PMID- 37983288
OWN - NLM
STAT- MEDLINE
DCOM- 20231206
LR  - 20240117
IS  - 1553-7358 (Electronic)
IS  - 1553-734X (Print)
IS  - 1553-734X (Linking)
VI  - 19
IP  - 11
DP  - 2023 Nov
TI  - Neural network models for sequence-based TCR and HLA association prediction.
PG  - e1011664
LID - 10.1371/journal.pcbi.1011664 [doi]
LID - e1011664
AB  - T cells rely on their T cell receptors (TCRs) to discern foreign antigens 
      presented by human leukocyte antigen (HLA) proteins. The TCRs of an individual 
      contain a record of this individual's past immune activities, such as immune 
      response to infections or vaccines. Mining the TCR data may recover useful 
      information or biomarkers for immune related diseases or conditions. Some TCRs 
      are observed only in the individuals with certain HLA alleles, and thus 
      characterizing TCRs requires a thorough understanding of TCR-HLA associations. 
      The extensive diversity of HLA alleles and the rareness of some HLA alleles 
      present a formidable challenge for this task. Existing methods either treat HLA 
      as a categorical variable or represent an HLA by its alphanumeric name, and have 
      limited ability to generalize to the HLAs that are not seen in the training 
      process. To address this challenge, we propose a neural network-based method 
      named Deep learning Prediction of TCR-HLA association (DePTH) to predict TCR-HLA 
      associations based on their amino acid sequences. We demonstrate that DePTH is 
      capable of making reasonable predictions for TCR-HLA associations, even when 
      neither the HLA nor the TCR have been included in the training dataset. 
      Furthermore, we establish that DePTH can be used to quantify the functional 
      similarities among HLA alleles, and that these HLA similarities are associated 
      with the survival outcomes of cancer patients who received immune checkpoint 
      blockade treatments.
CI  - Copyright: (c) 2023 Liu et al. This is an open access article distributed under the 
      terms of the Creative Commons Attribution License, which permits unrestricted 
      use, distribution, and reproduction in any medium, provided the original author 
      and source are credited.
FAU - Liu, Si
AU  - Liu S
AD  - Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer 
      Center, Seattle, Washington, United States of America.
FAU - Bradley, Philip
AU  - Bradley P
AD  - Herbold Computational Biology Program, Public Health Sciences Division, Fred 
      Hutchinson Cancer Center, Seattle, Washington, United States of America.
AD  - Institute for Protein Design. University of Washington, Seattle, Washington, 
      United States of America.
FAU - Sun, Wei
AU  - Sun W
AUID- ORCID: 0000-0002-6350-1107
AD  - Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer 
      Center, Seattle, Washington, United States of America.
AD  - Department of Biostatistics, University of Washington, Seattle, Washington, 
      United States of America.
AD  - Department of Biostatistics, University of North Carolina, Chapel Hill, North 
      Carolina, United States of America.
LA  - eng
GR  - R01 CA222833/CA/NCI NIH HHS/United States
GR  - R01 GM105785/GM/NIGMS NIH HHS/United States
GR  - R35 GM141457/GM/NIGMS NIH HHS/United States
GR  - R56 AI169192/AI/NIAID NIH HHS/United States
PT  - Journal Article
DEP - 20231120
PL  - United States
TA  - PLoS Comput Biol
JT  - PLoS computational biology
JID - 101238922
RN  - 0 (HLA Antigens)
RN  - 0 (Histocompatibility Antigens Class I)
RN  - 0 (Receptors, Antigen, T-Cell)
RN  - 0 (Histocompatibility Antigens Class II)
SB  - IM
UOF - bioRxiv. 2023 May 26;:. PMID: 37293077
MH  - Humans
MH  - *HLA Antigens/genetics/chemistry
MH  - *Histocompatibility Antigens Class I
MH  - Receptors, Antigen, T-Cell/genetics
MH  - Histocompatibility Antigens Class II
MH  - Neural Networks, Computer
PMC - PMC10695368
COIS- None.
EDAT- 2023/11/20 18:44
MHDA- 2023/12/06 06:42
PMCR- 2023/11/20
CRDT- 2023/11/20 13:52
PHST- 2023/06/21 00:00 [received]
PHST- 2023/11/06 00:00 [accepted]
PHST- 2023/12/04 00:00 [revised]
PHST- 2023/12/06 06:42 [medline]
PHST- 2023/11/20 18:44 [pubmed]
PHST- 2023/11/20 13:52 [entrez]
PHST- 2023/11/20 00:00 [pmc-release]
AID - PCOMPBIOL-D-23-00978 [pii]
AID - 10.1371/journal.pcbi.1011664 [doi]
PST - epublish
SO  - PLoS Comput Biol. 2023 Nov 20;19(11):e1011664. doi: 10.1371/journal.pcbi.1011664. 
      eCollection 2023 Nov.