Doctors treating B-cell acute lymphoblastic leukaemia (B-ALL) need a better way to read a patient’s cancer cells and predict whether the disease will return after treatment. The problem is twofold. For children, current treatment is harsh and long, causing lasting side effects—yet many could be cured with less. For adults, standard therapy often fails, and doctors lack a reliable way to identify who needs stronger or immune-based treatments early on. Existing risk predictions rely on age, white blood cell count, and DNA changes, but these are not accurate enough. The researcher has developed a new approach: matching RNA signals from leukaemia cells to those from healthy maturing blood cells, creating a “fingerprint of maturation.” Early work suggests that leukaemias with more immature fingerprints are more likely to relapse. This project will test whether that fingerprint reliably predicts relapse risk across patient groups, and explore the underlying biology of maturation. If successful, the method could give clinicians a practical tool to personalise treatment—sparing children unnecessary toxicity while steering adults toward more effective therapies sooner. All data will be shared openly to accelerate progress in the field.
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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 cured 3) Compare the maturation extremes within subgroups of B-ALL to understand what changes in DNA structure, DNA readability, and RNA affect maturation With 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.
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