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Spatial and quantitative mapping of glycolysis and hypoxia in glioblastoma as a predictor of radiotherapy response and sites of relapse

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Abstract

Introduction

Tumor hypoxia is a centerpiece of disease progression mechanisms such as neoangiogenesis or aggressive hypoxia-resistant malignant cells selection that impacts on radiotherapy strategies. Early identification of regions at risk for recurrence and prognostic-based classification of patients is a necessity to devise tailored therapeutic strategies. We developed an image-based algorithm to spatially map areas of aerobic and anaerobic glycolysis (Glyoxia).

Methods

18F-FDG and 18F-FMISO PET studies were used in the algorithm to produce DICOM-co-registered representations and maximum intensity projections combined with quantitative analysis of hypoxic volume (HV), hypoxic glycolytic volume (HGV), and anaerobic glycolytic volume (AGV) with CT/MRI co-registration. This was applied to a prospective clinical trial of 10 glioblastoma patients with post-operative, pre-radiotherapy, and early post-radiotherapy 18F-FDG and 18F-FMISO PET and MRI studies.

Results

In the 10 glioblastoma patients (5M:5F; age range 51–69 years), 14/18 18F-FMISO PET studies showed detectable hypoxia. Seven patients survived to complete post-radiotherapy studies. The patient with the longest overall survival showed non-detectable hypoxia in both pre-radiotherapy and post-radiotherapy 18F-FMISO PET. The three patients with increased HV, HGV, and AGV volumes after radiotherapy showed 2.8 months mean progression-free interval vs. 5.9 months for the other 4 patients. These parameters correlated at that time point with progression-free interval. Parameters combining hypoxia and glycolytic information (i.e., HGV and AGV) showed more prominent variation than hypoxia-based information alone (HV). Glyoxia-generated images were consistent with disease relapse topology; in particular, one patient had distant relapse anticipated by HV, HGV, and AGV maps.

Conclusion

Spatial mapping of aerobic and anaerobic glycolysis allows unique information on tumor metabolism and hypoxia to be evaluated with PET, providing a greater understanding of tumor biology and potential response to therapy.

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References

  1. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352:987–96. https://doi.org/10.1056/NEJMoa043330.

    Article  CAS  Google Scholar 

  2. Chen W. Clinical applications of PET in brain tumors. J Nucl Med. 2007;48:1468–81. https://doi.org/10.2967/jnumed.106.037689.

    Article  PubMed  Google Scholar 

  3. Galldiks N, Langen KJ, Holy R, Pinkawa M, Stoffels G, Nolte KW, et al. Assessment of treatment response in patients with glioblastoma using O-(2-18F-fluoroethyl)-L-tyrosine PET in comparison to MRI. J Nucl Med. 2012;53:1048–57. https://doi.org/10.2967/jnumed.111.098590.

    Article  CAS  PubMed  Google Scholar 

  4. Warburg O, Wind F, Negelein E. The metabolism of tumors in the body. J Gen Physiol. 1927;8:519–30. https://doi.org/10.1085/jgp.8.6.519.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Gray LH, Conger AD, Ebert M, Hornsey S, Scott OC. The concentration of oxygen dissolved in tissues at the time of irradiation as a factor in radiotherapy. Br J Radiol. 1953;26:638–48. https://doi.org/10.1259/0007-1285-26-312-638.

    Article  CAS  PubMed  Google Scholar 

  6. Rich JN. Cancer stem cells in radiation resistance. Cancer Res. 2007;67:8980–4. https://doi.org/10.1158/0008-5472.CAN-07-0895.

    Article  CAS  PubMed  Google Scholar 

  7. Monteiro AR, Hill R, Pilkington GJ, Madureira PA. the role of hypoxia in glioblastoma invasion. Cells. 2017;6. https://doi.org/10.3390/cells6040045.

  8. Yu L, Chen X, Wang L, Chen S. The sweet trap in tumors: aerobic glycolysis and potential targets for therapy. Oncotarget. 2016;7:38908–26. https://doi.org/10.18632/oncotarget.7676.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Ricci PE, Karis JP, Heiserman JE, Fram EK, Bice AN, Drayer BP. Differentiating recurrent tumor from radiation necrosis: time for re-evaluation of positron emission tomography? AJNR Am J Neuroradiol. 1998;19:407–13.

    CAS  PubMed  Google Scholar 

  10. Lee ST, Scott AM. Hypoxia positron emission tomography imaging with 18f-fluoromisonidazole. Semin Nucl Med. 2007;37:451–61. https://doi.org/10.1053/j.semnuclmed.2007.07.001.

    Article  PubMed  Google Scholar 

  11. Bell C, Dowson N, Fay M, Thomas P, Puttick S, Gal Y, et al. Hypoxia imaging in gliomas with 18F-fluoromisonidazole PET: toward clinical translation. Semin Nucl Med. 2015;45:136–50. https://doi.org/10.1053/j.semnuclmed.2014.10.001.

