Evidence map›Paper›PMID 40678247›Full record

ArticleResearch square2025

The ExoGAN generative AI framework enables extracellular vesicle-based immunotherapy.

Kiley Graim, Zachary Greenberg, Tina Salehi Torabi, Jiayu Huang, Franco Krepel, James Cahill, David Ostrov, Mei He

Abstract readPreprint
In one paragraph

Article in Research square, 2025. 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

8 authors.

Kiley GraimUniversity of Florida.ORCID 0000-0002-4569-8444
Zachary GreenbergUniversity of Florida.ORCID 0000-0001-9626-2694
Tina Salehi TorabiUniversity of Florida.ORCID 0009-0003-8365-7314
Jiayu HuangUniversity of Florida.
Franco KrepelUniversity of Florida.
James CahillUniversity of Florida.
David OstrovUniversity of Florida.
Mei HeUniversity of Florida.

Funding

Synthetic Chemical Biology CoreP20GM113117 · NIGMS · UNIVERSITY OF KANSAS LAWRENCE · PI OROZCO, ROBIN C. · 2016 to 2025
$23.9M
Synthetic Chemical Biology CoreP30GM145499 · NIGMS · UNIVERSITY OF KANSAS LAWRENCE · PI Susan M Lunte · 2022 to 2026
$6.9M
Leveraging Mammalian Cancers, Platinum-Quality Genome Assemblies, and Large-Scale Data to Identify Mechanisms of Rare Human CancersR01CA265907 · NCI · UNIVERSITY OF FLORIDA · PI Kiley Graim · 2022 to 2026
$1.6M
NCI NIH HHS R01 CA265907NIGMS NIH HHS P20 GM113117NIGMS NIH HHS P30 GM145499
6 · The paper itself

Abstract

Neoantigens, specifically tumor antigenic peptides presented by MHC-I, are essential for activating cytolytic CD8+ T cells via T Cell Receptor (TCR) recognition. Neoantigens have drawn growing attention for developing novel cancer immunotherapy approaches. However, current cancer immunotherapy suffers from low and unpredictable response rates due to the heterogeneity and unknown TCR binding properties among human patients. To address this issue, here we introduce the ExoGAN generative AI framework to guide the design of novel tumor antigenic peptides. ExoGAN is a generative adversarial network (GAN) that integrates HLA physiochemistry feature engineering with sequence-level data, used here to design new HLA-A*02:01-targeting peptides. We apply ExoGAN to design human-based neoantigens that bind to specific MHC-Is (HLAs) with improved TCR recognition and T cell activation, training the model on the largest IEDB dataset of HLA peptides. To deliver these ExoGAN-designed peptides to cells for effective T cell activation, we incorporate a membrane surface molecular engineering approach to fully decorate extracellular vesicles (EVs) with ExoGAN-designed peptides in MHC complex proteins. EVs have been recognized as essential immunity mediators, which carry costimulators with antigenic presentation for leading the emerging and advanced cancer immunotherapy, making them ideal for peptide delivery. Computational and experimental validation shown here, demonstrate that the ExoGAN-generated peptide carrying EVs consistently express antigenic presentation to T cell TCRs, resulting in precisely controlled and programmable EV agents for anti-tumor immunity activation. This greater coverage of effective antigens advances mechanistic understanding of neoantigen functionality, despite inconsistent TCR signaling across human patients. Combined with EV immunity activation, our ExoGAN framework is deployable for clinical translation and enabling precision cancer immunotherapy. The increased scientific understanding of neoantigen diversity across tumors, discovered by ExoGAN, will also accelerate the development of new functional immunotherapy agents in activating patient-specific TCRs.

Identifiers

PMID40678247
PMCPMC12270203

What Socratic holds

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LicenceCC BY
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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.