Radiogenomic Analysis of Vascular Endothelial Growth Factor in Patients With Glioblastoma.

OBJECTIVES: This article aims to predict the presence of vascular endothelial growth factor (VEGF) expression and to predict the expression level of VEGF by machine learning based on preoperative magnetic resonance imaging (MRI) of glioblastoma (GBM). METHODS: We analyzed the axial T2-weighted images (T2WI) and T1-weighted contrast-enhancement images of preoperative MRI in 217 patients with pathologically diagnosed GBM. Patients were divided into negative and positive VEGF groups, with the latter group further subdivided into low and high expression. The machine learning models were established with the maximum relevance and minimum redundancy algorithm and the extreme gradient boosting classifier. The area under the receiver operating curve (AUC) and accuracy were calculated for the training and validation sets. RESULTS: Positive VEGF in GBM was 63.1% (137/217), with a high expression ratio of 53.3% (73/137). To predict the positive and negative VEGF expression, 7 radiomic features were selected, with 3 features from T1CE and 4 from T2WI. The accuracy and AUC were 0.83 and 0.81, respectively, in the training set and were 0.73 and 0.74, respectively, in the validation set. To predict high and low levels, 7 radiomic features were selected, with 2 from T1CE, 1 from T2WI, and 4 from the data combinations of T1CE and T2WI. The accuracy and AUC were 0.88 and 0.88, respectively, in the training set and were 0.72 and 0.72, respectively, in the validation set. CONCLUSION: The VEGF expression status in GBM can be predicted using a machine learning model. Radiomic features resulting from data combinations of different MRI sequences could be helpful.

Long-term adjuvant administration of temozolomide impacts serum ions concentration in high-grade glioma.

BACKGROUND: Adjuvant temozolomide (TMZ) chemotherapy with standard regimen remarkably improves survival in patients with high-grade glioma (HGG). However, the influence of long-term TMZ chemotherapy on serum ions concentration is unclear. METHODS: One hundred and thirty-eight patients with HGG were included. Their blood samples were collected for blood biochemistry and routine test. The alteration in serum ions concentration, total protein, albumin, globin, and blood cells counts were used to identify the impact of long-term TMZ chemotherapy. RESULTS: Through the comparation of quantitative value of diverse parameters among different chemotherapy cycles, we identified that serum potassium concentration had a downward trend after TMZ administration (1st vs. 6th, p < 0.001; 1st vs. 12th, p < 0.001). Additionally, the correlation analysis showed that platelets was negatively correlated with chemotherapy cycles (r = - 0.649, p = 0.023). The hematological adverse events mainly centered on grade 1 to 2. CONCLUSION: Long-term administration of TMZ may lead to serum ions disturbance. Besides the myelosuppression, we should pay attention to the alteration in serum ions concentration, and give patients proper symptomatic treatment when necessary.

Automatic brain tumour diagnostic method based on a back propagation neural network and an extended set-membership filter.

BACKGROUND: Diagnosing brain tumours remains a challenging task in clinical practice. Despite their questionable accuracy, magnetic resonance image (MRI) scans are presently considered the optimal facility for assessing the growth of tumours. However, the efficiency of manual diagnosis is low, and high computational cost and poor convergence restrict the application of machine learning methods. This study aims to design a method that can reliably diagnose brain tumours from MRI scans. METHODS: First, image pre-processing (which includes background removal, size standardization, noise removal, and contrast enhancement) is utilized to normalize the images. Then, grey level co-occurrence matrix features are selected as texture features of the brain MRI scans. Finally, a method combining a back propagation neural network (BPNN) and an extended set-membership filter (ESMF) is proposed to classify features and perform image classification. RESULTS: A total of 304 patient MRI series (247 images of brains with tumours and 57 images of normal brains) were included and assessed in this study. The results revealed that our proposed method can achieve an accuracy of 95.40% and has classification accuracies of 97.14% and 88.24% for brain tumour and normal brain, respectively. CONCLUSION: This study proposes an automatic brain tumour detection model constructed using a combination of BPNN and ESMF. The model is found to be able to accurately classify brain MRI scans as normal or tumour images.

