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）是许多类型的人类癌症的致癌驱动因素。对于某些人 癌症，例如某些卵巢，乳房和子宫内膜癌，CNA很可能包括大部分 导致其高度恶性特性的遗传改变。 CNA也可能负责驾驶 肺癌的鳞状癌和胃癌和食管癌的亚群。相对较少的关注 正在为理解这类遗传改变而付费。更重要的是，通过癌症治疗 观点，没有路线图可以确定它们是否引起选择性依赖性 用于开发新的治疗剂。 正如我们的小组和其他人在过去几年中发现的那样，绝大多数CNA都包含 多个驱动器基因，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工具和 研究多基因CNA的模型，以便可以更容易地研究和利用它们 疗法。在AIM 1中，我们将结合CRISPR/CAS9和CRE-LOX基因组工程以准确建模 多基因CNA并确定它们如何影响正常乳腺上皮细胞中的致癌表型， 类似于目前研究了单基因改变（例如PIK3CA）中的突变。一旦我们有 经过验证的这些新细胞模型，我们将筛选诱导的依赖关系。在AIM 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 大规模综合癌症基因组数据集中存在的信息。我们有广泛的初步结果 这可以验证这种方法，包括预测CNA选择性依赖性。最后，真正了解 多基因CNA如何在癌症中发挥作用，我们必须在功能上探测多个的相互作用 司机。我们先前证明了这些相互作用是14q13的致癌性的关键特征 肺癌的扩增子和肝癌的第11季度扩增子。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可推广方法（AIM 3）。 我们的建议基于CNA是癌症中重要驱动因素的前提，但目前 需要改进研究方法。针对性治疗对患者的临床治疗 HER2扩增的乳腺癌强调了CNA的巨大翻译潜力。通过开发 该提案中描述的CNA的工具和模型，我们将对理解产生重大影响 多基因CNA，将为识别相关依赖性和治疗性奠定基础 策略。