PMID- 35813337
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220716
IS  - 2305-5839 (Print)
IS  - 2305-5847 (Electronic)
IS  - 2305-5839 (Linking)
VI  - 10
IP  - 11
DP  - 2022 Jun
TI  - Benchmarking of 5 algorithms for high-resolution genotyping of human leukocyte 
      antigen class I genes from blood and tissue samples.
PG  - 633
LID - 10.21037/atm-22-875 [doi]
LID - 633
AB  - BACKGROUND: Specific alterations in human leukocyte antigen class I (HLA-I) loci 
      are associated with clinical outcomes for immune checkpoint inhibitors, which 
      increase the clinical relevance of accurate high-resolution HLA genotyping in 
      immuno-oncology applications. Numerous algorithms have been developed for high- 
      to full-resolution HLA genotyping by next-generation sequencing (NGS) data; 
      however, Sanger sequencing-based typing (SBT) remains the gold standard. With the 
      increasing use of NGS for clinical oncology, it is important to identify the 
      computational tool with comparable performance as the gold standard. This study 
      aimed to benchmark 5 algorithms against SBT for the high-resolution typing of 
      classical HLA-I genes for targeted NGS data from blood and tissue samples. 
      METHODS: Paired white blood cell (WBC), plasma, and tissue deoxyribonucleic acid 
      (DNA) samples derived from 22 cancer patients with known HLA genotypes were 
      sequenced using a panel of all the following exons of classical HLA-I genes: 
      HLA-A, HLA-B, and HLA-C. NGS-based genotypes were generated by the 5 different 
      algorithms, including HLA-HD, HLAscan, OptiType, Polysolver, and xHLA. Accuracy 
      was defined as the concordance between the SBT and NGS-based algorithms. Accuracy 
      was computed as the fraction of all the alleles with concordant genotype using 
      the SBT and any of the algorithm over the total number of alleles. RESULTS: In 
      relation to the WBC, plasma, and tissue samples, all 5 algorithms were highly 
      accurate at low-resolution HLA-I genotyping, but had more varied accuracy at 
      high-resolution HLA-I genotyping, particularly in HLA-A. The in-silico analyses 
      revealed that high-resolution genotyping by all 5 algorithms achieved 
      approximately 90% accuracy at sequencing depths of 6,000x - 100x for the WBC 
      samples, at 6,000x - 700x for the plasma samples, and at 1,000x - 100x for the 
      tissue samples. Among the 5 algorithms, HLA-HD was consistently accurate at 
      high-resolution HLA-I genotyping, and had an accuracy of 93.9% for the WBC 
      samples, 87.9% for the plasma samples, and 94.2% for tissue samples even at a 50x 
      sequencing depth. CONCLUSIONS: We found that HLA-HD was an accurate algorithm for 
      the high-resolution genotyping of classical HLA-I genes sequenced by our targeted 
      panel, particularly at a sequencing depth >/=300x for blood and tissue samples.
CI  - 2022 Annals of Translational Medicine. All rights reserved.
FAU - Xin, Hua
AU  - Xin H
AD  - Department of Thoracic Surgery, China-Japan Union Hospital of Jilin University, 
      Changchun, China.
FAU - Li, Jiurong
AU  - Li J
AD  - Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital 
      of Xiamen University, Xiamen, China.
FAU - Sun, Hongbin
AU  - Sun H
AD  - Department of Thoracic Surgery, China-Japan Union Hospital of Jilin University, 
      Changchun, China.
FAU - Zhao, Nan
AU  - Zhao N
AD  - Department of Thoracic Surgery, China-Japan Union Hospital of Jilin University, 
      Changchun, China.
FAU - Yao, Bing
AU  - Yao B
AD  - Department of Urology, The Sixth Affiliated Hospital, Sun Yat-sen University, 
      Guangzhou, China.
FAU - Zhong, Wenwen
AU  - Zhong W
AD  - Department of Urology, The Sixth Affiliated Hospital, Sun Yat-sen University, 
      Guangzhou, China.
FAU - Ma, Bo
AU  - Ma B
AD  - Department of Urology, The Sixth Affiliated Hospital, Sun Yat-sen University, 
      Guangzhou, China.
FAU - Wang, Dejuan
AU  - Wang D
AD  - Department of Urology, The Sixth Affiliated Hospital, Sun Yat-sen University, 
      Guangzhou, China.
LA  - eng
PT  - Journal Article
PL  - China
TA  - Ann Transl Med
JT  - Annals of translational medicine
JID - 101617978
PMC - PMC9263794
OTO - NOTNLM
OT  - HLA class I
OT  - HLA genotyping
OT  - HLA-HD
OT  - Human leukocyte antigen genotyping
OT  - immunotherapy
COIS- Conflicts of Interest: All authors have completed the ICMJE uniform disclosure 
      form (available at 
      https://atm.amegroups.com/article/view/10.21037/atm-22-875/coif). JL reports that 
      this study was supported by grants from the Science and Technology Project of 
      Xiamen City (No. 3502Z20199006). BY reports that this study was supported by the 
      Medical Science Foundation of Guangdong Province (No. A2019347). The other 
      authors have no conflicts of interest to declare.
EDAT- 2022/07/12 06:00
MHDA- 2022/07/12 06:01
PMCR- 2022/06/01
CRDT- 2022/07/11 04:00
PHST- 2022/01/27 00:00 [received]
PHST- 2022/05/17 00:00 [accepted]
PHST- 2022/07/11 04:00 [entrez]
PHST- 2022/07/12 06:00 [pubmed]
PHST- 2022/07/12 06:01 [medline]
PHST- 2022/06/01 00:00 [pmc-release]
AID - atm-10-11-633 [pii]
AID - 10.21037/atm-22-875 [doi]
PST - ppublish
SO  - Ann Transl Med. 2022 Jun;10(11):633. doi: 10.21037/atm-22-875.