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Gene Expression Patterns in Human Tumors Identified Using Transcript Sequencing

使用转录测序鉴定人类肿瘤中的基因表达模式

基本信息

批准号：
8925212
负责人：
David N Hayes
金额：
$ 50万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-09-29 至 2016-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8925212
关键词：
Alternative Splicing Base Sequence Binding Sites Biological Assay Breast Cancer Etiology Characteristics Chromatin Clinical Code Coupled DNA DNA Methylation DNA Microarray Chip DNA Sequence DNA copy number Data Development Disease Distant Enhancers Epigenetic Process Event Formaldehyde Gene Expression Gene Expression Profile Gene Expression Profiling Gene Structure Genes Genome Genomic DNA Genomics Glioblastoma Head and neck structure Human Individual Infectious Agent Informatics Instruction Knowledge Lead Link Location Locus Control Region Malignant Neoplasms Malignant neoplasm of lung Mammalian Cell Maps Measurement Messenger RNA Methods MicroRNAs Molecular Profiling Mutate Mutation Nature Normal tissue morphology Nucleosomes Oligonucleotide Microarrays Outcome Output Paint Pathogen detection Patients Pattern Persons Phase Portraits Procedures Protein Isoforms Proteins RNA Splicing Regulation Regulatory Element Relative (related person)Research Infrastructure Role SNP genotyping Solid Technology Testing The Cancer Genome Atlas Time Tissues Transcript Tumor Biology Tumor Subtype Tumor Suppressor Genes Viral base cancer genome chromatin remodeling cost deep sequencing disorder subtype experience fusion gene gene function genome wide association study genome-wide histone modification improved infancy insight next generation next generation sequencing novel pathogen promoter research study tool transcription factor tumor

项目摘要

Gene expression provides a snapshot of the cellular changes that promote tumor malignancy. Quantitative gene expression analysis, especially as implemented by DNA microarrays, has proven to be an extremely valuable tool for cancer genome characterization, and has lead to the development of new genomic-based clinical tests. Our own experience with DNA microarrays to study gene expression patterns for breast, head & neck, and lung cancers has lead to the identification of novel subtypes of tumors with distinct patient outcomes and has identified new tumor suppressor genes. In the pilot phase of The Cancer Genome Atlas (TCGA) project, multiple platforms were used including tools to study gene expression (our role), tumor genomic DNA copy number alterations, SNP genotypes, DNA methylation and gene mutational analyses. Our collaborative efforts identified new tumor subtypes of glioblastoma and painted an integrated picture linking mutations to copy number changes to expression patterns, which identified biologically distinct subtypes of disease with differences in patient outcomes. For the second phase of TCGA project, we propose to continue to perform quantitative gene expression profiling of all protein-coding genes, non-protein coding mRNAs(ncRNAs) and microRNAs, on -2000 tumors per year. This approach has proven to be one of the most informative and comprehensive cancer genome characterization tools available. In addition, we propose to generate global chromatin organization profiles of cancer to identify regions of "open" chromatin domains (nucleosome-depleted regions). We will use FAIRE (Formaldehyde-Assisted isolation of Regulatory Elements), a simple, low-cost method amenable to use on small quantities of solid tissue, coupled to next-generation DNA sequencing. Since the function of most histone modifications and chromatin remodeling activities is to regulate nucleosome occupancy, FAIRE effectively summarizes the functional output of such epigenetic mechanisms in a single robust assay. Lastly, we propose to perform integrated analyses of transcript levels with chromatin stoicture to map important regulatory elements, which can be distant to the transcript(s) that they regulate. Our study of genome-wide transcript regulation with chromatin organization will provide a critical portrait of the cancer genome that can be integrated with (and indeed can sometimes generate) other important data, including mutations and copy number events.

基因表达提供了促进肿瘤恶性的细胞变化的快照。