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: 10625394
• 负责人: Marcy J Balunas
• 金额: $ 38.09万
• 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/10625394
• 关键词: Adoption; Algorithms; American; Animal Model; Attention; Biological Assay; Biological Markers; Biology; Blood; Butyrates; Cancer Biology; Cancer Research Project; Cancerous; Cells; Chemicals; Chromatography; Clinical; Colon; Colonic Neoplasms; Colonoscopy; Colorectal Cancer; Communities; Complement; Complex; Computer software; DNA; Data; Data Analyses; Data Set; Democracy; Detection; Diagnosis; Disease; Early Diagnosis; Early treatment; Elements; Environment; Exclusion; 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; Multiomic Data; Neoplasms; Pattern; Persons; Population; Prevention; 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; Writing; anticancer research; biomarker identification; 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; noninvasive diagnosis; open source; public health relevance; screening; synergism; 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 名美国人中就有 1 人患有结直肠癌 (CRC)，一旦确诊，超过三分之一的人不会患结直肠癌 存活5年。尽管可以进行筛查，但仍需进行粪便检测，例如粪便免疫化学检测 (FIT) 和 Cologuard 的真阳性率在 64-68% 之间，假阳性率在 5-10% 之间。 此外，结肠镜检查等其他方法是侵入性的、昂贵的，并且患者的患病率较低。 坚持。显然需要额外的生物标志物来补充现有的筛选程序 识别个体进行后续结肠镜检查，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常见的方法 来自粪便样本；然而，研究人员用来分析数据的一般方法排除了 95% 目前缺乏注释的代谢物。 CRC 动物模型和人群研究 表明肠道微生物群在该疾病中的作用未被充分认识。因此，至关重要的是我们 表征肠道微生物群产生的代谢物，以更好地了解该疾病。长期来看 这项研究的目标是开发生物标志物，以改善 CRC 的检测并加深我们对 CRC 的理解。 增加发生 CRC 风险的机制。该提案的目标是开发一个开放的 源 R 包 mums2，使研究人员能够识别与 癌症，无论它们是否已经被注释，或者它们是否是由人类或人类产生的 微生物细胞。通过这个包，我们将整合允许研究人员实现当前状态的工具 分析非靶向代谢组学的技术，我们将开发和验证改进方法 MS 特征的量化并根据未知代谢物的结构相似性进行聚类。三 提出了具体目标：(i) 开发 mums2 R 包，(ii) 构建预测丰度算法 为了更准确地量化 MS 特征丰度，以及 (iii) 构建可操作的代谢组学单元 （OMU）作为通过结构相似性对未知代谢物进行聚类的框架。顺利完成 这些目标将产生一个新的平台，用于分析 CRC 代谢组学数据，以识别生物标志物和 了解肿瘤发生的基本生物学。为了支持这个框架，我们将创建一个开源的 R 包 mums2，这对于扩大癌症微生物组和生物标志物群体很有用。这 一揽子计划将使代谢组学分析民主化，以扩大其采用范围、降低成本、提高严谨性和 分析的重现性，并增强使用各种方法进行非靶向代谢组学分析的能力 生物样本。最后，最重要的下一步是应用这些方法来更好地理解 代谢组、微生物组和肿瘤发生之间的相互作用，以确定诊断生物标志物并更好地 了解 CRC 疾病的进展。拟议研究补充的方法和目标 现有的癌症研究信息技术 (ITCR) 项目。