Prognosis Of Brain Tumors Research Articles

Expression and prognostic significance of microsomal triglyceride transfer protein in brain tumors: a retrospective cohort study.

Glioblastoma (GBM) is the most common malignant brain tumor and has poor survival. An elevated cholesterol level is involved occurrence and progression of brain tumors. Microsomal triglyceride transfer protein (MTTP) is a target for lowering lipids, and its inhibition helps to improve hyperlipidemia. However, whether the altered expression of MTTP affects the development and prognosis of brain tumors is currently unidentified. The purpose of this study is to determine MTTP as a prognostic marker for brain tumors. Data for patients with brain cancers and control brain tissue were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The datasets were analyzed using Mann-Whitney U-test or t-test to compare the expression of MTTP in normal and brain tumor tissues. To examine whether MTTP affected the prognosis of patients with brain tumors, log-rank test and multivariable Cox proportional hazard regression were conducted. The expression of MTTP was significantly upregulated in brain tumors and was correlated with age, tumor stage, and isocitrate dehydrogenase (IDH) mutation. Importantly, increased MTTP expression in brain tumors is associated with poor patient survival. High MTTP expression is associated with brain tumor development, tumor stage, and prognosis. Therefore, MTTP is an independent prognostic indicator for brain tumors, which can serve as one of the possible targets for adjuvant treatment of GBM.

Computational Fractal-Based Analysis of Brain Tumor Microvascular Networks.

Brain parenchyma microvasculature is set in disarray in the presence of tumors, and malignant brain tumors are among the most vascularized neoplasms in humans. As microvessels can be easily identified in histologic specimens, quantification of microvascularity can be used alone or in combination with other histological features to increase the understanding of the dynamic behavior, diagnosis, and prognosis of brain tumors. Different brain tumors, and even subtypes of the same tumor, show specific microvascular patterns, as a kind of "microvascular fingerprint," which is particular to each histotype. Reliable morphometric parameters are required for the qualitative and quantitative characterization of the neoplastic angioarchitecture, although the lack of standardization of a technique able to quantify the microvascular patterns in an objective way has limited the "morphometric approach" in neuro-oncology.In this chapter, we focus on the importance of computational-based morphometrics, for the objective description of tumoral microvascular fingerprinting. By also introducing the concept of "angio-space," which is the tumoral space occupied by the microvessels, we here present fractal analysis as the most reliable computational tool able to offer objective parameters for the description of the microvascular networks.The spectrum of different angioarchitectural configurations can be quantified by means of Euclidean and fractal-based parameters in a multiparametric analysis, aimed to offer surrogate biomarkers of cancer. Such parameters are here described from the methodological point of view (i.e., feature extraction) as well as from the clinical perspective (i.e., relation to underlying physiology), in order to offer new computational parameters to the clinicians with the final goal of improving diagnostic and prognostic power of patients affected by brain tumors.

Diagnostic Value of Nestin Expression in Adult Gliomas.

Introduction. Nestin, a type VI intermediate filament, is expressed in neuroepithelial cells during embryogenesis and has been expressed in various human tumors. Recent studies reported that the expression was associated with poor prognosis in brain tumors, but the results were inconclusive. In this study, we evaluated usefulness of nestin expression as a prognostic biomarker in consideration of IDH mutation and the World Health Organization (WHO) classification fifth edition. Methods. To investigate nestin expression, immunohistochemistry was performed on 92 adult brain gliomas using tissue microarrays. We analyzed the clinical characteristics and survival outcomes according to nestin expression and examined whether nestin expression alone affects the prognosis, independent of IDH mutation. Results. Sixty patients (65.2%) were nestin-positive (weak and strong). Nestin expression and intensity were significantly correlated with pathologic diagnosis and IDH mutation. The patients with high-grade gliomas showed a higher frequency and stronger intensity of nestin expression than those with low-grade gliomas (P < .001). The majority (93.6%) of gliomas with IDH mutation showed no expression or weak positivity. Multivariate Cox proportional hazard regression analysis for overall survival demonstrated that nestin expression (weak, hazard ratio [HR] 5.39, P = .036; strong, HR 8.43, P = .007) was an independent prognostic factor. Moreover, patients with nestin-expressing glioma showed shorter survival (P < .001). Conclusions. Nestin seems to be strongly expressed in the vast majority of glioblastomas, IDH-wildtype and rarely in IDH-mutant gliomas. Clear correlation between nestin expression and pathologic diagnosis makes an accurate patient diagnosis. Expression and intensity of nestin were significantly correlated with worse survival.

