Utilizing Bayesian modeling to improve mutational signature inference in large-scale datasets

利用贝叶斯建模改进大规模数据集中的突变特征推断

基本信息

批准号：
10684720
负责人：
Joshua D Campbell
金额：
$ 40.11万
依托单位：
BOSTON UNIVERSITY MEDICAL CAMPUS
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-17 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10684720
关键词：
Acceleration Adoption Algorithms Bayesian Modeling Biological Process Cancer Etiology Cancer Patient Cancer Research Project Carcinogens Chinese Clinical Communities Computer software Computing Methodologies Credentialing Cytidine Deaminase Data Data Set Disadvantaged Ensure Etiology Evolution Family Fingerprint Funding Genomics Goals Hemorrhage Human Individual Informatics Institution Joints Knowledge Learning Malignant Neoplasms Meta-Analysis Methods Modeling Mutation Noise Parameter Estimation Patients Pattern Probability Procedures Process Research Personnel Sampling Software Tools Statistical Methods Subgroup Techniques Technology Time Uncertainty Variant Visualization anticancer research base cBioPortal cancer genome cancer genomics cigarette smoke cohort flexibility genome analysis high dimensionality improved insight interest large scale data method development novel operation predictive signature protein activation software development targeted sequencing tool tumor

项目摘要

The goals of this proposal are to develop novel statistical methods, more accurate inference procedures, and interactive software tools to perform mutational signature deconvolution in cancer samples. Mutational signatures are patterns of co-occurring mutations that can reveal insights into a cancer's etiology and evolution. Currently, non-negative matrix factorization (NMF) is the “gold-standard” for mutational signature deconvolution. However, NMF has several deficiencies in that it cannot do the following things: 1) predict signatures in new samples, 2) perform joint learning of known and novel signatures at the same time, 3) alleviate problems from signature “bleeding”, 4) cluster tumors into subgroups based on mutational signature profiles, and 5) characterize uncertainty in model fit. In this proposal, we will develop a novel Bayesian hierarchical models that overcome the limitations of NMF. Furthermore, there is a lack of interactive software for mutational signature inference and visualization for non-computational users. We will also develop an R/Shiny interface on top of our R package to facilitate data preprocessing, inference, and visualization of large-scale datasets. This interface will have a cloud backend to facilitate computationally intensive operations. Overall, this software will streamline mutational signature analysis for noncomputational researchers and will have the capability to interface with other projects from the Informatics Technology for Cancer Research (ITCR) program. Finally, we will analyze a novel targeted sequencing dataset from Chinese patients and perform a meta-analysis of all publicly available variants to generate a novel reference set of mutational signatures for investigators to use in their own studies. Overall, our tools will be of great interest to the cancer community as it will provide greater insights into mutational signature patterns and will be useful in clinical settings to reveal insights into cancer etiology.

该提案的目标是开发新颖的统计方法、更准确的推理程序，以及用于在癌症样本中执行突变特征反卷积的交互式软件工具。特征是同时发生的突变的模式，可以揭示癌症的病因学和进化。目前，非负矩阵分解（NMF）是突变签名反卷积的“黄金标准”。然而，NMF 有几个缺陷，它不能做以下事情：1）预测新的签名样本，2）同时对已知和新颖的签名进行联合学习，3）缓解来自特征“出血”，4) 根据突变特征谱将肿瘤聚类为亚组，以及 5) 表征在本提案中，我们将开发一种新颖的贝叶斯分层模型来克服模型拟合的不确定性。此外，NMF 还缺乏用于突变特征推断的交互式软件。我们还将在 R 包之上开发一个 R/Shiny 界面，以供非计算用户使用。促进大规模数据集的数据预处理、推理和可视化。这个界面将有一个云。总体而言，该软件将简化突变操作。为非计算研究人员进行签名分析，并将具有与其他项目交互的能力最后，我们将分析一种新颖的靶向药物。对中国患者的数据集进行测序并对所有公开可用的变异进行荟萃分析生成一套新颖的突变特征参考集，供研究人员在自己的研究中使用。癌症界将对工具非常感兴趣，因为它将提供对突变特征的更深入的了解模式，并将在临床环境中有用，以揭示癌症病因学的见解。