Skip to main content

Advertisement

SpringerLink
Log in

Molecular characteristics and clinical features of multifocal glioblastoma

  • Clinical Study
  • Published:
Journal of Neuro-Oncology Aims and scope Submit manuscript

Abstract

Introduction

Glioblastomas (GBMs) usually occur as a solitary lesion; however, about 0.5–35% present with multiple lesions (M-GBM). The genetic landscape of GBMs have been thoroughly investigated; nevertheless, differences between M-GBM and single-foci GBM (S-GBM) remains unclear. The present study aimed to determine differences in clinical and molecular characteristics between M-GBM and S-GBM.

Methods

A retrospective review of multifocal/multicentric infiltrative gliomas (M-IG) from our institutional database was performed. Demographics, clinical, radiological, and genetic features were obtained and compared between M-GBM IDH-wild type (IDH-WT) vs 193 S-GBM IDH-WT. Mutations were examined by a targeted next-generation sequencing assay interrogating 315 genes.

Results

33M-IG were identified from which 94% were diagnosed as M-GBM IDH-WT, the remaining 6% were diagnosed as astrocytomas IDH-mutant. M-GBM and S-GBM comparison revealed that EGFR alterations were more frequent in M-GBM (65% vs 42% p = 0.019). Furthermore, concomitant EGFR/PTEN alterations were more common in M-GBM vs. S-GBM (36% vs 19%) as well as compared to TCGA (21%). No statistically significant differences in overall survival were observed between M-GBM and S-GBM; however, within the M-GBM cohort, patients harboring KDR alterations had a worse survival (KDR-altered 6.7 vs KDR-WT 16.6 months, p = 0.038).

Conclusions

The results of the present study demonstrate that M-GBM genetically resembles S-GBM, however, M-GBM harbor higher frequency of EGFR alterations and co-occurrence of EGFR/PTEN alterations, which may account for their highly malignant and invasive phenotype. Further study of genetic alterations including differences between multifocal and multicentric GBMs are warranted, which may identify potential targets for this aggressive tumor.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

References

  1. Ostrom QT, Gittleman H, Truitt G et al (2018) CBTRUS Statistical Report: primary brain and other central nervous system tumors diagnosed in the United States in 2011–2015. Neuro-oncology 20:iv1–iv86. https://doi.org/10.1093/neuonc/noy131

    Article  PubMed  PubMed Central  Google Scholar 

  2. Zhu P, Du XL, Zhu J-J, Esquenazi Y (2019) Improved survival of glioblastoma patients treated at academic and high-volume facilities: a hospital-based study from the National Cancer Database. J Neurosurg. https://doi.org/10.3171/2018.10.JNS182247

    Article  PubMed  PubMed Central  Google Scholar 

  3. Ferlay J, Colombet M, Soerjomataram I et al (2019) Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer 144:1941–1953. https://doi.org/10.1002/ijc.31937

    Article  CAS  PubMed  Google Scholar 

  4. Showalter TN, Andrel J, Andrews DW et al (2007) Multifocal glioblastoma multiforme: prognostic factors and patterns of progression. Int J Radiat Oncol Biol Phys 69:820–824. https://doi.org/10.1016/j.ijrobp.2007.03.045

    Article  PubMed  Google Scholar 

  5. Patil CG, Yi A, Elramsisy A et al (2012) Prognosis of patients with multifocal glioblastoma: a case–control study. J Neurosurg 117:705–711. https://doi.org/10.3171/2012.7.JNS12147

    Article  PubMed  Google Scholar 

  6. Thomas RP, Xu LW, Lober RM et al (2013) The incidence and significance of multiple lesions in glioblastoma. J Neurooncol 112:91–97. https://doi.org/10.1007/s11060-012-1030-1

    Article  PubMed  Google Scholar 

  7. Paulsson AK, Holmes JA, Peiffer AM et al (2014) Comparison of clinical outcomes and genomic characteristics of single focus and multifocal glioblastoma. J Neurooncol 119:429–435. https://doi.org/10.1007/s11060-014-1515-1

