
==== Front
J Crohns Colitis
J Crohns Colitis
eccojc
Journal of Crohn's & Colitis
1873-9946
1876-4479
Oxford University Press UK

38141256
10.1093/ecco-jcc/jjad213
jjad213
Original Articles
AcademicSubjects/MED00260
Eccojc/1020
Eccojc/1040
Baseline Serum and Stool Microbiome Biomarkers Predict Clinical Efficacy and Tissue Molecular Response After Ritlecitinib Induction Therapy in Ulcerative Colitis
Hassan-Zahraee Mina Pfizer Inc, Cambridge, MA, USA

Ye Zhan Pfizer Inc, Cambridge, MA, USA

Xi Li Pfizer Inc, Cambridge, MA, USA

Dushin Elizabeth Pfizer Inc, Cambridge, MA, USA

Lee Julie Pfizer Inc, Cambridge, MA, USA

Romatowski Jacek Provincial Complex Hospital, Gastroenterology, Bialystok, Poland

Leszczyszyn Jaroslaw Melita Medical, Gastroenterology, Wroclaw, Poland

https://orcid.org/0000-0001-7341-1351
Danese Silvio IRCCS Ospedale San Raffaele and University Vita-Salute San Raffaele, Milan, Italy

Sandborn William J University of California San Diego, La Jolla, CA, USA

Banfield Christopher Pfizer Inc, Cambridge, MA, USA

Gale Jeremy D Pfizer Inc, Cambridge, MA, USA

Peeva Elena Pfizer Inc, Cambridge, MA, USA

Longman Randy S Weill Cornell Medicine, Division of Gastroenterology and Hepatology, New York, NY, USA

Hyde Craig L Pfizer Inc, Cambridge, MA, USA

Hung Kenneth E Pfizer Inc, Cambridge, MA, USA

Corresponding author: Kenneth E. Hung, Pfizer Inc, 1 Portland Street, Cambridge, MA, USA. Tel: [617] 551-3000; Email: Kenneth.Hung@pfizer.com
9 2024
23 12 2023
23 12 2023
18 9 13611370
24 7 2023
10 11 2023
20 12 2023
12 1 2024
© The Author(s) 2023. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation.
2023
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

Background and Aims

Ritlecitinib, an oral JAK3/TEC family kinase inhibitor, was well-tolerated and efficacious in the phase 2b VIBRATO study in participants with moderate-to-severe ulcerative colitis [UC]. The aim of this study was to identify baseline serum and microbiome markers that predict subsequent clinical efficacy and to develop noninvasive serum signatures as potential real-time noninvasive surrogates of clinical efficacy after ritlecitinib.

Methods

Tissue and peripheral blood proteomics, transcriptomics, and faecal metagenomics were performed on samples before and after 8 weeks of oral ritlecitinib induction therapy [20 mg, 70 mg, 200 mg, or placebo once daily, N = 39, 41, 33, and 18, respectively]. Linear mixed models were used to identify baseline and longitudinal protein markers associated with efficacy. The combined predictivity of these proteins was evaluated using a logistic model with permuted efficacy data. Differential expression of faecal metagenomics was used to differentiate responders and nonresponders.

Results

Peripheral blood serum proteomics identified four baseline serum markers [LTA, CCL21, HLA-E, MEGF10] predictive of modified clinical remission [MR], endoscopic improvement [EI], histological remission [HR], and integrative score of tissue molecular improvement. In responders, 37 serum proteins significantly changed at Week 8 compared with baseline [false discovery rate of <0.05]; of these, changes in four [IL4R, TNFRSF4, SPINK4, and LAIR-1] predicted concurrent EI and HR responses. Faecal metagenomics analysis revealed baseline and treatment response signatures that correlated with EI, MR, and tissue molecular improvement.

