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FISH analysis reveals CDKN2A and IFNA14 co-deletion is heterogeneous and is a prominent feature of glioblastoma

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Abstract

Deletion of CDKN2A occurs in 50% of glioblastomas (GBM), and IFNA locus deletion in 25%. These genes reside closely on chromosome 9. We investigated whether CDKN2A and IFNA were co-deleted within the same heterogeneous tumour and their prognostic implications. We assessed CDKN2A and IFNA14 deletions in 45 glioma samples using an in-house three-colour FISH probe. We examined the correlation between p16INK4a protein expression (via IHC) and CDKN2A deletion along with the impact of these genomic events on patient survival. FISH analyses demonstrated that grades II and III had either wildtype (wt) or amplified CDKN2A/IFNA14, whilst 44% of GBMs harboured homozygous deletions of both genes. Cores with CDKN2A homozygous deletion (n = 11) were negative for p16INK4a. Twenty p16INK4a positive samples lacked CDKN2A deletion with some of cells showing negative p16INK4a. There was heterogeneity in IFNA14/CDKN2A ploidy within each GBM. Survival analyses of primary GBMs suggested a positive association between increased p16INK4a and longer survival; this persisted when considering CDKN2A/IFNA14 status. Furthermore, wt (intact) CDKN2A/IFNA14 were found to be associated with longer survival in recurrent GBMs. Our data suggest that co-deletion of CDKN2A/IFNA14 in GBM negatively correlates with survival and CDKN2A-wt status correlated with longer survival, and with second surgery, itself a marker for improved patient outcomes.

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Data availability

The patients’ datasets used in this work are not publicly available due to patients’ privacy concerns of the institutional review board policies on human tissue data. The CDKN2A and IFNs gene status profiles that were utilised in this work are available from the cBioPortal database.

References

  1. Weller M et al (2015) Glioma. Nat Rev Dis Prim 1:15017

    Article  PubMed  Google Scholar 

  2. Ostrom QT et al (2019) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2012–2016. Neuro Oncol 21(Suppl 5):v1–v100

    Article  PubMed  PubMed Central  Google Scholar 

  3. Stupp R et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352(10):987–996

    Article  PubMed  CAS  Google Scholar 

  4. Stupp R et al (2009) Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 10(5):459–466

    Article  PubMed  CAS  Google Scholar 

  5. Hanif F et al (2017) Glioblastoma multiforme: a review of its epidemiology and pathogenesis through clinical presentation and treatment. Asian Pac J Cancer Prev 18(1):3–9

    PubMed  PubMed Central  Google Scholar 

  6. Verhaak RG et al (2010) Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17(1):98–110

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Brennan CW et al (2013) The somatic genomic landscape of glioblastoma. Cell 155(2):462–477

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Cancer Genome Atlas Research (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455(7216):1061–1068

    Article  Google Scholar 

  9. Louis DN et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131(6):803–820

    Article  PubMed  Google Scholar 

  10. Louis DN et al (2021) The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol 23(8):1231–1251

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Gonzalez-Castro LN, Wesseling P (2021) The cIMPACT-NOW updates and their significance to current neuro-oncology practice. Neurooncol Pract 8(1):4–10

    PubMed  Google Scholar 

  12. Ma S et al (2020) Prognostic impact of CDKN2A/B deletion, TERT mutation, and EGFR amplification on histological and molecular IDH-wildtype glioblastoma. Neurooncol Adv 2(1):vdaa126

    PubMed  PubMed Central  Google Scholar 

  13. Gil J, Peters G (2006) Regulation of the INK4b-ARF-INK4a tumour suppressor locus: all for one or one for all. Nat Rev Mol Cell Biol 7(9):667–677

    Article  PubMed  CAS  Google Scholar 

  14. Kim WY, Sharpless NE (2006) The regulation of INK4/ARF in cancer and aging. Cell 127(2):265–275

    Article  PubMed  CAS  Google Scholar 

  15. Lu VM et al (2020) The prognostic significance of CDKN2A homozygous deletion in IDH-mutant lower-grade glioma and glioblastoma: a systematic review of the contemporary literature. J Neurooncol 148(2):221–229

