Revisiting Concurrent Radiation Therapy, Temozolomide, and the Histone Deacetylase Inhibitor Valproic Acid for Patients with Glioblastoma-Proteomic Alteration and Comparison Analysis with the Standard-of-Care Chemoirradiation.

BACKGROUND: Glioblastoma (GBM) is the most common brain tumor with an overall survival (OS) of less than 30% at two years. Valproic acid (VPA) demonstrated survival benefits documented in retrospective and prospective trials, when used in combination with chemo-radiotherapy (CRT). PURPOSE: The primary goal of this study was to examine if the differential alteration in proteomic expression pre vs. post-completion of concurrent chemoirradiation (CRT) is present with the addition of VPA as compared to standard-of-care CRT. The second goal was to explore the associations between the proteomic alterations in response to VPA/RT/TMZ correlated to patient outcomes. The third goal was to use the proteomic profile to determine the mechanism of action of VPA in this setting. MATERIALS AND METHODS: Serum obtained pre- and post-CRT was analyzed using an aptamer-based SOMAScan® proteomic assay. Twenty-nine patients received CRT plus VPA, and 53 patients received CRT alone. Clinical data were obtained via a database and chart review. Tests for differences in protein expression changes between radiation therapy (RT) with or without VPA were conducted for individual proteins using two-sided t-tests, considering p-values of <0.05 as significant. Adjustment for age, sex, and other clinical covariates and hierarchical clustering of significant differentially expressed proteins was carried out, and Gene Set Enrichment analyses were performed using the Hallmark gene sets. Univariate Cox proportional hazards models were used to test the individual protein expression changes for an association with survival. The lasso Cox regression method and 10-fold cross-validation were employed to test the combinations of expression changes of proteins that could predict survival. Predictiveness curves were plotted for significant proteins for VPA response (p-value < 0.005) to show the survival probability vs. the protein expression percentiles. RESULTS: A total of 124 proteins were identified pre- vs. post-CRT that were differentially expressed between the cohorts who received CRT plus VPA and those who received CRT alone. Clinical factors did not confound the results, and distinct proteomic clustering in the VPA-treated population was identified. Time-dependent ROC curves for OS and PFS for landmark times of 20 months and 6 months, respectively, revealed AUC of 0.531, 0.756, 0.774 for OS and 0.535, 0.723, 0.806 for PFS for protein expression, clinical factors, and the combination of protein expression and clinical factors, respectively, indicating that the proteome can provide additional survival risk discrimination to that already provided by the standard clinical factors with a greater impact on PFS. Several proteins of interest were identified. Alterations in GALNT14 (increased) and CCL17 (decreased) (p = 0.003 and 0.003, respectively, FDR 0.198 for both) were associated with an improvement in both OS and PFS. The pre-CRT protein expression revealed 480 proteins predictive for OS and 212 for PFS (p < 0.05), of which 112 overlapped between OS and PFS. However, FDR-adjusted p values were high, with OS (the smallest p value of 0.586) and PFS (the smallest p value of 0.998). The protein PLCD3 had the lowest p-value (p = 0.002 and 0.0004 for OS and PFS, respectively), and its elevation prior to CRT predicted superior OS and PFS with VPA administration. Cancer hallmark genesets associated with proteomic alteration observed with the administration of VPA aligned with known signal transduction pathways of this agent in malignancy and non-malignancy settings, and GBM signaling, and included epithelial-mesenchymal transition, hedgehog signaling, Il6/JAK/STAT3, coagulation, NOTCH, apical junction, xenobiotic metabolism, and complement signaling. CONCLUSIONS: Differential alteration in proteomic expression pre- vs. post-completion of concurrent chemoirradiation (CRT) is present with the addition of VPA. Using pre- vs. post-data, prognostic proteins emerged in the analysis. Using pre-CRT data, potentially predictive proteins were identified. The protein signals and hallmark gene sets associated with the alteration in the proteome identified between patients who received VPA and those who did not, align with known biological mechanisms of action of VPA and may allow for the identification of novel biomarkers associated with outcomes that can help advance the study of VPA in future prospective trials.

Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer.

PURPOSE: The ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), radiation oncology information systems, treatment planning systems, and other cancer care and outcomes databases. We sought to create a standardized ontology for clinical data, social determinants of health, and other radiation oncology concepts and interrelationships. METHODS AND MATERIALS: The American Association of Physicists in Medicine's Big Data Science Committee was initiated in July 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra-institutional databases from EHRs. The Big Data Science Committee adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community. RESULTS: We developed the Operational Ontology for Oncology (O3), which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, and the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to 4 constituencies: device manufacturers, centers of clinical care, researchers, and professional societies. CONCLUSIONS: O3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used to create large, representative, findable, accessible, interoperable, and reusable data sets to support the scientific objectives of grant programs. The construction of comprehensive "real-world" data sets and application of advanced analytical techniques, including artificial intelligence, holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative data sets.

