Informatics Tools for Quantitative Digital Pathology Profiling and Integrated Prognostic Modeling

用于定量数字病理学分析和综合预后建模的信息学工具

• 批准号: 10070213
• 负责人: Lee Cooper
• 金额: $ 42.55万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-12 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10070213
• 关键词: Informatics; Tools; Quantitative; Digital; Pathology

PROJECT SUMMARY Accurate biomarker-driven prognostic stratification, response prediction, and cohort enrichment are critical for realizing precision treatment strategies and population health management approaches that optimize quality of life and survival for cancer patients. Genomics holds promise for improving classification and prognostication of malignancies, yet oncology practice continues to rely heavily on immunohistochemistry (IHC) as a fundamental tool due to its practicality and ability to provide protein-level and subcellular localization information. The goal of this proposal is to create an open-source software resource for the quantitative analysis of IHC stained tissues and effective integration of IHC, genomic, and clinical features for cancer classification and prognostication. This proposal builds on our collective experience in computer-assisted analysis of microscopic images (including IHC images), development of machine-learning methods to address the challenges of classification and prognostication with heterogeneous and high-dimensional data, and leadership in collection and large-scale analysis of cancer outcomes involving collaboration with multiple medical centers. This effort for the first time will create tools to integrate quantitative IHC imaging, clinical, and genomic information that will in turn enable the research community to explore strategies for the classification of malignancies and prediction of outcomes. The proposed tools will be developed and extensively validated in close collaboration with clinical, genomic, and digital pathology data from the NCI-supported Lymphoma Epidemiology of Outcomes (LEO) cohort study. The software tools produced by this proposal will enable the characterization of subcellular protein expression in cell nuclei, membranes and cytoplasmic compartments. Spatial features of protein expression heterogeneity, along with patient-level summaries of protein expression will be used to develop machine-learning classifiers for cancer subtypes, using diffuse large b-cell lymphomas as a driving application. Technology for automatic tuning of machine learning algorithms will enable a broad class of clinically and biologically motivated users to utilize these tools in their investigations. We will also provide an interactive dashboard that enables users to integrate genomic and IHC-based features to explore prognostic models of patient survival. These tools will be released and documented under an open-source model, integrated with HistomicsTK (https://histomicstk.readthedocs.io/en/latest/), and available to the broader cancer research community.

项目摘要 准确的生物标志物驱动的预后分层，反应预测和队列富集对于 实现精确治疗策略和人口健康管理方法，以优化 癌症患者的生命和生存。基因组学有望改善分类和预后 恶性肿瘤，但肿瘤学实践继续严重依赖免疫组织化学（IHC）作为基本 工具由于其实用性和提供蛋白质级别和亚细胞定位信息的能力。目标 该建议的是创建一个开源软件资源，用于对IHC染色的定量分析 组织和有效整合IHC，基因组和临床特征，用于癌症分类和 预测。该建议基于我们在计算机辅助分析中的集体经验的基础 图像（包括IHC图像），开发机器学习方法来应对的挑战 通过异构和高维数据进行分类和预测，以及收集领域的领导 以及涉及与多个医疗中心合作的癌症结果的大规模分析。这项努力 首次将创建工具以整合定量的IHC成像，临床和基因组信息 反过 预测结果。提出的工具将在密切合作中开发和广泛验证 来自NCI支持的临床，基因组和数字病理学数据 结果（LEO）队列研究。该提案生产的软件工具将使您能够表征 细胞核，膜和细胞质室中的亚细胞蛋白表达。空间特征 蛋白质表达异质性以及蛋白质表达的患者水平摘要将用于 使用扩散的大B细胞淋巴瘤作为驱动器开发用于癌症亚型的机器学习分类器 应用。用于自动调整机器学习算法的技术将使一类广泛的类别 临床和生物学上动机的用户在调查中利用这些工具。我们还将提供 交互式仪表板使用户能够集成基因组和基于IHC的功能来探索预后 患者生存的模型。这些工具将在开源模型下发布和记录 与HistomicStk（https://thistomicstk.readthedocs.io/en/latest/）集成，可用于更广泛的癌症 研究社区。