TY - JOUR
T1 - Improve
T2 - a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition
AU - Borch, Annie
AU - Carri, Ibel
AU - Reynisson, Birkir
AU - Alvarez, Heli M Garcia
AU - Munk, Kamilla K
AU - Montemurro, Alessandro
AU - Kristensen, Nikolaj Pagh
AU - Tvingsholm, Siri A
AU - Holm, Jeppe Sejerø
AU - Heeke, Christina
AU - Moss, Keith Henry
AU - Hansen, Ulla Kring
AU - Schaap-Johansen, Anna-Lisa
AU - Bagger, Frederik Otzen
AU - de Lima, Vinicius Araujo Barbosa
AU - Rohrberg, Kristoffer S
AU - Funt, Samuel A
AU - Donia, Marco
AU - Svane, Inge Marie
AU - Lassen, Ulrik
AU - Barra, Carolina
AU - Nielsen, Morten
AU - Hadrup, Sine Reker
N1 - Copyright © 2024 Borch, Carri, Reynisson, Alvarez, Munk, Montemurro, Kristensen, Tvingsholm, Holm, Heeke, Moss, Hansen, Schaap-Johansen, Bagger, de Lima, Rohrberg, Funt, Donia, Svane, Lassen, Barra, Nielsen and Hadrup.
PY - 2024
Y1 - 2024
N2 - BACKGROUND: Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition.METHOD: To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy.RESULTS: We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity.CONCLUSION: Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.
AB - BACKGROUND: Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition.METHOD: To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy.RESULTS: We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity.CONCLUSION: Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.
KW - Humans
KW - T-Lymphocytes
KW - Neoplasms
KW - Immunotherapy/methods
KW - neoantigen
KW - immunotherapy
KW - immunoinformatics
KW - neoepitope prediction
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85190531570&partnerID=8YFLogxK
U2 - 10.3389/fimmu.2024.1360281
DO - 10.3389/fimmu.2024.1360281
M3 - Journal article
C2 - 38633261
SN - 1664-3224
VL - 15
JO - Frontiers in Immunology
JF - Frontiers in Immunology
M1 - 1360281
ER -