PMID- 32662817
OWN - NLM
STAT- MEDLINE
DCOM- 20211122
LR  - 20211122
IS  - 1477-4054 (Electronic)
IS  - 1467-5463 (Print)
IS  - 1467-5463 (Linking)
VI  - 22
IP  - 3
DP  - 2021 May 20
TI  - Investigations of sequencing data and sample type on HLA class Ia typing with 
      different computational tools.
LID - 10.1093/bib/bbaa143 [doi]
LID - bbaa143
AB  - Human leukocyte antigen (HLA) can encode the human major histocompatibility 
      complex (MHC) proteins and play a key role in adaptive and innate immunity. 
      Emerging clinical evidences suggest that the presentation of tumor neoantigens 
      and neoantigen-specific T cell response associated with MHC class I molecules are 
      of key importance to activate the adaptive immune systemin cancer immunotherapy. 
      Therefore, accurate HLA typing is very essential for the clinical application of 
      immunotherapy. In this study, we conducted performance evaluations of 4 widely 
      used HLA typing tools (OptiType, Phlat, Polysolver and seq2hla) for predicting 
      HLA class Ia genes from WES and RNA-seq data of 28 cancer patients. HLA 
      genotyping data using PCR-SBT method was firstly obtained as the golden standard 
      and was subsequently compared with HLA typing data by using NGS techniques. For 
      both WES data and RNA-seq data, OptiType showed the highest accuracy for HLA-Ia 
      typing than the other 3 programs at 2-digit and 4-digit resolution. Additionally, 
      HLA typing accuracy from WES data was higher than from RNA-seq data (99.11% for 
      WES data versus 96.42% for RNA-seq data). The accuracy of HLA-Ia typing by 
      OptiType can reach 100% with the average depth of HLA gene regions >20x. Besides, 
      the accuracy of 2-digit and 4-digit HLA-Ia typing based on control samples was 
      higher than tumor tissues. In conclusion, OptiType by using WES data from control 
      samples with the high average depth (>20x) of HLA gene regions can present a 
      probably superior performance for HLA-Ia typing, enabling its application in 
      cancer immunotherapy.
CI  - (c) The Author(s) 2020. Published by Oxford University Press.
FAU - Yi, Jian
AU  - Yi J
AD  - Cancer Translational Research Institute, YuceBio Technology Co., Ltd., Shenzhen, 
      China.
FAU - Chen, Longyun
AU  - Chen L
AD  - Cancer Translational Research Institute, YuceBio Technology Co., Ltd., Shenzhen, 
      China.
FAU - Xiao, Yajie
AU  - Xiao Y
AD  - Cancer Translational Research Institute, YuceBio Technology Co., Ltd., Shenzhen, 
      China.
FAU - Zhao, Zhikun
AU  - Zhao Z
AD  - Cancer Translational Research Institute, YuceBio Technology Co., Ltd., Shenzhen, 
      China.
FAU - Su, Xiaofan
AU  - Su X
AD  - Yucebio Cancer Translational Research Institute and Chief Medical Officer for 
      Yucebio Technology Co.
LA  - eng
PT  - Journal Article
PL  - England
TA  - Brief Bioinform
JT  - Briefings in bioinformatics
JID - 100912837
RN  - 0 (HLA Antigens)
SB  - IM
MH  - *Genotyping Techniques
MH  - HLA Antigens/*genetics
MH  - *Histocompatibility Testing
MH  - Humans
MH  - *RNA-Seq
MH  - *Software
PMC - PMC8138914
OTO - NOTNLM
OT  - Clinical genomics
OT  - HLA-Ia genotyping
OT  - NGS
OT  - OptiType
EDAT- 2020/07/15 06:00
MHDA- 2021/11/23 06:00
PMCR- 2020/07/14
CRDT- 2020/07/15 06:00
PHST- 2020/01/05 00:00 [received]
PHST- 2020/06/09 00:00 [revised]
PHST- 2020/07/15 06:00 [pubmed]
PHST- 2021/11/23 06:00 [medline]
PHST- 2020/07/15 06:00 [entrez]
PHST- 2020/07/14 00:00 [pmc-release]
AID - 5871189 [pii]
AID - bbaa143 [pii]
AID - 10.1093/bib/bbaa143 [doi]
PST - ppublish
SO  - Brief Bioinform. 2021 May 20;22(3):bbaa143. doi: 10.1093/bib/bbaa143.