    Article  PubMed  Google Scholar 

  12. Gerard M, Corroyer-Dulmont A, Lesueur P, Collet S, Cherel M, Bourgeois M, et al. Hypoxia imaging and adaptive radiotherapy: a state-of-the-art approach in the management of glioma. Front Med (Lausanne). 2019;6:117. https://doi.org/10.3389/fmed.2019.00117.

    Article  Google Scholar 

  13. Laprie A, Ken S, Filleron T, Lubrano V, Vieillevigne L, Tensaouti F, et al. Dose-painting multicenter phase III trial in newly diagnosed glioblastoma: the SPECTRO-GLIO trial comparing arm A standard radiochemotherapy to arm B radiochemotherapy with simultaneous integrated boost guided by MR spectroscopic imaging. BMC Cancer. 2019;19:167. https://doi.org/10.1186/s12885-019-5317-x.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Lim JL, Berridge MS. An efficient radiosynthesis of [18F]fluoromisonidazole. Appl Radiat Isot. 1993;44:1085–91.

    Article  CAS  Google Scholar 

  15. Macdonald DR, Cascino TL, Schold SC Jr, Cairncross JG. Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol. 1990;8:1277–80. https://doi.org/10.1200/JCO.1990.8.7.1277.

    Article  CAS  PubMed  Google Scholar 

  16. Rosset A, Spadola L, Ratib O. OsiriX: an open-source software for navigating in multidimensional DICOM images. J Digit Imaging. 2004;17:205–16. https://doi.org/10.1007/s10278-004-1014-6.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Klein S, Staring M, Murphy K, Viergever MA, Pluim JP. elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging. 2010;29:196–205. https://doi.org/10.1109/TMI.2009.2035616.

    Article  PubMed  Google Scholar 

  18. Cher LM, Murone C, Lawrentschuk N, Ramdave S, Papenfuss A, Hannah A, et al. Correlation of hypoxic cell fraction and angiogenesis with glucose metabolic rate in gliomas using 18F-fluoromisonidazole, 18F-FDG PET, and immunohistochemical studies. J Nucl Med. 2006;47:410–8.

    CAS  PubMed  Google Scholar 

  19. Chaddad A, Kucharczyk MJ, Daniel P, Sabri S, Jean-Claude BJ, Niazi T, et al. Radiomics in glioblastoma: current status and challenges facing clinical implementation. Front Oncol. 2019;9:374. https://doi.org/10.3389/fonc.2019.00374.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Shaver MM, Kohanteb PA, Chiou C, Bardis MD, Chantaduly C, Bota D, et al. Optimizing neuro-oncology imaging: a review of deep learning approaches for glioma imaging. Cancers (Basel). 2019;11. doi:https://doi.org/10.3390/cancers11060829.

  21. Chang K, Bai HX, Zhou H, Su C, Bi WL, Agbodza E, et al. Residual convolutional neural network for the determination of IDH status in low- and high-grade gliomas from MR imaging. Clin Cancer Res. 2018;24:1073–81. https://doi.org/10.1158/1078-0432.CCR-17-2236.

    Article  CAS  PubMed  Google Scholar 

  22. Chang P, Grinband J, Weinberg BD, Bardis M, Khy M, Cadena G, et al. Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas. AJNR Am J Neuroradiol. 2018;39:1201–7. https://doi.org/10.3174/ajnr.A5667.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hatt M, Lamare F, Boussion N, Turzo A, Collet C, Salzenstein F, et al. Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET. Phys Med Biol. 2007;52:3467–91. https://doi.org/10.1088/0031-9155/52/12/010.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Swanson KR, Chakraborty G, Wang CH, Rockne R, Harpold HL, Muzi M, et al. Complementary but distinct roles for MRI and 18F-fluoromisonidazole PET in the assessment of human glioblastomas. J Nucl Med. 2009;50:36–44. https://doi.org/10.2967/jnumed.108.055467.

    Article  PubMed  Google Scholar 

  25. De Clermont H, Huchet A, Lamare F, Riviere A, Fernandez P. Lack of concordance between the F-18 fluoromisonidazole PET and the F-18 FDG PET in human glioblastoma. Clin Nucl Med. 2011;36:e194–5. https://doi.org/10.1097/RLU.0b013e3182335e20.

    Article  PubMed  Google Scholar 

  26. Rajendran JG, Mankoff DA, O’Sullivan F, Peterson LM, Schwartz DL, Conrad EU, et al. Hypoxia and glucose metabolism in malignant tumors: evaluation by [18F]fluoromisonidazole and [18F]fluorodeoxyglucose positron emission tomography imaging. Clin Cancer Res. 2004;10:2245–52.

