Skip to main content
2023 BMES Annual Meeting Main banner
Icon Legend
This session is not in your schedule.
This session is in your schedule. Click again to remove it.
Back

    Bioinformatics, Computational and Systems Biology

    (B-49) Investigating Cancer-Specific Microbiomes: A Comprehensive Analysis of Tumor-Microenvironment Interactions Using Data Mining

    Thursday, October 12, 2023
    9:30 AM – 10:30 AM PDT
    Location: Exhibit Hall - Row B - Poster # 49

      Presenting Author(s)

    • ML

      Margaret Lindner

      undergraduate student
      University of South Dakota
      Sioux Falls, South Dakota, United States

      • Co-Author(s)

    • JZ

      Jessica Zylla

      Graduate Research Assistant
      University of South Dakota, United States

    • VP

      Vincent Peta

      Post-Doctoral Researcher
      University of South Dakota, United States

      • Primary Investigator(s)

    • EG

      Etienne Gnimpieba

      Research Assistant Professor
      University of South Dakota, United States

    Introduction:: The tumor microenvironment (TME) encompasses a vast array of normal cells, microbes, and molecules existing within the tumor. While the relationship between cancer progression and microbes is an emerging field, there is still much to uncover. A thorough understanding of the interaction between microbes and tumor cells may hold the key for uncovering novel therapeutics that exploit this relationship to treat cancer more effectively. Our objective was to use data mining to create a curated dataset of different cancer types and the microbiota discovered to occupy each tumor type, and then to use a data mining script to generalize and automate the process. Using this dataset, we aimed to identify a bacterial species capable of thriving in the TME for use in a 3D coculture model of tumor cells and bacteria.

    Materials and Methods:: Our methodology included a literature review of existing cancer and microbiome studies using keywords such as “tumor microbiome,” “tumor 16S sequencing,” etc. Then we completed a comprehensive data mining of the thirteen relevant tumor papers encompassing twelve different cancer types. Leveraging Jupyter Notebook that can be implemented on high performance computing clusters, a Python script was written to aid in generalizing and automating the process of going through the published data and collecting the names of the microbes found to be occupying each tumor type. 

    Results, Conclusions, and Discussions::

    Results: Tumor microbiome composition varies depending on the tumor location within the body, with ovarian tumors appearing to have the most unique composition of bacterial species of the cancer types we investigated. Bacterial beta diversity was provided for nine of the twelve tumor types comparing tumor and adjacent nontumor tissues. Nine bacterial genera were enriched in multiple tumor tissues and/or adjacent nontumor tissues. Six bacterial genera were enriched in three or more of the nine tumor tissues. Only one bacterial genus was enriched in three or more of the nine adjacent nontumor tissues. Four bacterial species were enriched in two or more of the nine tumor tissues, and only one bacterial species was enriched in two or more of the nine adjacent nontumor tissues. 




    Conclusion: Even though different regions of the body contain vastly different microbiomes, we identified several microbes that were enriched in multiple tumor tissues across the body. This suggests that there is a distinct correlation between tumor cells and certain bacterial taxa. Whether this correlation is simply from tumor tissue providing an ideal environment for certain taxa to flourish, or if these taxa play an active role in tumor progression and metastasis requires further investigation. 




    Discussion: Expanding upon this work, we will evolve our Python script to collect published data on protein and gene expression, in addition to bacterial composition data. This work can be combined with another project to leverage machine learning to analyze these data and help determine what bacterial species may enhance or inhibit drugs or therapies. We also plan to develop a 3D model of tumor cells cocultured with one of the bacterial species we have identified to attempt to ascertain the mechanisms through which the tumor cells and these microbes interact. 




    Acknowledgements (Optional): :

    This material is based upon work supported by the National Science Foundation/EPSCoR RII Track-1: Building on The 2020 Vision: Expanding Research, Education and Innovation in South Dakota, Award OIA-1849206 and by the South Dakota Board of Regents. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This research is also funded in part by the National Institutes of Health (NIH) Institutional Development Award #P20GM103443 (IDeA) program known as INBRE (IDeA Networks of Biomedical Research Excellence).



    References (Optional): :