TY - JOUR
T1 - A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis
AU - Huang, Yong
AU - Ma, Shwu Fan
AU - Vij, Rekha
AU - Oldham, Justin M.
AU - Herazo-Maya, Jose
AU - Broderick, Steven M.
AU - Strek, Mary E.
AU - White, Steven R.
AU - Hogarth, D. Kyle
AU - Sandbo, Nathan K.
AU - Lussier, Yves A.
AU - Gibson, Kevin F.
AU - Kaminski, Naftali
AU - Garcia, Joe G.N.
AU - Noth, Imre
N1 - Funding Information:
The authors are grateful to the individuals who participated in this study. The study was supported by the National Institutes of Health, National Heart, Lung, and Blood Institute [Grants HL080513, 1RC1HL099619-01, 1RC2HL1011740]; Pulmonary Fibrosis Foundation (Chicago, IL); and Coalition for Pulmonary Fibrosis (San Jose, CA).
Publisher Copyright:
© 2015 Huang et al.
PY - 2015/11/21
Y1 - 2015/11/21
N2 - 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.
AB - 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.
KW - Functional genomic model
KW - Gene expression profiling
KW - Idiopathic pulmonary fibrosis (IPF)
KW - Peripheral blood mononuclear cells (PBMCs)
KW - Prognosis prediction
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U2 - 10.1186/s12890-015-0142-8
DO - 10.1186/s12890-015-0142-8
M3 - Article
C2 - 26589497
AN - SCOPUS:84959176138
VL - 15
JO - BMC Pulmonary Medicine
JF - BMC Pulmonary Medicine
SN - 1471-2466
IS - 1
M1 - 147
ER -