Machine learning based survival prediction in Glioma using large-scale registry data.

Gliomas are the most common central nervous system tumors exhibiting poor clinical outcomes. The ability to estimate prognosis is crucial for both patients and providers in order to select the most appropriate treatment. Machine learning (ML) allows for sophisticated approaches to survival prediction using real world clinical parameters needed to achieve superior predictive accuracy. We employed Cox Proportional hazards (CPH) model, Support Vector Machine (SVM) model, Random Forest (RF) model in a large glioma dataset (3462 patients, diagnosed 2000-2018) to explore the most optimal approach to survival prediction. Features employed were age, sex, surgical resection status, tumor histology and tumor site, administration of radiation therapy (RT) and chemotherapy status. Concordance index (c-index) was employed to assess the accuracy of survival time prediction. All three models performed well with prediction accuracy (CI 0.767, 0.771, 0.57 for CPH, SVM, RF models respectively) with the best performance achieved when incorporating RT and chemotherapy administration status which emerged as key predictive features. Within the subset of glioblastoma patients, similar prediction accuracy was achieved. These findings should prompt stricter clinician oversight over registry data accuracy through quality assurance as we move towards meaningful predictive ability using ML approaches in glioma.

Bias and Class Imbalance in Oncologic Data-Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets.

Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to support precision/personalized medicine initiatives such as computer-aided detection, diagnosis, prognosis, and treatment planning by using large-scale medical data. Bias and class imbalance represent two of the most pressing challenges for machine learning-based problems, particularly in medical (e.g., oncologic) data sets, due to the limitations in patient numbers, cost, privacy, and security of data sharing, and the complexity of generated data. Depending on the data set and the research question, the methods applied to address class imbalance problems can provide more effective, successful, and meaningful results. This review discusses the essential strategies for addressing and mitigating the class imbalance problems for different medical data types in the oncologic domain.

Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications.

The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.

Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition.

Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.

Automated glioma grading on conventional MRI images using deep convolutional neural networks.

PURPOSE: Gliomas are the most common primary tumor of the brain and are classified into grades I-IV of the World Health Organization (WHO), based on their invasively histological appearance. Gliomas grading plays an important role to determine the treatment plan and prognosis prediction. In this study we propose two novel methods for automatic, non-invasively distinguishing low-grade (Grades II and III) glioma (LGG) and high-grade (grade IV) glioma (HGG) on conventional MRI images by using deep convolutional neural networks (CNNs). METHODS: All MRI images have been preprocessed first by rigid image registration and intensity inhomogeneity correction. Both proposed methods consist of two steps: (a) three-dimensional (3D) brain tumor segmentation based on a modification of the popular U-Net model; (b) tumor classification on segmented brain tumor. In the first method, the slice with largest area of tumor is determined and the state-of-the-art mask R-CNN model is employed for tumor grading. To improve the performance of the grading model, a two-dimensional (2D) data augmentation has been implemented to increase both the amount and the diversity of the training images. In the second method, denoted as 3DConvNet, a 3D volumetric CNNs is applied directly on bounding image regions of segmented tumor for classification, which can fully leverage the 3D spatial contextual information of volumetric image data. RESULTS: The proposed schemes were evaluated on The Cancer Imaging Archive (TCIA) low grade glioma (LGG) data, and the Multimodal Brain Tumor Image Segmentation (BraTS) Benchmark 2018 training datasets with fivefold cross validation. All data are divided into training, validation, and test sets. Based on biopsy-proven ground truth, the performance metrics of sensitivity, specificity, and accuracy are measured on the test sets. The results are 0.935 (sensitivity), 0.972 (specificity), and 0.963 (accuracy) for the 2D Mask R-CNN based method, and 0.947 (sensitivity), 0.968 (specificity), and 0.971 (accuracy) for the 3DConvNet method, respectively. In regard to efficiency, for 3D brain tumor segmentation, the program takes around ten and a half hours for training with 300 epochs on BraTS 2018 dataset and takes only around 50 s for testing of a typical image with a size of 160 × 216 × 176. For 2D Mask R-CNN based tumor grading, the program takes around 4 h for training with around 60 000 iterations, and around 1 s for testing of a 2D slice image with size of 128 × 128. For 3DConvNet based tumor grading, the program takes around 2 h for training with 10 000 iterations, and 0.25 s for testing of a 3D cropped image with size of 64 × 64 × 64, using a DELL PRECISION Tower T7910, with two NVIDIA Titan Xp GPUs. CONCLUSIONS: Two effective glioma grading methods on conventional MRI images using deep convolutional neural networks have been developed. Our methods are fully automated without manual specification of region-of-interests and selection of slices for model training, which are common in traditional machine learning based brain tumor grading methods. This methodology may play a crucial role in selecting effective treatment options and survival predictions without the need for surgical biopsy.

