Gene expression profile predicts response to antitumor necrosis factor in children with Crohn's disease: A Porto group biobank study, and a systematic review
Bibliographic record
Abstract
OBJECTIVES: There is a paucity of validated predictors of response to anti-tumor necrosis factor (TNF) in pediatric Crohn's disease (CD). We aimed to evaluate the predictive utility of intestinal gene expression to predict response to anti-TNF in children with CD. METHODS: We enrolled children with CD before initiating anti-TNF as part of the prospective biobank of the pediatric inflammatory bowel disease Porto group of ESPGHAN. Genes potentially associated with therapeutic response were first preselected from a systematic literature review. Ribonucleic acid was extracted and sequenced from inflamed ileal biopsies of 20 children before initiating anti-TNF (13 with steroid-free remission [SFR] at 12 months, and seven with primary nonresponse [PNR]). An external validation cohort including 22 children (21 SFR, 1 PNR) was enrolled from Germany and Canada. Using maximum relevance-minimum redundancy (mRMR) methods, we constructed a support vector machine-learning model evaluated via leave-one-out cross-validation and permutation testing. RESULTS: Of 1799 studies identified in the systematic review, 24 met the inclusion criteria, reporting on 150 genes possibly associated with anti-TNF response in children or adults. In the Porto group cohort, 30 genes were associated with treatment response, of which five (TREM1, IL23R, CCL7, IL17F, and YES1) were most frequently selected. A multivariable model of these genes achieved high predictive utility (area under receiver operating characteristic curve: 0.88 [95% confidence interval: 0.69-1.0], sensitivity/specificity/positive predictive value/negative predictive value: 92%/71%/86%/83%). The same genomic signature in external validation achieved accuracy of 82% (i.e., 18/22 samples were classified correctly, including the single PNR patient). CONCLUSION: Increased expression of five genes is associated with higher rate of anti-TNF response in pediatric CD. Prospective studies are now warranted to validate these genes as biomarkers for treatment selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".