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

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 muta...

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Published inMolecular & cellular proteomics Vol. 23; no. 9; p. 100825
Main Authors Salek, Mogjiborahman, Förster, Jonas D., Becker, Jonas P., Meyer, Marten, Charoentong, Pornpimol, Lyu, Yanhong, Lindner, Katharina, Lotsch, Catharina, Volkmar, Michael, Momburg, Frank, Poschke, Isabel, Fröhling, Stefan, Schmitz, Marc, Offringa, Rienk, Platten, Michael, Jäger, Dirk, Zörnig, Inka, Riemer, Angelika B.
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.09.2024
American Society for Biochemistry and Molecular Biology
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Summary: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 directly prove actual peptide presentation and 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 the improved detection of low abundant peptides that are very hard to detect using standard parameters. Applying this to immunopeptidomics, we detected a neoepitope in a patient-derived xenograft 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, optiPRM, a targeted MS workflow reaching ultra-high sensitivity by per peptide parameter optimization, makes the identification of actionable neoepitopes possible from sample sizes usually available in the clinic. [Display omitted] •optiPRM uses per peptide collision energy optimization to maximize detection sensitivity.•optiPRM detected a neoepitope from only 2.5 × 106 cells in a PDX-derived cell line.•It identifies actionable mutation-derived neoepitopes from small clinical samples.•MS-validated neoepitopes allow rational design of epitope-centric immunotherapies. Personalized cancer immunotherapies rely on the presentation of tumor-specific peptides by human leukocyte antigen molecules. Identification of such neoepitopes by mass spectrometry–based immunopeptidomics is often hampered by limited sensitivity of untargeted methods and limited input material. Here, we introduce optiPRM, a targeted immunopeptidomics workflow with ultra-high sensitivity utilizing per peptide parameter optimization by direct infusion. We use it to identify mutation-derived neoepitopes from as little as 2.5 × 106 cells and small patient tumor samples.
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These authors contributed equally to this work.
ISSN:1535-9476
1535-9484
1535-9484
DOI:10.1016/j.mcpro.2024.100825