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Antigens Involved in Adaptive Immunity

Antigens Involved in Adaptive Immunity

An antigen is any substance the immune system identifies as foreign and potentially harmful to the body, prompting an immune response. Antigens have two functional properties: immunogenicity and reactivity. Immunogenicity is the ability of an antigen to stimulate a specific immune response. At the same time, reactivity describes the antigen's ability to react with the cells and antibodies produced in response to it.
Complete Antigens
Complete antigens possess both immunogenicity and...

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Updated: Jul 14, 2025

Enrich and Expand Rare Antigen-specific T Cells with Magnetic Nanoparticles

Enrich and Expand Rare Antigen-specific T Cells with Magnetic Nanoparticles

Published on: November 17, 2018

Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction.

Markus Müller¹, Florian Huber², Marion Arnaud²

¹Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015 Lausanne, Switzerland.

|October 10, 2023

Summary

This summary is machine-generated.

Identifying immunogenic neoantigens is key for cancer immunotherapy. This study found that neoantigen location, binding ability, and gene function predict immunogenicity, improving neoantigen selection by 30%.

Keywords:

cancer immunotherapy machine learning neoantigen prioritization personalized cancer vaccine

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Last Updated: Jul 14, 2025

Enrich and Expand Rare Antigen-specific T Cells with Magnetic Nanoparticles

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Published on: November 17, 2018

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

Predictive Immune Modeling of Solid Tumors

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Published on: February 25, 2020

Area of Science:

Immunology
Oncology
Bioinformatics

Background:

Accurate neoantigen selection is critical for effective cancer immunotherapy pipelines.
Neoantigens must bind to human leukocyte antigen (HLA) class I and be recognized by T cells.

Purpose of the Study:

To identify predictive features of neoantigen immunogenicity beyond standard metrics.
To develop and validate machine learning classifiers for improved neoantigen ranking.

Main Methods:

Reprocessed whole-exome sequencing and RNA sequencing data from 131 cancer patients.
Combined external screening assays with an in-house dataset.
Identified somatic mutations, neo-peptides, and assessed immunogenicity.

Main Results:

Identified 46,017 somatic single-nucleotide variant mutations and 1,781,445 neo-peptides.
Confirmed 212 mutations and 178 neo-peptides as immunogenic.
Neo-peptide location in HLA presentation hotspots, binding promiscuity, and oncogenicity were predictive of immunogenicity.

Conclusions:

Machine learning classifiers accurately predicted neoantigen immunogenicity across datasets.
The developed methods improved neoantigen ranking by up to 30%.
Provided valuable homogenized datasets for developing and benchmarking neoantigen-based immunotherapy algorithms.