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ALK peptide vaccination restores the immunogenicity of ALK-rearranged non-small cell lung cancer

Nature Cancer volume 4, pages 1016–1035 (2023)Cite this article

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Abstract

Anaplastic lymphoma kinase (ALK)-rearranged non-small cell lung cancer (NSCLC) is treated with ALK tyrosine kinase inhibitors (TKIs), but the lack of activity of immune checkpoint inhibitors (ICIs) is poorly understood. Here, we identified immunogenic ALK peptides to show that ICIs induced rejection of ALK+ tumors in the flank but not in the lung. A single-peptide vaccination restored priming of ALK-specific CD8+ T cells, eradicated lung tumors in combination with ALK TKIs and prevented metastatic dissemination of tumors to the brain. The poor response of ALK+ NSCLC to ICIs was due to ineffective CD8+ T cell priming against ALK antigens and is circumvented through specific vaccination. Finally, we identified human ALK peptides displayed by HLA-A*02:01 and HLA-B*07:02 molecules. These peptides were immunogenic in HLA-transgenic mice and were recognized by CD8+ T cells from individuals with NSCLC, paving the way for the development of a clinical vaccine to treat ALK+ NSCLC.

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Fig. 1: ICIs do not increase the efficacy of ALK TKIs in ALK+ lung cancer mouse models.
Fig. 2: Identification of ALK immunogenic peptides in mouse models.
Fig. 3: Tumor localization dictates the strength of the anti-ALK spontaneous immune response and determines the response to ICIs.
Fig. 4: Enhancement of anti-ALK immune responses by vaccination leads to rejection of ALK+ lung tumors in combination with an ALK TKI.
Fig. 5: ALK vaccine prevents metastatic spread to the CNS in combination with an ALK TKI.
Fig. 6: Tumor escape in vaccinated mice is due to reversible MHC class I downregulation.
Fig. 7: Identification of immunogenic ALK peptides in individuals with ALK+ NSCLC.
Fig. 8: Lack of detectable toxicity of the ALK vaccine.

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Data availability

The MS data are deposited at https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp (Username: MSV000089110; Password: “a”) under the following dataset: MSV000089110.

Data were downloaded from GTEx Project (www.gtexportal.org), TCGA (http://cancergenome.nih.gov) and CPTAC (https://cptac-data-portal.georgetown.edu/cptac/s/S056). Source data are provided with this paper. All other data are available from the authors upon reasonable request.

References

  1. Shaw, A. T. et al. Ceritinib versus chemotherapy in patients with ALK-rearranged non-small-cell lung cancer previously given chemotherapy and crizotinib (ASCEND-5): a randomised, controlled, open-label, phase 3 trial. Lancet Oncol. 18, 874–886 (2017).

    CAS  PubMed  Google Scholar 

  2. Peters, S. et al. Alectinib versus crizotinib in untreated ALK-positive non-small-cell lung cancer. N. Engl. J. Med. 377, 829–838 (2017).

    CAS  PubMed  Google Scholar 

  3. Kwak, E. L. et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N. Engl. J. Med. 363, 1693–1703 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Shaw, A. T. et al. First-line lorlatinib or crizotinib in advanced ALK-positive lung cancer. N. Engl. J. Med. 383, 2018–2029 (2020).

    CAS  PubMed  Google Scholar 

  5. Camidge, D. R. et al. Brigatinib versus crizotinib in ALK-positive non-small-cell lung cancer. N. Engl. J. Med. 379, 2027–2039 (2018).

    CAS  PubMed  Google Scholar 

  6. Camidge, D. R. et al. Brigatinib versus crizotinib in ALK inhibitor-naive advanced ALK-positive NSCLC: final results of phase 3 ALTA-1L trial. J. Thorac. Oncol. 16, 2091–2108 (2021).

    CAS  PubMed  Google Scholar 

  7. Schneider, J. L., Lin, J. J. & Shaw, A. T. ALK-positive lung cancer: a moving target. Nat. Cancer 4, 330–343 (2023).

    CAS  PubMed  Google Scholar 

  8. Katayama, R. et al. Two novel ALK mutations mediate acquired resistance to the next-generation ALK inhibitor alectinib. Clin. Cancer Res. 20, 5686–5696 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Gainor, J. F. et al. Molecular mechanisms of resistance to first- and second-generation ALK inhibitors in ALK-rearranged lung cancer. Cancer Discov. 6, 1118–1133 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Sharma, G. G. et al. Tumor resistance against ALK targeted therapy—where it comes from and where it goes. Cancers 10, 62 (2018).

  11. Camidge, D. R. et al. Updated efficacy and safety data and impact of the EML4–ALK fusion variant on the efficacy of alectinib in untreated ALK-positive advanced non-small cell lung cancer in the global phase III ALEX study. J. Thorac. Oncol. 14, 1233–1243 (2019).

    CAS  PubMed  Google Scholar 

  12. Shaw, A. T. et al. ALK resistance mutations and efficacy of lorlatinib in advanced anaplastic lymphoma kinase-positive non-small-cell lung cancer. J. Clin. Oncol. 37, 1370–1379 (2019).

    PubMed  PubMed Central  Google Scholar 

  13. Zhang, I., Zaorsky, N. G., Palmer, J. D., Mehra, R. & Lu, B. Targeting brain metastases in ALK-rearranged non-small-cell lung cancer. Lancet Oncol. 16, e510–e521 (2015).

    CAS  PubMed  Google Scholar 

  14. Johung, K. L. et al. Extended survival and prognostic factors for patients with ALK-rearranged non-small-cell lung cancer and brain metastasis. J. Clin. Oncol. 34, 123–129 (2016).

