Bayesian integrative approaches for high-dimensional imaging and genomic data

本提案的主要目标是为现代生物医学研究中出现的高通量成像（放射组学）和基因组数据（例如基因表达、途径活性、甲基化数据和microRNA表达等）开发创新的贝叶斯统计框架。重点是结合放射组学和基因组学信息的原则技术，以深入了解潜在的生物学机制，以及为相关临床结果开发可靠的预测模型，以帮助转化医学的实践。***这两种数据类型在同一主题集上的可用性激励了这项工作。基因组数据来自癌症基因组图谱（TCGA）项目。患者影像资料和相应的临床资料摘自The Cancer Imaging Archive。影像学数据已被处理，以获得每个患者的图像衍生异质性的量度，称为灰度共现矩阵（GLCM）。目前的方法主要集中在基于GLCM的汇总统计（如熵、能量等）上，而可能忽略了GLCM中的其他结构属性。在这个项目中，我们的目标是研究一个使用GLCM作为显式协变量和内表型的框架，而不是限制其使用一些派生的汇总统计。***提出的方法和计算工具广泛适用于所有类型的疾病或具有相似数据的各种背景（例如：神经胶质瘤、神经认知障碍、阿尔茨海默病）。本研究旨在从两个方面推动成像基因组整合领域的发展；它回应了i)这些大规模研究提出的重要科学问题；Ii)需要有效的统计方法来帮助回答这些问题。更具体地说，我们将开发创新的贝叶斯统计框架，为集成高通量放射组学和基因组数据集定义新的先验分布，考虑数据集之间的依赖性。我们也将为所建议的方法开发计算效率高且可免费使用的软件。该方法将能够识别与临床结果相关的重要标记（如基因、影像学特征）。此外，我们期望提高这些结果的预测性能。该项目属于自然科学和工程领域，具有多样化和跨学科的下游应用。因此，我们的方法应用结果有望在临床上产生重要的积极影响，因为确定的潜在标记物可能为开发综合分析方法提供新的数据和方法，从而能够评估可能的疾病演变和相应的个性化治疗策略。**

The primary objective of this proposal is the development of innovative Bayesian statistical frameworks for high throughput imaging (radiomic) and genomic data (e.g. gene expression, pathway activities, Methylation data and microRNA expression, etc..) arising in modern biomedical studies. The key foci are principled techniques for combining radiomic and genomic information to provide insight into the underlying biological mechanisms, and the development of reliable prediction models for relevant clinical outcomes to aid the practice of translational medicine. ***The work is motivated by the availability of these two data types on the same set of subjects. Genomic data are obtained from The Cancer Genome Atlas (TCGA) project. Patient image data and corresponding clinical data are extracted from The Cancer Imaging Archive. Imaging data have been processed to obtain a measure of image-derived heterogeneity for each patient called gray-level co-occurrence matrix (GLCM). Current approaches have focused on deriving summary statistics (e.g. entropy, energy etc) based on the GLCM, and potentially overlook other structural properties in the GLCM. In this program, we aim to investigate a framework that uses the GLCM both as an explicit covariate and endophenotype rather than restricting its use to some derived summary statistics. ***The proposed methods and computational tools are broadly applicable in all types of disease or in a variety of contexts with similar data (e.g: glioma, neurocognitive impairment, Alzheimers disease). The proposed research is positioned to advance the field of imaging-genomic integration in two directions; it responds to i) important scientific questions raised by these large-scale studies; ii) to the need for efficient statistical methodologies to help answer those questions. More specifically, we will develop innovative Bayesian statistical frameworks that define new prior distributions for integrating high-throughput radiomic and genomic datasets, account for the dependence between the data-sets. we will also develop computationally efficient and freely accessible software for the proposed methods. The methods would be able to identify important markers (e.g genes, imaging features) that are associated with clinical outcomes. Moreover, we expect to improve the prediction performance of those outcomes.***This program, which is within the Natural Sciences and Engineering field, has diverse and interdisciplinary downstream applications. Hence, results from the application of our methods are expected to have an important positive impact clinically because the identified potential markers are likely to provide new data and methods for the development of integrative analysis methods that enable assessment of likely disease evolution and corresponding personalized treatment strategies. **