A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis

Persistent Link:
http://hdl.handle.net/10150/610303
Title:
A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis
Author:
Huang, Yong; Ma, Shwu-Fan; Vij, Rekha; Oldham, Justin M.; Herazo-Maya, Jose; Broderick, Steven M.; Strek, Mary E.; White, Steven R.; Hogarth, D. Kyle; Sandbo, Nathan K.; Lussier, Yves A.; Gibson, Kevin F.; Kaminski, Naftali; Garcia, Joe G.N.; Noth, Imre
Affiliation:
Section of Pulmonary & Critical Care Medicine, University of Chicago; Pulmonary, Critical Care and Sleep Medicine, Yale University; Institute for Genomics and Systems Biology, University of Chicago; Department of Medicine, Bio5 Institute, UA Cancer Center, University of Arizona; Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh; Arizona Respiratory Center and Department of Medicine, The University of Arizona
Issue Date:
2015
Publisher:
BioMed Central Ltd
Citation:
Huang et al. BMC Pulmonary Medicine (2015) 15:147 DOI 10.1186/s12890-015-0142-8
Journal:
BMC Pulmonary Medicine
Rights:
© 2015 Huang et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)
Collection Information:
This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.
Abstract:
BACKGROUND: The course of disease for patients with idiopathic pulmonary fibrosis (IPF) is highly heterogeneous. Prognostic models rely on demographic and clinical characteristics and are not reproducible. Integrating data from genomic analyses may identify novel prognostic models and provide mechanistic insights into IPF. METHODS: Total RNA of peripheral blood mononuclear cells was subjected to microarray profiling in a training (45 IPF individuals) and two independent validation cohorts (21 IPF/10 controls, and 75 IPF individuals, respectively). To identify a gene set predictive of IPF prognosis, we incorporated genomic, clinical, and outcome data from the training cohort. Predictor genes were selected if all the following criteria were met: 1) Present in a gene co-expression module from Weighted Gene Co-expression Network Analysis (WGCNA) that correlated with pulmonary function (p < 0.05); 2) Differentially expressed between observed "good" vs. "poor" prognosis with fold change (FC) >1.5 and false discovery rate (FDR) < 2 %; and 3) Predictive of mortality (p < 0.05) in univariate Cox regression analysis. "Survival risk group prediction" was adopted to construct a functional genomic model that used the IPF prognostic predictor gene set to derive a prognostic index (PI) for each patient into either high or low risk for survival outcomes. Prediction accuracy was assessed with a repeated 10-fold cross-validation algorithm and independently assessed in two validation cohorts through multivariate Cox regression survival analysis. RESULTS: A set of 118 IPF prognostic predictor genes was used to derive the functional genomic model and PI. In the training cohort, high-risk IPF patients predicted by PI had significantly shorter survival compared to those labeled as low-risk patients (log rank p < 0.001). The prediction accuracy was further validated in two independent cohorts (log rank p < 0.001 and 0.002). Functional pathway analysis revealed that the canonical pathways enriched with the IPF prognostic predictor gene set were involved in T-cell biology, including iCOS, T-cell receptor, and CD28 signaling. CONCLUSIONS: Using supervised and unsupervised analyses, we identified a set of IPF prognostic predictor genes and derived a functional genomic model that predicted high and low-risk IPF patients with high accuracy. This genomic model may complement current prognostic tools to deliver more personalized care for IPF patients.
EISSN:
1471-2466
DOI:
10.1186/s12890-015-0142-8
Keywords:
Idiopathic pulmonary fibrosis (IPF); Peripheral blood mononuclear cells (PBMCs); Gene expression profiling; Functional genomic model; Prognosis prediction
Version:
Final published version
Additional Links:
http://www.biomedcentral.com/1471-2466/15/147

