Lemke Steffen, Dubbelaar Marissa L, Zimmermann Patrick, Bauer Jens, Nelde Annika, Hoenisch Gravel Naomi, Scheid Jonas, Wacker Marcel, Jung Susanne, Dengler Anna, Maringer Yacine, Rammensee Hans-Georg, Gouttefangeas Cecile, Fillinger Sven, Bilich Tatjana, Heitmann Jonas S, Nahnsen Sven, Walz Juliane S
Department of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany.
Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, BW, Germany.
J Immunother Cancer. 2025 Apr 15;13(4):e011366. doi: 10.1136/jitc-2024-011366.
Various cancer immunotherapies rely on the T cell-mediated recognition of peptide antigens presented on human leukocyte antigens (HLA). However, the identification and selection of naturally presented peptide targets for the development of personalized as well as off-the-shelf immunotherapy approaches remain challenging.
Over 10,000 raw mass spectrometry (MS) files from over 3,000 tissue samples were analyzed, summing to approximately seven terabytes of data. The raw MS data were processed using the standardized and open-source nf-core pipelines MHCquant2 and epitopeprediction, providing a uniform procedure for data handling. A global false discovery rate was applied to minimize false-positive identifications.
Here, we introduce the open-access Peptides for Cancer Immunotherapy Database (PCI-DB, https://pci-db.org/), a comprehensive resource of immunopeptidome data originating from various malignant and benign primary tissues that provides the research community with a convenient tool to facilitate the identification of peptide targets for immunotherapy development. The PCI-DB includes >6.6 million HLA class I and >3.4 million HLA class II peptides from over 40 tissue types and cancer entities. First application of the database provided insights into the representation of cancer-testis antigens across malignant and benign tissues, enabling the identification and characterization of cross-tumor entity and entity-specific tumor-associated antigens (TAAs) as well as naturally presented neoepitopes from frequent cancer mutations. Further, we used the PCI-DB to design personalized peptide vaccines for two patients suffering from metastatic cancer. In a retrospective analysis, PCI-DB enabled the composition of both a multi-peptide vaccine comprising non-mutated, highly frequent TAAs matching the immunopeptidome of the individual patient's tumor and a neoepitope-based vaccine matching the mutational profile of a patient with cancer. Both vaccine approaches induced potent and long-lasting T-cell responses, accompanied by long-term survival of these patients with advanced cancer.
The PCI-DB provides a highly versatile tool to broaden the understanding of cancer-related antigen presentation and, ultimately, supports the development of novel immunotherapies.
各种癌症免疫疗法依赖于T细胞介导的对人类白细胞抗原（HLA）上呈递的肽抗原的识别。然而，识别和选择天然呈递的肽靶点以开发个性化和现成的免疫疗法仍然具有挑战性。
分析了来自3000多个组织样本的10000多个原始质谱（MS）文件，数据总量约为7太字节。使用标准化的开源nf-core管道MHCquant2和表位预测对原始MS数据进行处理，提供统一的数据处理程序。应用全局错误发现率以尽量减少假阳性识别。
在此，我们推出了开放获取的癌症免疫治疗肽数据库（PCI-DB，https://pci-db.org/），这是一个来自各种恶性和良性原发组织的免疫肽组数据的综合资源，为研究界提供了一个方便的工具，以促进免疫治疗开发中肽靶点的识别。PCI-DB包含来自40多种组织类型和癌症实体的超过660万个HLA I类肽和超过340万个HLA II类肽。该数据库的首次应用深入了解了癌症睾丸抗原在恶性和良性组织中的表现，能够识别和表征跨肿瘤实体和实体特异性肿瘤相关抗原（TAA）以及来自常见癌症突变的天然呈递的新表位。此外，我们使用PCI-DB为两名转移性癌症患者设计了个性化肽疫苗。在一项回顾性分析中，PCI-DB能够组成一种包含与个体患者肿瘤免疫肽组匹配的非突变、高频TAA的多肽疫苗和一种与癌症患者突变谱匹配的基于新表位的疫苗。两种疫苗方法均诱导了强效且持久的T细胞反应，并伴随着这些晚期癌症患者的长期生存。
PCI-DB提供了一个高度通用的工具，以拓宽对癌症相关抗原呈递的理解，并最终支持新型免疫疗法的开发。
