Integrated Cancer Modeling: A New Dimension

项目摘要 在癌症建模方面已经投入了大量的精力，探索可以更准确地 描述癌症结果/表型。利用耶鲁大学和其他机构收集的数据， 和公开可用的数据（特别是TCGA），我们对整合性癌症进行了广泛的研究， 使用各种类型的组学数据进行建模。在大多数癌症的临床实践中，成像技术 已被常规使用。具体而言，放射科医生使用CT/MRI/PET和其他技术，生成放射性图像， 图像，并报告肿瘤的“宏观”特征。然后，病理学家通过活检取出样本，进行审查 组织的代表性切片的幻灯片，并作出明确的诊断-这一程序产生 病理（诊断）图像，其中包含丰富的信息的“微观”特征的肿瘤。组学和 病理成像数据已被单独分析并被确立为对于癌症建模是高度有效的。 然而，一个关键的和实际上高度相关的问题，仍然没有答案，是“为了更准确地 癌症建模，有必要整合组学和病理成像数据吗？" 我们的最终目标是通过整合多种基因， 数据来源/类型，以推进癌症研究和实践。在这项研究中，我们将利用 最近根据耶鲁大学的多项研究和TCGA收集的数据，大大扩展了综合分析 为组学数据开发的范例，并创新性地整合各种类型的组学和病理成像 癌症建模的数据。制定了三个高度相互关联的目标， 补充研究数据集成的不同视角。目标1：评估重叠程度 癌症相关组学和成像特征/模型中的信息。这一分析将揭示， 整体组学和影像学数据包含了独立的信息及其程度，这是数据的基础 一体化目的2：识别与下列因素独立相关的个体成像（组学）特征： 癌症超越组学（成像）特征。该分析将识别最重要的成像/组学特征， 这可能具有最高的科学，临床和统计价值。目标3：构建综合模型 使用所有的组学和成像特征。这种分析可以导致“巨型”模型，这是上级那些 分别使用组学和成像数据以及严格的改进措施构建。等 结果将具有高度的临床相关性。 这项研究将提供一个创新的分析管道和多种新的方法，整合组学 和病理成像数据。随着组学和病理成像数据现在在癌症中被常规收集， 研究和实践，这一研究将开辟一个新的场地，并有很高的和持久的影响。

Project Summary Significant effort has been devoted to cancer modeling, exploring statistical models that can more accurately describe cancer outcomes/phenotypes. Taking advantage of data collected at Yale University and other institutes and publicly available data (especially TCGA), we have conducted extensive research on integrated cancer modeling using various types of omics data. In the clinical practice of most if not all cancers, imaging techniques have been routinely used. Specifically, radiologists use CT/MRI/PET and other techniques, generate radiological images, and report the “macro” features of tumors. Then with samples retrieved via biopsy, pathologists review the slides of representative sections of tissues and make definitive diagnosis – this procedure generates pathological (diagnostic) images, which contain rich information on the “micro” features of tumors. Omics and pathological imaging data have been separately analyzed and established as highly effective for cancer modeling. However, a critical and practically highly relevant question, which remains unanswered, is “for more accurate cancer modeling, is it necessary to integrate omics and pathological imaging data?”. Our ultimate goal is to more effectively model cancer outcomes/phenotypes by integrating multiple sources/types of data, so as to advance cancer research and practice. In this study, we will take advantage of data recently collected under multiple Yale studies and TCGA, significantly expand the integrated analysis paradigm developed for omics data, and innovatively integrate various types of omics and pathological imaging data for cancer modeling. Three highly interconnected aims have been designed to comprehensively and complementarily study different perspectives of data integration. Aim 1: Assess the degree of overlapping information in cancer-associated omics and imaging features/models. This analysis will reveal whether overall omics and imaging data contain independent information and its degree, which is the foundation of data integration. Aim 2: Identify individual imaging (omics) features that are independently associated with cancer beyond omics (imaging) features. This analysis will identify the most important imaging/omics features, which are likely to have the highest scientific, clinical, and statistical value. Aim 3: Construct integrated models using all omics and imaging features. This analysis can lead to “mega” models, which are superior to those constructed using omics and imaging data separately, as well as rigorous measures of improvement. Such results will have high clinical relevance. This study will deliver an innovative analysis pipeline and multiple novel methods for integrating omics and pathological imaging data. With omics and pathological imaging data now routinely collected in cancer research and practice, this study will open a new venue and have a high and long-lasting impact.