Super‐resolution of brain tumor MRI images based on deep learning

To explore and evaluate the performance of MRI-based brain tumor super-resolution generative adversarial network (MRBT-SR-GAN) for improving the MRI image resolution in brain tumors. A total of 237 patients from December 2018 and April 2020 with T2-fluid attenuated inversion recovery (FLAIR) MR images (one image per patient) were included in the present research to form the super-resolution MR dataset. The MRBT-SR-GAN was modified from the enhanced super-resolution generative adversarial networks (ESRGAN) architecture, which could effectively recover high-resolution MRI images while retaining the quality of the images. The T2-FLAIR images from the brain tumor segmentation (BRATS) dataset were used to evaluate the performance of MRBT-SR-GAN contributed to the BRATS task. The super-resolution T2-FLAIR images yielded a 0.062 dice ratio improvement from 0.724 to 0.786 compared with the original low-resolution T2-FLAIR images, indicating the robustness of MRBT-SR-GAN in providing more substantial supervision for intensity consistency and texture recovery of the MRI images. The MRBT-SR-GAN was also modified and generalized to perform slice interpolation and other tasks. MRBT-SR-GAN exhibited great potential in the early detection and accurate evaluation of the recurrence and prognosis of brain tumors, which could be employed in brain tumor surgery planning and navigation. In addition, this technique renders precise radiotherapy possible. The design paradigm of the MRBT-SR-GAN neural network may be applied for medical image super-resolution in other diseases with different modalities as well.

Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM.

Background and Objectives: Clinical diagnosis has become very significant in today’s health system. The most serious disease and the leading cause of mortality globally is brain cancer which is a key research topic in the field of medical imaging. The examination and prognosis of brain tumors can be improved by an early and precise diagnosis based on magnetic resonance imaging. For computer-aided diagnosis methods to assist radiologists in the proper detection of brain tumors, medical imagery must be detected, segmented, and classified. Manual brain tumor detection is a monotonous and error-prone procedure for radiologists; hence, it is very important to implement an automated method. As a result, the precise brain tumor detection and classification method is presented. Materials and Methods: The proposed method has five steps. In the first step, a linear contrast stretching is used to determine the edges in the source image. In the second step, a custom 17-layered deep neural network architecture is developed for the segmentation of brain tumors. In the third step, a modified MobileNetV2 architecture is used for feature extraction and is trained using transfer learning. In the fourth step, an entropy-based controlled method was used along with a multiclass support vector machine (M-SVM) for the best features selection. In the final step, M-SVM is used for brain tumor classification, which identifies the meningioma, glioma and pituitary images. Results: The proposed method was demonstrated on BraTS 2018 and Figshare datasets. Experimental study shows that the proposed brain tumor detection and classification method outperforms other methods both visually and quantitatively, obtaining an accuracy of 97.47% and 98.92%, respectively. Finally, we adopt the eXplainable Artificial Intelligence (XAI) method to explain the result. Conclusions: Our proposed approach for brain tumor detection and classification has outperformed prior methods. These findings demonstrate that the proposed approach obtained higher performance in terms of both visually and enhanced quantitative evaluation with improved accuracy.

Machine and Deep Learning Methods Enable the Accurate and Efficient Segmentation, Grading, Diagnosis and Prognosis of Brain Tumors