    CAS  PubMed  Google Scholar 

  15. Sankar, K., Nagrath, S. & Ramnath, N. Immunotherapy for ALK-rearranged non-small cell lung cancer: challenges inform promising approaches. Cancers 13, 1476 (2021).

  16. Gainor, J. F. et al. EGFR mutations and ALK rearrangements are associated with low response rates to PD-1 pathway blockade in non-small cell lung cancer: a retrospective analysis. Clin. Cancer Res. 22, 4585–4593 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Negrao, M. V. et al. Oncogene-specific differences in tumor mutational burden, PD-L1 expression, and outcomes from immunotherapy in non-small cell lung cancer. J. Immunother. Cancer 9, e002891 (2021).

  18. Shi, Y. et al. Integration of comprehensive genomic profiling, tumor mutational burden, and PD-L1 expression to identify novel biomarkers of immunotherapy in non-small cell lung cancer. Cancer Med. 10, 2216–2231 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Zeng, C. et al. Tumor-infiltrating CD8+ T cells in ALK-positive lung cancer are functionally impaired despite the absence of PD-L1 on tumor cells. Lung Cancer 150, 139–144 (2020).

    PubMed  Google Scholar 

  20. Fang, Y. et al. Comprehensive analyses reveal TKI-induced remodeling of the tumor immune microenvironment in EGFR/ALK-positive non-small-cell lung cancer. Oncoimmunology 10, 1951019 (2021).

    PubMed  PubMed Central  Google Scholar 

  21. Cheever, M. A. et al. The prioritization of cancer antigens: a national cancer institute pilot project for the acceleration of translational research. Clin. Cancer Res. 15, 5323–5337 (2009).

    PubMed  PubMed Central  Google Scholar 

  22. Ait-Tahar, K. et al. B and CTL responses to the ALK protein in patients with ALK-positive ALCL. Int. J. Cancer 118, 688–695 (2006).

    CAS  PubMed  Google Scholar 

  23. Ait-Tahar, K. et al. Correlation of the autoantibody response to the ALK oncoantigen in pediatric anaplastic lymphoma kinase-positive anaplastic large cell lymphoma with tumor dissemination and relapse risk. Blood 115, 3314–3319 (2010).

    CAS  PubMed  Google Scholar 

  24. Singh, V. K. et al. NPM–ALK-reactive T-cell responses in children and adolescents with NPM–ALK positive anaplastic large cell lymphoma. Oncoimmunology 8, e1625688 (2019).

    PubMed  PubMed Central  Google Scholar 

  25. Knorr, F. et al. Epitope mapping of anti-ALK antibodies in children with anaplastic large cell lymphoma. Clin. Immunol. 195, 77–81 (2018).

    PubMed  Google Scholar 

  26. Passoni, L. et al. In vivo T-cell immune response against anaplastic lymphoma kinase in patients with anaplastic large cell lymphomas. Haematologica 91, 48–55 (2006).

    CAS  PubMed  Google Scholar 

  27. Awad, M. M. et al. Epitope mapping of spontaneous autoantibodies to anaplastic lymphoma kinase (ALK) in non-small cell lung cancer. Oncotarget 8, 92265–92274 (2017).

    PubMed  PubMed Central  Google Scholar 

  28. Chiarle, R. et al. The anaplastic lymphoma kinase is an effective oncoantigen for lymphoma vaccination. Nat. Med. 14, 676–680 (2008).

    CAS  PubMed  Google Scholar 

  29. Voena, C. et al. Efficacy of a cancer vaccine against ALK-rearranged lung tumors. Cancer Immunol. Res. 3, 1333–1343 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Passoni, L. et al. ALK as a novel lymphoma-associated tumor antigen: identification of 2 HLA-A2.1-restricted CD8+ T-cell epitopes. Blood 99, 2100–2106 (2002).

    CAS  PubMed  Google Scholar 

  31. Maddalo, D. et al. In vivo engineering of oncogenic chromosomal rearrangements with the CRISPR/Cas9 system. Nature 516, 423–427 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Blasco, R. B. et al. Simple and rapid in vivo generation of chromosomal rearrangements using CRISPR/Cas9 technology. Cell Rep. 9, 1219–1227 (2014).

    CAS  PubMed  Google Scholar 

  33. Gainor, J. F. et al. EGFR mutations and ALK rearrangements are associated with low response rates to PD-1 pathway blockade in non-small cell lung cancer (NSCLC): a retrospective analysis. Clin. Cancer Res. 22, 4585–4593 (2016).

  34. Chiarle, R., Voena, C., Ambrogio, C., Piva, R. & Inghirami, G. The anaplastic lymphoma kinase in the pathogenesis of cancer. Nat. Rev. Cancer 8, 11–23 (2008).

    CAS  PubMed  Google Scholar 

  35. Dantoing, E., Piton, N., Salaun, M., Thiberville, L. & Guisier, F. Anti-PD1/PD-L1 immunotherapy for non-small cell lung cancer with actionable oncogenic driver mutations. Int. J. Mol. Sci. 22, 6288 (2021).

  36. Wang, L. & Lui, V. W. Y. Emerging roles of ALK in immunity and insights for immunotherapy. Cancers 12, 426 (2020).

  37. Powis, S. J. et al. Restoration of antigen presentation to the mutant cell line RMA-S by an MHC-linked transporter. Nature 354, 528–531 (1991).

    CAS  PubMed  Google Scholar 

  38. Palmer, R. H., Vernersson, E., Grabbe, C. & Hallberg, B. Anaplastic lymphoma kinase: signalling in development and disease. Biochem. J. 420, 345–361 (2009).

    CAS  PubMed  Google Scholar 

  39. Horton, B. L. et al. Lack of CD8+ T cell effector differentiation during priming mediates checkpoint blockade resistance in non-small cell lung cancer. Sci. Immunol. 6, eabi8800 (2021).

