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TERT promoter mutation status is necessary and sufficient to diagnose IDH-wildtype diffuse astrocytic glioma with molecular features of glioblastoma

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Abstract

The Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) update 3 recommends that histologic grade II and III IDH-wildtype diffuse astrocytic gliomas that harbor EGFR amplification, the combination of whole chromosome 7 gain and whole chromosome 10 loss (7 + /10 −), or TERT promoter (pTERT) mutations should be considered as glioblastomas (GBM), World Health Organization grade IV. In this retrospective study, we examined the utility of molecular classification based on pTERT status and copy-number alterations (CNAs) in IDH-wildtype lower grade gliomas (LGGs, grade II, and III). The impact on survival was evaluated for the pTERT mutation and CNAs, including EGFR gain/amplification, PTEN loss, CDKN2A homozygous deletion, and PDGFRA gain/amplification. We analyzed 46 patients with IDH-wildtype/pTERT-mutant (mut) LGGs and 85 with IDH-wildtype/pTERT-wildtype LGGs. EGFR amplification and a combination of EGFR gain and PTEN loss (EGFR + /PTEN −) were significantly more frequent in pTERT-mut patients (p < 0.0001). Cox regression analysis showed that the pTERT mutation was a significant predictor of poor prognosis (hazard ratio [HR] 2.79, 95% confidence interval [CI] 1.55–4.89, p = 0.0008), but neither EGFR amplification nor EGFR + /PTEN − was an independent prognostic factor in IDH-wildtype LGGs. PDGFRA gain/amplification was a significant poor prognostic factor in IDH-wildtype/pTERT-wildtype LGGs (HR 2.44, 95% CI 1.09–5.27, p = 0.03, Cox regression analysis). The IDH-wildtype LGGs with either pTERT-mut or PDGFRA amplification were mostly clustered with GBM by DNA methylation analysis. Thus, our study suggests that analysis of pTERT mutation status is necessary and sufficient to diagnose IDH-wildtype diffuse astrocytic gliomas with molecular features of glioblastoma. The PDGFRA status may help further delineate IDH-wildtype/pTERT-wildtype LGGs. Methylation profiling showed that IDH-wildtype LGGs without molecular features of GBM were a heterogeneous group of tumors. Some of them did not fall into existing categories and had significantly better prognoses than those clustered with GBM.

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References

  1. Aoki K, Nakamura H, Suzuki H, Matsuo K, Kataoka K, Shimamura T et al (2018) Prognostic relevance of genetic alterations in diffuse lower-grade gliomas. Neuro Oncol 20:66–77. https://doi.org/10.1093/neuonc/nox132

    Article  CAS  PubMed  Google Scholar 

  2. Arita H, Narita Y, Takami H, Fukushima S, Matsushita Y, Yoshida A et al (2013) TERT promoter mutations rather than methylation are the main mechanism for TERT upregulation in adult gliomas. Acta Neuropathol 126:939–941. https://doi.org/10.1007/s00401-013-1203-9

    Article  PubMed  Google Scholar 

  3. Arita H, Yamasaki K, Matsushita Y, Nakamura T, Shimokawa A, Takami H et al (2016) A combination of TERT promoter mutation and MGMT methylation status predicts clinically relevant subgroups of newly diagnosed glioblastomas. Acta Neuropathol Commun 4:79. https://doi.org/10.1186/s40478-016-0351-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Brat DJ, Aldape K, Colman H, Figrarella-Branger D, Fuller GN, Giannini C et al (2020) cIMPACT-NOW update 5: recommended grading criteria and terminologies for IDH-mutant astrocytomas. Acta Neuropathol 139:603–608. https://doi.org/10.1007/s00401-020-02127-9

    Article  PubMed  PubMed Central  Google Scholar 

  5. Brat DJ, Aldape K, Colman H, Holland EC, Louis DN, Jenkins RB et al (2018) cIMPACT-NOW update 3: recommended diagnostic criteria for “Diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma, WHO grade IV.” Acta Neuropathol 136:805–810. https://doi.org/10.1007/s00401-018-1913-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Brat DJ, Verhaak RG, Aldape KD, Yung WK, Salama SR, Cooper LA et al (2015) Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med 372:2481–2498. https://doi.org/10.1056/NEJMoa1402121

    Article  CAS  PubMed  Google Scholar 

  7. Brennan CW, Verhaak RG, McKenna A, Campos B, Noushmehr H, Salama SR et al (2013) The somatic genomic landscape of glioblastoma. Cell 155:462–477. https://doi.org/10.1016/j.cell.2013.09.034

