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Acta Neuropathologica

, Volume 134, Issue 2, pp 297–316 | Cite as

Identification of T cell target antigens in glioblastoma stem-like cells using an integrated proteomics-based approach in patient specimens

  • Carmen Rapp
  • Rolf Warta
  • Slava Stamova
  • Ali Nowrouzi
  • Christoph Geisenberger
  • Zoltan Gal
  • Saskia Roesch
  • Steffen Dettling
  • Simone Juenger
  • Mariana Bucur
  • Christine Jungk
  • Philip DaoTrong
  • Rezvan Ahmadi
  • Felix Sahm
  • David Reuss
  • Valentina Fermi
  • Esther Herpel
  • Volker Eckstein
  • Niels Grabe
  • Christoph Schramm
  • Markus A. Weigand
  • Juergen Debus
  • Andreas von Deimling
  • Andreas Unterberg
  • Amir Abdollahi
  • Philipp Beckhove
  • Christel Herold-MendeEmail author
Original Paper

Abstract

Glioblastoma (GBM) is a highly aggressive brain tumor and still remains incurable. Among others, an immature subpopulation of self-renewing and therapy-resistant tumor cells—often referred to as glioblastoma stem-like cells (GSCs)—has been shown to contribute to disease recurrence. To target these cells personalized immunotherapy has gained a lot of interest, e.g. by reactivating pre-existing anti-tumor immune responses against GSC antigens. To identify T cell targets commonly presented by GSCs and their differentiated counterpart, we used a proteomics-based separation of GSC proteins in combination with a T cell activation assay. Altogether, 713 proteins were identified by LC–ESI–MS/MS mass spectrometry. After a thorough filtering process, 32 proteins were chosen for further analyses. Immunogenicity of corresponding peptides was tested ex vivo. A considerable number of these antigens induced T cell responses in GBM patients but not in healthy donors. Moreover, most of them were overexpressed in primary GBM and also highly expressed in recurrent GBM tissues. Interestingly, expression of the most frequent T cell target antigens could also be confirmed in quiescent, slow-cycling GSCs isolated in high purity by the DEPArray technology. Finally, for a subset of these T cell target antigens, an association between expression levels and higher T cell infiltration as well as an increased expression of positive immune modulators was observed. In summary, we identified novel immunogenic proteins, which frequently induce tumor-specific T cell responses in GBM patients and were also detected in vitro in therapy-resistant quiescent, slow-cycling GSCs. Stable expression of these T cell targets in primary and recurrent GBM support their suitability for future clinical use.

Keywords

IDH1-wt glioblastoma T cell target antigen repertoire Quiescent stem-like cells Heterogeneity Plasticity 

Notes

Acknowledgements

We like to thank the Tissue Bank of the National Center for Tumor Diseases (NCT, Heidelberg, Germany) for providing us with tissue samples. We further thank Melanie Greibich, Mandy Barthel, Farzaneh Kashfi, Ilka Hearn, Hildegard Goeltzer, Axel Schoeffel, and Cinja Sackmann for excellent technical assistance.

Compliance with ethical standards

Funding

This project was supported by the Anni Hofmann Stiftung.

