# Anti-TNF Response Prediction from Pre-treatment Transcriptomics

## Objective
Develop a gene expression-based classifier to predict response to anti-TNF therapy (infliximab, adalimumab) in Crohn's disease patients using pre-treatment mucosal or blood transcriptomic profiles.

## Background
Anti-TNF biologics are first-line advanced therapy for moderate-to-severe Crohn's disease, but 30-40% of patients are primary non-responders. Early identification of non-responders could spare patients ineffective treatment, reduce costs, and accelerate time to effective therapy (e.g., vedolizumab, ustekinumab, or JAK inhibitors). Several small studies have identified candidate gene signatures (TREM1, oncostatin M signaling, OSMR pathway), but no robust, validated classifier exists. Aggregating multiple public datasets and applying modern machine learning could yield a clinically useful predictor.

## Data Sources
- **GSE16879**: Mucosal biopsies from 24 CD patients before/after infliximab, with response annotation (Arijs et al. 2009)
- **GSE12251**: Colonic biopsies from 22 CD patients pre-infliximab with response data (Arijs et al. 2009)
- **GSE73661**: Mucosal biopsies from 73 IBD patients (including CD) pre-anti-TNF treatment (Haberman et al. 2014)
- **GSE100833**: Blood transcriptomics from IBD patients on anti-TNF therapy
- **RISK cohort (GSE57945)**: Pediatric CD treatment-naive ileal biopsies with follow-up data
- **E-MTAB-7604**: Mucosal transcriptomics from the PANTS anti-TNF study
- **Paired 16S + transcriptomic cohorts (TBD)**: Identify cohorts with matched mucosal biopsy 16S rRNA sequencing and transcriptomics. Zafeiropoulou et al. (Front Cell Infect Microbiol 2026) used colonic biopsies from 125 patients (39 anti-TNFα) — evaluate whether raw 16S data are publicly deposited. RISK cohort may have paired microbiome data for a subset of patients.

