Systems Pharmacology of Therapeutic and Adverse Responses to ImmuneCheckpoint and Small Molecule Drugs

免疫检查点和小分子药物治疗和不良反应的系统药理学

基本信息

批准号：
10405812
负责人：
PETER Karl SORGER
金额：
$ 25.35万
依托单位：
HARVARD MEDICAL SCHOOL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2022-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10405812
关键词：
3-Dimensional Address Algorithms Atlases Award Behavior Cells Clinical Cloud Computing Code Communities Community Developments Computer software Coupled Crowding DNA sequencing Data Data Set Data Store Development Devices Diagnosis Disease Disease Management Docking Documentation Engineering Ensure Environment Evaluation Foundations Funding Hematoxylin and Eosin Staining Method Histology Hour Human Image Image Analysis Imaging Techniques Immune Immunohistochemistry Immunotherapy Individual International Laboratories Language Lateral Licensing Location Measures Memory Metadata Methods Microscopy Molecular Morphology Movement Mus Output Parents Performance Pharmaceutical Preparations Pharmacology Process Property Proteins Public Domains RNA Research Resolution Resources Running Specimen Staging Standardization Structure System Testing Therapeutic Time Tissue imaging Tissues Translational Research Translations United States National Institutes of Health Update Visual Visualization Visualization software algorithmic methodologies base cellular imaging cloud based cloud platform cost disease diagnosis file format flexibility human imaging human model image processing image visualization immune checkpoint improved insight interoperability microscopic imaging mouse model multiplexed imaging open source performance tests preservation prevent prototype quality assurance response small molecule symposium targeted treatment tumor tumor initiation tumor microenvironment whole slide imaging

项目摘要

SUMMARY ABSTRACT Single cell RNA profiling and DNA sequencing has revolutionized our understanding of the tumor microenvironment (TME) but such data lacks the spatial context and morphological information found in images. Histology makes extensive use of morphology, and in a clinical setting provides the primary means of diagnosing disease and managing treatment. However, relatively little molecular insight can be obtained from classical Hematoxylin and Eosin (H&E) or immunohistochemistry. These considerations have led to the recent development of multiple methods for performing highly multiplexed tissue imaging. It allows the properties of single cells to be determined in a preserved 3D environment in humans and mouse models. In a research setting, multiplexed imaging provides new insight into molecular mechanisms of tumor initiation, progression, immune editing, and escape. In a clinical setting, high-plex imaging promises to augment the traditional histopathological diagnosis of disease with molecular information needed to guide use of targeted and immuno-therapies. Multiple high-plex tissue images yield subcellular resolution data on 20-100 proteins or other biomolecules on resolvable structures having spatial scales from 100 nm to over 1 cm in specimens as large as 5 cm2. These images contain 106 to 107 cells, encoded in up to 1 TB of data. The primary barrier to wider use of high-plex imaging centers on the computational challenges associated with processing, managing, and disseminating images of this size. Many algorithms and methods have been developed to process images of cells grown in culture and these provide a foundation for analysis of tissue images. However, high-plex tissue imaging poses many additional challenges arising from the diversity and crowding of cells and as well as the size of the data. We have constructed a cloud-deployed pipeline (MCMICRO) that uses Docker-containers and a NextFlow pipeline to process large-scale tissue images and generate single-cell data in a standardized format. We propose to reengineering the components of this proof- of-concept implementation to make it performative and broadly useful. Aim 1 will improve the performance of individual modules through code profiling and optimization. Aim 2 will complete the general user and programmer documentation of MCICRO and its modules to enable continued contributions from the open- source community and to increase interoperability and perform testing. Aim 3 will add modules to MCMICRO based on existing proof-of concept code available in the public domain. Aim 4 will enable output of pipeline intermediate and final results - including image data itself - from the cloud without requiring download. These supplementary aims are directly relevant the approved aims of the parent award. Completing these aims will involve partial reengineering of existing software modules and evaluation of pipeline performance using real- world test data that we will release as part of this supplement. All Aims involve executing MCMICRO on Amazon and Google cloud platforms.