Pituicytoma: Report of three cases and a systematic literature review.

Pituicytoma is a rare subtype of WHO grade I glioma that originates in the neurohypophysis or infundibulum. Here we presented 3 cases of histopathologically diagnosed pituicytoma and subsequently performed a corresponding systematic literature review. A comprehensive literature search of the PubMed database was conducted. A total of 77 studies were eventually reviewed and 168 pituicytoma cases were identified. The epidemiology, clinical manifestations, radiological features, treatment, and pathological findings of all previous pituicytoma cases were summarized, and a "portrait" of this rare tumor was shown. It is hoped that the current study will afford a broader and more adequate understanding upon this rare disease.

Case report: tumor-treating fields prolongs IDH-mutant anaplastic astrocytoma progression-free survival and pathological evolution to glioblastoma.

Gliomas are one of the most common tumors of the central nervous system, which cause significant morbidity and mortality. Glioblastoma (GBM) is an aggressive and debilitating disease, which is the most common primary brain tumor in adults. Despite optimal surgical resection followed by radiotherapy and adjuvant chemotherapy, prognosis of patients with GBM remain poor. Tumor-treating fields (TTFields), as a novel treatment approach, is being explored through increased clinical application. Herein, we report the case of a 46-year-old man who was diagnosed as anaplastic astrocytoma [World Health Organization (WHO) III grade], IDH1 R132 mutation, IDH2 R172 mutation, and a methylated MGMT promoter, without 1p36 and 19q13 heterozygosity loss. About 2.5 months after surgery, the patient presented with seizures, aphasia, and memory loss. Following the first-line treatment, TTFields was shown to be a valuable treatment option to control clinical symptom burden. The use of TTFields was shown to prolong progression-free survival and delay the pathological upgradation to glioblastoma. Notably, combined TTFields and chemotherapy might be beneficial in terms of risk reduction for pathological upgradation. To the our current knowledge, this is the first case report that attempts to offer possible strategies based on TTFields toward delaying pathological upgradation from anaplastic astrocytoma (WHO III grade) to glioblastoma.

Clinical practice guidelines for the management of adult diffuse gliomas.

To follow the revision of the fourth edition of WHO classification and the recent progress on the management of diffuse gliomas, the joint guideline committee of Chinese Glioma Cooperative Group (CGCG), Society for Neuro-Oncology of China (SNO-China) and Chinese Brain Cancer Association (CBCA) updated the clinical practice guideline. It provides recommendations for diagnostic and management decisions, and for limiting unnecessary treatments and cost. The recommendations focus on molecular and pathological diagnostics, and the main treatment modalities of surgery, radiotherapy, and chemotherapy. In this guideline, we also integrated the results of some clinical trials of immune therapies and target therapies, which we think are ongoing future directions. The guideline should serve as an application for all professionals involved in the management of patients with adult diffuse glioma and also a source of knowledge for insurance companies and other institutions involved in the cost regulation of cancer care in China and other countries.

A novel extended Kalman filter with support vector machine based method for the automatic diagnosis and segmentation of brain tumors.

