The metabolomic detection of lung cancer biomarkers in sputum. (April 2016)

• Record Type: Journal Article
• Authors: Cameron, Simon J.S.; Lewis, Keir E.; Beckmann, Manfred; Allison, Gordon G.; Ghosal, Robin; Lewis, Paul D.; Mur, Luis A.J.
• Is Part Of: Lung cancer. Volume 94(2016)
• Page Start: 88
• Page End: 95
• Publication Date: 2016-04
• Subjects: Lung cancer -- Metabolomics -- Biomarkers -- Sputum -- Polyamines -- Gangliosides; Lungs -- Cancer -- Periodicals; Lung Neoplasms -- Abstracts; Lung Neoplasms -- Periodicals; Poumons -- Cancer -- Périodiques; Lungs -- Cancer; Periodicals; Electronic journals; Electronic journals; 616.99424
• DOI: 10.1016/j.lungcan.2016.02.006
• Languages: English
• ISSNs: 0169-5002

Abstract

Highlights: We examine sputum for non-invasive metabolomic biomarkers for lung cancer. Metabolome profiles of sputum constructed using two mass spectrometry methods. A number of metabolites differentiated between clinical samples and healthy smokers. Biomarkers identified which differentiated between LC positive and negative. Sputum metabolome biomarkers have potential for a population screen for lung cancer. Abstract: Objectives: Developing screening and diagnosis methodologies based on novel biomarkers should allow for the detection of the lung cancer (LC) and possibly at an earlier stage and thereby increase the effectiveness of clinical interventions. Here, our primary objective was to evaluate the potential of spontaneous sputum as a source of non-invasive metabolomic biomarkers for LC status. Materials and methods: Spontaneous sputum was collected and processed from 34 patients with suspected LC, alongside 33 healthy controls. Of the 34 patients, 23 were subsequently diagnosed with LC (LC +, 16 NSCLC, six SCLC, and one radiological diagnosis), at various stages of disease progression. The 67 samples were analysed using flow infusion electrospray ion mass spectrometry (FIE-MS) and gas-chromatography mass spectrometry (GC–MS). Results: Principal component analysis identified negative mode FIE-MS as having the main separating power between samples from healthy and LC. Discriminatory metabolites were identified using ANOVA and Random Forest. Indications of potential diagnostic accuracy involved the use of receiver operating characteristic/area under the curve (ROC/AUC) analyses. This approach identified metabolites changes that were only observed with LC. Metabolites with AUC values of greater than 0.8 which distinguished between LC + /LC − binary classifications where identified and included Ganglioside GM1 which has previously been linked to LC. Conclusion: This study indicates that metabolomics based on sputum can yield metabolites that can be used as a diagnostic and/or discriminator tool. These could aid clinical intervention and targeted diagnosis of LC within an 'at risk' LC − population group. The use of sputum as a non-invasive source of metabolite biomarkers may aid in the development of an at-risk population screening programme for lung cancer or enhanced clinical diagnostic pathways.