I_MDS: an inflammatory bowel disease molecular activity score to classify patients with differing disease-driving pathways and therapeutic response to anti-TNF treatment.

Crohn's disease and ulcerative colitis are driven by both common and distinct underlying mechanisms of pathobiology. Both diseases, exhibit heterogeneity underscored by the variable clinical responses to therapeutic interventions. We aimed to identify disease-driving pathways and classify individuals into subpopulations that differ in their pathobiology and response to treatment. We applied hierarchical clustering of enrichment scores derived from gene set variation analysis of signatures representative of various immunological processes and activated cell types, to a colonic biopsy dataset that included healthy volunteers, Crohn's disease and ulcerative colitis patients. Patient stratification at baseline or after anti-TNF treatment in clinical responders and non-responders was queried. Signatures with significantly different enrichment scores were identified using a general linear model. Comparisons to healthy controls were made at baseline in all participants and then separately in responders and non-responders. Fifty-nine percent of the signatures were commonly enriched in both conditions at baseline, supporting the notion of a disease continuum within ulcerative colitis and Crohn's disease. Signatures included T cells, macrophages, neutrophil activation and poly:IC signatures, representing acute inflammation and a complex mix of potential disease-driving biology. Collectively, identification of significantly enriched signatures allowed establishment of an inflammatory bowel disease molecular activity score which uses biopsy transcriptomics as a surrogate marker to accurately track disease severity. This score separated diseased from healthy samples, enabled discrimination of clinical responders and non-responders at baseline with 100% specificity and 78.8% sensitivity, and was validated in an independent data set that showed comparable classification. Comparing responders and non-responders separately at baseline to controls, 43% and 70% of signatures were enriched, respectively, suggesting greater molecular dysregulation in TNF non-responders at baseline. This methodological approach could facilitate better targeted design of clinical studies to test therapeutics, concentrating on patient subsets sharing similar underlying pathobiology, therefore increasing the likelihood of clinical response.

Longitudinally Stable, Clinically Defined Clusters of Patients with Asthma Independently Identified in the ADEPT and U-BIOPRED Asthma Studies.

BACKGROUND: ADEPT (Airways Disease Endotyping for Personalized Therapeutics) and U-BIOPRED (Unbiased Biomarkers for the Prediction of Respiratory Disease Outcome Consortium) are independent asthma biomarker studies that aim to enable personalization of therapies. METHODS: Patients in both studies were identified by similar criteria, and similar clinical parameters and biomarkers were assessed in blood, sputum, and airway samples. Fuzzy partition-around-medoid clustering was performed on the ADEPT dataset (n = 154) and independently on the U-BIOPRED asthma dataset (n = 82), filtered to match ADEPT inclusion criteria. For both studies, the same eight easily measurable clinical variables were used, and ADEPT also included methacholine airway hyperresponsiveness. Models for cluster classification probabilities were derived and applied to the 12-month longitudinal ADEPT data and the full U-BIOPRED adult asthma dataset (n = 397) as independent external validation. MEASUREMENTS AND MAIN RESULTS: Four clusters were identified in the ADEPT-asthma study population with distinct clinical and biomarker profiles. In general, Cluster 1 consists of patients with mild asthma not treated with steroids and well controlled with preserved lung function and a low-inflammatory phenotype; Cluster 2 is partially controlled, with mild airflow obstruction but severe airway hyperresponsiveness and a Th2 phenotype (brittle phenotype); Cluster 3 is partially controlled with mild airflow obstruction but reduced vital capacity, less bronchodilator reversibility, and a non-Th2 phenotype with neutrophilic inflammation (chronic obstructive pulmonary disease-like); and Cluster 4 is poorly controlled, with marked airflow obstruction, marked bronchodilator reversibility, and a mixed inflammatory phenotype. Overall, the ADEPT clusters were stable over 12 months and reproduced by identifying four analogous clusters in the U-BIOPRED asthma dataset, with distributions for most clustering and nonclustering variables similar to ADEPT. CONCLUSIONS: We report four clinical clusters in ADEPT and confirmed these by external validation in U-BIOPRED. The ADEPT clusters have distinct clinical and molecular characteristics, are stable over 12 months, and present opportunities for the development of tailored therapeutics for asthma.