Predicting Intermediate Phenotypes in Asthma Using Bronchoalveolar Lavage-Derived Cytokines
Allan R. Brasier M.D.
Sealy Center for Molecular Medicine, University of Texas Medical Branch (UTMB), Galveston, Texas, USA
Department of Internal Medicine, UTMB, Galveston, Texas, USA
Institute for Translational Sciences, UTMB, Galveston, Texas, USA
Search for more papers by this authorSundar Victor M.S.
Sealy Center for Molecular Medicine, University of Texas Medical Branch (UTMB), Galveston, Texas, USA
Search for more papers by this authorHyunsu Ju Ph.D.
Department of Internal Medicine, UTMB, Galveston, Texas, USA
Institute for Translational Sciences, UTMB, Galveston, Texas, USA
Search for more papers by this authorWilliam W. Busse M.D.
Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA and the National Heart, Lung and Blood Research Program (SARP ** )
Search for more papers by this authorDouglas Curran-Everett Ph.D.
National Jewish Health, Denver, Colorado, USA
Search for more papers by this authorEugene Bleecker M.D.
Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
Search for more papers by this authorMario Castro M.D.
Department of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
Search for more papers by this authorKian Fan Chung M.D., DSc.
Imperial College, London, United Kingdom
Search for more papers by this authorBenjamin Gaston M.D.
University of Virginia, Charlottesville, Virginia, USA
Search for more papers by this authorElliot Israel M.D.
Brigham & Women‘s Hospital, Boston, Massachusetts, USA
Search for more papers by this authorSally E. Wenzel M.D.
University of Pittsburgh, Pittsburgh, Pennsylvania, USA
Search for more papers by this authorNizar N. Jarjour M.D.
Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA and the National Heart, Lung and Blood Research Program (SARP ** )
Search for more papers by this authorWilliam J. Calhoun M.D.
Sealy Center for Molecular Medicine, University of Texas Medical Branch (UTMB), Galveston, Texas, USA
Department of Internal Medicine, UTMB, Galveston, Texas, USA
Institute for Translational Sciences, UTMB, Galveston, Texas, USA
Search for more papers by this authorAllan R. Brasier M.D.
Sealy Center for Molecular Medicine, University of Texas Medical Branch (UTMB), Galveston, Texas, USA
Department of Internal Medicine, UTMB, Galveston, Texas, USA
Institute for Translational Sciences, UTMB, Galveston, Texas, USA
Search for more papers by this authorSundar Victor M.S.
Sealy Center for Molecular Medicine, University of Texas Medical Branch (UTMB), Galveston, Texas, USA
Search for more papers by this authorHyunsu Ju Ph.D.
Department of Internal Medicine, UTMB, Galveston, Texas, USA
Institute for Translational Sciences, UTMB, Galveston, Texas, USA
Search for more papers by this authorWilliam W. Busse M.D.
Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA and the National Heart, Lung and Blood Research Program (SARP ** )
Search for more papers by this authorDouglas Curran-Everett Ph.D.
National Jewish Health, Denver, Colorado, USA
Search for more papers by this authorEugene Bleecker M.D.
Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
Search for more papers by this authorMario Castro M.D.
Department of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA
Search for more papers by this authorKian Fan Chung M.D., DSc.
Imperial College, London, United Kingdom
Search for more papers by this authorBenjamin Gaston M.D.
University of Virginia, Charlottesville, Virginia, USA
Search for more papers by this authorElliot Israel M.D.
Brigham & Women‘s Hospital, Boston, Massachusetts, USA
Search for more papers by this authorSally E. Wenzel M.D.
University of Pittsburgh, Pittsburgh, Pennsylvania, USA
Search for more papers by this authorNizar N. Jarjour M.D.
Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA and the National Heart, Lung and Blood Research Program (SARP ** )
Search for more papers by this authorWilliam J. Calhoun M.D.
