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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：
10228007
负责人：
Benjamin Deneen
金额：
$ 72.16万
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 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10228007
关键词：
Address Affect Algorithms Alleles Area Bar Codes 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 Dose Drug resistance Engineering Epithelial ovarian cancer Event Face Foundations Frequencies Gene Mutation Genes Genetic Genome Genomics Glioblastoma Heterogeneity Human Immune Industrialization Informatics International 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 Visualization Xenograft procedure algorithm development cancer cell cancer genome cancer heterogeneity cancer initiation cancer therapy cancer type cell type clinically relevant cohort combinatorial data management driver mutation 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 patient derived xenograft model 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 web platform

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一个肿瘤的纲要- 相关的基因组改变，以优先考虑最有前途的药物开发的治疗目标。这些努力揭示了癌症基因组复杂性的惊人水平。虽然我们知道关于众所周知的癌症基因中复发性畸变的功能和临床影响，更丰富的低频率突变如何促进肿瘤进展。有效将肿瘤基因组数据集转化为癌症治疗需要新的实验系统来提供信息，靶点在相关生物学背景下的功能活性，包括肿瘤间和肿瘤内异质性为了满足这些需求，我们提出了一个CTD 2中心，将提供研究界表征和验证致病性“驱动因子”高通量信息学和实验方法突变和融合基因，以及鉴定有意义地预测反应的分子标记物，对抗癌疗法的抵抗。我们将追求以下具体目标：在目标1中，我们将实施用于以高灵敏度和特异性鉴定驱动突变的算法框架。呢我们一起来算法开发，培训和测试工作，预测致癌，功能获得性突变驱动因素，多形性胶质母细胞瘤（GBM）、胰腺导管腺癌（PDAC）和上皮性卵巢癌（平机会）。这些计算方法将适用于所有癌症类型的分析。我们接下来将将约1，500个选定的突变和约400个融合基因工程化到表达载体中，沿着的还有个性化的、患者定义的编码突变。在目标2中，我们将输入突变等位基因和融合基因， GBM、PDAC和EOC环境特异性体内功能筛选，考虑到以下因素的重要性：遗传背景、肿瘤微环境和单一和组合驱动因子选择中的异质性肿瘤的发生。在目标3中，我们将确定肿瘤内异质性对肿瘤的影响。使用DNA条形码化的人患者来源的异种移植物模型对治疗剂的敏感性和抗性概括了癌症的异质性。我们将确定在何种程度上单一的目标代理人和它们的合理组合改变了肿瘤群体动态。我们还将利用Aim 1信息学，目标2和4中的功能表征，以表征“持久性”群体，从而识别畸变与耐药性有关。在目标4中，我们将使用高通量功能蛋白质组学，创新的蛋白质，蛋白质相互作用测定和告密者药物库筛选研究，以阐明潜在的机制，由有效驱动因素产生的治疗责任。我们的CTD 2中实施的基础平台中心将提供一个经过验证的管道，用于快速表征功能获得性畸变，跨肿瘤谱系工业化以指导癌症患者的临床管理。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

BRD4 facilitates replication stress-induced DNA damage response.

DOI：
10.1038/s41388-018-0194-3
发表时间：
2018-07
期刊：
Oncogene
影响因子：
8
作者：
Zhang J;Dulak AM;Hattersley MM;Willis BS;Nikkilä J;Wang A;Lau A;Reimer C;Zinda M;Fawell SE;Mills GB;Chen H
通讯作者：
Chen H

Common and distinct patterns of acquired uniparental disomy and homozygous deletions between lung squamous cell carcinomas and lung adenocarcinoma.

DOI：
10.1016/j.neo.2023.100932
发表时间：
2023-11
期刊：
NEOPLASIA
影响因子：
4.8
作者：
Tuna, Musaffe;Mills, Gordon B.;Amos, Christopher, I
通讯作者：
Amos, Christopher, I

Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma.

DOI：
10.1158/2767-9764.crc-23-0083
发表时间：
2023-08
期刊：
CANCER RESEARCH COMMUNICATIONS
影响因子：
0
作者：
Hill, Holly A.;Jain, Preetesh;Ok, Chi Young;Sasaki, Koji;Chen, Han;Wang, Michael L.;Chen, Ken
通讯作者：
Chen, Ken

Single-cell trajectory analysis reveals a CD9 positive state to contribute to exit from stem cell-like and embryonic diapause states and transit to drug-resistant states.

DOI：
10.1038/s41420-023-01586-9
发表时间：
2023-08-04
期刊：
CELL DEATH DISCOVERY
影响因子：
7
作者：
Li, Xi;Poire, Alfonso;Jeong, Kang Jin;Zhang, Dong;Chen, Gang;Sun, Chaoyang;Mills, Gordon B.
通讯作者：
Mills, Gordon B.