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

B细胞急性淋巴细胞白血病（B-ALL）是一种危及生命的血液癌症，可以影响年轻人和老年人。许多患有B-ALL的儿童现在可以治愈，但成人的前景不太乐观。在治疗B-ALL时，有两个主要挑战：1）确定哪些儿童可以通过较少的治疗治愈。B-ALL的治疗是长期和具有挑战性的。它对儿童和家庭造成心理、社会和教育后果。它还增加了晚年健康问题的风险，如心脏病，骨骼或关节问题和不孕症。2)确定哪些成年人最不可能通过标准治疗治愈。这些患者的选择包括使用利用免疫系统抗癌功能的药物和疗法的新组合。为了以这种方式进行个性化治疗，我们需要准确预测白血病复发的风险（治疗后复发）。目前，我们使用患者的年龄、诊断时的白色血细胞计数和白血病DNA（白血病细胞的遗传指令）的某些变化来预测风险。通过增加对RNA的测量（这是白血病细胞目前使用哪种DNA指令的指示），可能会改善这种预测。在最近的工作中，我将白血病RNA的信号与健康成熟血细胞的RNA信号进行了匹配。这种“成熟指纹”使我能够根据DNA变化分离出已知的B-ALL亚群。复发风险较高的亚组有更多的“不成熟”信号。然而，患者之间的信号不同，这意味着什么还不清楚。在下面的工作中，我的目标是：1）确保用于匹配信号的方法对成熟敏感并且在RNA数据集之间是可靠的2）通过比较来自复发患者与治愈患者的白血病细胞中的信号来测试不成熟信号是否与复发风险相关3）比较B-ALL亚组内的成熟极限以了解DNA结构、DNA可读性和RNA的变化影响成熟有了这些见解，我将确定确定RNA“成熟指纹”是否可以改善我们对复发风险的预测。如果成功，这种方法可以支持个性化的B-ALL治疗决策。对这些数据的进一步探索可以帮助确定改变B-ALL成熟的方法，以改变白血病细胞的行为和对治疗的反应。所有产生的RNA数据将与研究界共享，以最大限度地提高这种疾病的科学进步。

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.