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2017 ; 8
(?): 1367
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Hotspots of Antigen Presentation Revealed by Human Leukocyte Antigen
Ligandomics for Neoantigen Prioritization
#MMPMID29104575
Müller M
; Gfeller D
; Coukos G
; Bassani-Sternberg M
Front Immunol
2017[]; 8
(?): 1367
PMID29104575
show ga
The remarkable clinical efficacy of the immune checkpoint blockade therapies has
motivated researchers to discover immunogenic epitopes and exploit them for
personalized vaccines. Human leukocyte antigen (HLA)-binding peptides derived
from processing and presentation of mutated proteins are one of the leading
targets for T-cell recognition of cancer cells. Currently, most studies attempt
to identify neoantigens based on predicted affinity to HLA molecules, but the
performance of such prediction algorithms is rather poor for rare HLA class I
alleles and for HLA class II. Direct identification of neoantigens by mass
spectrometry (MS) is becoming feasible; however, it is not yet applicable to most
patients and lacks sensitivity. In an attempt to capitalize on existing
immunopeptidomics data and extract information that could complement HLA-binding
prediction, we first compiled a large HLA class I and class II immunopeptidomics
database across dozens of cell types and HLA allotypes and detected hotspots that
are subsequences of proteins frequently presented. About 3% of the peptidome was
detected in both class I and class II. Based on the gene ontology of their source
proteins and the peptide's length, we propose that their processing may partake
by the cellular class II presentation machinery. Our database captures the global
nature of the in vivo peptidome averaged over many HLA alleles, and therefore,
reflects the propensity of peptides to be presented on HLA complexes, which is
complementary to the existing neoantigen prediction features such as binding
affinity and stability or RNA abundance. We further introduce two
immunopeptidomics MS-based features to guide prioritization of neoantigens: the
number of peptides matching a protein in our database and the overlap of the
predicted wild-type peptide with other peptides in our database. We show as a
proof of concept that our immunopeptidomics MS-based features improved neoantigen
prioritization by up to 50%. Overall, our work shows that, in addition to
providing huge training data to improve the HLA binding prediction,
immunopeptidomics also captures other aspects of the natural in vivo presentation
that significantly improve prediction of clinically relevant neoantigens.
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