Quantitative histologic features of the inflammatory microenvironment using digital pathology prior to adalimumab treatment associate with response in patients with ulcerative colitis

Study Background 

Ulcerative colitis (UC) is a chronic disease characterized by inflammation. Despite the advent of anti-inflammatory biologics, most patients do not achieve a deep response to therapy. Furthermore, predicting response to therapy is challenging.1

Quantitative histologic features of the inflammatory microenvironment in UC have potential to inform treatment selection and maintenance efficacy once a regimen has started.

To address this hypothesis, we used a machine learning (ML) approach to quantify histologic features of baseline biopsies from patients with UC treated with adalimumab (ADA). We examined the association between these baseline features and end-of-maintenance endoscopic response status (Mayo endoscopic score; MES<2).

Conclusion

A quantitative understanding of the histologic changes in the colonic microenvironment is critical to advance our collective knowledge of UC and may aid in the prediction of a patient’s response.

Here, quantitative features of goblet cells and goblet cell mucin were especially prognostic of endoscopic response from baseline.

ML-derived quantitative histology features have the potential to guide therapeutic decisions in UC that leverage mechanistic rationale derived from histology, contributing to precision medicine in immunology.

Conference

Digestive Disease Week 2025

Partner

AbbVie