Comparative Performance Analysis of Neo-epitope Prediction Algorithms in Head and Neck Cancer

Leila Y Chihab ^1,2* Julie G Burel ¹ Aaron M Miller ^1,3 Luise Westernberg ¹ Brandee Brown ⁴ Jason Greenbaum ¹ Michael Korrer ⁴ Stephen P Schoenberger ¹ Sebastian Joyce ^5,6 Young Kim ⁷ Zeynep Kosaloglu Yalcin ¹ Bjoern Peters ^1,8*

• ¹ La Jolla Institute for Immunology (LJI), La Jolla, United States
• ² Department of Chemistry and Biochemistry UCSD, La Jolla, United States
• ³ Moores Cancer Center, School of Medicine, University of California, San Diego, La Jolla, California, United States
• ⁴ Department of Otolaryngology - Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States
• ⁵ Department of Pathology, Microbiology, and Immunology, Nashville, United States
• ⁶ Department of Veterans Affairs, Nashville, United States
• ⁷ Global Clinical Development, Regeneron Pharmaceuticals, New York, United States
• ⁸ Department of Medicine, UCSD, La Jolla, United States

Mutations in cancer cells can result in the production of neo-epitopes that can be recognized by T cells and trigger an immune response. A reliable pipeline to identify such immunogenic neoepitopes for a given tumor would be beneficial for the design of cancer immunotherapies. Current methods, such as the pipeline proposed by the Tumor Neoantigen Selection Alliance (TESLA), aim to select short peptides with the highest likelihood to be MHC-I restricted minimal epitopes. Typically, only a small percentage of these predicted epitopes are recognized by T cells when tested experimentally. This is particularly problematic as the limited amount of sample available from patients that are acutely sick restricts the number of peptides that can be tested in practice. This led our group to develop an inhouse pipeline termed Identify-Prioritize-Validate (IPV) that identifies long peptides that cover both CD4 and CD8 epitopes. Here, we systematically compared how IPV performs compared to the TESLA pipeline. Patient peripheral blood mononuclear cells were cultured in vitro with their corresponding candidate peptides, and immune recognition was measured using cytokine-secretion assays. The IPV pipeline consistently outperformed the TESLA pipeline in predicting neoepitopes that elicited an immune response in our assay. This was primarily due to the inclusion of longer peptides in IPV compared to TESLA. Our work underscores the improved predictive ability of IPV in comparison to TESLA in this assay system and highlights the need to clearly define which experimental metrics are used to evaluate bioinformatic epitope predictions.