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.

中文翻译：

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使用不同计算工具对 HLA Ia 类分型的测序数据和样本类型进行调查。

人类白细胞抗原 (HLA) 可以编码人类主要组织相容性复合体 (MHC) 蛋白，并在适应性和先天免疫中起关键作用。新出现的临床证据表明，与 MHC I 类分子相关的肿瘤新抗原和新抗原特异性 T 细胞反应的出现对于激活癌症免疫治疗中的适应性免疫系统至关重要。因此，准确的 HLA 分型对于免疫治疗的临床应用至关重要。在这项研究中，我们对 4 种广泛使用的 HLA 分型工具（OptiType、Phlat、Polysolver 和 seq2hla）进行了性能评估，用于从 28 名癌症患者的 WES 和 RNA-seq 数据预测 HLA Ia 类基因。使用 PCR-SBT 方法首先获得 HLA 基因分型数据作为金标准，随后使用 NGS 技术与 HLA 分型数据进行比较。对于 WES 数据和 RNA-seq 数据，OptiType 在 2 位和 4 位分辨率下显示出比其他 3 个程序更高的 HLA-Ia 分型准确度。此外，WES 数据的 HLA 分型准确性高于 RNA-seq 数据（WES 数据为 99.11%，而 RNA-seq 数据为 96.42%）。OptiType HLA-Ia分型准确率可达100%，HLA基因区域平均深度>20x。此外，基于对照样本的2位和4位HLA-Ia分型准确性高于肿瘤组织。总之，OptiType 通过使用来自具有高平均深度 (>20x) HLA 基因区域的对照样本的 WES 数据可以为 HLA-Ia 分型提供可能优越的性能，