Bioinformatics of metastatic migration histories

转移迁移历史的生物信息学

• 批准号: 10159969
• 负责人: Sudhir Kumar
• 金额: $ 33.96万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-06 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10159969
• 关键词: Atlas of Cancer Mortality in the United States; Awareness; Back; Benchmarking; Bioinformatics; Cancer Etiology; Cancerous; Cessation of life; Charge; Clonal Evolution; Clone Cells; Complement; Complex; Computer Analysis; Computer software; Computing Methodologies; Data; Data Analyses; Data Science; Data Set; Development; Distant; Evolution; Genetic Heterogeneity; Heterogeneity; Joints; Libraries; Malignant Neoplasms; Maps; Methodology; Methods; Modeling; Modernization; Molecular; Morbidity - disease rate; Mutagens; Mutation; Mutation Analysis; Neoplasm Metastasis; Nucleotides; Organ; Patients; Pattern; Phylogenetic Analysis; Phylogenetic Pattern; Phylogeny; Play; Primary Neoplasm; Process; Recording of previous events; Research; Research Personnel; Resistance; Resolution; Resources; Route; Secondary to; Site; Software Tools; Source Code; Structure; Technology; Time; Tissues; Training and Education; Translating; Tumor Cell Migration; Tumor Escape; United States; Variant; cancer cell; cancer genome; cancer type; cell motility; comparative; computerized tools; functional genomics; genome analysis; genome sciences; genomic data; genomic variation; graphical user interface; metastatic process; migration; mortality; neoplastic cell; novel strategies; reconstruction; research and development; software development; tool; tumor; tumor progression

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.

项目摘要/摘要 癌症在美国每年造成500,000多人死亡，其中90％以上的发病率 和死亡率归因于转移。转移是细胞癌克隆在体内的传播， 直到癌症发展的后期，其分散途径才被医学检测或可见。这些 迁移途径对于我们对影响患者肿瘤多样性的过程的理解至关重要 以及肿瘤的侵略性，耐药性和逃避治疗的逃避。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。我们建议开发用于准确推断癌细胞迁移的新方法。我们的新 方法将采用贝叶斯分子系统发育原理和突变特征的模式 癌细胞基因组首次产生肿瘤部位中细胞迁移的准确图。我们也会 开发用于检测突变特征的新方法。这些将克服当前的许多局限性 包含少数突变的数据集的方法，这是单一通常遇到的情况 患者克隆系统发育。我们的方法论发展将由图书馆的分布得到补充 包含我们新的和高级方法的功能，用于高通量和深入分析 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导致进步 在癌症中，生物信息学，功能基因组学和数据科学。将制作新软件及其源代码 所有用途免费提供，包括研究，教育和培训。