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Familial and sporadic idiopathic pulmonary fibrosis: making the diagnosis from peripheral blood.

Meltzer EB, Barry WT, Yang IV, Brown KK, Schwarz MI, Patel H, Ashley A, Noble PW, Schwartz DA, Steele MP - BMC Genomics (2014)

Bottom Line: Unsupervised clustering failed to discriminate between samples of different severity.The signature was successfully applied to the validation set, ROC area under the curve = 0.893, p < 0.0001.By using Bayesian probit regression to develop a model, we show that it is entirely possible to make a diagnosis of IPF from the peripheral blood with gene signatures.

View Article: PubMed Central - PubMed

Affiliation: Division of Allergy, Pulmonary, and Critical Care, Vanderbilt University Medical Center, 1313 21st Avenue South, 1105 Oxford House, Nashville, TN, USA. mark.p.steele@vanderbilt.edu.

ABSTRACT

Background: Peripheral blood biomarkers might improve diagnostic accuracy for idiopathic pulmonary fibrosis (IPF).

Results: Gene expression profiles were obtained from 89 patients with IPF and 26 normal controls. Samples were stratified according to severity of disease based on pulmonary function. The stratified dataset was split into subsets; two-thirds of the samples were selected to comprise the training set, while one-third was reserved for the validation set. Bayesian probit regression was used on the training set to develop a gene expression model for IPF versus normal. The gene expression model was tested by using it on the validation set to perform class prediction. Unsupervised clustering failed to discriminate between samples of different severity. Therefore, samples of all severities were included in the training and validation sets, in equal proportions. A gene signature model was developed from the training set. The model was built in an iterative fashion with the number of gene features selected to minimize the misclassification error in cross validation. The final model was based on the top 108 discriminating genes in the training set. The signature was successfully applied to the validation set, ROC area under the curve = 0.893, p < 0.0001. Using the optimal threshold (0.74) accurate class predictions were made for 77% of the test cases with sensitivity = 0.70, specificity = 1.00.

Conclusions: By using Bayesian probit regression to develop a model, we show that it is entirely possible to make a diagnosis of IPF from the peripheral blood with gene signatures.

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Related in: MedlinePlus

The peripheral blood gene signature. A heatmap displays the normalized expression values of 108 genes that comprise the model, derived from the peripheral blood of 17 normal controls and 59 subjects with IPF. A partial gene list (top ten genes) is displayed alongside the heatmap. The complete gene list (all 108 genes) is provided in the online supplement.
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Fig5: The peripheral blood gene signature. A heatmap displays the normalized expression values of 108 genes that comprise the model, derived from the peripheral blood of 17 normal controls and 59 subjects with IPF. A partial gene list (top ten genes) is displayed alongside the heatmap. The complete gene list (all 108 genes) is provided in the online supplement.

Mentions: The gene signature is visualized with a heatmap that highlights the necessary components of any functional gene signature: training samples (across the columns), gene features (across the rows) and the actual expression values (upon which the model is constructed) that are unique to this dataset (FigureĀ 5). The complete regression equation (with intercept and gene coefficients) is provided in the online supplement (Additional file 3). This regression equation can be used to predict the phenotype in any unknown sample. Thus, the diagnostic gene signature presented herein can be used to inform the diagnosis of IPF versus normal in any patient, using the gene expression profile derived from peripheral blood.Figure 5


Familial and sporadic idiopathic pulmonary fibrosis: making the diagnosis from peripheral blood.

Meltzer EB, Barry WT, Yang IV, Brown KK, Schwarz MI, Patel H, Ashley A, Noble PW, Schwartz DA, Steele MP - BMC Genomics (2014)

The peripheral blood gene signature. A heatmap displays the normalized expression values of 108 genes that comprise the model, derived from the peripheral blood of 17 normal controls and 59 subjects with IPF. A partial gene list (top ten genes) is displayed alongside the heatmap. The complete gene list (all 108 genes) is provided in the online supplement.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4288625&req=5

Fig5: The peripheral blood gene signature. A heatmap displays the normalized expression values of 108 genes that comprise the model, derived from the peripheral blood of 17 normal controls and 59 subjects with IPF. A partial gene list (top ten genes) is displayed alongside the heatmap. The complete gene list (all 108 genes) is provided in the online supplement.
Mentions: The gene signature is visualized with a heatmap that highlights the necessary components of any functional gene signature: training samples (across the columns), gene features (across the rows) and the actual expression values (upon which the model is constructed) that are unique to this dataset (FigureĀ 5). The complete regression equation (with intercept and gene coefficients) is provided in the online supplement (Additional file 3). This regression equation can be used to predict the phenotype in any unknown sample. Thus, the diagnostic gene signature presented herein can be used to inform the diagnosis of IPF versus normal in any patient, using the gene expression profile derived from peripheral blood.Figure 5

Bottom Line: Unsupervised clustering failed to discriminate between samples of different severity.The signature was successfully applied to the validation set, ROC area under the curve = 0.893, p < 0.0001.By using Bayesian probit regression to develop a model, we show that it is entirely possible to make a diagnosis of IPF from the peripheral blood with gene signatures.

View Article: PubMed Central - PubMed

Affiliation: Division of Allergy, Pulmonary, and Critical Care, Vanderbilt University Medical Center, 1313 21st Avenue South, 1105 Oxford House, Nashville, TN, USA. mark.p.steele@vanderbilt.edu.

ABSTRACT

Background: Peripheral blood biomarkers might improve diagnostic accuracy for idiopathic pulmonary fibrosis (IPF).

Results: Gene expression profiles were obtained from 89 patients with IPF and 26 normal controls. Samples were stratified according to severity of disease based on pulmonary function. The stratified dataset was split into subsets; two-thirds of the samples were selected to comprise the training set, while one-third was reserved for the validation set. Bayesian probit regression was used on the training set to develop a gene expression model for IPF versus normal. The gene expression model was tested by using it on the validation set to perform class prediction. Unsupervised clustering failed to discriminate between samples of different severity. Therefore, samples of all severities were included in the training and validation sets, in equal proportions. A gene signature model was developed from the training set. The model was built in an iterative fashion with the number of gene features selected to minimize the misclassification error in cross validation. The final model was based on the top 108 discriminating genes in the training set. The signature was successfully applied to the validation set, ROC area under the curve = 0.893, p < 0.0001. Using the optimal threshold (0.74) accurate class predictions were made for 77% of the test cases with sensitivity = 0.70, specificity = 1.00.

Conclusions: By using Bayesian probit regression to develop a model, we show that it is entirely possible to make a diagnosis of IPF from the peripheral blood with gene signatures.

Show MeSH
Related in: MedlinePlus