Conclusions

Blood and microbiome biomarkers stratify endoscopic, histological, and tissue molecular responses to ritlecitinib, which may help guide future precision medicine approaches to UC treatment. ClinicalTrials.gov NCT02958865

Biomarkers
JAK inhibitor
ulcerative colitis
Pfizer 10.13039/100004319
==== Body
pmc1. Introduction

Ulcerative colitis [UC] is a chronic disease affecting the large intestine and is characterized by abdominal pain, bloody diarrhoea, and urgency, as well as having a negative impact on the patient’s quality of life.1–3 The current treatment guidelines for UC include aminosalicylates, corticosteroids, immunomodulators, biologics, and small molecules, with the goal of therapy being a sustained period of steroid-free remission.4–6 The definition of remission in UC utilizes clinical parameters, such as rectal bleeding and number of bowel movements, or endoscopic appearance to monitor response to treatment. While these clinical markers and guidelines provide a framework for treatment of UC, there is still an unmet need to develop molecular diagnostics to guide therapy selection and to optimize response.7

Treatment options for UC have varying mechanisms of action, including anti-interleukin antibodies, anti-a4β7 integrin monoclonal antibodies, Janus kinase [JAK] inhibitors, and sphingosine 1-phosphate receptor modulators.8–11 Emerging head-to-head trials have started to evaluate differences in clinical efficacy, but the molecular signatures of the underlying biology driving these differing treatment responses are not clear.12–14 Moreover, the underlying disease pathophysiology may evolve following treatment and subsequently impact the disease state.15 Molecular biomarkers of baseline disease and response to ongoing treatment are therefore needed to inform a precision medicine strategy for the treatment of UC.

Ritlecitinib is an oral dual selective JAK3/TEC [tyrosine kinase expressed in hepatocellular carcinoma] family kinase inhibitor with high selectivity over the rest of the kinome that is in clinical development.16,17 Ritlecitinib selectively and irreversibly inhibits JAK3, as well as the TEC kinase family (Bruton’s tyrosine kinase, bone marrow tyrosine kinase on chromosome X, interleukin 2 [IL-2]-inducible T-cell kinase, TEC, and tyrosine kinase expressed in T cells).18,19 JAK3 exclusively modulates the γ-common chain cytokine pathways [e.g. IL-7, IL-9, IL-15, and IL-21], which are involved in the pathophysiology of UC but do not affect the cytokines that regulate and constrain gut inflammation. TEC kinases are involved in immunological regulation and inhibit the cytotoxic functions of CD8+ T and natural killer cells, which are also involved in the pathogenesis of inflammatory bowel disease [IBD].16,20–22

Ritlecitinib was evaluated in patients with UC in a phase 2b trial with an 8-week induction period and a 24-week chronic dosing period [VIBRATO; NCT02958865]. Ritlecitinib had a rapid onset, with significant improvement observed in the partial Mayo Score after 2 weeks. During the 8-week induction period, ritlecitinib demonstrated dose-dependent efficacy across clinical, endoscopic, histological, and patient-reported outcomes.23 The primary endpoint—the mean total Mayo Score at Week 8—was significantly lower in patients treated with ritlecitinib, and the rates of clinical remission, modified clinical remission [MR], endoscopic improvement [EI], histological improvement, and mucosal healing were higher with ritlecitinib than with placebo. During the 24-week chronic dosing period, improvements were generally maintained and/or continued to improve.24

The aim of this analysis was to identify baseline serum markers in the phase 2b VIBRATO study that predict achieving MR [modified Mayo score of endoscopic subscore ≤1, stool frequency ≤1, and rectal bleeding =0] and EI [Mayo endoscopic subscore ≤ 1] and to develop serum signatures that can serve as noninvasive indicators of histological remission [HR] or EI after ritlecitinib treatment. Here, we describe our efforts to identify blood and microbiome biomarkers that are able to stratify responders from nonresponders after ritlecitinib treatment based on clinical, endoscopic, and histological metrics.

2. Materials and Methods

2.1. Trial design and participant population

The VIBRATO study design was previously reported.23 Briefly, this was a phase 2b, randomized study to assess the safety and efficacy of ritlecitinib compared with placebo in participants with moderate-to-severe active UC. After a 6-week screening period, there was an 8-week double-blind, placebo-controlled induction period, a 24-week chronic dosing period, and a 4-week follow-up visit after the last dose. The study protocol was approved by an ethics committee or institutional review board at each study site [protocol number B7981005]. The study was conducted according to the ethical principles originating from the Declaration of Helsinki. All patients provided written informed consent. This study was registered at ClinicalTrials.gov NCT02958865 and EudraCT 2016-003708-29.

