Identification and characterization of cancer cell states by novel computational and experimental technologies - Resubmission - 1

通过新颖的计算和实验技术识别和表征癌细胞状态 - 重新提交 - 1

• 批准号: 10448890
• 负责人: Richard Mark White
• 金额: $ 59.3万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-21 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10448890
• 关键词: Ablation; Adopted; Atlas of Cancer Mortality in the United States; Biological Assay; Biological Models; Cancer Biology; Cancer Model; Cells; Clinical; Collaborations; Communities; Complex; Computing Methodologies; Data; Dependence; Detection; Development; Drug resistance; Elements; Environment; Fishes; Genes; Genetic; Genetic Engineering; Genetic Transcription; Goals; Heterogeneity; Human; Immune; Individual; Lead; Maintenance; Methods; Modeling; Molecular; Neoplasm Metastasis; Oncologist; Pharmaceutical Preparations; Play; Publications; Reporter; Research; Role; Sampling; System; Systems Biology; T-Lymphocyte; Technology; Testing; Time; Zebrafish; base; cancer cell; cancer therapy; cancer type; cell type; experimental study; flexibility; genetic manipulation; imaging capabilities; imaging genetics; improved; in vivo; innovation; insight; lens; melanoma; mortality; novel; predictive test; response; single-cell RNA sequencing; transcriptomics; treatment strategy; tumor; tumor heterogeneity; tumor initiation; tumor microenvironment; tumor progression; tumorigenesis

SUMMARY Tumors are complex systems composed of genetically and transcriptionally heterogeneous cells, and this heterogeneity has been implicated as a cause of drug resistance and overall mortality. Understanding intra- tumor heterogeneity is therefore likely to have widespread impact, both fundamental and clinical. The advent of single-cell RNA-Seq has led to the detection of transcriptionally distinct states among cancer cells across a wide range of tumor types and stages. However, the field lacks robust computational and experimental technologies to functionally identify and characterize these cancer cell states. In this project, we take a gene module-centric view to define cell states in a rigorous and widely applicable manner. We will validate the importance of each cell state using human samples and the zebrafish melanoma model, which has exceptional capabilities for imaging and perturbation of cell states throughout tumor progression and metastasis. To systematically characterize these cell states experimentally, we propose methods to assay their interdependencies within the tumor and with elements of the microenvironment. Our genetically engineered zebrafish will mark each cancer cell state with a fluorescent reporter and an ablation cassette, providing a flexible experimental platform to study and perturb each cell state. The reporter will enable us to sort cancer cell states and study them individually, with a particular emphasis on their plasticity. By systematically disrupting cancer cell states, we will further elucidate their individual contributions to tumor initiation, progression and metastasis. Using a spatial transcriptomics approach and integrating with single-cell RNA-seq data, we will map cancer cell states in relation to their microenvironment to screen for putative interactions. Finally, we will directly test predicted interactions between specific cancer cell states and the immune compartment using T cell-deficient fish. Throughout our three Aims, we adopt a systems biology workflow that iteratively cycles through modes of observations, perturbations, and refinement of our model of the functional role of cancer cell states during tumor progression. Our proposal collectively integrates the complementary expertise of the White and Yanai labs and sets out to significantly improve our understanding of intratumoral heterogeneity through the lens of cancer cell states.

概括 肿瘤是由遗传和转录异质细胞组成的复杂系统，这是 异质性被认为是抗药性和总体死亡率的原因。了解内部 因此，肿瘤异质性可能会产生广泛的影响，无论是基本还是临床。出现 单细胞RNA-seq已导致检测到癌细胞中癌细胞中转录不同的状态 广泛的肿瘤类型和阶段。但是，该领域缺乏强大的计算和实验性 在功能上识别和表征这些癌细胞态的技术。在这个项目中，我们采用一个基因 以模块为中心的视图以严格且广泛适用的方式定义单元格状态。我们将验证 使用人类样品和斑马鱼黑色素瘤模型的每个细胞状态的重要性 整个肿瘤进展和转移整个细胞态成像和扰动的能力。到 系统地以实验表征这些细胞状态，我们提出了分析它们的方法 肿瘤内的相互依赖性以及微环境的元素。我们的基因工程 斑马鱼将用荧光记者和消融盒标记每个癌细胞状态，提供 灵活的实验平台研究和扰动每个细胞状态。记者将使我们能够分类癌症 细胞状态并单独研究它们，特别着重于它们的可塑性。通过系统地 破坏癌细胞状态，我们将进一步阐明他们对肿瘤开始的个人贡献， 进展和转移。使用空间转录组学方法并与单细胞RNA-Seq集成 数据，我们将与其微环境相关的癌细胞状态以筛选推定的相互作用。 最后，我们将直接测试特定癌细胞态与免疫之间的预测相互作用 使用T细胞缺陷的鱼室。在我们的三个目标中，我们采用了系统生物学工作流程 通过观察模式，扰动和精炼我们的功能模型进行迭代循环 癌细胞态在肿瘤进展中的作用。我们的建议集体整合了补充 白色和亚奈实验室的专业知识，并着手显着提高我们对肿瘤内的理解 通过癌细胞态的晶状体异质性。