Microfluidic platforms for extracellular vesicle isolation, analysis and therapy in cancer.
Catarina M AbreuBruno Costa SilvaRui Luis ReisSubhas C KunduDavid CaballeroPublished in: Lab on a chip (2022)
Extracellular vesicles (EVs) are small lipidic particles packed with proteins, DNA, messenger RNA and microRNAs of their cell of origin that act as critical players in cell-cell communication. These vesicles have been identified as pivotal mediators in cancer progression and the formation of metastatic niches. Hence, their isolation and analysis from circulating biofluids is envisioned as the next big thing in the field of liquid biopsies for early non-invasive diagnosis and patient follow-up. Despite the promise, current benchtop isolation strategies are not compatible with point-of-care testing in a clinical setting. Microfluidic platforms are disruptive technologies capable of recovering, analyzing, and quantifying EVs within clinical samples with limited volume, in a high-throughput manner with elevated sensitivity and multiplexing capabilities. Moreover, they can also be employed for the controlled production of synthetic EVs and effective drug loading to produce EV-based therapies. In this review, we explore the use of microfluidic platforms for the isolation, characterization, and quantification of EVs in cancer, and compare these platforms with the conventional methodologies. We also highlight the state-of-the-art in microfluidic approaches for EV-based cancer therapeutics. Finally, we analyze the currently active or recently completed clinical trials involving EVs for cancer diagnosis, treatment or therapy monitoring and examine the future of EV-based point-of-care testing platforms in the clinic and EV-based therapy production by the industry.
Keyphrases
- single cell
- papillary thyroid
- high throughput
- clinical trial
- squamous cell
- cell therapy
- circulating tumor cells
- squamous cell carcinoma
- primary care
- randomized controlled trial
- lymph node metastasis
- emergency department
- stem cells
- small molecule
- ionic liquid
- deep learning
- childhood cancer
- current status
- mesenchymal stem cells
- machine learning
- combination therapy
- electronic health record
- ultrasound guided