Sealy Center for Molecular Medicine, University of Texas Medical Branch (UTMB), Galveston, Texas, USA
Department of Internal Medicine, UTMB, Galveston, Texas, USA
Institute for Translational Sciences, UTMB, Galveston, Texas, USA
Search for more papers by this authorThe SARP is a multicenter asthma research group funded by the NHLBI consisting of the following contributors (Principal Investigators are marked with an asterisk): Brigham and Women‘s Hospital—Elliot Israel*, Bruce D. Levy, Gautham Marigowda; Cleveland Clinic—Serpil C. Erzurum*, Raed A. Dweik, Suzy A.A. Comhair, Emmea Cleggett-Mattox, Deepa George, Marcelle Baaklini, Daniel Laskowski; Emory University—Anne M. Fitzpatrick, Eric Hunter, Denise Whitlock; Imperial College School of Medicine—Kian F. Chung*, Mark Hew, Patricia Macedo, Sally Meah, Florence Chow; University of Pittsburgh—Sally E. Wenzel*, Erin Aiken; University of Texas-Medical Branch—William J. Calhoun*, Bill T. Ameredes, Dori Smith; University of Virginia—Benjamin Gaston*, W. Gerald Teague*, Mike Davis; University of Wisconsin—William W. Busse*, Nizar Jarjour, Ronald Sorkness, Sean Fain, Erin Billmeyer, Cheri Swenson, Gina Crisafi, Laura Frisque, Dan Kolk; Wake Forest University—Eugene R. Bleecker*, Deborah Meyers, Wendy Moore, Stephen Peters, Annette Hastie, Gregory Hawkins, Jeffrey Krings, Regina Smith; Washington University in St Louis—Mario Castro*, Leonard Bacharier, Iftikhar Hussain, Jaime Tarsi; Data Coordinating Center—Douglas Curran-Everett*, Ruthie Knowles, Lori Silveira; NHLBI—Patricia Noel*, Robert Smith.
Abstract
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
Supporting Information
Figure S1. Inter-relationship of asthmatic phenotypes with SARP classification. Venn diagram analysis of phenotypes with clinical asthmatic groups. Shown is the intersection for various groups. (A) high BAL eosinophils (eosinophils); (B), high BAL neutrophils (neutrophils); (C) bronchodilation in response to 4 puffs of albuterol (bronchodilators); (D), methacholine sensitivity (``hyper-responder'') class. Note similar distribution as the larger SARP study.
Figure S2. Inter-relationship of asthmatic phenotypes. Shown is a Venn diagram of the membership for all classes. Note similar class relationships as the larger SARP study.
Figure S3 CART decision tree for eosinophils
Figure S4. CART for neutrophils
Figure S5 CART for bronchodilators
Figure S6 CART for hyper-responders
Table S1. Clinical/demographic features for the SARP subjects on whom cytokine arrays were performed.
Table SII. Logistic regression models for asthma phenotypes. Logistic regression with information criterion (AIC) was performed for high eosinophils, high neutrophil, bronchodilator and hyper-responder class. Abbreviations: OR5odds ratio; CI5confidence interval; AUC5area under ROC curve.
Table SIII. MARS models for asthma phenotypes Abbreviations: BF5basis function; OR5odds ratio; CI5confidence interval; AUC5area under ROC curve. At right is shown the performance metrics for the overall model.
Table SIV. Features used for high Eosinophil classifiers. Shown is rank-ordered feature for the two highest performance machine learning classifiers (LR and MARS) for predicting the high eosinophil phenotype. For the LR, the x2 score statistic is used, whereas rank-ordered variable importance is shown for the MARS model. Abbreviations: 5feature does not appear in model.
Table SV. Rank ordered features for high neutrophil classifiers. Rank-ordered feature list for LR and MARS models predicting the high neutrophil phenotype.
Table SVI. Rank ordered feature list for bronchodilators. Rank-ordered feature list for LR and MARS models predicting bronchodilator phenotype.
Table SVII. Rank ordered feature list for hyper-responders. Rank-ordered feature list for LR and MARS models predicting hyper-responder phenotype.
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