Early diagnosis and management of brain tumors are crucial and save patient from adverse effects or even death. Computer science advances have been made such that artificial intelligence (AI) brings enhancement to the classification, segmentation, grading and staging of brain tumors while assisting in the prediction of the diagnosis, treatment, future care and the prognosis, based on the analysis of MRI data. This review is aimed at enhancing the technical awareness of radiology residents, practitioners and physicians in general medicine, neurology and neurosurgery. Also, researchers from computer science, neuroscience and other relevant areas will find this review very helpful. We searched for relevant articles in Google Scholar, Scopus, and Pubmed, using these keywords: artificial intelligence, machine learning, deep learning, brain tumors, magnetic resonance imaging, MRI, and radiological image analyses. Out of 138 articles, 17 were selected, which described various AI algorithms to analyze brain tumor MRI data. This review presents the significant findings and conclusions of the selected articles, and the role of machine and deep learning methods in the classification, segmentation, diagnosis and prognosis of brain tumors. These AI methods enable radiologists to perform accurate, rapid and efficient mapping, classification, segmentation, grading and staging of brain tumors while predicting the diagnosis, treatment, future care and the clinical prognosis of patients with brain tumors.

Immunoengineering in glioblastoma imaging and therapy.

Patients diagnosed with glioblastoma have poor prognosis. Conventional treatment strategies such as surgery, chemotherapy, and radiation therapy demonstrated limited clinical success and have considerable side effects on healthy tissues. A central challenge in treating brain tumors is the poor permeability of the blood-brain barrier (BBB) to therapeutics. Recently, various methods based on immunotherapy and nanotechnology have demonstrated potential in addressing these obstacles by enabling precise targeting of brain tumors to minimize adverse effects, while increasing targeted drug delivery across the BBB. In addition to treating the tumors, these approaches may be used in conjunction with imaging modalities, such as magnetic resonance imaging and positron emission tomography to enhance the prognosis procedures. This review aims to provide mechanistic understanding of immune system regulation in the central nervous system and the benefits of nanoparticles in the prognosis of brain tumors. This article is characterized under: Diagnostic Tools > in vivo Nanodiagnostics and Imaging Nanotechnology Approaches to Biology > Cells at the Nanoscale Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Seizure characteristics are related to tumor pathology in children with brain tumors

ObjectivesBrain tumors are found in 1–3% of children with newly onset of seizures. Understanding the impact of seizures on pediatric brain tumors and factors between seizures and brain tumors are crucial. This study aimed to evaluate the association of pediatric brain tumors on seizure occurrence and the relationships between seizures and the diagnosis and prognosis of brain tumors. MethodsIn a cohort of 184 children with newly diagnosed brain tumors between August 2012 and September 2017, 56 patients with brain tumor-associated seizures were evaluated for their clinical characteristics, electroencephalography (EEG), tumor pathology, response to anti-epileptic drugs (AEDs) and their seizure outcome. ResultsThe prevalence of seizures in our cohort of children with brain tumors was 30.4% (n = 56/184). Supratentorial tumors were more commonly associated with seizures than were infratentorial tumors (89.3% vs. 10.7%; P < 0.01). Among patients with tumor-associated seizures, the tumors were most commonly located in the temporal lobe (32.1%), followed by the frontal (21.4%) and parietal lobes (16.1%) and other cortices. Thirty-four patients (60.7%) had benign tumor pathology and 22 patients (39.3%) had malignant tumor pathology; low grade astrocytoma (n = 15; 26.8%) was the most common histology type. Seizure onset at initial presentation was more frequently observed in the patients with benign tumors, whereas the patients with malignant tumors tended to have delayed seizure onset (P < 0.01). On EEGs, focal epileptiform discharges were more commonly seen in benign tumors, whereas focal and diffuse slow waves were most commonly associated with malignant tumors. Levetiracetam was the most commonly prescribed AED to treat brain-tumor-associated seizures. ConclusionsIn children with brain tumors, higher seizure frequency with normal neurologic examination was more prevalent in benign tumors, whereas less frequent seizures with focal weakness and signs of increased intracranial pressure were more indicative of malignancy. Delayed onset of seizures was more often observed in the children with malignant brain tumors. Seizure characteristics play an important role in relation to tumor biology and epileptogenesis in pediatric brain tumors.

Tandem High-dose Chemotherapy and Autologous Stem Cell Transplantation in Children with Brain Tumors : Review of Single Center Experience.

