Multigenic copy number alterations

多基因拷贝数改变

• 批准号: 9333598
• 负责人: Scott Powers
• 金额: $ 55.2万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-15 至 2022-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9333598
• 关键词: 11q13; 14q13; Address; Affect; Attention; Automobile Driving; Benchmarking; Biological; Breast Cancer cell line; Breast Epithelial Cells; CCND1 gene; CRISPR/Cas technology; Cataloging; Catalogs; Cell model; Cells; Clinical effectiveness; Communities; Computational algorithm; Computing Methodologies; Control Locus; Cre-LoxP; DHFR gene; DNA; DNA copy number; Data Set; Dependency; ERBB2 gene; Endometrial Carcinoma; Engineering; Epidermal Growth Factor Receptor; Event; Evolution; Experimental Models; FGF19 gene; GRB7 gene; Gene Combinations; Gene Mutation; Genes; Genetic; Genetic Recombination; Genetic study; Genome engineering; Glioblastoma; Goals; Human; Individual; KRAS2 gene; Link; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of esophagus; Malignant neoplasm of liver; Malignant neoplasm of lung; Malignant neoplasm of ovary; Methods; Methotrexate; Modeling; Mutation; Nature; Oncogenes; Oncogenic; Oncoproteins; PDGFRA gene; PIK3CA gene; Pathway interactions; Patients; Phenotype; Phosphoproteins; Phosphorylation; Plasmids; Play; Primary carcinoma of the liver cells; Property; Proteins; Proteomics; RNA; Research; Resources; Role; Sampling; Site; Squamous cell carcinoma; Stretching; Structure; System; Technology; Testing; The Cancer Genome Atlas; Therapeutic; Time; Work; anticancer research; base; cancer genome; cancer therapy; combinatorial; comparative; computerized tools; design; genomic profiles; improved; lung Carcinoma; malignant breast neoplasm; malignant stomach neoplasm; molecular subtypes; mutant; new therapeutic target; next generation sequencing; novel; novel therapeutics; screening; success; tool; tumor; tumor progression

Project Summary DNA copy number alterations (CNAs) are oncogenic drivers for many types of human cancer. For some cancers, e.g. certain ovarian, breast and endometrial cancers, it is very likely that CNAs, comprise the bulk of genetic alterations responsible for their highly malignant properties. CNAs may also be responsible for driving squamous carcinoma of the lung and for subsets of gastric and esophageal cancers. Relatively little attention is being paid to understanding this class of genetic alterations. More importantly, from a cancer treatment perspective, there is no roadmap for determining whether they induce selective dependencies that could be utilized for developing new therapeutics. As our group and others have discovered in the past several years, the vast majority of CNAs contain multiple driver genes, and this makes it considerably more difficult to study how they impact cancer progression compared to single-gene events. The overall goal of this project is to develop new tools and models to investigate multigenic CNAs so that they can be more readily studied and utilized in developing new therapeutics. In Aim 1, we will combine CRISPR/Cas9 and Cre-Lox genome engineering to accurately model multigenic CNAs and determine how they impact oncogenic phenotypes in normal mammary epithelial cells, similar to how mutations in single-gene alterations such as PIK3CA are currently studied. Once we have validated these new cell models, we will screen for induced dependencies. In Aim 2, we will develop and implement computational methods to extract information about specific CNAs from the warehouse of information present in large-scale integrated cancer genome datasets. We have extensive preliminary results that validate this approach, including the prediction of CNA-selective dependencies. Lastly, to truly understand how multigenic CNAs play a role in cancer, we must functionally probe the interactions between multiple drivers. We previously demonstrated that these interactions were key features of the oncogenicity of the 14q13 amplicon in lung cancer and 11q13 amplicon in liver cancer. Thus, our final goal is to develop and implement generalizable methods to study genetic interactions between multiple drivers (Aim 3). Our proposal is based on the premise that CNAs are important drivers in cancer but that the current research approach needs to be improved. The clinical effectiveness of targeted treatments for patients with HER2-amplified breast cancers underscores the enormous translational potential of CNAs. By developing the tools and models for CNAs described in this proposal, we will make a significant impact on understanding multigenic CNAs and will lay the groundwork for identifying associated dependencies and therapeutic strategies.

项目摘要 DNA拷贝数改变(CNA)是多种人类癌症的致癌驱动因素。对一些人来说 癌症，例如某些卵巢癌、乳腺癌和子宫内膜癌，很可能是CNA，占大部分 导致它们高度恶性的基因改变。国家公路交通安全管理局也可能负责驾驶 肺鳞癌以及胃癌和食道癌的亚类。相对较少的关注 被付钱来理解这类基因改变。更重要的是，从癌症治疗中 从角度来看，没有路线图来确定它们是否会导致选择性依赖 用于开发新的治疗方法。 正如我们小组和其他人在过去几年中发现的那样，绝大多数CNA包含 多个驱动基因，这使得研究它们如何影响癌症变得更加困难 与单基因事件相比，进展。该项目的总体目标是开发新的工具和 研究多基因CNA的模型，以便更容易地研究和利用它们开发新的 治疗学。在目标1中，我们将结合CRISPR/Cas9和CRE-Lox基因组工程来精确建模 并确定它们如何影响正常乳腺上皮细胞的致癌表型， 类似于目前研究单基因突变(如PIK3CA)的方式。一旦我们有了 验证了这些新的细胞模型，我们将筛选诱导依赖关系。在目标2中，我们将发展和 实施计算方法以从仓库中提取有关特定CNA的信息 信息存在于大规模整合的癌症基因组数据集中。我们已经有了广泛的初步结果 这验证了这种方法，包括对CNA选择性依赖的预测。最后，要真正理解 多基因cna如何在癌症中发挥作用，我们必须从功能上探索多个基因之间的相互作用。 司机。我们先前证明了这些相互作用是14q13致癌性的关键特征 肺癌为扩增子，肝癌为11q13扩增子。因此，我们的最终目标是开发和实施 研究多个驱动因素之间遗传相互作用的通用方法(目标3)。 我们的建议是基于这样一个前提，即CNA是癌症的重要驱动因素，但目前 研究方法有待改进。靶向治疗慢性粒细胞白血病的临床疗效观察 HER2扩增的乳腺癌突显了CNA的巨大翻译潜力。通过开发 对于本建议书中描述的CNA的工具和模型，我们将对理解产生重大影响 并将为确定相关的依赖性和治疗性奠定基础 战略。