Neoantigen Dissimilarity to the Self-Proteome Predicts Immunogenicity and Response to Immune Checkpoint Blockade.
Cell Syst
; 9(4): 375-382.e4, 2019 10 23.
Article
in En
| MEDLINE
| ID: mdl-31606370
ABSTRACT
Despite improved methods for MHC affinity prediction, the vast majority of computationally predicted tumor neoantigens are not immunogenic experimentally, indicating that high-quality neoantigens are beyond current algorithms to discern. To enrich for neoantigens with the greatest likelihood of immunogenicity, we developed an analytic method to parse neoantigen quality through rational biological criteria across five clinical datasets for 318 cancer patients. We explored four quality metrics, including analysis of dissimilarity to the non-mutated proteome that was predictive of peptide immunogenicity. In patient tumors, neoantigens with high dissimilarity were unique, enriched for hydrophobic sequences, and correlated with survival after PD-1 checkpoint therapy in patients with non-small cell lung cancer independent of predicted MHC affinity. We incorporated our neoantigen quality analysis methodology into an open-source tool, antigen.garnish, to predict immunogenic peptides from bulk computationally predicted neoantigens for which the immunogenic "hit rate" is currently low.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Autoantigens
/
Software
/
Carcinoma, Non-Small-Cell Lung
/
Computational Biology
/
Immunotherapy
/
Lung Neoplasms
/
Antigens, Neoplasm
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Cell Syst
Year:
2019
Document type:
Article
Affiliation country:
United States