MHCquant: Automated and Reproducible Data Analysis for Immunopeptidomics.

Area of Science:

• Computational biology
• Immunology
• Mass spectrometry

Background:

• Personalized multipeptide vaccines are crucial for tumor immunotherapy.
• Identifying tumor-specific epitopes requires analyzing human leukocyte antigen-presented peptides from cancer tissues.

Purpose of the Study:

• To present MHCquant, a fully automated computational pipeline for processing LC-MS/MS data.
• To generate annotated, false discovery rate-controlled lists of (neo-)epitopes with relative quantification.

Main Methods:

• Immunoaffinity purification of human leukocyte antigen-presented peptides from cancer tissue.
• Liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) analysis.
• Automated data processing using the MHCquant computational pipeline.

Main Results:

• MHCquant demonstrated higher sensitivity compared to established methods.
• It identified the highest number of unique peptides with a comparable rate of predicted MHC binders.
• Reprocessing of previous study data identified previously undetected neoepitopes.

Conclusions:

• MHCquant offers a sensitive and automated approach for (neo-)epitope identification in cancer immunotherapy research.
• The pipeline ensures reproducibility and facilitates large-scale immunopeptidomics data analysis.
• MHCquant is available as open-source software, promoting accessibility and further development.