The prognosis of brain tumors in children has improved for last a few decades. However, the prognosis remains dismal in patients with recurrent brain tumors. The outcome for infants and young children in whom the use of radiotherapy (RT) is very limited because of unacceptable long-term adverse effect of RT remains poor. The prognosis is also not satisfactory when a large residual tumor remains after surgery or when leptomeningeal seeding is present at diagnosis. In this context, a strategy using high-dose chemotherapy and autologous stem cell transplantation (HDCT/auto-SCT) has been explored to improve the prognosis of recurrent or high-risk brain tumors. This strategy is based on the hypothesis that chemotherapy dose escalation might result in improvement in survival rates. Recently, the efficacy of tandem HDCT/auto-SCT has been evaluated in further improving the outcome. This strategy is based on the hypothesis that further dose escalation might result in further improvement in survival rates. At present, the number of studies employing tandem HDCT/auto-SCT for brain tumors is limited. However, results of these pilot studies suggest that tandem HDCT/auto-SCT may further improve the outcome. In this review, we will summarize our single center experience with tandem HDCT/auto-SCT for recurrent or high-risk brain tumors.

Non-invasive grading and prognosis of Brain Tumours using fuzzy inferencing for rural India

A Brain tumour is an abnormal tissue that grows by uncontrolled cell division. CT and MRI scans are the two most important and commonly used imaging modalities to identify the presence of a brain tumour. The severity of the tumour is graded and prognosis can be predicted only after a biopsy procedure. We have developed a Non-invasive grading and prognosis prediction methodology using CT and MRI images that obviates the need of biopsy to confirm the presence of tumour and thereby helps the clinician in accurate diagnosis.

Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status.

Predicting methylation of the O6-methylguanine methyltransferase (MGMT) gene status utilizing MRI imaging is of high importance since it is a predictor of response and prognosis in brain tumors. In this study, we compare three different residual deep neural network (ResNet) architectures to evaluate their ability in predicting MGMT methylation status without the need for a distinct tumor segmentation step. We found that the ResNet50 (50 layers) architecture was the best performing model, achieving an accuracy of 94.90% (+/-3.92%) for the test set (classification of a slice as no tumor, methylated MGMT, or non-methylated). ResNet34 (34 layers) achieved 80.72% (+/-13.61%) while ResNet18 (18 layers) accuracy was 76.75% (+/-20.67%). ResNet50 performance was statistically significantly better than both ResNet18 and ResNet34 architectures (p<0.001). We report a method that alleviates the need of extensive preprocessing and acts as a proof of concept that deep neural architectures can be used to predict molecular biomarkers from routine medical images.

Abstract 2394: Subpopulation of infiltrating lymphocyte and MGMT methylation in brain tumor prognosis

Abstract Background: Clinicopathological features including MGMT methylation have been correlated to prognosis in Glioblastoma (GBM). Tumor infiltrating lymphocytes (TILs) are predictive of clinical outcome in different malignancies. We evaluated association between clinicopathological features including MGMT methylation and IHC infiltrating immune cell subpopulations in 43 resected GBM cases who came from 2008 to 2013. Methods: A prospective evaluation of TIL (CD3, CD4, CD8 and CD20) and macrophages (CD68 and CD163) through H&amp;E was performed by a pathologist and evaluated Immune cell subpopulations was evaluated through scanning, and software analysis (BX63 Olympus and TissueMorph-Visionpharm) with pathologist support. MGMT evaluation was prospectively performed by bisulphyte PCR test in paraffin samples. Results: Median age was 47.9 y, men (51.2%), Karnofsky scale 90% (62.8%), median tumor size of 5.1 cm, and partial/ complete tumor resection (22/21). Methylation MGMT test was performed in 31 cases and 17 showed un- methylated, 4 partial methylated and 10 methylated. TILs evaluation by HE showed low- intensity (29 cases), middle- intensity (1) and high- intensity level (9). Pattern of distribution was focal (9 cases), multi- focal (13) and diffuse (17). Median CD3, CD4, CD8, CD20, CD68 and CD163 were 1.6%, 0.032%, 1.6%, 0.03%, 9.1% and 2.2% (regarding total cells), respectively. Higher CD163 level was associated to male (p=0.03) and larger size (p=0.05). Higher levels of CD8 was associated to larger size (p=0.05). Higher CD8 and CD20 was associated to unmethylated MGMT (p=0.041 and 0.013). Median follow- up was 14 months. Only total resection (p&amp;lt;0.05) and MGMT methylation (p&amp;lt;0.05) was associated to outcome. Lymphocyte infiltration (H&amp;E) was not associated to outcome: Lymphocyte intensity (p=0.703), distribution (p=0.61). Longer OS was associated to lower CD4 levels (p=0.01). Conclusion: Tumor Infiltrating immune cell are not common in GBM, CD8 and CD20 was associated to unmethylation, and CD4 and MGMT methylation were associated to prognosis. Citation Format: Carlos Castaneda, Miluska Castillo, Carolina Belmar-Lopez, Sandro Casavilca, Luis Angel Bernabe, Tatiana Vidaurre. Subpopulation of infiltrating lymphocyte and MGMT methylation in brain tumor prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2394. doi:10.1158/1538-7445.AM2017-2394

