Background Several studies have established Glioblastoma Multiforme (GBM) prognostic and predictive models based on age and Karnofsky Performance Status (KPS), while very few studies evaluated the prognostic and predictive significance of preoperative MR-imaging. However, to date, there is no simple preoperative GBM classification that also correlates with a highly prognostic genomic signature. Thus, we present for the first time a biologically relevant, and clinically applicable tumor Volume, patient Age, and KPS (VAK) GBM classification that can easily and non-invasively be determined upon patient admission. Methods We quantitatively analyzed the volumes of 78 GBM patient MRIs present in The Cancer Imaging Archive (TCIA) corresponding to patients in The Cancer Genome Atlas (TCGA) with VAK annotation. The variables were then combined using a simple 3-point scoring system to form the VAK classification. A validation set (N = 64) from both the TCGA and Rembrandt databases was used to confirm the classification. Transcription factor and genomic correlations were performed using the gene pattern suite and Ingenuity Pathway Analysis. Results VAK-A and VAK-B classes showed significant median survival differences in discovery (P = 0.007) and validation sets (P = 0.008). VAK-A is significantly associated with P53 activation, while VAK-B shows significant P53 inhibition. Furthermore, a molecular gene signature comprised of a total of 25 genes and microRNAs was significantly associated with the classes and predicted survival in an independent validation set (P = 0.001). A favorable MGMT promoter methylation status resulted in a 10.5 months additional survival benefit for VAK-A compared to VAK-B patients. Conclusions The non-invasively determined VAK classification with its implication of VAK-specific molecular regulatory networks, can serve as a very robust initial prognostic tool, clinical trial selection criteria, and important step toward the refinement of genomics-based personalized therapy for GBM patients.
References
[1]
CBTRUS (2008) Central Brain Tumor registry of the United States. http://www.cbtrus.org/, accessed July 2011..
[2]
Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJ, et al. (2009) Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 10: 459–466.
[3]
Kahle KT, Kozono D, Ng K, Hsieh G, Zinn PO, et al. (2010) Functional genomics to explore cancer cell vulnerabilities. Neurosurg Focus 28: E5.
[4]
Marko NF, Toms SA, Barnett GH, Weil R (2008) Genomic expression patterns distinguish long-term from short-term glioblastoma survivors: a preliminary feasibility study. Genomics 91: 395–406.
[5]
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.
[6]
Lamborn KR, Chang SM, Prados MD (2004) Prognostic factors for survival of patients with glioblastoma: recursive partitioning analysis. Neuro Oncol 6: 227–235.
[7]
Li SW, Qiu XG, Chen BS, Zhang W, Ren H, et al. (2009) Prognostic factors influencing clinical outcomes of glioblastoma multiforme. Chin Med J (Engl) 122: 1245–1249.
Carson KA, Grossman SA, Fisher JD, Shaw EG (2007) Prognostic factors for survival in adult patients with recurrent glioma enrolled onto the new approaches to brain tumor therapy CNS consortium phase I and II clinical trials. J Clin Oncol 25: 2601–2606.
[10]
Wu W, Lamborn KR, Buckner JC, Novotny PJ, Chang SM, et al. (2010) Joint NCCTG and NABTC prognostic factors analysis for high-grade recurrent glioma. Neuro Oncol 12: 164–172.
[11]
Siker ML, Wang M, Porter K, Nelson DF, Curran WJ, et al. (2011) Age as an independent prognostic factor in patients with glioblastoma: a Radiation Therapy Oncology Group and American College of Surgeons National Cancer Data Base comparison. J Neurooncol 104: 351–356.
[12]
Wong ET, Hess KR, Gleason MJ, Jaeckle KA, Kyritsis AP, et al. (1999) Outcomes and prognostic factors in recurrent glioma patients enrolled onto phase II clinical trials. J Clin Oncol 17: 2572–2578.
[13]
McGirt MJ, Chaichana KL, Gathinji M, Attenello FJ, Than K, et al. (2009) Independent association of extent of resection with survival in patients with malignant brain astrocytoma. J Neurosurg 110: 156–162.
[14]
Sanai N, Berger MS (2008) Glioma extent of resection and its impact on patient outcome. Neurosurgery 62: 753–764; discussion 264–756.
[15]
Sanai N, Polley MY, McDermott MW, Parsa AT, Berger MS (2011) An extent of resection threshold for newly diagnosed glioblastomas. J Neurosurg.
[16]
Butowski N, Lamborn KR, Berger MS, Prados MD, Chang SM (2007) Historical controls for phase II surgically based trials requiring gross total resection of glioblastoma multiforme. J Neurooncol 85: 87–94.
[17]
Iwamoto FM, Reiner AS, Nayak L, Panageas KS, Elkin EB, et al. (2009) Prognosis and patterns of care in elderly patients with glioma. Cancer 115: 5534–5540.
[18]
Scott J, Tsai YY, Chinnaiyan P, Yu HH (2010) Effectiveness of Radiotherapy for Elderly Patients with Glioblastoma. Int J Radiat Oncol Biol Phys.
[19]
Park JK, Hodges T, Arko L, Shen M, Dello Iacono D, et al. (2010) Scale to predict survival after surgery for recurrent glioblastoma multiforme. J Clin Oncol 28: 3838–3843.
[20]
Zinn PO, Majadan B, Sathyan P, Singh SK, Majumder S, et al. (2011) Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme. PLoS One 6: e25451.
[21]
Barajas RF Jr, Hodgson JG, Chang JS, Vandenberg SR, Yeh RF, et al. (2010) Glioblastoma multiforme regional genetic and cellular expression patterns: influence on anatomic and physiologic MR imaging. Radiology 254: 564–576.
