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: 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
• 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

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.

该建议的目标是开发新颖的统计方法，更准确的推论程序以及 在癌症样品中执行突变签名反卷积的交互式软件工具。突变 特征是共发生突变的模式，可以揭示对癌症病因和进化的见解。 目前，非负基质分解（NMF）是突变特征反卷积的“金标准”。 但是，NMF有几种缺陷，因为它不能做以下事情：1）预测新的签名 样本，2）同时进行已知和新颖签名的联合学习，3）减轻问题 签名“出血”，4）基于突变签名曲线的亚组群集肿瘤，5）表征 模型拟合的不确定性。在此提案中，我们将开发一种新颖的贝叶斯分层模型来克服 NMF的局限性。此外，缺乏用于突变签名推理的交互式软件和 非计算用户的可视化。我们还将在R包装上开发R/闪亮的界面到 促进大型数据集的数据预处理，推理和可视化。该界面将具有云 后端促进计算密集型操作。总体而言，该软件将简化突变 针对非计算研究人员的签名分析，并有能力与其他项目进行交互 从癌症研究信息学技术（ITCR）计划。最后，我们将分析针对性的新颖 对中国患者的数据集进行测序，并对所有公开变体进行荟萃分析 为研究人员在自己的研究中使用一组新型的突变签名集。总体而言，我们的 工具将引起癌症社区的极大兴趣，因为它将为突变签名提供更多的见解 模式，将在临床环境中有用，以揭示对癌症病因的见解。