Bioinformatics of metastatic migration histories

项目总结/摘要 癌症在美国每年造成50多万人死亡，发病率超过90% 和归因于转移的死亡率。转移是癌细胞克隆在体内的扩散， 其扩散途径直到癌症发展的后期才被医学检测到或可见。这些 迁移途径对于我们理解影响患者肿瘤多样性的过程至关重要， 以及肿瘤的攻击性、抵抗性和逃避治疗。广泛的比较分析 肿瘤中存在的遗传异质性可用于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迁移。我们的新 方法将采用贝叶斯分子遗传学的原理和突变特征的模式， 癌症细胞基因组首次产生肿瘤部位之间细胞迁移的准确地图。我们还将 开发新的检测突变特征的方法。这些将克服目前的许多局限性 包含少量突变的数据集的方法，这种情况在单个 病人的克隆基因。我们的方法论发展将通过分发一个图书馆来补充 包含我们的新的和先进的方法，用于高通量和深入分析的功能， 转移性肿瘤基因组数据。总的来说，拟议的软件和研究开发将导致进步 癌症、生物信息学、功能基因组学和数据科学。新的软件及其源代码将在 免费提供给所有使用者，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.