Potential Biomarkers in Diagnosis, Prognosis and Prediction of Treatment Response in Malignant Glioma

A B S T R A C T

Gliomas are the most common type of primary central nervous system malignancies with poor prognosis in adults. There are several challenges in developing a treatment protocol for this malignancy including presence of blood-brain barrier that inhibit drug delivery to brain tissue, drug and radiation resistance of tumor cells, and inter and intra-tumor heterogeneity of glioma. In addition, early treatment assessment is difficult for glioma patients because of phenomenon of pseudo-progression. Due to the challenges involved in treatment and monitoring of treatment response for glioma, it is very helpful to identify specific and non-invasive molecular and imaging markers in order to provide useful prognostic information. The aim of this article is to summarize several potential biological and imaging markers regarding malignant glioma. A brief description of the proteins involved in the glioma signaling pathways is provided in order to introduce potential biological markers. Furthermore, the role of imaging techniques in treatment management is discussed. Finally, correlation between tumor characteristics and values of angiogenesis and physiological factors measured in perfusion magnetic resonance imaging techniques as well as metabolites in MRS, and PET tracer’s uptake is investigated.

Keywords

Biomarkers, imaging techniques, malignant glioma



Get access to the full version of this article.

Article Info

Article Type
Research Article
Publication history
Received: Fri 27, Aug 2021
Accepted: Sat 11, Sep 2021
Published: Wed 29, Sep 2021
Copyright
© 2023 Parvaneh Shokrani. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Hosting by Science Repository.
DOI: 10.31487/j.IJCST.2021.02.05

Author Info

Corresponding Author
Parvaneh Shokrani
Professor of Medical Physics, Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Figures & Tables



Get access to the full version of this article.

References

1.     Nelson SJ (2011) Assessment of therapeutic response and treatment planning for brain tumors using metabolic and physiological MRI. NMR Biomed 24: 734-749. [Crossref]

2.     da Cruz Jr LCH, Rodriguez I, Domingues RC, Gasparetto EL, Sorensen AG (2011) Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma. AJNR Am J Neuroradiol 32: 1978-1985. [Crossref]

3.     Keunen O, Taxt T, Grüner R, Lund Johansen M, Tonn JC et al. (2014) Multimodal imaging of gliomas in the context of evolving cellular and molecular therapies. Adv Drug Deliv Rev 76: 98-115. [Crossref]

4.     Areeb Z, Stylli SS, Koldej R, Ritchie DS, Siegal T et al. (2015) MicroRNA as potential biomarkers in Glioblastoma. J Neurooncol 125: 237-248. [Crossref]

5.     Pirtoli L, Gravina GL, Giordano A (2016) Radiobiology of Glioblastoma. Recent Advances and Related Pathobiology. Springer.

6.     Han X, Xue X, Zhou H, Zhang G (2017) A molecular view of the radioresistance of gliomas. Oncotarget 8: 100931-100941. [Crossref]

7.     Noch EK, Ramakrishna R, Magge R (2018) Challenges in the Treatment of Glioblastoma: Multisystem Mechanisms of Therapeutic Resistance. World Neurosurg 116: 505-517. [Crossref]

8.     Verhaak RGW, Hoadley KA, Purdom E, Wang V, Qi Y et al. (2010) Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17: 98-110. [Crossref]

9.     Phillips HS, Kharbanda S, Chen R, Forrest WF, Soriano RH 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: 157-173. [Crossref]

10.  Parvez K, Parvez A, Zadeh G (2014) The diagnosis and treatment of pseudoprogression, radiation necrosis and brain tumor recurrence. Int J Mol Sci 15: 11832-11846. [Crossref]

11.  Juhász C, Dwivedi S, Kamson DO, Michelhaugh SK, Mittal S (2014) Comparison of amino acid positron emission tomographic radiotracers for molecular imaging of primary and metastatic brain tumors. Mol Imaging 13: 10.2310/7290.2014.00015. [Crossref]

12.  Olar A, Aldape KD (2012) Biomarkers classification and therapeutic decision-making for malignant gliomas. Curr Treat Options Oncol 13: 417-436. [Crossref]

