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A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
Published on: March 25, 2014
Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer
Leila Y Chihab1,2, Julie G Burel1, Aaron M Miller1,3
1Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States.
Background:
Mutations in cancer cells can result in the production of neoepitopes 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 in-house pipeline termed Identify-Prioritize-Validate (IPV) that identifies long peptides that cover both CD4 and CD8 epitopes.
Methods:
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.
Results:
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.
Conclusions:
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.
Insights
The Identify-Prioritize-Validate (IPV) pipeline shows improved prediction of immunogenic neoepitopes compared to the Tumor Neoantigen Selection Alliance (TESLA) pipeline. IPV
Area of Science:
- Immunology
- Bioinformatics
- Cancer Research
Background:
- Cancer mutations can create neoepitopes, recognized by T cells, driving immune responses crucial for cancer immunotherapy.
- Current neoepitope prediction pipelines, like TESLA, focus on short peptides, often showing limited experimental validation.
- The Identify-Prioritize-Validate (IPV) pipeline was developed to identify longer peptides encompassing both CD4 and CD8 epitopes.
Purpose of the Study:
- To systematically compare the performance of the in-house IPV pipeline against the established TESLA pipeline.
- To evaluate the predictive accuracy of neoepitopes identified by both pipelines using experimental validation.
Main Methods:
- Patient peripheral blood mononuclear cells (PBMCs) were cultured in vitro with candidate peptides from both IPV and TESLA.
- Immune recognition of the candidate peptides was assessed via cytokine-secretion assays.
Main Results:
- The IPV pipeline demonstrated superior performance in predicting neoepitopes that elicited a T cell response compared to TESLA.
- This improved predictive capability of IPV was attributed to its strategy of including longer peptides.
Conclusions:
- The IPV pipeline offers enhanced predictive accuracy for immunogenic neoepitopes in the tested assay system.
- This study highlights the importance of peptide length in neoepitope prediction and the need for standardized validation metrics.
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