    CAS  PubMed  Google Scholar 

  40. Le, D. T. et al. Evaluation of ipilimumab in combination with allogeneic pancreatic tumor cells transfected with a GM–CSF gene in previously treated pancreatic cancer. J. Immunother. 36, 382–389 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Gubin, M. M. et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Keskin, D. B. et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234–239 (2019).

    CAS  PubMed  Google Scholar 

  43. Mahadevan, N. R. et al. Intrinsic Immunogenicity of small cell lung carcinoma revealed by its cellular plasticity. Cancer Discov. 11, 1952–1969 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Gillette, M. A. et al. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell 182, 200–225 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Keskin, D. B. et al. Direct identification of an HPV-16 tumor antigen from cervical cancer biopsy specimens. Front. Immunol. 2, 75 (2011).

    PubMed  PubMed Central  Google Scholar 

  46. Reinherz, E. L., Keskin, D. B. & Reinhold, B. Forward vaccinology: CTL targeting based upon physical detection of HLA-bound peptides. Front. Immunol. 5, 418 (2014).

    PubMed  PubMed Central  Google Scholar 

  47. Blasco, R. B., Patrucco, E., Mota, I., Tai, W. T. & Chiarle, R. Comment on “ALK is a therapeutic target for lethal sepsis”. Sci. Transl. Med. 10, eaar4321 (2018).

  48. Martinez, P., Peters, S., Stammers, T. & Soria, J. C. Immunotherapy for the first-line treatment of patients with metastatic non-small cell lung cancer. Clin. Cancer Res. 25, 2691–2698 (2019).

    CAS  PubMed  Google Scholar 

  49. Petrazzuolo, A. et al. Pharmacological inhibitors of anaplastic lymphoma kinase (ALK) induce immunogenic cell death through on-target effects. Cell Death Dis. 12, 713 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Hegde, S. et al. Dendritic cell paucity leads to dysfunctional immune surveillance in pancreatic cancer. Cancer Cell 37, 289–307 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Zagorulya, M. et al. Tissue-specific abundance of interferon-γ drives regulatory T cells to restrain DC1-mediated priming of cytotoxic T cells against lung cancer. Immunity 56, 386–405 (2023).

    CAS  PubMed  Google Scholar 

  52. Herbst, R. S. et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515, 563–567 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Sanmamed, M. F. et al. A Burned-out CD8+ T-cell subset expands in the tumor microenvironment and curbs cancer immunotherapy. Cancer Discov. 11, 1700–1715 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Baumeister, S. H., Freeman, G. J., Dranoff, G. & Sharpe, A. H. Coinhibitory pathways in immunotherapy for cancer. Annu. Rev. Immunol. 34, 539–573 (2016).

    CAS  PubMed  Google Scholar 

  55. Pedicord, V. A., Montalvo, W., Leiner, I. M. & Allison, J. P. Single dose of anti-CTLA-4 enhances CD8+ T-cell memory formation, function, and maintenance. Proc. Natl Acad. Sci. USA 108, 266–271 (2011).

    CAS  PubMed  Google Scholar 

  56. Sharma, N., Vacher, J. & Allison, J. P. TLR1/2 ligand enhances antitumor efficacy of CTLA-4 blockade by increasing intratumoral Treg depletion. Proc. Natl Acad. Sci. USA 116, 10453–10462 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. McGranahan, N. et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171, 1259–1271 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Tran, E. et al. T-cell transfer therapy targeting mutant KRAS in cancer. N. Engl. J. Med. 375, 2255–2262 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Le Naour, J., Zitvogel, L., Galluzzi, L., Vacchelli, E. & Kroemer, G. Trial watch: STING agonists in cancer therapy. Oncoimmunology 9, 1777624 (2020).

    PubMed  PubMed Central  Google Scholar 

  60. Stronen, E. et al. Targeting of cancer neoantigens with donor-derived T cell receptor repertoires. Science 352, 1337–1341 (2016).

    CAS  PubMed  Google Scholar 

  61. Reynisson, B., Alvarez, B., Paul, S., Peters, B. & Nielsen, M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 48, W449–W454 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Andreatta, M. & Nielsen, M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 32, 511–517 (2016).

    CAS  PubMed  Google Scholar 

  63. Liu, H. et al. Structure-based programming of lymph-node targeting in molecular vaccines. Nature 507, 519–522 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Schultze, J. L. et al. Follicular lymphomas can be induced to present alloantigen efficiently: a conceptual model to improve their tumor immunogenicity. Proc. Natl Acad. Sci. USA 92, 8200–8204 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Liebig, T. M., Fiedler, A., Zoghi, S., Shimabukuro-Vornhagen, A. & von Bergwelt-Baildon, M. S. Generation of human CD40-activated B cells. J. Vis. Exp. https://doi.org/10.3791/1373 (2009).