Supplementary material

401_2017_1702_MOESM1_ESM.pptx (108 kb)
Immunohistochemistry and multicolor immunofluorescent stainings: Two consecutive tissue sections, cytospins or wells of adherent cells on one slide allowed the concurrent staining of antibodies and the respective control. All primary antibodies were diluted in Antibody Diluent (Dako, Hamburg, Germany) and incubated for 1 h. All incubation steps were performed at room temperature. After the application of first and secondary antibodies as well as after the incubation with the ABC reagent, three washing steps with PBS containing 0.05 % Tween (Sigma-Aldrich, Taufkirchen, Germany) were performed. For immunohistochemical stainings, appropriate biotinylated secondary antibodies of the Vectastain Elite ABC Kit (Vector Laboratories, Burlingame, USA) were diluted (1:200) with serum (1:66) in DPBS and incubated for 30 minutes. In the next step, the ABC reagent was prepared 30 min. before the sections were incubated with peroxidase substrate solution until the desired staining intensity. The duration of incubation was based on the isotype control staining. After stopping incubation with water, nuclei were counterstained with hematoxylin. Finally, all slides were mounted with Elvanol (Roth, Karlsruhe, Germany). For all stainings, appropriate concentration and species of isotype controls were used (PPTX 107 kb)
401_2017_1702_MOESM2_ESM.jpg (5.1 mb)
Supplementary Fig. 1 Characterization of glioblastoma stem-like cells. a, c Phase-contrast images of glioblastoma stem-like cells (GSCs) from NCH663 and NCH711d growing as neurospheres (scale bar: 200 µm), while b, d expression of the stem cell marker CD133 was determined by flow cytometry (grey outline: isotype control, red outline: CD133 staining). e, f Immunofluorescence analysis of stem cell markers Nestin (green) and CD133 (red) as well as g, h the lineage markers GFAP (purple), MBP (red), and ßIII-tubulin (green) stained on cytospins (scale bar: 50 µM). i mRNA expression analysis of stem cell markers POU5F1, ID1, BMI1, FABP7, and SOX2 in undifferentiated and ATRA-treated GSCs from NCH663 and NCH711d. Expression levels were assessed by qPCR and normalized against the housekeeping gene GAPDH. Stem cell marker expression decreased markedly upon ATRA-induced differentiation (JPEG 5199 kb)
401_2017_1702_MOESM3_ESM.jpg (2.3 mb)
Supplementary Fig. 2 Expression of stem cell and differentiation markers before and after differentiation of glioblastoma stem-like cells. Percentage of positive GSCs expressing a stem cell markers Nestin and CD133 or b lineage markers GFAP, MBP, and ßIII-tubulin before and after ATRA-induced differentiation. Three representative areas of immunofluorescence stainings were quantitatively evaluated. Significant differences are indicated by asterisks (p < 0.05 *, p < 0.01 **, p < 0.001 ***). Error bars show the standard error of the mean (SEM) of triplicates (JPEG 2365 kb)
401_2017_1702_MOESM4_ESM.jpg (4.2 mb)
Supplementary Fig. 3 Tumorigenicity of glioblastoma stem-like cells. Anti-human Nuclei and Ki67 staining of an exemplary tumor-bearing mouse brain, derived from a NCH663 or b NCH711d GSCs, after xenotransplantation of 1x105 GSCs (dashed scale bar: 500 µm, insert: 20 µM). Mice were sacrificed upon occurrence of neurological symptoms (NCH663: symptom-free until the 8th week; NCH711d: symptom-free until the 14th week) or after 20 weeks at the latest (JPEG 4283 kb)
401_2017_1702_MOESM5_ESM.jpg (2.7 mb)
Supplementary Fig. 4 Characterization of ATRA-treated glioblastoma stem-like cells. a, b Phase-contrast images of adherently growing ATRA-treated GSCs from NCH663 and NCH711d (scale bar: 200 µm) c ATRA-treated NCH663 and d NCH711d cells were stained by immunofluorescence for the expression of the differentiation markers GFAP (purple), MBP (red), and ßIII-tubulin (green). e, f ATRA-treated cells were further stained for Nestin and CD133. Scale bar: 50 µM (JPEG 2787 kb)
401_2017_1702_MOESM6_ESM.jpg (1.1 mb)
