Xin Hua, Li Jiurong, Sun Hongbin, Zhao Nan, Yao Bing, Zhong Wenwen, Ma Bo, Wang Dejuan
Department of Thoracic Surgery, China-Japan Union Hospital of Jilin University, Changchun, China.
Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Xiamen University, Xiamen, China.
Ann Transl Med. 2022 Jun;10(11):633. doi: 10.21037/atm-22-875.
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.
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: , , and . 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.
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 . The analyses revealed that high-resolution genotyping by all 5 algorithms achieved approximately 90% accuracy at sequencing depths of 6,000× - 100× for the WBC samples, at 6,000× - 700× for the plasma samples, and at 1,000× - 100× 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 50× sequencing depth.
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.
人类白细胞抗原I类（HLA-I）基因座的特定改变与免疫检查点抑制剂的临床疗效相关，这增加了在免疫肿瘤学应用中进行准确的高分辨率HLA基因分型的临床相关性。已经开发了许多算法用于通过下一代测序（NGS）数据进行高分辨率至全分辨率的HLA基因分型；然而，基于桑格测序的分型（SBT）仍然是金标准。随着NGS在临床肿瘤学中的使用日益增加，识别具有与金标准相当性能的计算工具非常重要。本研究旨在针对来自血液和组织样本的靶向NGS数据，将5种算法与SBT进行基准测试，以对经典HLA-I基因进行高分辨率分型。
对来自22名已知HLA基因型的癌症患者的配对白细胞（WBC）、血浆和组织脱氧核糖核酸（DNA）样本进行测序，使用经典HLA-I基因的以下所有外显子：、和。基于NGS的基因型由5种不同算法生成，包括HLA-HD、HLAscan、OptiType、Polysolver和xHLA。准确性定义为SBT与基于NGS的算法之间的一致性。准确性计算为使用SBT和任何一种算法的基因型一致的所有等位基因的分数除以等位基因总数。
对于WBC、血浆和组织样本，所有5种算法在低分辨率HLA-I基因分型时都具有很高的准确性，但在高分辨率HLA-I基因分型时准确性差异更大，尤其是在。分析显示，对于WBC样本，所有5种算法在测序深度为6,000× - 100×时实现了约90%的高分辨率基因分型准确性，对于血浆样本在6,000× - 700×时，对于组织样本在1,000× - 100×时。在这5种算法中，HLA-HD在高分辨率HLA-I基因分型时始终保持准确，即使在50×测序深度下，对于WBC样本的准确性为93.9%，对于血浆样本为87.9%，对于组织样本为94.2%。
我们发现HLA-HD是一种用于通过我们的靶向面板测序的经典HLA-I基因进行高分辨率基因分型的准确算法，特别是对于血液和组织样本，在测序深度≥300×时。
