P0694 Clinical response trajectories identify distinct subpopulations of ulcerative colitis patients and are linked to disease outcome across different therapy classes. Patient level analysis of 2378 participants from 6 randomized controlled trials in moderate-severe UC representing 8 different mechanisms of action.
Authors: Schreiber S W , Mueller-Breckenridge A , Cheng J , Wu C , Jelinsky S A , Liu J , Agueusop I , Freitag-Wolf S , Hall A , Baribaud F , El Houjajji C , Paun A , McBride J , Branigan P , Hart A , Waterworth D , Gale J , Woolcott J C , Flechsenhar K , Rawlings C A , Candela N , Yajnik V , Krawczak M , Thakker P , Hecht P 
Published: January 2025

Background

In randomized placebo-controlled trials (RCTs) for ulcerative colitis (UC), remission rates below 40% (wk52). Landmark analyses using independent endpoints provide approvable estimands for overall efficacy but overlook individual variability.Notably, a subset of patients achieves deeper remission levels. Post hoc analysis of the SELECTION trial (filgotinib) demonstrated that treating daily PRO2 scores (stool frequency, rectal bleeding) as time-series data reveals discrete subpopulations.Patients with in the faster response trajectory groups achieve wk52 endpoints at higher rates including disease control.

Methods

In a cross-industry/academic collaboration, data from 2,378 UC responders from RCTs of adalimumab, brepocitinib, etrasimod, etrolizumab, ozanimod, ritlecitinib, ustekinumab, and vedolizumab in UC were analyzed for PRO2-based treatment response trajectories using Group-based trajectory modeling (GBTM). Using random-effects logistic regression, trajectory groups were linked to wk 12 and 52 outcomes, including comprehensive disease control (CDC), defined as achieving remission in endoscopy, symptoms, lab markers, and histology. Meta-analyses of baseline demographics/disease characteristics examined predictors of trajectory group affiliation and CDC at wk 52.

Results

GBTM identified 3–5 distinct response trajectories for each mechanism of action (MOA), ranging from super-fast responses to slow and incomplete responses or relapse, based on PRO2 dynamics.

Meta-analysis revealed baseline factors associated with faster response, including prior biologic failure, lower endoscopic activity (Mayo-ES 2 vs. 3), lower Robarts Histology Index, lower baseline disease activity and male sex (0.78 [0.66, 0.93]) (Fig1).

Across therapies, remission, mucosal healing, and comprehensive disease control (CDC) were most frequently achieved in fast and super-fast responders (15–37%), compared to slow or non-responders (2.6–8.1%). Random-effects logistic regression confirmed better clinical outcomes for faster responders (Tab1).

Conclusion

Distinct subgroups (response trajectories) with specific PRO2-based response dynamics were consistently identified. In all trials, patients with (super)fast responses had significantly higher odds of achieving mucosal healing, deep remission, and comprehensive disease control (CDC) at wk52. While some baseline characteristics were linked to faster responses, they only partially account for the observed separation of patient groups. Response trajectories likely reflect underlying heterogeneity in disease biology in the interaction with the different MOA (i.e. endotypes). RNASeq analysis of transcriptome regulation in these patients aims to further explore this hypothesis.

References

Schreiber S et al. UEG Journal, doi:10.1002/ueg2.12686