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

每20个美国人中就有一个罹患结直肠癌，而一旦确诊，超过三分之一的人不会。 存活5年。虽然可以进行筛查，但粪便免疫化学检测(FIT)和 科洛瓦的真阳性率在-68%之间，假阳性率在5%-10%之间。 此外，其他方法，如结肠镜检查，具有侵入性，费用昂贵，病人发生率低。 坚持不懈。显然需要额外的生物标志物来补充现有的筛查程序，以 确定个体进行随后的结肠镜检查，并更好地了解导致肿瘤的生物学因素。 非靶向代谢组学已成为鉴定此类生物标志物来源的一种日益普遍的方法 然而，研究人员用来分析数据的一般方法排除了95%的 目前缺少注释的代谢物。结直肠癌的动物模型和人类种群研究 表明肠道微生物区系在疾病中的作用被低估。因此，至关重要的是，我们 确定肠道微生物区系产生的代谢物的特征，以更好地了解疾病。长期的 这项研究的目标是开发生物标志物，以提高结直肠癌的检测和我们对结直肠癌的认识 增加发展儿童权利公约风险的机制。这项提议的目标是开发一种开放的 来源R包，MUMS2，允许研究人员识别与以下因素相关的代谢生物标记物 癌症，不管它们是否已经被注解，或者它们是由人类还是 微生物细胞。在这个包中，我们将包含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艺术，我们将开发和验证改进 MS特征的量化和基于结构相似性的未知代谢物的聚类。三 具体目标是：(I)开发Mums2R程序包；(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)作为根据结构相似性对未知代谢物进行分类的框架。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，以识别生物标记物和 了解肿瘤发生的潜在生物学基础。为了支持这个框架，我们将创建一个开源的 R包，Mums2，这将对不断扩大的癌症微生物组和生物标记物社区有用。这 一揽子将使代谢分析大众化，以扩大其采用范围，降低成本，提高严谨性，并 分析的重复性，并增强使用各种方法进行非靶向代谢组学分析的能力 生物有机磷农药。最后，最重要的下一步将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记物并更好地 了解结直肠癌的进展情况。拟议研究的方法和目标是补充 现有的癌症研究信息技术(ITCR)项目。

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.