Molecular Patterns in Human Ulcerative Colitis and Correlation with Response to Infliximab
Bibliographic record
Abstract
BACKGROUND: As a T cell-mediated disease of the colonic epithelium, ulcerative colitis (UC) is likely to share pathogenic elements with other T cell-mediated inflammatory diseases. Recently, microarray analysis revealed large-scale molecular changes in T cell-mediated rejection of kidney and heart transplants. We hypothesized that similar disturbances might be operating in UC and could provide insights into responsiveness to therapy. METHODS: We studied 56 colon biopsies from patients with colitis characterizing the clinical and histological features and using microarrays to define the messenger RNA phenotype. We expressed the microarray results using previously defined pathogenesis-based transcript sets. We also studied 48 published microarray files from human colon biopsies downloaded from the Gene Expression Omnibus database, classified by response to infliximab therapy, to examine whether the molecular measurements derived from our studies correlated with nonresponsiveness to treatment. RESULTS: UC biopsies manifested coordinate transcript changes resembling rejecting transplants, with effector T cell, IFNG-induced, macrophage, and injury transcripts increasing while parenchymal transcripts decreased. The disturbance in gene expression, summarized as principal component 1 (PC1), correlated with conventional clinical and histologic assessments. When assessed in microarray results from published studies, the disturbance (PC1) predicted response to infliximab: patients with intense disturbance did not achieve clinical response, although quantitative improvement was seen even in many clinical nonresponders. Similar changes were seen in Crohn's colitis. CONCLUSIONS: The molecular phenotype of UC manifests a large-scale coordinate disturbance reflecting changes in inflammatory cells and parenchymal elements that correlates with conventional features and predicts response to infliximab.
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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 | 0.002 | 0.002 |
| 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".