Metabolism and response to stress gene signatures reveal ulcerative colitis heterogeneity and identify patients with increased response to therapy

Abstract

Background and aims: Ulcerative colitis (UC) therapies lead to variable remission and response rates in patients participating in clinical trials, likely due to interindividual target variability, differences in active biological pathways, feedback, and/or resistance mechanisms. Here, we stratified patients into subtypes by characterizing heterogeneity using mucosal biopsy transcriptomics data.

Methods: Transcriptomics data from an andecaliximab phase 2/3 study in patients with UC were scored for gene signature enrichment. Eleven Reactome gene sets, moderately correlated with histological disease activity using Robarts Histopathology Index with low correlation to each other, were selected and evaluated in baseline gene expression data of ustekinumab, infliximab, and adalimumab clinical trials in patients with UC.

Results: Of 11 gene sets, referred to as "Metabolism and Response to Stress" (MARS) signatures, 5 correlated with "non-disease" mucosa and 6 with "disease-related" mucosa. Clustering baseline andecaliximab samples scored with MARS revealed 3 clusters with low non-disease/high disease-related, high non-disease/low disease-related, or a mixture. Importantly, these clusters did not correlate with patient demographics, clinical characteristics, or disease activity metrics. Clustering baseline data from other clinical trials (anti-interleukin-12/23 and anti-tumor necrosis factor) in patients with UC scored with MARS showed that patients in low non-disease/high disease-related baseline score clusters less likely to achieve treatment response.

Conclusions: We identified and evaluated a novel, multi-dimensional signature gene set to characterize previously undefined heterogeneity in patients with UC and identify patients less likely to respond to therapy. This approach offers potential utility to define clinical trial populations, enrich for clinical responders, and identify difficult-to-treat populations for therapeutic development.

Keywords: biomarkers; clinical trials; heterogeneity.

Figure 1.

Andecaliximab dataset baseline clustering revealed differences in gene expression in patient samples. (A) RNA from mucosal biopsy baseline samples from patients with ulcerative colitis in the andecaliximab study were scored with the MARS signatures (right side of heatmap) and sorted using unsupervised hierarchical clustering at both the sample and gene signature level. Values are scaled 1 to –1 on the signature level (row) with red as high and blue as low. (B) Radar plots further depict the mean signature value for each cluster from panel (A). The chart is organized such that each signature is a different axis of the center point, with high values radiating farthest from the center and low scores near the center (scaled 0% to 100% of all sample scores). The radar plots of these clusters are generally described, respectively, as (1) right-shifted representing higher disease-related signature scores than non-disease scores, (2) left-shifted, representing higher non-disease scores than disease-related scores, and (3) narrow, representing a mixture of scores that average out to a circle near the center. AAAM, aspartate and asparagine metabolism; BCAAC, branched-chain amino acid catabolism; FZD, frizzled receptors; I4AI1S, interleukin 4 and interleukin 13 signaling; MARS, metabolism response to stress; PATEC, platelet adhesion to exposed collagen; PPI, peroxisomal protein import; PTFS, potential therapeutics for SARS; ROFBU, regulation of FZD by ubiquitination; ROIS, regulation of interferon-alpha signaling; SAAM, sulfur amino acid metabolism; SARS, severe acute respiratory syndrome; TC, tryptophan catabolism; UPR, unfolded protein response.

Figure 2.

Comparison of MARS signatures with 13 public UC signatures. The heatmap depicts the correlation between the MARS signature enrichment scores and enrichment scores for 13 other public UC signatures. Rows and columns are ordered by pairwise Pearson correlation coefficient using hierarchical clustering. Dark red and dark blue indicate strong positive and negative correlations, respectively, while lighter shades of red/blue indicate weaker correlations. Pearson correlation values are indicated as values within the cells. Details of the source of signatures are provided in Table 4. Published UC dataset PMIDs (year): 33907256 (2021); 29981298 (2018); 36881820 (2023); 36109152 (2023); 22605655 (2013); 27802155 (2018); 34521888 (2021); 31253778 (2019). PMID, PubMed Identifier; UC, ulcerative colitis.

Figure 3.