BACKGROUND: Brain tumors are life-threatening, and their early detection is crucial for improving survival rates. Conventionally, brain tumors are detected by radiologists based on their clinical experience. However, this process is inefficient. This paper proposes a machine learning-based method to 1) determine the presence of a tumor, 2) automatically segment the tumor, and 3) classify it as benign or malignant. METHODS: We implemented an Extended Kalman Filter with Support Vector Machine (EKF-SVM), an image analysis platform based on an SVM for automated brain tumor detection. A development dataset of 120 patients which supported by Tiantan Hospital was used for algorithm training. Our machine learning algorithm has 5 components as follows. Firstly, image standardization is applied to all the images. This is followed by noise removal with a non-local means filter, and contrast enhancement with improved dynamic histogram equalization. Secondly, a gray-level co-occurrence matrix is utilized for feature extraction to get the image features. Thirdly, the extracted features are fed into a SVM for classify the MRI initially, and an EKF is used to classify brain tumors in the brain MRIs. Fourthly, cross-validation is used to verify the accuracy of the classifier. Finally, an automatic segmentation method based on the combination of k-means clustering and region growth is used for detecting brain tumors. RESULTS: With regard to the diagnostic performance, the EKF-SVM had a 96.05% accuracy for automatically classifying brain tumors. Segmentation based on k-means clustering was capable of identifying the tumor boundaries and extracting the whole tumor. CONCLUSION: The proposed EKF-SVM based method has better classification performance for positive brain tumor images, which was mainly due to the dearth of negative examples in our dataset. Therefore, future work should obtain more negative examples and investigate the performance of deep learning algorithms such as the convolutional neural networks for automatic diagnosis and segmentation of brain tumors.

Whole-transcriptome sequencing profiling identifies functional and prognostic signatures in patients with PTPRZ1-MET fusion-negative secondary glioblastoma multiforme.

Gliomas are the most common type of primary brain tumor in adults with a high mortality rate. Low-grade gliomas progress to glioblastoma multiforme (GBM) in the majority of cases, forming secondary GBM (sGBM), followed by rapid fatal clinical outcomes. Protein tyrosine phosphatase receptor type Z1 (PTPRZ1)-MET proto-oncogene receptor tyrosine kinase (MET) (ZM) fusion has been identified as a biomarker for sGBM that is involved in glioma progression, but the mechanism of gliomagenesis and pathology of ZM-negative sGBM has remained to be fully elucidated. A whole-transcriptome signature is thus required to improve the outcome prediction for patients with sGBM without ZM fusion. In the present study, whole-transcriptome sequencing on 42 sGBM samples with or without ZM fusion from the Chinese Glioma Genome Atlas database identified mRNAs with differential expression between patients with and without ZM fusion and the most significant survival-associated genes were identified. A 6-gene signature was identified as a novel prognostic model reflecting survival probability in patients with ZM-negative sGBM. Clinical characteristics in patients with a high or low risk score value were analyzed with the Kaplan-Meier method and a two-sided log-rank test. In addition, ZM-negative sGBM patients with a high risk score exhibited an increase in immune cells, NF-κB-induced pathway activation and a decrease in endothelial cells compared with those with a low risk score. The present study demonstrated the potential use of a next-generation sequencing-based cancer gene signature in patients with ZM-negative sGBM, indicating possible clinical therapeutic strategies for further treatment of such patients.

Pre-treatment neutrophils count as a prognostic marker to predict chemotherapeutic response and survival outcomes in glioma: a single-center analysis of 288 cases.

BACKGROUND: Glioma is the most common and deadliest malignant primary intracranial brain tumor in adults. It remains unclear whether the pre-treatment peripheral blood test parameters might serve as biomarkers for treatment outcome. The purpose of the current study was to investigate the predictive and prognostic value of pre-treatment peripheral blood test parameters in glioma. METHODS: In total, 288 glioma patients with complete results of pre-operation peripheral blood test, clinical information and tumor transcriptome data from Chinese Glioma Genome Atlas (CGGA project) were enrolled in our study. Receiver operating characteristic (ROC) curve, Kaplan-Meier analysis and Cox proportional hazards models were performed to evaluate the diagnostic and prognostic value of pre-treatment peripheral blood test parameters in glioma patients. RESULTS: The white blood cells (WBC) and neutrophils (NEU) counts and neutrophil to lymphocyte ratio (NLR) were positively correlated with tumor grade. IDH mutation and 1p/19q codeletion occurred frequently in patients with higher NEU counts and NLR. We also found that glioma patients with higher NEU or NLR were more likely to have a significantly decreased overall survival. Meanwhile, NEU count was a prognostic marker for TMZ standard treatment GBM patients or IDH wild-type GBM patients. Further biological and functional analysis revealed that NEU count was positively associated with cell cycle and DNA duplication. CONCLUSION: Our study was first to highlight the clinical significance of NEU count in GBM clinical treatment, which should be fully valued for clinical prediction and precise management.

Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.

This study aimed to identify the optimal radiomic machine-learning classifier for differentiating glioblastoma (GBM) from solitary brain metastases (MET) preoperatively. Four hundred and twelve patients with solitary brain tumors (242 GBM and 170 solitary brain MET) were divided into training (n = 227) and test (n = 185) cohorts. Radiomic features extraction was performed with PyRadiomics software. In the training cohort, twelve feature selection methods and seven classification methods were evaluated to construct favorable radiomic machine-learning classifiers. The performance of the classifiers was evaluated using the mean area under the curve (AUC) and relative standard deviation in percentile (RSD). In the training cohort, thirteen classifiers had favorable predictive performances (AUC≥0.95 and RSD ≤6). In the test cohort, receiver operating characteristic (ROC) curve analysis revealed that support vector machines (SVM) + least absolute shrinkage and selection operator (LASSO) (AUC, 0.90) classifiers had the highest prediction efficacy. Furthermore, the clinical performance of the best classifier was superior to neuroradiologists in accuracy, sensitivity, and specificity. In conclusion, employing radiomic machine-learning technology could help neuroradiologist in differentiating GBM from solitary brain MET preoperatively.

Classifying lower grade glioma cases according to whole genome gene expression.

OBJECTIVE: To identify a gene-based signature as a novel prognostic model in lower grade gliomas. RESULTS: A gene signature developed from HOXA7, SLC2A4RG and MN1 could segregate patients into low and high risk score groups with different overall survival (OS), and was validated in TCGA RNA-seq and GSE16011 mRNA array datasets. Receiver operating characteristic (ROC) was performed to show that the three-gene signature was more sensitive and specific than histology, grade, age, IDH1 mutation and 1p/19q co-deletion. Gene Set Enrichment Analysis (GSEA) and GO analysis showed high-risk samples were associated with tumor associated macrophages (TAMs) and highly invasive phenotypes. Moreover, HOXA7-siRNA inhibited migration and invasion in vitro, and downregulated MMP9 at the protein level in U251 glioma cells. METHODS: A cohort of 164 glioma specimens from the Chinese Glioma Genome Atlas (CGGA) array database were assessed as the training group. TCGA RNA-seq and GSE16011 mRNA array datasets were used for validation. Regression analyses and linear risk score assessment were performed for the identification of the three-gene signature comprising HOXA7, SLC2A4RG and MN1. CO NCLUSIONS: We established a three-gene signature for lower grade gliomas, which could independently predict overall survival (OS) of lower grade glioma patients with higher sensitivity and specificity compared with other clinical characteristics. These findings indicate that the three-gene signature is a new prognostic model that could provide improved OS prediction and accurate therapies for lower grade glioma patients.

Comparative Analysis of Matrix Metalloproteinase Family Members Reveals That MMP9 Predicts Survival and Response to Temozolomide in Patients with Primary Glioblastoma.

