HLA ligandome analysis identifies the underlying specificities of spontaneous antileukemia immune responses in chronic lymphocytic leukemia (CLL)

Abstract

The breakthrough development of clinically effective immune checkpoint inhibitors illustrates the potential of T-cell-based immunotherapy to effectively treat malignancies. A remaining challenge is to increase and guide the specificities of anticancer immune responses, e.g., by therapeutic vaccination or by adoptive T-cell transfer. By analyzing the landscape of naturally presented HLA class I and II ligands of primary chronic lymphocytic leukemia (CLL), we delineated a novel category of tumor-associated T-cell antigens based on their exclusive and frequent representation in the HLA ligandome of leukemic cells. These antigens were validated across different stages and mutational subtypes of CLL and found to be robustly represented in HLA ligandomes of patients undergoing standard chemo-/immunotherapy. We demonstrate specific immune recognition of these antigens exclusively in CLL patients, with the frequencies of representation in CLL ligandomes correlating with the frequencies of immune recognition by patient T cells. Moreover, retrospective survival analysis revealed survival benefits for patients displaying immune responses to these antigens. These results directly imply these nonmutant self-peptides as pathophysiologically relevant tumor antigens and encourages their implementation for cancer immunotherapy.

Keywords: HLA; cancer immunotherapy; chronic lymphocytic leukemia; therapeutic vaccination; tumor-associated antigens.

Fig. 1.

HLA surface expression of primary CLL samples. HLA class I (A) and HLA class II (B) expression on CD5⁺CD19⁺ CLL cells compared with autologous CD5⁻CD19⁺ B cells in 7 primary CLL samples. Data are expressed as mean ± SD of triplicate experiments. (C) Mean HLA class I and (D) HLA class II expression on CD5⁺CD19⁺ CLL cells compared with autologous CD5⁻CD19⁺ B cells (n = 7). *P < 0.01. UPN, uniform patient number.

Fig. 2.

Identification of a novel category of tumor-associated antigens by HLA ligandome profiling. (A) Overlap of HLA class I ligand source proteins of primary CLL samples (n = 30) and HV PBMC (n = 30). (B) Comparative profiling of HLA class I ligand source proteins based on the frequency of HLA restricted representation in CLL and HV PBMC ligandomes. Frequencies [%] of CLL patients/HVs positive for HLA restricted presentation of the respective source protein (x axis) are indicated on the y axis. The box on the left highlights the subset of source proteins showing CLL-exclusive representation in >20% of patients (LiTAAs, ligandome-defined tumor-associated antigens). (C) Regression analysis of the number of LiTAAs yielded at different cohort sizes. Reduced-size CLL and HV PBMC cohorts were randomly assembled from the complete respective cohorts (n = 30) and LiTAAs were defined as CLL-exclusive proteins presented on ≥20% of CLL patients. The process of random cohort assembly and LiTAA definition was repeated 100 times and the resultant mean number of LiTAAs was plotted for cohort sizes of n = 5, 10, 15, 20 and 25. Regression analysis using an exponential model extrapolated the plateau value for the number of LiTAAs resulting at high values of n to be 69.7. (D) Statistical analysis of the proportion of false positive LiTAA identifications at different representation frequencies. The numbers of original LiTAAs identified based on the analysis of the CLL and HV PBMC cohorts were compared with random virtual LiTAAs. Virtual CLL patients and HV PBMCs were generated in silico based on random weighted sampling from the entirety of protein identifications in both original cohorts. These randomized virtual ligandomes of defined size (n = 1130 proteins, which is the mean number of protein identifications in all analyzed samples) were used to define LiTAAs based on simulated cohorts of 30 CLL versus 30 HV PBMC. The process of protein randomization, cohort assembly and LiTAA identification was repeated 1,000 times and the mean value of resultant virtual LiTAAs was calculated and plotted for the different threshold values. The corresponding false discovery rates for any chosen LiTAA threshold are listed below the x axis. (E) Representation of published CLL-associated antigens in HLA class I ligandomes. Bars indicate relative representation [%] of respective antigens by HLA class I ligands on CLL and HV PBMC. Dashed lines divide the antigens into three groups according to their degree of CLL-association (CLL exclusive, CLL overrepresented, not overrepresented).

Fig. 3.

LiTAAs are robustly represented across different CLL disease stages and risk strata. (A) Source protein overlaps of CLL samples from different stages of disease [Binet A (n = 9), Binet B (n = 7), Binet C (n = 14)]. (B) Heatmap analysis of the representation frequencies [%] of LiTAAs across different disease stages (Binet A–C, as in A). (C) Heatmap analysis of LiTAA representation [%] on primary CLL samples with del17p (n = 5) and without del17p (n = 25). CLL, chronic lymphocytic leukemia; HV, healthy volunteer.

Fig. 4.

