Richman and Vonderheide et al. evaluated the relationship of metrics of neoantigen quality to immunogenicity, and identified dissimilarity to the non-mutated self-proteome as a key predictive determinate of neoantigen immunogenicity and of PFS to PD-1 blockade in NSCLC. A rare, unique subset of predicted, distinctly hydrophobic, immunogenic neoantigens with high dissimilarity to self-proteome was observed to correlate with clinical response independently of predicted MHC affinity. These data identify dissimilarity and high dissimilarity to self-proteome as metrics to improve selection of immunogenic neoantigens with predictive clinical activity.
Contributed by Samuel Goldman
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.
Author Info: (1) Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA. (2) Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic
Author Info: (1) Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA. (2) Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: rhv@upenn.edu. (3) Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: rech@rech.io.
