Transcriptional assessment of haematopoietic differentiation to risk-stratify acute lymphoblastic leukaemia

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B-cell acute lymphoblastic leukaemia (B-ALL) is a life-threatening blood cancer which can affect both the young and the elderly. Many children with B-ALL can now be cured, but the outlook for adults is less favourable. In treating B-ALL there are two main challenges:1) Identifying which children can be cured with less treatment. B-ALL treatment is long and challenging. It has psychological, social and educational consequences for children and families. It also increases the risk of health problems in later life, such as heart disease, bone or joint problems and infertility. 2) Identifying which adults are least likely to be cured with standard treatments. Options for these patients include using new combinations of drugs and therapies that harness the anti-cancer functions of the immune system. To personalise treatment in this way, we need to accurately predict the risk of leukaemia relapsing (coming back after treatment). At present, we predict risk using the patient's age, white blood cell count at diagnosis and certain changes in the leukaemia DNA (the genetic instructions of the leukaemia cell). It may be possible to improve this prediction by adding measurements of RNA (an indication of which DNA instructions the leukaemia cell is currently using).In recent work, I matched signals from leukaemia RNA to signals from the RNA of healthy maturing blood cells. This 'fingerprint of maturation' allowed me to separate the known subgroups of B-ALL, which are based on DNA changes. Subgroups with a higher risk of relapse had more 'immature' signals. However, signals varied between patients, and it isn't yet clear what this means.In the following work, I aim to:1) Ensure that the method for matching signals is sensitive to maturation and reliable across RNA datasets 2) Test whether immature signals correlate with relapse risk, by comparing signals in leukaemia cells from patients who relapsed versus patients who were cured3) Compare the maturation extremes within subgroups of B-ALL to understand what changes in DNA structure, DNA readability, and RNA affect maturationWith these insights, I will establish whether determining the RNA 'fingerprint of maturation' could improve our predictions of relapse risk. If successful, this method could support personalised B-ALL treatment decisions. Further exploration of the data could help identify ways of changing B-ALL maturation to modify how the leukaemia cells behave and respond to treatment. All RNA data generated will be shared with the research community to maximise scientific advances in this disease.