LiTAAs are specifically recognized by CLL patient immune responses. (A) HLA class I LiTAAs and corresponding LiTAPs (3 HLA-A*03, 5 HLA-A*02, 6 HLA-B*07) functionally evaluated in IFNγ ELISPOT assays. Absolute numbers and frequencies of peptide-specific immune recognition by CLL patient PBMC are summarized in the right hand column. (B) Example of A*03 LiTAPs evaluated in ELISPOT using HV PBMC as a control. An EBV epitope mix containing five frequently recognized peptides [BRLF_109–117 YVLDHLIVV (A*02), EBNA3_471–479 RLRAEAQVK (A*03), EBNA3_247–255 RPPIFIRRL (B*07), BZLF1_190–197 RAKFKQLL (B*08), EBNA6_162–171 AEGGVGWRHW (B*44)] was used as positive control, HIV GAG_18–26 A*03 peptide KIRLRPGGK served as negative control. (C) Example of ELISPOT assays using HLA-A*03 LiTAPs (n = 3) on PBMC of three different CLL patients. Results are shown for immunoreactive LiTAPs. EBV epitope mix served as positive control, HIV GAG_18–26 A*03 peptide as negative control. (D) Example of HLA-A*03 benign tissue-derived LiBAPs (n = 3) tested on CLL patient PBMC as internal control for the target selection strategy. EBV epitope mix served as positive control, HIV GAG_18–26 A*03 peptide as negative control. (E) Scatterplot of the allele-adjusted frequencies of LiTAP presentation in CLL ligandomes (as detected by MS) and the corresponding allele-adjusted frequencies of immune recognition by CLL patient PBMC in IFNγ ELISPOT. Data points are shown for the 14 of 15 LiTAPs showing immune recognition. HV, healthy volunteer; LiBAP, ligandome-defined benign tissue-associated peptide; LiTAP, ligandome-defined tumor-associated peptide; MS, mass spectrometry; neg., negative; pos., positive; UPN, uniform patient number.

Fig. 5.

Identification of additional/synergistic HLA class II LiTAAs and LiTAPs. (A) Overlap of HLA class II ligand source proteins of primary CLL samples (n = 20) and HV PBMC (n = 13). (B) Comparative profiling of HLA class II ligand source proteins based on the frequency of HLA restricted representation in CLL and HV PBMC ligandomes. Frequencies [%] of CLL patients/HVs positive for HLA restricted presentation of the respective source protein (x axis) are indicated on the y axis. The box on the left highlights the subset of source proteins showing CLL-exclusive representation in >20% of patients (LiTAAs, ligandome-defined tumor-associated antigens). (C) HLA class II LiTAAs and corresponding LiTAPs (n = 6) functionally evaluated in IFNγ ELISPOT assays. Absolute numbers and frequencies of peptide-specific immune recognition by CLL patient PBMC are summarized in the right column. (D) Example of HLA class II LiTAPs evaluated in ELISPOT using HV PBMC as a control. PHA was used as positive control. FLNA_1669–1683 HLA-DR peptide (ETVITVDTKAAGKGK) served as negative control. (E) Example of ELISPOT assays using HLA class II LiTAPs (n = 6) on PBMC of three different CLL patients. Results are shown for immunoreactive LiTAPs. PHA was used as positive control, FLNA_1669–1683 HLA-DR peptide served as negative control. (F) Overlap analysis of CLL-exclusive HLA class I and HLA class II ligand source proteins for shared/synergistic vaccine targets. (G) Heatmap analysis of the 132 shared HLA class I/II LiTAAs identified in D. The two source proteins showing representation in ≥20% of both, HLA class I and II CLL patient ligandomes are specified.

Fig. 6.

Longitudinal HLA class I ligandome analysis of CLL patients undergoing chemo- or immunotherapy. Volcano plots of the relative abundance of HLA ligands in the class I ligandomes of patients after treatment compared with their respective abundance before therapy (ratio post- and pretherapy). Dashed lines indicate the thresholds for differential peptide presentation (defined as ≥twofold ratio with P < 0.05 after Bonferroni correction), with up-regulated ligands in the upper right and down-regulated ligands in the upper left areas. Frequencies and absolute numbers of differentially presented ligands are specified in the respective quadrants. LiTAPs showing differential presentation over the course of therapy are marked in red and their sequences are specified. (A) Analysis of a CLL patient ligandome before therapy, 48 h after treatment with rituximab (375 mg/m²), and 24 h after treatment with bendamustin (90 mg/m²). One of 25 (4.0%) of detectable LiTAPs showed differential presentation above the specified thresholds. (B) Analysis of a CLL patient ligandome before therapy and after the first 7 d of treatment with alemtuzumab (three doses of alemtuzumab, 10 mg, 20 mg, and 30 mg on day 1, 3, and 5; ligandome analysis on day 7). Three of 24 (12.5%) of detectable LiTAPs showed differential presentation above threshold. (C) Analysis of a CLL patient ligandome before therapy and 24h after treatment with 300 mg of atumumab. Two of 10 (20.0%) of detectable LiTAPs showed differential presentation above threshold.

Fig. 7.

Retrospective survival analysis of CLL patients (n = 45) with respect to their immune recognition of LiTAPs. Overall survival of subjects evaluated for LiTAP-specific immune responses grouped as follows: black, CLL patients showing immune responses to >1 LiTAPs (n = 13). Red, CLL patients showing immune reactions to 0–1 LiTAPs (n = 32). (A) Follow-up of patient survival from time of study enrollment. (B) Follow-up of patient survival from time of diagnosis.