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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

Validation testing. (A) Unsupervised hierarchical clustering of the validation set, includes 9 peripheral blood samples from normal subjects (black) and 30 samples from IPF subjects (red). (B) Each sample from the validation set is assigned a probability of IPF, and a credible interval to that value (solid bars), by Bayesian modeling to the gene signature. Normal subjects (blue) tend to receive low probability scores while IPF subjects (red) tend to receive high probability scores. The optimal threshold for this test was determined by the Youden index (dotted line).
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Fig6: Validation testing. (A) Unsupervised hierarchical clustering of the validation set, includes 9 peripheral blood samples from normal subjects (black) and 30 samples from IPF subjects (red). (B) Each sample from the validation set is assigned a probability of IPF, and a credible interval to that value (solid bars), by Bayesian modeling to the gene signature. Normal subjects (blue) tend to receive low probability scores while IPF subjects (red) tend to receive high probability scores. The optimal threshold for this test was determined by the Youden index (dotted line).

Mentions: FigureĀ 6A shows hierarchical clustering of the validation dataset. The validation dataset includes 30 samples from IPF subjects (familial and sporadic) and 9 samples from normal subjects (Additional file 5). The validation dataset was filtered by coefficient of variation. Visual inspection shows that hierarchical clustering of the complete (filtered) validation dataset does not distinguish IPF from normal.Figure 6


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)

Validation testing. (A) Unsupervised hierarchical clustering of the validation set, includes 9 peripheral blood samples from normal subjects (black) and 30 samples from IPF subjects (red). (B) Each sample from the validation set is assigned a probability of IPF, and a credible interval to that value (solid bars), by Bayesian modeling to the gene signature. Normal subjects (blue) tend to receive low probability scores while IPF subjects (red) tend to receive high probability scores. The optimal threshold for this test was determined by the Youden index (dotted line).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig6: Validation testing. (A) Unsupervised hierarchical clustering of the validation set, includes 9 peripheral blood samples from normal subjects (black) and 30 samples from IPF subjects (red). (B) Each sample from the validation set is assigned a probability of IPF, and a credible interval to that value (solid bars), by Bayesian modeling to the gene signature. Normal subjects (blue) tend to receive low probability scores while IPF subjects (red) tend to receive high probability scores. The optimal threshold for this test was determined by the Youden index (dotted line).
Mentions: FigureĀ 6A shows hierarchical clustering of the validation dataset. The validation dataset includes 30 samples from IPF subjects (familial and sporadic) and 9 samples from normal subjects (Additional file 5). The validation dataset was filtered by coefficient of variation. Visual inspection shows that hierarchical clustering of the complete (filtered) validation dataset does not distinguish IPF from normal.Figure 6

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