Research
Research Grants:
Oldfield, L., "Advancing biomarkers and screening devices for earlier detection of pancreatic cancer". Jan 2025 £298,000 Pancreatic Cancer UK Career Foundation Fellowship
Costello, E., Oldfield, L., Halloran, C., Greenhalf, W., Slupsky, J., Evans, E. “Dissecting the biology underpinning pancreatic cancer-associated diabetes”. June 2021 £100,000 Pancreatic Cancer UK Innovation Fund Award
Chouhan, M., Hoskins, C., Chang, D., Oldfield, L., Acedo Nunez, P., Ni, M., Golbabaee, M. “Early detection of pre-malignant pancreatic cancer using multifunctional targeted nanoparticles for MR imaging with super-resolution reconstruction and MR fingerprinting”. May 2021 £100,000 Cancer Research UK Sandpit Innovation Award
Acedo Nunez, P., Santa Olalla, A., Kunzmann, A., Tan, P., Golbabaee, M., Oldfield, L., Brennan, P. “Earlier detection of pancreatic cancer through personalised assessment of risk combined with non-invasive infrared spectroscopy”. May 2021 £99,997 Cancer Research UK Sandpit Innovation Award
Goldring, C., Lister, A., Oldfield, L., Evans, A., Costello, E., Plagge, A., Owen, A. “Development of a high-throughput in vitro compound screening assay for the determination of anti-SARS CoV-2 activity”. July 2020 £18,078 BBSRC IAA: COVID-19 Award
Oldfield, L., Costello, E., Greenhalf, W., Halloran, C., and Psarelli, E. “Application of deep proteomics using an aptamer-based technology to advance early detection of pancreatic cancer”. Sept 2020 £104,000 CRUK Primer Award
Costello, E., Halloran, C., Greenhalf, W., Ghaneh, P., Palmer, D., Oldfield, L., Van Der Meer, R., Alison, L., Purewal, T. and Psarelli, E. “Detecting pancreatic cancer in the largest high-risk group for this disease: a top-down approach”. April 2019 £2,180,000 Cancer Research UK Programme Award
Oldfield, L. “Applying state-of-the art mass spectrometry-based proteomic techniques and quantitative methodologies to the search for novel biomarkers of pancreatic cancer”. Sept 2016 €3,000 EU COST Research Exchange Grant
Application of deep proteomics using an aptamer-based technology to advance early detection of pancreatic cancer
Aim
To identify protein biomarkers that distinguish type 3c diabetes (which includes PDAC-associated) from type 2 diabetes, paving the way for the development of diagnostic tools capable of selecting individuals for PDAC screening at the point of diagnosis of diabetes.
Using an aptamer-based platform, 210 plasma samples were analysed from key case/control groups including PDAC +/- diabetes, chronic pancreatitis +/- diabetes, NOD and long-standing diabetes (Figure 1). Over 7,500 proteins, from >6500 genes were identified per sample. Univariate analysis incorporating false discovery rate (FDR) <0.05 and fold change (FC) >1.5 thresholds identified proteins that were significantly differentially expressed between 1) type-3c and NOD, 2) PDAC and NOD (Figure 2a-b). A classifier was subsequently trained for the distinction of type-3c from type-2 diabetes. An ensemble of models was created using a bootstrapping approach, and only biomarkers appearing in 90% of runs were carried forward. Predictive modelling (random forest) of the 6 highest ranked proteins (Figure 2d) yielded a model, which when tested in an independent subset of discovery samples gave an accuracy of 0.84, with a sensitivity of 0.91 and specificity of 0.79 for the distinction of type 3c from type 2 diabetes.
This work is being continued as part of a Pancreatic Cancer UK fellowship
UK Early Detection Initiative for Pancreatic Cancer (UK-EDI)
Introduction: Pancreatic cancer is a leading cause of cancer deaths worldwide. Screening for this disease has potential to improve survival. It is not feasible, with current screening modalities, to screen the asymptomatic adult population. However, screening of individuals in high-risk groups is recommended. Our study aims to provide resources and data that will inform strategies to screen individuals with new-onset diabetes (NOD) for pancreatic cancer.
Methods and analysis: The UK-EDI study is a national, prospective, observational cohort study that aims to recruit 2,500 individuals with NOD (< 6 months post-diagnosis) aged 50 years and over, with follow-up every 6 months, over a 3-year period. For study eligibility, diagnosis of diabetes is considered to be clinical measurement of HbA1c ≥ 48 mmol/mol. Detailed clinical information and biospecimens will be collected at baseline and follow-up to support the development of molecular, epidemiological and demographic biomarkers for earlier detection of pancreatic cancer in the high-risk NOD group. Socio-economic impacts and cost-effectiveness of earlier detection of pancreatic cancer in individuals with NOD will be evaluated. The UK-EDI NOD cohort will provide a bio-resource for future early detection research to be conducted.
Advancing biomarkers and screening devices for earlier detection of pancreatic cancer
Mass spectrometry (MS)-based multiple reaction monitoring (MRM) methods will be established to advance the development of a 6-panel biomarker discovered by Oldfield et. al. This biomarker has demonstrated capacity to identify type 3c diabetes (which harbours PDAC) from among individuals newly diagnosed with type 2 diabetes (accuracy 0.84; sensitivity/specificity 0.90/0.79). Secondary specialist screening and surveillance of the type 3c group would support detection of PDAC from benign type 3c disease. Using samples from independent relevant cohorts containing of type 3c (PDAC and CP-related) diabetes and type 2 (longstanding and new-onset) diabetes, a predictive model will be trained and calibrated. Data generated will support the evidence-base for assessment of the 6-panel biomarker in pre-diagnostic human cohorts.
To support research and future screening strategies for PDAC, MS-based proteomics methodologies will be utilised to develop multi-analyte quantitative measurement of proteins from dried blood spots (DBS). DBS sampling has potential to transform biomarker development and translation by simplifying blood collection and enhancing accessibility. Using untargeted MS-proteomics I will evaluate extraction/desorption efficiencies and protein coverage using commercial devices, and will explore modified materials for enhanced protein stabilisation (trypsin embedded, gel adsorption). Following DBS optimisation, MS-(MRM) methods will be applied to evaluate targeted multi-protein quantitate analysis.
Research grants
PaNanoMRI - Early detection of pre-malignant pancreatic cancer using multifunctional targeted nanoparticles for MR imaging with super-resolution and MR fingerprinting
CANCER RESEARCH UK (UK)
May 2021 - December 2023
Dissecting the biology underpinning pancreatic cancer-associated diabetes
PANCREATIC CANCER UK (UK)
September 2021 - September 2024