    Article  PubMed  CAS  Google Scholar 

  16. Park JW et al (2021) The prognostic significance of p16 expression pattern in diffuse gliomas. J Pathol Transl Med 55(2):102–111

    Article  PubMed  Google Scholar 

  17. Appay R et al (2019) CDKN2A homozygous deletion is a strong adverse prognosis factor in diffuse malignant IDH-mutant gliomas. Neuro Oncol 21(12):1519–1528

    PubMed  PubMed Central  CAS  Google Scholar 

  18. Reis GF et al (2015) CDKN2A loss is associated with shortened overall survival in lower-grade (World Health Organization Grades II-III) astrocytomas. J Neuropathol Exp Neurol 74(5):442–452

    Article  PubMed  CAS  Google Scholar 

  19. Romagosa C et al (2011) p16(Ink4a) overexpression in cancer: a tumor suppressor gene associated with senescence and high-grade tumors. Oncogene 30(18):2087–2097

    Article  PubMed  CAS  Google Scholar 

  20. Milde-Langosch K et al (2001) Overexpression of the p16 cell cycle inhibitor in breast cancer is associated with a more malignant phenotype. Breast Cancer Res Treat 67(1):61–70

    Article  PubMed  CAS  Google Scholar 

  21. Lee CT et al (1999) Overexpression of the cyclin-dependent kinase inhibitor p16 is associated with tumor recurrence in human prostate cancer. Clin Cancer Res 5(5):977–983

    PubMed  CAS  Google Scholar 

  22. Gutiontov SI et al (2021) CDKN2A loss-of-function predicts immunotherapy resistance in non-small cell lung cancer. Sci Rep 11(1):20059

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Parkin J, Cohen B (2001) An overview of the immune system. Lancet 357(9270):1777–1789

    Article  PubMed  CAS  Google Scholar 

  24. de Padilla CML, Niewold TB (2016) The type I interferons: basic concepts and clinical relevance in immune-mediated inflammatory diseases. Gene 576(1):14–21

    Article  Google Scholar 

  25. Ferrantini M, Capone I, Belardelli F (2007) Interferon-alpha and cancer: mechanisms of action and new perspectives of clinical use. Biochimie 89(6–7):884–893

    Article  PubMed  CAS  Google Scholar 

  26. Vidal P (2020) Interferon α in cancer immunoediting: from elimination to escape. Scand J Immunol 91(5):e12863

    Article  PubMed  Google Scholar 

  27. Tarhini AA, Gogas H, Kirkwood JM (2012) IFN-α in the treatment of melanoma. J Immunol 189(8):3789–3793

    Article  PubMed  CAS  Google Scholar 

  28. Kankuri-Tammilehto M et al (2023) Long-term outcome with prolonged use of interferon-alpha administered intermittently for metastatic renal cell carcinoma: a phase II study. Anticancer Res 43(6):2645–2657

    Article  PubMed  CAS  Google Scholar 

  29. Guo J et al (2019) Empowering therapeutic antibodies with IFN-α for cancer immunotherapy. PLoS ONE 14(8):e0219829

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Guo C et al (2023) Adjuvant temozolomide chemotherapy with or without interferon Alfa among patients with newly diagnosed high-grade gliomas: a randomized clinical trial. JAMA Netw Open 6(1):e2253285

    Article  PubMed  Google Scholar 

  31. Fujita M et al (2010) Role of type 1 IFNs in antiglioma immunosurveillance–using mouse studies to guide examination of novel prognostic markers in humans. Clin Cancer Res 16(13):3409–3419

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Yu R, Zhu B, Chen D (2022) Type I interferon-mediated tumor immunity and its role in immunotherapy. Cell Mol Life Sci 79(3):191

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Rafique I, Kirkwood JM, Tarhini AA (2015) Immune checkpoint blockade and interferon-α in melanoma. Semin Oncol 42(3):436–447

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Aricò E et al (2019) Type I interferons and cancer: an evolving story demanding novel clinical applications. Cancers (Basel) 11(12):1943

    Article  PubMed  Google Scholar 

  35. Tarhini AA et al (2012) Differing patterns of circulating regulatory T cells and myeloid-derived suppressor cells in metastatic melanoma patients receiving anti-CTLA4 antibody and interferon-α or TLR-9 agonist and GM-CSF with peptide vaccination. J Immunother 35(9):702–710

