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

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），但这些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，并在临床环境中提供了主要手段， 诊断疾病和管理治疗。然而，相对较少的分子洞察力可以从 经典的苏木精和伊红（H&E）或免疫组织化学。这些考虑导致最近 开发用于执行高度复用组织成像的多种方法。它允许的属性 在人类和小鼠模型中保存的3D环境中确定单个细胞。在一个研究 多重成像提供了对肿瘤发生，进展， 免疫编辑和逃脱在临床环境中，高分辨率成像有望增强传统的 组织病理学诊断疾病的分子信息需要指导使用靶向和 免疫疗法多个高倍组织图像产生20-100种蛋白质或 在样品中具有100 nm至超过1 cm空间尺度的可分辨结构上的其他生物分子， 大至5 cm 2。这些图像包含106至107个细胞，编码数据高达1 TB。 更广泛使用高分辨率成像的主要障碍集中在与以下相关的计算挑战上： 处理、管理和传播这种大小的图像。许多算法和方法已经被 开发用于处理培养物中生长的细胞的图像，这些图像为组织分析提供了基础。 图像.然而，高复杂度的组织成像提出了许多额外的挑战， 单元格的拥挤以及数据的大小。我们已经构建了一个云部署管道， （MCMICRO），其使用Docker-containers和NextFlow管道来处理大规模组织图像， 以标准化格式生成单个单元格数据。我们建议重新设计证据的组成部分- 概念的实现，使其具有表演性和广泛的实用性。目标1将提高 通过代码分析和优化单个模块。目标2将完成一般用户和 MCICRO及其模块的程序员文档，以使来自开放的 源代码社区，并增加互操作性和执行测试。Aim 3将为MCMICRO添加模块 基于公共领域现有的概念验证代码。目标4将实现管道输出 中间和最终结果-包括图像数据本身-从云端无需下载。这些 补充目标与原奖项的核定目标直接相关。实现这些目标将 涉及现有软件模块的部分再工程和使用真实的 世界测试数据，我们将发布作为本补充的一部分。所有目标都涉及执行MCMICRO， 亚马逊和谷歌云平台。

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.