Supplementary Fig. 5 IFN-γ ELISpot assays of 1st and 2nd PF2D dimension fractions. 1st (left) and 2nd PF2D dimension fractions (right) of a differentiated NCH663 GSCs, b undifferentiated NCH711d GSCs, and c differentiated NCH711d GSCs are shown. Fractions of the 1st PF2D dimension triggering higher T cell responses than the control (PBMC lysate) were further fractionated in the 2nd PF2D dimension (marked in black). 2nd PF2D fractions which had shown a significantly higher immune response than the control were analyzed by LC-ESI-MS/MS to identify protein contents (marked in black). Significant differences are indicated by asterisks (p < 0.05 *, p < 0.01 **, p < 0.001 ***). Abbreviations: GSCs = glioblastoma stem-like cells (JPEG 1154 kb)
401_2017_1702_MOESM7_ESM.jpg (666 kb)
Supplementary Fig. 6 Filtering process to select the most interesting potential T cell target antigens. Mass spectrometry analyses of 2D PF2D fractions identified 713 proteins, which passed the following filtering process: First, false positive peptide identifications were reduced by applying cutoffs for the protein sequence coverage (> 10 %) and protein matches (> 5) as well as common contaminations resulting in 332 proteins. Additionally, literature and publicly available databases such as UniProt, GeneCards, and PubMed of the National Center for Biotechnology Information were used to characterize all proteins regarding their known function. Proteins (i) already described in the context of tumor diseases, (ii) involved in tumor-related signaling pathways such as cell cycle control, cell proliferation, angiogenesis, apoptosis, or invasion, and (iii) proteins with immunomodulatory effects were selected revealing 201 proteins which were further assessed regarding protein expression using The Human Protein Atlas. Proteins with a strong and homogenous expression in normal tissues (especially in the brain) were excluded. Based on this approach, 32 proteins were chosen for further characterization (JPEG 665 kb)
401_2017_1702_MOESM8_ESM.jpg (997 kb)
Supplementary Fig. 7 Epitope prediction of immunogenic epitopes. a The Immune Epitope Database (IEDB) was used to predict the most immunogenic epitope of selected proteins for the HLA alleles HLA-A*01:01, HLA-A*02:01, HLA-A*24:02, HLA-A*03:01, HLA-B*07:02. Epitopes were selected based on the calculation of a low HLA IC50 value (< 500 nm) (grey line) combined with a high product of the number of epitopes within the respective sequence and the number of HLA types (inverse, black line). Selection of long peptide sequences is exemplarily shown for HSPA5 (red box). b Amino acid sequence for HSPA5 containing the selected epitope marked in red (JPEG 996 kb)
401_2017_1702_MOESM9_ESM.jpg (708 kb)
Supplementary Fig. 8 Validation of synthesized peptides in the patients of origin. All selected peptides were validated for immunogenicity with peripheral autologous blood of the patients of origin by IFN-γ ELISpot assays. All peptides showing significantly higher T cell responses relative to the control (IgG) were further validated (marked in grey). Significant differences are indicated by asterisks (p < 0.05 *, p < 0.01 **, p < 0.001 ***). Error bars show the standard error of the mean (SEM) of triplicates (JPEG 708 kb)
401_2017_1702_MOESM10_ESM.jpg (564 kb)
Supplementary Fig. 9 Validation of immunogenicity of candidate proteins in additional GBM patients and healthy donors. Peptides corresponding to 11 potential TAAs identified by PF2D analysis were tested for immunogenicity in an independent cohort of GBM patients (n = 28) as well as in healthy donors (n = 22) by IFN-γ ELISpot analyses. GBM patients showed a significantly higher immune response against peptides than healthy donors. Asterisks indicate significant differences (***, p < 0.001) (JPEG 563 kb)
401_2017_1702_MOESM11_ESM.jpg (646 kb)
