Computational modelling human stem cell fitness of pre-leukaemic mutations

这个跨学科的项目将涉及三个研究小组之间的密切合作的学生：舒马赫（计算建模），钱德拉（生物信息学）和Kirschner（干细胞测定）年龄是许多血液（血液）恶性肿瘤发病的单一最大风险因素。不确定潜能的克隆性造血（CHIP）影响超过10%的60岁以上的个体，并与血液系统癌症的风险增加有关。关于个体突变如何导致造血干细胞克隆生长并因此导致这种风险，人们知之甚少。了解个体CHIP赋予干细胞的适应性优势可以使我们能够预测进展为白血病的风险。学生将使用来自Lothian出生队列的数据，这是一个老年人的纵向队列，我们已经在15年的时间跨度内对晚年的染色体位置进行了深度测序。学生将量化CHIP突变的变异等位基因频率（VAF），并使用数学建模来推断体内干细胞的适应性。这将使我们能够测量特定的CHIP突变如何增加干细胞适应性，并揭示它们如何扰乱体内平衡（血液生成）。这些结果将使建立一个年龄依赖的预测个人的白血病风险的基础上，他们的CHIP突变频率。为了测试这些预测，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联合收割机15年来体内克隆扩增的纵向数据和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有助于白血病发展的理解。

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.