Department of Bioengineering and ∥Department of Electrical and Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States.
Division of Gastroenterology and §Division of Hematology-Oncology, Department of Medicine, Perelman School of Medicine and ⊥Perelman School of Medicine, University of Pennsylvania , Philadelphia, Pennsylvania 19104, United States.
ACS Nano. 2017 Nov 28;11(11):11182-11193. doi: 10.1021/acsnano.7b05503. Epub 2017 Oct 17.
Circulating exosomes contain a wealth of proteomic and genetic information, presenting an enormous opportunity in cancer diagnostics. While microfluidic approaches have been used to successfully isolate cells from complex samples, scaling these approaches for exosome isolation has been limited by the low throughput and susceptibility to clogging of nanofluidics. Moreover, the analysis of exosomal biomarkers is confounded by substantial heterogeneity between patients and within a tumor itself. To address these challenges, we developed a multichannel nanofluidic system to analyze crude clinical samples. Using this platform, we isolated exosomes from healthy and diseased murine and clinical cohorts, profiled the RNA cargo inside of these exosomes, and applied a machine learning algorithm to generate predictive panels that could identify samples derived from heterogeneous cancer-bearing individuals. Using this approach, we classified cancer and precancer mice from healthy controls, as well as pancreatic cancer patients from healthy controls, in blinded studies.
循环外泌体包含丰富的蛋白质组学和遗传学信息，为癌症诊断提供了巨大的机会。虽然微流控方法已被用于成功地从复杂样本中分离细胞，但这些方法在用于外泌体分离时受到低通量和纳米流道易堵塞的限制。此外，外泌体生物标志物的分析受到患者之间和肿瘤内部存在的实质性异质性的影响。为了解决这些挑战，我们开发了一种多通道纳流控系统来分析原始临床样本。使用这个平台，我们从健康和患病的小鼠和临床队列中分离出外泌体，分析这些外泌体内部的 RNA 货物，并应用机器学习算法生成可以识别来自异质癌症患者的样本的预测面板。使用这种方法，我们在盲法研究中对癌症和癌前小鼠与健康对照进行了分类，对胰腺癌患者与健康对照进行了分类。
