Image Guided Genome-Epigenome Analysis of Tumor Heterogeneity and Evolution

肿瘤异质性和进化的图像引导基因组-表观基因组分析

• 批准号: 8738071
• 负责人: Joseph F Costello
• 金额: $ 16.09万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-27 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8738071
• 关键词: Address; Aftercare; Biopsy; Blood Volume; Cell Cycle; Cerebrum; Choline; Clinical Trials Design; DNA; Data; Detection; Diagnosis; Diffusion; Epigenetic Process; Event; Evolution; Excision; Exhibits; Exons; Functional Imaging; Gene Expression; Gene Expression Profile; Genetic; Genome; Genomics; Glioblastoma; Goals; Growth; Heterogeneity; Histologic; Histones; Human; Hypoxia; Image; Image Analysis; In Vitro; Individual; Induced Mutation; Instruction; Knowledge; Light; Limes; Link; Malignant - descriptor; Malignant Neoplasms; Messenger RNA; Metabolic; Methylation; Metric; Molecular Profiling; Mutation; Mutation Analysis; Newly Diagnosed; Outcome; Pathway interactions; Patient Care; Patients; Physiological; Primary Neoplasm; Property; Recurrence; Recurrent disease; Recurrent tumor; Relative (related person); Sampling; Techniques; Testing; The Cancer Genome Atlas; Time; Tissue Sample; Tissues; Tumor Tissue; base; cancer cell; chemotherapy; data integration; design; epigenome; epigenomics; exome; genome-wide; imaging modality; in vivo; indexing; innovation; mutant; next generation sequencing; novel; transcriptome sequencing; treatment planning; tumor; tumor growth; tumor progression

PROJECT SUMMARY (See instructions): This project will use novel quantitative imaging methods to guide biopsies to biologically distinct regions of primary and post-treatment recurrent GBM for targeted exome, epigenome and transcriptome analysis. Our goal is to identify naturally evolving and treatment-induced mutations and epimutations that promote the selective outgrowth of malignant subclones over lime. Genomic analysis of cancer is typically conducted at a single time point and on a single piece of the bulk resection without knowledge of its original context within the heterogeneous tumor. In contrast to these traditional genomic studies, an image guided approach to newly diagnosed and recurrent tumors could enrich for the detection of drivers of tumor growth by linking mutations and epimutations to regions of aggressive tumor growth in vivo. We will use innovative metabolic and physiologic imaging to identify regions with different levels of proliferation and hypoxia within the same patient. To our knowledge, this would be the first time that advanced imaging will be used to guide genomic or epigenomic analysis of any human tumor. In Aim 1, we will identify functional mutations and epimutations that exhibit intratumoral heterogeneity within newly diagnosed GBM. In Aim 2, we will identify functional mutations and epimutations commonly acquired during tumor progression using image guided tissue samples from treated, recurrent GBM, including paired samples from individual patients over time. Our preliminary data show that chemotherapy can have a profound effect on selective outgrowth of malignant subclones. The integration of data from Aims 1 and 2 will identify subclones in newly diagnosed tumor that exhibit selective outgrowth to become the dominant clone(s) at recurrence, and the sequential biallelic events involving intersecting genetic and epigenetic mechanisms that contribute to their enhanced growth potential. Candidate driver alterations will be evaluated using a mature computational pipeline, and will experimentally be tested for predicted functional effect. These studies could therefore impact patient care by the identification of common drivers specific to recurrence, defining the influence of therapy on tumor evolution, and incorporating profiles of primary and recurrent tumors into personalized treatment plans.

项目概述（见说明）：