Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA.
Division of Molecular Oncology and Immunology, the Netherlands Cancer Institute, Amsterdam, the Netherlands; T Cell Immunology, Biopharmaceutical New Technologies (BioNTech) Corporation, BioNTech US, Cambridge, MA, USA.
Cell. 2020 Oct 29;183(3):818-834.e13. doi: 10.1016/j.cell.2020.09.015. Epub 2020 Oct 9.
Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.
许多鉴定治疗相关的新抗原的方法都将肿瘤测序与生物信息算法和推断的肿瘤表位免疫原性规则相结合。然而，目前还没有参考数据来比较这些方法，并且控制肿瘤表位免疫原性的参数仍不清楚。在这里，我们组建了一个全球性的联盟，每个参与者都从共享的肿瘤测序数据中预测免疫原性表位。随后，在患者匹配的样本中评估了 608 个表位的 T 细胞结合情况。通过整合与呈递和识别相关的肽特征，我们开发了一种肿瘤表位免疫原性模型，该模型可以过滤掉 98%的非免疫原性肽，其精度高于 0.70。优先考虑模型特征的流水线具有更好的性能，并且利用这些特征的流水线改变可以提高预测性能。这些发现通过对从肿瘤测序数据中优先排序并评估 T 细胞结合的 310 个表位的独立队列进行了验证。这个数据资源可以帮助确定有效的抗肿瘤免疫的基础参数，并可供研究界使用。
