Introduction
Vedolizumab (VDZ) therapy can lead to histological and endoscopic improvement for patients with ulcerative colitis (UC), but its effects on the inflammatory microenvironment (IME) at the regional and cellular level have not been fully characterised. Uncovering biomarkers that can predict response to VDZ remains a critical step towards developing a precision medicine approach to treatment.
Aims & Methods
This study utilised artificial intelligence (AI)-powered quantification of IME histology to identify biomarkers that predict endoscopic response in VDZ-treated patients with UC. This study included retrospective data from VARSITY (NCT02497469), a phase 3b study of VDZ in patients with UC. Haematoxylin and eosin-stained whole slide images (WSIs; n=148) of biopsies from 50 patients were analysed at baseline, after induction (Week 14), and maintenance (Week 52) therapy with VDZ using IBDExplore1,* (PathAI, version 2.0), a suite of machine learning (ML) models trained to analyse tissue regions and cells. Human interpretable features (HIFs) were extracted from each WSI and correlated with endoscopic response using the Mayo Endoscopic Score (MES). Classification models were trained to predict mucosal healing (MES = 0 or 1) after one year of VDZ treatment using features at baseline and Week 14. IBDExplore results were associated with VARSITY mucosal transcriptomic data results described previously.2
Results
Univariate correlative analysis found that ML-derived HIFs are associated with MES severity. Features with a strong correlation to MES severity primarily describe the density of goblet cells in the mucosal epithelium, with lower density correlating with higher MES. Multivariate models were also developed leveraging baseline and Week 14 HIFs to predict mucosal healing after 52 weeks of VDZ treatment. Receiver operating characteristic area under the curve (ROC-AUC) scores were 0.85. The highest contributing feature to VDZ non-response was plasma cell density, while the most predictive feature of VDZ response was goblet cell density (Table 1). These findings were supported by gene co-expression network modules identified from tissue transcriptomic data, prioritised based on pathway and cell-type enrichment as well as study response endpoints. Cross-modality analysis integrating histology and transcriptomics further reinforced the epithelial and inflammatory cellular and molecular features associated with VDZ response.
| Table 1: IBDExplore Features With Highest Predictive Power of MES Severity |
| IBDExplore Feature | Feature Coefficient/Importancea |
| DENSITY [PLASMA CELLS] IN [TISSUE SURROUNDING (40UM) SURFACE EPITHELIUM WITHIN LAMINA PROPRIA]_week14 | −0.01230 |
| DENSITY [PLASMA CELLS] IN [TISSUE SURROUNDING (20UM) SURFACE EPITHELIUM WITHIN LAMINA PROPRIA]_week14 | −0.01229 |
| DENSITY [GOBLET CELLS] IN [CRYPT EPITHELIUM]_week14 | 0.01062 |
| DENSITY [GOBLET CELLS] IN [EPITHELIUM]_week14 | 0.01062 |
aFeature coefficients indicate the direction and magnitude of predictive power. Higher values for positive coefficients increase the likelihood for response, whereas higher values for features with negative coefficients decrease the likelihood for response.
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Conclusion
Using an ML-based approach to assess the IME in patients with UC, a significant association between goblet cell density and the severity of MES was identified. Certain features at baseline and Week 14 were found to predict endoscopic response to VDZ therapy, with immune cell abundance and goblet cell density exhibiting the highest predictive power. These findings highlight the potential of histological biomarkers to predict therapeutic outcomes in VDZ-treated patients with UC and support further validation of these results in larger cohorts.
*IBDExplore is for research use only. Not for use in diagnostic procedures.
References
1. Altepeter TA, et al. Gastroenterology. 2025;169(suppl 1):S-882. Abstract Su1904.
2. Paraskevopoulou MD, et al. United European Gastroenterol J. 2024;12(suppl 8):26–7. Abstract OP025.
Disclosure
This study was sponsored by Takeda Pharmaceutical Company Limited. Fernando Magro has received speaking fees from AbbVie, Arena, Biogen, Bristol Myers Squibb, Eli Lilly and Company, Falk, Ferring, Hospira, Janssen, Laboratórios Vitoria, Merck Sharp & Dohme, Pfizer, Sandoz, Takeda, UCB, and Vifor. Pratik Panchal, Maria D. Paraskevopoulou, Saman Farahmand, Christian Agboton, Mukesh Bansal, Vinayagam Arunachalam, Natasha Darras, and Vandana Gupta are employees of Takeda and holds Takeda stock/stock options. Mariana Ferreira-Duarte reports no disclosures. Alyson R. Warr, Jake Conway, John Tauber, and Christina Jayson are employees of PathAI and hold PathAI stock/stock options.