Evolutionary Heterogeneity of High Grade Glioma

高级别胶质瘤的进化异质性

• 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
• 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

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.

项目总结/摘要 癌症是一组精心策划的基因组改变的结果， 细胞生长失控。解剖这些变化的时间和空间关联， 不同的时间点将提供开始、进展、转移和/或转移的里程碑。 对某些治疗方案的抵抗力。阐明这些里程碑可以提供宝贵的 生物标志物的背景下，不同阶段的治疗，并将有助于定制个性化 基于基因组信息的治疗。我们将开发一个模型来推断可能的排序， 从纵向、多区域、转录组和单细胞数据中的改变， 使用严格的方法， 基因组方法。接下来，我们将推断每个患者的潜在进化动作， 序列样本与统计方法植根于进化生物学。第三，我们将 为患者队列构建一个进化网络， 基因组改变的途径。