title: A methodology for exploring biomarker--phenotype associations: application to flow cytometry data and systemic sclerosis clinical manifestations.
abstract: BACKGROUND
  This work seeks to develop a methodology for identifying reliable biomarkers of disease activity, progression and outcome through the identification of significant associations between high-throughput flow cytometry (FC) data and interstitial lung disease (ILD) - a systemic sclerosis (SSc, or scleroderma) clinical phenotype which is the leading cause of morbidity and mortality in SSc. A specific aim of the work involves developing a clinically useful screening tool that could yield accurate assessments of disease state such as the risk or presence of SSc-ILD, the activity of lung involvement and the likelihood to respond to therapeutic intervention. Ultimately this instrument could facilitate a refined stratification of SSc patients into clinically relevant subsets at the time of diagnosis and subsequently during the course of the disease and thus help in preventing bad outcomes from disease progression or unnecessary treatment side effects. The methods utilized in the work involve: (1) clinical and peripheral blood flow cytometry data (Immune Response In Scleroderma, IRIS) from consented patients followed at the Johns Hopkins Scleroderma Center. (2) machine learning (Conditional Random Forests - CRF) coupled with Gene Set Enrichment Analysis (GSEA) to identify subsets of FC variables that are highly effective in classifying ILD patients; and (3) stochastic simulation to design, train and validate ILD risk screening tools.
  
  
  RESULTS
  Our hybrid analysis approach (CRF-GSEA) proved successful in predicting SSc patient ILD status with a high degree of success (>82% correct classification in validation; 79 patients in the training data set, 40 patients in the validation data set).
  
  
  CONCLUSIONS
  IRIS flow cytometry data provides useful information in assessing the ILD status of SSc patients. Our new approach combining Conditional Random Forests and Gene Set Enrichment Analysis was successful in identifying a subset of flow cytometry variables to create a screening tool that proved effective in correctly identifying ILD patients in the training and validation data sets. From a somewhat broader perspective, the identification of subsets of flow cytometry variables that exhibit coordinated movement (i.e., multi-variable up or down regulation) may lead to insights into possible effector pathways and thereby improve the state of knowledge of systemic sclerosis pathogenesis.
authors.affiliation: Department of Geography and Environmental Engineering, GWC Whiting School of Engineering, The Johns Hopkins University, Baltimore, MD, USA. Hongtai@jhu.edu.
authors.forename: Hongtai
authors.initials: H
authors.lastname: Huang
authors.affiliation: Division of Rheumatology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. afava1@jhmi.edu.
authors.forename: Andrea
authors.initials: A
authors.lastname: Fava
authors.affiliation: Division of Rheumatology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. tguhr@jhmi.edu.
authors.forename: Tara
authors.initials: T
authors.lastname: Guhr
authors.affiliation: Division of Rheumatology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. rcimbro1@jhmi.edu.
authors.forename: Raffaello
authors.initials: R
authors.lastname: Cimbro
authors.affiliation: Division of Rheumatology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. arosen@jhmi.edu.
authors.forename: Antony
authors.initials: A
authors.lastname: Rosen
authors.affiliation: Division of Rheumatology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. francesco.boin@ucsf.edu.|Present address: Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA. francesco.boin@ucsf.edu.
authors.forename: Francesco
authors.initials: F
authors.lastname: Boin
authors.affiliation: Department of Geography and Environmental Engineering, GWC Whiting School of Engineering, The Johns Hopkins University, Baltimore, MD, USA. hugh.ellis@jhu.edu.
authors.forename: Hugh
authors.initials: H
authors.lastname: Ellis
mesh_terms: D015415:Biomarkers / Q000032:analysis*; D015331:Cohort Studies; D018450:Disease Progression; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D017563:Lung Diseases, Interstitial / Q000175:diagnosis* / Q000378:metabolism; D000069550:Machine Learning; D010641:Phenotype; D012372:ROC Curve; D012595:Scleroderma, Systemic / Q000175:diagnosis* / Q000378:metabolism; D060388:Support Vector Machine
chemical_list: D015415:Biomarkers