Exhaled breath condensate identifies metabolic dysregulation in patients with radiation-induced lung injury.

Radiation-induced lung injury (RILI) is a consequence of therapeutic thoracic irradiation (TR) for many cancers, and there are no FDA-approved curative strategies. Studies report that 80% of patients who undergo TR will have CT-detectable interstitial lung abnormalities, and strategies to limit the risk of RILI may make radiotherapy less effective at treating cancer. Our lab and others have reported that lung tissue from patients with idiopathic pulmonary fibrosis (IPF) exhibits metabolic defects including increased glycolysis and lactate production. In this pilot study, we hypothesized that patients with radiation-induced lung damage will exhibit distinct changes in lung metabolism that may be associated with the incidence of fibrosis. Using liquid chromatography/tandem mass spectrometry to identify metabolic compounds, we analyzed exhaled breath condensate (EBC) in subjects with CT-confirmed lung lesions after TR for lung cancer, compared with healthy subjects, smokers, and cancer patients who had not yet received TR. The lung metabolomic profile of the irradiated group was significantly different from the three nonirradiated control groups, highlighted by increased levels of lactate. Pathway enrichment analysis revealed that EBC from the case patients exhibited concurrent alterations in lipid, amino acid, and carbohydrate energy metabolism associated with the energy-producing tricarboxylic acid (TCA) cycle. Radiation-induced glycolysis and diversion of lactate to the extracellular space suggests that pyruvate, a precursor metabolite, converts to lactate rather than acetyl-CoA, which contributes to the TCA cycle. This TCA cycle deficiency may be compensated by these alternate energy sources to meet the metabolic demands of chronic wound repair. Using an "omics" approach to probe lung disease in a noninvasive manner could inform future mechanistic investigations and the development of novel therapeutic targets.NEW & NOTEWORTHY We report that exhaled breath condensate (EBC) identifies cellular metabolic dysregulation in patients with radiation-induced lung injury. In this pilot study, untargeted metabolomics revealed a striking metabolic signature in EBC from patients with radiation-induced lung fibrosis compared to patients with lung cancer, at-risk smokers, and healthy volunteers. Patients with radiation-induced fibrosis exhibit specific changes in tricarboxylic acid (TCA) cycle energy metabolism that may be required to support the increased energy demands of fibroproliferation.

A plasma miRNA-based classifier for small cell lung cancer diagnosis.

Introduction: Small cell lung cancer (SCLC) is characterized by poor prognosis and challenging diagnosis. Screening in high-risk smokers results in a reduction in lung cancer mortality, however, screening efforts are primarily focused on non-small cell lung cancer (NSCLC). SCLC diagnosis and surveillance remain significant challenges. The aberrant expression of circulating microRNAs (miRNAs/miRs) is reported in many tumors and can provide insights into the pathogenesis of tumor development and progression. Here, we conducted a comprehensive assessment of circulating miRNAs in SCLC with a goal of developing a miRNA-based classifier to assist in SCLC diagnoses. Methods: We profiled deregulated circulating cell-free miRNAs in the plasma of SCLC patients. We tested selected miRNAs on a training cohort and created a classifier by integrating miRNA expression and patients' clinical data. Finally, we applied the classifier on a validation dataset. Results: We determined that miR-375-3p can discriminate between SCLC and NSCLC patients, and between SCLC and Squamous Cell Carcinoma patients. Moreover, we found that a model comprising miR-375-3p, miR-320b, and miR-144-3p can be integrated with race and age to distinguish metastatic SCLC from a control group. Discussion: This study proposes a miRNA-based biomarker classifier for SCLC that considers clinical demographics with specific cut offs to inform SCLC diagnosis.