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.

项目概要 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 实现精准治疗策略和人口健康管理方法，优化治疗质量 癌症患者的生命和生存。基因组学有望改善疾病的分类和预测 恶性肿瘤，但肿瘤学实践仍然严重依赖免疫组织化学 (IHC) 作为基础 工具的实用性和提供蛋白质水平和亚细胞定位信息的能力。目标 该提案的目的是创建一个开源软件资源，用于 IHC 染色的定量分析 组织和 IHC、基因组和临床特征的有效整合，以进行癌症分类和 预测。该提案建立在我们在计算机辅助微观分析方面的集体经验的基础上 图像（包括 IHC 图像），开发机器学习方法以应对挑战 利用异构高维数据进行分类和预测，并在收集方面处于领先地位 以及涉及与多个医疗中心合作的癌症结果的大规模分析。这个努力 将首次创建整合定量 IHC 成像、临床和基因组信息的工具， 反过来将使研究界能够探索恶性肿瘤的分类策略和 结果的预测。拟议的工具将在密切合作中开发和广泛验证 包含来自 NCI 支持的淋巴瘤流行病学的临床、基因组和数字病理学数据 结果（LEO）队列研究。该提案产生的软件工具将能够表征 细胞核、细胞膜和细胞质区室中的亚细胞蛋白表达。的空间特征 蛋白质表达异质性以及患者水平的蛋白质表达摘要将用于 使用弥漫性大 B 细胞淋巴瘤作为驱动力，开发癌症亚型的机器学习分类器 应用。机器学习算法的自动调整技术将使广泛的 临床和生物学动机的用户在他们的研究中使用这些工具。我们还将提供一个 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 患者生存模型。这些工具将在开源模型下发布和记录， 与 HistomicsTK (https://histomicstk.readthedocs.io/en/latest/) 集成，可用于更广泛的癌症 研究社区。