A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis

Yong Huang, Shwu Fan Ma, Rekha Vij, Justin M. Oldham, Jose Herazo-Maya, Steven M. Broderick, Mary E. Strek, Steven R. White, D. Kyle Hogarth, Nathan K. Sandbo, Yves A Lussier, Kevin F. Gibson, Naftali Kaminski, Joe GN Garcia, Imre Noth

Research output: Contribution to journalArticle

17 Citations (Scopus)

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.

Original languageEnglish (US)
JournalBMC Pulmonary Medicine
DOIs
StateAccepted/In press - Nov 21 2015

Fingerprint

Idiopathic Pulmonary Fibrosis
Genes
Survival
Regression Analysis
Gene Expression
Survival Analysis
T-Cell Antigen Receptor
Cell Biology
Blood Cells

Keywords

  • Functional genomic model
  • Gene expression profiling
  • Idiopathic pulmonary fibrosis (IPF)
  • Peripheral blood mononuclear cells (PBMCs)
  • Prognosis prediction

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine

Cite this

Huang, Y., Ma, S. F., Vij, R., Oldham, J. M., Herazo-Maya, J., Broderick, S. M., ... Noth, I. (Accepted/In press). A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis. BMC Pulmonary Medicine. https://doi.org/10.1186/s12890-015-0142-8

A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis. / 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 GN; Noth, Imre.

In: BMC Pulmonary Medicine, 21.11.2015.

Research output: Contribution to journalArticle

Huang, Y, Ma, SF, Vij, R, Oldham, JM, Herazo-Maya, J, Broderick, SM, Strek, ME, White, SR, Hogarth, DK, Sandbo, NK, Lussier, YA, Gibson, KF, Kaminski, N, Garcia, JGN & Noth, I 2015, 'A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis', BMC Pulmonary Medicine. https://doi.org/10.1186/s12890-015-0142-8
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 GN ; Noth, Imre. / A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis. In: BMC Pulmonary Medicine. 2015.
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keywords = "Functional genomic model, Gene expression profiling, Idiopathic pulmonary fibrosis (IPF), Peripheral blood mononuclear cells (PBMCs), Prognosis prediction",
author = "Yong Huang and Ma, {Shwu Fan} and Rekha Vij and Oldham, {Justin M.} and Jose Herazo-Maya and Broderick, {Steven M.} and Strek, {Mary E.} and White, {Steven R.} and Hogarth, {D. Kyle} and Sandbo, {Nathan K.} and Lussier, {Yves A} and Gibson, {Kevin F.} and Naftali Kaminski and Garcia, {Joe GN} and Imre Noth",
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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 GN

AU - Noth, Imre

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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