Evidence map›Paper›PMID 38835776›Full record

ArticleFrontiers in immunology2024

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

Nan Deng, Krishna M Sinha, Eduardo Vilar

Abstract read
In one paragraph

Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Nan DengDepartment of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Krishna M SinhaDepartment of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Eduardo VilarDepartment of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8M
Project 3: Inhibiting Oxidative Phosphorylation in Pancreatic CancerP50CA221707 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI KOPETZ, SCOTT · 2019 to 2023
$11.0M
Dietary HDAC Inhibitors in Colon Cancer PreventionR01CA122959 · NCI · TEXAS A&M UNIVERSITY HEALTH SCIENCE CTR · PI DASHWOOD, RODERICK H · 2008 to 2023
$4.3M
Neoantigen Vaccination for Lynch Syndrome ImmunopreventionU01CA233056 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI LIPKIN, STEVEN M, VILAR SANCHEZ, EDUARDO · 2018 to 2022
$3.8M
Cancer Immune-Interception for Lynch SyndromeR01CA257375 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI LIPKIN, STEVEN M, VILAR SANCHEZ, EDUARDO · 2021 to 2025
$3.3M
Cancer Immune-Interception in a Spontaneous Non-Human Primate Model of Lynch SyndromeR01CA260761 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI STANTON BRADLEY GRAY, Eduardo Vilar Sanchez · 2022 to 2026
$3.3M
Immunoepigenetic targeting of MHC regulators in FAPR01CA257559 · NCI · TEXAS A&M UNIVERSITY HEALTH SCIENCE CTR · PI Roderick H Dashwood, Eduardo Vilar Sanchez · 2023 to 2026
$2.3M
NCI NIH HHS P30 CA016672NCI NIH HHS P50 CA221707NCI NIH HHS R01 CA122959NCI NIH HHS R01 CA257375NCI NIH HHS R01 CA257559NCI NIH HHS R01 CA260761NCI NIH HHS U01 CA233056
6 · The paper itself

Abstract

Background: 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: 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: 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: 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.

Indexed as

Antigens, NeoplasmDatabases, GeneticMicrosatellite RepeatsComputational BiologyFrameshift MutationHumansMicrosatellite InstabilityNeoplasmsSoftwareAntigens, NeoplasmindelsLynch syndromemicrosatellitemismatch repairneoantigensomatic mutation

Identifiers

PMID38835776
PMCPMC11148240

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.