AstroPath Integration Resource Core

摘要 AstroPath-基因组学核心将成为传播强大的分子病理学、信息学、大数据 科学和基因组学方法，以实现前列腺癌计划的目标。它将提供 对所有三个项目的关键支持。关键战略是利用现有的基础设施、设备和 服务，同时还提供专门的方法和能力，这些方法和能力是为 支持TBEL计划中的活动。这一模式将允许高效和经济高效地利用 这些强大的技术通过当前AstroPath基因组学的专注努力和专业知识 通过与这里所有项目和核心的密切互动，核心人员，而不必建立 设备、行政基础设施、生物检疫标准操作规程和实验室信息管理系统 (LIMS)，从头开始。分子病理学、信息学和大数据科学的这种集中，以及 数学/计算集成方法将产生成本效益高的策略，避免重复工作 在TBEL计划的每个组成部分中。核心小组将在三个主要目标下开展工作。目标1将 部署先进的数字病理工具，用于多分析免疫组织化学、免疫荧光和 微环境深层次表征的原位杂交方法和图像分析 从炎症到肿瘤的过渡。具体技术包括多光谱多分析物成像， 显色迭代多重IHC(ChIM-IHC)和多重ACD原位杂交。目标2将利用批量， 单细胞和空间基因组学方法来测量基因组和表观基因组的变化 PIA、PIN和癌病变的上皮室和间质室。具体技术包括，基因组 广泛和超深度的靶向DNA甲基化分析，多模式单核ATAC-和偶联RNA- SEQ(SnATAC-/SnRNA-seq)和空间转录组学。跨多模式数据流的集成将 通过实施创新的贝叶斯非负矩阵分解和转移是可能的 通过CoGAPS和项目R框架的学习方法。目标3将实施和扩展 用于高维单胞和块体计算分析的AstroPath框架 基因组学/表观基因组学、多分析数字病理学和先进的成像数据。要实现这些目标 为了实现目标，核心领导人组建了一支令人印象深刻的专家团队，以支持塔贝尔中心，包括 在大数据科学/信息学、分子病理学、基因组学技术、 免疫病理学、计算/系统生物学和应用数学。

SUMMARY The AstroPath-Genomics Core will be a hub to disseminate powerful molecular pathology, informatics, big data science, and genomics methods tailored to accomplish the goals of the Prostate TBEL Program. It will provide critical support for ALL three projects. The key strategy is to leverage existing infrastructure, equipment and services at the SKCCC, while also providing specialized approaches and capabilities that are highly tailored to support the activities in the TBEL Program. This model will allow efficient and cost-effective harnessing of these powerful technologies through the dedicated effort and expertise of the current AstroPath-Genomics Core personnel through the close interaction with all Projects and Cores here, without having to establish the equipment, administrative infrastructure, biospecimen SOPs, and laboratory informatics management systems (LIMS), from scratch. This centralization of the molecular pathology, informatics and big data science, and mathematical/computational integration methods will yield cost-effective strategies that avoid duplicating effort in each component of the TBEL Program. The Core will carry out its work in three major Aims. Aim 1 will deploy advanced digital pathology tools for multi-analyte immunohistochemistry, immunofluorescence, and in situ hybridization approaches and image analysis for deep characterization of the microenvironmental transitions from inflammation to neoplasia. Specific technologies include multi-spectral multi-analyte imaging, Chromogenic iterative multiplex-IHC(ChIM-IHC), and multiplex ACD in situ hybridization. Aim 2 will utilize bulk, single cell, and spatial genomics approaches to measure the genomic and epigenomic alterations to the epithelial and stromal compartments of PIA, PIN, and cancer lesions. Specific technologies include, genome wide and ultra-deep targeted DNA methylation analysis, multi-modal single nucleus ATAC- and coupled RNA- seq (snATAC-/snRNA-seq), and spatial transcriptomics. Integration across the multi-modal data streams will be possible through implementation of the innovative Bayesian non-negative matrix factorization and transfer learning approaches through the CoGAPS and ProjectR framework. Aim 3 will implement and extend the AstroPath framework for computational analysis of high dimensional single cell and bulk genomics/epigenomics, multi-analyte digital pathology, and advanced imaging data. To accomplish these goals, the core leaders have assembled an impressive team of experts to support the TBEL Center, with complementary expertise in big data science/informatics, molecular pathology, genomics technologies, immunopathology, computational/systems biology and applied mathematics.