BACKGROUND: Glioblastoma multiform (GBM) is the most common malignant primary brain tumor in adults. Radiotherapy plus concomitant and adjuvant TMZ chemotherapy is the current standard of care for patients with GBM. Matrix metalloproteinases (MMPs), a family of zinc-dependent endopeptidases, are key modulators of tumor invasion and metastasis due to their ECM degradation capacity. The aim of the present study was to identify the most informative MMP member in terms of prognostic and predictive ability for patients with primary GBM. METHOD: The mRNA expression profiles of all MMP genes were obtained from the Chinese Glioma Genome Atlas (CGGA), the Repository for Molecular Brain Neoplasia Data (REMBRANDT) and the GSE16011 dataset. MGMT methylation status was also examined by pyrosequencing. The correlation of MMP9 expression with tumor progression was explored in glioma specimens of all grades. Kaplan-Meier analysis and Cox proportional hazards regression models were used to investigate the association of MMP9 expression with survival and response to temozolomide. RESULTS: MMP9 was the only significant prognostic factor in three datasets for primary glioblastoma patients. Our results indicated that MMP9 expression is correlated with glioma grade (p<0.0001). Additionally, low expression of MMP9 was correlated with better survival outcome (OS: p = 0.0012 and PFS: p = 0.0066), and MMP9 was an independent prognostic factor in primary GBM (OS: p = 0.027 and PFS: p = 0.032). Additionally, the GBM patients with low MMP9 expression benefited from temozolomide (TMZ) chemotherapy regardless of the MGMT methylation status. CONCLUSIONS: Patients with primary GBMs with low MMP9 expression may have longer survival and may benefit from temozolomide chemotherapy.

Classification based on mutations of TERT promoter and IDH characterizes subtypes in grade II/III gliomas.

BACKGROUND: Grade II and III gliomas have variable clinical behaviors, showing the distinct molecular genetic alterations from glioblastoma (GBM), many of which eventually transform into more aggressive tumors. Since the classifications of grade II/III gliomas based on the genetic alterations have been recently emerging, it is now a trend to include molecular data into the standard diagnostic algorithm of glioma. METHODS: Here we sequenced TERT promoter mutational status (TERTp-mut) in the DNA of 377 grade II/III gliomas and analyzed the clinical factors, molecular aberrations, and transcriptome profiles. RESULTS: We found that TERTp-mut occurred in 145 of 377 grade II and III gliomas (38.5%), mutually exclusive with a TP53 mutation (TP53-mut; P < .001) and coincident with a 1p/19q co-deletion (P = .002). TERTp-mut was an independent predictive factor of a good prognosis in all patients (P = .048). It has been an independent factor associated with a good outcome in the IDH mutation (IDH-mut) subgroup (P = .018), but it has also been associated with a poor outcome in the IDH wild-type (IDH-wt) subgroup (P = .049). Combining TERTp-mut and IDH-mut allowed the grade II/III malignancies to be reclassified into IDH-mut/TERTp-mut, IDH-mut only, TERTp-mut only, and IDH-wt/TERTp-wt. 1p/19q co-deletion, TP53-muts, Ki-67 expression differences, and p-MET expression differences characterized IDH-mut/TERTp-mut, IDH-mut only, TERTp-mut only, and IDH-wt/TERTp-wt subtypes, respectively. CONCLUSIONS: Our results showed that TERTp-mut combined with IDH-mut allowed simple classification of grade II/III gliomas for stratifying patients and clarifying diagnostic accuracy by supplementing standard histopathological criteria.

CGCG clinical practice guidelines for the management of adult diffuse gliomas.

The Chinese Glioma Cooperative Group (CGCG) Guideline Panel for adult diffuse gliomas provided recommendations for diagnostic and therapeutic procedures. The Panel covered all fields of expertise in neuro-oncology, i.e. neurosurgeons, neurologists, neuropathologists, neuroradiologists, radiation and medical oncologists and clinical trial experts. The task made clearer and more transparent choices about outcomes considered most relevant through searching the references considered most relevant and evaluating their value. The scientific evidence of papers collected from the literature was evaluated and graded based on the Oxford Centre for Evidence-based Medicine Levels of Evidence and recommendations were given accordingly. The recommendations will provide a framework and assurance for the strategy of diagnostic and therapeutic measures to reduce complications from unnecessary treatment and cost. The guideline should serve as an application for all professionals involved in the management of patients with adult diffuse glioma and also as a source of knowledge for insurance companies and other institutions involved in the cost regulation of cancer care in China.