Patients were aged 18–75 years and had moderate-to-severe active UC as defined by a total Mayo Score of ≥6, rectal bleeding subscore of ≥1, and a Mayo endoscopic subscore of ≥2 [on centrally read endoscopy], with active disease >15 cm from the anal verge. Patients were randomly assigned to oral once-daily doses of ritlecitinib [20, 70, or 200 mg] or matched placebo. Endpoints included: MR, which was based on modified Mayo score [total Mayo score minus the physician’s global assessment]; endoscopic sub-score ≤1 AND stool frequency ≤1 and rectal bleeding = 0; EI, which was defined as a Mayo endoscopic subscore of ≤1; and HR, which was defined as a Geboes Score of ≤3.0.

2.2. Biomarker sampling and testing

Samples from participants before and after they received 8 weeks of oral ritlecitinib induction therapy were included [20 mg, 70 mg, 200 mg, or placebo once daily, N = 39, 41, 33, and 18, respectively]. These participants had similar baseline demographics and clinical characteristics as the previously published full VIBRATO population [Supplementary Table S1].23

Colonic mucosal biopsies were taken from the most inflamed and from noninflamed tissue within 15–30 cm from the anal verge and peripheral sera were obtained from participants for biomarker profiling before and after they received 8 weeks of induction therapy with 20, 70, or 200 mg ritlecitinib, or placebo once daily. Inflamed and noninflamed colon biopsies for transcriptomics were immediately stored in RNAlater [Ambion]. RNA was extracted from these biopsies, processed, and sequenced to a depth of 40 million paired-end pass-filter reads [Fulgent Therapeutics]. Protein extracts were prepared from flash frozen inflamed biopsies and normalized to 1 mg/mL [CellCarta]. Tissue and serum proteins were measured using the Olink Explore Inflammation panel [Olink Proteomics AB].

Stool samples were collected at screening [baseline, before start of treatment] and at Week 8 in Sarstedt tubes prefilled with 9 mL of preservative buffer. Samples were processed by Enterome and its service providers. Samples were shipped to Cell & Co, the service provider selected by Enterome to provide sample biobanking. Samples were aliquoted, and bacterial DNA was extracted at GenBio. Deep shotgun sequencing [Illumina, 250 samples, 40 million reads] was performed at IntegraGen. FASTQ files from each sample were then processed by Enterome’s bioinformatics pipeline and summarized at different levels for further statistical analyses. HUMAnN 3.0 and PanPhlan [MetaPhlan] were utilized to generate sample-level taxonomy, pathways, and enzyme data.25,26

2.3. Analyses

A UC transcriptome [1046 genes upregulated, 383 genes downregulated] was defined in paired inflamed versus noninflamed colon tissues by meta-analyses of three UC studies.27 Tissue molecular improvement was defined by a gene set improvement score [GSI] that measures the normalization of the UC transcriptome [Supplementary Methods].27 Differential analysis used EI, HR, and MR to evaluate treatment changes in responders and nonresponders.

For identifying baseline serum biomarkers, a linear model was used to evaluate serum biomarkers that differentiate clinical responders and nonresponders of EI and MR at Week 8; the findings were further evaluated in a logistics model. Linear mixed models were used to estimate the change from baseline of each protein or gene at Week 8 by treatment and clinical response. Differences between response groups and treatment arms were also analysed. Responders were defined by EI and MR. Significant proteins were identified for EI with a false discovery rate [FDR] < 0.2; three of four had FDR < 0.2 and one protein had p < 0.05 for MR at baseline.

To identify baseline serum biomarkers that predict efficacy of ritlecitinib, a logistic model was applied to evaluate predictive probabilities of selected baseline serum markers with EI and MR at Week 8, followed by permutation-based evaluation of area under the receiver operating characteristic curve [AUROC].

