抗原肽的准确识别和优先排序对于个性化癌症免疫疗法的开发至关重要。预测临床新抗原的公开渠道不允许直接整合质谱免疫肽组学数据，而质谱免疫肽组学数据可以揭示源自各种规范和非规范来源的抗原肽。为了解决这个问题，我们提出了一个名为 NeoDisc 的端到端临床蛋白质组学管道，它将用于免疫肽组学、基因组学和转录组学的最先进的公开和内部软件与用于识别、预测的计算机工具相结合优先考虑多个来源的肿瘤特异性和免疫原性抗原，包括新抗原、病毒抗原、高置信度肿瘤特异性抗原和肿瘤特异性非典型抗原。我们证明了 NeoDisc 在准确优先考虑免疫原性新抗原方面优于最近的优先级划分流程。我们展示 NeoDisc 提供的各种功能，这些功能支持基于规则和机器学习的方法，用于个性化抗原发现和新抗原癌症疫苗设计。此外，我们还演示了 NeoDisc 的多组学整合如何识别细胞抗原呈递机制中的缺陷，这些缺陷会影响异质肿瘤抗原景观。© 2024。作者。

The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc's multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.© 2024. The Author(s).