Full metadata record

DC FieldValue Language
dc.contributor.authorHuang, Yongen
dc.contributor.authorMa, Shwu-Fanen
dc.contributor.authorVij, Rekhaen
dc.contributor.authorOldham, Justin M.en
dc.contributor.authorHerazo-Maya, Joseen
dc.contributor.authorBroderick, Steven M.en
dc.contributor.authorStrek, Mary E.en
dc.contributor.authorWhite, Steven R.en
dc.contributor.authorHogarth, D. Kyleen
dc.contributor.authorSandbo, Nathan K.en
dc.contributor.authorLussier, Yves A.en
dc.contributor.authorGibson, Kevin F.en
dc.contributor.authorKaminski, Naftalien
dc.contributor.authorGarcia, Joe G.N.en
dc.contributor.authorNoth, Imreen
dc.date.accessioned2016-05-20T09:03:41Z-
dc.date.available2016-05-20T09:03:41Z-
dc.date.issued2015en
dc.identifier.citationHuang et al. BMC Pulmonary Medicine (2015) 15:147 DOI 10.1186/s12890-015-0142-8en
dc.identifier.doi10.1186/s12890-015-0142-8en
dc.identifier.urihttp://hdl.handle.net/10150/610303-
dc.description.abstractBACKGROUND: The course of disease for patients with idiopathic pulmonary fibrosis (IPF) is highly heterogeneous. Prognostic models rely on demographic and clinical characteristics and are not reproducible. Integrating data from genomic analyses may identify novel prognostic models and provide mechanistic insights into IPF. METHODS: Total RNA of peripheral blood mononuclear cells was subjected to microarray profiling in a training (45 IPF individuals) and two independent validation cohorts (21 IPF/10 controls, and 75 IPF individuals, respectively). To identify a gene set predictive of IPF prognosis, we incorporated genomic, clinical, and outcome data from the training cohort. Predictor genes were selected if all the following criteria were met: 1) Present in a gene co-expression module from Weighted Gene Co-expression Network Analysis (WGCNA) that correlated with pulmonary function (p < 0.05); 2) Differentially expressed between observed "good" vs. "poor" prognosis with fold change (FC) >1.5 and false discovery rate (FDR) < 2 %; and 3) Predictive of mortality (p < 0.05) in univariate Cox regression analysis. "Survival risk group prediction" was adopted to construct a functional genomic model that used the IPF prognostic predictor gene set to derive a prognostic index (PI) for each patient into either high or low risk for survival outcomes. Prediction accuracy was assessed with a repeated 10-fold cross-validation algorithm and independently assessed in two validation cohorts through multivariate Cox regression survival analysis. RESULTS: A set of 118 IPF prognostic predictor genes was used to derive the functional genomic model and PI. In the training cohort, high-risk IPF patients predicted by PI had significantly shorter survival compared to those labeled as low-risk patients (log rank p < 0.001). The prediction accuracy was further validated in two independent cohorts (log rank p < 0.001 and 0.002). Functional pathway analysis revealed that the canonical pathways enriched with the IPF prognostic predictor gene set were involved in T-cell biology, including iCOS, T-cell receptor, and CD28 signaling. CONCLUSIONS: Using supervised and unsupervised analyses, we identified a set of IPF prognostic predictor genes and derived a functional genomic model that predicted high and low-risk IPF patients with high accuracy. This genomic model may complement current prognostic tools to deliver more personalized care for IPF patients.en
dc.language.isoenen
dc.publisherBioMed Central Ltden
dc.relation.urlhttp://www.biomedcentral.com/1471-2466/15/147en
dc.rights© 2015 Huang et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)en
dc.subjectIdiopathic pulmonary fibrosis (IPF)en
dc.subjectPeripheral blood mononuclear cells (PBMCs)en
dc.subjectGene expression profilingen
dc.subjectFunctional genomic modelen
dc.subjectPrognosis predictionen
dc.titleA functional genomic model for predicting prognosis in idiopathic pulmonary fibrosisen
dc.typeArticleen
dc.identifier.eissn1471-2466en
dc.contributor.departmentSection of Pulmonary & Critical Care Medicine, University of Chicagoen
dc.contributor.departmentPulmonary, Critical Care and Sleep Medicine, Yale Universityen
dc.contributor.departmentInstitute for Genomics and Systems Biology, University of Chicagoen
dc.contributor.departmentDepartment of Medicine, Bio5 Institute, UA Cancer Center, University of Arizonaen
dc.contributor.departmentDivision of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburghen
dc.contributor.departmentArizona Respiratory Center and Department of Medicine, The University of Arizonaen
dc.identifier.journalBMC Pulmonary Medicineen
dc.description.collectioninformationThis item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
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