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Bayesian probit regression model for the diagnosis of pulmonary fibrosis: proof-of-principle.

Publication ,  Journal Article
Meltzer, EB; Barry, WT; D'Amico, TA; Davis, RD; Lin, SS; Onaitis, MW; Morrison, LD; Sporn, TA; Steele, MP; Noble, PW
Published in: BMC Med Genomics
October 5, 2011

BACKGROUND: The accurate diagnosis of idiopathic pulmonary fibrosis (IPF) is a major clinical challenge. We developed a model to diagnose IPF by applying Bayesian probit regression (BPR) modelling to gene expression profiles of whole lung tissue. METHODS: Whole lung tissue was obtained from patients with idiopathic pulmonary fibrosis (IPF) undergoing surgical lung biopsy or lung transplantation. Controls were obtained from normal organ donors. We performed cluster analyses to explore differences in our dataset. No significant difference was found between samples obtained from different lobes of the same patient. A significant difference was found between samples obtained at biopsy versus explant. Following preliminary analysis of the complete dataset, we selected three subsets for the development of diagnostic gene signatures: the first signature was developed from all IPF samples (as compared to controls); the second signature was developed from the subset of IPF samples obtained at biopsy; the third signature was developed from IPF explants. To assess the validity of each signature, we used an independent cohort of IPF and normal samples. Each signature was used to predict phenotype (IPF versus normal) in samples from the validation cohort. We compared the models' predictions to the true phenotype of each validation sample, and then calculated sensitivity, specificity and accuracy. RESULTS: Surprisingly, we found that all three signatures were reasonably valid predictors of diagnosis, with small differences in test sensitivity, specificity and overall accuracy. CONCLUSIONS: This study represents the first use of BPR on whole lung tissue; previously, BPR was primarily used to develop predictive models for cancer. This also represents the first report of an independently validated IPF gene expression signature. In summary, BPR is a promising tool for the development of gene expression signatures from non-neoplastic lung tissue. In the future, BPR might be used to develop definitive diagnostic gene signatures for IPF, prognostic gene signatures for IPF or gene signatures for other non-neoplastic lung disorders such as bronchiolitis obliterans.

Duke Scholars

Published In

BMC Med Genomics

DOI

EISSN

1755-8794

Publication Date

October 5, 2011

Volume

4

Start / End Page

70

Location

England

Related Subject Headings

  • Tissue Donors
  • Sensitivity and Specificity
  • Regression Analysis
  • ROC Curve
  • Predictive Value of Tests
  • Phenotype
  • Middle Aged
  • Male
  • Lung Transplantation
  • Lung
 

Citation

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Meltzer, E. B., Barry, W. T., D’Amico, T. A., Davis, R. D., Lin, S. S., Onaitis, M. W., … Noble, P. W. (2011). Bayesian probit regression model for the diagnosis of pulmonary fibrosis: proof-of-principle. BMC Med Genomics, 4, 70. https://doi.org/10.1186/1755-8794-4-70
Meltzer, Eric B., William T. Barry, Thomas A. D’Amico, Robert D. Davis, Shu S. Lin, Mark W. Onaitis, Lake D. Morrison, Thomas A. Sporn, Mark P. Steele, and Paul W. Noble. “Bayesian probit regression model for the diagnosis of pulmonary fibrosis: proof-of-principle.BMC Med Genomics 4 (October 5, 2011): 70. https://doi.org/10.1186/1755-8794-4-70.
Meltzer EB, Barry WT, D’Amico TA, Davis RD, Lin SS, Onaitis MW, et al. Bayesian probit regression model for the diagnosis of pulmonary fibrosis: proof-of-principle. BMC Med Genomics. 2011 Oct 5;4:70.
Meltzer, Eric B., et al. “Bayesian probit regression model for the diagnosis of pulmonary fibrosis: proof-of-principle.BMC Med Genomics, vol. 4, Oct. 2011, p. 70. Pubmed, doi:10.1186/1755-8794-4-70.
Meltzer EB, Barry WT, D’Amico TA, Davis RD, Lin SS, Onaitis MW, Morrison LD, Sporn TA, Steele MP, Noble PW. Bayesian probit regression model for the diagnosis of pulmonary fibrosis: proof-of-principle. BMC Med Genomics. 2011 Oct 5;4:70.
Journal cover image

Published In

BMC Med Genomics

DOI

EISSN

1755-8794

Publication Date

October 5, 2011

Volume

4

Start / End Page

70

Location

England

Related Subject Headings

  • Tissue Donors
  • Sensitivity and Specificity
  • Regression Analysis
  • ROC Curve
  • Predictive Value of Tests
  • Phenotype
  • Middle Aged
  • Male
  • Lung Transplantation
  • Lung