PepQueryMHC: rapid and comprehensive tumor antigen prioritization from immunopeptidomics data

Area of Science:

• Immunology
• Bioinformatics
• Oncology

Background:

• Accurate identification of tumor-specific peptides presented by Major Histocompatibility Complex (MHC) molecules is essential for developing effective cancer immunotherapies.
• Current methods face challenges in analyzing MHC class II peptides and sequences not found in reference databases.

Purpose of the Study:

• To introduce PepQueryMHC, an ultra-fast computational tool designed for efficient tumor antigen prioritization.
• To enhance the analysis of MHC-bound peptides by integrating peptide sequences with translated RNA-sequencing (RNA-seq) reads.

Main Methods:

• Development and application of PepQueryMHC, a novel bioinformatics tool.
• Integration of MHC-bound peptide data with translated RNA-seq reads.
• Validation of the tool's performance in antigen prioritization and peptide origin mapping.

Main Results:

• PepQueryMHC demonstrates high efficiency in prioritizing both MHC class I and class II tumor antigens.
• The tool successfully maps the cellular origins of presented peptides.
• PepQueryMHC helps resolve questions regarding the occurrence of proteasome-spliced peptides in cancer.

Conclusions:

• PepQueryMHC offers a versatile and rapid solution for tumor antigen discovery in cancer immunotherapy research.
• The tool advances the ability to analyze complex peptide repertoires, including MHC class II and non-reference sequences.
• This work facilitates the development of more targeted and effective cancer immunotherapies.