Gene and protein sequence features augment HLA class I ligand predictions (mRNA sequencing). Gene and protein sequence features augment HLA class I ligand predictions (mRNA sequencing)

The sensitivity of malignant tissues to T cell-based cancer immunotherapies is dependent on the presence of targetable HLA class I ligands on the tumor cell surface. Peptide intrinsic factors, such as HLA class I affinity, likelihood of proteasomal processing, and transport into the ER lumen have all been established as determinants of HLA ligand presentation. However, the role of sequence features at the gene and protein level as determinants of epitope presentation has not been systematically evaluated. To address this, we performed HLA ligandome mass spectrometry on patient-derived melanoma lines and used this data-set to evaluate the contribution of 7,124 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turn-over, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of gene and protein sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as predictive models that include experimental measures of gene expression. Our study highlights the value of gene and protein features to HLA ligand predictions. Overall design: Three different melanoma cell lines were cultered by an approximate density of 80%, harvested and snap frozen. mRNA was extracted and used for mRNA sequencing. Please note that each processed data was generated from both replicates and is linked to the corresponding 'replicate 1' sample records.

恶性组织对基于T细胞的癌症免疫疗法的敏感性，取决于肿瘤细胞表面可靶向的HLA I类配体的存在。肽段内在因素——包括HLA I类结合亲和力、蛋白酶体加工潜力以及向内质网腔的转运能力——均已被证实为HLA配体呈递的关键决定因素。然而，基因与蛋白质层面的序列特征作为表位呈递的决定因素，其相关作用尚未得到系统评估。为填补这一研究空白，我们对患者来源的黑色素瘤细胞系开展了HLA配体组质谱分析，并依托该数据集评估了7124个基因与蛋白质序列特征对HLA采样的贡献。本分析结果显示，多种可预测的mRNA与蛋白质丰度及周转调控因子（包括预测的mRNA甲基化位点与蛋白质泛素化位点），能够有效推断HLA配体的存在情况。值得注意的是，将基因与蛋白质序列特征整合至机器学习模型中，其对HLA配体预测性能的提升幅度，与纳入基因表达实验测量数据的预测模型相当。本研究凸显了基因与蛋白质特征在HLA配体预测中的应用价值。实验整体设计：将3种不同的黑色素瘤细胞系以约80%汇合密度进行培养，收集细胞并快速冷冻。提取mRNA并用于mRNA测序。请注意，所有处理后的数据均来源于两份重复样本，并与对应的“重复1”样本记录相关联。