13.  Fischer I, Aldape K (2010) Molecular tools: biology, prognosis, and therapeutic triage. Neuroimaging Clin N Am 20: 273-282. [Crossref]

14.  de Groot JF, Sulman EP, Aldape KD (2011) Multigene sets for clinical application in glioma. J Natl Compr Canc Netw 9: 449-456. [Crossref]

15.  Hayward RM, Kirk MJ, Sproull M, Scott T, Smith S et al. (2008) Post-collection, pre-measurement variables affecting VEGF levels in urine biospecimens. J Cell Mol Med 12: 343-350. [Crossref]

16.  Ahir BK, Engelhard HH, Lakka SS (2020) Tumor Development and Angiogenesis in Adult Brain Tumor: Glioblastoma. Mol Neurobiol 57: 2461-2478. [Crossref]

17.  Stacker SA, Caesar C, Baldwin ME, Thornton GE, Williams RA et al. (2001) VEGF-D promotes the metastatic spread of tumor cells via the lymphatics. Nat Med 7: 186-191. [Crossref]

18.  Lacerda S, Law M (2009) Magnetic resonance perfusion and permeability imaging in brain tumors. Neuroimaging Clin N Am 19: 527-557. [Crossref]

19.  Fathi Kazerooni A, Bakas S, Saligheh Rad H, Davatzikos C (2020) Imaging signatures of glioblastoma molecular characteristics: A radiogenomics review. J Magn Reson Imaging 52: 54-69. [Crossref]

20.  Palanichamy K, Erkkinen M, Chakravarti A (2006) Predictive and prognostic markers in human glioblastomas. Curr Treat Options Oncol 7: 490-504. [Crossref]

21.  Staedtke V, a Dzaye OD, Holdhoff M (2016) Actionable molecular biomarkers in primary brain tumors. Trends Cancer 2: 338-349. [Crossref]

22.  Arvold ND, Lee EQ, Mehta MP (2016) Molecular pathology in adult neuro-oncology: an update on diagnostic, prognostic and predictive markers. Neuro Oncol 18: 1043-1065.

23.  Hatanpaa KJ, Burma S, Zhao D, Habib AA (2010) Epidermal growth factor receptor in glioma: signal transduction, neuropathology, imaging, and radioresistance. Neoplasia 12: 675-684. [Crossref]

24.  Bazzichetto C, Conciatori F, Pallocca M, Falcone I, Fanciulli M et al. (2019) PTEN as a Prognostic/Predictive Biomarker in Cancer: An Unfulfilled Promise? Cancers (Basel) 11: 435. [Crossref]

25.  Jonas S, Izaurralde E (2015) Towards a molecular understanding of microRNA-mediated gene silencing. Nat Rev Genet 16: 421-433. [Crossref]

26.  Santangelo A, Tamanini A, Cabrini G, Dechecchi MC (2017) Circulating microRNAs as emerging non-invasive biomarkers for gliomas. Ann Transl Med 5: 277. [Crossref]

27.  Xiao Y, Zhang L, Song Z, Guo C, Zhu J et al. (2016) Potential Diagnostic and Prognostic Value of Plasma Circulating MicroRNA-182 in Human Glioma. Med Sci Monit 22: 855-862. [Crossref]

28.  Herman A, Gruden K, Blejec A, Podpečan V, Motaln H et al. (2015) Analysis of Glioblastoma Patients’ Plasma Revealed the Presence of MicroRNAs with a Prognostic Impact on Survival and Those of Viral Origin. PLoS One 10: e0125791. [Crossref]

29.  Ilhan Mutlu A, Wagner L, Wöhrer A, Furtner J, Widhalm G et al. (2012) Plasma MicroRNA-21 concentration may be a useful biomarker in glioblastoma patients. Cancer Invest 30: 615-621. [Crossref]

30.  Zhi F, Shao N, Wang R, Deng D, Xue L et al. (2015) Identification of 9 serum microRNAs as potential noninvasive biomarkers of human astrocytoma. Neuro Oncol 17: 383-391. [Crossref]

