Computational modelling human stem cell fitness of pre-leukaemic mutations

这个跨学科项目将涉及学生在三个研究小组之间的密切合作：Schumacher（计算建模），Chandra（生物信息学）和Kirschner（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。潜在不确定克隆造血（CHIP）影响超过10%的60岁以上人群，并与血液学癌症风险增加相关。对于个体突变如何导致造血干细胞克隆生长，从而导致这种风险，我们所知甚少。了解个体CHIP赋予干细胞的适应性优势可以使我们预测白血病进展的风险。该学生将处理来自洛锡安出生队列（Lothian Birth Cohort）的数据，这是一组老年人的纵向队列，我们从这些数据中深度排序了15年以上生命后期的目标染色体位置。该学生将量化CHIP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应性。这将使我们能够测量特定的CHIP突变如何增加干细胞的适应性，并揭示它们如何扰乱体内平衡（血液生产）。该结果将使建立一个年龄依赖的预测个体白血病风险的预测仅仅基于他们的CHIP突变频率。为了验证这些预测，我们获得了苏格兰一代的数据（20,000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增超过15年的纵向数据和来自Kirschner实验室的体外数据，以增加转化影响，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.