A model that includes key parameters that govern tumor epitope immunogenicity has been developed, and is available as a resource for studies evaluating antitumor immunity, such as neoantigen-based therapies, according to a study published in Cell.1
Neoantigens are promising treatment targets due to their tumor specificity and lack of preexisting immune tolerance. Although rules that predict epitope immunogenicity have been developed, they were derived based on techniques and models with limitations and similar parameters neoantigen immunogenicity have not yet been developed. This study was part of a consortium effort to improve neoantigen prediction.
The study included data from tumor samples provided by teams of researchers from academia, industry, and nonprofit groups of the Tumor Neoantigen Selection Alliance (TESLA) consortium. Data included whole-exome sequencing, tumor RNA sequencing, and clinical-grade Human leukocyte antigen typing. Neoepitope predictions were generated using these data, and a ranked list of neoepitopes predicted to bind to major histocompatibility complex (MHC) class I molecules was produced. The ranking was evaluated in vitro and further validated using independent samples from patients with melanoma and non-small cell lung cancer.
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During the process of developing the model, the teams identified and ranked the immunogenicity of peptides, characterized the presentation features of immunogenic peptides, and determined features of immunogenic peptides. There were 4 independent MHC class I features that enriched immunogenicity: strong binding affinity, high tumor abundance, high binding stability, and peptide recognition.
“These results comprehensively characterize approximately 50% of immunogenic tumor epitopes: they are those tumor peptides that have strong MHC binding affinity and long half-life; are expressed highly; and have either low agretopicity or high foreignness,” the authors wrote.
The authors concluded that “it is our goal that the data assembled here be a resource for the community to identify new features that differentiate immunogenic pMHC, calibrate and tune neoepitope prediction pipelines for particular use cases, and ultimately, be used to benchmark and improve neoantigen prediction methods.” The data are available open-source at: https://www.synapse.org/#!Synapse:syn21048999.
Reference
Wells DK, van Buuren MM, Dang KK, et al. Key parameters of tumor epitope immunogenicity revealed through a consortium approach improve neoantigen prediction. Cell. Published online October 9, 2020.