摘要摘要单细胞 RNA 分析和 DNA 测序彻底改变了我们对肿瘤的理解微环境（TME），但此类数据缺乏空间背景和形态信息图像。组织学广泛使用形态学，在临床环境中提供了研究形态学的主要手段。诊断疾病和管理治疗。然而，从中获得的分子洞察相对较少。经典苏木精和曙红 (H&E) 或免疫组织化学。这些考虑导致了最近开发执行高度多重组织成像的多种方法。它允许以下属性在人类和小鼠模型中保存的 3D 环境中确定单细胞。在一项研究中设置，多重成像提供了对肿瘤发生、进展的分子机制的新见解，免疫编辑和逃脱。在临床环境中，高复数成像有望增强传统的成像技术疾病的组织病理学诊断需要分子信息来指导靶向和治疗的使用免疫疗法。多个高复数组织图像可生成 20-100 种蛋白质或样本中空间尺度从 100 nm 到超过 1 cm 的可解析结构上的其他生物分子大至5cm2。这些图像包含 106 到 107 个单元，编码为高达 1 TB 的数据。更广泛使用高复数成像的主要障碍集中在与相关的计算挑战处理、管理和传播这种尺寸的图像。许多算法和方法已经被开发用于处理培养细胞的图像，这些为组织分析提供了基础图像。然而，高复数组织成像由于多样性和多样性而带来了许多额外的挑战。细胞的拥挤程度以及数据的大小。我们构建了云端部署的管道 (MCMICRO) 使用 Docker 容器和 NextFlow 管道来处理大规模组织图像和以标准化格式生成单细胞数据。我们建议重新设计这个证明的组成部分- 概念实施，使其具有执行性和广泛实用性。目标 1 将提高性能通过代码分析和优化来分析各个模块。目标 2 将完成一般用户和 MCICRO 及其模块的程序员文档，以确保开放的持续贡献源社区并提高互操作性和执行测试。 Aim 3 将向 MCMICRO 添加模块基于公共领域现有的概念验证代码。目标 4 将启用管道输出中间和最终结果 - 包括图像数据本身 - 来自云端，无需下载。这些补充目标与母奖项的批准目标直接相关。完成这些目标将涉及现有软件模块的部分重新设计以及使用真实的管道性能评估我们将作为本补充的一部分发布世界测试数据。所有目标都涉及执行 MCMICRO 亚马逊和谷歌云平台。

项目成果

期刊论文数量（114）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms.

可微生物学：使用深度学习进行基于生物物理学和数据驱动的分子机制建模。

DOI：
发表时间：
2021-10
期刊：
影响因子：
48
作者：
AlQuraishi, Mohammed;Sorger, Peter K
通讯作者：
Sorger, Peter K

Developmental Stage Classification of Embryos Using Two-Stream Neural Network with Linear-Chain Conditional Random Field.

使用具有线性链条件随机场的双流神经网络对胚胎发育阶段进行分类。

DOI：
发表时间：
2021
期刊：
影响因子：
0
作者：
Lukyanenko, Stanislav;Jang, Won;Wei, Donglai;Struyven, Robbert;Kim, Yoon;Leahy, Brian;Yang, Helen;Rush, Alexander;Ben;Needleman, Daniel;Pfister, Hanspeter
通讯作者：
Pfister, Hanspeter

Combination treatment optimization using a pan-cancer pathway model.

使用泛癌途径模型优化联合治疗。

DOI：
发表时间：
2021-12
期刊：
影响因子：
4.3
作者：
Schmucker, Robin;Farina, Gabriele;Faeder, James;Fröhlich, Fabian;Saglam, Ali Sinan;Sandholm, Tuomas
通讯作者：
Sandholm, Tuomas

Three-dimensional spatial transcriptomics uncovers cell type localizations in the human rheumatoid arthritis synovium.

三维空间转录组学揭示了人类类风湿关节炎滑膜中的细胞类型定位。

DOI：
发表时间：
2022-02-11
期刊：
影响因子：
5.9
作者：
Vickovic, Sanja;Schapiro, Denis;Carlberg, Konstantin;Lötstedt, Britta;Larsson, Ludvig;Hildebrandt, Franziska;Korotkova, Marina;Hensvold, Aase H;Catrina, Anca I;Sorger, Peter K;Malmström, Vivianne;Regev, Aviv;Ståhl, Patrik L
通讯作者：
Ståhl, Patrik L

Copper induces cell death by targeting lipoylated TCA cycle proteins.

铜通过靶向硫酰化 TCA 循环蛋白来诱导细胞死亡。

DOI：
发表时间：
2022-03-18
期刊：
影响因子：
0
作者：
Tsvetkov, Peter;Coy, Shannon;Petrova, Boryana;Dreishpoon, Margaret;Verma, Ana;Abdusamad, Mai;Rossen, Jordan;Joesch;Humeidi, Ranad;Spangler, Ryan D;Eaton, John K;Frenkel, Evgeni;Kocak, Mustafa;Corsello, Steven M;Lutsenko, Svetlana
通讯作者：
Lutsenko, Svetlana