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EGFR amplification and classical subtype are associated with a poor response to bevacizumab in recurrent glioblastoma

  • Koos E. Hovinga
  • Heather J. McCrea
  • Cameron Brennan
  • Jason Huse
  • Junting Zheng
  • Yoshua Esquenazi
  • Katherine S. Panageas
  • Viviane TabarEmail author
Clinical Study

Abstract

Purpose

The highly vascular malignant brain tumor glioblastoma (GBM) appears to be an ideal target for anti-angiogenic therapy; however, clinical trials to date suggest the VEGF antibody bevacizumab affects only progression-free survival. Here we analyze a group of patients with GBM who received bevacizumab treatment at recurrence and are stratified according to tumor molecular and genomic profile (TCGA classification), with the goal of identifying molecular predictors of the response to bevacizumab.

Methods

We performed a retrospective review of patients with a diagnosis of glioblastoma who were treated with bevacizumab in the recurrent setting at our hospital, from 2006 to 2014. Treatment was discontinued by the treating neuro-oncologists, based on clinical and radiographic criteria. Pre- and post-treatment imaging and genomic subtype were available on 80 patients. We analyzed time on bevacizumab and time to progression. EGFR gene amplification was determined by FISH.

Results

Patients with classical tumors had a significantly shorter time on bevacizumab than mesenchymal, and proneural patients (2.7 vs. 5.1 vs. 6.4 and 6.0 months respectively, p = 0.011). Classical subtype and EGFR gene amplification were significantly associated with a shorter time to progression both in univariate (p < 0.001 and p = 0.007, respectively) and multivariate analysis (both p = 0.010).

Conclusion

EGFR gene amplification and classical subtype by TCGA analysis are associated with significantly shorter time to progression for patients with recurrent GBM when treated with bevacizumab. These findings can have a significant impact on decision-making and should be further validated prospectively.

Keywords

Bevacizumab Classical EGFR Glioblastoma Mesenchymal Proneural 

Notes

Compliance with ethical standards

Conflict of interest

All the authors declare that there is no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

The research protocol was submitted to the institutional research board and deemed exempt. No patient consent was required.

Supplementary material

11060_2019_3102_MOESM1_ESM.docx (16 kb)
Supplementary material 1 (DOCX 15 KB)

