Response to Anti-α4β7 Blockade in Patients With Ulcerative Colitis Is Associated With Distinct Mucosal Gene Expression Profiles at Baseline

Insights into Therapeutic Response Prediction for Ustekinumab in Ulcerative Colitis Using an Ensemble Bioinformatics Approach

INTRODUCTION

Optimizing treatment with biological agents is an ideal goal for patients with ulcerative colitis (UC). Recent data suggest that mucosal inflammation patterns and serum cytokine profiles differ between patients who respond and those who do not. Ustekinumab, a monoclonal antibody targeting the p40 subunit of interleukin (IL)-12 and IL-23, has shown promise, but predicting treatment response remains a challenge. We aimed to identify prognostic markers of response to ustekinumab in patients with active UC, utilizing information from their mucosal transcriptome.

METHODS

We performed a prospective observational study of 36 UC patients initiating treatment with ustekinumab. Colonic mucosal biopsies were obtained before treatment initiation for a gene expression analysis using a microarray panel of 84 inflammatory genes. A differential gene expression analysis (DGEA), correlation analysis, and network centrality analysis on co-expression networks were performed to identify potential biomarkers. Additionally, machine learning (ML) models were employed to predict treatment response based on gene expression data.

RESULTS

Seven genes, including BCL6, CXCL5, and FASLG, were significantly upregulated, while IL23A and IL23R were downregulated in non-responders compared to responders. The co-expression analysis revealed distinct patterns between responders and non-responders, with key genes like BCL6 and CRP highlighted in responders and CCL11 and CCL22 in non-responders. The ML algorithms demonstrated a high predictive power, emphasizing the significance of the IL23R, IL23A, and BCL6 genes.

CONCLUSIONS

Our study identifies potential biomarkers associated with ustekinumab response in UC patients, shedding light on its underlying mechanisms and variability in treatment outcomes. Integrating transcriptomic approaches, including gene expression analyses and ML, offers valuable insights for personalized treatment strategies and highlights avenues for further research to enhance therapeutic outcomes for patients with UC.

Effectiveness and Persistency of Ustekinumab Treatment for Ulcerative Colitis: A Phoenix retrospective Cohort Study

Background

Real-world data regarding ustekinumab (UST) for ulcerative colitis (UC) particularly in biologics-naïve patients is currently limited. This study aimed to elucidate the real-world effectiveness and safety of UST for UC.

Methods

Overall, 150 patients with UC treated with UST from March 2020 to January 2023 were enrolled across 7 referral hospitals. To assess the clinical efficacy and persistence of UST, retrospective analyses were conducted from weeks 8 to 56. Predictive factors concerning the response and persistence of UST were examined through univariate and multivariate analyses.

Results

Of the 150 patients, 125 received UST for remission induction, including 36% biologics-naïve. The response and remission rates were 72.8% and 56.0% at week 8 and 73.2% and 63.4% at week 56, respectively. Biologics-naïve patients represented higher response and remission rates at week 8 (84.4% and 73.3%) than those with biologics exposure (66.2% and 46.2%). Patients with prior antitumor necrosis factor (anti-TNF) and vedolizumab (VDZ) exposure had relatively lower response and remission rates (34.5% and 24.1%, respectively). The 1-year cumulative persistence rate was 84.0%. Multivariate analysis revealed that the chronic continuous type and prior anti-TNF and VDZ exposure were negative predictive factors for week 8 responsiveness. Clinical response at week 8 was a predictor of 1-year persistence. Adverse event incidence remained notably low at 6.4%.

Conclusions

This study highlights the safety and effectiveness of UST as an induction and maintenance therapy for UC. Chronic continuous type and previous anti-TNF and VDZ exposure negatively contributed to short-term effectiveness, whereas short-term effectiveness provided good persistency.

