Developing personalised treatments for brain-directed chemotherapy in childhood acute lymphoblastic leukaemia
ALLTogether1 CSF-FLOW Study
A molecular approach to predict recurrence or spread of cancer that will lead to improving outcomes for children with rhabdomyosarcoma
MG-fnRMS and CINSARC gene expression signatures to predict relapse in fusion gene negative rhabdomyosarcomas: assessing an approach to improve patient outcomes
‘Radiogenomics’: A novel non-invasive approach to identify different types of ependymoma brain tumours
Exploring whether a new approach to identifying ependymoma tumours can help support better, more tailored, and potentially less invasive treatment for children with this type of cancer.
Stopping ‘cut-and-run’ DNA errors to help treat leukaemia and predict relapse
Towards inhibiting cut-and-run: An aberrant V(D)J recombination reaction that leads to lymphoid cancers
Using artificial intelligence to diagnose brain tumours
Moving Artificial Intelligence of Functional Imaging for children’s tumours into a multi-centre environment through a Clinical Decision Support System
Improving the outlook for children with Philadelphia positive ALL
Unravelling clinical heterogeneity in Philadelphia positive ALL
"Getting it right first time" for children with renal tumours
Improving prediction of relapse, treatment delivery and outcomes for children with renal tumours in the UK
Developing a blood test to monitor ependymoma treatment and follow-up
Minimal residual disease detection of ependymoma by microRNA biomarker profile evaluation as part of SIOP Ependymoma II trial
Finding new drugs to treat high-risk rhabdomyosarcoma
High-risk rhabdomyosarcoma patient derived 3D cultures to screen for novel drugs that enhance sensitivity to standard treatment