## Methodology
1. **Data acquisition**: Download CEL/raw files from GEO/ArrayExpress. Process with consistent pipeline (RMA for microarray, salmon for RNA-seq). Map to common gene symbols.
2. **Batch correction**: Apply ComBat-seq/ComBat across studies. Verify batch removal via PCA and UMAP visualization while preserving biological signal (responder/non-responder separation).
3. **Differential expression**: Identify genes differentially expressed between responders and non-responders pre-treatment (limma/DESeq2). Perform pathway enrichment (GSEA with Hallmark and Reactome gene sets).
4. **Feature selection**: Use stability selection (randomized lasso) and recursive feature elimination to identify a compact gene signature (target: 10-30 genes). Cross-reference with known biology (TNF signaling, Th17, innate immunity, fibrosis). **Candidate feature sets to evaluate:**
   - *pediCD-TIME vector genes* (eLife Nov 2025, doi:10.7554/eLife.91792): Composite cellular vector (T cell, innate lymphocyte, myeloid, epithelial states) predicting anti-TNF response with r=-0.87 correlation. Validated in E-MTAB-7604 (adult, n=43) — one of our training datasets. Extract gene signature via ARBOL package (GitHub, R and Python). Note convergence with GIMATS module (Martin et al. 2019). Key caveat: anti-TNF treatment pushes pediatric cellular ecosystem toward adult, treatment-refractory state — treatment-naïve vs post-treatment samples may perform differently.
   - *Cell-type deconvolution features*: Use InstaPrism (fast BayesPrism implementation, Bioinformatics 2024, doi:10.1093/bioinformatics/btae440) — top-performing deconvolution method per Briefings in Bioinformatics 2025 benchmark — with pediCD-TIME single-cell reference (254,911 cells, Broad Single Cell Portal) or IBDverse atlas (1.1M cells) to estimate cell-type proportions from bulk data as additional classifier features.
   - *Porto 5-gene mucosal signature* (PubMed 40819280, 2025): TREM1, IL23R, CCL7, IL17F, YES1 — increased expression associates with higher anti-TNF response rates. AUC=0.88 (sensitivity 92%, specificity 71%) in pediatric CD, validated externally in 22 children (82% accuracy). TREM1 converges with our existing OSMR/TREM1 candidate features — strong cross-study validation signal. IL23R links anti-TNF response to IL-23 biology (cross-project relevance with il23-single-cell-response-atlas). Discovery cohort was small (n=20), so all 5 genes should be evaluated as candidates in stability selection rather than adopted as a fixed panel.
   - *HLA-DQA1*05 genotype*: Validated predictor of anti-drug antibody formation for infliximab and adalimumab (PMC12133927). Include if available in training cohorts.
   - *RORγT/RORC and JNK pathway features* (Int Immunopharmacol 2026, PMID 41806689): Mucosal RORγT expression discriminates secondary loss of response (LOR) from sustained response (AUC 0.83, HR 4.90). JNK pathway hyperactivation drives pathogenic Th17 cells (IL-17A+IFN-γ+CD4+). Add RORC expression as individual feature and JNK pathway gene set score. IL-17 axis converges with Porto IL17F (above). Relevant primarily for secondary LOR prediction — see two-stage model below.
   - *ECM/fibroblast gene signatures* (IBD Jan 2026): CD81+ fibroblasts expressing MMP2 drive anti-TNF nonresponse via extracellular matrix remodeling. This extends beyond the OSMR/oncostatin M axis. Evaluate MMP2, COL1A1, and CD81 expression as candidate features during stability selection alongside existing OSMR pathway genes. ECM remodeling may represent a fibrosis-driven mechanism of primary non-response distinct from inflammatory pathway features.
   - *Mucosal microbiome features (multi-modal extension)*: Colonic biopsy 16S rRNA signatures achieve AUC 0.90 for anti-TNFα response prediction alone (Zafeiropoulou et al., Front Cell Infect Microbiol 2026, 125 patients). Key taxa: Ruminococcus gnavus ASVs (failure), Blautia ASVs (response), baseline alpha-diversity (response). Treatment-specific signatures — models trained for anti-TNF do not generalize to vedolizumab/ustekinumab, validating our drug-class-focused approach. **Bifidobacterium-TCDCA-Treg stratification axis**: B. catenulatum depletion → TCDCA deficiency → M1 macrophage dominance + Treg depletion → infliximab failure (LDA AUC 80.5%, causally validated by FMT). This represents a mechanistically distinct failure mode — patients with depleted Bifidobacterium may fail anti-TNF regardless of mucosal transcriptomic profile. Include Bifidobacterium abundance and TCDCA bile acid pathway gene expression (e.g., bile salt hydrolase genes) as stratification features. Multi-modal transcriptomics + microbiome model could exceed AUC 0.90 benchmark. **NAMPT/NAD+ salvage pathway features** (Advanced Science 2025, doi:10.1002/advs.202413128): F. nucleatum activates p38 MAPK via NAD+ salvage pathway, inhibiting anti-TNF response. Extracellular NAMPT elevated in anti-TNF non-responders. Include: (1) F. nucleatum and Romboutsia relative abundance from 16S data, (2) NAMPT gene expression from mucosal transcriptomics, (3) NAD+ pathway gene set score (NAMPT, NMNAT1, NMNAT2, NAPRT). Cross-project convergence: NAD metabolism bridges anti-TNF prediction (mechanism of resistance) and microbiome-metabolome subtyping (tryptophan-NAD axis). Patients with elevated NAMPT + F. nucleatum colonization may define a metabolic subtype resistant to anti-TNF regardless of inflammatory profile.
5. **Classifier training**: Train and compare multiple models — elastic net logistic regression, random forest, XGBoost. Use leave-one-study-out cross-validation to assess generalization across cohorts. **Two-stage model architecture**: Consider separating primary non-response (PNR) from secondary loss of response (LOR). These are clinically distinct endpoints with different biology — PNR may reflect non-TNF-driven inflammation (e.g., TL1A/DR3 axis, which remains active under anti-TNF per Gut 2025 PMID 41448881), while secondary LOR involves pathogenic Th17/RORγT expansion and anti-drug antibody formation. Train separate classifiers if cohort annotation permits PNR vs LOR distinction. **Stratification**: Prior anti-TNF exposure degrades prediction accuracy (EPIC-CD: vedolizumab methylation AUC drops from 0.75 to 0.66 in anti-TNF-exposed patients). Include prior biologic exposure as a stratification variable.
6. **Signature analysis**: Interpret final gene signature biologically. Map to cell types using single-cell reference atlases (e.g., Smillie et al. 2019 UC atlas). Assess overlap with OSMR/oncostatin M pathway.

## Expected Outputs
- Validated multi-gene classifier predicting anti-TNF response in CD
- Gene signature with biological interpretation (pathways, cell types)
- Cross-study performance metrics (AUC, sensitivity, specificity per study)
- Comparison with existing published signatures
- Risk stratification visualization (predicted probability vs actual outcome)

## Success Criteria
- Leave-one-study-out AUC > 0.75 for transcriptomic-only model (clinically meaningful improvement over random); multi-modal (transcriptomics + microbiome) target AUC > 0.85
- Gene signature enriched in biologically interpretable pathways
- Consistent performance across mucosal biopsy datasets (AUC > 0.70 in each)
- Signature size <= 30 genes (practical for clinical translation)
- **Benchmark**: Demonstrate complementary value beyond adalimumab ML model (PLOS One 2025, doi:10.1371/journal.pone.0331447) which achieved AUC=0.935 at 48 weeks using clinical/lab features only (calprotectin, CRP, hemoglobin). Our transcriptomic classifier should either exceed this in a comparable setting or show additive value when combined with clinical features.
- **External AUC benchmarks**: (1) EPIC-CD blood DNA methylation — adalimumab model failed validation (AUC 0.25), vindicating mucosal transcriptomics over blood epigenomics for anti-TNF prediction; vedolizumab/ustekinumab methylation models achieved AUC 0.75 (Lancet GH 2025, PMID 40614748). (2) Multimodal deep learning (TabNet) — histopathology + CT enterography + clinical labs achieved AUC 0.858 for IFX PNR on external validation, highest reported (J Crohns Colitis 2026, PMID 41298328). Our mucosal transcriptomic approach is complementary (molecular mechanism vs structural disease burden).

## Labels
biomarker, clinical, immunology, high-priority, promising