Cluster membership predicts response to ustekinumab. (A) Baseline samples (n = 364) in the UNIFI trial (NCT02407236) were grouped into 4 clusters. Green indicates mucosal healing at week 8, and red indicates lack of mucosal healing at week 8. (B) Radar plots summarize the mean score for each signature per cluster. (C) Proportion of mucosal healing responders at week 8, per cluster (4.3% vs 23%, 22%, and 19%, for clusters 1, 2, 3, and 4, respectively, adjusted P = .001). Clusters defined by MARS signatures identified patients with ~4- to 5-fold lower mucosal healing rates at follow-up. AAAM, aspartate and asparagine metabolism; BCAAC, branched-chain amino acid catabolism; FZD, frizzled receptors; I4AI1S, interleukin 4 and interleukin 13 signaling; MARS, metabolism response to stress; PATEC, platelet adhesion to exposed collagen; PPI, peroxisomal protein import, PTFS, potential therapeutics for SARS; ROFBU, regulation of FZD by ubiquitination; ROIS, regulation of interferon-alpha signaling; SAAM, sulphur amino acid metabolism; SARS, severe acute respiratory syndrome; TC, tryptophan catabolism; UPR, unfolded protein response.

Figure 4.

Cluster membership predicts response to anti-TNF therapies at baseline. (A) Baseline mucosal biopsy samples (n = 138) in the anti-TNF therapy meta-analysis cohort (GSE16879, GSE23597, GSE73661, and GSE92415) were scored for the MARS signatures. Response at week 4, 6, or 8 is indicated by green lines (non-response is red). (B) Radar plots summarize the mean values of each signature by cluster. (C) Proportion of patients in each cluster who responded at follow-up (week 4, 6, or 8, depending on cohort). The response rate was very high for clusters 1 and 2 (69% and 70%, respectively) but moderate for clusters 3 and 4 (35% and 40%, respectively). AAAM, aspartate and asparagine metabolism; BCAAC, branched-chain amino acid catabolism; FZD, frizzled receptors; I4AI1S, interleukin 4 and interleukin 13 signaling; MARS, metabolism response to stress; PATEC, platelet adhesion to exposed collagen; PPI, peroxisomal protein import; PTFS, potential therapeutics for SARS; ROFBU, regulation of FZD by ubiquitination; ROIS, regulation of interferon-A signaling; SAAM, sulfur amino acid metabolism; SARS, severe acute respiratory syndrome; TC, tryptophan catabolism; UPR, unfolded protein response.

Figure 5.

Change in MARS scores from baseline to follow-up (n = 89) in the anti-TNF therapy meta-analysis cohort. (A) The change in MARS signature scores between baseline and follow-up (week 4, 6, or 8, depending on cohort) identified 2 main clusters. Response at week 4, 6, or 8 is indicated by green lines (non-response is red). (B) Radar plots show these 2 clusters as right-shifted and left-shifted, respectively. (C) Proportion of responders in clusters 1 and 2. Patients in cluster 2 had a higher proportion of responders compared to cluster 1 (64% and 24%, respectively). AAAM, aspartate and asparagine metabolism; BCAAC, branched-chain amino acid catabolism; FZD, frizzled receptors; I4AI1S, interleukin 4 and interleukin 13 signaling; MARS, metabolism response to stress; PATEC, platelet adhesion to exposed collagen; PPI, peroxisomal protein import; PTFS, potential therapeutics for SARS; ROFBU, regulation of FZD by ubiquitination; ROIS, regulation of interferon-A signaling; SAAM, sulfur amino acid metabolism; SARS, severe acute respiratory syndrome; TC, tryptophan catabolism; TNF, tumor necrosis factor; UPR, unfolded protein response.

Figure 6.

MARS signature scores for follow-up samples in the anti-TNF therapy meta-analysis cohort. (A) Follow-up data for patients treated with anti-TNF therapies clustered with MARS signatures revealed 2 clusters. Response at week 4, 6, or 8 is indicated by green lines (non-response is red). (B) Radar plots depict these clusters as right-shifted (cluster 1) and left-shifted (cluster 2). (C) Proportion of patients who responded, by cluster. Cluster 1 contained a significantly lower proportion of responders compared to cluster 2 (16% vs 57%, respectively; adjusted P = .009). AAAM, aspartate and asparagine metabolism; BCAAC, branched-chain amino acid catabolism; FZD, frizzled receptors; I4AI1S, interleukin 4 and interleukin 13 signaling; MARS, metabolism response to stress; PATEC, platelet adhesion to exposed collagen; PPI, peroxisomal protein import; PTFS, potential therapeutics for SARS; ROFBU, regulation of FZD by ubiquitination; ROIS, regulation of interferon-A signaling; SAAM, sulfur amino acid metabolism; SARS, severe acute respiratory syndrome; TC, tryptophan catabolism; UPR, unfolded protein response.