Informatics Tools for Quantitative Digital Pathology Profiling and Integrated Prognostic Modeling

项目总结 准确的生物标记物驱动的预后分层、反应预测和队列浓缩对于 实现精准治疗战略和人口健康管理方法，优化医疗质量 癌症患者的生命和生存。基因组学有望改善肿瘤的分类和预测 恶性肿瘤，然而肿瘤学实践仍然严重依赖免疫组织化学(IHC)作为基础 这一工具因其实用性和提供蛋白质水平和亚细胞定位信息的能力而受到高度重视。目标是 这项建议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集成用于癌症分类和 预言。这项建议建立在我们在计算机辅助显微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 利用异类和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。这一努力 将首次创建工具来集成定量IHC成像、临床和基因组信息 将反过来使研究界能够探索对恶性肿瘤进行分类的策略和 对结果的预测。建议的工具将在密切合作中开发和广泛验证 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。由该提案产生的软件工具将能够描述 亚细胞蛋白在胞核、胞膜和胞质中的表达。的空间特征 蛋白质表达的异质性，以及患者水平的蛋白质表达总结将被用于 开发以弥漫性大b细胞淋巴瘤为驱动力的癌症亚型机器学习分类器 申请。机器学习算法的自动调整技术将使更广泛的类别 临床和生物动机的使用者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，以探索预后 病人生存的典范。这些工具将在开源模式下发布和记录， 与历史学集成TK(https://histomicstk.readthedocs.io/en/latest/)，，可用于更广泛的癌症 研究社区。

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.