Late toxicity in long-term survivors from a phase 2 study of concurrent radiation therapy, temozolomide and valproic acid for newly diagnosed glioblastoma.

BACKGROUND: Valproic acid (VPA) is an antiepileptic agent with histone deacetylase inhibitor activity shown to enhance overall survival and progression free survival in patients with newly diagnosed glioblastoma (GBM). This reports on the late toxicity of the VPA/radiotherapy (RT)/temozolomide (TMZ) combination in the long-term survivors of a phase 2 study evaluating this regimen. METHODS: 37 patients with newly diagnosed GBM were initially enrolled on this trial and received combination therapy. VPA/RT/TMZ related late toxicities were evaluated in the 6 patients that lived greater than 3 years using the Cancer Therapy and Evaluation Program Common Toxicity Criteria (CTC) Version 4.0 for toxicity and adverse event reporting as well as the RTOG/EORTC Radiation Morbidity Scoring Scheme. RESULTS: The median duration of follow-up for these 6 patients was 69.5m. In this cohort, the median OS was 73.8m (60.8-103.8m) and median PFS was 53.1m (37.3 - 103.8m). The most common late toxicity of VPA in conjunction with RT/TMZ were the CTC classifications of neurological, pain, and blood/ bone marrow toxicity and most were grade 1/2. There were only two grade 3/4 toxicities. CONCLUSIONS: The addition of VPA to concurrent RT/TMZ in patients with newly diagnosed GBM was well tolerated with little late toxicity. Additionally, VPA may result in improved outcomes as compared to historical data and merits further study.

Differentiating pseudoprogression from true progression: analysis of radiographic, biologic, and clinical clues in GBM.

INTRODUCTION: Pseudoprogression (PsP) is a diagnostic dilemma in glioblastoma (GBM) after chemoradiotherapy (CRT). Magnetic resonance imaging (MRI) features may fail to distinguish PsP from early true progression (eTP), however clinical findings may aid in their distinction. METHODS: Sixty-seven patients received CRT for GBM between 2003 and 2016, and had pre- and post-treatment imaging suitable for retrospective evaluation using RANO criteria. Patients with signs of progression within the first 12-weeks post-radiation (P-12) were selected. Lesions that improved or stabilized were defined as PsP, and lesions that progressed were defined as eTP. RESULTS: The median follow up for all patients was 17.6 months. Signs of progression developed in 35/67 (52.2%) patients within P-12. Of these, 20/35 (57.1%) were subsequently defined as eTP and 15/35 (42.9%) as PsP. MRI demonstrated increased contrast enhancement in 84.2% of eTP and 100% of PsP, and elevated CBV in 73.7% for eTP and 93.3% for PsP. A decrease in FLAIR was not seen in eTP patients, but was seen in 26.7% PsP patients. Patients with eTP were significantly more likely to require increased steroid doses or suffer clinical decline than PsP patients (OR 4.89, 95% CI 1.003-19.27; p = 0.046). KPS declined in 25% with eTP and none of the PsP patients. CONCLUSIONS: MRI imaging did not differentiate eTP from PsP, however, KPS decline or need for increased steroids was significantly more common in eTP versus PsP. Investigation and standardization of clinical assessments in response criteria may help address the diagnostic dilemma of pseudoprogression after frontline treatment for GBM.

Correction to expert consensus on re-irradiation for recurrent glioma.

In the original publication [1] two author names were missing the middle names. The corrected versions can be found in this Erratum.

Multiparametric MRI for the detection of local recurrence of prostate cancer in the setting of biochemical recurrence after low dose rate brachytherapy.