    Article  PubMed  PubMed Central  Google Scholar 

  8. Liu Q, Liu Y, Li W et al (2015) Genetic, epigenetic, and molecular landscapes of multifocal and multicentric glioblastoma. Acta Neuropathol 130:587–597. https://doi.org/10.1007/s00401-015-1470-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hassaneen W, Levine NB, Suki D et al (2011) Multiple craniotomies in the management of multifocal and multicentric glioblastoma. J Neurosurg 114:576–584. https://doi.org/10.3171/2010.6.JNS091326

    Article  PubMed  Google Scholar 

  10. Yan H, Parsons DW, Jin G et al (2009) Mutations in gliomas. N Engl J Med 360:765–773. https://doi.org/10.1056/NEJMoa0808710

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Brennan CW, Verhaak RGW, McKenna A et al (2013) The somatic genomic landscape of glioblastoma. Cell 155:462. https://doi.org/10.1016/j.cell.2013.09.034

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Brat DJ, Aldape K, Colman H 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 

  13. Harris PA, Taylor R, Thielke R et al (2009) Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 42:377–381. https://doi.org/10.1016/j.jbi.2008.08.010

    Article  PubMed  Google Scholar 

  14. Harris PA, Taylor R, Minor BL et al (2019) The REDCap Consortium: building an international community of software platform partners. J Biomed Inform 95:103208. https://doi.org/10.1016/j.jbi.2019.103208

    Article  PubMed  PubMed Central  Google Scholar 

  15. Louis DN, Perry A, Reifenberger G 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 

  16. Zorofchian S, El-Achi H, Yan Y, et al (2018) Characterization of genomic alterations in primary central nervous system lymphomas. J Neurooncol 140:509–517. https://doi.org/10.1007/s11060-018-2990-6

    Article  CAS  Google Scholar 

  17. Frampton GM, Fichtenholtz A, Otto GA et al (2013) Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol 31:1023–1031. https://doi.org/10.1038/nbt.2696

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Schwaederle M, Krishnamurthy N, Daniels GA et al (2018) Telomerase reverse transcriptase promoter alterations across cancer types as detected by next-generation sequencing: a clinical and molecular analysis of 423 patients. Cancer 124:1288–1296. https://doi.org/10.1002/cncr.31175

    Article  CAS  PubMed  Google Scholar 

  19. McLendon R, Friedman A, Bigner D et al (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455:1061–1068. https://doi.org/10.1038/nature07385

    Article  CAS  Google Scholar 

  20. Gao J, Aksoy BA, Dogrusoz U et al (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6:1–20. https://doi.org/10.1126/scisignal.2004088

    Article  CAS  Google Scholar 

  21. Nonoguchi N, Ohta T, Eun J (2013) TERT promoter mutations in primary and secondary glioblastomas. 931–937. https://doi.org/10.1007/s00401-013-1163-0

  22. Cerami E, Gao J, Dogrusoz U 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 

  23. Thakkar JP, Dolecek TA, Horbinski C et al (2014) Epidemiologic and molecular prognostic review of glioblastoma. Cancer Epidemiol Biomark Prev 23:1985–1996. https://doi.org/10.1158/1055-9965.EPI-14-0275

    Article  CAS  Google Scholar 

  24. Kanda Y (2013) Investigation of the freely available easy-to-use software “EZR” for medical statistics. Bone Marrow Transplant 48:452–458. https://doi.org/10.1038/bmt.2012.244

    Article  CAS  PubMed  Google Scholar 

  25. Stupp R, Mason WP, van den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996. https://doi.org/10.1056/NEJMoa043330

    Article  CAS  PubMed  Google Scholar 

  26. Park YW, Han K, Ahn SS et al (2018) Prediction of IDH1-mutation and 1p/19q-codeletion status using preoperative MR imaging phenotypes in lower grade gliomas. Am J Neuroradiol 39:37–42. https://doi.org/10.3174/ajnr.A5421