[22]
Pope WB, Chen JH, Dong J, Carlson MR, Perlina A, et al. (2008) Relationship between gene expression and enhancement in glioblastoma multiforme: exploratory DNA microarray analysis. Radiology 249: 268–277.
[23]
Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, et al. (2005) MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352: 997–1003.
[24]
Etienne MC, Formento JL, Lebrun-Frenay C, Gioanni J, Chatel M, et al. (1998) Epidermal growth factor receptor and labeling index are independent prognostic factors in glial tumor outcome. Clin Cancer Res 4: 2383–2390.
[25]
Verhaak RG, 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.
[26]
Diehn M, Nardini C, Wang DS, McGovern S, Jayaraman M, et al. (2008) Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci U S A 105: 5213–5218.
[27]
TCGA (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455: 1061–1068.
[28]
Madhavan S, Zenklusen JC, Kotliarov Y, Sahni H, Fine HA, et al. (2009) Rembrandt: helping personalized medicine become a reality through integrative translational research. Mol Cancer Res 7: 157–167.
[29]
Gering DT, Nabavi A, Kikinis R, Hata N, O'Donnell LJ, et al. (2001) An integrated visualization system for surgical planning and guidance using image fusion and an open MR. J Magn Reson Imaging 13: 967–975.
[30]
Pichon E, Tannenbaum A, Kikinis R (2004) A statistically based flow for image segmentation. Med Image Anal 8: 267–274.
[31]
Archip N, Jolesz FA, Warfield SK (2007) A validation framework for brain tumor segmentation. Acad Radiol 14: 1242–1251.
[32]
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, et al. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4: 249–264.
[33]
Pierre Bady DS, Annie-Claire Diserens, Jocelyne Bloch, Martin J van den Bent, Christine Marosi, Pierre-Yves Dietrich, Michael Weller, Luigi Mariani, Frank L Heppner, David R Macdonald, Denis Lacombe, Roger Stupp, Mauro Delorenzi, Monika Hegi (2012) MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMPstatus. Acta Neuropathologica, in press.
Dweep H, Sticht C, Pandey P, Gretz N (2011) miRWalk – Database: Prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform.
[36]
Iliadis G, Selviaridis P, Kalogera-Fountzila A, Fragkoulidi A, Baltas D, et al. (2009) The importance of tumor volume in the prognosis of patients with glioblastoma: comparison of computerized volumetry and geometric models. Strahlenther Onkol 185: 743–750.
[37]
Mirimanoff RO, Gorlia T, Mason W, Van den Bent MJ, Kortmann RD, et al. (2006) Radiotherapy and temozolomide for newly diagnosed glioblastoma: recursive partitioning analysis of the EORTC 26981/22981-NCIC CE3 phase III randomized trial. J Clin Oncol 24: 2563–2569.
[38]
Vasudevan S, Tong Y, Steitz JA (2007) Switching from repression to activation: microRNAs can up-regulate translation. Science 318: 1931–1934.
[39]
Brase JC, Johannes M, Schlomm T, Falth M, Haese A, et al. (2011) Circulating miRNAs are correlated with tumor progression in prostate cancer. Int J Cancer 128: 608–616.
[40]
Squadrito Mario L, Pucci F, Magri L, Moi D, Gilfillan Gregor D, et al. (2012) miR-511-3p Modulates Genetic Programs of Tumor-Associated Macrophages. Cell Reports 1: 141–154.
[41]
Li L, Halaby MJ, Hakem A, Cardoso R, El Ghamrasni S, et al. (2010) Rnf8 deficiency impairs class switch recombination, spermatogenesis, and genomic integrity and predisposes for cancer. J Exp Med 207: 983–997.
[42]
Westbrook TF, Martin ES, Schlabach MR, Leng Y, Liang AC, et al. (2005) A genetic screen for candidate tumor suppressors identifies REST. Cell 121: 837–848.
[43]
Babel I, Barderas R, Diaz-Uriarte R, Martinez-Torrecuadrada JL, Sanchez-Carbayo M, et al. (2009) Identification of tumor-associated autoantigens for the diagnosis of colorectal cancer in serum using high density protein microarrays. Mol Cell Proteomics 8: 2382–2395.
[44]
Lee EJ, Gusev Y, Jiang J, Nuovo GJ, Lerner MR, et al. (2007) Expression profiling identifies microRNA signature in pancreatic cancer. Int J Cancer 120: 1046–1054.
[45]
Ahmed FE, Jeffries CD, Vos PW, Flake G, Nuovo GJ, et al. (2009) Diagnostic microRNA markers for screening sporadic human colon cancer and active ulcerative colitis in stool and tissue. Cancer Genomics Proteomics 6: 281–295.
[46]
Jukic DM, Rao UN, Kelly L, Skaf JS, Drogowski LM, et al. (2010) Microrna profiling analysis of differences between the melanoma of young adults and older adults. J Transl Med 8: 27.
[47]
Podhajcer OL, Benedetti L, Girotti MR, Prada F, Salvatierra E, et al. (2008) The role of the matricellular protein SPARC in the dynamic interaction between the tumor and the host. Cancer Metastasis Rev 27: 523–537.
[48]
Resch U, Schichl YM, Sattler S, de Martin R (2008) XIAP regulates intracellular ROS by enhancing antioxidant gene expression. Biochem Biophys Res Commun 375: 156–161.
[49]
Martin NL, Saba-El-Leil MK, Sadekova S, Meloche S, Sauvageau G (2005) EN2 is a candidate oncogene in human breast cancer. Oncogene 24: 6890–6901.
[50]
Luise C, Capra M, Donzelli M, Mazzarol G, Jodice MG, et al. (2011) An atlas of altered expression of deubiquitinating enzymes in human cancer. PLoS One 6: e15891.
