Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.

中文翻译：

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增强基于质谱的肿瘤免疫肽识别：利用 HLA 结合亲和力、脂肪族指数和保留时间偏差的机器学习过滤器

准确识别新抗原对于开发有效的癌症疫苗和改善肿瘤免疫治疗至关重要。基于质谱的免疫肽组学已成为鉴定癌细胞表面存在的人类白细胞抗原 (HLA) 肽的一种有前途的方法，但假阳性鉴定仍然是一个重大挑战。在这项研究中，利用基于液相色谱-串联质谱的蛋白质组学和新一代测序来鉴定来自 18 名肾细胞癌或胰腺癌患者的肿瘤组织中非同义单核苷酸变异所产生的 HLA 呈递新抗原肽。利用机器学习通过预测 MS/MS 谱图一致性来评估 Mascot 识别，并选择每个候选序列的四个描述符：最大 Mascot 离子得分、预测的 HLA 结合亲和力、脂肪族指数和保留时间偏差作为重要特征。过滤掉碎片一致性不足的标识。这表明，与主要用于蛋白质组学的传统 Mascot 方法相比，结合基于肽理化特征的重新评分过滤器可以提高基于 MS 的免疫肽组学的识别率，这表明优化新抗原识别流程以及临床应用的潜力。