Data4CYP awards
CRUK has made a £5million investment in a new research programme to support data-driven research questions to develop new, scalable and generalisable solutions to common challenges in children’s and young people’s cancers.
The first phase of this will be eight Pilot awards, which will run up to two years. Four of the awards have been jointly funded through partnerships – two with Great Ormond Street Hospital Children’s Charity and two with Children with Cancer UK.
Here are the awardees and what they will be working on…
Anthony Moorman, Newcastle University
ALLTogether for Research (A2G4R) Knowledge Hub
There are at least 25 different genetic subtypes of acute lymphoblastic leukaemia (ALL). A current clinical trial for CYP-ALL (ALLTogether-01) uses 10 leukaemia genetic subtypes to guide treatment, however for the remaining subtypes, there is a lack of robust data regarding the relationship with risk of relapse.
This project will investigate the clinical relevance of the remaining 15 subtypes and establish whether they can be used prospectively to guide treatment.
Children with Cancer UK
Anindita Roy, University of Oxford
Scientific Advances in Infant ALL (SAIL)
The project will bring together the clinical and scientific data available for all infants with ALL in the UK to learn about the efficacy of these newer therapies compared to historic treatments. A consortium of expert clinicians, scientists, data strategists and parent advocates will work on SAIL. The aim is to use the integrated datasets to understand why some infants with ALL do better than others, hopefully leading to a personalised medicine approach in the future.
Great Ormond Street Hospital Children’s Charity
Martin McCabe, University of Manchester
Routinely collected treatment data to evaluate the uptake and utility of UK paediatric early phase trial infrastructure
The evidence base for treatment of recurrent childhood cancer is poor. And, although 500-600 UK children and adolescents/young adults (AYA) die of cancer annually, there has been no systematic analysis of treatments or treatment efficacy in this setting. This project will use data collected by NHS England on cancer treatments, genetic data from the SMPaeds study and whole genome sequencing with individual patients’ records to describe treatment patterns and treatment efficacy across the childhood/AYA cancer spectrum.
Great Ormond Street Hospital Children’s Charity
Katie Harron, Institute of Child Health and University College London
Education and longer-term health outcomes for childhood cancer survivors: linkage of cancer registration data to the ECHILD database for use by UK researchers
The project will create a longitudinal, population-level database of hospital and education records linked with childhood cancer registrations data. It will link national cancer registration data with existing linked health and education data captured for 20 million children in ECHILD – a dataset capturing information from NHS hospitals and state-schools in England for children born since 1984. The aim is to quantify the difference in academic attainment trajectories and school support up to age 16, and hospitalisations and mortality into adulthood, between childhood cancer survivors and healthy peers.
Children with Cancer UK
Emma Woodward, University of Manchester
CanCYP: a tool to enable cancer risk prediction in children and young people with a cancer predisposing gene alteration
This study will use hereditary genetic tests carried out in the NHS, along with NHS records about cancers that children and young people have already developed and the treatment they received. The aim is to use the information to calculate for a person with a gene alteration that can increase cancer risk, the actual chance of developing a cancer.
Simon Bomken, Newcastle University
Identification of prognostic biomarkers for childhood Burkitt lymphoma through multiomic data integration
This project will look at how the core Burkitt lymphoma (the most common childhood non-Hodgkin lymphoma) transcriptome differs from normal germinal states. Using highly curated clinical cohort of childhood sBL cases to identify relapse associated transcriptional features and investigate their potential as risk-stratification biomarkers. Integration of multiomic data from rare sBL patient samples will provide an understanding of the transcriptome of this disease and a novel dataset for future additional integration.
Maria Hawkins, University College London
PROVIDENTIA 1000 Proton and radiation data combined with biology, imaging and long term outcomes to advance radiation combined modality treatments in CYP
The project will assemble a comprehensive database of 1000 children and young people treated with radiation (proton and photon) focussing on brain malignancies to answer 3 questions: Can we refine radiation indications to improve outcomes using unbiased molecular and imaging data? Can we propose new solutions to mitigate toxicity development? Can we use AI to generate high-fidelity virtual synthetic data on a relatively limited real-world dataset?
Benjamin Hall, University College London
Elucidating the molecular pathways to relapse in childhood leukaemia through computational modelling
The project will use computational modelling, combining the gene levels with information about how genes interact, to build simulations that can predict the behaviour of cancer cells and exploit these models to find differences associated with poor patient outcomes and in turn to predict vulnerabilities that we might target to improve treatments.
