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A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset
Journal article   Open access   Peer reviewed

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset

Cankun Wang, Anjun Ma, Yingjie Li, Megan E McNutt, Shiqi Zhang, Jiangjiang Zhu, Rebecca Hoyd, Caroline E Wheeler, Lary A Robinson, Carlos H F Chan, …
Cancer research communications, Vol.4(2), pp.293-302
02/05/2024
DOI: 10.1158/2767-9764.CRC-23-0213
PMCID: PMC10840455
PMID: 38259095
url
https://doi.org/10.1158/2767-9764.CRC-23-0213View
Published (Version of record) Open Access

Abstract

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, MEGA, to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of 9 cancer centers in the Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2704 tumor RNA-seq samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors.

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