Existential distress among caregivers of patients with brain tumors: a review of the literature.

Attention to existential needs is a component of comprehensive oncologic care, and understanding these needs among informal caregivers of patients with brain tumors is necessary to provide them with comprehensive psychosocial care. The purpose of this systematic review was to synthesize the literature on existential distress experienced by these informal caregivers to inform the development of psychotherapeutic interventions for this population. A systematic review was conducted using electronic medical databases. Studies that examined any element of existential distress among informal caregivers of patients with brain tumors were included. A final sample of 35 articles was reviewed. Six existential themes emerged: identity; isolation; responsibility and guilt; death anxiety; deriving meaning and personal growth; and spirituality and religion. The unique existential experience of parenting a patient with a brain tumor also emerged. Existential distress in all areas was identified as experienced early in the cancer trajectory and as a critical, unmet need. Existential distress is well documented among informal caregivers of patients with brain tumors and is a significant driving force of burden. Awareness and acknowledgement of this distress, as well as interventions to ameliorate this suffering, are needed. More candid communication between health care providers and caregivers about brain tumor prognosis and caregivers' existential distress may improve their psychosocial outcomes.

Epidemiology of brain tumors in children aged two and under: A 10-year single-institute study.

The aim of present study was to evaluate the incidence, clinical symptoms, pathological characteristics, surgical treatment strategies and prognosis of brain tumors in children aged two and under. The current study obtained data regarding 32 consecutive infants and young children aged two years and under, who were treated for brain tumors in the Children’s Hospital of Fudan University (Shanghai, China) between 2003 and 2013. The types of tumor, clinical manifestations, location, histological features, applied treatment strategies and outcomes were retrospectively evaluated. The male to female ratio was 1.13:1, and among a total of 32 tumors, 14 (43.8%) were suptratentorial and 18 (56.3%) were infratentorial. Intracranial hypertension was the most common onset symptom, and astrocytoma was the most common tumor type (10 cases; 31.3%), followed by ependymoma (nine cases; 28.1%) and medulloblastoma (six cases; 18.8%). Surgical tumor resection was performed in 20 patients (62.5%), who experienced a mean post-operative survival time of 67.6 months. By contrast, conservative treatment with medications was administered in 12 patients (37.5%), with a mean survival time of 25.3 months. Furthermore, four patients underwent conservative therapy combined with ventriculoperitoneal shunting to relieve intracranial pressure arising from cerebrospinal fluid accumulation, resulting in a mean survival time of 10.5 months. In conclusion, the present study indicates that surgical tumor resection may improve the overall prognosis of infants and young children aged two years and under who presented with brain tumors. In addition, ventriculoperitoneal shunts may facilitate pre- and post-operative improvement in clinical symptoms by relieving intracranial pressure; however, the shunts do not appear to increase long-term survival. Furthermore, high surgical risk is an important prognostic factor in this pediatric patient population.

