« Previous AbstractClassification of lung cancer histology by gold nanoparticle sensors    Next AbstractAnalytical and sensorial characterization of the aroma of wines produced with sour rotten grapes using GC-O and GC-MS: identification of key aroma compounds »

Oncotarget

Title:		Differentiation between genetic mutations of breast cancer by breath volatolomics
Author(s):		Barash O; Zhang W; Halpern JM; Hua QL; Pan YY; Kayal H; Khoury K; Liu H; Davies MP; Haick H;
Address:		"Department of Chemical Engineering and Russel Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, Israel. Department of Oncology, The First Affiliated Hospital of Anhui Medical University, Anhui, China. Present Address: Department of Chemical Engineering, University of New Hampshire, Durham, NH, USA. Molecular & Clinical Cancer Medicine, University of Liverpool, Cancer Research Centre, Liverpool, United Kingdom"
Journal Title:		Oncotarget
Year:		2015
Volume:		6
Issue:		42
Page Number:		44864 - 44876
DOI:		10.18632/oncotarget.6269
ISSN/ISBN:		1949-2553 (Electronic) 1949-2553 (Linking)
Abstract:		"Mapping molecular sub-types in breast cancer (BC) tumours is a rapidly evolving area due to growing interest in, for example, targeted therapy and screening high-risk populations for early diagnosis. We report a new concept for profiling BC molecular sub-types based on volatile organic compounds (VOCs). For this purpose, breath samples were collected from 276 female volunteers, including healthy, benign conditions, ductal carcinoma in situ (DCIS) and malignant lesions. Breath samples were analysed by gas chromatography mass spectrometry (GC-MS) and artificially intelligent nanoarray technology. Applying the non-parametric Wilcoxon/Kruskal-Wallis test, GC-MS analysis found 23 compounds that were significantly different (p < 0.05) in breath samples of BC patients with different molecular sub-types. Discriminant function analysis (DFA) of the nanoarray identified unique volatolomic signatures between cancer and non-cancer cases (83% accuracy in blind testing), and for the different molecular sub-types with accuracies ranging from 82 to 87%, sensitivities of 81 to 88% and specificities of 76 to 96% in leave-one-out cross-validation. These results demonstrate the presence of detectable breath VOC patterns for accurately profiling molecular sub-types in BC, either through specific compound identification by GC-MS or by volatolomic signatures obtained through statistical analysis of the artificially intelligent nanoarray responses"
Keywords:		"Adult Aged Artificial Intelligence Biomarkers, Tumor/*genetics/*metabolism Breast Neoplasms/*genetics/*metabolism Breath Tests/*methods Carcinoma, Intraductal, Noninfiltrating/*genetics/*metabolism Case-Control Studies Diagnosis, Differential Female *Gas;"
Notes:		"MedlineBarash, Orna Zhang, Wei Halpern, Jeffrey M Hua, Qing-Ling Pan, Yue-Yin Kayal, Haneen Khoury, Kayan Liu, Hu Davies, Michael P A Haick, Hossam eng Research Support, Non-U.S. Gov't 2015/11/06 Oncotarget. 2015 Dec 29; 6(42):44864-76. doi: 10.18632/oncotarget.6269"