Bronchoalveolar lavage proteomics in patients with suspected lung cancer

© The Author(s) 2017. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/Lung cancer configures as one of the deadliest types of cancer. The future implementation of early screening methods such as exhaled breath condensate analysis and low dose computed tomography (CT) as an alternative to current chest imaging based screening will lead to an increased burden on bronchoscopy units. New approaches for improvement of diagnosis in bronchoscopy units, regarding patient management, are likely to have clinical impact in the future. Diagnostic approaches to address mortality of lung cancer include improved early detection and stratification of the cancers according to its prognosis and further response to drug treatment. In this study, we performed a detailed mass spectrometry based proteome analysis of acellular bronchoalveolar lavage (BAL) fluid samples on an observational prospective cohort consisting of 90 suspected lung cancer cases which were followed during two years. The thirteen new lung cancer cases diagnosed during the follow up time period clustered, based on liquid chromatography-mass spectrometry (LC-MS) data, with lung cancer cases at the time of BAL collection. Hundred and thirty-tree potential biomarkers were identified showing significantly differential expression when comparing lung cancer versus non-lung cancer. The regulated biomarkers showed a large overlap with biomarkers detected in tissue samples.All experiments including MS analysis were supported by Fundação para a Ciência e Tecnologia project EXPL/DTP-PIC/0616/2013. RM is supported by FCT investigator program 2012 (IF/01002/2012). ASC is supported by grant SFRH/BPD/85569/2012 funded by Fundação para a Ciência e Tecnologia.