For faecal metagenomics analyses, differential expression analysis between responders and nonresponders was evaluated by the nonparametric Mann–Whitney U test, with significance of FDR < 0.2. For unsupervised clustering of the baseline metagenomic profile, the k-means algorithm of biclusters was applied to baseline genus-level relative abundance data obtained from taxonomy data derived from PanPhlan based on metagenomics sequencing. All baseline samples were used in the unsupervised clustering and included only genera with at least 10% sample prevalence.

3. Results

3.1. Defining a UC tissue molecular signature of dose-dependent clinical response to ritlecitinib

To determine a tissue molecular signature of UC tissue inflammation, we defined differentially expressed genes at baseline in inflamed and noninflamed tissue [‘UC transcriptome’] [Figure 1A]. Using these transcriptional markers, we compared transcriptional changes between tissue biopsies at the most inflamed site at baseline and Week 8 of ritlecitinib therapy. Differential expression analysis revealed significant changes in 1397 genes [98% of UC transcriptome] between baseline and Week 8 and response clustered by MR [Figure 1A, defined as treatment transcriptome]. Pathway analysis of these differentially regulated genes revealed the highest changes in extracellular matrix, innate immune, and T-cell markers, with a dose-dependent response in change from baseline [Figure 1B]. To develop a metric for tissue molecular improvement, we defined a gene set improvement [GSI] score using the set of genes in the treatment transcriptome with an FDR < 0.05 and fold-change >1.5 that were selected from inflamed biopsy [Figure 1C]. The tissue-specific molecular GSI score revealed a dose-dependent improvement and was effective at separating responders vs nonresponders for both EI and MR [Figure 1D]. To confirm and validate the transcriptional signature at the protein level, we performed proteomics analysis of the inflamed tissue. Consistent with the tissue molecular improvement, Th17, Th1, TREM1, and IL-6 pathways were differentially regulated in MR responders compared with nonresponders [Figure 1D].

Figure 1 Differential analysis. [A] Differentially expressed genes in inflamed and noninflamed tissues at baseline [left] and Week 8 following ritlecitinib therapy [right] in responders and nonresponders [MR]. [B] First column from left: baseline gene expression changes between inflamed and noninflamed tissues; other columns: gene expression changes from baseline following Week 8 treatment. [C] Gene set improvement scores using treatment-modulated genes in all participants; responders vs nonresponders are defined by endoscopic improvement and modified clinical remission, respectively. [D] Pathways modulated following ritlecitinib therapy at Week 8 in responders and nonresponders based on inflamed tissue proteomics and transcriptomics. CFB, change from baseline; ECM, extracellular matrix; GSI, gene set improvement; IL, interleukin; JAK, Janus kinase; LS, lesions; NL, nonlesions; Th, T helper.

To define specific marker genes within the treatment transcriptome that were associated with response, we evaluated genes whose transcription levels changed in responders, but not in nonresponders, at Week 8 of ritlecitinib therapy. At the 70- and 200-mg doses, eight genes were differentially regulated in responders defined by MR, and 27 genes were differentially regulated in responders defined by EI. Seven genes overlapped between MR and EI: LCN2, CASP1, SORD, SAA2, CFB, KCND3, and NOS2 [Figure 2A]. The MR and EI gene set enrichment revealed significantly higher GSI scores in responders who received the 70- and 200-mg doses than in those who received the 20-mg dose [Figure 2B].

Figure 2 Genes with treatment changes in responders and nonresponders in endoscopic improvement and modified clinical remission: [A] genes changing in responders but not in nonresponders at Week 8 of ritlecitinib therapy; [B] gene set improvement scores at all dose levels in responders and nonresponders.

