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.
journal_name
Cell Systjournal_title
Cell systemsauthors
Richman LP,Vonderheide RH,Rech AJdoi
10.1016/j.cels.2019.08.009subject
Has Abstractpub_date
2019-10-23 00:00:00pages
375-382.e4issue
4eissn
2405-4712issn
2405-4720pii
S2405-4712(19)30307-2journal_volume
9pub_type
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