References

  1. 1.
    Kim KJ, Li B, Winer J et al (1993) Inhibition of vascular endothelial growth factor-induced angiogenesis suppresses tumour growth in vivo. Nature 362(6423):841–844.  https://doi.org/10.1038/362841a0 Google Scholar
  2. 2.
    Ferrara N, Adamis AP (2016) Ten years of anti-vascular endothelial growth factor therapy. Nat Rev Drug Discov 15(6):385.  https://doi.org/10.1038/nrd.2015.17 Google Scholar
  3. 3.
    Thomas AA, Brennan CW, DeAngelis LM, Omuro AM (2014) Emerging therapies for glioblastoma. JAMA Neurol 71(11):1437–1444.  https://doi.org/10.1001/jamaneurol.2014.1701 Google Scholar
  4. 4.
    Gilbert MR, Dignam JJ, Armstrong TS et al (2014) A randomized trial of bevacizumab for newly diagnosed glioblastoma. N Engl J Med 370(8):699–708.  https://doi.org/10.1056/NEJMoa1308573 Google Scholar
  5. 5.
    Chinot OL, Wick W, Mason W et al (2014) Bevacizumab plus radiotherapy-temozolomide for newly diagnosed glioblastoma. N Engl J Med 370(8):709–722.  https://doi.org/10.1056/NEJMoa1308345 Google Scholar
  6. 6.
    Yang S-B, Gao K-D, Jiang T, Cheng S-J, Li W-B (2017) Bevacizumab combined with chemotherapy for glioblastoma: a meta-analysis of randomized controlled trials. Oncotarget 8(34):57337.  https://doi.org/10.18632/oncotarget.16924 Google Scholar
  7. 7.
    Schaub C, Tichy J, Schäfer N et al (2016) Prognostic factors in recurrent glioblastoma patients treated with bevacizumab. J Neurooncol 129(1):93–100.  https://doi.org/10.1007/s11060-016-2144-7 Google Scholar
  8. 8.
    Saran F, Chinot OL, Henriksson R et al (2016) Bevacizumab, temozolomide, and radiotherapy for newly diagnosed glioblastoma: comprehensive safety results during and after first-line therapy. Neuro-Oncol 18(7):991–1001  https://doi.org/10.1093/neuonc/nov300 Google Scholar
  9. 9.
    Laviv Y, Rappaport ZH (2014) Extremely late wound dehiscence following bevazicumab treatment in a long term survival glioblastoma patient. Clin Neurol Neurosurg 127:125–127.  https://doi.org/10.1016/j.clineuro.2014.10.012 Google Scholar
  10. 10.
    Lai A, Tran A, Nghiemphu PL et al (2011) Phase II study of bevacizumab plus temozolomide during and after radiation therapy for patients with newly diagnosed glioblastoma multiforme. J Clin Oncol 29(2):142–148.  https://doi.org/10.1200/JCO.2010.30.2729 Google Scholar
  11. 11.
    Ladha H, Pawar T, Gilbert MR et al (2015) Wound healing complications in brain tumor patients on Bevacizumab. J Neurooncol 124(3):501–506.  https://doi.org/10.1007/s11060-015-1868-0 Google Scholar
  12. 12.
    Friedman HS, Prados MD, Wen PY et al (2009) Bevacizumab alone and in combination with irinotecan in recurrent glioblastoma. J Clin Oncol 27(28):4733–4740.  https://doi.org/10.1200/JCO.2008.19.8721 Google Scholar
  13. 13.
    Chamberlain MC (2011) Radiographic patterns of relapse in glioblastoma. J Neurooncol 101(2):319–323.  https://doi.org/10.1007/s11060-010-0251-4 Google Scholar
  14. 14.
    Mamo A, Baig A, Azam M et al (2016) Progression pattern and adverse events with bevacizumab in glioblastoma. Curr Oncol 23(5):468.  https://doi.org/10.3747/co.23.3108 Google Scholar
  15. 15.
    Blouw B, Song H, Tihan T et al (2003) The hypoxic response of tumors is dependent on their microenvironment. Cancer Cell 4(2):133–146.  https://doi.org/10.1016/S1535-6108(03)00194-6 Google Scholar
  16. 16.
    Brennan CW, Verhaak RGW, McKenna A et al (2013) The somatic genomic landscape of glioblastoma. Cell 155(2):462–477.  https://doi.org/10.1016/j.cell.2013.09.034 Google Scholar
  17. 17.
    Verhaak RGW, Hoadley KA, Purdom E 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.  https://doi.org/10.1016/j.ccr.2009.12.020 Google Scholar
  18. 18.
    Omuro A, DeAngelis LM, KR P et al (2013) Glioblastoma and other malignant gliomas. JAMA 310(17):1842.  https://doi.org/10.1001/jama.2013.280319 Google Scholar
  19. 19.
    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(6):803–820.  https://doi.org/10.1007/s00401-016-1545-1 Google Scholar
  20. 20.
    Erdem-Eraslan L, van den Bent MJ, Hoogstrate Y et al (2016) Identification of patients with recurrent glioblastoma who may benefit from combined bevacizumab and CCNU therapy: a report from the BELOB trial. Cancer Res 76(3):525–534.  https://doi.org/10.1158/0008-5472.CAN-15-0776 Google Scholar
  21. 21.
    Omuro A, Beal K, Gutin P et al (2014) Phase II study of bevacizumab, temozolomide, and hypofractionated stereotactic radiotherapy for newly diagnosed glioblastoma. Clin Cancer Res 20(19):5023–5031.  https://doi.org/10.1158/1078-0432.CCR-14-0822 Google Scholar
  22. 22.
    Kastenhuber ER, Huse JT, Berman SH et al (2014) Quantitative assessment of intragenic receptor tyrosine kinase deletions in primary glioblastomas: their prevalence and molecular correlates. Acta Neuropathol 127(5):747–759.  https://doi.org/10.1007/s00401-013-1217-3 Google Scholar
  23. 23.
    Huang RY, Rahman R, Ballman KV et al (2016) The impact of T2/FLAIR evaluation per RANO criteria on response assessment of recurrent glioblastoma patients treated with Bevacizumab. Clin Cancer Res 22(3):575–581.  https://doi.org/10.1158/1078-0432.CCR-14-3040 Google Scholar
  24. 24.
    Verhoeff JJC, van Tellingen O, Claes A et al (2009) Concerns about anti-angiogenic treatment in patients with glioblastoma multiforme. BMC Cancer 9:444.  https://doi.org/10.1186/1471-2407-9-444 Google Scholar
  25. 25.
    Eskilsson E, Verhaak RGW (2016) Longitudinal genomic characterization of brain tumors for identification of therapeutic vulnerabilities. Neuro Oncol 18(8):1037–1039.  https://doi.org/10.1093/neuonc/now064 Google Scholar
  26. 26.
    Aldape K, Amin SB, Ashley DM et al (2018) Glioma through the looking GLASS: molecular evolution of diffuse gliomas and the Glioma Longitudinal Analysis Consortium. Neuro Oncol 20(7):873–884.  https://doi.org/10.1093/neuonc/noy020 Google Scholar
  27. 27.
    van den Bent MJ, Gao Y, Kerkhof M et al (2015) Changes in the EGFR amplification and EGFRvIII expression between paired primary and recurrent glioblastomas. Neuro Oncol 17(7):935–941.  https://doi.org/10.1093/neuonc/nov013 Google Scholar
  28. 28.
    Phillips HS, Kharbanda S, Chen R et al (2006) Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9(3):157–173.  https://doi.org/10.1016/j.ccr.2006.02.019 Google Scholar
  29. 29.
    Sandmann T, Bourgon R, Garcia J et al (2015) Patients with proneural glioblastoma may derive overall survival benefit from the addition of bevacizumab to first-line radiotherapy and temozolomide: retrospective analysis of the AVAglio trial. J Clin Oncol 33(25):2735–2744.  https://doi.org/10.1200/JCO.2015.61.5005 Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of NeurosurgeryMemorial Sloan Kettering Cancer CenterNew YorkUSA
  2. 2.Department of PathologyM.D. Anderson Cancer CenterHoustonUSA
  3. 3.Department of Epidemiology and BiostatisticsMemorial Sloan Kettering Cancer CenterNew YorkUSA

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