An Explainable Graph Neural Framework to Identify Cancer‐Associated Intratumoral Microbial Communities - Zhaoqian Liu; Yuhan Sun; Yingjie Li; Anjun Ma; Nyelia F. Willaims; Shiva Jahanbahkshi; Rebecca Hoyd; Xiaoying Wang; Shiqi Zhang; Jiangjiang Zhu; Dong Xu; Daniel Spakowicz; et al. (Advanced Science, 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.