PURPOSE: Prostate multiparametric magnetic resonance imaging (mpMRI) has utility in detecting post-radiotherapy local recurrence. We conducted a multireader study to evaluate the diagnostic performance of mpMRI for local recurrence after low dose rate (LDR) brachytherapy. METHODS: A total of 19 patients with biochemical recurrence after LDR brachytherapy underwent 3T endorectal coil mpMRI with T2-weighted imaging, dynamic contrast-enhanced imaging (DCE) and diffusion-weighted imaging (DWI) with pathologic confirmation. Prospective reads by an experienced prostate radiologist were compared with reads from 4 radiologists of varying experience. Readers identified suspicious lesions and rated each MRI detection parameter. MRI-detected lesions were considered true-positive with ipsilateral pathologic confirmation. Inferences for sensitivity, specificity, positive predictive value (PPV), kappa, and index of specific agreement were made with the use of bootstrap resampling. RESULTS: Pathologically confirmed recurrence was found in 15 of 19 patients. True positive recurrences identified by mpMRI were frequently located in the transition zone (46.7%) and seminal vesicles (30%). On patient-based analysis, average sensitivity of mpMRI was 88% (standard error [SE], 3.5%). For highly suspicious lesions, specificity of mpMRI was 75% (SE, 16.5%). On lesion-based analysis, the average PPV was 62% (SE, 6.7%) for all lesions and 78.7% (SE, 10.3%) for highly suspicious lesions. The average PPV for lesions invading the seminal vesicles was 88.8% (n=13). The average PPV was 66.6% (SE, 5.8%) for lesions identified with T2-weighted imaging, 64.9% (SE, 7.3%) for DCE, and 70% (SE, 7.3%) for DWI. CONCLUSION: This series provides evidence that mpMRI after LDR brachytherapy is feasible with a high patient-based cancer detection rate. Radiologists of varying experience demonstrated moderate agreement in detecting recurrence.

Expert consensus on re-irradiation for recurrent glioma.

PURPOSE: To investigate radiation oncologists' opinions on important considerations to offering re-irradiation (re-RT) as a treatment option for recurrent glioma. MATERIALS AND METHODS: A survey was conducted with 13 radiation oncologists involved in the care of central nervous system tumor patients. The survey was comprised of 49 questions divided into 2 domains: a demographic section (10 questions) and a case section (5 re-RT cases with 5 to 6 questions representing one or several re-RT treatment dilemmas as may be encountered in the clinic). Respondents were asked to rate the relevance of various factors to offering re-RT, respond to the cases with a decision to offer re-RT vs. not, volume to be treated, margins to be employed, dose/fractionation suggested and any additional comments with respect to rationale in each scenario. RESULTS: Sixty nine percent of responders have been practicing for greater than 10 years and 61% have re-RT 20 to 100 patients to date, with 54% seeing 2-5 re-RT cases per month and retreating 1-2 patients per month. Recurrent tumor volume, time since previous radiation therapy, previously administered dose to organs at risk and patient performance status were rated by the majority of responders (85%, 92%, 77%, and 69% respectively) as extremely relevant or very relevant to offering re-RT as an option. CONCLUSION: The experts' practice of re-RT is still heterogeneous, reflecting the paucity of high-quality prospective data available for decision-making. Nevertheless, practicing radiation oncologists can support own decisions by referring to the cases found suitable for re-RT in this survey.

Brain tumor segmentation using holistically nested neural networks in MRI images.

PURPOSE: Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 yr of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically nested neural networks that could be employed for brain tumor segmentation of MRI images. METHODS: Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multiscale and multilevel hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. RESULTS: The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training datasets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training datasets with high-grade gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to ten clinical datasets with HGG from a locally developed database. DSC and sensitivity of 0.83 and 0.85 were achieved. A quantitative comparison indicated that the proposed method outperforms the popular fully convolutional network (FCN) method. In terms of efficiency, the proposed method took around 10 h for training with 50,000 iterations, and approximately 30 s for testing of a typical MRI image in the BRATS 2013 dataset with a size of 160 × 216 × 176, using a DELL PRECISION workstation T7400, with an NVIDIA Tesla K20c GPU. CONCLUSIONS: An effective brain tumor segmentation method for MRI images based on a HNN has been developed. The high level of accuracy and efficiency make this method practical in brain tumor segmentation. It may play a crucial role in both brain tumor diagnostic analysis and in the treatment planning of radiation therapy.

Optimizing the Benefit of CNS Radiation Therapy in the Pediatric Population-PART 1: Understanding and Managing Acute and Late Toxicities.

Radiation therapy continues to be a key component in the management of pediatric malignancies. Increasing the likelihood of cure while minimizing late treatment toxicity in these young patients remains the primary goal. Within the realm of central nervous system neoplasms, efforts to further improve the efficacy of radiation therapy continue, while balancing risks of damage to uninvolved tissue. Radiation therapy can result in second malignancies, as well as cerebrovascular, neurotoxic, neurocognitive, endocrine, psychosocial, and quality-of-life effects. In this article we describe these acute and late effects and their implications, and we highlight strategies that have emerged to reduce both the volume of tissue that is irradiated and the radiation dose delivered. The feasibility, efficacy, and risks of these newer approaches to radiation therapy continue to be evaluated and monitored; robust outcome data are needed.