3.2. Integrated tissue transcriptomics and serum proteomics reveals baseline biomarkers and signature of endoscopic improvement and modified remission

While the tissue transcriptomics revealed a key signature of ritlecitinib treatment and response to therapy, no baseline changes in the UC or ritlecitinib treatment transcriptome were identified to separate responders and nonresponders when defined by either MR or EI. Serum proteomics was performed to determine if peripheral blood baseline markers differentiated clinical, endoscopic, and tissue molecular responders from nonresponders [Figure 3A]. Four baseline serum proteins, HLA-E, LTA, CCL21, and MEGF10, significantly differentiated responders from nonresponders for both MR and EI, with FDR ≤ 0.15 when combining the 70- and 200-mg ritlecitinib treatment groups. A logistic regression model based on these four proteins revealed robust predicted MR and EI following treatment with 200 mg ritlecitinib, with an AUROC and 95% confidence interval [CI] of 0.83 [0.70–0.97] and 0.88 [0.77–0.99], respectively [Figure 3B]. The predicted responders using 70- and 200-mg ritlecitinib samples showed a significant correlation with tissue molecular improvement by GSI score of at least 75% following treatment [p = 0.04 for EI; p = 0.08 for MR].

Figure 3 [A] Serum protein baseline markers to determine responders and nonresponders in participants with EI or MR in the combined 70- and 200-mg ritlecitinib treatment groups. [B] Area under the ROC using a logistic model predicting EI and MR at Week 8 with HLA-E, LTA, CCL21, and MEGF10 proteins based on participants treated with 200 mg ritlecitinib. AUC, area under the curve; EI, endoscopic improvement; MR, modified clinical remission; ROC, receiver operating curve.

3.3. Serum signatures of endoscopic improvement and histological remission after ritlecitinib treatment at Week 8

Analysis of serum proteins showed that in responders, 37 proteins had changed significantly at Week 8 compared with baseline [FDR < 0.05]. Changes in four of these proteins [IL4R, TNFRSF4, SPINK4, and LAIR-1] correlated with both EI and HR and separated responders from nonresponders (EI area under the curve [AUC] = 0.84 [95% CI, 0.67–0.94] and HR AUC = 0.64 [95% CI, 0.43–0.82]) [Figure 4A]. To determine whether these 37 proteins reflect tissue inflammation, we evaluated the differential expression of genes encoding these proteins in RNA-sequencing [RNA-seq] from inflamed biopsies. Ten genes [CXCL1, FCAR, CKAP4, SPINK4, CXCL17, OSM, CD4, CXCL9, IL17A, and GZMB] had significant changes from baseline between responders and nonresponders at Week 8 [FDR < 0.1] in either EI or HR. Furthermore, the levels of transcription of these ten genes were significantly increased at baseline in inflamed vs noninflamed colon biopsies. Finally, colon biopsy transcription levels of TNFRSF4, SPINK4, and LAIR1 were elevated in inflamed versus noninflamed biopsies at baseline.

Figure 4 Serum protein signatures of EI and HR after ritlecitinib treatment at Week 8: [A] area under the ROC using a logistic model predicting EI and HR at Week 8 with IL4R, TNFRSF4, SPINK4, and LAIR-1 changes based on participants treated with 200 mg ritlecitinib; [B] baseline gene expression changes of IL4R, TNFRSF4, SPINK4, and LAIR1, between inflamed and noninflamed colon biopsy. AUC, area under the curve; EI, endoscopic improvement; HR, histological remission; ROC, receiver operating characteristic.

3.4. Faecal metagenomics analysis reveals baseline and response to therapy signature

Analysis of the faecal microbiome has described dysbiosis as a clinical biomarker of IBD.28,29 To determine the ability of the microbiome to serve as a biomarker for the response to ritlecitinib, we performed a metagenomics analysis. Differential expression analysis between responders and nonresponders revealed significant differential expression for five bacterial taxa [Butyricimonas, Peptostreptococcus, Alistipes, Methanobrevibacter, and Parabacteroides] for participants defined as responders by EI and three taxa for those defined as responders by MR [Figure 5A and 5B]. Pathway analysis revealed a significant increase in the inosine 5-phosphate degradation pathway in responders at Week 8 compared with no change in nonresponders at Week 8 based on MR [Figure 5E].

Figure 5 Faecal metagenomic analysis: differential expression analysis between responders and nonresponders for [A] endoscopic improvement and [B] modified clinical remission; [C] baseline taxa biclustering using the k-means algorithm, with differential responses for each cluster in participants with active treatment; [D] GSI of meta-analyses derived from the UC transcriptome, using clusters 1 and 2; [E] inosine 5-phosphate degradation pathway in responders and nonresponders at Week 8 based on modified remission. GSI, gene set improvement; NR, nonresponder; R, responder; UC, ulcerative colitis.

