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Mutation position is an important determinant for predicting cancer neoantigens
The Journal of Experimental Medicine Pub Date : 2020-01-15 , DOI: 10.1084/jem.20190179 Aude-Hélène Capietto 1 , Suchit Jhunjhunwala 1 , Samuel B Pollock 1 , Patrick Lupardus 1 , Jim Wong 1 , Lena Hänsch 1 , James Cevallos 1 , Yajun Chestnut 1 , Ajay Fernandez 1 , Nicolas Lounsbury 1 , Tamaki Nozawa 1 , Manmeet Singh 1 , Zhiyuan Fan 1 , Cecile C de la Cruz 1 , Qui T Phung 1 , Lucia Taraborrelli 1 , Benjamin Haley 1 , Jennie R Lill 1 , Ira Mellman 1 , Richard Bourgon 1 , Lélia Delamarre 1
The Journal of Experimental Medicine Pub Date : 2020-01-15 , DOI: 10.1084/jem.20190179 Aude-Hélène Capietto 1 , Suchit Jhunjhunwala 1 , Samuel B Pollock 1 , Patrick Lupardus 1 , Jim Wong 1 , Lena Hänsch 1 , James Cevallos 1 , Yajun Chestnut 1 , Ajay Fernandez 1 , Nicolas Lounsbury 1 , Tamaki Nozawa 1 , Manmeet Singh 1 , Zhiyuan Fan 1 , Cecile C de la Cruz 1 , Qui T Phung 1 , Lucia Taraborrelli 1 , Benjamin Haley 1 , Jennie R Lill 1 , Ira Mellman 1 , Richard Bourgon 1 , Lélia Delamarre 1
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Tumor-specific mutations can generate neoantigens that drive CD8 T cell responses against cancer. Next-generation sequencing and computational methods have been successfully applied to identify mutations and predict neoantigens. However, only a small fraction of predicted neoantigens are immunogenic. Currently, predicted peptide binding affinity for MHC-I is often the major criterion for prioritizing neoantigens, although little progress has been made toward understanding the precise functional relationship between affinity and immunogenicity. We therefore systematically assessed the immunogenicity of peptides containing single amino acid mutations in mouse tumor models and divided them into two classes of immunogenic mutations. The first comprises mutations at a nonanchor residue, for which we find that the predicted absolute binding affinity is predictive of immunogenicity. The second involves mutations at an anchor residue; here, predicted relative affinity (compared with the WT counterpart) is a better predictor. Incorporating these features into an immunogenicity model significantly improves neoantigen ranking. Importantly, these properties of neoantigens are also predictive in human datasets, suggesting that they can be used to prioritize neoantigens for individualized neoantigen-specific immunotherapies.
中文翻译:
突变位置是预测癌症新抗原的重要决定因素
肿瘤特异性突变可以产生新抗原,驱动 CD8 T 细胞针对癌症做出反应。下一代测序和计算方法已成功应用于识别突变和预测新抗原。然而,只有一小部分预测的新抗原具有免疫原性。目前,预测的 MHC-I 肽结合亲和力通常是优先考虑新抗原的主要标准,尽管在理解亲和力和免疫原性之间的精确功能关系方面进展甚微。因此,我们系统地评估了小鼠肿瘤模型中含有单一氨基酸突变的肽的免疫原性,并将其分为两类免疫原性突变。第一个包括非锚定残基处的突变,我们发现预测的绝对结合亲和力可以预测免疫原性。第二个涉及锚定残基的突变;在这里,预测的相对亲和力(与 WT 对应物相比)是更好的预测因子。将这些特征纳入免疫原性模型可显着提高新抗原排名。重要的是,新抗原的这些特性在人类数据集中也具有预测性,这表明它们可用于优先考虑新抗原,以进行个体化新抗原特异性免疫疗法。
更新日期:2020-01-15
中文翻译:
突变位置是预测癌症新抗原的重要决定因素
肿瘤特异性突变可以产生新抗原,驱动 CD8 T 细胞针对癌症做出反应。下一代测序和计算方法已成功应用于识别突变和预测新抗原。然而,只有一小部分预测的新抗原具有免疫原性。目前,预测的 MHC-I 肽结合亲和力通常是优先考虑新抗原的主要标准,尽管在理解亲和力和免疫原性之间的精确功能关系方面进展甚微。因此,我们系统地评估了小鼠肿瘤模型中含有单一氨基酸突变的肽的免疫原性,并将其分为两类免疫原性突变。第一个包括非锚定残基处的突变,我们发现预测的绝对结合亲和力可以预测免疫原性。第二个涉及锚定残基的突变;在这里,预测的相对亲和力(与 WT 对应物相比)是更好的预测因子。将这些特征纳入免疫原性模型可显着提高新抗原排名。重要的是,新抗原的这些特性在人类数据集中也具有预测性,这表明它们可用于优先考虑新抗原,以进行个体化新抗原特异性免疫疗法。