Cancer cell state interactions with immune populations of the tumor microenvironment

癌细胞状态与肿瘤微环境免疫群体的相互作用

• 批准号: 10362539
• 负责人: Dalia Barkley
• 金额: $ 5.18万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10362539
• 关键词: Algorithms; Atlas of Cancer Mortality in the United States; Biological; Biological Assay; Biological Models; Cancer Biology; Cancer Model; Cell Communication; Cell physiology; Cells; Coculture Techniques; Complex; Data; Disease; Drug resistance; Elements; Environment; Epigenetic Process; Experimental Models; Gene Expression; Genes; Genetic; Genetic Transcription; Goals; Heterogeneity; Human; Immune; Immune Evasion; Immune system; Immunotherapy; In Vitro; Location; Maintenance; Maps; Measures; Methods; Molecular; Mus; Natural Selections; Neoplasm Metastasis; Patients; Pattern; Phenotype; Physicians; Play; Population; Regimen; Relapse; Research; Resolution; Role; Sampling; Scientist; Spottings; System; Techniques; Technology; Testing; Therapeutic Intervention; Time; Tissue-Specific Gene Expression; Tissues; Training; Treatment Failure; Variant; Work; cancer cell; cancer type; cell type; clinical application; dynamic system; experimental study; improved; in vitro Assay; in vivo Model; innovation; insight; interdisciplinary approach; lens; mortality; neoplastic cell; rational design; single-cell RNA sequencing; transcriptome; transcriptomics; tumor; tumor heterogeneity; tumor microenvironment; tumor progression; tumorigenesis

SUMMARY Tumors are complex systems composed of genetically and transcriptionally heterogeneous cells, and this variation has been implicated as a cause of drug resistance and mortality. Understanding intra-tumor heterogeneity is therefore likely to have widespread scientific implications and clinical applications. The advent of single-cell RNA-Seq has enabled the mapping of transcriptionally distinct states among cancer cells across a wide range of tumor types and disease stages. In this project, I take a gene module-centric view to define cell states in a rigorous and widely applicable manner. In my preliminary work, I have identified specific cancer cell states that are conserved across many cancer types as well as some that are unique to specific tumors. This proposal aims to further characterize these states in relation to the tumor microenvironment, with the goal of furthering our understanding of tumors as complex dynamical systems amenable to therapeutic intervention. In order to systematically map interactions between tumor cell populations within their native context, I will integrate paired single-cell and spatial transcriptomic data obtained in primary patient samples. This will enable me to identify cell populations that interact within the tumor, and to characterize the gene expression changes that occur in these interactions. In order to further establish how cancer cell state function relates to the tumor microenvironment, I will take advantage of experimental model systems amenable to perturbation. I will use orthotopic mouse cancer models as a platform to deplete specific populations of the immune system and measure their effect on cancer cell states. Next, I will perform co-culture experiments followed by transcriptomic and phenotypic profiling to measure direct effects on one cell population on another. Collectively, the experiments and algorithms proposed will significantly improve our understanding of intratumoral heterogeneity through the lens of cancer cell states. Dissecting the complex interactions between cancer cell states and immune cell populations will be of particular translational relevance, as it may enable the rational design of immunotherapy regimens. The research proposed capitalizes on the strengths of the Yanai lab in cutting-edge molecular techniques, computational innovation, and in vitro and in vivo modeling of cancer. Together with the outlined training plan, this work will set me on a path to independent research as a physician-scientist.

概括 肿瘤是由遗传和转录异质细胞组成的复杂系统，以及 这种变异已被视为耐药性和死亡率的原因。了解肿瘤内 因此，异质性可能具有广泛的科学意义和临床应用。出现 单细胞RNA-seq的作用已使跨癌细胞之间的转录不同状态绘制 广泛的肿瘤类型和疾病阶段。在这个项目中，我采用以基因模块为中心的视图来定义细胞 以严格且广泛适用的方式国家。在我的初步工作中，我已经确定了特定的癌细胞 在许多癌症类型中保守的州以及特定肿瘤独有的国家。这 提案旨在进一步描述与肿瘤微环境有关的这些状态，目的是 进一步了解我们对肿瘤作为复杂动力学系统的理解，适合治疗干预。在 为了在其天然环境中系统地绘制肿瘤细胞种群之间的相互作用，我将 集成在原发性患者样品中获得的成对的单细胞和空间转录组数据。这将启用 我确定在肿瘤中相互作用的细胞群体，并表征基因表达的变化 这发生在这些相互作用中。为了进一步确定癌细胞状态的功能与肿瘤的关系 微环境，我将利用可扰动的实验模型系统。我会用 原位小鼠癌模型是耗尽免疫系统和特定种群的平台 衡量它们对癌细胞态的影响。接下来，我将执行共培养实验，然后进行 转录组和表型分析，以衡量对一个细胞群对另一个细胞群的直接影响。 总的来说，提出的实验和算法将显着提高我们对 肿瘤内异质性通过癌细胞态的晶状体。剖析复杂的相互作用 癌细胞状态和免疫细胞群体将具有特殊的转化相关性，因为它可能使 免疫疗法方案的合理设计。该研究提议利用Yanai的优势 尖端分子技术，计算创新以及癌症体内建模的实验室。 与概述的培训计划一起，这项工作将使我踏上独立研究的途径 医师科学家。