Volume 3, Issue 4 p. 147-157

Predicting Intermediate Phenotypes in Asthma Using Bronchoalveolar Lavage-Derived Cytokines

Allan R. Brasier M.D.

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

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Sundar Victor M.S.

Sundar Victor M.S.

Sealy Center for Molecular Medicine, University of Texas Medical Branch (UTMB), Galveston, Texas, USA

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Hyunsu Ju Ph.D.

Hyunsu Ju Ph.D.

Department of Internal Medicine, UTMB, Galveston, Texas, USA

Institute for Translational Sciences, UTMB, Galveston, Texas, USA

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William W. Busse M.D.

William W. Busse M.D.

Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA and the National Heart, Lung and Blood Research Program (SARP ** )

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Douglas Curran-Everett Ph.D.

Douglas Curran-Everett Ph.D.

National Jewish Health, Denver, Colorado, USA

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Eugene Bleecker M.D.

Eugene Bleecker M.D.

Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA

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Mario Castro M.D.

Mario Castro M.D.

Department of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA

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Kian Fan Chung M.D., DSc.

Kian Fan Chung M.D., DSc.

Imperial College, London, United Kingdom

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Benjamin Gaston M.D.

Benjamin Gaston M.D.

University of Virginia, Charlottesville, Virginia, USA

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Elliot Israel M.D.

Elliot Israel M.D.

Brigham & Women‘s Hospital, Boston, Massachusetts, USA

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Sally E. Wenzel M.D.

Sally E. Wenzel M.D.

University of Pittsburgh, Pittsburgh, Pennsylvania, USA

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Serpil C. Erzurum M.D.

Serpil C. Erzurum M.D.

Cleveland Clinic, Cleveland, Ohio, USA.

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Nizar N. Jarjour M.D.

Nizar N. Jarjour M.D.

Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA and the National Heart, Lung and Blood Research Program (SARP ** )

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William J. Calhoun M.D.

William 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

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First published: 16 July 2010
Citations: 58
AR Brasier ([email protected])

The 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