Unsupervised clustering using the k-means algorithm of biclusters and linear modelling was used to identify the clusters at baseline and differential analyses with response at Week 8, respectively. Participants in cluster 1 showed enrichment in the clinical response of EI and MR [50 and 40%, respectively] in combined active doses compared with those in cluster 2 [25 and 20%, p < 0.05] [Figure 5C]. In addition, participants in cluster 1 showed a significantly higher Week 8 GSI score, consistent with a tissue molecular response, compared with those in cluster 2 [Figure 5D]. Twenty genera drove the differences between cluster 1 and cluster 2 [Supplementary Fig. S1]. Ten metagenomic pathways were significantly downregulated in cluster 1 compared with cluster 2, consistent with metabolic dysbiosis at baseline. An association analysis followed on the two clusters with clinical outcomes. Unsupervised clustering of the baseline metagenomic profile resulted in two clusters that predict both the clinical outcomes of EI and MR [p = 0.01 and p = 0.033, respectively] and the tissue molecular improvement using GSI score [p = 0.031].

4. Discussion

The VIBRATO study of ritlecitinib treatment in patients with moderate-to-severely active UC demonstrated dose-dependent efficacy with ritlecitinib across multiple metrics encompassing clinical, endoscopic, histological, and patient-reported outcome dimensions during an 8-week induction period.23 Here, we tested whether an integrated approach using tissue transcriptomics and proteomics would help predict and monitor responses to ritlecitinib, potentially paving the way for precision medicine therapy with emerging small molecules that are capable of selectively targeting specific immune signalling pathways. Using longitudinal tissue transcriptomics, we identified a treatment transcriptome that models both clinical [MR] and endoscopic [EI] response to ritlecitinib. GSI scores derived from baseline and Week 8 samples defined a dose-dependent tissue molecular improvement metric that was validated by tissue proteomics and stratified MR and EI responders at Week 8. The analyses identified seven specific marker genes in both MR and EI responders, with an additional 20 gene markers specific for responders defined by EI. Mechanistically, these biomarkers may reflect cellular and molecular pathways of early responses shared between MR and EI. Overall, these findings highlight the potential diagnostic utility of tissue transcriptomics as an outcome metric for ritlecitinib response; however, its mechanistic specificity for JAK3/TEC family kinase inhibition compared with that of other strategies still needs to be evaluated.

Although tissue markers of response are arguably the most specific for treatment response in UC, the ease of peripheral blood sampling is desirable for monitoring. Serum proteomics analysis performed in this study revealed a promising signature of 37 proteins that reflect EI and HR in participants with UC who received ritlecitinib. Changes in three of these serum proteins [TNFRSF4, SPINK4, and LAIR-1] parallel changes in tissue gene expression: TNFRSF4 [OX40] is a co-stimulatory receptor on lymphocytes which regulates multiple inflammatory and autoimmune diseases30,31; LAIR-1 is expressed broadly on myeloid cells and enables stromal cell regulation of tissue monocytes32; and SPINK4 is highly expressed in goblet cells and may function as a secreted gastrointestinal peptide.33 Collectively, these findings highlight the potential role for serum markers to offer a portal into tissue-specific mechanisms and may offer opportunities for a less invasive companion monitoring test to guide clinical management of patients with UC who are treated with ritlecitinib.

While biomarkers of treatment response will be useful for monitoring and guiding therapy duration, predictive markers hold promise for guiding treatment selection. Although the transcriptional analysis of both the tissue and peripheral blood [data not shown] did not discover any baseline markers associated with response, proteomics analysis identified four baseline serum proteins—HLA-E, LTA, CCL21, and MEGF10—that differentiated responders from nonresponders by EI, MR, and tissue molecular transcription by GSI. Serum levels of HLA-E [a molecule involved in innate immune cell activation],34 LTA [a soluble homotrimer of the TNF superfamily involved in immune development and lymphocyte maintenance],35 and CCL21 [a ligand for CCR7 guiding lymphocytes to lymphatic organs]36 highlight the potential for selective immune phenotypes in stratifying treatment response. The intestinal role for MEGF10 [a scavenger receptor expressed on astrocytes and myosatellite cells associated with myopathy]37 requires further investigation. Collectively, these findings highlight the potential role for peripheral blood biomarkers in predicting response to drug therapy but additional studies are needed to assess the selectivity of these findings for ritlecitinib prospectively.

