Abstract Background Clinical classification frameworks for IBD have recently been revised and acknowledge the pressing need for molecular classification tools, especially if they can be harnessed to map and predict patient trajectories. Methods Mucosal transcriptional modules (TMs) were defined with weighted gene network correlation analysis (WGCNA) in a large population with moderate-to-severe active Ulcerative Colitis (UC, n = 358).1 TM enrichment scores in baseline biopsies were correlated with key future outcomes, including treatment response and resistance. TM signatures were validated across multiple independent UC datasets and treatments. Functional roles of TMs were inferred with over-representation analysis. Module-trait analyses determined relation to outcomes. Machine learning (ML) and elastic net regression identified the most discriminatory features of TMs associated with outcome. Results Overall, 23 modules were defined. TM1 (n = 325 genes) enrichment associated most highly with ustekinumab resistance (ρ: -0.26), along with TM16 (n = 313, ρ: -0.25) and TM4 (n = 168, ρ: -0.23), whilst TM7 (n = 439, ρ: 0.25), TM15 (n = 139, ρ: 0.24), TM3 (n = 714, ρ: 0.23) and TM18 (n = 387, ρ: 0.22) correlated most strongly with response. Resistance modules represented neutrophil activity, extracellular matrix dysregulation and cellular stress pathways, respectively. Response modules related to protein glycosylation (TM3) and mitochondrial respiration (TM15). TM1, TM16 and TM4 were highly expressed by inflammatory monocytes, inflammatory fibroblasts and B cells, respectively. All response modules were highly enriched in epithelial cells. Fewer than 5% of patients with the highest TM1, TM16 or TM4 enrichment, or lowest TM7 or TM15 enrichment achieved combined endoscopic and histologic healing at week 8 with ustekinumab, or week 6 with infliximab/golimumab. They predicted treatment resistance/response, with area under the curve (AUC) up to 0.72 for ustekinumab and 0.96 for anti-TNFs. We considered if these inversely related TM groups could classify UC patients. Two new modules were derived: (1) top 50 genes from the resistance modules (n = 150) and (2) top 50 genes from the response modules (n = 200). Patients were labelled if they belonged to the top tertile of either group. This resulted in full separation along PC1 in four datasets (fig 1). ML identified the most discriminatory transcripts driving outcomes, which could be refined into feature panels and likely converted to a clinically tractable biomarker platform. Conclusion Refined modular analysis of tissue transcriptomics can be harnessed as a framework for a novel molecular classification of UC that speaks directly to the predominating biological pathways of disease in individuals and their likely treatment outcomes. Reference: 1. Sands et al, NEJM, 2019. Conflict of interest: Dr. Saifuddin, Aamir: Personal Fees: I have received speaker fees from Galapagos (now, Alfasigma) and Ferring. I have received travel support from Galapagos (now, Alfasigma), Janssen Pharmaceuticals and Dr Falk Pharma. Cozzetto, Domenico: No conflict of interest Liu, Yufan: No conflict of interest Thomas, John P: No conflict of interest Hart, Ailsa: Grant: Takeda Personal Fees: Abbvie, Amgen, Arena, AZ, Falk, Celltrion, Eli Lilly, Ferring, Genentech/ Roche, GSK, Pfizer, Takeda, Napp, Pharmacosmos, Janssen (J & J), Bristol-Myers Squibb, Gilead, Galapagos, Alfasigma Korcsmaros, Tamas: Grant: Unilever, Roche Powell, Nick: Grant: Takeda, BMS, Pfizer, Astra-Zeneca Personal Fees: Abbvie, Abivax, Allergan, Astra-Zeneca, Bristol-Myers Squibb, Celgene, Celltrion, Dr Falk Pharma UK Ltd, Ferring, Galapagos, GSK, Janssen, MSD, Roche, Pfizer, Sobi, Takeda, Tillotts
Saifuddin et al. (Thu,) studied this question.