Breath biomarkers for lung cancer detection and assessment of smoking related effects — confounding variables, influence of normalization and statistical algorithms

• Published in: Clinica chimica acta Vol. 411; no. 21; pp. 1637 - 1644
• Main Authors: Kischkel, Sabine, Miekisch, Wolfram, Sawacki, Annika, Straker, Eva M., Trefz, Phillip, Amann, Anton, Schubert, Jochen K.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 11.11.2010
• Subjects: Adult; Aged; Algorithms; Biomarkers - analysis; Breath gas analysis; Breath Tests - methods; Case-Control Studies; Confounding variables; Data Interpretation, Statistical; Data processing; Female; Humans; Inspired concentrations; Lung cancer; Lung Neoplasms - diagnosis; Male; Reference Values; Research Design - standards; Smoking - adverse effects; SPME-GC-MS; Data processing; Confounding variables; Breath gas analysis; SPME-GC-MS; Inspired concentrations; Lung cancer
• ISSN: 0009-8981; 1873-3492
• DOI: 10.1016/j.cca.2010.06.005

Up to now, none of the breath biomarkers or marker sets proposed for cancer recognition has reached clinical relevance. Possible reasons are the lack of standardized methods of sampling, analysis and data processing and effects of environmental contaminants. Concentration profiles of endogenous and exogenous breath markers were determined in exhaled breath of 31 lung cancer patients, 31 smokers and 31 healthy controls by means of SPME-GC-MS. Different correcting and normalization algorithms and a principal component analysis were applied to the data. Differences of exhalation profiles in cancer and non-cancer patients did not persist if physiology and confounding variables were taken into account. Smoking history, inspired substance concentrations, age and gender were recognized as the most important confounding variables. Normalization onto PCO 2 or BSA or correction for inspired concentrations only partially solved the problem. In contrast, previous smoking behaviour could be recognized unequivocally. Exhaled substance concentrations may depend on a variety of parameters other than the disease under investigation. Normalization and correcting parameters have to be chosen with care as compensating effects may be different from one substance to the other. Only well-founded biomarker identification, normalization and data processing will provide clinically relevant information from breath analysis.