Background-Asthma is a heterogeneous clinical disorder. Methods for objective identification of disease subtypes will focus on clinical interventions and help identify causative pathways. Few studies have explored phenotypes at a molecular level.

Abstract. Dengue virus infections are a major cause of morbidity in tropical countries. Early detection of dengue hemorrhagic fever (DHF) may help identify individuals that would benefit from intensive therapy. Predictive modeling was performed using 11 laboratory values of 51 individuals (38 DF and 13 DHF) obtained on initial presentation using logistic regression. We produced a robust model with an area under the curve of 0.9615 that retained IL-10 levels, platelets, and lymphocytes as the major predictive features. A classification and regression tree was developed on these features that were 86% accurate on cross-validation. The IL-10 levels and platelet counts were also identified as the most informative features associated with DHF using a Random Forest classifier. In the presence of polymerase chain reaction-proven acute dengue infections, we suggest a complete blood count and rapid measurement of IL-10 can assist in the triage of potential DHF cases for close follow-up or clinical intervention improving clinical outcome.

An important problem in realizing personalized medicine is the development of methods for identifying disease subtypes using quantitative proteomics. Recently we found that bronchoalveolar lavage (BAL) cytokine patterns contain information about dynamic lung responsiveness. In this study, we examined physiological data from 1,048 subjects enrolled in the US Severe Asthma Research Program (SARP) to identify four largely separable, quantitative intermediate phenotypes. Upper extremes in the study population were identified for eosinophil‐ or neutrophil‐predominant inflammation, bronchodilation in response to albuterol treatment, or methacholine sensitivity. We evaluated four different statistical (“machine”) learning methods to predict each intermediate phenotype using BAL ­cytokine measurements on a 76 subject subset. Comparison of these models using area under the ROC curve and overall classification accuracy indicated that logistic regression and multivariate adaptive regression splines produced the most accurate methods to predict intermediate asthma phenotypes. These robust classification methods will aid future translational studies in asthma targeted at specific intermediate phenotypes. Clin Trans Sci 2010; Volume 3: 147–157

Secondary Dengue viral infection can produce capillary leakage associated with increased mortality known as Dengue Hemorrhagic Fever (DHF). Because the mortality of DHF can be reduced by early detection and intensive support, improved methods for its detection are needed. We applied multidimensional protein profiling to predict outcomes in a prospective Dengue surveillance study in South America. Plasma samples taken from initial clinical presentation of acute Dengue infection were subjected to proteomics analyses using ELISA and a recently developed biofluid analysis platform. Demographics, clinical laboratory measurements, 9 cytokines and 419 plasma proteins collected at the time of initial presentation were compared between the DF and DHF outcomes. Here, the subject’s gender, clinical parameters, 2 cytokines and 42 proteins discriminated between the outcomes. These factors were reduced by multivariate adaptive regression splines (MARS) that a highly accurate classification model based on 8 discriminant features with an AUC of 0.999. Model analysis indicated that the feature-outcome relationship were non-linear. Although this DHF risk model will need validation in a larger cohort, we conclude that approaches to develop predictive biomarker models for disease outcome will need to incorporate nonparametric modeling approaches.

Asthmatic patients are currently classified as either severe or non-severe based primarily on their response to glucocorticoids. However, because this classification is based on a post-hoc assessment of treatment response, it does not inform the rational staging of disease or therapy. Recent studies in other diseases suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. We therefore measured cytokine values in bronchoalveolar lavage (BAL) samples of the lower respiratory tract obtained from 83 asthma patients, and used bipartite network visualizations with associated quantitative measures to conduct an exploratory analysis of the co-occurrence of cytokines across patients. The analysis helped to identify three clusters of patients which had a complex but understandable interaction with three clusters of cytokines, leading to insights for a state-based classification of asthma patients. Furthermore, while the patient clusters were significantly different based on key pulmonary functions, they appeared to have no significant relationship to the current classification of asthma patients. These results suggest the need to define a molecular-based classification of asthma patients, which could improve the diagnosis and treatment of this disease.