| Title: | Multimodal chemometric approach for the analysis of human exhaled breath in lung cancer patients by TD-GC?ª+x?ª+GC-TOFMS |
| Author(s): | Pesesse R; Stefanuto PH; Schleich F; Louis R; Focant JF; |
| Address: | "Organic and Biological Analytical Chemistry Group, MolSys Research Unit, University of Liege, B6c, Agora District, 4000 Liege, Belgium. Pneumology and Allergology, GIGA Research Group, CHU of Liege, University of Liege, B35, Hospital District, Liege, Belgium. Organic and Biological Analytical Chemistry Group, MolSys Research Unit, University of Liege, B6c, Agora District, 4000 Liege, Belgium. Electronic address: JF.Focant@uliege.be" |
| Journal Title: | J Chromatogr B Analyt Technol Biomed Life Sci |
| DOI: | 10.1016/j.jchromb.2019.01.029 |
| ISSN/ISBN: | 1873-376X (Electronic) 1570-0232 (Linking) |
| Abstract: | "Lung cancer is the deadliest cancer in developed countries. To reduce its mortality rate, it is important to enhance our capability to detect it at earlier stages by developing early diagnostic methods. In that context, the analysis of exhaled breath is an interesting approach because of the simplicity of the medical act and its non-invasiveness. Thermal desorption comprehensive two-dimensional gas chromatography time of flight mass spectrometry (TD-GC?ª+x?ª+GC-TOFMS) has been used to characterize and compare the volatile content of human breath of lung cancer patients and healthy volunteers. On the sampling side, the contaminations induced by the bags membrane and further environmental migration of VOCs during and after the sampling have also been investigated. Over a realistic period of 6?ª+h, the concentration of contaminants inside the bag can increase from 2 to 3 folds based on simulated breath samples. On the data processing side, Fisher ratio (FR) and random forest (RF) approaches were applied and compared in regards to their ability to reduce the data dimensionality and to extract the significant information. Both approaches allow to efficiently smooth the background signal and extract significant features (27 for FR and 17 for RF). Principal component analysis (PCA) was used to evaluate the clustering capacity of the different models. For both approaches, a separation along PC-1 was obtained with a variance score around 35%. The combined model provides a partial separation with a PC-1 score of 52%. This proof-of-concept study further confirms the potential of breath analysis for cancer detection but also underlines the importance of quality control over the full analytical procedure, including the processing of the data" |
| Keywords: | Aged Breath Tests/*methods Case-Control Studies Female Gas Chromatography-Mass Spectrometry/*methods Humans Lung Neoplasms/diagnosis/*metabolism Male Middle Aged Principal Component Analysis Specimen Handling Volatile Organic Compounds/*analysis Breath an; |
| Notes: | "MedlinePesesse, R Stefanuto, P-H Schleich, F Louis, R Focant, J-F eng Netherlands 2019/02/13 J Chromatogr B Analyt Technol Biomed Life Sci. 2019 May 1; 1114-1115:146-153. doi: 10.1016/j.jchromb.2019.01.029. Epub 2019 Jan 31" |
