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The efficacy of breath volatile organic compounds (VOCs) analysis for the screening of patients bearing breast cancer lesions has been demonstrated by using gas chromatography and artificial olfactory systems. On the other hand, in-vitro studies suggest that VOCs detection could also give important indications regarding molecular and tumorigenic characteristics of tumor cells. Aim of this study was to analyze VOCs in the headspace of breast cancer cell lines in order to ascertain the potentiality of VOCs signatures in giving information about these cells and set-up a new sensor system able to detect breast tumor-associated VOCs. We identified by Gas Chromatography-Mass Spectrometry analysis a VOCs signature that discriminates breast cancer cells for: i) transformed condition; ii) cell doubling time (CDT); iii) Estrogen and Progesterone Receptors (ER, PgR) expression, and HER2 overexpression. Moreover, the signals obtained from a temperature modulated metal oxide semiconductor gas sensor can be classified in order to recognize VOCs signatures associated with breast cancer cells, CDT and ER expression. Our results demonstrate that VOCs analysis could give clinically relevant information about proliferative and molecular features of breast cancer cells and pose the basis for the optimization of a low-cost diagnostic device to be used for tumors characterization.

Results collected in more than 20 years of studies suggest a relationship between the volatile organic compounds exhaled in breath and lung cancer. However, the origin of these compounds is still not completely elucidated. In spite of the simplistic vision that cancerous tissues in lungs directly emit the volatile metabolites into the airways, some papers point out that metabolites are collected by the blood and then exchanged at the air-blood interface in the lung. To shed light on this subject we performed an experiment collecting both the breath and the air inside both the lungs with a modified bronchoscopic probe. The samples were measured with a gas chromatography-mass spectrometer (GC-MS) and an electronic nose. We found that the diagnostic capability of the electronic nose does not depend on the presence of cancer in the sampled lung, reaching in both cases an above 90% correct classification rate between cancer and non-cancer samples. On the other hand, multivariate analysis of GC-MS achieved a correct classification rate between the two lungs of only 76%. GC-MS analysis of breath and air sampled from the lungs demonstrates a substantial preservation of the VOCs pattern from inside the lung to the exhaled breath.

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