Using GP data to improve cancer survival rates

Julia Hippisley–Cox is Professor of Clinical Epidemiology and General Practice based in the Nuffield Department of Primary Healthcare. Julia trained in Medicine at Sheffield University and held a number of academic positions before being appointed full professor at the University of Nottingham moving to Oxford in 2019. Julia’s research interests include large-scale clinical epidemiology, drug safety and the development of risk prediction algorithms using electronic databases from general practices. She is the co-founder of the QResearch database which is one of the largest clinical research databases worldwide.

The UK has one of the poorest survival rates for cancer in Europe. This is thought to be partly related to late presentation, and delays in diagnosis and treatment. Earlier diagnosis could improve with more targeted investigation of symptomatic patients and increased public awareness of symptoms. It has been estimated that such an approach may save 5000 lives a year without any new medical advances. Current screening programs tend to prioritise patients for cancer screening based on very simple measure (e.g. those in a particular age band) rather than those people at highest risk of developing a cancer for whom interventions might be more beneficial. QCancer aims to address these shortcomings in screening, early detection, and disease.

QCancer® is an evolving set of prediction models developed using the QResearch database linked to cancer registry, mortality and hospital data. Broadly the prediction models are designed to (1) quantify the absolute risk that a patient has an existing cancer based on combinations of readily available risk factors and symptoms, (2) estimate future risk of major cancers over the next 10 years to improve the evidence base for screening and targeting interventions to those at highest risk, and (3) estimate survival among those with an existing cancer taking into account information about their tumour (type, stage, grade) as well as information on risk factors and treatments.

Such information is essential to ensure that patients are fully informed so they can make appropriate choices regarding their treatment. Using very large linked electronic health datasets, prediction models are being developed which can improve the information available to patients.

In Oxford, Julia collaborates with Brian Nicholson and Eric O’Neil. She also works with Juliet Usher Smith (Cambridge), Carol Coupland (Nottingham), Rosie Loftus (Macmillan), the NHS England and CRUK ACE program, EMIS Health, Pancreatic Cancer UK. The QCancer tool was highlighted by the All Party Parliamentary Working Group on Pancreatic Cancer in their report, The Need for Speed. Julia is also medical director of ClinRisk Ltd, a software company that develops open and closed sourced software to implement risk prediction tools into clinical systems.

Julia’s latest funding comes from the PCUK ADEPTS trial which has begun in Oxford to look at improving earlier diagnosis of pancreatic cancer.

Find out more below

The search for pancreatic cancer biomarkers

Nuffield Department of Population Health researchers are using the China Kadoorie Biobank to identify potential protein biomarkers in the blood that could be used to predict the presence or development of pancreatic cancer

Early stage ‘red flag’ symptoms for pancreatic cancer

The ADEPTS study uses the QResearch database to better understand what ‘red flag’ symptoms may be associated with pancreatic cancer, in the hopes of promoting earlier diagnosis from primary care.

Understanding breast cancer risk in Chinese populations

Researchers from the Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU) are utilising the China Kadoorie Biobank to better understand how breast cancer risk factors may differ between individuals from Western and Eastern populations
Women

Using big data in breast cancer research

The Cancer Epidemiology Unit has been using the largest epidemiological data set of its kind to unlock the secrets of breast cancer, what can be done to prevent it, and which women are most likely to develop it

IL-22 pathway linked to poor prognosis in colon cancer

Research shows how IL-22 interacts with KRAS mutant tumours to promote excessive growth in colorectal cancer

The relationship between unexpected weight loss & cancer

New research will help GPs to identify the signs they should look for to swiftly diagnose cancer in people with unexpected weight loss

Virtual Annual Cancer Symposium 2020

Registration is now open for our 9th annual Symposium, now being held virtually.

AI research discovers link between smell genes and colon cancer

Dr Heba Sailem’s new discovery shows a connection between your sense of smell and the spread of colon cancer.