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

• 批准号: 10490301
• 负责人: Joshua D Campbell
• 金额: $ 40.26万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 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/10490301
• 关键词: 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; Gold; Hemorrhage; Human; Individual; Informatics; Institution; Joints; Knowledge; Learning; Malignant Neoplasms; Meta-Analysis; Methods; Modeling; Mutation; Noise; 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

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.

这项提议的目标是开发新的统计方法，更准确的推断程序，以及 在癌症样本中执行突变签名去卷积的交互式软件工具。突变 签名是一种共生突变的模式，可以揭示癌症的病因和进化。 目前，非负矩阵分解(NMF)是突变签名去卷积的“黄金标准”。 然而，NMF有几个不足之处，它不能做以下事情：1)在新的 样本，2)同时执行已知签名和新签名的联合学习，3)缓解以下问题 特征“出血”，4)基于突变特征曲线将肿瘤分类为亚组，以及5)表征 模型拟合中的不确定性。在这个方案中，我们将开发一种新的贝叶斯分层模型，该模型克服了 NMF的局限性。此外，还缺乏用于突变签名推理和 面向非计算性用户的可视化。我们还将在我们的R包上开发一个R/SHINY界面，以 促进大规模数据集的数据预处理、推理和可视化。此界面将有一个云 后端，以方便计算密集型操作。总体而言，这款软件将简化变异 用于非计算性研究人员的签名分析，并将能够与其他项目接口 来自癌症研究信息学技术(ITCR)计划。最后，我们将分析一部有针对性的小说 对来自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 生成一组新的突变签名，供研究人员在自己的研究中使用。总的来说，我们的 这些工具将引起癌症社区的极大兴趣，因为它将提供对突变签名的更深入的见解 模式，并将在临床环境中有用，以揭示对癌症病因的洞察。