Immunopeptidomics-Guided Warehouse Design for Peptide-Based Immunotherapy in Chronic Lymphocytic Leukemia

Abstract

Antigen-specific immunotherapies, in particular peptide vaccines, depend on the recognition of naturally presented antigens derived from mutated and unmutated gene products on human leukocyte antigens, and represent a promising low-side-effect concept for cancer treatment. So far, the broad application of peptide vaccines in cancer patients is hampered by challenges of time- and cost-intensive personalized vaccine design, and the lack of neoepitopes from tumor-specific mutations, especially in low-mutational burden malignancies. In this study, we developed an immunopeptidome-guided workflow for the design of tumor-associated off-the-shelf peptide warehouses for broadly applicable personalized therapeutics. Comparative mass spectrometry-based immunopeptidome analyses of primary chronic lymphocytic leukemia (CLL) samples, as representative example of low-mutational burden tumor entities, and a dataset of benign tissue samples enabled the identification of high-frequent non-mutated CLL-associated antigens. These antigens were further shown to be recognized by pre-existing and de novo induced T cells in CLL patients and healthy volunteers, and were evaluated as pre-manufactured warehouse for the construction of personalized multi-peptide vaccines in a first clinical trial for CLL (NCT04688385). This workflow for the design of peptide warehouses is easily transferable to other tumor entities and can provide the foundation for the development of broad personalized T cell-based immunotherapy approaches.

Keywords: HLA peptides; chronic lymphocytic leukemia; immunopeptidomics; immunotherapy; mass spectrometry; peptide vaccines; peptide warehouse.

Figure 1

Comparative immunopeptidome profiling identifies CLL-associated antigens. (A) Mass spectrometry-based workflow for the design of a CLL-associated immunopeptidome-derived peptide warehouse. (B) Saturation analysis of source proteins of HLA class I-presented peptides. Number of unique source protein identifications shown as a function of cumulative immunopeptidome analysis of CLL samples (n = 52). Exponential regression allowed for the robust calculation of the maximum attainable number of different source protein identifications (dotted line). The dashed red line depicts the source proteome coverage achieved in the CLL cohort. (C) HLA-A*02, -A*24, and -B*07 allotype coverage within the CLL cohort (n = 61). The frequencies of individuals within the CLL cohort carrying up to three HLA allotypes (x-axis) are indicated as gray bars on the left y-axis. The cumulative percentage of population coverage is depicted as black dots on the right y-axis. (D, E) Overlap analysis of (D) HLA-A*02- and (E) HLA class II-restricted peptide identifications of primary CLL samples (D, n = 30; E, n = 49) and benign tissue samples (D, n = 351 including 162 HLA-A*02⁺; E, n = 312). (F, G) Comparative immunopeptidome profiling of (F) HLA-A*02- and (G) HLA class II-presented peptides based on the frequency of HLA-restricted presentation in immunopeptidomes of CLL and benign tissue samples. Frequencies of positive immunopeptidomes for the respective HLA-presented peptides (x-axis) are indicated on the y-axis. To allow for better readability, HLA-presented peptides identified in < 5% of the samples within the respective cohort were not depicted. The box on the left highlights the subset of CLL-associated antigens that show CLL-exclusive high-frequent presentation. IDs, identifications.

Figure 2

Immunopeptidome coverage of common CLL mutation sites and spectral validation of CLL-associated peptides. (A, B) Hotspot and dark spot analysis by HLA class I (above x-axis) and HLA class II (below x-axis) peptide clustering. All identified HLA class I- and HLA class II-presented peptides of the CLL and benign tissue immunopeptidomes were mapped to their amino acid positions within the respective source protein. Representative examples are shown for (A) XPO1 and (B) DNS2A. Representation frequencies of amino acid counts for the respective amino acid position (x-axis) were calculated and are indicated on the y-axis. The red lines highlight the analyzed mutation sites of recurrent CLL-associated mutations. (C, D) Representative examples of the validation of the experimentally eluted (C) HLA class I-restricted peptide P1_B07 and (D) the HLA class II-restricted peptide P2_II using synthetic isotope-labeled peptides. Comparison of fragment spectra (m/z on the x-axis) of peptides eluted from primary CLL patient samples (identification) to their corresponding synthetic peptides (validation). The spectra of the synthetic peptides are mirrored on the x-axis. Identified b- and y-ions are marked in red and blue, respectively. The calculated spectral correlation coefficients are depicted on the right graph, respectively. aa, amino acid; n_pep, number of peptides.

Figure 3

Immunogenicity analyses of CLL-associated peptides. (A) Representative example of P3_B07-specific tetramer staining of CD8⁺ T cells after 4 cycles of aAPC-based in vitro priming. Graphs show single, viable cells stained for CD8 and PE-conjugated multimers of indicated specificity. The upper panel displays P3_B07-tetramer staining of T cells primed with P3_B07. The lower panel (negative control) depicts P3_B07-tetramer staining of T cells from the same donor primed with an HLA-matched irrelevant control peptide. (B) Functional characterization of induced P1_B07-specific CD8⁺ T cells after in vitro aAPC-based priming by intracellular cytokine (IFN-γ, TNF) and degranulation marker (CD107a) staining. Representative example of IFN-γ and TNF production as well as CD107a expression after stimulation with the peptide P1_B07 compared to an HLA-matched negative control peptide. (C, D) Representative examples of preexisting T cell responses to (C) HLA-B*07- and (D) HLA class II-restricted peptides as evaluated by IFN-γ ELISpot assays after 12-day in vitro expansion using PBMC samples of CLL patients (C, UPN064; D, UPN066). Data are presented as scatter dot plot with mean. FSC, forward scatter; Neg, negative control; Pos, positive control.