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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

Thetraining set. An unsupervised hierarchical cluster of peripheral blood gene expression from 17 normal subjects (blue) and 59 IPF subjects (red).
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Fig3: Thetraining set. An unsupervised hierarchical cluster of peripheral blood gene expression from 17 normal subjects (blue) and 59 IPF subjects (red).

Mentions: The training dataset was examined with hierarchical clustering. The training dataset consisted of 59 samples obtained from the peripheral blood of IPF subjects (including both sporadic and familial cases) and 17 samples obtained from the peripheral blood of normal subjects (Additional file 2). The training dataset was filtered for the top 90th percentile of coefficient of variation (resulting in a dataset with 2208 genes). Visual inspection of the hierarchical cluster could not distinguish between IPF patients and normal subjects (FigureĀ 3).Figure 3


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)

Thetraining set. An unsupervised hierarchical cluster of peripheral blood gene expression from 17 normal subjects (blue) and 59 IPF subjects (red).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: Thetraining set. An unsupervised hierarchical cluster of peripheral blood gene expression from 17 normal subjects (blue) and 59 IPF subjects (red).
Mentions: The training dataset was examined with hierarchical clustering. The training dataset consisted of 59 samples obtained from the peripheral blood of IPF subjects (including both sporadic and familial cases) and 17 samples obtained from the peripheral blood of normal subjects (Additional file 2). The training dataset was filtered for the top 90th percentile of coefficient of variation (resulting in a dataset with 2208 genes). Visual inspection of the hierarchical cluster could not distinguish between IPF patients and normal subjects (FigureĀ 3).Figure 3

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