Predictive, preventive and personalised approach as a conceptual and technological innovation in primary and secondary care of inflammatory bowel disease benefiting affected individuals and populations

Inflammatory bowel disease (IBD) is a global health burden which carries lifelong morbidity affecting all age groups in populations with the disease-specific peak of the age groups ranging between 15 and 35 years, which are of great economic importance for the society. An accelerating incidence of IBD is reported for newly industrialised countries, whereas stabilising incidence but increasing prevalence is typical for countries with a Westernised lifestyle, such as the European area and the USA. Although the aetiology of IBD is largely unknown, the interplay between the genetic, environmental, immunological, and microbial components is decisive for the disease manifestation, course, severity and individual outcomes. Contextually, the creation of an individualised patient profile is crucial for the cost-effective disease management in primary and secondary care of IBD. The proposed pathomechanisms include intestinal pathoflora and dysbiosis, chronic inflammation and mitochondrial impairments, amongst others, which collectively may reveal individual molecular signatures defining IBD subtypes and leading to clinical phenotypes, patient stratification and cost-effective protection against health-to-disease transition and treatments tailored to individualised patient profiles-all the pillars of an advanced 3PM approach. The paradigm change from reactive medical services to predictive diagnostics, cost-effective targeted prevention and treatments tailored to individualised patient profiles in overall IBD management holds a promise to meet patient needs in primary and secondary care, to increase the life-quality of affected individuals and to improve health economy in the area of IBD management. This article analyses current achievements and provides the roadmap for future developments in the area in the context of 3P medicine benefiting society at large.

Responsiveness to Vedolizumab Therapy in Ulcerative Colitis is Associated With Alterations in Immune Cell-Cell Communications

BACKGROUND

Ulcerative colitis (UC) and Crohn's disease are 2 types of inflammatory bowel disease (IBD), a group of chronic digestive disorders caused by aberrant immune responses to intestinal microbes. Although changes in the composition of immune cell subsets in the context of IBD have been previously described, the interactions and communication among cells are less well understood. Moreover, the precise mechanisms of action underlying many biologic therapies, including the anti-α4β7 integrin antagonist vedolizumab, remain incompletely understood. Our study aimed to explore possible additional mechanisms through which vedolizumab acts.

METHODS

We performed cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) on peripheral blood and colon immune cells derived from patients with ulcerative colitis treated with the anti-α4β7 integrin antagonist vedolizumab. We applied a previously published computational approach, NicheNet, to predict immune cell-cell interactions, revealing putative ligand-receptor pairs and key transcriptional changes downstream of these cell-cell communications (CCC).

RESULTS

We observed decreased proportions of T helper 17 (TH17) cells in UC patients who responded to vedolizumab and therefore focused the study on identifying cell-cell communications and signals of TH17 cells with other immune cells. For example, we observed that colon TH17 cells from vedolizumab nonresponders were predicted to have a greater degree of interactions with classical monocytes compared with responders, whereas colon TH17 cells from vedolizumab responders exhibited more interactions with myeloid dendritic cells compared with nonresponders.

CONCLUSIONS

Overall, our results indicate that efforts to elucidate cell-cell communications among immune and nonimmune cell types may increase the mechanistic understanding of current and investigational therapies for IBD.

Expression and function of α4β7 integrin predict the success of vedolizumab treatment in inflammatory bowel disease

Inflammatory bowel diseases are medically intractable and require constant therapy in many cases. While a growing number of biologicals and small molecules is available for treatment, a substantial portion of patients experiences primary non-response to these compounds and head-to-head evidence for therapy selection is scarce. Thus, approaches to predict treatment success in individual patients are a huge unmet need. We had previously suggested that the expression and function of α4β7 integrin on T cells in the peripheral blood correlate to outcomes of therapy with the anti-α4β7 integrin antibody vedolizumab. Here, we conducted a translational multicenter trial to prospectively evaluate this hypothesis. In a cohort of 89 patients with inflammatory bowel disease undergoing regular therapy with vedolizumab, lower baseline expression of α4β7 was associated with short-term clinical response. Consistently, low α4β7 expression in patients achieving remission predicted sustained remission in week 30. Moreover, high dynamic adhesion of CD4⁺ T cells to MAdCAM-1 and high reduction of adhesion by vedolizumab in vitro at baseline were associated with clinical remission. These data substantiate the potential of α4β7 integrin function and expression to forecast outcomes of vedolizumab therapy. Further translational efforts are necessary to improve the performance of the assays and to implement the concept in clinical practice.

