TY - JOUR
T1 - The immunopeptidome from a genomic perspective
T2 - Establishing the noncanonical landscape of MHC class I–associated peptides
AU - Bedran, Georges
AU - Gasser, Hans-Christof
AU - Weke, Kenneth
AU - Wang, Tongjie
AU - Bedran, Dominika
AU - Laird, Alexander
AU - Battail, Christophe
AU - Zanzotto, Fabio Massimo
AU - Pesquita, Catia
AU - Axelson, Håkan
AU - Rajan, Ajitha
AU - Harrison, David J
AU - Palkowski, Aleksander
AU - Pawlik, Maciej
AU - Parys, Maciej
AU - O'Neill, J Robert
AU - Brennan, Paul M
AU - Symeonides, Stefan N
AU - Goodlett, David R
AU - Litchfield, Kevin
AU - Fahraeus, Robin
AU - Hupp, Ted R
AU - Kote, Sachin
AU - Alfaro, Javier A
PY - 2023/3/24
Y1 - 2023/3/24
N2 - Tumor antigens can emerge through multiple mechanisms, including translation of non-coding genomic regions. This non-canonical category of antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry to enable the discovery of non-canonical MHC-associated peptides (ncMAPs) from non-coding regions. Considering that the emergence of tumor antigens can also involve post-translational modifications, we included an open search component in our pipeline. Leveraging the wealth of mass spectrometry-based immunopeptidomics, we analyzed 26 MHC class I immunopeptidomic studies of 9 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant post-translational modifications, using spectral matching and controlled their false discovery rate (FDR) to 1%. Interestingly, the non-canonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 54.85%. Here, we reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targeting agents for T-cell therapies or vaccine development.
AB - Tumor antigens can emerge through multiple mechanisms, including translation of non-coding genomic regions. This non-canonical category of antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry to enable the discovery of non-canonical MHC-associated peptides (ncMAPs) from non-coding regions. Considering that the emergence of tumor antigens can also involve post-translational modifications, we included an open search component in our pipeline. Leveraging the wealth of mass spectrometry-based immunopeptidomics, we analyzed 26 MHC class I immunopeptidomic studies of 9 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant post-translational modifications, using spectral matching and controlled their false discovery rate (FDR) to 1%. Interestingly, the non-canonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 54.85%. Here, we reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targeting agents for T-cell therapies or vaccine development.
KW - Cancer
KW - tumor antigens
KW - non-canonical MHC class I–associated peptides
KW - mass spectrometry
KW - shared antigens
U2 - 10.1158/2326-6066.CIR-22-0621
DO - 10.1158/2326-6066.CIR-22-0621
M3 - Article
C2 - 36961404
SP - 1
EP - 40
JO - Cancer Immunology Research
JF - Cancer Immunology Research
SN - 2326-6066
M1 - 22-0621
ER -