  66. Sarkizova, S. et al. A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nat. Biotechnol. 38, 199–209 (2020).

    CAS  PubMed  Google Scholar 

  67. Klaeger, S. et al. Optimized liquid and gas phase fractionation increases HLA–peptidome coverage for primary cell and tissue samples. Mol. Cell. Proteomics 20, 100133 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Gregus, M., Kostas, J. C., Ray, S., Abbatiello, S. E. & Ivanov, A. R. Improved sensitivity of ultralow flow LC–MS-based proteomic profiling of limited samples using monolithic capillary columns and FAIMS technology. Anal. Chem. 92, 14702–14712 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Reinhold, B., Keskin, D. B. & Reinherz, E. L. Molecular detection of targeted major histocompatibility complex I-bound peptides using a probabilistic measure and nanospray MS3 on a hybrid quadrupole-linear ion trap. Anal. Chem. 82, 9090–9099 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank N. Chamberlin for editing of the manuscript and M. S. Scalzo, R. Dall’Olio, P. Le, L. Li, S. Peola and B. Castella for technical assistance. I.M. was supported by the European Union Horizon 2020 Marie Skłodowska–Curie Innovative Training Network Grant (675712) for the European Research Initiative for ALK-Related Malignancies (ERIA). R.B.B. was supported by a fellowship from the Ramon Areces Foundation. The work was supported by TRP Pilot grants from the Boston Children’s Hospital (to R.C.), by the Bridge Project, a partnership between the Koch Institute for Integrative Cancer Research at MIT and the Dana-Farber/Harvard Cancer Center (to D.J.I. and R.C.), the Koch Institute Dana–Farber/Harvard Cancer Center Extended Bridge Project (to D.J.I., M.M.A. and R.C.), the DFCI Lung Cancer Development Award and ALK Positive/LUNGevity Lung Cancer Research Awards (to M.M.A. and R.C.), the 1 P50 CA265826-01A1 Dana–Farber/Harvard Cancer Center SPORE in Lung Cancer (to R.C, D.A.B. and M.M.A.), the Ellison Foundation Boston grant (to R.C.), the Grant for Oncology Innovation from Merck Healthcare KGaA (to R.C.), a Pfizer research grant 53232955 (to R.C.), the V Foundation (to D.J.I.) and the NIH (EB022433; to D.J.I.). D.B.K. and C.J.W. are supported in part by NIH/NCI U24CA224331. D.B.K. is supported by 1R01HL157174-01A1. Z.M.S. and Z.S. were supported by the Department of Defense through the Lung Cancer Research Program (award number W81XWH-18-1-0751) and the Breast Cancer Research Foundation (BCRF-21-159). E.B. was supported by the American–Italian Cancer Foundation fellowship. T.-C.C. is supported by a OFD/BTREC/CTREC Career Development Fellowship. C.A. is supported by the Giovanni Armenise–Harvard Foundation, the International Lung Cancer Foundation, the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 101001288), AIRC under IG 2021 ID 25737 project and the Zanon di Valgiurata family through Justus s.s.

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Authors and Affiliations

  1. Department of Pathology, Boston Children’s Hospital, Boston, MA, USA

    Ines Mota, Elisa Bergaggio, Taek-Chin Cheong, Giulia Leonardi, Elif Karaca-Atabay, Marco Campisi, Rafael B. Blasco & Roberto Chiarle

  2. Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy

    Enrico Patrucco, Cristina Mastini, Teresa Poggio, Matteo Menotti, Chiara Ambrogio, Dario L. Longo, Claudia Voena & Roberto Chiarle

  3. Department of Medical Oncology, Dana–Farber Cancer Institute, Boston, MA, USA

    Navin R. Mahadevan, Tran C. Thai, Marco Campisi, Derin B. Keskin, Jonathan S. Duke-Cohan, Bruce Reinhold, Catherine J. Wu, David A. Barbie, Ellis L. Reinherz & Mark M. Awad

  4. Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA

    Navin R. Mahadevan

  5. Molecular Imaging Center, University of Torino, Torino, Italy

    Dario L. Longo

  6. Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), Torino, Italy

    Dario L. Longo

  7. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Susan Klaeger, Hasmik Keshishian, Derin B. Keskin, Steven A. Carr & Catherine J. Wu

  8. Danish Cancer Society Research Center, Copenhagen, Denmark

    Zsófia M. Sztupinszki & Zoltan Szallasi

  9. Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, USA

    Zsófia M. Sztupinszki, Zoltan Szallasi & Ellis L. Reinherz

  10. Department of Bioinformatics, Semmelweis University, Budapest, Hungary

    Zoltan Szallasi

  11. Translational Immunogenomics Laboratory, Dana–Farber Cancer Institute, Boston, MA, USA

    Derin B. Keskin

  12. Department of Computer Science, Metropolitan College, Boston University, Boston, MA, USA

    Derin B. Keskin

  13. Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Lyngby, Denmark

    Derin B. Keskin

  14. Laboratory of Immunobiology, Dana–Farber Cancer Institute, Boston, MA, USA

    Jonathan S. Duke-Cohan & Bruce Reinhold

  15. Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Catherine J. Wu & Mark M. Awad

  16. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Kelly D. Moynihan & Darrell J. Irvine

  17. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Darrell J. Irvine

Authors
  1. Ines Mota
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  2. Enrico Patrucco
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  17. Zsófia M. Sztupinszki
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  18. Zoltan Szallasi
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  19. Derin B. Keskin
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  20. Jonathan S. Duke-Cohan
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  27. Ellis L. Reinherz
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  28. Claudia Voena
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  29. Mark M. Awad
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  30. Rafael B. Blasco
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  31. Roberto Chiarle
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Contributions

I.M., R.B.B. and R.C. conceptualized the study. I.M., E.P., C.M., N.R.M., T.C.T., G.L., E.K.A., M.C., T.P., M.M., C.A., D.L.L., D.B.K., J.S.D.-C., B.R., K.D.M., C.V. and R.B.B. performed in vitro experiments. S.K., H.K. and S.A.C. performed MS experiments. J.S.D.-C. and B.R. performed LC–DIAMS experiments. I.M., E.B., T.-C.C., E.P., C.M., T.P., M.M. and R.B.B. performed mouse experiments. Z.M.S. and Z.S. analyzed CPTAC and TCGA datasets. I.M., D.B.K., J.S.D.-C., B.R., K.D.M., C.V., R.B.B. and R.C. analyzed data. C.J.W., D.A.B., D.J.I., M.M.A. and E.L.R. supervised experiments and analyzed data. M.M.A. provided human samples. I.M., N.R.M., M.M.A., D.A.B., E.L.R., R.B.B. and R.C. wrote and edited the manuscript.