Clinicopathological factors predictive of postoperative seizures in patients with gliomas.

PURPOSE: Epilepsy is one of the most common manifestations in gliomas and has a severe effect on the life expectancy and quality of life of patients. The aim of our study was to assess the potential connections between clinicopathological factors and postoperative seizure. METHOD: We retrospectively investigated a group of 147 Chinese high-grade glioma (HGG) patients with preoperative seizure to examine the correlation between postoperative seizure and clinicopathological factors and prognosis. Univariate analyses and multivariate logistic regression analyses were performed to identify factors associated with postoperative seizures. Survival function curves were calculated using the Kaplan-Meier method. RESULTS: 53 patients (36%) were completely seizure-free (Engel class I), and 94 (64%) experienced a postoperative seizure (Engel classes II, III, and IV). A Chi-squared analysis showed that anaplastic oligodendroglioma/anaplastic oligoastrocytoma (AO/AOA) (P=0.05), epidermal growth factor receptor (EGFR) expression (P=0.0004), O(6)-methylguanine DNA methyltransferase (MGMT) expression (P=0.011), and phosphatase and tensin homolog (PTEN) expression (P=0.045) were all significantly different. A logistic regression analysis showed that MGMT expression (P=0.05), EGFR expression (P=0.001), and AO/AOA (P=0.038) are independent factors of postoperative seizure. Patients with lower MGMT and EGFR expression and AO/AOA showed more frequent instances of postoperative seizure. Postoperative seizure showed no statistical significance on overall survival (OS) and progression-free survival (PFS). CONCLUSION: Our study identified clinicopathological factors related to postoperative seizure in HGGs and found two predictive biomarkers of postoperative seizure: MGMT and EGFR. These findings provided insight treatment strategies aimed at prolonging survival and improving quality of life.

Intracranial benign fibrous histiocytoma mimicking parasagittal meningioma: report of two cases and review of literature.

Primary benign fibrous histiocytoma involving the central nervous system is an exceedingly rare tumor with most cases originating from the dura or parenchymal tissue. Diagnosis of primary benign fibrous histiocytoma is difficult due to its confusing histopathological characteristics. Two cases of primary intracranial benign fibrous histiocytoma mimicking parasagittal meningioma are presented in this report. Both tumors were gross totally resected and the patients showed no evidence of recurrence at follow-up of 12 months. The clinical features and prognosis of this rare tumor that were reported previously in the literature were also reviewed. Histopathological examination coupled with immunohistochemical staining is proved to be the convincing diagnostic method for the primary benign fibrous histiocytoma. Surgical resection is the recommendation for the therapy of the tumor.

IDH mutation and MGMT promoter methylation in glioblastoma: results of a prospective registry.

BACKGROUND: The relative contribution of isocitrate dehydrogenase mutations (mIDH) and O6-methylguanine-DNA methyltransferase promoter methylation (methMGMT) as biomarkers in glioblastoma remain poorly understood. METHODS: We investigated the association between methMGMT and mIDH with progression free survival and overall survival in a prospectively collected molecular registry of 274 glioblastoma patients. RESULTS: For glioblastoma patients who underwent Temozolomide and Radiation Therapy, OS and PFS was most favorable for those with tumors harboring both mIDH and methMGMT (median OS: 35.8 mo, median PFS: 27.5 mo); patients afflicted glioblastomas with either mIDH or methMGMT exhibited intermediate OS and PFS (mOS: 36 and 17.1 mo; mPFS: 12.2 mo and 9.9 mo, respectively); poorest OS and PFS was observed in wild type IDH1 (wtIDH1) glioblastomas that were MGMT promoter unmethylated (mOS: 15 mo, mPFS: 9.7 mo). For patients with wtIDH glioblastomas, TMZ+RT was associated with improved OS and PFS relative to patients treated with RT (OS: 15.4 mo v 9.6 mo, p < 0.001; PFS: 9.9 mo v 6.5 mo, p < 0.001). While TMZ+RT and RT treated mIDH patients exhibited improved overall survival relative to those with wtIDH, there were no differences between the TMZ+RT or RT group. These results suggest that mIDH1 conferred resistance to TMZ. Supporting this hypothesis, exogenous expression of mIDH1 in independent astrocytoma/glioblastoma lines resulted in a 3-10 fold increase in TMZ resistance after long-term passage. CONCLUSIONS: Our study demonstrates IDH mutation and MGMT promoter methylation status independently associate with favorable outcome in TMZ+RT treated glioblastoma patients. However, these biomarkers differentially impact clinical TMZ response.

