微生物广泛存在于各种癌症组织中，在癌发生和治疗反应中发挥着关键作用。然而，瘤内微生物与肿瘤之间的潜在关系仍然知之甚少。在此，提出了使用异质图转换器 (MICAH) 来识别肿瘤内癌症相关微生物群落的微生物癌症关联分析。 MICAH 将微生物之间的代谢和系统发育关系整合为异质图表示。它使用图形转换器来全面捕获肿瘤内微生物与癌症组织之间的关系，从而提高了已识别的微生物群落与癌症之间关联的可解释性。 MICAH 应用于 5 种癌症类型和 5 种真菌数据集的瘤内细菌数据，并证明了其普遍性和再现性。使用肿瘤-微生物-免疫相互作用的小鼠模型对代表性观察结果进行实验测试后，观察到与 MICAH 确定的关系一致的结果。来源追踪分析表明，已知的与癌症相关的微生物群落的主要贡献者是受癌症类型影响的器官。总体而言，该图神经网络框架将可用于后续实验验证的微生物数量从数千种细化至数十种，从而有助于加速对肿瘤与瘤内微生物组之间关系的理解。© 2024 作者。 《Advanced Science》由 Wiley‐VCH GmbH 出版。

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.© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.