Corresponding authors

Correspondence to Rafael B. Blasco or Roberto Chiarle.

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Competing interests

R.C. and R.B.B. have filed IP related to human ALK vaccine sequences. R.C. and D.J.I. hold stock options in Elicio Therapeutics. D.J.I. is a consultant to Elicio Therapeutics. R.B.B. is a current employee of Moderna Therapeutics. C.J.W. receives research funding from Pharmacyclics and holds equity in BioNTech, Inc. D.B.K. is a scientific advisor for Immunitrack and Breakbio. D.B.K. owns equity in Affimed N.V., Agenus, Armata Pharmaceuticals, Breakbio, BioMarin Pharmaceutical, Celldex Therapeutics, Editas Medicine, Gilead Sciences, Immunitybio, ImmunoGen, IMV, Lexicon Pharmaceuticals and Neoleukin Therapeutics. BeiGene, a Chinese biotech company, supported unrelated severe acute respiratory syndrome coronavirus 2 research at Translational Immunogenomics Lab (TIGL) at DFCI.

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Extended data

Extended Data Fig. 1 Immune checkpoint inhibitors (ICIs) do not increase the efficacy of ALK TKIs in ALK+ lung cancer mouse models.

(a) Kaplan-Meier curves showing overall survival of mice described in Fig. 1c. Log-rank test, n/s, not significant (N = 3 mice per group). (b) Kaplan-Meier curves showing overall survival of mice described in Fig. 1d. Log-rank test; n/s, not significant (N = 4 mice per group) (c, d, e) Quantification of volume change compared with baseline tumor volume (change from baseline, % ± SEM) in hEML4-ALK Tg mice treated with higher doses (crizotinib: 100 mg/kg DIE; lorlatinib 10 mg/kg DIE) of ALK inhibitors and ICIs at T0 (c), T4 (d), and T8 (e) (Rat IgG, N = 12; anti-PD-1, N = 8; anti-PD-L1, N = 8; Crizotinib + Rat IgG, N = 13; Crizotinib + anti-PD-1, N = 8; Crizotinib + anti-PD-L1, N = 7; Lorlatinib + Rat IgG, N = 14; Lorlatinib + anti-PD-1, N = 7; Lorlatinib + anti-PD-L1, N = 8 mice). P values were calculated using an ordinary one-way Anova. (f) Kaplan-Meier curves showing overall survival of hEML4-ALK Tg mice treated with higher doses of ALK inhibitors and ICIs. Log-rank test; P values not significant. (Rat IgG, N = 12; anti-PD-1, N = 12; anti-PD-L1, N = 12; Crizotinib + Rat IgG, N = 13; Crizotinib + anti-PD-1, N = 8; Crizotinib + anti-PD-L1, N = 7; Lorlatinib + Rat IgG, N = 14; Lorlatinib + anti-PD-1, N = 9; Lorlatinib + anti-PD-L1, N = 8 mice). (g) Schematic representation of ALK peptide screening. Mice were vaccinated with a DNA encompassing the cytoplasmic portion of ALK, as previously described29. A set of 21 synthetic long peptides (SLPs) encompassing the coding region of the ALK-DNA vaccine was synthesized. Peptides A/B were synthetized to cover the cytoplasmic portion of hALK protein that is not represented in the ALK-DNA vaccine. (h-j) Benchmarking MHC-I algorithms for peptide-binding affinity prediction. NetMHC4.0 showing peptide affinity (predicted IC50 values in nM) vs. NetMHCpan4.1 showing the rank of the elution ligand likelihood (%Rank_EL). Computational predictions of peptide binding to MHC-I of SLPA (h), SLP7 (i) and SLP20 and 21 (j). Each dot represents a peptide. Peptide sequences and their correspondent predicted MHC-I allele can be found in Supplementary Table 3. Peptides represented in red or green indicate the best binder candidates.

Source data

Extended Data Fig. 2 Identification of ALK immunogenic peptides in mouse models.

(a, b) IFN-γ intracellular staining of CD4+-gated (a) and CD8+-gated (b) peripheral mononuclear cells (PBMCs) isolated from mice vaccinated with the indicated peptides and stimulated in vitro with the same peptide (%± SEM). Each dot represents a mouse (ALK1058-1066, n = 3; remaining groups, N = 4 mice). (c) IFN-γ intracellular staining in CD4+ and CD8+ splenocytes isolated from a representative mouse (N = 3 independent mice) vaccinated with SLP7 and pulsed with 10 µg/mL of the same peptide. (d) Representative flow cytometric analysis of H-2Kd and H-2Dd expression on ASB-XIV and ASB-XIVTAP2KO cells. Experiment performed three times. (e) H-2Dd and H-2Kd staining of ASB XIVTAP2KO cells incubated with increasing concentrations of PGPGRVAKI peptide displayed as mean fluorescence intensity (MFI) ( ± SEM) (n = 3 independent experiments). (f) Schematic representation of the generation of the mEml4-Alk immortalized cell lines. (g) Sanger sequencing chromatogram showing the mElm4-Alk inversion. The mouse Eml4-Alk inversion involves exon 14 of Eml4 and exon 20 of mouse Alk. (h) Representative immunoblot analysis (N = 3 independent experiments with similar results) showing mEML4-ALK protein expression in two mEml4-Alk (mEml4-Alk1 and mEml4-Alk2) immortalized cell lines. The K-RasG12D KP1233 cell line was used as a negative control; ALK SP8 antibody recognizes the murine EML4-ALK (arrow); ALK D5F3 antibody recognizes the human EML4-ALK in NCI-H3122. *Indicates a non-specific band recognized by the SP8 antibody. (i) Dose response curves of mElm4-Alk1 cells to different ALK TKIs (crizotinib, N = 2 independent experiments; alectinib, N = 1; ceritinib, N = 1; brigatinib, N = 2 independent experiments; and lorlatinib, N = 2 independent experiments). (j) Representative immunoblot for the indicated proteins in mEml4-Alk1 cells treated with crizotinib and lorlatinib at the indicated concentrations for 6 h (N = 2 independent experiments with similar results). (k) Subcutaneous tumor growth (mm3 ± SEM) of mEml4-Alk-1 and mEml4-Alk-2 immortalized cell lines in NSG and syngeneic BALB/c mice (N = 4 mice per group). (l) Quantification of volume changes compared with baseline tumor volume (change from baseline, % ± SEM) in syngeneic BALB/c mice injected subcutaneously with a mEml4-Alk cell line and treated as indicated. Red arrow indicates the end of treatment (N = 5 mice per group).

