High resolution profiling of cellular communities in the tumor microenvironment

项目摘要/摘要 肿瘤微环境(TME)由不同的免疫和基质成分组成-每种成分都有 与环境相关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细胞群在形成转移和利用它进行治疗方面的作用。然而，一种泛癌症 原发和转移性肿瘤沉积中TME内单细胞群落的特征是 目前还不够。此外，外周血浆中循环中的游离核酸已经出现 在癌症的非侵入性检测和来源问题测绘方面有希望的生物标记物。然而，没有液体 已经开发了活组织检查来监测TME的细胞状态和细胞群落。 我推测，对TME社区的大规模分析可能会带来新的治疗方法 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，来自大量的肿瘤表达数据。使用EcoTyper，我构建了第一个全球地图集 来自16种类型的6,000个原发肿瘤样本中转录定义的细胞状态和生态型 癌症和1,000例弥漫性大B细胞淋巴瘤。尽管这些地图集是 了解TME，他们不能实现单细胞分辨率。虽然努力构建泛癌症单一- 已经描述了细胞图谱，它们不能识别多细胞群落，也不能提供自动化 在新数据中发现新的单元状态或询问它们的方法。 我建议大规模的生态型分析(1)可以在单细胞分辨率下通过 对EcoTyper平台的专门改进，(2)可以描述进步的决定因素 到转移性疾病，(3)，并可用于无创监测临床相关的异质性。 液体活组织检查的TME。在K99阶段，我将显著改进EcoTyper，将其扩展到 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。我还将定义一个单元格状态的全局单元格图谱，它扩展了我们之前发布的 泛癌图谱；并将得出包括肝脏在内的多个转移部位的全球生态型图谱， 大脑和淋巴结，通过分析数千个转移性癌症。在R00阶段，我的团队将 开发生物信息学工具来解析生态型的表观基因组特征，包括利用 单细胞和批量甲基化数据，以定义TME生态型的甲基化特征，并将利用它们 测试是否可以从循环核酸分子中可靠地检测出肿瘤生态型。

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.