Bioinformatics of metastatic migration histories

项目摘要/摘要 在美国，癌症每年造成50多万人死亡，其中90%以上的发病率 并将死亡归因于转移。转移是指癌细胞的克隆在全身扩散， 其扩散路径直到癌症发展的后期才被医学检测到或可见。这些 迁移路线对于我们理解影响AS患者肿瘤多样性的过程至关重要。 以及肿瘤的侵袭性、抵抗力和逃避治疗。粗放型的比较分析 肿瘤中存在的遗传异质性可以用来绘制癌细胞迁移的动态历史 时间和空间。我们建议开发新的方法来准确地推断癌细胞的迁移。我们的新产品 方法将使用贝叶斯分子系统发育学原理和突变特征模式 癌症细胞基因组第一次制作了肿瘤部位之间细胞迁移的准确地图。我们还将 开发检测突变特征的新方法。这些将克服当前的许多限制 方法，对于包含少量突变的数据集，这是在单个- 病人克隆系统学。我们的方法发展将得到一个图书馆的分发的补充 包含我们用于高吞吐量和深入分析的新的和先进的方法的功能 转移性肿瘤基因组数据。总体而言，拟议的软件和研究开发将带来进步 在癌症、生物信息学、功能基因组学和数据科学方面。将制作新的软件及其源代码 免费提供所有用途，包括研究、教育和培训。

Project Summary/Abstract Cancer causes more than 500,000 deaths a year in the United States, with more than 90% of the morbidity and mortality attributed to metastases. Metastasis is the spread of cancerous clones of cells across a body, whose dispersal routes are not medically detected or visible until later stages of cancer development. These migratory routes are critical to our understanding of the processes that influence tumor diversity in patients as well as aggressiveness, resistance, and escape of tumors from therapy. Comparative analysis of the extensive genetic heterogeneity present in tumors can be used to map the dynamic history of cancer cell migration over time and space. We propose to develop new methods for inferring cancer cell migrations accurately. Our new approaches will employ principles of Bayesian molecular phylogenetics and patterns of mutational signatures in cancer cell genomes for the first time to produce accurate maps of cell migration among tumor sites. We will also develop new methods for detecting mutational signatures. These will overcome many limitations of the current methods for datasets containing a small number of mutations, a situation commonly encountered in single- patient clone phylogenies. Our methodological developments will be complemented by the distribution of a library of functions containing our new and advanced approaches for high-throughput and in-depth analysis of metastatic tumor genomic data. Overall, the proposed software and research developments will lead to advances in cancer, bioinformatics, functional genomics, and data science. New software and its source code will be made available free of charge for all uses, including research, education, and training.