Evidence map›Paper›PMID 40234091›Full record

ArticleJournal for immunotherapy of cancer2025

PCI-DB: a novel primary tissue immunopeptidome database to guide next-generation peptide-based immunotherapy development.

Steffen Lemke, Marissa L Dubbelaar, Patrick Zimmermann, Jens Bauer, Annika Nelde, Naomi Hoenisch Gravel, Jonas Scheid, Marcel Wacker, Susanne Jung, Anna Dengler and 8 more

Abstract read
In one paragraph

Article in Journal for immunotherapy of cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
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  4. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Steffen LemkeDepartment of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany.ORCID http://orcid.org/0009-0009-8121-082X
Marissa L DubbelaarDepartment of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany.ORCID http://orcid.org/0000-0002-4930-1467
Patrick ZimmermannDepartment of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany.
Jens BauerDepartment of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany.
Annika NeldeDepartment of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany.ORCID http://orcid.org/0000-0001-8504-8481
Naomi Hoenisch GravelDepartment of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany.ORCID http://orcid.org/0000-0001-9465-0175
Jonas ScheidDepartment of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany.
Marcel WackerDepartment of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany.
Susanne JungDepartment of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany.
Anna DenglerDepartment of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany.
Yacine MaringerDepartment of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany.
Hans-Georg RammenseeCluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, BW, Germany.ORCID http://orcid.org/0000-0003-1614-2647
Cecile GouttefangeasCluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, BW, Germany.ORCID http://orcid.org/0000-0002-6410-2378
Sven FillingerQuantitative Biology Center (QBiC), University of Tübingen, Tübingen, BW, Germany.
Tatjana BilichDepartment of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany.
Jonas S HeitmannDepartment of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany.
Sven NahnsenCluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, BW, Germany.
Juliane S WalzDepartment of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Tübingen, BW, Germany juliane.walz@med.uni-tuebingen.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVarious 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.

methodsOver 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.

resultsHere, 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.

conclusionThe PCI-DB provides a highly versatile tool to broaden the understanding of cancer-related antigen presentation and, ultimately, supports the development of novel immunotherapies.

Indexed as

ImmunotherapyNeoplasmsPeptidesHumansPeptidesHuman leukocyte antigen - HLAImmunotherapyVaccine

Identifiers

PMID40234091
PMCPMC12001369

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.