Benchmarking of 5 algorithms for high-resolution genotyping of human leukocyte antigen class I genes from blood and tissue samples.

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: 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 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 ≥300× for blood and tissue samples.