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.

抗原肽的准确鉴定和优先排序对于开发个性化癌症免疫疗法至关重要。用于预测临床新抗原的公开管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来自各种经典和非经典来源的抗原肽。为了解决这个问题，我们提出了一种称为 NeoDisc 的端到端临床蛋白质基因组学管道，它结合了用于免疫肽组学、基因组学和转录组学的最先进的公开可用和内部软件，以及用于识别、预测和确定来自多个来源的肿瘤特异性和免疫原性抗原的优先级，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经典抗原。我们证明了 NeoDisc 在准确优先处理免疫原性新抗原方面优于最近的优先流程。我们展示了 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，我们还展示了 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性肿瘤抗原景观。