Oxford success at the early detection sandpit on pancreatic cancer

Pancreatic cancer is a devastating disease with low survival rates that have hardly improved in the last 40 years. These cancers are very challenging to treat, in part due to their frequently late diagnosis when the cancer is already advanced.

To address this need for earlier detection, Cancer Research UK, Pancreatic Cancer UK and the Engineering and Physical Sciences Research Council convened a 3-day virtual workshop in November 2020. Multidisciplinary teams worked together to generate innovative research ideas for detecting pancreatic cancer earlier. At the end of the workshop, the teams pitched their ideas to receive seed funding for feasibility testing from a Cancer Research UK Early Detection Innovation Award. Two successful teams involved Oxford researchers.

Team ReTHOMS: Real-time high-sensitivity optrode metabolic sensor for pancreatic cyst fluids

Team ‘ReTHOMS’ includes Oxford’s Professor Eric O’Neill (Department of Oncology) who is working with Dr Paolo Bertoncello (Swansea University), Dr David Chang (University of Glasgow) and Dr George Gordon (University of Nottingham). The team aims to develop a new sensor device to detect malignant transformation in people with pancreatic cysts, a condition that puts them at higher risk of pancreatic cancer.

Pancreatic cysts are fluid-filled sacs on or in the pancreas that are mostly benign. However, 2-3% are precancerous and can develop into pancreatic cancer. Cysts are often identified incidentally and are then monitored for malignant transformation using either clinical imaging or analysis of the cyst fluid for cancer biomarkers such as mucins. Despite this surveillance regime, early cancers are still being missed since these methods have limited sensitivity and specificity.

To improve the early detection of malignant cyst transformation, the team aims to develop real-time and highly sensitive detection of an expanded range of cancer biomarkers. In addition to mucin, raised cellular levels of the chemical hydrogen peroxide are associated with cancer. So-called optrode technology will be used to detect hydrogen peroxide and mucin in cyst fluid. Optrodes are optical sensor devices that detect light emitted as a result of an electrochemical reaction with the biomarkers being analysed.

During this short project, the team will build the optrode device for measuring hydrogen peroxide and mucin, and undertake technical and biological validation. The longer-term aim of this research is to detect pancreatic cancer earlier by screening pancreatic cyst fluid at the point-of-care and determining further action based on the risk of cancer.

Team EDPAN: Earlier detection of pancreatic cancer through personalised assessment of risk combined with non-invasive infrared spectroscopy

Oxford’s Dr Pui San Tan (Nuffield Department of Primary Care Health Sciences) will work as part of team EDPAN with Dr Pilar Acedo Nunez (University College London), Dr Aida Santaolalla (King’s College London), Dr Paul Brennan (University of Edinburgh), Dr Lucy Oldfield (University of Liverpool), Dr Andrew Kunzmann (Queen’s University Belfast) and Dr Mohammad Golbabaee (University of Bath). This team aims to identify individuals at higher risk of pancreatic cancer for further diagnostic screening.

One of the reasons that pancreatic cancer is often diagnosed late is that symptoms are non-specific and cannot discriminate those that require investigation for pancreatic cancer. Team EDPAN will develop an approach for personalised risk stratification to identify individuals at higher risk that would benefit from more in-depth screening for pancreatic cancer.

During the project, the team will make use of existing cohorts for pancreatic cancer (ADEPTS (UCL) and PanDIA (Liverpool)) and larger cohorts for epidemiology research (UK Biobank, AMORIS). They will combine clinical and demographic information with analysis of serum and urine samples using a technique called infrared spectroscopy. They will also evaluate changes in immune components. These approaches aim to identify individuals at high-risk of pancreatic cancer and investigate whether addition of infrared spectroscopy data and immune analysis improves the accuracy of the risk prediction model.