Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.
Sinotech Genomics, Shenzhen, China.
Mol Oncol. 2021 Jul;15(7):1764-1782. doi: 10.1002/1878-0261.12895. Epub 2021 Jan 24.
Human leukocyte antigen (HLA) genotyping gains intensive attention due to its critical role in cancer immunotherapy. It is still a challenging issue to generate reliable HLA genotyping results through in silico tools. In addition, the survival impact of HLA alleles in tumor prognosis and immunotherapy remains controversial. In this study, the benchmarking of HLA genotyping on TCGA is performed and a 'Gun-Bullet' model which helps to clarify the survival impact of HLA allele is presented. The performance of HLA class I genotyping is generally better than class II. POLYSOLVER, OptiType, and xHLA perform generally better at HLA class I calling with an accuracy of 0.954, 0.949, and 0.937, respectively. HLA-HD obtained the highest accuracy of 0.904 on HLA class II alleles calling. Each HLA genotyping tool displayed specific error patterns. The ensemble HLA calling from the top-3 tools is superior to any individual one. HLA alleles show distinct survival impact among cancers. Cytolytic activity (CYT) was proposed as the underlying mechanism to interpret the survival impact of HLA alleles in the 'Gun-Bullet' model for fighting cancer. A strong HLA allele plus a high tumor mutation burden (TMB) could stimulate intensive immune CYT, leading to extended survival. We established an up to now most reliable TCGA HLA benchmark dataset, composing of concordance alleles generated from eight prevalently used HLA genotyping tools. Our findings indicate that reliable HLA genotyping should be performed based on concordance alleles integrating multiple tools and incorporating TMB background with HLA genotype, which helps to improve the survival prediction compared to HLA genotyping alone.
人类白细胞抗原 (HLA) 基因分型因其在癌症免疫治疗中的关键作用而受到广泛关注。通过计算机工具生成可靠的 HLA 基因分型结果仍然是一个具有挑战性的问题。此外，HLA 等位基因在肿瘤预后和免疫治疗中的生存影响仍存在争议。在这项研究中，对 TCGA 的 HLA 基因分型进行了基准测试，并提出了一种“枪弹”模型，有助于阐明 HLA 等位基因的生存影响。HLA Ⅰ类基因分型的性能通常优于Ⅱ类。POLYSOLVER、OptiType 和 xHLA 在 HLA Ⅰ类基因分型的准确性方面表现较好，分别为 0.954、0.949 和 0.937。HLA-HD 在 HLA Ⅱ类等位基因分型中获得了最高的准确性，为 0.904。每种 HLA 基因分型工具都显示出特定的错误模式。前 3 种工具的 HLA 综合调用优于任何单一工具。HLA 等位基因在不同癌症中表现出不同的生存影响。细胞溶解活性 (CYT) 被提出作为 HLA 等位基因生存影响的潜在机制，用于对抗癌症的“枪弹”模型。强有力的 HLA 等位基因加上高肿瘤突变负担 (TMB) 可以刺激强烈的免疫 CYT，从而延长生存时间。我们建立了迄今为止最可靠的 TCGA HLA 基准数据集，由八种常用 HLA 基因分型工具生成的一致等位基因组成。我们的研究结果表明，应基于整合多个工具的一致等位基因，并结合 HLA 基因型和 TMB 背景来进行可靠的 HLA 基因分型，与单独进行 HLA 基因分型相比，这有助于提高生存预测。
