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Wang C, Ma A, Li Y, McNutt ME, Zhang S, Zhu J, Hoyd R, Wheeler CE, Robinson LA, Chan CH, Zakharia Y, Dodd RD, Ulrich CM, Hardikar S, Churchman ML, Tarhini AA, Singer EA, Ikeguchi AP, McCarter MD, Denko N, Tinoco G, Husain M, Jin N, Osman AE, Eljilany I, Tan AC, Coleman SS, Denko L, Riedlinger G, Schneider BP, Spakowicz D, Ma Q. A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset. CANCER RESEARCH COMMUNICATIONS 2024; 4:293-302. [PMID: 38259095 PMCID: PMC10840455 DOI: 10.1158/2767-9764.crc-23-0213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/26/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024]
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, 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.
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Affiliation(s)
- Cankun Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
| | - Anjun Ma
- 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
| | - Yingjie Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
| | - Megan E. McNutt
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio
| | - Shiqi Zhang
- Department of Human Sciences, College of Education and Human Ecology, The Ohio State University, Columbus, Ohio
| | - Jiangjiang Zhu
- Department of Human Sciences, College of Education and Human Ecology, The Ohio State University, Columbus, Ohio
| | - Rebecca Hoyd
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Caroline E. Wheeler
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Lary A. Robinson
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Carlos H.F. Chan
- University of Iowa, Holden Comprehensive Cancer Center, Iowa City, Iowa
| | - Yousef Zakharia
- Division of Oncology, Hematology and Blood & Marrow Transplantation, University of Iowa, Holden Comprehensive Cancer Center, Iowa City, Iowa
| | - Rebecca D. Dodd
- Department of Internal Medicine, University of Iowa, Iowa City, Iowa
| | - Cornelia M. Ulrich
- Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Sheetal Hardikar
- Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | | | - Ahmad A. Tarhini
- Departments of Cutaneous Oncology and Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Eric A. Singer
- Department of Urologic Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Alexandra P. Ikeguchi
- Department of Hematology/Oncology, Stephenson Cancer Center of University of Oklahoma, Oklahoma City, Oklahoma
| | - Martin D. McCarter
- Department of Surgery, University of Colorado School of Medicine, Aurora, Colorado
| | - Nicholas Denko
- Department of Radiation Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Gabriel Tinoco
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Marium Husain
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Ning Jin
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio
| | - Afaf E.G. Osman
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah
| | - Islam Eljilany
- Clinical Science Lab – Cutaneous Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Aik Choon Tan
- Departments of Oncological Science and Biomedical Informatics, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Samuel S. Coleman
- Departments of Oncological Science and Biomedical Informatics, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Louis Denko
- 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
| | - Gregory Riedlinger
- Department of Precision Medicine, Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey
| | - Bryan P. Schneider
- Indiana University Simon Comprehensive Cancer Center, Indianapolis, Indiana
| | - Daniel Spakowicz
- 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
| | - Qin Ma
- 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
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Douglas GM, Kim S, Langille MGI, Shapiro BJ. Efficient computation of contributional diversity metrics from microbiome data with FuncDiv. Bioinformatics 2022; 39:6909011. [PMID: 36519836 PMCID: PMC9825779 DOI: 10.1093/bioinformatics/btac809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 11/21/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
MOTIVATION Microbiome datasets with taxa linked to the functions (e.g. genes) they encode are becoming more common as metagenomics sequencing approaches improve. However, these data are challenging to analyze due to their complexity. Summary metrics, such as the alpha and beta diversity of taxa contributing to each function (i.e. contributional diversity), represent one approach to investigate these data, but currently there are no straightforward methods for doing so. RESULTS We addressed this gap by developing FuncDiv, which efficiently performs these computations. Contributional diversity metrics can provide novel insights that would be impossible to identify without jointly considering taxa and functions. AVAILABILITY AND IMPLEMENTATION FuncDiv is distributed under a GNU Affero General Public License v3.0 and is available at https://github.com/gavinmdouglas/FuncDiv. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Sunu Kim
- Department of Microbiology & Immunology, McGill University, Montréal, QC H3A 2B4, Canada
| | - Morgan G I Langille
- Department of Pharmacology, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - B Jesse Shapiro
- Genome Centre, McGill University, Montréal, QC H3A 0G1, Canada ,Department of Microbiology & Immunology, McGill University, Montréal, QC H3A 2B4, Canada
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