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Related Experiment Video

Updated: May 27, 2025

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

Published on: October 15, 2021

Toward equitable major histocompatibility complex binding predictions.

Eric Glynn^1,2, Dario Ghersi³, Mona Singh^2,4

¹Department of Molecular Biology, Princeton University, Princeton, NJ 08544.

Proceedings of the National Academy of Sciences of the United States of America

|February 18, 2025

Summary

This summary is machine-generated.

Deep learning for predicting major histocompatibility complex (MHC) binding shows racial disparities. A new framework and model mitigate these data imbalances, promoting equitable personalized immunotherapies.

Keywords:

cancer immunotherapies deep learning health equity in precision oncology neoantigen prediction predicting major histocompatibility complex (MHC) binding

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Last Updated: May 27, 2025

Immunopeptidomics: Isolation of Mouse and Human MHC Class I- and II-Associated Peptides for Mass Spectrometry Analysis

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Published on: October 15, 2021

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

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Published on: March 25, 2014

Stability and Structure of Bat Major Histocompatibility Complex Class I with Heterologous β2-Microglobulin

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Published on: March 10, 2021

Area of Science:

Immunoinformatics
Computational Biology
Genomics

Background:

Major histocompatibility complex (MHC) binding prediction is crucial for personalized cancer immunotherapies and vaccines.
Equitable application requires methods that perform well across diverse human populations and MHC alleles.
Significant disparities exist in the amount of binding data available for different MHC alleles across racial and ethnic groups.

Purpose of the Study:

To assess the impact of data imbalance on MHC binding prediction across diverse populations.
To develop an advanced MHC binding prediction model that accounts for and mitigates data disparities.
To establish a foundation for equitable MHC binding models in personalized medicine.

Main Methods:

Developed a machine learning framework to evaluate data imbalance effects on MHC allele binding predictions.
Applied the framework to create a state-of-the-art MHC binding prediction model with per-allele performance estimates.
Devised an algorithmic strategy for targeted data collection to address remaining inequities.

Main Results:

Identified alarming disparities in MHC binding data availability across racial and ethnic groups.
The developed MHC binding model significantly mitigates performance disparities linked to data imbalance.
The model provides per-allele performance estimates, enhancing transparency and reliability.

Conclusions:

The study highlights critical data equity issues in MHC binding prediction for immunotherapies.
The novel framework and model advance the development of fairer and more accurate predictive tools.
This work is foundational for creating equitable MHC binding models essential for personalized cancer treatments.