Multigenic copy number alterations

项目摘要 DNA拷贝数改变（CNA）是许多类型人类癌症的致癌驱动因素。对于一些 对于某些癌症，例如某些卵巢癌、乳腺癌和子宫内膜癌，CNA很可能包括大部分的 遗传变异导致了它们的高度恶性特性。CNA也可能负责驾驶 肺鳞状细胞癌和胃癌和食管癌的亚类。关注相对较少 是为了理解这类基因改变更重要的是，从癌症治疗 从这个角度来看，没有路线图来确定它们是否会引起可能被 用于开发新疗法。 正如我们的团队和其他人在过去几年中发现的那样，绝大多数CNA包含 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，该项目的总体目标是开发新的工具， 研究多基因CNA的模型，以便它们可以更容易地研究和利用， 治疗学在目标1中，我们将联合收割机CRISPR/Cas9和Cre-Lox基因组工程相结合， 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， 类似于目前研究的单基因改变中的突变，如PIK 3CA。一旦我们 验证了这些新的细胞模型，我们将筛选诱导的依赖性。在目标2中，我们将开发和 实施计算方法，从仓库中提取有关特定CNA的信息， 大规模整合癌症基因组数据集中存在的信息。我们有大量的初步结果 验证了这种方法，包括预测CNA选择性依赖性。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基因CNA之间的相互作用， 司机我们先前证明，这些相互作用是14 q13基因致瘤性的关键特征。 肺癌中的11 q13扩增子和肝癌中的11 q13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（目标3）。 我们的建议是基于这样的前提，即CNA是癌症的重要驱动因素，但目前的癌症 研究方法有待改进。针对性治疗患者的临床有效性 HER 2扩增的乳腺癌强调了CNA的巨大翻译潜力。通过发展 本提案中描述的CNA工具和模型，我们将对理解 多基因CNA，并将奠定基础，确定相关的依赖性和治疗 战略布局

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.