TY - JOUR
T1 - Computational prediction of MHC anchor locations guides neoantigen identification and prioritization
AU - Xia, Huiming
AU - McMichael, Joshua
AU - Becker-Hapak, Michelle
AU - Onyeador, Onyinyechi C.
AU - Buchli, Rico
AU - McClain, Ethan
AU - Pence, Patrick
AU - Supabphol, Suangson
AU - Richters, Megan M.
AU - Basu, Anamika
AU - Ramirez, Cody A.
AU - Puig-Saus, Cristina
AU - Cotto, Kelsy C.
AU - Freshour, Sharon L.
AU - Hundal, Jasreet
AU - Kiwala, Susanna
AU - Goedegebuure, S. Peter
AU - Johanns, Tanner M.
AU - Dunn, Gavin P.
AU - Ribas, Antoni
AU - Miller, Christopher A.
AU - Gillanders, William E.
AU - Fehniger, Todd A.
AU - Griffith, Obi L.
AU - Griffith, Malachi
N1 - Funding Information:
M.G. was supported by the National Human Genome Research Institute (NHGRI) of the NIH under award number R00HG007940. M.G. and O.L.G. were supported by the NIH National Cancer Institute (NCI) under award numbers U01CA209936, U01CA231844, and U24CA237719. M.G. was supported by the V Foundation for Cancer Research under award number V2018-007. M.G., O.L.G., H.X., W.E.G., and T.A.F. were supported by the NCI under award number U01CA248235. T.A.F. was also supported by the Jamie Erin Follicular Lymphoma Consortium and the Siteman Cancer Center (P30CA91842). This work was also supported in part by the Washington University Institute of Clinical and Translational Sciences from the National Center for Advancing Translational Sciences (NCATS) of NIH under award number UL1TR002345. M.M.R. was supported by Washington University’s Genome Analysis Training Program (GATP) from the National Human Genome Research Institute (NHGRI) of the NIH under award number T32HG000045. S.S. was supported by the Prince Mahidol Award Youth Program of the Prince Mahidol Award Foundation under the Royal Patronage of HM the King of Thailand.
Publisher Copyright:
Copyright © 2023 The Authors, some rights reserved.
PY - 2023/4
Y1 - 2023/4
N2 - Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient’s specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.
AB - Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient’s specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.
UR - http://www.scopus.com/inward/record.url?scp=85152049697&partnerID=8YFLogxK
U2 - 10.1126/sciimmunol.abg2200
DO - 10.1126/sciimmunol.abg2200
M3 - Article
C2 - 37027480
AN - SCOPUS:85152049697
SN - 2470-9468
VL - 8
JO - Science immunology
JF - Science immunology
IS - 82
M1 - eabg2200
ER -