RNA sequencing analysis of Cancer

癌症的RNA测序分析

基本信息

批准号：
10000909
负责人：
KATHERINE A. HOADLEY
金额：
$ 41.58万
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：
2016
资助国家：
美国
起止时间：
2016-09-15 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10000909
关键词：
Address Algorithms Alternative Splicing Area Atlases B-Lymphocytes Back Breast Cancer Center Candidate Disease Gene Cells Classification Clinic Clinical Clinical Trials Cohort Studies Communities Competence Complex Cox Proportional Hazards Models DNA DNA Sequence Alteration DNA sequencing Data Data Set Development Diagnosis Disease Event Exons Fibroblasts Formalin Freezing Gene Expression Gene Expression Profile Gene Expression Profiling Gene Fusion Genes Genetic Materials Genetic Transcription Genome Data Analysis Center Genomics Genotype Germ Cells Head and neck structure Histology Human Immune Individual Leadership Malignant Neoplasms Manuscripts Maps Methods Modeling Molecular Mutation Outcome Paper Pathway interactions Patient-Focused Outcomes Patients Pattern Pharmacotherapy Phase Protein Isoforms Quality Control RNA RNA Sequences RNA analysis Relapse Role Sampling Signal Pathway Somatic Mutation Structure Supervision T-Cell Receptor T-Lymphocyte Technology Testing The Cancer Genome Atlas Tissues Tumor Subtype Variant Visualization Work base cancer genomics cancer heterogeneity cancer therapy clinically relevant cohort feature selection follow-up fusion gene genetic signature genomic data immunological diversity improved individualized medicine insight mRNA Expression malignant breast neoplasm member novel novel marker novel therapeutics response survival outcome therapy development tool transcriptome transcriptome sequencing treatment response tumor variant detection working group

项目摘要

Abstract Cancer is a complex disease and represents hundreds of different disease types. It is important to identify these disease types and their underlying causative alterations to help guide and tailor treatments. Genomic technologies have been used in projects such as The Cancer Genome Atlas (TCGA) to characterize a large number of cancers. However, these early genomics projects were mostly on unselected cohorts with limited follow-up and many clinically relevant datasets were not feasible for analysis due to limitations in technologies using formalin-fixed and small starting quantities of tissue. New initiatives of the Center for Cancer Genomics will help us address more clinically-meaningful questions. We propose to use our expertise in gene expression and RNA-sequencing analysis to further characterize cancer to help identify novel markers for diagnosis, novel drug therapies and clinical associations. We will approach this in three aims. For Aim 1, we will use RNA sequence information to identify somatic mutations, improve mapping assembly and quantification of B and T cells, identify structural variations, and perform high level quality control including genotype checks across sequence data for the same sample. For Aim 2, we will calculate gene and isoform levels that will be used to identify tumor subtypes, alternative isoform usage, and application of previously defined gene signatures and tumor subtypes. For Aim 3, we will use supervised analyses to find genes significantly associated with molecular features and model gene expression data to look for association with clinical outcome or drug treatment response. We expect our data, integrated with the data from other Genome Data Analysis Centers, will uncover novel insights into cancer development, progression and treatment of cancer. We will also leverage the information we have learned from pan-cancer analyses to identify shared genomic alterations or pathway activity that may accelerate therapy development.

抽象的癌症是一种复杂的疾病，代表数百种不同的疾病类型。识别很重要这些疾病类型及其潜在的病因改变有助于指导和定制治疗。基因组癌症基因组图谱 (TCGA) 等项目已使用这些技术来表征大量癌症癌症的数量。然而，这些早期的基因组学项目大多是针对未经选择的群体，且研究有限。由于技术限制，随访和许多临床相关数据集无法进行分析使用福尔马林固定的少量起始组织。癌症基因组学中心的新举措将帮助我们解决更多具有临床意义的问题。我们建议利用我们在基因表达方面的专业知识和 RNA 测序分析，以进一步表征癌症，帮助识别新的诊断标记物、新的药物治疗和临床关联。我们将通过三个目标来实现这一目标。对于目标 1，我们将使用 RNA 序列信息可识别体细胞突变，改进 B 和 T 的定位组装和定量细胞，识别结构变异，并执行高水平的质量控制，包括基因型检查同一样本的序列数据。对于目标 2，我们将计算将用于识别肿瘤亚型、替代异构体的使用以及先前定义的基因特征的应用肿瘤亚型。对于目标 3，我们将使用监督分析来寻找与以下因素显着相关的基因：分子特征和模型基因表达数据，以寻找与临床结果或药物的关联治疗反应。我们期望我们的数据与其他基因组数据分析中心的数据相结合，将揭示有关癌症发生、进展和治疗的新见解。我们也会利用我们从泛癌分析中学到的信息来识别共享的基因组改变或可能加速治疗开发的途径活动。