31.  Yue X, Lan F, Hu M, Pan Q, Wang Q et al. (2016) Downregulation of serum microRNA-205 as a potential diagnostic and prognostic biomarker for human glioma. J Neurosurg 124: 122-128. [Crossref]

32.  Dong L, Li Y, Han C, Wang X, She L et al. (2014) miRNA microarray reveals specific expression in the peripheral blood of glioblastoma patients. Int J Oncol 45: 746-756. [Crossref]

33.  Yang C, Wang C, Chen X, Chen S, Zhang Y et al. (2013) Identification of seven serum microRNAs from a genome-wide serum microRNA expression profile as potential noninvasive biomarkers for malignant astrocytomas. Int J Cancer 132: 116-127. [Crossref]

34.  Hermansen SK, Kristensen BW (2013) MicroRNA biomarkers in glioblastoma. J Neurooncol 114: 13-23. [Crossref]

35.  Shea A, Harish V, Afzal Z, Chijioke J, Kedir H et al. (2016) MicroRNAs in glioblastoma multiforme pathogenesis and therapeutics. Cancer Med 5: 1917-1946. [Crossref]

36.  Paulson ES, Erickson B, Schultz C, Allen Li X (2015) Comprehensive MRI simulation methodology using a dedicated MRI scanner in radiation oncology for external beam radiation treatment planning. Med Phys 42: 28-39. [Crossref]

37.  Maier SE, Sun Y, Mulkern RV (2010) Diffusion imaging of brain tumors. NMR Biomed 23: 849-864. [Crossref]

38.  Melhem ER, Mori S, Mukundan G, Kraut MA, Pomper MG et al. (2002) Diffusion tensor MR imaging of the brain and white matter tractography. AJR Am J Roentgenol 178: 3-16. [Crossref]

39.  Sugahara T, Korogi Y, Kochi M, Ikushima I, Shigematu Y et al. (1999) Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reason Imaging 9: 53-60. [Crossref]

40.  Durand Muñoz C, Flores Alvarez E, Moreno Jimenez S, Roldan Valadez E (2019) Pre-operative apparent diffusion coefficient values and tumour region volumes as prognostic biomarkers in glioblastoma: correlation and progression-free survival analyses. Insights Imaging 10: 36. [Crossref]

41.  Moffat BA, Chenevert TL, Meyer CR, Mckeever PE, Hall DE et al. (2006) The functional diffusion map: an imaging biomarker for the early prediction of cancer treatment outcome. Neoplasia 8: 259-267. [Crossref]

42.  Lemaire L, Howe FA, Rodrigues LM, Griffiths JR (1999) Assessment of induced rat mammary tumour response to chemotherapy using the apparent diffusion coefficient of tissue water as determined by diffusion-weighted 1 H-NMR spectroscopy in vivo. MAGMA 8: 20-26. [Crossref]

43.  Mardor Y, Roth Y, Ocherashvilli A, Spiegelmann R, Tichler T et al. (2004) Pretreatment prediction of brain tumors’ response to radiation therapy using high b-value diffusion-weighted MRI. Neoplasia 6: 136-142. [Crossref]

44.  Folkman J (1971) Tumor angiogenesis: therapeutic implications. N Eng J Med 285: 1182-1186. [Crossref]

45.  Nguyen TB, Cron GO, Mercier JF, Foottit C, Torres CH et al. (2015) preoperative prognostic value of dynamic contrast-enhanced MRI-derived contrast transfer coefficient and plasma volume in patients with cerebral gliomas. AJNR Am J Neuroradiol 36: 63-69. [Crossref]

46.  Wong TZ, van der Westhuizen GJ, Coleman RE (2002) Positron emission tomography imaging of brain tumors. Neuroimaging Clin N Am 12: 615-626. [Crossref]

47.  Niyazi M, Geisler J, Siefert A, Schwarz SB, Ganswindt U et al. (2011) FET-PET for malignant glioma treatment planning. Radiother Oncol 99: 44-48. [Crossref]

