Cancer immunotherapy has gained significant momentum from recent clinical successes of checkpoint blockade inhibition. Massively parallel sequence analysis suggests a connection between mutational load and response to this class of therapy. Methods to identify which tumor-specific mutant peptides (neoantigens) can elicit anti-tumor T cell immunity are needed to improve predictions of checkpoint therapy response and to identify targets for vaccines and adoptive T cell therapies. Here, we present a flexible, streamlined computational workflow for identification of personalized Variant Antigens by Cancer Sequencing (pVAC-Seq) that integrates tumor mutation and expression data (DNA- and RNA-Seq). pVAC-Seq is available at https://github.com/griffithlab/pVAC-Seq .

中文翻译：

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pVAC-Seq：一种基因组引导的计算机识别肿瘤新抗原的方法。

癌症免疫疗法从最近检查点阻断抑制的临床成功中获得了显着的动力。大规模并行序列分析表明突变负荷与对此类治疗的反应之间存在联系。需要确定哪些肿瘤特异性突变肽（新抗原）可以引发抗肿瘤 T 细胞免疫的方法，以提高对检查点治疗反应的预测，并确定疫苗和过继性 T 细胞治疗的靶点。在这里，我们提出了一种灵活、简化的计算工作流程，用于通过整合肿瘤突变和表达数据（DNA 和 RNA 序列）的癌症测序 (pVAC-Seq) 识别个性化变异抗原。pVAC-Seq 可在 https://github.com/griffithlab/pVAC-Seq 获得。