A scalable "lab-on-a-chip" technology based on inkjet printing methods detected breast cancer in plasma samples from patients with more than 90% accuracy, according to a new study. The new chips, which sniffed out both early-stage and metastatic tumors when tested using plasma from 100 individuals, could one day allow clinicians to detect new cancers and monitor existing tumors less invasively compared with current diagnostic methods.
Catching cancers during their early stages is key to achieve the best clinical outcomes, as advanced tumors that have metastasized are far more difficult to eradicate. But current cancer detection techniques suffer from a plethora of drawbacks: standard tissue biopsies are invasive and cannot be repeated, and imaging techniques such as radiography fail to capture important changes in the growth of tumors. A liquid biopsy - a test that detects cancers with a simple blood or plasma draw - would overcome many of these issues, but despite years of research there is still not a liquid biopsy test that has entered widespread clinical practice.
Peng Zhang and colleagues have now taken a step toward liquid biopsies with their EV-CLUE chip, a device that they manufactured using colloidal inkjet printing methods. The chip works by capturing extracellular vesicles - tiny structures that ferry molecules between cells - and scanning for the presence and activity of MMP14, an enzyme that has been linked to the progression and metastasis of tumors. EV-CLUE successfully differentiated between controls and patients with early-stage or metastatic breast cancer with an accuracy of 96.7% in an initial group of 30 individuals and an accuracy of 92.9% in a second group of 70 people. Zhang et al. add that their printing technique's robustness and scalability will allow for clinical studies with larger numbers of patients in the future.
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Journal reference:
Zhang, P., et al. (2020) Molecular and functional extracellular vesicle analysis using nanopatterned microchips monitors tumor progression and metastasis. Science Translational Medicine. doi.org/10.1126/scitranslmed.aaz2878.