muMS2: an open source R package for analyzing and integrating multi-omics datasets to improve early detection and understanding of colorectal cancer

muMS2：一个开源 R 包，用于分析和集成多组学数据集，以改善结直肠癌的早期检测和理解

• 批准号: 10415579
• 负责人: Marcy J Balunas
• 金额: $ 40.81万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10415579
• 关键词: Adoption; Algorithms; American; Animal Model; Attention; Biological Assay; Biological Markers; Biology; Blood; Butyrates; Cancer Biology; Cancer Research Project; Cancerous; Cells; Chemicals; Clinical; Colon; Colonic Neoplasms; Colonoscopy; Colorectal Cancer; Communities; Complement; Complex; Computer software; DNA; Data; Data Analyses; Data Set; Detection; Diagnosis; Disease; Early Diagnosis; Early treatment; Elements; Environment; Feces; Fees; Funding; Genetic Markers; Goals; Health; Human; Human Microbiome; In Vitro; Incidence; Individual; Industrialization; Informatics; Licensing; Malignant Neoplasms; Malignant neoplasm of gastrointestinal tract; Mass Spectrum Analysis; Metabolic; Metabolism; Methods; Modeling; Molecular Weight; Neoplasms; Pattern; Persons; Population; Prevention; Prevention therapy; R programming language; Reproducibility; Research; Research Personnel; Risk; Role; Sampling; Screening procedure; Signal Transduction; Source; Specimen; Structure; Taxonomy; Techniques; Technology; Testing; Time; Tumor Burden; Urine; Volatile Fatty Acids; anticancer research; base; colorectal cancer progression; colorectal cancer screening; compliance behavior; cost; design; diagnostic biomarker; dysbiosis; fecal microbiota; gut microbiota; human population study; improved; in silico; interest; metabolic profile; metabolome; metabolomics; microbial; microbiome; microbiota; mortality; multiple omics; noninvasive diagnosis; open source; public health relevance; screening; tool; tumor; tumorigenesis

One in every 20 Americans develops colorectal cancer (CRC) and, once diagnosed, more than one-third will not survive 5 years. Although screening is available, stool assays such as fecal immunochemical test (FIT) and Cologuard have true positive rates ranging between 64-68% and false positive rate ranging between 5-10%. Moreover, other approaches such as colonoscopy are invasive and expensive and have low rates of patient adherence. There is clearly a need for additional biomarkers that complement existing screening procedures to identify individuals for subsequent colonoscopy and to better understand the biology that gives rise to tumors. Untargeted metabolomics has become an increasingly common approach to identify sources of such biomarkers from fecal samples; however, the general approach researchers use to analyze the data excludes the 95% of metabolites that currently lack an annotation. Animal models of CRC and human population studies have indicated that the gut microbiota has an underappreciated role in the disease. Therefore, it is critical that we characterize the metabolites generated by the gut microbiota to better understand the disease. The long-term goal of this research is to develop biomarkers that improve the detection of CRC and our understanding of the mechanisms that increase the risk of developing CRC. The objective of this proposal is to develop an open source R package, mums2, that allows researchers to identify metabolic biomarkers that can be associated with cancer regardless of whether they have already been annotated or whether they are produced by human or microbial cells. With this package, we will incorporate tools that allow researchers to implement the current state of the art for analyzing untargeted metabolomics and we will develop and validate methods for improving the quantification of MS features and clustering unknown metabolites based on their structural similarity. Three specific aims are proposed: (i) develop the mums2 R package, (ii) construct a predictive abundance algorithm for more accurate quantification of MS feature abundance, and (iii) construct operational metabolomics units (OMUs) as a framework for clustering unknown metabolites by structural similarity. Successful completion of these aims will result in a new platform for analyzing CRC metabolomics data for identifying biomarkers and understanding the underlying biology of tumorigenesis. To support this framework, we will create an open source R package, mums2, which will be useful for the expanding cancer microbiome and biomarker community. This package will democratize metabolomic analyses to broaden their adoption, reduce costs, improve the rigor and reproducibility of analyses, and enhance the ability to perform untargeted metabolomics analyses using a variety of biospecimens. Finally, the most important next step will be to apply these methods to better understand the interaction between the metabolome, microbiome, and tumorigenesis to identify diagnostic biomarkers and better understand the progression of CRC disease. The approaches and goals of the proposed research complement existing Informatics Technology for Cancer Research (ITCR) projects.

每20个美国人中有一个患有大肠癌（CRC），一旦被诊断出，超过三分之一将不会 生存5年。尽管有筛查，但粪便免疫化学测试（FIT）等粪便测定法和 Cologuard的真正正率在64-68％之间，误报率在5-10％之间。 此外，其他方法（例如结肠镜检查）是侵入性且昂贵的，患者发生率较低 坚持。显然需要其他生物标志物，以补充现有筛查程序 识别个人进行随后的结肠镜检查，并更好地了解引起肿瘤的生物学。 非靶向代谢组学已成为识别此类生物标志物来源的越来越普遍的方法 来自粪便样品；但是，研究人员用来分析数据的一般方法不包括95％ 目前缺乏注释的代谢产物。 CRC和人口研究的动物模型 表明肠道菌群在该疾病中的作用不足。因此，我们至关重要 表征由肠道微生物群产生的代谢产物，以更好地了解该疾病。长期 这项研究的目标是开发生物标志物，以改善CRC的检测以及我们对 增加了CRC风险的机制。该建议的目的是开放 源R软件包，MUMS2，允许研究人员识别可以与之相关的代谢生物标志物 癌症是不论已经被注释还是由人类或人类产生的癌症 微生物细胞。使用此软件包，我们将合并工具，使研究人员能够实施当前状态 用于分析非靶向代谢组学的艺术，我们将开发和验证改进的方法 基于其结构相似性的MS特征和聚类未知代谢产物的量化。三 提出了具体目的：（i）开发MUMS2 R软件包，（ii）构建一种预测丰度算法 为了更准确地量化MS特征丰度，（iii）构建操作代谢组学单元 （Omus）作为结构相似性聚集未知代谢物的框架。成功完成 这些目标将导致一个新的平台，用于分析CRC代谢组学数据，以识别生物标志物和 了解肿瘤发生的潜在生物学。为了支持此框架，我们将创建一个开源 R包，MUMS2，这将对扩大的癌症微生物组和生物标志物社区有用。这 包装将民主化代谢组分分析，以扩大其采用，降低成本，改善严格和 分析的可重复性，并增强使用一种品种进行非靶向代谢组学分析的能力 生物测量。最后，下一步最重要的是应用这些方法，以更好地了解 代谢组，微生物组和肿瘤发生之间的相互作用，以鉴定诊断生物标志物和更好 了解CRC疾病的进展。拟议研究的方法和目标补充 现有的癌症研究信息技术（ITCR）项目。