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Therapies for ulcerative colitis (UC) result in varying remission and response rates in patients enrolled in clinical trials, likely due to individual differences in therapeutic targets, variations in active biological pathways, feedback, or resistance mechanisms, researchers noted in the Journal of Crohn’s and Colitis. Furthermore, research has identified heterogeneity among patient populations as a primary factor contributing to the differences in patient responses observed in clinical trials.

Niels Vande Casteele, PhD, and colleagues used transcriptomic data obtained from mucosal biopsies to identify RNA signatures associated with histological disease activity in this patient population, noting that mucosal inflammation is a major component of this inflammatory bowel disease.

The study team scored for gene signature enrichment using transcriptomic data from patients with moderate to severe active UC in a phase 2/3 study. Eleven Reactome gene sets, each showing moderate correlation with histological disease activity using Robarts Histopathology Index and minimal correlation with one another, were selected and analyzed using baseline gene expression data from clinical trials of treatments in patients with UC.

11 Gene Sets in MARS

This approach yielded 11 gene sets, known as Metabolism and Response to Stress (MARS). This group contained four signatures associated with metabolism, two signatures connected with the immune system, and one each affiliated with disease, hemostasis, cellular response to stimuli, signal transducers, and protein localization, according to the researchers.

Dr. Casteele and colleagues found that these gene sets characterized patient heterogeneity, contained novel pathways related to UC pathogenesis, and identified patients with an increased response to therapy. Of the 11 gene sets, four correlated with “non-disease” mucosa and six with “disease-related” mucosa, study results showed.

“The use of molecular biomarkers from disease sites (eg, mucosal biopsies) may be the next step to obtain higher-resolution information that can identify clinically relevant differences among patients,” the researchers wrote.

Implications for Clinical & Research Settings

 Analysis of multiple datasets consistently identified two to three main patterns of scores using MARS: high non-disease/low disease, low non-disease/high disease, and mixed. Dr. Casteele noted these clusters showed no association with patient demographics, clinical characteristics, or disease activity measures.

Furthermore, clustering baseline data from other UC clinical trials involving anti-IL-12/23 and anti-TNF therapies, as scored with MARS, revealed that patients with low non-disease/high disease-related baseline scores were less likely to respond to treatment.

 “This exploratory approach broadens our knowledge of these principles and has the potential to define clinical trial populations, enrich for clinical responders, and identify difficult-to-treat populations for therapeutic development in the future,” the researchers wrote.