A label-free and real-time Graphene bioSensor for exosome-driven point-of-care detection of early CANcers (Gr-SensorCAN)

Background Developing reliable, ultrasensitive, simple, real-time, and non-invasive cancer diagnostic tools is crucial to tackle cancer at very early stage. Meanwhile, findings of new biomarkers are essential for any progress in early stage cancer detection.

Exosomes and other smaller extracellular vesicles (30-200 nm) in the circulating blood have been identified as promising cancer biomarkers for early detection of diseases because they carry rich information about tumour cells and are released by the cells in high concentration.

Biosensors for exosome-derived detection are promising for non-invasive point-of-care testing of cancers because of their low-cost and fast detection.

Aims Based on our initial work on graphene field effect transistor (gFET) sensors for exosome detection, we propose to develop miniaturized chip-based sensors comprising smart sensing modality and electrical signal readout, as a universal technology platform for clinical cancer diagnosis through rapid and quantitative monitoring of valid protein markers carried by cancer derived exosomes.

Our detection technology can offer accurate, ultrasensitive, selective, and label-free detection of cancerous exosome biomarkers.

Methods The methodology of this project will be divided into four parts. 1) Manufacturing of gFET sensing chips: graphene sensor arrays will be designed and manufactured using thin film and cleanroom facilities available in the department.

Sensors will then be bio-functionalised with surface linker chemistry to attach bio-specific antibodies for antigen capture and recognition.

Microfluidic channels will be prepared and integrated onto the chip to form a sensing system for real time and label-free detection. 2) Detection of cancerous exosomes in buffer: pancreatic cancer (PC) cell derived exosomes will be characterized for positive cancer marker and then quantified by using gFET sensors.

Sensitivity, specificity, and detection limits will be investigated for optimization of the assay for optimum performance. 3) Collection of blood samples from patients: 5 mL of blood will be collected from various stages of PC patients, as well as healthy donors. 4) Characterizing patient sample using gFET sensors: The results of clinical PC testing will be compared with “Gold Standard Diagnostics” for the evaluation of the clinical potential of our method.

How the results of this research will be used. The research results will be published throughout open access referred journals.

The data gained such as images, graphs, numerical tables will also be made available for sharing with other scientists in a safe and feasible way. A patent will be filed, and possible spin-off opportunities will be explored after the project.