Alterations in the gut microbiome are strongly associated with active IBD, and emerging studies highlight the potential for microbial biomarkers and pathways to segregate treatment response.28 Using unsupervised clustering, our results identified a distinct microbiome composition that correlated with clinical response to ritlecitinib. In contrast, with the microbiome response reported for other treatments, pathway analysis showed a baseline decrease in amino acid biosynthesis. These findings may indicate a select response in patients with microbiome metabolic dysbiosis in which nitrogen is used as a carbon source.29 Five specific bacterial taxa associated with responders at baseline were identified, including three [Odoribacter, Parabacteroides, and Alistipes] previously reported to be enriched in centenarians.38 or UC subjects who responded to faecal transplant.39 Longitudinal pathway analysis revealed a restoration in inosine 5-phosphate degradation in responders. Metabolites in this pathway included hypoxanthine, which can serve as an energy source for maintaining colonic barrier function.40 Further strain and metabolic analysis are needed to understand ritlecitinib’s potential mechanistic contribution and/or specific pathway regulation following treatment.

Whereas these findings highlight exciting potential candidate biomarker signatures that are predictive of response, there are some limitations to the current study. As the protein profiling was biased in nature due to the composition of the Olink Explore Inflammation panel, expansion of these efforts may lead to further refinement of the candidate biomarker signatures. Future efforts involving an integrated multi-omics effort may yield additional refinement of these candidate biomarker signatures. Finally, as this was an initial exploratory study of a large panel of biomarkers in a limited sample size, the generalizability of these signatures to a broader patient population still needs confirmation in subsequent clinical trials.

Collectively, these findings provide a framework for integrative biomarker analysis in randomized controlled clinical trials that have led to the identification of transcriptional markers of tissue response, baseline proteomic and bacterial markers predictive of response, and serum markers to monitor response. These results need further validation in subsequent clinical trials to enable future noninvasive predictive precision medicine strategies.

Supplementary Data

Supplementary data are available online at ECCO-JCC online.

jjad213_suppl_Supplementary_Material

Acknowledgments

Writing and scientific support were funding by Pfizer, Inc. Third-party medical writing assistance provided by Denise Kenski, PhD, of Health Interactions, Inc., was funded by Pfizer, Inc.

Funding

This work was supported by Pfizer Inc.