    Article  CAS  Google Scholar 

  27. Zimny M, Gagel B, DiMartino E, Hamacher K, Coenen HH, Westhofen M, et al. FDG--a marker of tumour hypoxia? A comparison with [18F]fluoromisonidazole and pO2-polarography in metastatic head and neck cancer. Eur J Nucl Med Mol Imaging. 2006;33:1426–31. https://doi.org/10.1007/s00259-006-0175-6.

    Article  CAS  PubMed  Google Scholar 

  28. Spence AM, Muzi M, Swanson KR, O’Sullivan F, Rockhill JK, Rajendran JG, et al. Regional hypoxia in glioblastoma multiforme quantified with [18F]fluoromisonidazole positron emission tomography before radiotherapy: correlation with time to progression and survival. Clin Cancer Res. 2008;14:2623–30. https://doi.org/10.1158/1078-0432.CCR-07-4995.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Cuneo KC, Vredenburgh JJ, Sampson JH, Reardon DA, Desjardins A, Peters KB, et al. Safety and efficacy of stereotactic radiosurgery and adjuvant bevacizumab in patients with recurrent malignant gliomas. Int J Radiat Oncol Biol Phys. 2012;82:2018–24. https://doi.org/10.1016/j.ijrobp.2010.12.074.

    Article  CAS  PubMed  Google Scholar 

  30. Fabrini MG, Perrone F, De Franco L, Pasqualetti F, Grespi S, Vannozzi R, et al. Perioperative high-dose-rate brachytherapy in the treatment of recurrent malignant gliomas. Strahlenther Onkol. 2009;185:524–9. https://doi.org/10.1007/s00066-009-1965-0.

    Article  PubMed  Google Scholar 

  31. Helseth R, Helseth E, Johannesen TB, Langberg CW, Lote K, Ronning P, et al. Overall survival, prognostic factors, and repeated surgery in a consecutive series of 516 patients with glioblastoma multiforme. Acta Neurol Scand. 2010;122:159–67. https://doi.org/10.1111/j.1600-0404.2010.01350.x.

    Article  CAS  PubMed  Google Scholar 

  32. Patel M, Siddiqui F, Jin JY, Mikkelsen T, Rosenblum M, Movsas B, et al. Salvage reirradiation for recurrent glioblastoma with radiosurgery: radiographic response and improved survival. J Neuro-Oncol. 2009;92:185–91. https://doi.org/10.1007/s11060-008-9752-9.

    Article  Google Scholar 

  33. Villavicencio AT, Burneikiene S, Romanelli P, Fariselli L, McNeely L, Lipani JD, et al. Survival following stereotactic radiosurgery for newly diagnosed and recurrent glioblastoma multiforme: a multicenter experience. Neurosurg Rev. 2009;32:417–24. https://doi.org/10.1007/s10143-009-0212-6.

    Article  PubMed  Google Scholar 

  34. Chen W, Cloughesy T, Kamdar N, Satyamurthy N, Bergsneider M, Liau L, et al. Imaging proliferation in brain tumors with 18F-FLT PET: comparison with 18F-FDG. J Nucl Med. 2005;46:945–52.

    CAS  PubMed  Google Scholar 

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Author information

Author notes

  1. Antoine Leimgruber and Kevin Hickson contributed equally to this work.

Authors and Affiliations

  1. Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, Victoria, Australia

    Antoine Leimgruber, Kevin Hickson, Sze Ting Lee, John I Sachinidis, Graeme J O’Keefe & Andrew M Scott

  2. Service of Medical Imaging, Hospital Riviera Chablais, Rennaz, Switzerland

    Antoine Leimgruber & Hui K Gan

  3. Medical Physics and Radiation Safety, South Australia Medical Imaging, Adelaide, Australia

    Kevin Hickson

  4. Tumour Targeting Laboratory, Olivia Newton-John Cancer Research Institute, Austin Health, Heidelberg, Australia

    Sze Ting Lee & Andrew M Scott

  5. School of Cancer Medicine, La Trobe University, Melbourne, Australia

    Sze Ting Lee, Hui K Gan & Andrew M Scott

  6. Department of Medicine, The University of Melbourne, Austin Health, PO Box 5555, Heidelberg, Vic, 3084, Australia

    Lawrence M Cher & Andrew M Scott

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  1. Antoine Leimgruber
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Correspondence to Andrew M Scott.

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All patients provided written informed consent. Patients with previous history of glioma and/or brain radiotherapy were excluded. The study was approved by the Austin Health’s Human Research Ethics Committee.

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Leimgruber, A., Hickson, K., Lee, S.T. et al. Spatial and quantitative mapping of glycolysis and hypoxia in glioblastoma as a predictor of radiotherapy response and sites of relapse. Eur J Nucl Med Mol Imaging 47, 1476–1485 (2020). https://doi.org/10.1007/s00259-020-04706-0

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