A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset.
cancer
deep learning
microbiome
tumor microbiome
Journal
bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187
Informations de publication
Date de publication:
24 May 2023
24 May 2023
Historique:
pubmed:
9
6
2023
medline:
9
6
2023
entrez:
9
6
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37292990
doi: 10.1101/2023.05.24.541982
pmc: PMC10245834
pii:
doi:
Types de publication
Preprint
Langues
eng
Subventions
Organisme : NCI NIH HHS
ID : P30 CA086862
Pays : United States
Déclaration de conflit d'intérêts
CONFLICTS OF INTEREST Carlos Chan: None related to this project. Other unrelated projects and clinical trials (Research support from Checkmate Pharmaceuticals, Regeneron, Angiodynamics, Optimum Therapeutics) Yousef Zakharia: Advisory Board: Bristol Myers Squibb, Amgen, Roche Diagnostics, Novartis, Janssen, Eisai, Exelixis, Castle Bioscience, Genzyme Corporation, Astrazeneca, Array, Bayer, Pfizer, Clovis, EMD serono, Myovant. Grant/research support from: Institution clinical trial support from NewLink Genetics, Pfizer, Exelixis, Eisai. DSMC: Janssen Research and Development Consultant honorarium: Pfizer, Novartis Ahmad Tarhini: Contracted research grants with institution from Bristol Myers Squib, Genentech-Roche, Regeneron, Sanofi-Genzyme, Nektar, Clinigen, Merck, Acrotech, Pfizer, Checkmate, OncoSec. Personal consultant/advisory board fees from Bristol Myers Squibb, Merck, Easai, Instil Bio Clinigin, Regeneron, Sanofi-Genzyme, Novartis, Partner Therapeutics, Genentech/Roche, BioNTech, Concert AI, AstraZeneca outside the submitted work. Eric Singer: Astellas/Medivation: research support (clinical trial); Johnson & Johnson: advisory board; Merck: advisory board; Vyriad: advisory board; Aura Biosciences: data safety monitoring board Gregory Riedlinger: AstraZeneca advisory board Bryan Schneider: Genentech-Research support (drug supply only); Pfizer-Research support (Drug supply only); Foundation Medicine-research support (sequencing support)