Computational modelling human stem cell fitness of pre-leukaemic mutations

这个跨学科的项目将让学生参与三个研究小组的密切合作：舒马赫(计算模型)、钱德拉(生物信息学)和克尔施纳(干细胞分析)年龄是许多血液(血液)恶性肿瘤发病的单一最大风险因素。克隆性造血不确定潜能(CHIP)影响超过10%的60岁以上的人，并与血液学癌症的风险增加有关。关于个体突变如何导致血液干细胞的克隆性生长，从而导致这种风险，人们知之甚少。了解单个芯片赋予干细胞的健康优势，可以使我们预测发展为白血病的风险。这名学生将使用洛锡安出生队列的数据，这是一个由老年人组成的纵向队列，我们从这些数据中对晚年的染色体位置进行了深入的测序，时间跨度为15年。学生将量化芯片突变的变异等位基因频率(VAF)，并使用数学模型来推断干细胞在体内的适合性。这将使我们能够测量特定的芯片突变如何增加干细胞的健康，并揭示它们如何扰乱体内平衡(血液生产)。这一结果将使仅根据个体的芯片突变频率建立个体白血病风险的年龄相关预测因子成为可能。为了验证这些预测，我们获得了苏格兰一代的数据(20000名参与者)。数学模型的使用将使我们能够定量地结合体内克隆扩张超过15年的纵向数据和来自Kirschner实验室的体外数据，以增加翻译影响，提高我们对CHIP对正常造血的影响以及这如何促进白血病发展的理解。

This interdisciplinary project will involve the student in a close collaboration between three research groups: Schumacher (computational modelling), Chandra (bioinformatics), and Kirschner (stem cell assays)Age is the single biggest risk factor underlying the onset of many haematological (blood) malignancies. Clonal haematopoiesis of indeterminate potential (CHIP) affects more than 10% of individuals over the age of 60 years and is associated with increased risk for haematological cancers. Little is known about how individual mutations cause clonal outgrowth of blood stem cells and thus contribute to this risk. Knowing the fitness advantage conferred on stem cells by individual CHIP could enable us to predict the risk of progression to leukaemia.The student will be working with data from the Lothian Birth Cohort, a longitudinal cohort of aged individuals, from which we have deeply sequence targeted chromosomal locations in late life over a 15 year span. The student will quantify variant allele frequency (VAF) for CHIP mutations and use mathematical modelling to infer stem cell fitness in vivo. This will allow us to measure how specific CHIP mutations increase stem cell fitness and reveal how they perturb homeostasis (blood production). The results will enable building of an age-dependent predictor of an individual's leukaemic risk based solely on their CHIP mutation frequencies. To test these predictions, we have gained access to Generation Scotland data (20,000 participants). The use of mathematical models will allow us to quantitatively combine both longitudinal data on clonal expansion in vivo over 15 years and in vitro data from the Kirschner lab for increased translational impact, improving our understanding of the effects of CHIP on normal haematopoiesis and how this contributes to leukaemia development.