This project aims to develop a method of diagnosing pancreatic cancer from the analysis of infrared (IR) signatures of blood. This approach has the potential to satisfy the urgent clinical need for an early diagnosis of this disease, a major problem that must be overcome in order to reducing mortality.
Pancreatic ductal adenocarcinoma (PDAC) is the most lethal of the common cancers, with overall five-year survival around 5% to 7%, and its incidence is disproportionally high in the Liverpool area. For 80% of patients, the diagnosis of PDAC comes after the disease has spread to the liver and other organs, limiting treatment options. When patients are eligible for surgery, work led by Liverpool and others show that the five-year survival reaches 30% and above, compared to <5% for unresectable patients. Thus, strategies to enhance early detection of PDAC are urgently required .
The PhD student will be a key member of a strong interdisciplinary team of physicists (Peter Weightman and Stephen Barrett) that have recently developed a patented machine learning algorithm (MLA) for the diagnosis of oral cancer [2,3] and pancreatologists (Eithne Costello, Christopher Halloran and Pedro Perez-Mancera) with a long track record of research on pancreatic cancer.
The physics group have shown that the application of the MLA to infrared spectral images of tissue can predict whether oral lesions will become malignant with an accuracy of 80% [4,5]. This is a significant advance on current histopathological techniques which at best are only accurate to 40%, less than tossing a coin and failing 60% of patients. This research was funded by Cancer Research UK and the National Institute of Health and Care research has recently made an award to develop prototype of a device, the Liverpool Diagnostic Infrared Wand (LDIR Wand), to translate this advance into clinic. If this PhD research project is successful in developing a diagnostic for PDAC from the analysis of blood then the LDIR Wand would be cheap and accurate method of exploiting this advance.
The key to this project is that the pancreatologists, who have been working on pancreatic cancer for >20 years [6,7], have developed a large biobank of samples from over 4,500 patients treated for pancreatic disorders and additional samples will be available from relevant disease and healthy control subjects.
The strategy is to use IR spectroscopy to detect exosomes (EVs) in blood. EVs are found in many body fluids and which have been shown to carry macromolecular signatures, including nucleic acids, proteins and metabolites, specific to their cell of origin from their release point to distant parts of the body. Evidence suggests that they are important in intercellular communication under normal physiological conditions and in many disorders, including cancer. Their presence in extracellular fluids make them ideal as a source of biomarkers in liquid biopsies and they thus have merit in the development of high throughput, minimally invasive preliminary screening tests. It is expected that the sensitivity and specificity of biomarkers based solely on data from EVs will not be sufficient in themselves to categorically identify patients with underlying PDAC. Rather, it is intended that the IR biomarkers will augment the current biomarkers of early detection by the production of a multivariate model.
Please apply via this link and ensure you quote the following reference on your application: PPPR035 - Early diagnosis of pancreatic cancer utilizing an IR fingerprint of exosomes in blood.
Open to students worldwide
An application has been submitted to the EPSRC DTP allocation of PhD studentships to the condensed matter group of the physics department of the University of Liverpool. The project will be strongly interdisciplinary by design, and aligns with the faculty ‘healthcare technology’ theme.
1 S.P. Pereira et. al. Lancet Gastroenterol Hepatol. 7 698 (2020)
2 J. Ingham et. al. Infrared Physics and Technology 102 103007 (2019)
3 J. Ingham et. al. WIPO Patent Application: PCT/GB2019/050998 5/4/19.
4 B.G. Ellis et. al. PLoS One 17 e0266043 (2022)
5 J. Ingham et. al. IOP SciNotes 3 034001 (2022)
6 L. N. Barrera et. al. Cancer Res. 80 2861 (2020)
7 C. Jenkinson et. al. Clin. Can. Res. 22 1734 (2016)