Integrative bioinformatics and functional characterization of oncogenic driver aberrations in cancer

癌症中致癌驱动畸变的综合生物信息学和功能表征

基本信息

批准号：
9756341
负责人：
Benjamin Deneen
金额：
$ 70万
依托单位：
OREGON HEALTH & SCIENCE UNIVERSITY
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：
2018-08-08 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9756341
关键词：
Address Affect Algorithms Alleles Area Bioinformatics Biological Biological Assay CRISPR/Cas technology Cancer Center Cancer Etiology Cancer Patient Categories Cell Line Clinical Clinical Management Code Combination Drug Therapy Communities Complement DNA DNA Sequence Alteration Data Data Set Development Dose Drug resistance Engineering Epithelial ovarian cancer Event Face Foundations Frequencies Gene Mutation Genes Genetic Genome Genomics Glioblastoma Heterogeneity Human Imagery Immune Industrialization Informatics International Internet Investigation Libraries MCF10A cells Malignant Neoplasms Modeling Modification Molecular Mus Mutation Nature Neoplasm Metastasis Oncogenes Oncogenic Pancreatic Ductal Adenocarcinoma Pathogenicity Pathway interactions Patient Care Patients Pharmaceutical Preparations Pharmacology Pharmacotherapy Phenotype Population Population Dynamics Proteins Proteomics RNA Interference Reagent Recurrence Research Resistance Role Sensitivity and Specificity Series Somatic Mutation System Testing The Cancer Genome Atlas Therapeutic Therapeutic Agents Training Translations Tumor-Derived Tumorigenicity Validation Xenograft Model Xenograft procedure actionable mutation cancer cell cancer genome cancer heterogeneity cancer initiation cancer therapy cancer type cell type clinically relevant cohort combinatorial data management drug development expression cloning expression vector fusion gene gain of function gain of function mutation gene cloning gene function genomic data high throughput screening improved in vivo in vivo Model individual patient innovation loss of function molecular marker mutant new therapeutic target next generation sequencing novel optimal treatments predicting response prediction algorithm predictive marker programs protein protein interaction response screening targeted agent therapeutic target tumor tumor behavior tumor heterogeneity tumor microenvironment tumor progression tumorigenesis validation studies vector

项目摘要

Project Summary Large-scale national and international cancer sequencing programs are generating a compendium of tumor- associated genomic alterations to prioritize the most promising therapeutic targets for drug development. These efforts have uncovered a staggering level of genome complexity in cancer. Although much is known about the function and clinical impact of recurrent aberrations in well-known cancer genes, less is known about which and how the more abundant, low-frequency mutations contribute to tumor progression. Effective translation of tumor genomic datasets into cancer therapeutics will require new experimental systems to inform the functional activity of targets in the relevant biological context encompassing inter- and intra-tumoral heterogeneity. To address these needs, we propose a CTD2 Center that will provide the research community high-throughput informatic and experimental approaches to characterize and validate pathogenic “driver” mutations and fusion genes as well as identify molecular markers that meaningfully predict responses or resistance to anticancer therapies. We will pursue the following Specific Aims: In Aim 1 we will implement an algorithmic framework for identifying driver mutations with high sensitivity and specificity. We will focus our algorithm development, training and testing efforts on predicting oncogenic, gain-of-function mutation drivers of glioblastoma multiforme (GBM), pancreatic ductal adenocarcinoma (PDAC) and epithelial ovarian cancer (EOC). These computational approaches will be amenable to the analysis of all cancer types. We will next engineer ~1,500 selected mutations and ~400 fusion genes into expression vectors along with cohorts of personalized, patient-defined coding mutations. In Aim 2 we will enter mutant alleles and fusion genes into GBM, PDAC and EOC context-specific, in vivo functional screens that take into account the importance of genetic context, tumor microenvironment and heterogeneity in the selection of single and combinatorial drivers of tumorigenesis. In Aim 3 we will determine the consequences of intra-tumoral heterogeneity on tumor sensitivity and resistance to therapeutic agents using DNA-barcoded, human patient-derived xenograft models that recapitulate the heterogeneity of cancer. We will determine the extent to which single targeted agents and their rational combinations alter tumor population dynamics. We will also leverage Aim 1 informatics and functional characterizations in Aim 2 and 4 to characterize “persistor” populations to identify aberrations associated with drug resistance. In Aim 4 we will use high-throughput functional proteomics, innovative protein- protein interaction assays and informer drug library screening studies to elucidate underlying mechanisms and therapeutic liabilities engendered by validated drivers. The foundational platform implemented in our CTD2 Center will provide a validated pipeline for the rapid characterization of gain-of-function aberrations that can be industrialized across tumor lineages to guide clinical management of cancer patients.

项目摘要大规模的国家和国际癌症测序计划正在产生肿瘤的纲要相关的基因组改变以优先考虑药物开发的最有希望的治疗靶点。这些努力发现了癌症中基因组复杂性的惊人水平。虽然众所周知关于众所周知的癌症基因中复发畸变的功能和临床影响，对哪种以及更丰富的低频突变有助于肿瘤进展。有效的将肿瘤基因组数据集转化为癌症治疗将需要新的实验系统来告知靶标在相关的生物学环境中的功能活性，包括腹部间和腹部内部异质性。为了满足这些需求，我们提出了一个CTD2中心，该中心将为研究社区提供高通量的信息和实验方法来表征和验证致病性“驱动器” 突变和融合基因以及确定有意义预测反应或的分子标记物对抗癌疗法的抗性。我们将追求以下具体目标：在AIM 1中，我们将实施一个用于识别具有高灵敏度和特异性的驱动器突变的算法框架。我们将集中精力算法开发，培训和测试工作，以预测致癌，功能收益突变驱动因素胶质母细胞瘤多形（GBM），胰腺导管腺癌（PDAC）和上皮卵巢癌（EOC）。这些计算方法将适合所有癌症类型的分析。我们接下来工程师〜1,500个选定的突变和〜400个融合基因与表达矢量一起个性化的，患者定义的编码突变。在AIM 2中，我们将进入突变等位基因和融合基因 GBM，PDAC和EOC上下文特定于体内功能屏幕，这些屏幕考虑到重要性遗传环境，肿瘤微环境和异质性在选择单一和组合驱动器中肿瘤发生。在AIM 3中，我们将确定肿瘤内异质性对肿瘤的后果使用DNA-Barcoded，人类患者衍生的Xenogroticon模型对热剂的敏感性和抗性概括了癌症的异质性。我们将确定单个目标药物和它们的合理组合改变了肿瘤种群的动态。我们还将利用目标1信息和 AIM 2和4中的功能性字符以表征“ persistor”人群以识别畸变与耐药性有关。在AIM 4中，我们将使用高通量功能蛋白蛋白质相互作用测定和告密者药物库筛查研究，以阐明潜在的机制和由经过验证的驾驶员产生的治疗责任。在我们的CTD2中实现的基础平台中心将提供经过验证的管道，以快速表征功能收益的畸变可能是跨肿瘤谱系的工业化，以指导癌症患者的临床管理。