PMID- 35833513
OWN - NLM
STAT- MEDLINE
DCOM- 20230913
LR  - 20231006
IS  - 1541-0420 (Electronic)
IS  - 0006-341X (Print)
IS  - 0006-341X (Linking)
VI  - 79
IP  - 3
DP  - 2023 Sep
TI  - Prioritizing candidate peptides for cancer vaccines through predicting peptide 
      presentation by HLA-I proteins.
PG  - 2664-2676
LID - 10.1111/biom.13717 [doi]
AB  - Cancer (treatment) vaccines that are made of neoantigens, or peptides unique to 
      tumor cells due to somatic mutations, have emerged as a promising method to 
      reinvigorate the immune response against cancer. A key step to prioritizing 
      neoantigens for cancer vaccines is computationally predicting which neoantigens 
      are presented on the cell surface by a human leukocyte antigen (HLA). We propose 
      to address this challenge by training a neural network using mass spectrometry 
      (MS) data composed of peptides presented by at least one of several HLAs of a 
      subject. We embed the neural network within a mixture model and train the neural 
      network by maximizing the likelihood of the mixture model. After evaluating our 
      method using data sets where the peptide presentation status was known, we 
      applied it to analyze somatic mutations of 60 melanoma patients and identified a 
      group of neoantigens more immunogenic in tumor cells than in normal cells. 
      Moreover, neoantigen burden estimated by our method was significantly associated 
      with a measurement of the immune system activity, suggesting these neoantigens 
      could induce an immune response.
CI  - (c) 2022 The International Biometric Society.
FAU - Zhou, Laura Y
AU  - Zhou LY
AD  - Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel 
      Hill, North Carolina.
FAU - Zou, Fei
AU  - Zou F
AD  - Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel 
      Hill, North Carolina.
FAU - Sun, Wei
AU  - Sun W
AUID- ORCID: 0000-0002-6350-1107
AD  - Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel 
      Hill, North Carolina.
AD  - Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer 
      Center, Seattle, Washington.
AD  - Department of Biostatistics, University of Washington, Seattle, Washington.
LA  - eng
GR  - R01 GM105785/GM/NIGMS NIH HHS/United States
GR  - R56 LM013784/LM/NLM NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20220729
PL  - England
TA  - Biometrics
JT  - Biometrics
JID - 0370625
RN  - 0 (Cancer Vaccines)
RN  - 0 (Antigens, Neoplasm)
RN  - 0 (Peptides)
RN  - 0 (HLA Antigens)
SB  - IM
MH  - Humans
MH  - *Cancer Vaccines/chemistry
MH  - Antigens, Neoplasm/genetics
MH  - *Neoplasms/genetics
MH  - Peptides/chemistry/genetics
MH  - HLA Antigens/genetics
MH  - *Melanoma/genetics
PMC - PMC10548401
MID - NIHMS1908078
OTO - NOTNLM
OT  - cancer vaccine
OT  - melanoma
OT  - mixture model
OT  - neoantigens
OT  - neural network
OT  - peptide-HLA association
EDAT- 2022/07/15 06:00
MHDA- 2023/09/13 06:41
PMCR- 2023/10/04
CRDT- 2022/07/14 06:32
PHST- 2021/10/29 00:00 [received]
PHST- 2022/07/01 00:00 [accepted]
PHST- 2023/09/13 06:41 [medline]
PHST- 2022/07/15 06:00 [pubmed]
PHST- 2022/07/14 06:32 [entrez]
PHST- 2023/10/04 00:00 [pmc-release]
AID - 10.1111/biom.13717 [doi]
PST - ppublish
SO  - Biometrics. 2023 Sep;79(3):2664-2676. doi: 10.1111/biom.13717. Epub 2022 Jul 29.