A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset.

Autor:	Wang C; Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio., Ma A; Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio.; Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio., Li Y; Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio., McNutt ME; Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio., Zhang S; Department of Human Sciences, College of Education and Human Ecology, The Ohio State University, Columbus, Ohio., Zhu J; Department of Human Sciences, College of Education and Human Ecology, The Ohio State University, Columbus, Ohio., Hoyd R; Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio., Wheeler CE; Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio., Robinson LA; Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida., Chan CHF; University of Iowa, Holden Comprehensive Cancer Center, Iowa City, Iowa., Zakharia Y; Division of Oncology, Hematology and Blood & Marrow Transplantation, University of Iowa, Holden Comprehensive Cancer Center, Iowa City, Iowa., Dodd RD; Department of Internal Medicine, University of Iowa, Iowa City, Iowa., Ulrich CM; Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah., Hardikar S; Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah., Churchman ML; Clinical & Life Sciences, M2GEN, Tampa, Florida., Tarhini AA; Departments of Cutaneous Oncology and Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida., Singer EA; Department of Urologic Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio., Ikeguchi AP; Department of Hematology/Oncology, Stephenson Cancer Center of University of Oklahoma, Oklahoma City, Oklahoma., McCarter MD; Department of Surgery, University of Colorado School of Medicine, Aurora, Colorado., Denko N; Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio., Tinoco G; Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio., Husain M; Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio., Jin N; Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio., Osman AEG; Department of Internal Medicine, University of Utah, Salt Lake City, Utah., Eljilany I; Clinical Science Lab - Cutaneous Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida., Tan AC; Departments of Oncological Science and Biomedical Informatics, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah., Coleman SS; Departments of Oncological Science and Biomedical Informatics, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah., Denko L; Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio.; Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio., Riedlinger G; Department of Precision Medicine, Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey., Schneider BP; Indiana University Simon Comprehensive Cancer Center, Indianapolis, Indiana., Spakowicz D; Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio.; Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio., Ma Q; Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio.; Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio.
Jazyk:	angličtina
Zdroj:	Cancer research communications [Cancer Res Commun] 2024 Feb 05; Vol. 4 (2), pp. 293-302.
DOI:	10.1158/2767-9764.CRC-23-0213
Abstrakt:	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, microbial graph attention (MEGA), to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of nine cancer centers in the Oncology Research Information Exchange Network. This package has three 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 2,704 tumor RNA sequencing 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. Significance: Studying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep learning tool, MEGA, to refine the organisms that interact with tumors. (© 2024 The Authors; Published by the American Association for Cancer Research.)
Databáze:	MEDLINE
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