Statin use and survival following glioblastoma multiforme

AimWhile some studies indicate a potential chemopreventive effect of statin use on the risk of glioma, the effect of statins on the prognosis of brain tumours has not yet been examined. We thus conducted a cohort study evaluating the influence of statin use on survival in patients with glioblastoma multiforme (GBM). MethodsWe identified 1562 patients diagnosed with GBM during 2000–2009 from the Danish Cancer Registry and linked this cohort to Danish nationwide demographic and health registries. Within the GBM cohort, each patient recorded as using statins prior to diagnosis (defined as ≥2 redeemed prescriptions) was matched to two statin non-users (<2 redeemed prescriptions) by propensity scores based on age, gender, year of diagnosis, comorbidity, and use of selected drugs. Cox proportional hazard models were used to compute hazard ratios (HRs) and 95% confidence intervals (CI) for all-cause death associated with prediagnostic statin use. ResultsA total of 339 GBM patients were included in the analyses. Of these, 325 died during median follow-up of 6.9 months (interquartile range: 3.8–13.4 months). Prediagnostic statin use was associated with a reduced HR of death (0.79; 95% CI: 0.63–1.00). The HRs decreased with increasing duration or intensity of prediagnostic statin use [long-term (≥5 years) statin use: HR 0.75 (95% CI: 0.47–1.20); high-intensity statin use: HR 0.66 (95% CI: 0.44–0.98)]. Additional adjustment for oncotherapeutic modalities yielded similar results (overall HR 0.80, 95% CI: 0.63–1.01). ConclusionLong-term prediagnostic statin use may improve survival following GBM.

Seizure prognosis in brain tumors: new insights and evidence-based management.

Brain tumor-related epilepsy (BTE) is common in low- and high-grade gliomas. The risk of seizures varies between 60% and 100% among low-grade gliomas and between 40% and 60% in glioblastomas. The presence of seizures in patients with brain tumors implies favorable and unfavorable factors. New-onset seizures represent an early warning sign for the presence of a brain tumor and count as a good prognostic factor for survival. Recurrence or worsening of seizures during the course of disease may signal tumor progression. Each of the modalities for tumor control (i.e., surgery, radiotherapy, chemotherapy) contributes to seizure control. Nevertheless, one third of BTE shows pharmacoresistance to antiepileptic drugs (AEDs) and may severely impair the burden of living with a brain tumor. For symptomatic therapy of BTE, seizure type and individual patient factors determine the appropriate AED. Randomized controlled trials in partial epilepsy in adults to which type BTE belongs and additional studies in gliomas indicate that levetiracetam is the agent of choice, followed by valproic acid (VPA). In the case of recurring seizures, combining these two drugs (polytherapy) seems effective and possibly synergistic. If either one is not effective or not well tolerated, lacosamide, lamotrigine, or zonisamide are additional options. A new and exciting insight is the potential contribution of VPA to prolonged survival, particularly in glioblastomas. A practice guideline on symptomatic medical management including dose schedules of AEDs is supplied.

The Degree of Global DNA Hypomethylation in Peripheral Blood Correlates with That in Matched Tumor Tissues in Several Neoplasia

There are no good blood and serum biomarkers for detection, follow up, or prognosis of brain tumors. However, they are needed for more detailed tumor classification, better prognosis estimation and selection of an efficient therapeutic strategy. The aim of this study was to use the epigenetic changes in DNA of peripheral blood samples as a molecular marker to diagnose brain tumors as well as other diseases. We have applied a very precise thin-layer chromatography (TLC) analysis of the global amount of 5-methylcytosine (m5C) in DNA from brain tumors, colon and breast cancer tissues and peripheral blood samples of the same patients. The m5C level in tissue DNA from different brain tumor types, expressed as R coefficient, changes within the range of 0.2–1.6 and overlaps with R of that of blood samples. It negatively correlates with the WHO malignancy grade. The global DNA hypomethylation quantitative measure in blood, demonstrates a big potential for development of non-invasive applications for detection of a low and a high grade brain tumors. We have also used this approach to analyze patients with breast and colon cancers. In all these cases the m5C amount in DNA cancer tissue match with data of blood. This study is the first to demonstrate the potential role of global m5C content in blood DNA for early detection of brain tumors and others diseases. So, genomic DNA hypomethylation is a promising marker for prognosis of various neoplasms as well as other pathologies.

Stem cell in alternative treatments for brain tumors: potential for gene delivery.

