Combining Machine Learning and Nanofluidic Technology To Diagnose Pancreatic Cancer Using Exosomes

Abstract

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.