Monell Chemical Senses Center, Philadelphia, Pennsylvania, United States of America.
PLoS One. 2010 Jan 27;5(1):e8819. doi: 10.1371/journal.pone.0008819.
A potential strategy for diagnosing lung cancer, the leading cause of cancer-related death, is to identify metabolic signatures (biomarkers) of the disease. Although data supports the hypothesis that volatile compounds can be detected in the breath of lung cancer patients by the sense of smell or through bioanalytical techniques, analysis of breath samples is cumbersome and technically challenging, thus limiting its applicability. The hypothesis explored here is that variations in small molecular weight volatile organic compounds ("odorants") in urine could be used as biomarkers for lung cancer. To demonstrate the presence and chemical structures of volatile biomarkers, we studied mouse olfactory-guided behavior and metabolomics of volatile constituents of urine. Sensor mice could be trained to discriminate between odors of mice with and without experimental tumors demonstrating that volatile odorants are sufficient to identify tumor-bearing mice. Consistent with this result, chemical analyses of urinary volatiles demonstrated that the amounts of several compounds were dramatically different between tumor and control mice. Using principal component analysis and supervised machine-learning, we accurately discriminated between tumor and control groups, a result that was cross validated with novel test groups. Although there were shared differences between experimental and control animals in the two tumor models, we also found chemical differences between these models, demonstrating tumor-based specificity. The success of these studies provides a novel proof-of-principle demonstration of lung tumor diagnosis through urinary volatile odorants. This work should provide an impetus for similar searches for volatile diagnostic biomarkers in the urine of human lung cancer patients.
诊断肺癌（癌症相关死亡的主要原因）的一种潜在策略是确定疾病的代谢特征（生物标志物）。尽管有数据支持这样的假设，即通过嗅觉或通过生物分析技术可以检测到肺癌患者呼吸中的挥发性化合物，但呼吸样本的分析既繁琐又具有技术挑战性，因此限制了其适用性。这里探讨的假设是，尿液中小分子挥发性有机化合物（“气味剂”）的变化可以用作肺癌的生物标志物。为了证明挥发性生物标志物的存在和化学结构，我们研究了小鼠嗅觉引导行为和尿液中挥发性成分的代谢组学。可以对传感器小鼠进行训练，以区分有无实验肿瘤的小鼠的气味，这证明了挥发性气味剂足以识别携带肿瘤的小鼠。与这一结果一致，对尿液挥发物的化学分析表明，几种化合物的数量在肿瘤和对照小鼠之间存在显著差异。使用主成分分析和有监督的机器学习，我们可以准确地区分肿瘤和对照组，这一结果通过新的测试组进行了交叉验证。尽管在两种肿瘤模型中，实验动物和对照动物之间存在共同的差异，但我们也发现了这些模型之间的化学差异，证明了基于肿瘤的特异性。这些研究的成功为通过尿液挥发性气味进行肺癌肿瘤诊断提供了一个新颖的原理验证。这项工作应该为在人类肺癌患者的尿液中寻找挥发性诊断生物标志物提供类似的动力。