Identifying predictive signalling networks for Vedolizumab response in ulcerative colitis

BACKGROUND

In ulcerative colitis (UC), the molecular mechanisms that drive disease development and patient response to therapy are not well understood. A significant proportion of patients with UC fail to respond adequately to biologic therapy. Therefore, there is an unmet need for biomarkers that can predict patients' responsiveness to the available UC therapies as well as ascertain the most effective individualised therapy. Our study focused on identifying predictive signalling pathways that predict anti-integrin therapy response in patients with UC.

METHODS

We retrieved and pre-processed two publicly accessible gene expression datasets (GSE73661 and GSE72819) of UC patients treated with anti-integrin therapies: (1) 12 non-IBD controls and 41 UC patients treated with Vedolizumab therapy, and (2) 70 samples with 58 non-responder and 12 responder UC patient samples treated with Etrolizumab therapy without non-IBD controls. We used a diffusion-based signalling model which is mainly focused on the T-cell receptor signalling network. The diffusion model uses network connectivity between receptors and transcription factors.

RESULTS

The network diffusion scores were able to separate VDZ responder and non-responder patients before treatment better than the original gene expression. On both anti-integrin treatment datasets, the diffusion model demonstrated high predictive performance for discriminating responders from non-responders in comparison with 'nnet'. We have found 48 receptor-TF pairs identified as the best predictors for VDZ therapy response with AUC ≥ 0.76. Among these receptor-TF predictors pairs, FFAR2-NRF1, FFAR2-RELB, FFAR2-EGR1, and FFAR2-NFKB1 are the top best predictors. For Etrolizumab, we have identified 40 best receptor-TF pairs and CD40-NFKB2 as the best predictor receptor-TF pair (AUC = 0.72). We also identified subnetworks that highlight the network interactions, connecting receptors and transcription factors involved in cytokine and fatty acid signalling. The findings suggest that anti-integrin therapy responses in cytokine and fatty acid signalling can stratify UC patient subgroups.

CONCLUSIONS

We identified signalling pathways that may predict the efficacy of anti-integrin therapy in UC patients and personalised therapy alternatives. Our results may lead to the advancement of a promising clinical decision-making tool for the stratification of UC patients.

Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis

Predicting the response of patients with ulcerative colitis (UC) to a biologic such as vedolizumab (VDZ) before administration is an unmet need for optimizing individual patient treatment. We hypothesized that the machine-learning approach with daily clinical information can be a new, promising strategy for developing a drug-efficacy prediction tool. Random forest with grid search and cross-validation was employed in Cohort 1 to determine the contribution of clinical features at baseline (week 0) to steroid-free clinical remission (SFCR) with VDZ at week 22. Among 49 clinical features including sex, age, height, body weight, BMI, disease duration/phenotype, treatment history, clinical activity, endoscopic activity, and blood test items, the top eight features (partial Mayo score, MCH, BMI, BUN, concomitant use of AZA, lymphocyte fraction, height, and CRP) were selected for logistic regression to develop a prediction model for SFCR at week 22. In the validation using the external Cohort 2, the positive and negative predictive values of the prediction model were 54.5% and 92.3%, respectively. The prediction tool appeared useful for identifying patients with UC who would not achieve SFCR at week 22 during VDZ therapy. This study provides a proof-of-concept that machine learning using real-world data could permit personalized treatment for UC.