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Molecular and functional extracellular vesicle analysis using nanopatterned microchips monitors tumor progression and metastasis

An inkjet-printed nanochip integrating multiparametric extracellular vesicle analyses monitors tumor burden via liquid biopsy. Probing plasma with patterned chips Liquid biopsy of blood or other biofluids has shown promise for cancer detection and monitoring response to treatment. Zhang et al. used colloidal inkjet printing to create nanopatterned polydimethylsiloxane/glass microfluidic chips to analyze extracellular vesicles (EVs) in plasma. The chips captured EVs expressing different surface markers of interest and measured the expression and activity of EV-bound MMP14. EVs derived from cancer cell lines in vitro and breast cancer tumors in mouse models and patients analyzed using chips revealed differential expression and activity of EV-bound MMP14 with metastasis or cancer stage. These chips provide a useful platform for characterizing EVs with implications for noninvasive cancer diagnosis and surveillance. Longitudinal cancer monitoring is crucial to clinical implementation of precision medicine. There is growing evidence indicating important functions of extracellular vesicles (EVs) in tumor progression and metastasis, including matrix remodeling via transporting matrix metalloproteases (MMPs). However, the clinical relevance of EVs remains largely undetermined, partially owing to challenges in EV analysis. Distinct from existing technologies mostly focused on characterizing molecular constituents of EVs, here we report a nanoengineered lab-on-a-chip system that enables integrative functional and molecular phenotyping of tumor-associated EVs. A generalized, high-resolution colloidal inkjet printing method was developed to allow robust and scalable manufacturing of three-dimensional (3D) nanopatterned devices. With this nanochip platform, we demonstrated integrative analysis of the expression and proteolytic activity of MMP14 on EVs to detect in vitro cell invasiveness and monitor in vivo tumor metastasis, using cancer cell lines and mouse models. Analysis of clinical plasma specimen showed that our technology could be used for cancer detection including accurate classification of age-matched controls and patients with ductal carcinoma in situ, invasive ductal carcinoma, or locally metastatic breast cancer in a training cohort (n = 30, 96.7% accuracy) and an independent validation cohort (n = 70, 92.9% accuracy). With clinical validation, our technology could provide a useful liquid biopsy tool to improve cancer diagnostics and real-time surveillance of tumor evolution in patients to inform personalized therapy.