Evolutionary Heterogeneity of High Grade Glioma

高级别胶质瘤的进化异质性

基本信息

批准号：
10249322
负责人：
Erik Ladewig
金额：
$ 10.07万
依托单位：
SLOAN-KETTERING INST CAN RESEARCH
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10249322
关键词：
Address Aftercare Age Algorithms Biological Markers Biology Biopsy Brain Brain Neoplasms Cells Clinical Clinical Data Collection DNA DNA Sequence Alteration Data Data Analyses Development Disease Evolution Future Genetic Genomic approach Genomics Glioblastoma Glioma Heterogeneity Human Human Cell Line Individual International Laboratories Lead Liquid substance Longitudinal cohort Malignant Neoplasms Manuscripts Mathematics Methods Modeling Mutation Neoplasm Metastasis Pathway interactions Patients Pattern Pharmaceutical Preparations Pharmacotherapy Plant Roots Prognosis RNA Recording of previous events Recurrence Regimen Relapse Research Research Project Grants Resistance Resources Route Sampling Solid Solid Neoplasm Statistical Methods Statistical Models Techniques Testing The Cancer Genome Atlas Therapeutic Time Transforming Growth Factor beta Trees Validation Work analysis pipeline base cancer genome clinically relevant cohort computer framework driving force exhaustion exome genomic data human data insight interest next generation sequencing personalized medicine reconstruction statistics targeted treatment therapeutic target transcriptomics tumor tumor heterogeneity uncontrolled cell growth

Address Aftercare Age Algorithms Biological Markers Biology Biopsy Brain Brain Neoplasms Cells Clinical Clinical Data Collection DNA DNA Sequence Alteration Data Data Analyses Development Disease Evolution Future Genetic Genomic approach Genomics Glioblastoma Glioma Heterogeneity Human Human Cell Line Individual International Laboratories Lead Liquid substance Longitudinal cohort Malignant Neoplasms Manuscripts Mathematics Methods Modeling Mutation Neoplasm Metastasis Pathway interactions Patients Pattern Pharmaceutical Preparations Pharmacotherapy Plant Roots Prognosis RNA Recording of previous events Recurrence Regimen Relapse Research Research Project Grants Resistance Resources Route Sampling Solid Solid Neoplasm Statistical Methods Statistical Models Techniques Testing The Cancer Genome Atlas Therapeutic Time Transforming Growth Factor beta Trees Validation Work analysis pipeline base cancer genome clinically relevant cohort computer framework driving force exhaustion exome genomic data human data insight interest next generation sequencing personalized medicine reconstruction statistics targeted treatment therapeutic target transcriptomics tumor tumor heterogeneity uncontrolled cell growth

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项目摘要

PROJECT SUMMARY/ABSTRACT Cancer is the result of an orchestrated set of genomic alterations that conspire to drive uncontrolled cell growth. Dissecting temporal and spatial association of these alterations into varying time points will provide milestones into initiation, progression, metastasis and/or resistance to certain therapeutic regimens. Elucidating these milestones could provide invaluable biomarkers in the context of different stages for treatments and would help to tailor personalized therapies based on genomic information. We will develop a model to infer a possible ordering of alterations from longitudinal, multi-region, transcriptomic and single cell data by first discerning the totality of significant alterations and most likely contributors to progression using a rigorous genomic approach. Next we will infer potential evolutionary moves of each patient using sequential samples with a statistical approach rooted in evolutionary biology. Third we will construct an evolutionary network for the cohort of patients that will delineate and order significant routes of genomic alteration.

项目摘要/摘要癌症是共同策划的基因组改变的结果不受控制的细胞生长。将这些改变的时间和空间关联解剖为不同的时间点将为启动，进展，转移和/或对某些治疗方案的抗性。阐明这些里程碑可能会提供无价的生物标志物在不同阶段的治疗阶段，将有助于量身定制个性化基于基因组信息的疗法。我们将开发一个模型来推断可能的顺序首先辨别纵向，多区域，转录组和单细胞数据的变化重大变化的总体以及最有可能使用严格的进步贡献者基因组方法。接下来，我们将使用使用具有基于进化生物学的统计方法的顺序样品。第三我们会构建一个将描绘和订购重要的患者队列的进化网络基因组改变的途径。