Comparison of the clinical efficacy of temozolomide (TMZ) versus nimustine (ACNU)-based chemotherapy in newly diagnosed glioblastoma.

Although temozolomide (TMZ) replaced nitrosoureas as the standard initial chemotherapy for glioblastoma (GBM), no studies have compared TMZ with nimustine (ACNU), a nitrosourea agent widely used in central Europe and most Asian regions. One hundred thirty-five patients with GBM who underwent extensive tumor resection in our institution received both radiation and chemotherapy as initial treatment, 34 received TMZ and 101 ACNU-based (ACNU plus teniposide or cisplatin) chemotherapy. Efficacy analysis included overall survival (OS) and progression-free survival (PFS). The following prognostic factors were taken into account: age, performance status, extent of resection, and O(6)-methylguanine-DNA-methyltransferase (MGMT) gene status. The median OS was superior in the TMZ versus the ACNU group (p = 0.011), although MGMT gene silencing, which is associated with a striking survival benefit from alkylating agents, was more frequent in the ACNU group. In multivariate Cox analysis adjusting for the common prognostic factors, TMZ chemotherapy independently predicted a favorable outcome (p = 0.002 for OS, hazard ratio [HR], 0.45; p = 0.011 for PFS, HR, 0.56). Given that >40 % of patients in ACNU group did not receive the intensive chemotherapy cycles because of severe hematological and nonhematological toxicity, we performed a further subanalysis for patients who received at least 4 cycles of chemotherapy. Although a modest improvement in survival occurred in this ACNU subgroup, the efficacy was still inferior to that in the TMZ cohort. Our data suggest that the survival benefit of TMZ therapy is superior to that of an ACNU-based regimen in patients with extensive tumor resection, also shows greater tolerability.

Prognostic and predictive value of p53 in low MGMT expressing glioblastoma treated with surgery, radiation and adjuvant temozolomide chemotherapy.

OBJECTIVE: To assess the prognostic and predictive significance of p53 protein expression in low O6-methylguanine-DNA methyltransferase (MGMT) expressing glioblastoma multiform (GBM) treated with combined therapy. METHODS: The authors reviewed the clinical outcomes of 46 low MGMT expressing GBM patients who had undergone surgery, conventional local radiotherapy and temozolomide chemotherapy. Correlation between p53 expression level and clinical outcomes were analysed with univariate and multivariate Cox model. RESULTS: Patients with low p53 expression had a significantly improved progression free survival (PFS) (p=0.015) and overall survival (OS) (p=0.047) compared to those with high expression. On both univariate and multivariate analyses, low p53 expression persisted as a significant independent favorable prognostic factor for PFS (p=0.017). Pre-operative Karnofsky performance status score (p=0.029), tumor resection extent (p=0.045) and p53 expression level (p=0.038) were significant independent prognostic factors for OS. CONCLUSION: In these low MGMT expressing GBM patients with combined treatment, low p53 expression was a significant independent favorable prognostic factor for both PFS and OS. In addition to MGMT, p53 may be another stratification variable in the future therapeutic trials.