High resolution profiling of cellular communities in the tumor microenvironment

肿瘤微环境中细胞群落的高分辨率分析

• 批准号: 10572355
• 负责人: Bogdan Alexandru Luca
• 金额: $ 16.97万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2023-12-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10572355
• 关键词: Atlases; Biological Assay; Biological Markers; Blood specimen; Brain; Cancer Biology; Cancer Etiology; Carcinoma; Cells; Cessation of life; Clinical; Collection; Communities; Complex; DNA Methylation; Data; Data Science; Data Set; Dedications; Dependence; Deposition; Detection; Development; Disease; Disease Management; Ecosystem; Elements; Evolution; Genetic Transcription; Goals; Immune; Immunofluorescence Immunologic; Individual; Joints; Ligands; Liver; Malignant Neoplasms; Maps; Metastatic Carcinoma; Methods; Methylation; Modeling; Molecular Profiling; Monitor; Neoplasm Metastasis; Non-Invasive Cancer Detection; Nucleic Acids; Patients; Pattern; Phase; Phenotype; Plasma; Population; Predictive Value; Primary Neoplasm; Process; Publishing; Recovery; Research; Resolution; Role; Sampling; Shapes; Signal Pathway; Site; Solid Neoplasm; Techniques; Testing; Therapeutic; Tumor Biology; bioinformatics tool; cancer cell; cancer therapy; cell community; cell free DNA; clinically relevant; computing resources; epigenomics; improved; indexing; large cell Diffuse non-Hodgkin' s lymphoma; liquid biopsy; lymph nodes; machine learning framework; non-invasive monitor; novel; novel therapeutics; peripheral blood; prognostic; prognostic value; receptor; single-cell RNA sequencing; transcriptomics; treatment response; tumor; tumor DNA; tumor heterogeneity; tumor microenvironment; tumor progression

PROJECT SUMMARY/ABSTRACT The tumor microenvironment (TME) is comprised of diverse immune and stromal elements – each with context-dependent phenotypic states and distinct functions – that interact with cancer cells to form unique cellular communities. In recent years, major advances have been made in understanding the cross-talk between tumor and TME cell populations in shaping metastasis, and in leveraging it for therapies. However, a pan-cancer characterization of single-cell communities within the TME, both in primary and metastatic tumor deposits, is currently lacking. Moreover, circulating cell-free nucleic acids in peripheral blood plasma have emerged as promising biomarkers for noninvasive detection of cancer, and for issue-of-origin mapping. However, no liquid biopsy assays have been developed to monitor the cell states and cellular communities of the TME. I hypothesize that large-scale profiling of TME communities could present new therapeutic opportunities to transform cancer treatment. To study TME communities at scale, I recently developed EcoTyper, a new machine learning framework for delineating cell states and multicellular communities, termed ecotypes, from bulk tumor expression data. Using EcoTyper, I constructed the first global atlas of transcriptionally-defined cell states and ecotypes in >6,000 primary bulk tumor samples from 16 types of carcinoma and >1,000 diffuse large B cell lymphomas. Although these atlases are major milestones toward understanding the TME, they do not achieve single-cell resolution. While efforts to construct pan-cancer single- cell atlases have been described, they do not identify multicellular communities, nor do they provide automated methods to discover new cell states or interrogate them in new data. I propose that large-scale ecotype profiling (1) can be performed at single-cell resolution via dedicated improvements to the EcoTyper platform, (2) can delineate the determinants of progression to metastatic disease, (3) and can be used to noninvasively monitor clinically relevant heterogeneity in the TME from liquid biopsies. In the K99 phase, I will significantly improve upon EcoTyper by extending it to identify cell states and ecotypes from the joint analysis of large collections of single-cell RNA sequencing (scRNA-seq) data. I will also define a global single-cell atlas of cell states that extends our previously published pan-carcinoma atlas; and will derive a global atlas of ecotypes across multiple metastatic sites, including liver, brain and lymph nodes, by analyzing thousands of metastatic carcinomas. In the R00 phase, my group will develop bioinformatics tools for resolving epigenomic signatures of ecotypes, including methods that leverage single-cell and bulk methylation data to define methylation signatures of TME ecotypes, and will leverage them to test whether tumor ecotypes can be reliably detected from circulating nucleic acid molecules.

项目总结/摘要 肿瘤微环境（TME）由不同的免疫和基质成分组成，每种成分都具有 背景依赖的表型状态和不同的功能-与癌细胞相互作用，形成独特的细胞 社区.近年来，在理解肿瘤之间的串扰方面取得了重大进展， 和TME细胞群在形成转移中的作用，并将其用于治疗。然而，一个泛癌症 在原发性和转移性肿瘤沉积物中，TME内的单细胞群落的表征是 目前缺乏。此外，外周血血浆中的循环无细胞核酸已经出现， 用于癌症的非侵入性检测和用于起源定位的有前景的生物标志物。然而，没有液体 已经开发了活组织检查测定来监测TME的细胞状态和细胞群落。 我假设，大规模的TME社区分析可以提供新的治疗方法， 改变癌症治疗的机会。为了大规模研究TME社区，我最近开发了 EcoTyper是一种新的机器学习框架，用于描绘细胞状态和多细胞群落，称为 生态型，来自大量肿瘤表达数据。使用EcoTyper，我构建了第一个全球地图集， 在来自16种类型的肿瘤的> 6，000个原发性大块肿瘤样品中， 癌和> 1，000例弥漫性大B细胞淋巴瘤。尽管这些地图集是 然而，在理解TME的情况下，它们不能实现单细胞分辨率。虽然努力构建泛癌症单一- 虽然已经描述了细胞图谱，但它们不能识别多细胞群落，也不能提供自动化的 发现新的细胞状态或在新数据中询问它们的方法。 我建议，大规模的生态型剖析（1）可以在单细胞分辨率进行，通过 EcoTyper平台的专门改进，（2）可以描述进展的决定因素 转移性疾病，（3）并可用于非侵入性监测临床相关的异质性， 液体活检的TME在K99阶段，我将对EcoTyper进行重大改进， 从大量单细胞RNA测序的联合分析中识别细胞状态和生态型 （scRNA-seq）数据。我还将定义一个细胞状态的全局单细胞图谱， 泛癌图谱;并将获得跨多个转移位点的生态型的全球图谱，包括肝脏， 脑和淋巴结，通过分析成千上万的转移癌。在R 00阶段，我的团队将 开发生物信息学工具，用于解析生态类型的表观基因组特征，包括利用 单细胞和批量甲基化数据来定义TME生态型的甲基化特征，并将利用它们 以测试是否可以从循环核酸分子中可靠地检测肿瘤生态型。