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D, Sturm D et al (2018) DNA methylation-based classification of central nervous system tumours. Nature 555:469–474. https://doi.org/10.1038/nature26000

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ceccarelli M, Barthel FP, Malta TM, Sabedot TS, Salama SR, Murray BA et al (2016) Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell 164:550–563. https://doi.org/10.1016/j.cell.2015.12.028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA et al (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2:401–404. https://doi.org/10.1158/2159-8290.CD-12-0095

    Article  PubMed  Google Scholar 

  11. Eckel-Passow JE, Lachance DH, Molinaro AM, Walsh KM, Decker PA, Sicotte H et al (2015) Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 372:2499–2508. https://doi.org/10.1056/NEJMoa1407279

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO et al (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6:pl1. https://doi.org/10.1126/scisignal.2004088

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Jeuken J, Cornelissen S, Boots-Sprenger S, Gijsen S, Wesseling P (2006) Multiplex ligation-dependent probe amplification: a diagnostic tool for simultaneous identification of different genetic markers in glial tumors. J Mol Diagn 8:433–443. https://doi.org/10.2353/jmoldx.2006.060012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Johann PD, Jager N, Pfister SM, Sill M (2019) RF_Purify: a novel tool for comprehensive analysis of tumor-purity in methylation array data based on random forest regression. BMC Bioinform 20:428. https://doi.org/10.1186/s12859-019-3014-z

    Article  CAS  Google Scholar 

  15. Jonsson P, Lin AL, Young RJ, DiStefano NM, Hyman DM, Li BT et al (2019) Genomic correlates of disease progression and treatment response in prospectively characterized gliomas. Clin Cancer Res 25:5537–5547. https://doi.org/10.1158/1078-0432.CCR-19-0032

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820. https://doi.org/10.1007/s00401-016-1545-1

    Article  PubMed  Google Scholar 

  17. Mulholland S, Pearson DM, Hamoudi RA, Malley DS, Smith CM, Weaver JM et al (2012) MGMT CpG island is invariably methylated in adult astrocytic and oligodendroglial tumors with IDH1 or IDH2 mutations. Int J Cancer 131:1104–1113. https://doi.org/10.1002/ijc.26499

    Article  CAS  PubMed  Google Scholar 

  18. Pekmezci M, Rice T, Molinaro AM, Walsh KM, Decker PA, Hansen H et al (2017) Adult infiltrating gliomas with WHO 2016 integrated diagnosis: additional prognostic roles of ATRX and TERT. Acta Neuropathol 133:1001–1016. https://doi.org/10.1007/s00401-017-1690-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Phillips JJ, Aranda D, Ellison DW, Judkins AR, Croul SE, Brat DJ et al (2013) PDGFRA amplification is common in pediatric and adult high-grade astrocytomas and identifies a poor prognostic group in IDH1 mutant glioblastoma. Brain Pathol 23:565–573. https://doi.org/10.1111/bpa.12043

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Reuss DE, Kratz A, Sahm F, Capper D, Schrimpf D, Koelsche C et al (2015) Adult IDH wild type astrocytomas biologically and clinically resolve into other tumor entities. Acta Neuropathol 130:407–417. https://doi.org/10.1007/s00401-015-1454-8

    Article  CAS  PubMed  Google Scholar 

  21. Richardson TE, Hatanpaa KJ, Walker JM (2021) Molecular characterization of “true” low-grade IDH-wildtype astrocytomas. J Neuropathol Exp Neurol 80:431–435. https://doi.org/10.1093/jnen/nlab023

    Article  PubMed  Google Scholar 

  22. Shirahata M, Ono T, Stichel D, Schrimpf D, Reuss DE, Sahm F et al (2018) Novel, improved grading system(s) for IDH-mutant astrocytic gliomas. Acta Neuropathol 136:153–166. https://doi.org/10.1007/s00401-018-1849-4

    Article  CAS  PubMed  Google Scholar 

  23. Stichel D, Ebrahimi A, Reuss D, Schrimpf D, Ono T, Shirahata M et al (2018) Distribution of EGFR amplification, combined chromosome 7 gain and chromosome 10 loss, and TERT promoter mutation in brain tumors and their potential for the reclassification of IDHwt astrocytoma to glioblastoma. Acta Neuropathol 136:793–803. https://doi.org/10.1007/s00401-018-1905-0

    Article  PubMed  Google Scholar 

  24. Umehara T, Arita H, Yoshioka E, Shofuda T, Kanematsu D, Kinoshita M et al (2019) Distribution differences in prognostic copy number alteration profiles in IDH-wild-type glioblastoma cause survival discrepancies across cohorts. Acta Neuropathol Commun 7:99. https://doi.org/10.1186/s40478-019-0749-8

