Dataset Information

OptiPRM: A targeted immunopeptidomics LC-MS workflow with ultra-high sensitivity for the detection of mutation-derived tumor neoepitopes from limited input material

ABSTRACT: Personalized cancer immunotherapies such as vaccines and T cell receptor (TCR)-transgenic T cells rely on the presentation of tumor-specific peptides by human leukocyte antigen (HLA) class I molecules to cytotoxic T cells. Such neoepitopes can for example arise from somatic mutations and their identification is crucial for the rational design of new therapeutic interventions. For their detection by liquid chromatography mass spectrometry (LC-MS), we have developed a parameter optimization workflow to tune targeted assays for maximum detection sensitivity on a per peptide basis, termed optiPRM. Optimization of collision energy using optiPRM allows for improved detection of low abundant peptides that are very hard to detect using standard parameters. Applying this for to immunopeptidomics, we detected a neoepitope in a patient-derived xenograft (PDX) from as little as 2.5×106 cells input. Application of the workflow on small patient tumor samples allowed for the detection of five mutation-derived neoepitopes in three patients. One neoepitope was confirmed to be recognized by patient T cells. In conclusion, we here present optiPRM, a targeted MS workflow reaching ultra-high sensitivity by per peptide parameter optimization, which allowed for the identification of actionable neoepitopes from sample sizes usually available in the clinic.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Jonas D. Förster

LAB HEAD: Angelika B. Riemer

PROVIDER: PXD043542 | Pride | 2024-08-09

REPOSITORIES: Pride

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optiPRM: A Targeted Immunopeptidomics LC-MS Workflow With Ultra-High Sensitivity for the Detection of Mutation-Derived Tumor Neoepitopes From Limited Input Material.

Salek Mogjiborahman M Förster Jonas D JD Becker Jonas P JP Meyer Marten M Charoentong Pornpimol P Lyu Yanhong Y Lindner Katharina K Lotsch Catharina C Volkmar Michael M Momburg Frank F Poschke Isabel I Fröhling Stefan S Schmitz Marc M Offringa Rienk R Platten Michael M Jäger Dirk D Zörnig Inka I Riemer Angelika B AB

Molecular & cellular proteomics : MCP 20240805 9

Personalized cancer immunotherapies such as therapeutic vaccines and adoptive transfer of T cell receptor-transgenic T cells rely on the presentation of tumor-specific peptides by human leukocyte antigen class I molecules to cytotoxic T cells. Such neoepitopes can for example arise from somatic mutations and their identification is crucial for the rational design of new therapeutic interventions. Liquid chromatography mass spectrometry (LC-MS)-based immunopeptidomics is the only method to direct ...[more]

PMID: 39111711

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Project description:Various cancer immunotherapies rely on the T cell 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 remains challenging. 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 analyzed uniformly using high-sensitive nf-core bioinformatics pipelines and applying a global peptide false discovery rate (FDR) approach. First application of the database provided insights into the representation of cancer-testis antigens (CTA) across malignant and benign tissues and enabled the identification and characterization of the cross-tumor entity and entity-specific tumor-associated antigens 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. PCI-DB enabled the composition of both a multi-peptide vaccine comprising non-mutated, highly frequent tumor-associated antigens matching the immunopeptidome of the individual patient´s tumor and a neoepitope-based vaccine matching the mutational profile of the cancer patient. Both vaccine approaches induced potent and long-lasting T-cell responses, accompanied by long-term survival of these advanced cancer patients. In conclusion, 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.

Project description:The Major histocompatibility complex (MHC) class I peptidome is thought to be generated mostly through proteasomal degradation of cellular proteins, a notion that is based on the alterations in presentation of selected peptides following proteasome inhibition. We evaluated the effects of proteasome inhibitors, epoxomicin and bortezomib, on human cultured cancer cells. Since the inhibitors did not reduce the level of presentation of the cell surface human leukocyte antigen (HLA) molecules, we followed their effects on the rates of synthesis of both HLA peptidome and proteome of the cells, using dynamic stable isotope labeling in tissue culture (dynamic-SILAC). The inhibitors reduced the rates of synthesis of most cellular proteins and HLA peptides, yet the synthesis rates of some of the proteins and HLA peptides was not decreased by the inhibitors and of some even increased. Therefore, we concluded that the inhibitors affected the production of the HLA peptidome in a complex manner, including modulation of the synthesis rates of the source proteins of the HLA peptides in addition to their effect on their degradation. The collected data may suggest that the current reliance on proteasome inhibition may overestimate the centrality of the proteasome in the generation of the MHC peptidome. It is therefore suggested that the relative contribution of the proteasomal and non-proteasomal pathways to the production of the MHC peptidome should be revaluated in accordance with the inhibitors effects on the synthesis rates of the source proteins of the MHC peptides. Bioinformatics & data processing: Peptides were identified and the dynamic-SILAC data were quantified using the MaxQuant and the Proteome Discoverer software tools. Graphical representation of the bioinformatics results was performed with Perseus, version 1.3.0.4. MaxQuant [54] version 1.3.0.5 was used with the Andromeda search engine [55] and the human section of Nov 2011 of UniProt containing 20257 entries and mass tolerance of 6 ppm for the precursor masses and 0.5 Da for the fragments. Methionine oxidation was accepted as variable modification for both tryptic and HLA peptides. Carbamidomethyl cysteine and n-acetylation were accepted as modification for the proteomics analyses. Minimal peptide length was set to 7 amino acids and a maximum of two missed cleavages was allowed for tryptic peptides. The false discovery rate (FDR) was set for tryptic peptides to 0.01 for protein identifications, and 0.05 for the MHC peptides. The resulting identified protein tables were filtered to eliminate the identifications derived from the reverse database, as well as common contaminants. Proteome Discoverer version 1.3 (Thermo-Fisher) was used with UniProt of April 2012 for the Sequest search (containing 20220 entries) and July 2012 for the Mascot search (containing 20306 entries). Masses tolerance was set to 6 ppm for the precursors and 0.5 Da for the fragments. Methionine oxidation and n-acetylation were accepted as variable modification for both tryptic and HLA peptides. Carbamidomethyl cysteine was set as fixed modification for the proteomics analyses. Mass range of 350-5000 Da was used for tryptic peptides and mass range of 750-2500 Da was used for the MHC peptides. For both tryptic and MHC peptides the PSMs were filtered with at least 0.05 FDR (medium confidence), peptide maximum rank was set to 1. Minimal number of identified peptides per proteins was set to 2.

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