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Extended Data Fig. 3 Generation and immunogenicity of Eml4-AlkPGPGRVAKI immortalized cell lines.

(a) Representative H&E image of lung tumors generated in syngeneic BALB/c mice injected intravenously with mEml4-Alk. 2x magnification (left panel); 40x magnification (right panel) (n = 3 independent mice). Scale bar left = 1.5 mm; Scale bar right= 100 µm. (b) Schematic illustration of mouse and human ALK short peptide 7 sequences differences and CRISPR/Cas9-edited DNA bases (Red). NetMHC4.0 showing peptide affinity (predicted IC50 values in nM) for H-2Dd and NetMHCpan4.1 showing the rank of the elution ligand likelihood (%Rank_EL) for H-2Dd. (c) Sanger sequencing chromatogram showing the cDNA sequence of mEml4-Alk (upper panel), Eml4-AlkPGPGRVAKI-1 (middle panel), and Eml4-AlkPGPGRVAKI-2 (lower panel) of PGPGRVAKI peptide. (d,e) Dose response curves of Eml4-AlkPGPGRVAKI-1 (d) and Eml4-AlkPGPGRVAKI-2 (e) cell lines to different ALK inhibitors (crizotinib, alectinib, ceritinib, brigatinib, and lorlatinib). Two independent experiments were performed for each ALK inhibitor in each cell line. (f) Representative immunoblot for the indicated proteins in Eml4-AlkPGPGRVAKI-1 cells treated with lorlatinib at the indicated concentrations for 6 h (N = 2 independent experiments with similar results). (g) Representative H&E of lung tumors generated in syngeneic BALB/c mice injected intravenously with Eml4-AlkPGPGRVAKI-1. 2x magnification (left panel); 40x magnification (right panel) (n = 3 independent mice). Scale bar left = 1.5 mm; Scale bar right= 100 µm. (h) IFN-γ-ELISPOT analysis of freshly isolated splenocytes from tumor-bearing BALB/c mice 15 days after been injected either subcutaneously (flank) or intravenously (lung) with mEml4-Alk (left panel), Eml4-AlkPGPGRVAKI-1 (middle panel), and Eml4-AlkPGPGRVAKI-2 (right panel) cells. Data is shown as average number of spot forming units (SFU ± SEM). Unpaired two-tailed Student’s t test, not significant, P = 0.2038; **P < 0.005; ***P < 0.001. The N shown on the figure corresponds to the number of mice per group. (i,j) Dextramer staining of PGPGRVAKI-specific CD8+ T cells isolated from splenocytes (i) and PGPGRVAKI-specific tumor infiltrating T lymphocytes (TILs) isolated from lung tumors (j) at day 15 post subcutaneous (flank) and intravenous (lung) injection. Mice were injected with mElm4-Alk (left panel), with Eml4-AlkPGPGRVAKI-1 (middle panel), and with Eml4-AlkPGPGRVAKI-2 (right panel) cells (%±SEM). Unpaired two-tailed Student’s t test. The N shown on the figure corresponds to the number of mice per group. (k) Tumor growth of mice injected subcutis in the flank with mEml4-Alk1 cells and treated as indicated. Data are shown as average tumor volume (mm3 ± SEM). (l) Kaplan-Meier curves showing overall survival of mice injected intravenously with Eml4-AlkPGPGRVAKI-1 and treated as indicated. Log-rank test), not significant, P = 0.6040).

Source data

Extended Data Fig. 4 Immunogenicity of the ALK vaccine in mice.

