An Explainable Graph Neural Framework to Identify Cancer-Associated Intratumoral Microbial Communities.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Published Date: Sep 3, 2024

Abstract

Microbes are extensively present among various cancer tissues and play critical roles in carcinogenesis and treatment responses. However, the underlying relationships between intratumoral microbes and tumors remain poorly understood. Here, a MIcrobial Cancer-association Analysis using a Heterogeneous graph transformer (MICAH) to identify intratumoral cancer-associated microbial communities is presented. MICAH integrates metabolic and phylogenetic relationships among microbes into a heterogeneous graph representation. It uses a graph transformer to holistically capture relationships between intratumoral microbes and cancer tissues, which improves the explainability of the associations between identified microbial communities and cancers. MICAH is applied to intratumoral bacterial data across 5 cancer types and 5 fungi datasets, and its generalizability and reproducibility are demonstrated. After experimentally testing a representative observation using a mouse model of tumor-microbe-immune interactions, a result consistent with MICAH's identified relationship is observed. Source tracking analysis reveals that the primary known contributor to a cancer-associated microbial community is the organs affected by the type of cancer. Overall, this graph neural network framework refines the number of microbes that can be used for follow-up experimental validation from thousands to tens, thereby helping to accelerate the understanding of the relationship between tumors and intratumoral microbiomes.

Authors

  • Zhaoqian Liu
    School of Mathematics, Shandong University, and now she is a visiting scholar at Ohio State University.
  • Yuhan Sun
    Clinical Data Center, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, 310052 Hangzhou, Zhejiang, China.
  • Yingjie Li
    School of Communication and Information Engineering, Shanghai University, China.
  • Anjun Ma
    Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
  • Nyelia F Willaims
    Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
  • Shiva Jahanbahkshi
    Department of Food Science and Technology, College of Food, Agricultural, and Environmental Sciences, The Ohio State University, Columbus, OH, 43210, USA.
  • Rebecca Hoyd
    Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
  • XiaoYing Wang
  • Shiqi Zhang
    Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark.
  • Jiangjiang Zhu
    Department of Human Sciences, College of Education and Human Ecology, The Ohio State University, Columbus, OH, 43210, USA.
  • Dong Xu
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
  • Daniel Spakowicz
    Pelotonia Institute for Immuno-Oncology, The Ohio State University, Columbus, OH, 43210, USA.
  • Qin Ma
    Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, University of Georgia, GA 30602, USA BioEnergy Science Center, TN 37831, USA.
  • Bingqiang Liu

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