Bioinformatics of metastatic migration histories

转移迁移历史的生物信息学

基本信息

批准号：
9981255
负责人：
Sudhir Kumar
金额：
$ 35.42万
依托单位：
TEMPLE UNIV OF THE COMMONWEALTH
依托单位国家：
美国
项目类别：
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/9981255
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

项目摘要

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.

项目概要/摘要在美国，癌症每年导致超过 50 万人死亡，其中发病率超过 90% 以及转移引起的死亡率。转移是癌细胞克隆在全身的扩散，其传播途径直到癌症发展的后期才被医学检测到或可见。这些迁移路线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侵袭性、抵抗力和逃避治疗。广泛的对比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。我们建议开发新方法来准确推断癌细胞迁移。我们的新方法将采用贝叶斯分子系统发育学原理和突变特征模式首次对癌细胞基因组进行分析，以生成肿瘤部位之间细胞迁移的准确图谱。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包含少量突变的数据集的方法，这是单样本中常见的情况患者克隆系统发育。我们的方法论发展将通过图书馆的分发得到补充包含我们用于高通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癌症、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免费供所有用途使用，包括研究、教育和培训。