鉴定肿瘤特异性新抗原（TSN）的免疫原性对于开发基于肽/ mRNA的抗肿瘤疫苗和/或采用T细胞免疫疗法至关重要；因此，准确的体外分类/优先级确定对于成本效益的临床应用至关重要。虽然有一些方法被提出作为TSN免疫原性的预测器，但由于缺乏充分记录和充足的TSN数据库，尚缺乏全面的性能比较。在这里，通过开发一个新的精选数据库，其中包含199个经过实验证实的MHC-I呈递和阳性/阴性免疫反应的TSN（ITSNdb），对十六个度量标准进行了免疫原性预测器的评估。此外，通过使用模拟患者源TSN和包含预测TSN的免疫疗法队列的数据集，在肿瘤新抗原负荷（TNB）相关性方面，将这些度量标准作为TSN优先级确定器和免疫疗法反应生物标志物进行了评估。我们的研究结果显示了不同方法之间的高性能变异性，突显了对实质性改进的需求。深度学习预测器在ITSNdb上排名靠前，但在验证数据库上存在差异。总的来说，当前的预测TNB并未超越现有的生物标志物。我们提出了关于它们的临床应用和ITSNdb的建议，以促进计算TSN免疫原性预测器的发展和比较。版权所有© 2023 Nibeyro, Baronetto, Folco, Pastore, Girotti, Prato, Morón, Luján and Fernández.

Identification of tumor specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based anti-tumoral vaccines and/or adoptive T-cell immunotherapies; thus, accurate in-silico classification/prioritization proves critical for cost-effective clinical applications. Several methods were proposed as TSNs immunogenicity predictors; however, comprehensive performance comparison is still lacking due to the absence of well documented and adequate TSN databases.Here, by developing a new curated database having 199 TSNs with experimentally-validated MHC-I presentation and positive/negative immune response (ITSNdb), sixteen metrics were evaluated as immunogenicity predictors. In addition, by using a dataset emulating patient derived TSNs and immunotherapy cohorts containing predicted TSNs for tumor neoantigen burden (TNB) with outcome association, the metrics were evaluated as TSNs prioritizers and as immunotherapy response biomarkers.Our results show high performance variability among methods, highlighting the need for substantial improvement. Deep learning predictors were top ranked on ITSNdb but show discrepancy on validation databases. In overall, current predicted TNB did not outperform existing biomarkers.Recommendations for their clinical application and the ITSNdb are presented to promote development and comparison of computational TSNs immunogenicity predictors.Copyright © 2023 Nibeyro, Baronetto, Folco, Pastore, Girotti, Prato, Morón, Luján and Fernández.