Noninvasive quantification of airway inflammation following segmental allergen challenge with functional MR imaging: A proof of concept study

Purpose To evaluate oxygen-enhanced T1-mapping magnetic resonance (MR) imaging as a noninvasive method for visualization and quantification of regional inflammation after segmental allergen challenge in asthmatic patients compared with control subjects. Materials and Methods After institutional review board approval, nine asthmatic and four healthy individuals gave written informed consent. MR imaging (1.5 T) was performed by using an inversion-recovery snapshot fast low-angle shot sequence before (0 hours) and 6 hours and 24 hours after segmental allergen challenge by using either normal- or low-dose allergen or saline. The volume of lung tissue with increased relaxation times was determined by using a threshold-based method. As a biomarker for oxygen transfer from the lungs into the blood, the oxygen transfer function (OTF) was calculated. After the third MR imaging examination, eosinophils in bronchoalveolar lavage fluid were counted. Differences between times and segments were analyzed with nonparametric Wilcoxon matched-pairs test and Spearman correlation. Results In lung segments treated with the standard dose of allergen, the OTF was decreased at 6 hours in asthmatic patients, compared with saline-treated segments (P = .0078). In asthmatic patients at 24 hours, the volume over threshold was significantly increased in normal allergen dose–treated segments compared with saline-treated segments (P = .004). In corresponding lung segments, the volume over threshold at 24 hours in the asthmatic group showed a positive correlation (r = 0.65, P = .0001) and the OTF at 6 hours showed an inverse correlation (r = -0.67, P = .0001) with the percentage of eosinophils in the bronchoalveolar lavage fluid. Conclusion OTF and volume over threshold are noninvasive MR imaging-derived parameters to visualize and quantify the regional allergic reaction after segmental endobronchial allergen challenge

A computational framework for complex disease stratification from multiple large-scale datasets.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine

U-BIOPRED clinical adult asthma clusters linked to a subset of sputum omics

Background: Asthma is a heterogeneous disease in which there is a differential response to asthma treatments. This heterogeneity needs to be evaluated so that a personalized management approach can be provided. Objectives: We stratified patients with moderate-to-severe asthma based on clinicophysiologic parameters and performed an omics analysis of sputum. Methods: Partition-around-medoids clustering was applied to a training set of 266 asthmatic participants from the European Unbiased Biomarkers for the Prediction of Respiratory Diseases Outcomes (U-BIOPRED) adult cohort using 8 prespecified clinic-physiologic variables. This was repeated in a separate validation set of 152 asthmatic patients. The clusters were compared based on sputum proteomics and transcriptomics data. Results: Four reproducible and stable clusters of asthmatic patients were identified. The training set cluster T1 consists of patients with well-controlled moderate-to-severe asthma, whereas cluster T2 is a group of patients with late-onset severe asthma with a history of smoking and chronic airflow obstruction. Cluster T3 is similar to cluster T2 in terms of chronic airflow obstruction but is composed of nonsmokers. Cluster T4 is predominantly composed of obese female patients with uncontrolled severe asthma with increased exacerbations but with normal lung function. The validation set exhibited similar clusters, demonstrating reproducibility of the classification. There were significant differences in sputum proteomics and transcriptomics between the clusters. The severe asthma clusters (T2, T3, and T4) had higher sputum eosinophilia than cluster T1, with no differences in sputum neutrophil counts and exhaled nitric oxide and serum IgE levels. Conclusion: Clustering based on clinicophysiologic parameters yielded 4 stable and reproducible clusters that associate with different pathobiological pathways