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Al Shboul S et al (2021) Kinomics platform using GBM tissue identifies BTK as being associated with higher patient survival. Life Sci Alliance 4(12):e202101054

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Kononen J et al (1998) Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat Med 4(7):844–847

    Article  PubMed  CAS  Google Scholar 

  38. Purkait S et al (2013) CDKN2A deletion in pediatric versus adult glioblastomas and predictive value of p16 immunohistochemistry. Neuropathology 33(4):405–412

    Article  PubMed  CAS  Google Scholar 

  39. Jubb A, Boyle S (2020) Visualizing genome reorganization using 3D DNA FISH. Methods Mol Biol 2148:85–95

    Article  PubMed  CAS  Google Scholar 

  40. Boyle S et al (2011) Fluorescence in situ hybridization with high-complexity repeat-free oligonucleotide probes generated by massively parallel synthesis. Chromosome Res 19(7):901–909

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Boyle S et al (2020) A central role for canonical PRC1 in shaping the 3D nuclear landscape. Genes Dev 34(13–14):931–949

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Pollard SM et al (2009) Glioma stem cell lines expanded in adherent culture have tumor-specific phenotypes and are suitable for chemical and genetic screens. Cell Stem Cell 4(6):568–580

    Article  PubMed  CAS  Google Scholar 

  43. Schindelin J et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9(7):676–682

    Article  PubMed  CAS  Google Scholar 

  44. Bankhead P et al (2017) QuPath: open source software for digital pathology image analysis. Sci Rep 7(1):16878

    Article  PubMed  PubMed Central  Google Scholar 

  45. Delaunay T et al (2020) Frequent homozygous deletions of type I interferon genes in pleural mesothelioma confer sensitivity to oncolytic measles virus. J Thorac Oncol 15(5):827–842

    Article  PubMed  CAS  Google Scholar 

  46. Kim UJ et al (1992) Stable propagation of cosmid sized human DNA inserts in an F factor based vector. Nucleic Acids Res 20(5):1083–1085

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Brennan PM et al (2021) Second surgery for progressive glioblastoma: a multi-centre questionnaire and cohort-based review of clinical decision-making and patient outcomes in current practice. J Neurooncol 153(1):99–107

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Cottone L et al (2020) Frequent alterations in p16/CDKN2A identified by immunohistochemistry and FISH in chordoma. J Pathol Clin Res 6(2):113–123

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Kamiryo T et al (2002) Analysis of homozygous deletion of the p16 gene and correlation with survival in patients with glioblastoma multiforme. J Neurosurg 96(5):815–822

    Article  PubMed  CAS  Google Scholar 

  50. Burns KL et al (1998) Molecular genetic correlates of p16, cdk4, and pRb immunohistochemistry in glioblastomas. J Neuropathol Exp Neurol 57(2):122–130

    Article  PubMed  CAS  Google Scholar 

  51. Wang H et al (2020) Identification of genomic alterations and associated transcriptomic profiling reveal the prognostic significance of MMP14 and PKM2 in patients with pancreatic cancer. Aging (Albany NY) 12(18):18676–18692

    Article  PubMed  CAS  Google Scholar 

  52. Worst TS et al (2018) CDKN2A as transcriptomic marker for muscle-invasive bladder cancer risk stratification and therapy decision-making. Sci Rep 8(1):14383

    Article  PubMed  PubMed Central  Google Scholar 

  53. Chen Z et al (2021) Comprehensive analysis revealed that CDKN2A is a biomarker for immune infiltrates in multiple cancers. Front Cell Dev Biol 9:808208

    Article  PubMed  PubMed Central  Google Scholar 

  54. Liu W et al (2020) Loss of CDKN2A at chromosome 9 has a poor clinical prognosis and promotes lung cancer progression. Mol Genet Genom Med 8(12):e1521

    Article  CAS  Google Scholar 

  55. Peng Y et al (2022) Co-occurrence of CDKN2A/B and IFN-I homozygous deletions correlates with an immunosuppressive phenotype and poor prognosis in lung adenocarcinoma. Mol Oncol 16(8):1746–1760

