Unknown,Transcriptomics,Genomics,Proteomics

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Validated Gene Expression Signatures of Idiopathic Pulmonary Fibrosis

ABSTRACT: Idiopathic pulmonary fibrosis (IPF) is a chronic fibrosing lung disease that is difficult to diagnose and follows an unpredictable clinical course. The object of this study was to develop a predictive gene signature model of IPF from whole lung tissue. We collected whole lung samples from 11 IPF patients undergoing diagnostic surgical biopsy or transplantation. Whenever possible, samples were obtained from different lobes. Normals consisted of healthy organs donated for transplantation. We measured gene expression on microarrays. Data were analyzed by hierarchical clustering and Principal Component Analysis. By this approach, we found that gene expression was similar in the upper and lower lobes of individuals with IPF. We also found that biopsied and explanted specimens contained different patterns of gene expression; therefore, we analyzed biopsies and explants separately. Signatures were derived by fitting top genes to a Bayesian probit regression model. We developed a 153-gene signature that discriminates IPF biopsies from normal. We also developed a 70-gene signature that discriminates IPF explants from normal. Both signatures were validated on an independent cohort. The IPF Biopsy signature correctly diagnosed 76% of the validation cases (p < 0.01), while IPF Explant correctly diagnosed 78% (p < 0.001). Examination of differentially expressed genes revealed partial overlap between IPF Biopsy and IPF Explant and almost no overlap with previously reported IPF gene lists. However, several overlapping genes may provide a basis for developing therapeutic targets. 17 samples from 11 patients with IPF (6 patients provided a pair of samples from upper and lower lobes; 5 patients contributed singleton samples); 6 control specimens were obtained from routine lung volume reduction of healthy donor lungs at the time of lung transplantation.

ORGANISM(S): Homo sapiens

SUBMITTER: Eric Meltzer

PROVIDER: E-GEOD-24206 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

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

Meltzer Eric B EB Barry William T WT D'Amico Thomas A TA Davis Robert D RD Lin Shu S SS Onaitis Mark W MW Morrison Lake D LD Sporn Thomas A TA Steele Mark P MP Noble Paul W PW

BMC medical genomics 20111005

<h4>Background</h4>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.<h4>Methods</h4>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 explor ...[more]

PMID: 21974901

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Project description:Rationale: The diagnosis of idiopathic pulmonary fibrosis (IPF) requires exclusion of an underlying autoimmune disease, as present in interstitial lung diseases associated with connective tissue diseases (CTD-ILD). The prevalence of autoantibodies in IPF patients is currently unknown. Objectives: An unbiased assay for de novo discovery of autoantigens can help characterizing autoreactivities in IPF patients beyond clinically established autoimmune panels. Methods: We developed the proteomic Differential Antigen Capture (DAC) assay, capturing patient antibodies from plasma, followed by affinity purification coupled to mass spectrometry (AP-MS). The DAC assay quantifies the binding capacity of patient antibodies to proteins in a pooled native extract from lung explants (ILD explants n=41; donor controls n=12). Plasma antibodies from patients with IPF (n=35), CTD-ILD (n=24) and age-matched controls (n=32) were analyzed and validated in an independent cohort (IPF: n=40; CTD-ILD: n=20). Plasma antibody binding profiles were associated with clinical meta-data including diagnosis, lung function and transplant free survival. Measurements and Main Results: We identified 586 putative autoantigens in both study cohorts with a broad heterogeneity among disease entities and cohorts. On average, in IPF a mean±SD of 16±40 autoantigens and in CTD-ILD a mean±SD of 9±15 autoantigens were identified per patient. We identified 18 IPF-specific autoantigens validated in the second cohort. Interestingly, there was also a high number of shared autoantigens in IPF and CTD-ILD patients, with 17 being present in IPF and CTD-ILD of both cohorts. Presence of antibodies to Thrombospondin 1 (THBS1) and tubulin beta-1 chain (TUBB1) was associated with a significantly reduced survival in patients with IPF (p=0.002 and p=0.019, respectively). This signature was often associated with autoreactivity against talin-1 (TLN1), latent-transforming growth factor beta-binding protein 1 (LTBP1), epididymis secretory protein Li 112 (HEL-S-112), zyxin (ZYX), the LIM and senescent cell antigen-like-containing domain protein 1 (LIMS1) and caldesmon (CALD1). Conclusions: Unbiased proteomic profiling reveals that the overall prevalence of autoantibodies is similar in IPF and CTD-ILD patients and identifies novel IPF specific autoantigens associated with patient survival.

Dataset Information

Validated Gene Expression Signatures of Idiopathic Pulmonary Fibrosis

Publications

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

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