OP40 Transcriptomic signature of response to vedolizumab in patients with moderate-to-severe ulcerative colitis: Results from an international, multicentre, retrospective cohort study
Bibliographic record
Abstract
Abstract Background Transcriptomic analysis has the potential to facilitate drug development and clinical decision making in inflammatory bowel disease. Current understanding of pharmacodynamic response to vedolizumab is limited by a lack of publicly available mucosal biopsy RNA samples/sequencing data and corresponding clinical information. We aimed to identify gene signatures associated with response to vedolizumab in ulcerative colitis (UC) patients by pooling data from 3 international sites. Methods Patients receiving vedolizumab for the treatment of moderate-to-severe UC at Hospital Clínic de Barcelona (IDIBAPS), Mount Sinai Hospital (MSH), and University of California, San Diego (UCSD) with baseline and post-treatment (week 14 [± 4 weeks]) mucosal biopsy RNA-sequencing data and Mayo Clinic endoscopic subscores (MCES) were eligible for inclusion. Endoscopic response was defined as an MCES <1. Demographic and clinical data were collected. Differential expressed genes (DEGs) between baseline and week 14 were identified using linear mixed models with a >1.5 or <-1.5-fold change (adjusted P-value <.05). Enrichment was performed using Reactome pathway analysis and a previously published UC gene signature (adjusted P-value <.05).1 Analyses were performed using R version 4.3.0. Results Twenty-three patients were included (IDIBAPS: n=12; MSH: n=8; UCSD: n=3). Site-specific differences in sex, surgical history, and corticosteroid use were observed (Table). Analysing data by site yielded a relatively small number of DEGs between baseline and week 14 (IDIBAPS=57 [27 up-regulated, 30 down-regulated]; MSH=0; UCSD=0) among endoscopic responders (IDIBAPS: n=5; MSH: n=7; UCSD: n=2). Combining data from the 3 sites increased the number of DEGs among endoscopic responders at week 14 (n=14) to 1162 (662 up-regulated and 500 down-regulated). The down-regulated DEGs were enriched for genes involved in 37 of 103 up-regulated Reactome pathways in UC, including neutrophil degranulation, interleukin-4 and interleukin-13 signalling, interleukin-10 signalling, and integrin cell surface interactions. The up-regulated DEGs were enriched for genes involved in 1 of 39 down-regulated Reactome pathways in UC (Drug ADME). Conclusion We defined a pharmacodynamic signature using mucosal biopsies from UC patients who had endoscopic response to vedolizumab at week 14. The signature was enriched for several, but not all, pathways involved in UC. In addition to providing insight into vedolizumab’s mechanism of action, we underscored the utility of multi-site collaboration to access data, increase statistical power, and enhance the generalizability of research findings. Reference: 1. Linggi et al. Sci Rep 2021;11:18243.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".