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Ye Z et al (2018) Prevalent homozygous deletions of type I interferon and defensin genes in human cancers associate with immunotherapy resistance. Clin Cancer Res 24(14):3299–3308

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Barriga FM et al (2022) MACHETE identifies interferon-encompassing chromosome 9p21.3 deletions as mediators of immune evasion and metastasis. Nature Cancer 3(11):1367–1385

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Nassar A et al (2010) Intratumoral heterogeneity of immunohistochemical marker expression in breast carcinoma: a tissue microarray-based study. Appl Immunohistochem Mol Morphol 18(5):433–441

    Article  PubMed  CAS  Google Scholar 

  59. Kündig P et al (2018) Limited utility of tissue micro-arrays in detecting intra-tumoral heterogeneity in stem cell characteristics and tumor progression markers in breast cancer. J Transl Med 16(1):118

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank Helen Caldwell from the pathology department at the University of Edinburgh for IHC preparation and staining. We would like to thank the members of the Advanced Imaging Resource facility at the Institute of Genetics and Cancer (IGC) for facilitating the use of microscopes and image processing software. The authors are sincerely thankful to NHS Lothian Bioresource for granting access to tissue samples.

Funding

Sofian Al Shboul (SAS) and Tareq Saleh (TS) are supported by the Deanship of Scientific Research, The Hashemite University (SAS: grants no. 785/48/2022 and 738/54/2022; TS: grants no. 465/83/2019 and 418/84/2019).

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Author notes

  1. Shaden Abu Baker

    Present address: Department of Pathology and Microbiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 22110, Jordan

  2. Ola Abu Al Karsaneh

    Present address: Department of Microbiology, Pathology, and Forensic Medicine, Faculty of Medicine, The Hashemite University, Zarqa, 13133, Jordan

Authors and Affiliations

  1. Department of Pharmacology and Public Health, Faculty of Medicine, The Hashemite University, Zarqa, 13133, Jordan

    Sofian Al Shboul, Tareq Saleh, Amel Mryyian & Rand Dawoud

  2. MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Crewe Road, Edinburgh, EH4 2XU, UK

    Shelagh Boyle

  3. Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XR, Scotland, UK

    Ashita Singh, Sinem Gul, Kathryn Ball & Ted Hupp

  4. Department of Pathology and Microbiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 22110, Jordan

    Moath Alrjoub

  5. Translational Neurosurgery, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK

    Paul M. Brennan

  6. Translational Neurosurgery, Department of Clinical Neurosciences, Royal Infirmary of Edinburgh, Edinburgh, EH16 4SB, Scotland, UK

    Paul M. Brennan

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Contributions

SAS: conceptualization, acquiring FFPE FISH and IHC images, analysis of FISH and IHC data, designing and creating the figures and tables, writing—original draft, review, and editing. SB: designed and labelled FISH probes, conducted FISH on the FFPE slides. AS: conceptualization, data analysis and writing original draft. TS: FISH and IHC analysis, designing figures, writing—original draft, review, and editing. MA, OAK and SAB: performed pathological assessment of the protein marker expression. AM and RD: FISH image analysis. SG: performed FISH on cells and obtained the images. KB: conceptualization and supervision of the project. TH: conceptualization, resources, supervision of the project, funding acquisition, analysis of FISH and IHC data, designing and creating the figures and tables, writing—original draft, review, and editing. PMB: conceptualization, resources, acquired the FFPE samples and supervised the construction of the TMA, supervision of the project, analysis of FISH and IHC data, designing and creating the figures and tables, writing—original draft, review, and editing. *All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Sofian Al Shboul, Ted Hupp or Paul M. Brennan.

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All authors have no relevant financial or non-financial interests to disclose.

Ethical approval

GBM FFPE samples used to construct the TMA were obtained under ethical approvals from the regional ethics committee (LREC 115/ES/0094).

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Al Shboul, S., Boyle, S., Singh, A. et al. FISH analysis reveals CDKN2A and IFNA14 co-deletion is heterogeneous and is a prominent feature of glioblastoma. Brain Tumor Pathol 41, 4–17 (2024). https://doi.org/10.1007/s10014-023-00473-6

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