48.  Niyazi M, Schnell O, Suchorska B, Schwarz SB, Ganswindt U et al. (2012) FET-PET assessed recurrence pattern after radio-chemotherapy in newly diagnosed patients with glioblastoma is influenced by MGMT methylation status. Radiother Oncol 104: 78-82. [Crossref]

49.  Piroth MD, Pinkawa M, Holy R, Klotz J, Schaar S et al. (2012) Integrated boost IMRT with FET-PET-adapted local dose escalation in glioblastomas. Results of a prospective phase II study. Strahlenther Onkol 188: 334-339. [Crossref]

50.  Jansen NL, Suchorska B, Wenter V, Eigenbrod S, Schmid Tannwald C et al. (2014) Dynamic 18F-FET PET in newly diagnosed astrocytic low-grade glioma identifies high-risk patients. J Nucl Med 55: 198-203. [Crossref]

51.  Kunz M, Thon N, Eigenbrod S, Hartmann C, Egensperger R et al. (2011) Hot spots in dynamic (18)FET-PET delineate malignant tumor parts within suspected WHO grade II gliomas. Neuro Oncol 13: 307-316. [Crossref]

52.  Miyake K, Shinomiya A, Okada M, Hatakeyama T, Kawai N et al. (2012) Usefulness of FDG, MET and FLT-PET studies for the management of human gliomas. J Biomed Biotechnol 2012: 205818. [Crossref]

53.  Bakas S, Akbari H, Pisapia J, Martinez Lage M, Rozycki M et al. (2005) In vivo Detection of EGFRvIII in Glioblastoma via Perfusion Magnetic Resonance Imaging Signature. Canc Res 11: 1462-1466.

54.  Ahn SS, Shin NY, Chang JH, Kim SH, Kim EH et al. (2014) Prediction of methylguanine methyltransferase promoter methylation in glioblastoma using dynamic contrast-enhanced magnetic resonance and diffusion tensor imaging. J Neurosurg 121: 367-373. [Crossref]

55.  Rundle Thiele D, Day B, Stringer B, Fay M, Martin J et al. (2015) Using the apparent diffusion coefficient to identifying MGMT promoter methylation status early in glioblastoma: importance of analytical method. J Med Radiat Sci 62: 92-98. [Crossref]

56.  Chang P, Grinband J, Weinberg BD, Bardis M, Khy M et al. (2018) Deep-learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas. AJNR Am J Neuroradiol 39: 1201-1207. [Crossref]

57.  Akbari H, Bakas S, Pisapia JM, Nasrallah MP, Rozycki M et al. (2018) In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature. Neuro Oncol 20: 1068-1079. [Crossref]

58.  Cha J, Kim ST, Kim HJ, Kim BJ, Kim YK et al. (2014) Differentiation of tumor progression from pseudoprogression in patients with posttreatment glioblastoma using multiparametric histogram analysis. AJNR Am J Neuroradiol 35: 1309-1317. [Crossref]

59.  Park JE, Kim HS, Goh MJ, Kim SJ, Kim JH (2015) Pseudoprogression in Patients with Glioblastoma: Assessment by Using Volume-weighted Voxel-based Multiparametric Clustering of MR Imaging Data in an Independent Test Set. Radiology 275: 792-802. [Crossref]

60.  Yun TJ, Park CK, Kim TM, Lee SH, Kim JH et al. (2015) Glioblastoma treated with concurrent radiation therapy and temozolomide chemotherapy: differentiation of true progression from pseudoprogression with quantitative dynamic contrast-enhanced MR imaging. Radiology 274: 830-840. [Crossref]

61.  Artzi M, Liberman G, Nadav G, Blumenthal DT, Bokstein F et al. (2016) Differentiation between treatment-related changes and progressive disease in patients with high grade brain tumors using support vector machine classification based on DCE MRI. J Neurooncol 127: 515-524. [Crossref]

62.  Qian X, Tan H, Zhang J, Zhao W, Chan MD et al. (2016) Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation. Med Phys 43: 5889-5902. [Crossref]

