Molecular-Based Recursive Partitioning Analysis Model for Glioblastoma in the Temozolomide Era: A Correlative Analysis Based on NRG Oncology RTOG 0525

Importance There is a need for a more refined, molecularly based classification model for glioblastoma (GBM) in the temozolomide era. Objective To refine the existing clinically based recursive partitioning analysis (RPA) model by incorporating molecular variables. Design, Setting, and Participants NRG Oncology RTOG 0525 specimens (n = 452) were analyzed for protein biomarkers representing key pathways in GBM by a quantitative molecular microscopy-based approach with semiquantitative immunohistochemical validation. Prognostic significance of each protein was examined by single-marker and multimarker Cox regression analyses. To reclassify the prognostic risk groups, significant protein biomarkers on single-marker analysis were incorporated into an RPA model consisting of the same clinical variables (age, Karnofsky Performance Status, extent of resection, and neurologic function) as the existing RTOG RPA. The new RPA model (NRG-GBM-RPA) was confirmed using traditional immunohistochemistry in an independent data set (n = 176). Main Outcomes and Measures Overall survival (OS). Results In 452 specimens, MGMT (hazard ratio [HR], 1.81; 95% CI, 1.37-2.39; P < .001), survivin (HR, 1.36; 95% CI, 1.04-1.76; P = .02), c-Met (HR, 1.53; 95% CI, 1.06-2.23; P = .02), pmTOR (HR, 0.76; 95% CI, 0.60-0.97; P = .03), and Ki-67 (HR, 1.40; 95% CI, 1.10-1.78; P = .007) protein levels were found to be significant on single-marker multivariate analysis of OS. To refine the existing RPA, significant protein biomarkers together with clinical variables (age, Karnofsky Performance Status, extent of resection, and neurological function) were incorporated into a new model. Of 166 patients used for the new NRG-GBM-RPA model, 97 (58.4%) were male (mean [SD] age, 55.7 [12.0] years). Higher MGMT protein level was significantly associated with decreased MGMT promoter methylation and vice versa (1425.1 for methylated vs 1828.0 for unmethylated; P < .001). Furthermore, MGMT protein expression (HR, 1.84; 95% CI, 1.38-2.43; P < .001) had greater prognostic value for OS compared with MGMT promoter methylation (HR, 1.77; 95% CI, 1.28-2.44; P < .001). The refined NRG-GBM-RPA consisting of MGMT protein, c-Met protein, and age revealed greater separation of OS prognostic classes compared with the existing clinically based RPA model and MGMT promoter methylation in NRG Oncology RTOG 0525. The prognostic significance of the NRG-GBM-RPA was subsequently confirmed in an independent data set (n = 176). Conclusions and Relevance This new NRG-GBM-RPA model improves outcome stratification over both the current RTOG RPA model and MGMT promoter methylation, respectively, for patients with GBM treated with radiation and temozolomide and was biologically validated in an independent data set. The revised RPA has the potential to contribute to improving the accurate assessment of prognostic groups in patients with GBM treated with radiation and temozolomide and to influence clinical decision making. Trial Registration clinicaltrials.gov Identifier: NCT00304031

[1]  Do-Hyun Nam,et al.  Prognostic significance of c‐Met expression in glioblastomas , 2009, Cancer.

[2]  H. Eyre,et al.  Correlation of tumor O6 methylguanine-DNA methyltransferase levels with survival of malignant astrocytoma patients treated with bis-chloroethylnitrosourea: a Southwest Oncology Group study. , 1998, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[3]  Arata Tomiyama,et al.  Crosstalk Between the PI3K/mTOR and MEK/ERK Pathways Involved in the Maintenance of Self‐Renewal and Tumorigenicity of Glioblastoma Stem‐Like Cells , 2010, Stem cells.

[4]  J. Laterra,et al.  In Vivo c-Met Pathway Inhibition Depletes Human Glioma Xenografts of Tumor-Propagating Stem-Like Cells. , 2013, Translational oncology.

[5]  M. Schemper,et al.  Predictive Accuracy and Explained Variation in Cox Regression , 2000, Biometrics.

[6]  Walter J Curran,et al.  Dose-dense temozolomide for newly diagnosed glioblastoma: a randomized phase III clinical trial. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[7]  J. A. van der Laak,et al.  Effects of Dual Targeting of Tumor Cells and Stroma in Human Glioblastoma Xenografts with a Tyrosine Kinase Inhibitor against c-MET and VEGFR2 , 2013, PloS one.

[8]  D. Nelson,et al.  Recursive partitioning analysis of prognostic factors in three Radiation Therapy Oncology Group malignant glioma trials. , 1993, Journal of the National Cancer Institute.

