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NEOPANC-01 is a window of opportunity study looking to assess gene expression in patients with resectable pancreatic cancer. It aims to evaluate the utility of whole transcriptome RNA sequencing as a potential biomarker for future window-of-opportunity trials that will assess existing and/or new treatments for pancreatic cancer. An understanding of the variability in the transcriptomic signature between biopsies will also inform power calculations for future studies. Lastly, the data generated will be available for use as historical controls for future studies. 2 sets of biopsy samples will be taken throughout the trial. Firstly during an endoscopic ultrasound procedure and then again, up to 6 weeks later, during the patients surgery to remove the cancer. Once all patients have been recruited, the mRNA from the samples will be sequenced and analysed. Further samples will also be taken at the same time points for storage into a biobank and for use in future research. This is a single site study opening at the Churchill Hospital looking to recruit 10 patients over 18 months.
2022-2024 cohort
Find out about our 2022-2024 alumni.
Our Projects
PHI has work that spans the worlds of climate change and health equity. Explore some of the themes of our work below, and find project pages, links and publications within them.
Amelia Doran
Amelia is a Science Communicator, and joined the PHI Lab in September 2023. She has a background in Biochemistry and research experience in science communication, and now leads on the various parts of PHI’s outputs.
Real-World Data and Federated Analytics
One key feature of our research is using data collected from the world around us. This could be de-identified patient records, weather station measurements or satellite imagery. This real-world international evidence uses federated analytics to safely and securely combine individual person-level data into valuable learning.
AI for Planetary Health
Artificial Intelligence is one of the biggest areas of development and innovation today. In our research, we are applying AI technology to tackle one of the greatest challenges facing healthcare systems across the globe: the impact of the environment and climate change.
COVID-19 Pandemic Response
Medical statistics was a key part of the response to the COVID-19 pandemic. Like many teams, we applied our experience with data analysis, real-world evidence and international collaboration to help healthcare systems around the world respond to the health crisis, and learn from it.
Machine Learning and Medical Statistics
Our lab applies machine learning in medical statistics, allowing us to identify patterns and insights in large amounts of health data. This can lead to improved diagnoses, treatments, and overall healthcare outcomes.