(a) Quantification of the number of tumors counted on a histology section taken in the middle of the lungs from mice shown in Fig. 3g. Each dot represents an individual mouse (n = 4 biological independent mice per group). Data are represented as mean ± SEM. Unpaired two-tailed Student’s t test, not significant, P = 0.6790). (b) Schematic representation of treatment protocol of hEML4-ALK Tg mice vaccinated with either an ALK DNA vaccine29 or an ALK amph-vaccine63. (c) In vivo cytotoxic activity represented as % of lysis of target ALK+ cells ( ± SEM) by ALK-specific CD8+ T-cells in mice treated as in b. Each dot represents a mouse (Untreated, N = 7; ALK vax, N = 13, ALK-DNA vax, N = 8 mice). Unpaired two-tailed Student’s t test, not significant, P = 0.0882; ***P = 0.0004; ****P < 0.0001. (d) Tumor volumes indicated as mm3 ( ± SEM) were measured by MRI at the indicated time points in hEML4-ALK Tg mice treated with an ALK peptide vaccine as in b. Unpaired two-tailed Student’s t test, *P = 0.0173; **P = 0.0027. The N shown on the figure corresponds to the number of mice per group. (e) Kaplan-Meier curves showing overall survival of mice injected intravenously with Eml4-AlkPGPGRVAKI-1 and vaccinated with the PGPGRVAKI peptide with CDN adjuvant at days 1, 7, and 14 after tumor injection, alone or in combination with an anti-CTLA-4 antibody (administered at day 3, 6, and 9 after tumor injection). Log-rank test: not significant, P = 0.2985, P = 0.1276; *P = 0.0228; *P = 0.0153. The N shown on the figure corresponds to the number of mice per group. (f) Quantitative analysis of intratumoral CD8+ T cells per high power field (HPF) from mice in indicated treatment regimens (Untreated, N = 7; Lorlatinib, N = 3; Lorlatinib + ALK vax, N = 5 mice). ALK vax: PGPGRVAKI peptide with CDN adjuvant. Data are represented as in average number of four high power field per mouse (% ± SEM). Unpaired two-tailed Student’s t test, not significant, P = 0.1096; ***P = 0.0003; ****P < 0.0001. (g) Representative H&E and paired CD8 immunohistochemistry staining of lung tumors from mice treated as indicated. Scale bars = 50 µm. (h) Dextramer staining of PGPGRVAKI-specific CD8+ T cells isolated from lung tumors, displayed as percentage (% ± SEM). Mice were treated as indicated, and tumors collected upon death. Each dot represents an individual mouse (N = 4 mice per group). Unpaired two-tailed Student’s t test, not significant, P = 0.3545, P = 0.2740, P = 0.1935. (i) Relative normalized expression of Alk mRNA in ex vivo cell lines isolated from lung tumors that escaped the indicated treatments. Normalized expression relative to the parental tumor line ( ± SEM). The N shown on the figure corresponds to the number of ex vivo cell lines isolated and analyzed per group.(j) Representative immunoblot for the indicated proteins from four MHC-Ihigh and four MHC-Ilow cell lines isolated from lung tumors that escaped treatment. Eml4-AlkPGPGRVAKI-1 parental cell line was used as control. Two independent experiments were performed with similar results. (k) Representative chromatogram of Sanger sequencing showing the PGPGRVAKI peptide cDNA sequences in representative cell lines isolated from lung tumors that escaped the indicated treatments lung tumor escapers from each indicated treatment group. mEml4-Alk represents the unedited sequence.

Source data

Extended Data Fig. 5 Characterization of tumors that escaped the ALK vaccine and MHC-I expression in human ALK + NSCLC.

(a) PD-L1 expression of cell lines isolated from lung tumors that escaped the indicated treatment, displayed as mean fluorescence intensity (MFI) ( ± SEM). Each dot represents an individual tumor. (b-i) Relative normalized mRNA expression of Lpm2(b), Lpm7(c), Tap1(d), Tap2(e), B2m (f), Tapasin(g), Mecl1 (h), and Sting (i) genes in tumor lines isolated ex vivo from lung tumors that escaped the indicated treatments. For all samples the values were normalized to the expression levels of the parental Eml4-AlkPGPGRVAKI-1 cell line cultivated in vitro (± SEM). The N shown on the figure corresponds to the number of ex vivo cell lines isolated and analyzed per group. (j-o) ALK+ NSCLC expresses HLA-A/B mRNA and protein at levels comparable to NSCLC with other oncogenic drivers (CPTAC dataset). Boxplot showing mRNA and protein expression of ALK (j, k), HLA-A (l,m), HLA-B (n, o), from cases of ALK+ NSCLC from the Clinical Proteomic Tumor Analysis Consortium (CPTAC)44. Fusion variants of the 7 ALK + NSCLC cases: 5 EML4-ALK, 1 ANKRD36B-ALK, 1 HMBOX1-ALK. Number of samples with ALK-fusion: n = 7, KRAS-mutation: n = 32, EGFR-mutation: n = 35, wild type samples not represented on the plot: n = 36. The boxplots show the median (line), and the interquartile range (IQR), and the whiskers represent 1.5× the IQR ± the upper and lower quartiles, respectively.

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Extended Data Fig. 6 Characterization of MHC-I expression in human ALK + NSCLC and identification of MHC-I human ALK peptides in human ALK+ lymphoma cell lines.

(a-b) Boxplot showing mRNA and protein expression of HLA-BC from cases of ALK+ NSCLC from the Clinical Proteomic Tumor Analysis Consortium (CPTAC)44. (c-f) ALK+ NSCLC express HLA-A/B/C mRNA and protein at levels comparable to NSCLC with other oncogenic drivers (TCGA dataset). Boxplot showing mRNA expression of ALK (c), HLA-A (d), HLA-B (e), and HLA-C (f) from cases of ALK+ NSCLC from The Cancer Genome Atlas (TCGA) LUAD gene expression (http://cancergenome.nih.gov). All 4 ALK+ NSCLC had an EML4-ALK fusion. Number of samples with ALK-fusion: n = 4, KRAS-mutation: n = 155, EGFR-mutation: n = 56, wild type samples not represented on the plot: n = 301. The boxplots show the median (line), and the interquartile range (IQR), and the whiskers represent 1.5× the IQR ± the upper and lower quartiles, respectively. (g) Targeted mass spectrometry from HLA A*02:01 immunoprecipitations of the ALCL cell line DEL. Oxidized and non-oxidized methionine forms of the peptide AMLDLLHVA were monitored. (h) Poisson LC-DIAMS plots from pan-HLA immunoprecipitations of the ALK+ ALCL cell lines DEL. Poisson plots combine an extracted ion chromatogram (XIC) for the peptides’ precursor m/z (black trace) with an inverted, scaled Poisson chromatogram (blue trace, see Methods). Peptide detection (see Methods) at an elution point is associated with coincident precursor and Poisson peaks, here marked by blue arrows The elution position lies near the predicted elution position determined by a retention time peptide set added to the DEL (h, lower panel) samples and, after sample data was collected, the synthetic set of ALK peptides.

