PDF(Pubmed)
Abstract：
High-precision human leukocyte antigen (HLA) genotyping is crucial for anti-cancer immunotherapy, but existing tools predicting HLA genotypes using next-generation sequencing (NGS) data are insufficiently accurate.
We compared availability, accuracy, correction score, and complementary ratio of eight HLA genotyping tools (OptiType, HLA-HD, PHLAT, seq2HLA, arcasHLA, HLAscan, HLA*LA, and Kourami) using 1,005 cases from the 1000 Genomes Project data. We created a new HLA-genotyping algorithm combining tools based on the precision and the accuracy of tools\' combinations. Then, we assessed the new algorithm\'s performance in 39 in-house samples with normal whole-exome sequencing (WES) data and polymerase chain reaction-sequencing-based typing (PCR-SBT) results.
Regardless of the type of tool, the calls presented by more than six tools concordantly showed high accuracy and precision. The accuracy of the group with at least six concordant calls was 100% (97/97) in HLA-A, 98.2% (112/114) in HLA-B, 97.3% (142/146) in HLA-C. The precision of the group with at least six concordant calls was over 98% in HLA-ABC. We additionally calculated the accuracy of the combination tools considering the complementary ratio of each tool and the accuracy of each tool, and the accuracy was over 98% in all groups with six or more concordant calls. We created a new algorithm that matches the above results. It was to select the HLA type if more than six out of eight tools presented a matched type. Otherwise, determine the HLA type experimentally through PCR-SBT. When we applied the new algorithm to 39 in-house cases, there were more than six matching calls in all HLA-A, B, and C, and the accuracy of these concordant calls was 100%.
HLA genotyping accuracy using NGS data could be increased by combining the current HLA genotyping tools. This new algorithm could also be useful for preliminary screening to decide whether to perform an additional PCR-based experimental method instead of using tools with NGS data.
We compared availability, accuracy, correction score, and complementary ratio of eight HLA genotyping tools (OptiType, HLA-HD, PHLAT, seq2HLA, arcasHLA, HLAscan, HLA*LA, and Kourami) using 1,005 cases from the 1000 Genomes Project data. We created a new HLA-genotyping algorithm combining tools based on the precision and the accuracy of tools\' combinations. Then, we assessed the new algorithm\'s performance in 39 in-house samples with normal whole-exome sequencing (WES) data and polymerase chain reaction-sequencing-based typing (PCR-SBT) results.
Regardless of the type of tool, the calls presented by more than six tools concordantly showed high accuracy and precision. The accuracy of the group with at least six concordant calls was 100% (97/97) in HLA-A, 98.2% (112/114) in HLA-B, 97.3% (142/146) in HLA-C. The precision of the group with at least six concordant calls was over 98% in HLA-ABC. We additionally calculated the accuracy of the combination tools considering the complementary ratio of each tool and the accuracy of each tool, and the accuracy was over 98% in all groups with six or more concordant calls. We created a new algorithm that matches the above results. It was to select the HLA type if more than six out of eight tools presented a matched type. Otherwise, determine the HLA type experimentally through PCR-SBT. When we applied the new algorithm to 39 in-house cases, there were more than six matching calls in all HLA-A, B, and C, and the accuracy of these concordant calls was 100%.
HLA genotyping accuracy using NGS data could be increased by combining the current HLA genotyping tools. This new algorithm could also be useful for preliminary screening to decide whether to perform an additional PCR-based experimental method instead of using tools with NGS data.
摘要：
高精度人类白细胞抗原（HLA）基因分型对于抗癌免疫疗法至关重要，但是使用下一代测序（NGS）数据预测HLA基因型的现有工具不够准确。
我们比较了可用性，准确度,校正分数,和八个HLA基因分型工具的互补比例(OptiType，HLA-HD,PHLAT,seq2HLA,arcasHLA,Hlascan,HLA*LA,和Kourami)使用1000个基因组项目数据中的1,005个案例。我们基于工具组合的精度和准确性创建了一种新的HLA基因分型算法。然后，我们利用正常全外显子组测序(WES)数据和基于聚合酶链反应测序的分型(PCR-SBT)结果,评估了新算法在39个内部样本中的性能.
不管工具的类型，六个以上工具提出的呼叫一致显示出很高的准确性和精确度。在HLA-A中，具有至少六个一致呼叫的组的准确性为100％（97/97），HLA-B的98.2%(112/114),HLA-C中的97.3%(142/146)在HLA-ABC中，具有至少六个一致呼叫的组的精确度超过98％。我们还计算了组合工具的精度，考虑了每个工具的互补比和每个工具的精度，在所有有6个或更多一致呼叫的组中，准确率超过98%。我们创建了一个与上述结果相匹配的新算法。如果八个工具中的六个以上呈现匹配的类型，则选择HLA类型。否则,通过PCR-SBT实验确定HLA类型。当我们将新算法应用于39个内部案例时，在所有HLA-A中有超过6个匹配的呼叫,B,C,这些一致呼叫的准确性是100%。
使用NGS数据的HLA基因分型准确性可以通过组合当前的HLA基因分型工具来提高。这种新算法也可用于初步筛选，以决定是否执行额外的基于PCR的实验方法，而不是使用具有NGS数据的工具。
我们比较了可用性，准确度,校正分数,和八个HLA基因分型工具的互补比例(OptiType，HLA-HD,PHLAT,seq2HLA,arcasHLA,Hlascan,HLA*LA,和Kourami)使用1000个基因组项目数据中的1,005个案例。我们基于工具组合的精度和准确性创建了一种新的HLA基因分型算法。然后，我们利用正常全外显子组测序(WES)数据和基于聚合酶链反应测序的分型(PCR-SBT)结果,评估了新算法在39个内部样本中的性能.
不管工具的类型，六个以上工具提出的呼叫一致显示出很高的准确性和精确度。在HLA-A中，具有至少六个一致呼叫的组的准确性为100％（97/97），HLA-B的98.2%(112/114),HLA-C中的97.3%(142/146)在HLA-ABC中，具有至少六个一致呼叫的组的精确度超过98％。我们还计算了组合工具的精度，考虑了每个工具的互补比和每个工具的精度，在所有有6个或更多一致呼叫的组中，准确率超过98%。我们创建了一个与上述结果相匹配的新算法。如果八个工具中的六个以上呈现匹配的类型，则选择HLA类型。否则,通过PCR-SBT实验确定HLA类型。当我们将新算法应用于39个内部案例时，在所有HLA-A中有超过6个匹配的呼叫,B,C,这些一致呼叫的准确性是100%。
使用NGS数据的HLA基因分型准确性可以通过组合当前的HLA基因分型工具来提高。这种新算法也可用于初步筛选，以决定是否执行额外的基于PCR的实验方法，而不是使用具有NGS数据的工具。
