Informatics Tools for Quantitative Digital Pathology Profiling and Integrated Prognostic Modeling

项目摘要 准确的生物标志物驱动的预后分层、缓解预测和队列富集对于 实现精确的治疗战略和人口健康管理方法， 癌症患者的生命和生存。基因组学有望改善分类和分类， 虽然免疫组化是治疗恶性肿瘤的基本方法，但肿瘤学实践仍然严重依赖免疫组化（IHC）作为基本的免疫组化方法。 工具，由于其实用性和能力，提供蛋白质水平和亚细胞定位信息。目标 该提案的目的是创建一个开源软件资源，用于定量分析IHC染色的 组织和有效整合IHC、基因组和临床特征，用于癌症分类， 精确化。这一建议是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的。 图像（包括IHC图像），开发机器学习方法以应对 对异构和高维数据进行分类和解释，并在收集方面发挥领导作用 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 首次将创建整合定量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分类的策略， 预测结果。将在密切合作下开发和广泛验证拟议的工具 临床，基因组和数字病理学数据来自NCI支持的淋巴瘤流行病学， 结局（LEO）队列研究。该提案所产生的软件工具将能够表征 在细胞核、膜和细胞质区室中的亚细胞蛋白质表达。空间特征 蛋白质表达异质性以及患者水平的蛋白质表达总结将沿着用于 开发癌症亚型的机器学习分类器，使用弥漫性大b细胞淋巴瘤作为驱动因素， 应用程序.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调查中使用这些工具。我们还将提供一个 交互式仪表板，使用户能够整合基因组和基于IHC的功能，以探索预后 患者生存模型。这些工具将以开放源码模式发布和记录， 与HistomicsTK（https：//histomicstk.readthedocs.io/en/latest/）集成，并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.