Validation of Gene Expression Biomarker Analysis for Biopsy-based Clinical Trials in Crohnʼs Disease
Bibliographic record
Abstract
BACKGROUND: The ability to measure the expression of proinflammatory cytokines from intestinal biopsies in patients with Crohn's disease in an accurate and reproducible way is critical for proof-of-concept and mechanism-of-action trials; however, the number of biopsies from a segment of the ileum or colon required to yield reproducible results has not been rigorously evaluated. We examined intestinal biopsies from patients with Crohn's disease to validate methods for detecting changes in inflammatory gene expression. METHODS: To evaluate the reproducibility of gene expression measurements, intestinal biopsies were obtained from designated segments from 6 healthy controls, 6 patients with active Crohn's disease, and 6 patients with inactive Crohn's disease. Disease activity was based on the simple endoscopic score for Crohn's disease. Expression of 7 proinflammatory genes was measured from each biopsy using quantitative polymerase chain reaction. Using a linear mixed effects model, the power to detect transcriptional changes corresponding to active and inactive Crohn's disease was calculated. RESULTS: Total simple endoscopic score for Crohn's disease score corresponds with expression of most inflammatory biomarkers. For most genes, 2 to 5 biopsies are needed to reduce sampling error to <25% for most genes. To measure changes in mRNA expression corresponding to active versus inactive Crohn's disease, 1 to 2 intestinal biopsies from 3 patients before and after treatment are needed to yield power of at least 80%. CONCLUSIONS: Measuring proinflammatory gene expression from mucosal biopsies from patients with Crohn's disease is practicable and provides objective biomarkers that can be used in proof-of-concept and mechanism-of-action trials to assess response to therapy.
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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 | 0.198 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".