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Extended Data Fig. 7 Identification of MHC-I human ALK peptides in human ALK+ cell lines.

(a) Identification of ALK-specific peptides by discovery mass spectrometry from pan-HLA immunoprecipitations of the ALCL cell line Karpas-299. (b) Poisson LC-DIAMS plots from pan-HLA immunoprecipitations of the ALK+ ALCL cell lines Karpas-299. Poisson plots combine an extracted ion chromatogram (XIC) for the peptides’ precursor m/z (black trace) with an inverted, scaled Poisson chromatogram (blue trace, see Methods). Peptide detection (see Methods) at an elution point is associated with coincident precursor and Poisson peaks, here marked by blue arrows The elution position lies near the predicted elution position determined by a retention time peptide set added to the Karpas-299 (b, lower panel) samples and, after sample data was collected, the synthetic set of ALK peptides. (c) Poisson analysis of the same LC-DIAMS datasets detecting AMLDLLHVA in DEL but not in NCI-H2228, using reference information for a set of common HLA-A*02:01 peptides, identifies substantially better A02:01 peptide recovery from NCI-H2228. Hence, that NCI-H2228 cells do not present HLA-A*02:01 peptides from ALK, including the strong binding AMLDLLHVA peptide detected in the ALCL cell line DEL, cannot be associated with poor overall recovery of A*02:01 peptides.

Extended Data Fig. 8 Identification of MHC-I human ALK peptides and characterization of the immunoproteasome in human ALK+ cell lines.

(a) Cell type specificity in antigen presentation. HLA A*02:01 restricted ALK peptide AMLDLLHVA is displayed in DEL cells (i) but not in H2228 cells (ii). These lines are HLA A*02:01 positive and an abundant display of common HLA-A2 peptides was detected. Both HLA B*07:02 restricted ALK peptides RPRPSQPSSL and VPRKNITLI are displayed in Karpas-299 cells (iii and v) while RPRPSQPSSL is displayed in H2228 cells (iv) and VPRKNITLI is not (vi). Blue arrows mark detection signatures associated with coincident XIC and Poisson peaks. For the negative detections in (ii) and (vi) orange arrows mark the expected elution position as calculated from the elution of added retention time peptides (see Methods). Expected elution positions and detection positions overlap for positive detection, as is the case for the other 4 panels. (b-g) Expression of Tap1, Tap2, B2M, Tapasin, Lmp2 and Lmp7 in ALK+ lung cancer and lymphoma cell lines. Relative normalized mRNA expression of (b)Tap1, (c)Tap2, (d) B2m, (e) Tapasin, (f) Lpm2, (g) Lpm7 genes in NCI-H2228, Karpas-299 and DEL cell lines either at baseline or stimulated for 24 hours with 10 ng/mL IFN-γ. Values are normalized to the expression levels of NCI-H2228 line at baseline. Dots represent technical replicates from a single experiment. (h) Expression of HLA molecules was determined by flow cytometry with a pan-HLA antibody in NCI-H2228, Karpas-299 and DEL cell lines either at baseline or stimulated for 24 hours with 10 ng/mL IFN-γ.

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Extended Data Fig. 9 Immunogenicity of ALK-specific peptides in HLA transgenic mice.

(a) Scheme of vaccination of HLA-A*02:01 and HLA-B*07:02 transgenic mice. (b) Sequence comparison of the ALK peptides between mouse and human. (c) Quantification of IFN-γ ELISPOT assays of splenocytes isolated from HLA-A*02:01 transgenic mice vaccinated with AMLshort or AMLlong peptides with CDN adjuvant. PBS was used as a vaccination control (N = 5 mice per group). Each bar represents spot forming units (SFU) from splenocytes isolated from an individual mouse and incubated with either peptide AMLDLLHV or peptide solvent (DMSO) as a negative control. Two independent experiments were performed. Data are represented as three technical replicates from one experiment. (d) Schematic representation of the expansion protocol for human CD8+ T cells from PBMCs of patients with NSCLC in the presence of ALK-specific peptides. The displayed alternative expansion methods were applied for those patients with lower amount of PBMCs available.

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Extended Data Fig. 10 Physiologic ALK expression is low and limited to certain areas in the brain with low expression of HLA-A, HLA-B and HLA-C.

ALK (a), HLA-A (b), HLA-B (c) and HLA-C (d) mRNA expression in the indicated tissues according to the Genotype-Tissue Expression (GTEx) project (https://gtexportal.org/home/). 1) Amygdala (n = 152); 2) Anterior cingulate cortex (BA24) (n = 176); 3) Caudate (basal ganglia) (n = 246); 4) Cerebellar Hemisphere (n = 215); 5) Cerebellum (n = 241); 6) Cortex (n = 255); 7) Frontal Cortex (BA9) (n = 209); 8) Hippocampus (n = 197); 9) Hypothalamus (n = 202); 10) Nucleus accumbens (basal ganglia) (n = 246); 11) Putamen (basal ganglia) (n = 205); 12) Spinal cord (cervical c-1) (n = 159); 13) Substantia nigra (n = 139); 14) Colon – Sigmoid (n = 373); 15) Colon – Transverse (n = 406); 16) Lung (n = 578); 17) Small Intestine - Terminal Ileum (n = 187); 18) Testis (n = 361); 19) Whole Blood (n = 755). The line inside each box is the median, upper box border represents the 75th quartile, lower box border represents the 25th quartile and whiskers represent the range.

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Mota, I., Patrucco, E., Mastini, C. et al. ALK peptide vaccination restores the immunogenicity of ALK-rearranged non-small cell lung cancer. Nat Cancer 4, 1016–1035 (2023). https://doi.org/10.1038/s43018-023-00591-2

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