Biomarkers Help Predict Outcome in Deadly Lung Disease

A gene expression profile has been identified that can predict outcomes and lead to better treatment for one of the most lethal lung diseases, idiopathic pulmonary fibrosis (IPF).

In most cases of IPF the cause cannot be identified, and there is no cure other than a lung transplant and while some patients experience a progressive course that leads to death within one to two years, others experience a relative stable disease.


Scientists from Yale University (New Haven, CT, USA) with colleagues from other institutions recruited a discovery cohort of 45 patients from the University of Chicago (IL, USA) and 75 patients in a replication cohort from the University of Pittsburgh (PA, USA). The goal of the study was to identify changes in expression of genes in the blood that are predictive of poor outcomes among patients with IPF.

The teams analyzed the expression of the genes in the whole genome of patients with IPF, using peripheral blood mononuclear cell (PBMC). They identified 52 genes that significantly correlated with outcome. They further found that the decreased expression of four genes that encode for cluster of differentiation 28 (CD28), inducible T-cell co-stimulator (ICOS), lymphocyte-specific protein tyrosine kinase (LCK), and interleukin 2 (IL2)-inducible T-cell kinase (ITK). These genes predicted shorter survival time in patients with IPF.

The investigators believe the discovery of these biomarkers will help physicians better predict disease presence, severity, and prognosis in IPF patients. Naftali Kaminski, MD, a professor at Yale School of Medicine and a senior author of the study said, “Given the fact that lung transplantation is the only therapy that has shown to improve survival in IPF, our test could allow physicians to refer IPF patients for lung transplant at the right time, not too late and not too early.”

The author concluded that microarray-derived 52-gene expression profile or quantitative reverse transcription polymerase chain reaction (qRT-PCR) of CD28, ICOS, LCK, and ITK members of this signature was sufficient to identify IPF patients destined for poor outcomes. Combining gene expression data with clinical parameters enhanced outcome prediction; thus, the results could have considerable value in clinical evaluations and management of patients with this devastating lung disease. The study was published on October 2, 2013, in the journal Science Translational Medicine.

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