Integrated Cancer Modeling: A New Dimension

项目摘要 在癌症建模方面投入了大量的努力，探索能够更准确地 描述癌症结果/表型。利用耶鲁大学和其他机构收集的数据 和公开的数据(特别是TCGA)，我们对整合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 已经被常规使用了。具体地，放射科医生使用CT/MRI/PET和其他技术，产生放射学 图像，并报告肿瘤的“宏观”特征。然后，通过活检取回样本，病理学家审查 具有代表性的组织切片并做出明确的诊断--该程序生成 病理(诊断)图像，其中包含丰富的肿瘤“微观”特征的信息。经济学和 病理成像数据已经被单独分析，并被确定为对癌症建模非常有效。 然而，一个关键的和实际上高度相关的问题仍然没有得到回答，那就是“为了更准确 癌症建模，是否有必要将组学和病理成像数据整合起来？ 我们的最终目标是通过集成多个 数据来源/类型，以促进癌症研究和实践。在这项研究中，我们将利用 根据多项耶鲁研究和TCGA最近收集的数据，极大地扩展了综合分析 为组学数据开发的范式，创新地整合了各种类型的组学和病理成像 用于癌症建模的数据。设计了三个高度相关的目标，以全面和 互补研究数据整合的不同视角。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 整体组学和影像数据包含独立的信息及其程度，这是数据的基础 整合。目标2：确定与以下各项独立相关的个人成像(组学)特征 超越组学(成像)特征的癌症。该分析将确定最重要的成像/组学特征， 它们可能具有最高的科学、临床和统计价值。目标3：构建集成模型 使用所有的组学和成像功能。这种分析可以产生“巨型”模型，这些模型比那些 分别使用组学和成像数据构建，以及严格的改进措施。是这样的 结果将具有很高的临床相关性。 这项研究将提供一个创新的分析渠道和多种新的方法来整合组学 和病理影像数据。随着组学和病理成像数据现在在癌症中的常规收集 研究和实践，将为本次研究开辟一个新的场所，并产生较高和持久的影响。

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.