Despite ongoing research efforts and attempts to bring new drugs into trial, the prognosis for brain tumors remains poor. Patients with the most common and lethal intracranial neoplasia, glioblastoma multiforme (GBM), have an average survival of one year with combination of surgical resection, radiotherapy and temozolomide. One of the main problems in the treatment of GBM is getting drugs across the blood brain barrier (BBB) efficiently. In an attempt to solve this problem, there are ongoing experimental and clinical trials to deliver drugs within stem cells. The purpose for this method is the ease by which stem cells home to the brain. This review discusses the experimental and clinical applications of stem cells for GBM. We also discuss the different properties of stem cells. This information is important to understand why one stem cell would be advantageous over another in cell therapy. We provide an overview of the different drug delivery methods, gene-based treatments and cancer vaccines for GBM, including the stem cell subset.

129 Urinary Biomarkers Predict the Onset and Progression of Medulloblastoma in a Spontaneous Murine Model

INTRODUCTION: Our laboratory is interested in the development of non-invasive urinary biomarkers to improve the accuracy of diagnosis and prognosis of brain tumors. In order to better study the efficacy of urinary biomarkers in a reproducible manner, with a particular focus on the study of early onset of tumor development, we now present our experience with correlating urinary biomarker expression with tumor progression in a transgenic murine model of spontaneous medulloblastoma. METHODS: SMO/SMO mice were genotyped to confirm presence of the mutation associated with tumor development. Tumor progression naturally occurred over months 2-6 of life, with urine as collected and correlated to simultaneous imaging and pathology. Tumor presence was confirmed radiographically by PET scan and pathologically by harvesting tissue analyzed by immunohistochemistry staining for cyclin D1, a medulloblastoma-specific marker. Urine was analyzed for the presence of putative biomarker species with Western blot and quantitatively assessed with ELISA, compared with matched control animals, and subjected to statistical analysis. RESULTS: Elevated levels of urinary cathepsin-B correlated with medulloblastoma presence (confirmed by imaging and pathology). After performing a Kolmogorov-Smirnov test to prove normality, samples were subjected to a Student's 2-tailed T-test. Densitometry of western blots for Cathepsin-B showed elevated levels of protein in the urine of tumor-bearing mice but not in controls (n = 21, P = .0002). ELISA data confirmed significant increases in urinary Cathepsin-B levels in tumor-bearing SMO/SMO mice as compared to healthy controls (n = 18, P = .001). Using the 95% confidence interval, tumor mice have an average of 3-10 fold higher levels of Cathepsin-B in their urine. CONCLUSION: Urinary biomarkers reliably indicate presence of medulloblastoma in a spontaneous murine tumor model. This provides proof-of-principle data that facilitates study of tumor onset in a reliable fashion not possible in humans and has utility in the development of novel diagnostic and therapeutic techniques.

High-dose chemotherapy and autologous stem cell transplantation for pediatric brain tumors

T prognosis of brain tumors in children has improved for the last 2-3 decades. However, the prognosis remains dismal in patients with relapsed tumors. The outcome for infants and young children is also poor. For younger children, the ability to use of radiotherapy (RT) is very limited because of the unacceptable long-term adverse effects of RT. The prognosis is also not satisfactory when a large residual tumor remains after surgery or when leptomeningeal seeding is present at diagnosis. In this context, a strategy using high-dose chemotherapy and autologous stem cell transplantation (HDCT/autoSCT) has been explored to improve the prognosis of recurrent or high-risk brain tumors. It was found that at least some patients with relapsed tumors can be salvaged with HDCT/autoSCT. For infants and young children, it was possible to avoid or defer RT until 3 years of age while maintaining or improving survival rates. Investigators also have explored the efficacy of HDCT/autoSCT in patients with newly diagnosed embryonal tumors to further improve the survival rate or to reduce the craniospinal RT dose without jeopardizing the survival rate. Preliminary results were encouraging although the numbers of patients was small. Recently, a few investigators have evaluated the efficacy of sequential HDCT/autoSCT to further improve the outcome. This strategy is based on the hypothesis that further dose escalation might result in further improvement in survival rates. At present, the number of studies employing a sequential HDCT/autoSCT strategy is limited. However, preliminary results of these studies suggest that sequential HDCT/autoSCT may further improve outcomes.