Bayesian integrative approaches for high-dimensional imaging and genomic data

高维成像和基因组数据的贝叶斯综合方法

• 批准号: RGPIN-2019-04810
• 负责人: ChekouoTekougang, Thierry
• 金额: $ 1.68万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758929
• 关键词: Bayesian; integrative; approaches; dimensional; imaging

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.

该提案的主要目标是开发创新的贝叶斯统计框架，用于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、通路活动、甲基化数据和 microRNA 表达等）。焦点是结合放射组学和基因组信息的原则技术，以深入了解潜在的生物学机制，并开发相关临床结果的可靠预测模型，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的可用性。在同一组受试者的基因组数据来自癌症基因组图谱 (TCGA) 项目，并从癌症成像档案中提取相应的临床数据，以获得图像衍生异质性的测量。每个患者都称为灰度共生矩阵（GLCM）。当前的方法侧重于基于 GLCM 导出汇总统计数据（例如熵、能量等），并且可能忽略了 GLCM 中的其他结构特性。在该计划中，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，而不是限制其使用某些派生的汇总统计数据。所提出的方法和计算工具广泛适用于所有类型的疾病或各种背景。具有类似的数据（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；它回答了这些大规模研究提出的重要科学问题； ii) 需要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，以整合高通量放射组学和基因组有效数据集，并解释数据集之间的依赖性。我们还将为所提出的方法开发可计算且可免费访问的软件，这些方法将能够识别与临床结果相关的重要标记（例如基因、成像特征）。此外，我们期望提高这些结果的预测性能。这个节目，它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，因为所识别的潜在标记可能会为该领域提供新的数据和方法。开发综合分析方法，能够评估可能的疾病演变和相应的个性化治疗策略。