Using next-generation gene technology to help children with blood cancers in low-income countries.
Defining the molecular landscape of paediatric and adolescent acute leukaemia in Tanzania
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
New markers to select children with cancer for trials with PARP inhibitors
Novel biomarkers for PARP inhibitor trials for children with cancer
Developing personalised treatments for brain-directed chemotherapy in childhood acute lymphoblastic leukaemia
ALLTogether1 CSF-FLOW Study
‘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.
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
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
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