A novel sensor platform for early detection of pancreatic cancer

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This project aims to develop a point-of-care diagnostic tool for the early-onset detection of pancreatic cancer. Pancreatic cancer is predicted to become the second leading cause of cancer-death in the next few years. It is mostly asymptomatic, and currently 80% of cases are diagnosed at an advanced stage. Late detection leads to an extremely poor survival prognosis, with average survival of around 5 months after diagnosis. An analysis methodology that leads to early diagnosis, such as that proposed here, will have a profoundly positive effect on pancreatic cancer survival. When tumours are present in the body, specific proteins, indicative of disease progression, are produced and appear in the blood. These proteins, referred to as biomarkers, are indicators of disease. In pancreatic cancer there are two significant biomarkers: CA19-9 and CEA. Unfortunately, these biomarkers alone cannot be used to make a diagnosis in the general population. However, a large number of other proteins have been identified and linked to disease progression. For our project, 30 biomarkers indicative of pancreatic cancer have been carefully selected. Current methods to identify key biomarkers lack the necessary sensitivity and specificity for the detection of early-stage pancreatic cancer, are time consuming to run, and require skilled operators to ensure results are reliable. Therefore, a new approach is needed to achieve early onset pancreatic cancer detection. Effective point-of-care diagnosis will significantly reduce preventable cases. Here we propose to develop an integrated sensor platform that makes measurements indicating the presence of biomarkers using novel sensors. It will then make use of machine leaning approaches to combine these measurements with secondary data, to enhance diagnosis. The secondary data will include 'risk' factors from patient medical history, such as having diabetes. To detect biomarkers at the very low levels they manifest themselves at pancreatic cancer onset, we propose to design a novel sensitive and selective transistor-based sensor system. Normally transistors are operated by directly applying an electrical signal to their channels. Here the sensor system will be based upon an array of transistors which are tuned for the detection of specific biomarkers by using "aptamers" placed onto their channels. The detection process relies upon on specific biomarkers binding to aptamers, which acts like an input signal to change the overall transistor electrical characteristics, which can be subsequently measured. To ensure effective and reliable biomarker detection, it is essential the transistor sensors are built in a consistent fashion, especially regarding aptamer-loaded channel construction, since this greatly affects operation. To achieve this, a 3D-bioprinter will be used to deliver controlled volumes of aptamers to the transistor in an automated fashion. The experimental phase of the research will systematically progress from simple to complex detection tasks. Phase 1 of will focus on creating the sensors for initial characterisation studies. In the second phase, sensor capacity to detect known biomarker concentrations will determine the sensor sensitivity and detection limits. The final stage 3 will examine biomarker detection in serum samples from patients with pancreatic cancer at varying stages of disease progression, as well as healthy controls. Sensor data will be combined with risk-life factors to train a machine learning system to detect the presence of pancreatic cancer. Finally, we will evaluate sensor performance as a diagnostic tool to predict early-onset pancreatic cancer.