Pfitzer Lena, Boons Gitta, Lybaert Lien, van Criekinge Wim, Bogaert Cedric, Fant Bruno
myNEO Therapeutics, 9000 Ghent, Belgium.
Department of Bioinformatics, Ghent University, 9000 Ghent, Belgium.
Vaccines (Basel). 2025 Aug 15;13(8):865. doi: 10.3390/vaccines13080865.
: Antigen-targeting immunotherapies hinge on the accurate identification of immunogenic epitopes that elicit robust T-cell responses. However, current computational approaches focus primarily on MHC binding affinity, leading to high false-positive rates and limiting the clinical utility of antigen selection methods. : We developed the neoIM (for "neoantigen immunogenicity") model, a first-in-class, high-precision immunogenicity prediction tool that overcomes these limitations by focusing exclusively on overall CD8 T-cell response rather than MHC binding. neoIM, a random forest classifier, was trained solely on MHC-presented non-self peptides (n = 61.829). Its performance was assessed against that of currently existing alternatives on several in vitro immunogenicity datasets. In addition, its clinical impact was investigated in two retrospective analyses of clinical trial data by assessing the effect of neoIM-based antigen selection on the positive immunogenicity rate of personal vaccine designs. Finally, the potential for neoIM as a biomarker was investigated by assessing the correlation between neoIM scores and overall survival in a melanoma patient cohort treated with checkpoint inhibitors (CPI). : neoIM was found to substantially outperform publicly available tools in regards to in vitro benchmarks based on ELISpot assays, with an increase in predictive power of at least 30%, reducing false positives and improving target selection efficiency. In addition, using neoIM scores during patient-specific antigen prioritization and selection was shown to yield up to 50% more clinically actionable antigens for individual patients in two recent clinical trials. Finally, we showed that neoIM could further refine response prediction to checkpoint inhibition therapy, further demonstrating the importance of evaluating neoantigen immunogenicity. : These findings establish neoIM as the first computational tool capable of accurately predicting epitope immunogenicity beyond MHC affinity. By enabling more precise target discovery and prioritization, neoIM has the potential to accelerate the development of next-generation antigen-based immunotherapies.
抗原靶向免疫疗法依赖于准确识别能引发强大T细胞反应的免疫原性表位。然而，当前的计算方法主要侧重于MHC结合亲和力，导致假阳性率高，限制了抗原选择方法的临床应用。我们开发了neoIM（“新抗原免疫原性”）模型，这是一流的高精度免疫原性预测工具，通过专门关注整体CD8 T细胞反应而非MHC结合来克服这些限制。neoIM是一种随机森林分类器，仅在MHC呈递的非自身肽（n = 61,829）上进行训练。在几个体外免疫原性数据集上，将其性能与现有替代方法进行了比较评估。此外，通过评估基于neoIM的抗原选择对个人疫苗设计的阳性免疫原性率的影响，在两项临床试验数据的回顾性分析中研究了其临床影响。最后，通过评估neoIM评分与接受检查点抑制剂（CPI）治疗的黑色素瘤患者队列的总生存期之间的相关性，研究了neoIM作为生物标志物的潜力。结果发现，在基于ELISpot测定的体外基准测试中，neoIM的表现明显优于公开可用的工具，预测能力至少提高了30%，减少了假阳性并提高了靶点选择效率。此外，在最近的两项临床试验中，在患者特异性抗原优先排序和选择过程中使用neoIM评分，可为个体患者产生多达50%的更具临床可操作性的抗原。最后，我们表明neoIM可以进一步优化对检查点抑制疗法的反应预测，进一步证明了评估新抗原免疫原性的重要性。这些发现确立了neoIM作为首个能够准确预测超出MHC亲和力的表位免疫原性的计算工具。通过实现更精确的靶点发现和优先排序，neoIM有潜力加速下一代基于抗原的免疫疗法的开发。