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Karlowee V, Amatya VJ, Hirano H et al (2017) Multicentric glioma develops via a mutant IDH1-independent pathway: immunohistochemical study of multicentric glioma. Pathobiology 84:99–107. https://doi.org/10.1159/000447951

    Article  CAS  PubMed  Google Scholar 

  28. Heaphy CM, De Wilde RF, Jiao Y et al (2011) Altered telomeres in tumors with ATRX and DAXX mutations. Science (80-) 333:425. https://doi.org/10.1126/science.1207313

    Article  CAS  Google Scholar 

  29. Pekmezci M, Rice T, Molinaro AM 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 

  30. Abou-El-Ardat K, Seifert M, Becker K et al (2017) Comprehensive molecular characterization of multifocal glioblastoma proves its monoclonal origin and reveals novel insights into clonal evolution and heterogeneity of glioblastomas. Neuro-oncology 19:546–557. https://doi.org/10.1093/neuonc/now231

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Parsons DW, Jones S, Zhang X et al (2008) An integrated genomic analysis of human glioblastoma multiforme. Science (80-) 321:1807–1812. https://doi.org/10.1126/science.1164382

    Article  CAS  Google Scholar 

  32. Lassman AB, Van Den Bent MJ, Gan HK et al (2019) Safety and efficacy of depatuxizumab mafodotin + temozolomide in patients with EGFR-amplified, recurrent glioblastoma: results from an international phase I multicenter trial. Neuro-oncology 21:106–114. https://doi.org/10.1093/neuonc/noy091

    Article  CAS  PubMed  Google Scholar 

  33. Lassman AB, Pugh SL, Wang TJC, Aldape K, Gan HK, Preusser M, Vogelbaum MA, Sulman E, Won M, Zhang P, Moazami G, Macsai MS, Gilbert MR, Bain E, Blot V, Ansell PJ, Samanta S, Kundu MG, Seidel C, de Vos FY, Hsu S, Cardona AF, Lombardi G, Bentsion D, Peterson R, Gedye C, Lebrun-Frenay C, Wick A, Curran WJ, Mehta M (2019) Epidermal Growth Factor Receptor (EGFR) amplified (amp) newly diagnosed glioblastoma (nGBM). In: Paper presented at the annual meeting of Society of Neuro-Oncology, Phoenix, AZ

  34. Talasila KM, Soentgerath A, Euskirchen P, et al (2013) EGFR wild-type amplification and activation promote invasion and development of glioblastoma independent of angiogenesis. 683–698. https://doi.org/10.1007/s00401-013-1101-1

  35. Syed M, Liermann J, Verma V et al (2018) Survival and recurrence patterns of multifocal glioblastoma after radiation therapy. Cancer Manag Res 10:4229–4235. https://doi.org/10.2147/CMAR.S165956

    Article  PubMed  PubMed Central  Google Scholar 

  36. Pérez-Beteta J, Molina-García D, Villena M et al (2019) Morphologic features on MR imaging classify multifocal glioblastomas in different prognostic groups. Am J Neuroradiol 40:634–640. https://doi.org/10.3174/ajnr.A6019

    Article  PubMed  PubMed Central  Google Scholar 

  37. Singh G, Mehrotra A, Das K et al (2015) Multiple glioblastomas: are they different from their solitary counterparts? Asian J Neurosurg 10:266. https://doi.org/10.4103/1793-5482.162685

    Article  PubMed  PubMed Central  Google Scholar 

  38. Burger MC, Breuer S, Cieplik HC et al (2017) Bevacizumab for patients with recurrent multifocal glioblastomas. Int J Mol Sci 18:1–11. https://doi.org/10.3390/ijms18112469

