Neon Therapeutics, Cambridge, MA, USA.
Proteomics. 2018 Jun;18(12):e1700259. doi: 10.1002/pmic.201700259. Epub 2018 Feb 23.
A challenge in developing personalized cancer immunotherapies is the prediction of putative cancer-specific antigens. Currently, predictive algorithms are used to infer binding of peptides to human leukocyte antigen (HLA) heterodimers to aid in the selection of putative epitope targets. One drawback of current epitope prediction algorithms is that they are trained on datasets containing biochemical HLA-peptide binding data that may not completely capture the rules associated with endogenous processing and presentation. The field of MS has made great improvements in instrumentation speed and sensitivity, chromatographic resolution, and proteogenomic database search strategies to facilitate the identification of HLA-ligands from a variety of cell types and tumor tissues. As such, these advances have enabled MS profiling of HLA-binding peptides to be a tractable, orthogonal approach to lower throughput biochemical assays for generating comprehensive datasets to train epitope prediction algorithms. In this review, we will highlight the progress made in the field of HLA-ligand profiling enabled by MS and its impact on current and future epitope prediction strategies.
开发个性化癌症免疫疗法的一个挑战是预测潜在的癌症特异性抗原。目前，预测算法被用于推断肽与人类白细胞抗原（HLA）异二聚体的结合，以帮助选择潜在的表位靶标。当前表位预测算法的一个缺点是，它们是在包含生化 HLA-肽结合数据的数据集上进行训练的，这些数据可能无法完全捕获与内源性加工和呈递相关的规则。MS 领域在仪器速度和灵敏度、色谱分辨率以及蛋白质基因组数据库搜索策略方面取得了重大进展，以促进从各种细胞类型和肿瘤组织中鉴定 HLA 配体。因此，这些进展使得 HLA 结合肽的 MS 分析成为一种可行的、正交的方法，可以替代高通量生化测定来生成全面的数据集，以训练表位预测算法。在这篇综述中，我们将重点介绍 MS 技术在 HLA 配体分析方面取得的进展及其对当前和未来表位预测策略的影响。