Conflict of Interest

Mina Hassan-Zahraee: employee and stockholder of Pfizer, Inc. Zhan Ye: employee and stockholder of Pfizer, Inc. Li Xi: employee and stockholder of Pfizer, Inc. Elizabeth Dushin: employee and stockholder of Pfizer, Inc. Julie Lee: employee and stockholder of Pfizer, Inc. Jacek Romatowski: no disclosures. Jaroslaw Leszczyszyn: no disclosures. Silvio Danese: consultancy fees from AbbVie, Alimentiv, Allergan, Amgen, AstraZeneca, Athos Therapeutics, Biogen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Celltrion, Dr Falk Pharma, Eli Lilly, Enthera, Ferring Pharmaceuticals Inc., Gilead, Hospira, Inotrem, Janssen, Johnson & Johnson, MSD, Mundipharma, Mylan, Pfizer, Roche, Sandoz, Sublimity Therapeutics, Takeda, TiGenix, UCB Inc., and Vifor; lecture fees from Abbvie, Amgen, Ferring Pharmaceuticals Inc., Gilead, Janssen, Mylan, Pfizer, and Takeda. William J. Sandborn: research grants from Abbie, Abivax, Arena Pharmaceuticals, Boehringer Ingelheim, Celgene, Genentech, Gilead Sciences, GSK, Janssen, Lilly, Pfizer, Prometheus Laboratories, Seres Therapeutics, Shire Pharmaceuticals, Takeda, and Theravance Biopharma; consulting fees from AbbVie, Abivax, Admirx, Alfasigma, Alimentiv, Alivio Therapeutics, Allakos, Amgen, Arena Pharmaceuticals, AstraZeneca, Atlantic Pharmaceuticals, Bausch Health [Salix], Beigene, Bellatrix Pharmaceuticals, Biora [Progenity], Boehringer Ingelheim, Boston Pharmaceuticals, Bristol Meyers Squibb, Celgene, Celltrion, Clostrabio, Codexis, Equillium, Forbion, Galapagos, Genentech, Gilead Sciences, GSK, Gossamer Bio, Immunic [Vital Therapies], Index Pharmaceuticals, Inotrem, Intact Therapeutics, Iota Biosciences, Janssen, Kiniksa Pharmaceuticals, Kyverna Therapeutics, Landos Biopharma, Lilly, Morphic Therapeutics, Novartis, Ono Pharmaceuticals, Oppilan Pharma [now Ventyx Biosciences], Otsuka, Pandion Therapeutics, Pfizer, Pharm Olam, Polpharm, Prometheus Biosciences, Protagonist Therapeutics, PTM Therapeutics, Quell Therapeutics, Reistone Biopharma, Seres Therapeutics, Shanghai Pharma Biotherapeutics, Shoreline Biosciences, Sublimity Therapeutics, Surrozen, Takeda, Theravance Biopharma, Thetis Pharmaceuticals, Tillotts Pharma, Vedanta Biosciences, Ventyx Biosciences, Vimalan Biosciences, Vivelix Pharmaceuticals, Vividion Therapeutics, Vivreon Gastrosciences, Xencor, and Zealand Pharma; stock or stock options from Allakos, BeiGene, Biora [Progenity], Gossamer Bio, Oppilan Pharma [now Ventyx Biosciences], Prometheus Biosciences, Prometheus Laboratories, Protagnoists Therapeutics, Shoreline Biosciences, Ventyx Biosciences, Vimalan Biosciences, and Vivreon Gastrosciences; and employee at Shoreline Biosciences and Ventyx Biosciences. Spouse: Iveric Bio—consultant, stock options; Progenity—stock; Oppilan Pharma [now Ventyx Biosciences]—stock; Prometheus Biosciences—employee, stock, stock options; Prometheus Laboratories—stock, stock options, consultant, Ventyx Biosciences—stock, stock options; Vimalan Biosciences—stock. Jeremy D. Gale: employee and stockholder of Pfizer, Inc. Elena Peeva: employee and stockholder of Pfizer, Inc. Michael S. Vincent: employee and stockholder of Pfizer, Inc. Randy S. Longman: consultant for Pfizer, Inc. Craig L. Hyde: employee and stockholder of Pfizer, Inc. Kenneth E. Hung: employee and stockholder of Pfizer, Inc. Christopher Banfield: employee and stockholder of Pfizer, Inc.

Author Contributions

Wrote the manuscript: RSL, MHZ, KEH. Designed the research: MHZ, KEH. Performed the research: MHZ, KEH, CH, ZY, LX. Data acquisition: ED, JL. Analysed the data: RSL, ZY, MHZ, KEH, LX, CH, WJS.

Data Availability

Upon request, and subject to review, Pfizer will provide the data that support the findings of this study. Subject to certain criteria, conditions, and exceptions, Pfizer may also provide access to the related individual de-identified participant data. See https://www.pfizer.com/science/clinical-trials/trial-data-and-results for more information.

Conference presented: UEGW, Vienna, Austria, 2022. Baseline serum biomarkers predict clinical efficacy and molecular tissue response to induction therapy with ritlecitinib, an oral JAK3/TEC inhibitor, in ulcerative colitis [UC] [phase 2b VIBRATO study]; ECCO, virtual, 2021 [DOP080]. Integrated tissue transcriptomic and serum proteomic interrogation reveals biomarkers for endoscopic improvement and histologic remission after JAK3/TEC inhibition in ulcerative colitis [UC] [phase 2b Vibrato study].
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