MONET: a database for prediction of neoantigens derived from microsatellite loci.


Journal

Frontiers in immunology
ISSN: 1664-3224
Titre abrégé: Front Immunol
Pays: Switzerland
ID NLM: 101560960

Informations de publication

Date de publication:
2024
Historique:
received: 01 03 2024
accepted: 03 05 2024
medline: 5 6 2024
pubmed: 5 6 2024
entrez: 5 6 2024
Statut: epublish

Résumé

Microsatellite instability (MSI) secondary to mismatch repair (MMR) deficiency is characterized by insertions and deletions (indels) in short DNA sequences across the genome. These indels can generate neoantigens, which are ideal targets for precision immune interception. However, current neoantigen databases lack information on neoantigens arising from coding microsatellites. To address this gap, we introduce The MicrOsatellite Neoantigen Discovery Tool (MONET). MONET identifies potential mutated tumor-specific neoantigens (neoAgs) by predicting frameshift mutations in coding microsatellite sequences of the human genome. Then MONET annotates these neoAgs with key features such as binding affinity, stability, expression, frequency, and potential pathogenicity using established algorithms, tools, and public databases. A user-friendly web interface (https://monet.mdanderson.org/) facilitates access to these predictions. MONET predicts over 4 million and 15 million Class I and Class II potential frameshift neoAgs, respectively. Compared to existing databases, MONET demonstrates superior coverage (>85% vs. <25%) using a set of experimentally validated neoAgs. MONET is a freely available, user-friendly web tool that leverages publicly available resources to identify neoAgs derived from microsatellite loci. This systems biology approach empowers researchers in the field of precision immune interception.

Sections du résumé

Background UNASSIGNED
Microsatellite instability (MSI) secondary to mismatch repair (MMR) deficiency is characterized by insertions and deletions (indels) in short DNA sequences across the genome. These indels can generate neoantigens, which are ideal targets for precision immune interception. However, current neoantigen databases lack information on neoantigens arising from coding microsatellites. To address this gap, we introduce The MicrOsatellite Neoantigen Discovery Tool (MONET).
Method UNASSIGNED
MONET identifies potential mutated tumor-specific neoantigens (neoAgs) by predicting frameshift mutations in coding microsatellite sequences of the human genome. Then MONET annotates these neoAgs with key features such as binding affinity, stability, expression, frequency, and potential pathogenicity using established algorithms, tools, and public databases. A user-friendly web interface (https://monet.mdanderson.org/) facilitates access to these predictions.
Results UNASSIGNED
MONET predicts over 4 million and 15 million Class I and Class II potential frameshift neoAgs, respectively. Compared to existing databases, MONET demonstrates superior coverage (>85% vs. <25%) using a set of experimentally validated neoAgs.
Conclusion UNASSIGNED
MONET is a freely available, user-friendly web tool that leverages publicly available resources to identify neoAgs derived from microsatellite loci. This systems biology approach empowers researchers in the field of precision immune interception.

Identifiants

pubmed: 38835776
doi: 10.3389/fimmu.2024.1394593
pmc: PMC11148240
doi:

Substances chimiques

Antigens, Neoplasm 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1394593

Informations de copyright

Copyright © 2024 Deng, Sinha and Vilar.

Déclaration de conflit d'intérêts

EV has a consulting or advisory role with Janssen Research and Development, Recursion Pharma, Guardant Health, The Rising Tide Foundation, and Nouscom, s.r.l. EV has received research support from Janssen Research and Development. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Nan Deng (N)

Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.

Krishna M Sinha (KM)

Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.

Eduardo Vilar (E)

Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Department of Clinical Cancer Genetics Program, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.

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