Supplementary Fig. 10 mRNA expression of immunogenic T cell target antigens in GSCs before and after ATRA-induced differentiation. mRNA expression of HSPD1, PPIA, FSCN1, ANXA1, and CSTA was analyzed in undifferentiated and differentiated GSCs (n = 9) by microarray analyses. Data of NCH663 and NCH711d are represented by a clear circle. Abbreviations: diff. = differentiated; undiff. = undifferentiated (JPEG 646 kb)
401_2017_1702_MOESM12_ESM.jpg (648 kb)
Supplementary Fig. 11 Survival analysis of CSTA. IDH1-wt pGBM cases retrieved from the TCGA microarray data set were used to calculate a Cox proportional hazard model. Results of a the univariate analysis (n = 356) and b the multivariate analysis (n = 259) are shown. All significant covariates of the univariate model were included into the multivariate model. P-values were calculated by log-rank test (p < 0.05 *, p < 0.01 **, p < 0.001 ***) (JPEG 648 kb)
401_2017_1702_MOESM13_ESM.jpg (986 kb)
Supplementary Fig. 12 Survival analysis of CSTA combined with CD8 (A) and TGFB1 (B). 356 IDH1-wt pGBM cases were used to calculate a Cox proportional hazard model. Groups which did not show significant differences in a and c were summarized in one group for a better visualization (b, d) (JPEG 986 kb)
401_2017_1702_MOESM14_ESM.jpg (983 kb)
Supplementary Fig. 13 Association of CSTA expression and cytokines. a TCGA microarray data of 357 IDH1-wt pGBM cases were median-grouped for the expression of CXCL10, TNF-α (TNF), and TGF-β1 (TGFB1) while CSTA expression was analyzed in the respective groups. b Correlation between CSTA and the respective immunomodulators. Significant correlations are indicated by asterisks (p < 0.05 *, p < 0.01 **, p < 0.001 ***) (JPEG 983 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Carmen Rapp
    • 1
  • Rolf Warta
    • 1
  • Slava Stamova
    • 2
  • Ali Nowrouzi
    • 3
    • 4
    • 5
  • Christoph Geisenberger
    • 1
  • Zoltan Gal
    • 1
  • Saskia Roesch
    • 1
  • Steffen Dettling
    • 1
  • Simone Juenger
    • 2
  • Mariana Bucur
    • 2
  • Christine Jungk
    • 1
    • 3
  • Philip DaoTrong
    • 1
  • Rezvan Ahmadi
    • 1
  • Felix Sahm
    • 3
    • 6
    • 7
  • David Reuss
    • 3
    • 6
    • 7
  • Valentina Fermi
    • 1
  • Esther Herpel
    • 8
    • 9
  • Volker Eckstein
    • 10
  • Niels Grabe
    • 11
  • Christoph Schramm
    • 12
  • Markus A. Weigand
    • 12
  • Juergen Debus
    • 3
    • 4
    • 5
  • Andreas von Deimling
    • 3
    • 6
    • 7
  • Andreas Unterberg
    • 1
  • Amir Abdollahi
    • 3
    • 4
    • 5
  • Philipp Beckhove
    • 2
  • Christel Herold-Mende
    • 1
    • 3
    • 13
    Email author
  1. 1.Division of Experimental Neurosurgery, Department of NeurosurgeryHeidelberg University HospitalHeidelbergGermany
  2. 2.Translational Immunology DepartmentGerman Cancer Research CenterHeidelbergGermany
  3. 3.German Cancer Consortium (DKTK)HeidelbergGermany
  4. 4.Molecular and Translational Radiation OncologyNational Center for Tumor Diseases (NCT), Heidelberg University Hospital and German Cancer Research Center (DKFZ)HeidelbergGermany
  5. 5.Department of Radiation OncologyHeidelberg University Medical School, Heidelberg Institute of Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCOR)HeidelbergGermany
  6. 6.Clinical Cooperation Unit NeuropathologyGerman Cancer Research Center (DKFZ)HeidelbergGermany
  7. 7.Department of NeuropathologyInstitute of Pathology, Ruprecht-Karls-UniversityHeidelbergGermany
  8. 8.Institute of PathologyRuprecht-Karls-UniversityHeidelbergGermany
  9. 9.NCT Tissue BankNational Center for Tumor Diseases (NCT)HeidelbergGermany
  10. 10.Department of Internal Medicine VHeidelberg University HospitalHeidelbergGermany
  11. 11.Hamamatsu Tissue Imaging and Analysis Center (TIGA)BIOQUANT, University of HeidelbergHeidelbergGermany
  12. 12.Department of AnaesthesiologyHeidelberg University HospitalHeidelbergGermany
  13. 13.Sektion Neurochirurgische ForschungNeurochirurgische UniversitätsklinikHeidelbergGermany

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