Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas (CIDIE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)/Universidad Católica de Córdoba (UCC) & Fundación para el Progreso de la Medicina, Córdoba, Argentina.
Facultad de Ingeniería, Universidad Católica de Córdoba (UCC), Córdoba, Argentina.
Front Immunol. 2023 Jul 25;14:1094236. doi: 10.3389/fimmu.2023.1094236. eCollection 2023.
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.
鉴定肿瘤特异性新抗原（TSN）的免疫原性对于开发基于肽/信使 RNA 的抗肿瘤疫苗和/或过继性 T 细胞免疫疗法至关重要；因此，准确的计算机分类/优先级划分对于具有成本效益的临床应用至关重要。已经提出了几种作为 TSN 免疫原性预测因子的方法；然而，由于缺乏记录良好且充分的 TSN 数据库，因此仍然缺乏全面的性能比较。
在这里，通过开发一个具有 199 个经实验验证的 MHC-I 呈递和阳性/阴性免疫反应的新的经过精心整理的数据库（ITSNdb），我们评估了十六个指标作为免疫原性预测因子。此外，通过使用模拟患者衍生的 TSN 并包含与免疫疗法结果相关的预测 TSN 的肿瘤新抗原负担（TNB）的数据集，我们评估了这些指标作为 TSN 的优先级和免疫疗法反应的生物标志物。
我们的结果表明，方法之间的性能差异很大，这突显了需要进行实质性改进的必要性。深度学习预测因子在 ITSNdb 上排名最高，但在验证数据库上存在差异。总的来说，目前预测的 TNB 并不优于现有生物标志物。
为了促进计算 TSN 免疫原性预测因子的开发和比较，提出了它们的临床应用和 ITSNdb 的建议。
