non-responders we also aimed to propose novel targets for therapy. Pre-treatment colonic endoscopic biopsies were collected in RNAlater from 22 patients (14 CD and 8 UC) who were starting anti-TNFa therapy. RNA was extracted using RNeasy columns (Qiagen) and quantified by Qubit (Life Technologies). 0.5ng RNA was used to prepare uniquely indexed cDNA QIAseq UPX 3’ Transcriptome libraries according to manufacturer’s instructions (Qiagen). Libraries were quantified and quality-controlled using the QIAseq Library Quant Assay Kit and tapestation analysis and sequenced on the Miseq and Nextseq Illumina platforms using the illumina sequencing primer and PhiX 15% to a depth of 1-3milion reads/sample. Read numbers were set as 101 bp (R1) x 51 bp (R2). Fastq files were obtained through BaseSpace and reads de-multiplexed, aligned to human genome GRCh38, quantified and normalised by the TPM method using the CLC Genomics Workbench (Qiagen). The following differential expression procedure was performed for participants classified as responders, partial responders or non-responders according to their endoscopic presentation 12 weeks after therapy. For UC patients the Mayo score was used: Responders Mayo= 0, Partial responders 1<= Mayo <3 and Non Responders Mayo >=3. For CD patients % reduction in the SES-CD score was used: Responders >=50% of SES-CD, Partial responders 50%<SES-CD<75%, Non responders SES-CD > 75%. Normalised values were log-transformed with a pseudocount of 1 added. Transcripts with < 1 transformed count in any sample were excluded from further analysis, as were transcripts with low variance (defined as less than 10% unique counts across both conditions and greater than a 19:1 ratio of the most frequent count to the second most frequent across both conditions). Differential expression analysis between conditions was conducted with the limma package. Library size was estimated using reduced maximum likelihood estimator with 500 iterations. Initial fitting was performed using a robust M-estimation, and moderated test statistics computed by empirical Bayes. A FDR corrected p value <0.05 was considered and filtered for further downstream data analysis. Partial least squares discriminant analysis (PLS-DA) modelling was performed on these genes to calculate their variable Importance in Projection (VIP) score. To understand the biological significance and pathways represented in the DEGS, we also performed enrichment analysis using the DAVID Gene Functional Classification Tool (REF I,II). Finally, strongest indicators of response were predicted by Random Forest Area Under the Curve (AUC) analysis.

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Longitudinal peripheral blood DNA methylation profiling of endoscopic response to tofacitinib in moderate-to-severe ulcerative colitis patients

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