Barash Orna, Zhang Wei, Halpern Jeffrey M, Hua Qing-Ling, Pan Yue-Yin, Kayal Haneen, Khoury Kayan, Liu Hu, Davies Michael P A, Haick Hossam
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.
Oncotarget. 2015 Dec 29;6(42):44864-76. doi: 10.18632/oncotarget.6269.
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.
由于对靶向治疗以及筛查高危人群进行早期诊断等方面的兴趣日益浓厚，乳腺癌（BC）肿瘤分子亚型的研究领域正在迅速发展。我们报告了一种基于挥发性有机化合物（VOCs）对BC分子亚型进行分析的新概念。为此，我们收集了276名女性志愿者的呼吸样本，包括健康者、良性疾病患者、原位导管癌（DCIS）患者和恶性病变患者。呼吸样本通过气相色谱 - 质谱联用仪（GC-MS）和人工智能纳米阵列技术进行分析。应用非参数Wilcoxon/Kruskal-Wallis检验，GC-MS分析发现23种化合物在不同分子亚型的BC患者呼吸样本中有显著差异（p < 0.05）。纳米阵列的判别函数分析（DFA）确定了癌症与非癌症病例之间独特的挥发组学特征（盲测准确率为83%），并且在留一法交叉验证中，对于不同分子亚型，准确率在82%至87%之间，灵敏度在81%至88%之间，特异性在76%至96%之间。这些结果表明，通过GC-MS识别特定化合物或通过对人工智能纳米阵列响应进行统计分析获得的挥发组学特征，存在可检测的呼吸VOC模式，能够准确分析BC的分子亚型。
