Bayesian integrative approaches for high-dimensional imaging and genomic data

该提案的主要目标是为高通量成像（放射组学）和基因组数据（例如基因表达，通路活性，甲基化数据和microRNA表达等）开发创新的贝叶斯统计框架。在现代生物医学研究中出现。关键焦点是结合放射组学和基因组信息的原则性技术，以深入了解潜在的生物学机制，并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。 这项工作的动机是这两种数据类型在同一组主题的可用性。基因组数据来自癌症基因组图谱（TCGA）项目。患者图像数据和相应的临床数据从癌症成像档案中提取。成像数据已被处理，以获得一个衡量图像衍生的异质性为每个病人称为灰度共生矩阵（GLCM）。当前的方法集中于基于GLCM导出汇总统计量（例如熵、能量等），并且潜在地忽略了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框架，使用GLCM作为一个明确的协变量和内表型，而不是限制其使用一些派生的汇总统计。 所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类似数据的各种背景（例如：神经胶质瘤、神经认知障碍、阿尔茨海默病）。拟议的研究旨在从两个方向推进成像-基因组整合领域;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，定义新的先验分布，用于整合高通量放射组学和基因组数据集，解释数据集之间的依赖性。我们亦会为建议的方法开发计算效率高及可免费使用的软件。这些方法将能够识别与临床结果相关的重要标志物（例如基因，成像特征）。此外，我们希望提高这些结果的预测性能。该计划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，我们的方法的应用结果预计将在临床上产生重要的积极影响，因为所确定的潜在标志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.