Merck & Co, Inc.; West Point, PA USA.
Oncoimmunology. 2012 Nov 1;1(8):1258-1270. doi: 10.4161/onci.21355.
Functional T-cell epitope discovery is a key process for the development of novel immunotherapies, particularly for cancer immunology. In silico epitope prediction is a common strategy to try to achieve this objective. However, this approach suffers from a significant rate of false-negative results and epitope ranking lists that often are not validated by practical experience. A high-throughput platform for the identification and prioritization of potential T-cell epitopes is the iTopia(TM) Epitope Discovery System(TM), which allows measuring binding and stability of selected peptides to MHC Class I molecules. So far, the value of iTopia combined with in silico epitope prediction has not been investigated systematically. In this study, we have developed a novel in silico selection strategy based on three criteria: (1) predicted binding to one out of five common MHC Class I alleles; (2) uniqueness to the antigen of interest; and (3) increased likelihood of natural processing. We predicted in silico and characterized by iTopia 225 candidate T-cell epitopes and fixed-anchor analogs from three human tumor-associated antigens: CEA, HER2 and TERT. HLA-A2-restricted fragments were further screened for their ability to induce cell-mediated responses in HLA-A2 transgenic mice. The iTopia binding assay was only marginally informative while the stability assay proved to be a valuable experimental screening method complementary to in silico prediction. Thirteen novel T-cell epitopes and analogs were characterized and additional potential epitopes identified, providing the basis for novel anticancer immunotherapies. In conclusion, we show that combination of in silico prediction and an iTopia-based assay may be an accurate and efficient method for MHC Class I epitope discovery among tumor-associated antigens.
功能 T 细胞表位发现是开发新型免疫疗法的关键过程，特别是在癌症免疫学方面。计算机预测表位是实现这一目标的常用策略。然而，这种方法存在显著的假阴性率，而且表位排序列表通常未经实际经验验证。一种用于鉴定和优先考虑潜在 T 细胞表位的高通量平台是 iTopia（TM）表位发现系统（TM），它允许测量选定肽与 MHC Ⅰ类分子的结合和稳定性。到目前为止，iTopia 与计算机预测表位结合的价值尚未系统地进行研究。在这项研究中，我们开发了一种新的基于三个标准的计算机选择策略：（1）预测与五种常见 MHC Ⅰ类等位基因之一结合；（2）对感兴趣的抗原具有独特性；（3）自然加工的可能性增加。我们预测了来自三种人类肿瘤相关抗原的 225 个候选 T 细胞表位和固定锚定类似物，并通过 iTopia 进行了特征描述：CEA、HER2 和 TERT。进一步筛选 HLA-A2 限制片段，以评估它们在 HLA-A2 转基因小鼠中诱导细胞介导反应的能力。iTopia 结合测定仅略有信息量，而稳定性测定被证明是一种有价值的实验筛选方法，可与计算机预测互补。我们鉴定了 13 个新的 T 细胞表位和类似物，并鉴定了其他潜在的表位，为新型抗癌免疫疗法提供了基础。总之，我们表明，计算机预测与基于 iTopia 的测定相结合可能是一种在肿瘤相关抗原中发现 MHC Ⅰ类表位的准确有效的方法。