63.  Yoon RG, Kim HS, Koh MJ, Shim WH, Jung SC et al. (2017) Differentiation of Recurrent Glioblastoma from Delayed Radiation Necrosis by Using Voxel-based Multiparametric Analysis of MR Imaging Data. Radiology 285: 206-213. [Crossref]

64.  Booth TC, Larkin TJ, Yuan Y, Kettunen MI, Dawson SN et al. (2017) Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma. PloS One 12: e0176528. [Crossref]

65.  Nam JG, Kang KM, Choi SH, Lim WH, Yoo RE et al. (2017) Comparison between the Prebolus T1 Measurement and the Fixed T1 Value in Dynamic Contrast-Enhanced MR Imaging for the Differentiation of True Progression from Pseudoprogression in Glioblastoma Treated with Concurrent Radiation Therapy and Temozolomide Chemotherapy. AJNR Am J Neuroradiol 38: 2243-2250. [Crossref]

66.  Kim JY, Park JE, Jo Y, Shim WH, Nam SJ et al. (2019) Incorporating diffusion-and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro Oncol 21: 404-414. [Crossref]

67.  Jang BS, Jeon SH, Kim IH, Kim IA (2018) Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma. Sci Rep 8: 1-9. [Crossref]

68.  Chen W, Delaloye S, Silverman DH, Geist C, Czernin J et al. (2007) Predicting treatment response of malignant gliomas to bevacizumab and irinotecan by imaging proliferation with [18F] fluorothymidine positron emission tomography: a pilot study. J Clin Oncol 25: 4714-4721. [Crossref]

69.  Galldiks N, Kracht LW, Burghaus L, Thomas A, Jacobs AH et al. (2006) Use of 11C-methionine PET to monitor the effects of temozolomide chemotherapy in malignant gliomas. Eur J Nucl Med Mol Imaging May 33: 516-524. [Crossref]

70.  Schwarzenberg J, Czernin J, Cloughesy TF, Ellingson BM, Pope WB et al. (2012) 3′-deoxy-3′-18F-fluorothymidine PET and MRI for early survival predictions in patients with recurrent malignant glioma treated with bevacizumab. J Nucl Med 53: 29-36. [Crossref]

71.  Galldiks N, Langen KJ, Holy R, Pinkawa M, Stoffels G et al. (2012) Assessment of treatment response in patients with glioblastoma using O-(2-18F-fluoroethyl)-l-tyrosine PET in comparison to MRI. J Nucl Med 53: 1048-1057. [Crossref]

72.  Möller Hartmann W, Herminghaus S, Krings T, Marquardt G, Lanfermann H et al. (2002) Clinical application of proton magnetic resonance spectroscopy in the diagnosis of intracranial mass lesions. Neuroradiology 44: 371-381. [Crossref]

73.  Li Y, Lupo JM, Parvataneni R, Lamborn KR, Cha S et al. (2013) Survival analysis in patients with newly diagnosed glioblastoma using pre-and postradiotherapy MR spectroscopic imaging. Neuro Oncol 15: 607-617. [Crossref]

74.  Rock JP, Hearshen D, Scarpace L, Croteau D, Gutierrez J et al. (2002) Correlations between magnetic resonance spectroscopy and image-guided histopathology, with special attention to radiation necrosis. Neurosurgery 51: 912-919. [Crossref]

75.  Venkatasubramanian R, Arenas RB, Henson MA, Forbes NS (2010) Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic response. Br J Cancer 103: 486-497. [Crossref]

76.  Aronen HJ, Gazit IE, Louis DN, Buchbinder BR, Pardo FS et al. (1994) Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 191: 41-51. [Crossref]

77. Elson A, Bovi J, Siker M, Schultz C, Paulson E (2015) Evaluation of absolute and normalized apparent diffusion coefficient (ADC) values within the post-operative T2/FLAIR volume as adverse prognostic indicators in glioblastoma. J Neurooncol 122: 549-558. [Crossref]