Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction

Area of Science:

• Immunology
• Oncology
• Proteomics

Background:

• CD4+ T cells recognize cancer-specific antigens, offering therapeutic potential.
• Predicting human leukocyte antigen class II (HLA-II) presented antigens is challenging due to inaccurate binding predictions and pathway complexities.
• Targeting HLA-II is hindered by difficulties in identifying relevant tumor antigens.

Purpose of the Study:

• To develop improved technology for discovering HLA-II binding motifs.
• To analyze tumor ligandomes and understand processing rules in the tumor microenvironment.
• To create algorithms for accurate prediction of HLA-II ligandomes for improved cancer therapies.

Main Methods:

• Profiling of over 40 HLA-II alleles to determine binding motifs.
• Analysis of tumor ligandomes to identify antigen processing rules.
• Development of predictive algorithms integrating HLA-DM sensitivity and antigen-presenting cell (APC) dominance.

Main Results:

• HLA-II binding motifs are highly sensitive to the peptide-loading chaperone HLA-DM.
• Intratumoral HLA-II presentation is primarily by professional APCs, not cancer cells.
• Developed algorithms accurately predict APC ligandomes, including peptides from phagocytosed cancer cells.

Conclusions:

• Improved HLA-II binding motif discovery and ligandomes analysis provide crucial biological insights.
• Accurate prediction of APC ligandomes enables better targeting of HLA-II.
• These advancements facilitate the development of improved HLA-II-directed cancer immunotherapies.