Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes.
Algorithms
Animals
Antigens, Neoplasm
/ immunology
Cancer Vaccines
/ immunology
Computational Biology
/ methods
Epitopes, T-Lymphocyte
/ immunology
Female
Histocompatibility Antigens Class I
/ immunology
Histocompatibility Antigens Class II
/ immunology
Humans
Machine Learning
Male
Mice
Mice, Inbred BALB C
Mice, Inbred C57BL
Neoplasms
/ drug therapy
Peptide Fragments
/ immunology
T-Lymphocytes, Cytotoxic
/ immunology
Journal
Cancer immunology research
ISSN: 2326-6074
Titre abrégé: Cancer Immunol Res
Pays: United States
ID NLM: 101614637
Informations de publication
Date de publication:
10 2019
10 2019
Historique:
received:
02
03
2019
revised:
19
05
2019
accepted:
12
08
2019
pubmed:
14
9
2019
medline:
25
9
2020
entrez:
14
9
2019
Statut:
ppublish
Résumé
Current tumor neoantigen calling algorithms primarily rely on epitope/major histocompatibility complex (MHC) binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity using approaches modeled from tumor-specific antigen data. Here, we describe peptide-intrinsic biochemical features associated with neoantigen and minor histocompatibility mismatch antigen immunogenicity and present a gradient boosting algorithm for predicting tumor antigen immunogenicity. This algorithm was validated in two murine tumor models and demonstrated the capacity to select for therapeutically active antigens. Immune correlates of neoantigen immunogenicity were studied in a pan-cancer data set from The Cancer Genome Atlas and demonstrated an association between expression of immunogenic neoantigens and immunity in colon and lung adenocarcinomas. Lastly, we present evidence for expression of an out-of-frame neoantigen that was capable of driving antitumor cytotoxic T-cell responses. With the growing clinical importance of tumor vaccine therapies, our approach may allow for better selection of therapeutically relevant tumor-specific antigens, including nonclassic out-of-frame antigens capable of driving antitumor immunity.
Identifiants
pubmed: 31515258
pii: 2326-6066.CIR-19-0155
doi: 10.1158/2326-6066.CIR-19-0155
pmc: PMC6774822
mid: NIHMS1537822
doi:
Substances chimiques
Antigens, Neoplasm
0
Cancer Vaccines
0
Epitopes, T-Lymphocyte
0
Histocompatibility Antigens Class I
0
Histocompatibility Antigens Class II
0
Peptide Fragments
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1591-1604Subventions
Organisme : NCI NIH HHS
ID : F30 CA225136
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA016086
Pays : United States
Organisme : NIEHS NIH HHS
ID : P30 ES010126
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM008719
Pays : United States
Informations de copyright
©2019 American Association for Cancer Research.
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