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

> **NIH NIH U01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $408,122

## Abstract

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 id...

## Key facts

- **NIH application ID:** 10415579
- **Project number:** 1U01CA264071-01A1
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Marcy J Balunas
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $408,122
- **Award type:** 1
- **Project period:** 2022-06-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10415579

## Citation

> US National Institutes of Health, RePORTER application 10415579, muMS2: an open source R package for analyzing and integrating multi-omics datasets to improve early detection and understanding of colorectal cancer (1U01CA264071-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10415579. Licensed CC0.

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