Metabolism and response to stress gene signatures reveal ulcerative colitis heterogeneity and identify patients with increased response to therapy
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".