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

基本信息

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
项目类别：
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&apos 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

项目摘要

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.

项目概要/摘要肿瘤微环境 (TME) 由不同的免疫和基质元素组成 - 每个元素都具有上下文相关的表型状态和独特的功能——与癌细胞相互作用形成独特的细胞近年来，在理解肿瘤之间的相互作用方面取得了重大进展。然而，TME 细胞群在塑造转移以及利用其进行治疗方面具有重要作用。原发性和转移性肿瘤沉积物中 TME 内单细胞群落的特征是此外，目前还缺乏外周血浆中的循环游离核酸。用于癌症无创检测和起源问题定位的有前途的生物标志物但是，没有液体。活检检测已被开发用于监测 TME 的细胞状态和细胞群落。我认为对 TME 社区进行大规模分析可以提供新的治疗方法为了大规模研究 TME 社区，我最近开发了改变癌症治疗的机会。 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，根据大量肿瘤表达数据，我使用 EcoTyper 构建了第一个全球图谱。来自 16 种类型的超过 6,000 个原发性大块肿瘤样本中转录定义的细胞状态和生态型癌和超过 1,000 个弥漫性大 B 细胞淋巴瘤。尽管他们努力构建泛癌单细胞，但了解 TME 并没有实现单细胞分辨率。细胞图谱已被描述，它们不识别多细胞群落，也不提供自动化发现新细胞状态或在新数据中询问它们的方法。我建议大规模生态型分析 (1) 可以通过以下方式以单细胞分辨率进行对 EcoTyper 平台的专门改进，(2) 可以描绘进展的决定因素转移性疾病，（3）并且可用于无创监测临床相关的异质性在 K99 阶段，我将通过将其扩展到 EcoTyper 来显着改进。通过对大量单细胞 RNA 测序的联合分析来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球单细胞图谱，该图谱扩展了我们之前发布的数据。泛癌图谱；并将得出跨多个转移部位（包括肝脏）的全球生态型图谱；通过分析数以千计的 R00 期转移癌，我的团队将研究大脑和淋巴结。开发生物信息学工具来解析生态型的表观基因组特征，包括利用的方法单细胞和批量甲基化数据来定义 TME 生态型的甲基化特征，并将利用它们测试是否可以从循环核酸分子中可靠地检测肿瘤生态型。