    Article  CAS  PubMed  Google Scholar 

  25. van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605

    Google Scholar 

  26. Weller M, Weber RG, Willscher E, Riehmer V, Hentschel B, Kreuz M et al (2015) Molecular classification of diffuse cerebral WHO grade II/III gliomas using genome- and transcriptome-wide profiling improves stratification of prognostically distinct patient groups. Acta Neuropathol 129:679–693. https://doi.org/10.1007/s00401-015-1409-0

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thank all clinicians who took care of the patients and contributed to this study by providing specimens and clinical information. We would like to thank Editage (www.editage.com) for English language editing. This work was supported partially by JSPS KAKENHI Grant Number 25462283 (K.I.) and Practical Research for Innovative Cancer Control program of the Japan Agency for Medical Research and Development, Grant/Award Number: 17ck0106140 h0003 (K.I.).

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Authors and Affiliations

  1. Division of Brain Tumor Translational Research, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan

    Kenji Fujimoto, Hideyuki Arita, Kaishi Satomi, Kai Yamasaki, Yuko Matsushita, Taishi Nakamura & Koichi Ichimura

  2. Department of Neurosurgery, Graduate School of Life Sciences, Kumamoto University, Kumamoto, Japan

    Kenji Fujimoto

  3. Department of Neurosurgery, Osaka University Graduate School of Medicine, Osaka, Japan

    Hideyuki Arita & Toru Umehara

  4. Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, Japan

    Kaishi Satomi

  5. Department of Pediatric Hematology and Oncology, Osaka City General Hospital, Osaka, Japan

    Kai Yamasaki

  6. Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, Tokyo, Japan

    Yuko Matsushita, Yasuji Miyakita & Yoshitaka Narita

  7. Department of Neurosurgery, Graduate School of Medicine, Yokohama City University, Yokohama, Japan

    Taishi Nakamura

  8. Department of Neurosurgery, Faculty of Medicine, Kyorin University, Tokyo, Japan

    Keiichi Kobayashi & Motoo Nagane

  9. Department of Neurosurgery, Tokyo Medical and Dental University, Tokyo, Japan

    Kaoru Tamura & Taketoshi Maehara

  10. Department of Neurosurgery, The University of Tokyo, Tokyo, Japan

    Shota Tanaka

  11. Department of Neurosurgery, Dokkyo Medical University, Tochigi, Japan

    Fumi Higuchi

  12. Department of Neurosurgery, Osaka International Cancer Institute, Osaka, Japan

    Yoshiko Okita

  13. Department of Biomedical Research and Innovation, Institute for Clinical Research, National Hospital Organization Osaka National Hospital, Osaka, Japan

    Yonehiro Kanemura

  14. Department of Neurological Surgery, Wakayama Medical University, Wakayama, Japan

    Junya Fukai

  15. Department of Neurosurgery, Hyogo College of Medicine, Hyogo, Japan

    Daisuke Sakamoto

  16. Department of Neurosurgery, Osaka City University Graduate School of Medicine, Osaka, Japan

    Takehiro Uda

  17. Biostatistics Division, Center for Research Administration and Support, National Cancer Center, Tokyo, Japan

    Ryunosuke Machida

  18. Department of Neuro-Oncology/Neurosurgery, Saitama Medical University International Medical Center, Saitama, Japan

    Aya Kuchiba & Ryo Nishikawa

  19. Department of Pathology and Laboratory Medicine, National Hospital Organization, Sendai Medical Center, Sendai, Japan

    Hiroyoshi Suzuki

  20. Central Clinical Laboratory, Hachioji Medical Center, Tokyo Medical University, Tokyo, Japan

    Makoto Shibuya

  21. Department of Laboratory Medicine and Pathology (Neuropathology), Tokyo Metropolitan Neurological Hospital, Tokyo, Japan

    Takashi Komori

  22. Department of Brain Disease Translational Research, Juntendo University Faculty of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan

    Koichi Ichimura

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  1. Kenji Fujimoto
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Correspondence to Koichi Ichimura.

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Fujimoto, K., Arita, H., Satomi, K. et al. TERT promoter mutation status is necessary and sufficient to diagnose IDH-wildtype diffuse astrocytic glioma with molecular features of glioblastoma. Acta Neuropathol 142, 323–338 (2021). https://doi.org/10.1007/s00401-021-02337-9

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  • DOI: https://doi.org/10.1007/s00401-021-02337-9

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