Computational pathology software for integrative cancer research with three-dimensional digital slides

项目概要： 基于组织的研究仍然是癌症研究的基石。随着经济高效的数字技术的出现， 扫描仪，大规模的定量调查，现在是可行的，使用高通量分析的两个- 三维（2D）图像数据集。然而，2D图像分析具有其局限性，因为病理性疾病 在三维（3D）空间中发生，并且2D表示遭受显著的信息损失。那里 是3D分析数字病理学的主要差距，包括缺乏图像分析工具， 处理3D数据量，缺乏有效且可扩展的数据管理和分析基础设施 对大规模空间病理学特征和生物标志物进行建模、策划、查询和挖掘。我们建议填补 这些差距与一个新的信息学解决方案，旨在更好地了解三维肿瘤微环境， 在增强免疫细胞浸润胰腺导管的免疫治疗研究中， 脑胶质母细胞瘤快速进展的病理生理学研究 （GBM）。根据人类肿瘤图谱计划，我们建议创建一个新颖而全面的3D数字 病理分析框架，用于定量分析病理标志的空间模式， 在真实的3D组织环境中与疾病进展相关的生物标志物， 病理图像体积处理，空间整合组织学-分子图像分析，大规模 空间数据分析，以及用于临床治疗反应测试的关键细胞区室跟踪， 免疫疗法的发展。为了能够广泛使用3D数字病理学成像数据的信息学工具 在癌症研究方面，我们会进一步提升一个全面的网上多模式显微镜系统， 图像管理、传播和可视化。我们将利用大量的信息学工具， 我们已经开发了用于显微镜图像分析，综合转化癌症研究， 病理学空间分析和高性能计算。开发的工具将是 经过一系列资金充足的癌症研究项目的测试和使用， 以及颈癌、肝癌和肺癌拟议的信息学工具将能够准确和全面地 在关键过渡阶段的组织学，分子，细胞和组织水平的相互作用的特点， 癌症进展它们还将允许精确地询问物理和空间特征， 免疫细胞浸润肿瘤，宿主免疫系统和肿瘤细胞之间的相互作用 在复杂的肿瘤微环境结构中的转移，对于免疫疗法的开发至关重要。 这项研究的完成将大大提高我们的信息技术能力， 显微镜图像分析，帮助癌症研究人员准确了解癌症生物学和进展 机制，并使临床医生能够轻松获得大规模的临床相关信息 显微镜图像用于基于计算机的诊断和治疗开发。

PROJECT SUMMARY: Tissue-based investigation remains a cornerstone of cancer research. With the advent of cost-effective digital scanners, large-scale quantitative investigations are now feasible using high throughput analysis of two- dimensional (2D) image datasets. However, 2D image analytics has its limitations, since pathologic diseases occur in three-dimensional (3D) space and 2D representations suffer from significant information loss. There are major gaps for 3D analytical digital pathology, including lack of image analysis tools to quantitatively process 3D data volumes and lack of an effective and scalable data management and analytical infrastructure to model, curate, query and mine large-scale spatial pathology features and biomarkers. We propose to fill these gaps with a new informatics solution directed at better understanding of 3D tumor micro-environments, with driving use cases on immunotherapy study for enhanced immune cell infiltration for pancreatic ductal adenocarcinoma (PDAC) and pathophysiological study of rapid tumor progression in brain tumor glioblastoma (GBM). In line with Human Tumor Atlas program, we propose to create a novel and comprehensive 3D digital pathology analytics framework to quantitatively analyze spatial patterns of pathologic hallmarks and biomarkers related to disease progression in an authentic 3D tissue environment with quantitative digital pathology image volume processing, spatially integrative histology-molecular image analysis, large-scale spatial data analytics, and key cellular compartment tracking for clinical treatment response test and immunotherapy development. To enable a wide use of informatics tools for 3D digital pathology imaging data in cancer research, we will further upgrade a comprehensive, web-based system for multi-modality microscopy image management, dissemination, and visualization. We will leverage a large set of informatics tools and algorithms we have developed for microscopy image analysis, integrative translational cancer research, pathology spatial analytics, and high performance computing in the past 14 years. The developed tools will be tested and used by a suite of well-funded cancer research projects on pancreatic cancer, brain tumor, head and neck, liver, and lung cancers. The proposed informatics tools will enable precise and comprehensive characterizations of the histologic, molecular, cellular and tissue-level interactions at critical transition stages in cancer progression. They will also allow for a precise interrogation of physical and spatial signatures of immune cell infiltration into tumors, and the interactions between the host immune system and tumor cell metastasis within a complex tumor micro-environment architecture, essential for immunotherapy development. The completion of the proposed study will boost our informatics technology capabilities for large scale microscopy image analytics, help cancer researchers accurately understand cancer biology and progression mechanisms, and enable clinicians an easy access to clinically relevant information from large scale microscopy images for computer based diagnosis and therapeutic development.