    Article  CAS  Google Scholar 

  39. Vasconcelos VCA, Lourenço GJ, Brito ABC et al (2019) Associations of VEGFA and KDR single-nucleotide polymorphisms and increased risk and aggressiveness of high-grade gliomas. Tumor Biol 41:1–10. https://doi.org/10.1177/1010428319872092

    Article  CAS  Google Scholar 

  40. Zhang SD, Leung KL, McCrudden CM, Kwok HF (2015) The prognostic significance of combining VEGFA, FLT1 and KDR mRNA expressions in brain tumors. J Cancer 6:812–818. https://doi.org/10.7150/jca.11975

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Sjöström S, Wibom C, Andersson U et al (2011) Genetic variations in VEGF and VEGFR2 and glioblastoma outcome. J Neurooncol 104:523–527. https://doi.org/10.1007/s11060-010-0504-2

    Article  CAS  PubMed  Google Scholar 

  42. Wu HB, Yang S, Weng HY et al (2017) Autophagy-induced KDR/VEGFR-2 activation promotes the formation of vasculogenic mimicry by glioma stem cells. Autophagy 13:1528–1542. https://doi.org/10.1080/15548627.2017.1336277

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hovinga KE, McCrea HJ, Brennan C et al (2019) EGFR amplification and classical subtype are associated with a poor response to bevacizumab in recurrent glioblastoma. J Neurooncol 142:337–345. https://doi.org/10.1007/s11060-019-03102-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Michaelsen SR, Staberg M, Pedersen H et al (2018) VEGF-C sustains VEGFR2 activation under bevacizumab therapy and promotes glioblastoma maintenance. Neuro-oncology 20:1462–1474. https://doi.org/10.1093/neuonc/noy103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zhang SD, McCrudden CM, Meng C et al (2015) The significance of combining VEGFA, FLT1, and KDR expressions in colon cancer patient prognosis and predicting response to bevacizumab. Oncotargets Ther 8:835–843. https://doi.org/10.2147/OTT.S80518

    Article  CAS  Google Scholar 

Download references

Acknowledgements

None.

Funding

No funding to disclose.

Author information

Authors and Affiliations

  1. Vivian L. Smith Department of Neurosurgery, The University of Texas Health Science Center at Houston, Houston, TX, USA

    Antonio Dono, Nitin Tandon, Leomar Y. Ballester & Yoshua Esquenazi

  2. Department of Pathology and Laboratory Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA

    Antonio Dono & Leomar Y. Ballester

  3. Rice University, Houston, TX, USA

    Emily Wang & Arvind V. Ramesh

  4. Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, USA

    Victor Lopez-Rivera

  5. Memorial Hermann Hospital-TMC, Houston, TX, USA

    Nitin Tandon, Leomar Y. Ballester & Yoshua Esquenazi

  6. Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA

    Yoshua Esquenazi

Authors
  1. Antonio Dono
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emily Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Victor Lopez-Rivera
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arvind V. Ramesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nitin Tandon
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Leomar Y. Ballester
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yoshua Esquenazi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Study design: AD, LYB, and YE. Data recollection: AD, EW, and AR Data analysis: AD and VLR. Manuscript writing: AD, EW, and YE. Manuscript revision and editing: AD, NT, LYB, and YE. Study supervision: LYB and YE. Approved final manuscript: all authors.

Corresponding authors

Correspondence to Leomar Y. Ballester or Yoshua Esquenazi.

Ethics declarations

Conflict of interest

The authors declared no conflict of interest.

Ethical approval

This retrospective study was approved by the Institutional Review Board of The University of Texas Health Science Center at Houston and Memorial Hermann Hospital, Houston, TX following the 1964 Helsinki declaration and its later amendments.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dono, A., Wang, E., Lopez-Rivera, V. et al. Molecular characteristics and clinical features of multifocal glioblastoma. J Neurooncol 148, 389–397 (2020). https://doi.org/10.1007/s11060-020-03539-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11060-020-03539-z

Keywords

Search

Navigation