Melanoma and Human Leukocyte Antigen (HLA): Immunogenicity of 69 HLA Class I Alleles With 11 Antigens Expressed in Melanoma Tumors.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2023-01-01 DOI:10.1177/11769351231172604
Apostolos P Georgopoulos, Lisa M James, Spyros A Charonis, Matthew Sanders
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引用次数: 3

Abstract

Host immunogenetics play a critical role in the human immune response to melanoma, influencing both melanoma prevalence and immunotherapy outcomes. Beneficial outcomes that stimulate T cell response hinge on binding affinity and immunogenicity of human leukocyte antigen (HLA) with melanoma antigen epitopes. Here, we use an in silico approach to characterize binding affinity and immunogenicity of 69 HLA Class I human leukocyte antigen alleles to epitopes of 11 known melanoma antigens. The findings document a significant proportion of positively immunogenic epitope-allele combinations, with the highest proportions of positive immunogenicity found for the Q13072/BAGE1 melanoma antigen and alleles of the HLA B and C genes. The findings are discussed in terms of a personalized precision HLA-mediated adjunct to immune checkpoint blockade immunotherapy to maximize tumor elimination.

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黑色素瘤与人类白细胞抗原(HLA):在黑色素瘤肿瘤中表达的69个HLA I类等位基因的免疫原性。
宿主免疫遗传学在人类对黑色素瘤的免疫反应中起关键作用,影响黑色素瘤的患病率和免疫治疗结果。刺激T细胞应答的有益结果取决于人白细胞抗原(HLA)与黑色素瘤抗原表位的结合亲和力和免疫原性。在这里,我们使用计算机方法来表征69个HLA I类人白细胞抗原等位基因与11种已知黑色素瘤抗原表位的结合亲和力和免疫原性。研究结果表明,免疫原性阳性的表位-等位基因组合占很大比例,其中Q13072/BAGE1黑色素瘤抗原和HLA B和C基因等位基因的免疫原性阳性比例最高。研究结果在个性化的精确hla介导的辅助免疫检查点阻断免疫治疗方面进行了讨论,以最大限度地消除肿瘤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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