[9]  Arnab Chakravarti,et al.  Insulin-like growth factor receptor I mediates resistance to anti-epidermal growth factor receptor therapy in primary human glioblastoma cells through continued activation of phosphoinositide 3-kinase signaling. , 2002, Cancer research.

[10]  Alona Muzikansky,et al.  The prognostic significance of phosphatidylinositol 3-kinase pathway activation in human gliomas. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[11]  Benjamin Haibe-Kains,et al.  A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all? , 2008, Bioinform..

[12]  A. Chakravarti,et al.  The epidermal growth factor receptor pathway mediates resistance to sequential administration of radiation and chemotherapy in primary human glioblastoma cells in a RAS-dependent manner. , 2002, Cancer research.

[13]  Joshua M. Korn,et al.  Comprehensive genomic characterization defines human glioblastoma genes and core pathways , 2008, Nature.

[14]  M. Pencina,et al.  Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation , 2004, Statistics in medicine.

[15]  H. Akaike A new look at the statistical model identification , 1974 .

[16]  N. Mantel Evaluation of survival data and two new rank order statistics arising in its consideration. , 1966, Cancer chemotherapy reports.

[17]  P. Álvarez,et al.  MGMT promoter methylation status and MGMT and CD133 immunohistochemical expression as prognostic markers in glioblastoma patients treated with temozolomide plus radiotherapy , 2012, Journal of Translational Medicine.

[18]  S. Torp Diagnostic and prognostic role of Ki67 immunostaining in human astrocytomas using four different antibodies. , 2002, Clinical neuropathology.

[19]  Jacob Cohen,et al.  Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .

[20]  J. Barnholtz-Sloan,et al.  CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2007-2011. , 2012, Neuro-oncology.

[21]  Daniel B. Mark,et al.  TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .

[22]  Shin Jung,et al.  THE CORRELATION AND PROGNOSTIC SIGNIFICANCE OF MGMT PROMOTER METHYLATION AND MGMT PROTEIN IN GLIOBLASTOMAS , 2009, Neurosurgery.

[23]  D. Allred,et al.  Prognostic and predictive factors in breast cancer by immunohistochemical analysis. , 1998, Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc.

[24]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .

[25]  Jeffrey W. Clark,et al.  Rapid radiographic and clinical improvement after treatment of a MET-amplified recurrent glioblastoma with a mesenchymal-epithelial transition inhibitor. , 2012, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[26]  D. Rimm,et al.  Quantitative, fluorescence-based in-situ assessment of protein expression. , 2009, Methods in molecular biology.

[27]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[28]  M. Tainsky Tumor Biomarker Discovery , 2009, Methods in Molecular Biology.

[29]  F. Harrell,et al.  Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors , 2005 .

[30]  N. Obuchowski Receiver operating characteristic curves and their use in radiology. , 2003, Radiology.

[31]  E. Shaw,et al.  Validation and simplification of the Radiation Therapy Oncology Group recursive partitioning analysis classification for glioblastoma. , 2011, International journal of radiation oncology, biology, physics.

[32]  K. Aldape,et al.  Temozolomide-Mediated Radiation Enhancement in Glioblastoma: A Report on Underlying Mechanisms , 2006, Clinical Cancer Research.

[33]  Z L Gokaslan,et al.  A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. , 2001, Journal of neurosurgery.

[34]  J. Uhm Comprehensive genomic characterization defines human glioblastoma genes and core pathways , 2009 .

[35]  R. Mirimanoff,et al.  Radiotherapy and temozolomide for newly diagnosed glioblastoma: recursive partitioning analysis of the EORTC 26981/22981-NCIC CE3 phase III randomized trial. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[36]  Ewout W Steyerberg,et al.  Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers , 2011, Statistics in medicine.

[37]  H. Heinzl,et al.  Anti‐O6‐Methylguanine‐Methyltransferase (MGMT) Immunohistochemistry in Glioblastoma Multiforme: Observer Variability and Lack of Association with Patient Survival Impede Its Use as Clinical Biomarker * , 2008, Brain pathology.

[38]  R. Mirimanoff,et al.  MGMT gene silencing and benefit from temozolomide in glioblastoma. , 2005, The New England journal of medicine.

[39]  A. Viera,et al.  Understanding interobserver agreement: the kappa statistic. , 2005, Family medicine.

[40]  D. Haussler,et al.  The Somatic Genomic Landscape of Glioblastoma , 2013, Cell.

[41]  D.,et al.  Regression Models and Life-Tables , 2022 .

[42]  B. Everitt,et al.  Large sample standard errors of kappa and weighted kappa. , 1969 .