Towards designing improved cancer immunotherapy targets with a peptide-MHC-I presentation model, HLApollo

William John Thrift, Nicolas W. Lounsbury, Quade Broadwell, Amy Heidersbach, Emily Freund, Yassan Abdolazimi, Qui T. Phung, Jieming Chen, Aude-Hélène Capietto, Ann-Jay Tong, Christopher M. Rose, Craig Blanchette, Jennie R. Lill, Benjamin Haley, Lélia Delamarre, Richard Bourgon, Kai Liu () and Suchit Jhunjhunwala ()
Additional contact information
William John Thrift: Genentech
Nicolas W. Lounsbury: Genentech
Quade Broadwell: Genentech
Amy Heidersbach: Genentech
Emily Freund: Genentech
Yassan Abdolazimi: Genentech
Qui T. Phung: Genentech
Jieming Chen: Genentech
Aude-Hélène Capietto: Genentech
Ann-Jay Tong: Genentech
Christopher M. Rose: Genentech
Craig Blanchette: Genentech
Jennie R. Lill: Genentech
Benjamin Haley: Genentech
Lélia Delamarre: Genentech
Richard Bourgon: Genentech
Kai Liu: Genentech
Suchit Jhunjhunwala: Genentech

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract Based on the success of cancer immunotherapy, personalized cancer vaccines have emerged as a leading oncology treatment. Antigen presentation on MHC class I (MHC-I) is crucial for the adaptive immune response to cancer cells, necessitating highly predictive computational methods to model this phenomenon. Here, we introduce HLApollo, a transformer-based model for peptide-MHC-I (pMHC-I) presentation prediction, leveraging the language of peptides, MHC, and source proteins. HLApollo provides end-to-end treatment of MHC-I sequences and deconvolution of multi-allelic data, using a negative-set switching strategy to mitigate misassigned negatives in unlabelled ligandome data. HLApollo shows a 12.65% increase in average precision (AP) on ligandome data and a 4.1% AP increase on immunogenicity test data compared to next-best models. Incorporating protein features from protein language models yields further gains and reduces the need for gene expression measurements. Guided by clinical use, we demonstrate pan-allelic generalization which effectively captures rare alleles in underrepresented ancestries.

Date: 2024
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DOI: 10.1038/s41467-024-54887-7

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