Optimizing the Benefit of CNS Radiation Therapy in the Pediatric Population-PART 2: Novel Methods of Radiation Delivery.

Newer approaches in the field of radiation therapy have raised the bar in the treatment of central nervous system (CNS) malignancies, with recognized advances that have aimed to increase the therapeutic index by improving conformality of the radiation dose to the planned target volume. Beyond these advances, the continued evolution of more effective systems for delivery of radiation to the CNS may offer further benefit not only to adults but also to pediatric patients, a cohort of the population that may be more sensitive to the long-term effects of radiation. This article describes several novel irradiation techniques under investigation that hold promise in the pediatric population. These include newer approaches to intensity-modulated radiation therapy; stereotactic radiosurgery and radiation therapy; particle therapy, most notably proton therapy, which may be of particular benefit in enabling young patients to avoid radiation-related adverse effects; and radioimmunotherapy strategies that spare healthy tissue from radiotoxicity by delivering therapy directly to tumor tissue. Although emerging strategies for the delivery of radiation therapy hold promise for improved outcomes in pediatric patients, there must be rigorous long-term evaluation of consequences associated with the various techniques employed, to weigh risks, benefits, and impact on quality of life.

A Phase 2 Study of Concurrent Radiation Therapy, Temozolomide, and the Histone Deacetylase Inhibitor Valproic Acid for Patients With Glioblastoma.

PURPOSE: Valproic acid (VPA) is an antiepileptic agent with histone deacetylase inhibitor (HDACi) activity shown to sensitize glioblastoma (GBM) cells to radiation in preclinical models. We evaluated the addition of VPA to standard radiation therapy (RT) plus temozolomide (TMZ) in patients with newly diagnosed GBM. METHODS AND MATERIALS: Thirty-seven patients with newly diagnosed GBM were enrolled between July 2006 and April 2013. Patients received VPA, 25 mg/kg orally, divided into 2 daily doses concurrent with RT and TMZ. The first dose of VPA was given 1 week before the first day of RT at 10 to 15 mg/kg/day and subsequently increased up to 25 mg/kg/day over the week prior to radiation. VPA- and TMZ-related acute toxicities were evaluated using Common Toxicity Criteria version 3.0 (National Cancer Institute Cancer Therapy Evaluation Program) and Cancer Radiation Morbidity Scoring Scheme for toxicity and adverse event reporting (Radiation Therapy Oncology Group/European Organization for Research and Treatment). RESULTS: A total of 81% of patients took VPA according to protocol. Median overall survival (OS) was 29.6 months (range: 21-63.8 months), and median progression-free survival (PFS) was 10.5 months (range: 6.8-51.2 months). OS at 6, 12, and 24 months was 97%, 86%, and 56%, respectively. PFS at 6, 12, and 24 months was 70%, 43%, and 38% respectively. The most common grade 3/4 toxicities of VPA in conjunction with RT/TMZ therapy were blood and bone marrow toxicity (32%), neurological toxicity (11%), and metabolic and laboratory toxicity (8%). Younger age and class V recursive partitioning analysis (RPA) results were significant for both OS and PFS. VPA levels were not correlated with grade 3 or 4 toxicity levels. CONCLUSIONS: Addition of VPA to concurrent RT/TMZ in patients with newly diagnosed GBM was well tolerated. Additionally, VPA may result in improved outcomes compared to historical data and merits further study.

Predictive value of tumor recurrence using urinary vascular endothelial factor levels in patients receiving radiation therapy for Glioblastoma Multiforme (GBM).

BACKGROUND: Glioblastoma Multiforme (GBM) is the most common primary malignant tumor of the central nervous system. Standard of care includes maximal resection followed by chemoradiotherapy. Tumors need adequate perfusion and neovascularization to maintain oxygenation and for removal of wastes. Vascular endothelial growth factor (VEGF) is a well characterized pro-angiogenic factor. We hypothesized that the increases in urinary VEGF levels would occur early in the course of tumor recurrence or progression. We examine the feasibility of collecting and analyzing urinary VEGF levels in a prospective, multi-institutional trial (Radiation Therapy Oncology Group, RTOG, 0611) as well as the role of VEGF as a marker of tumor recurrence. METHOD: We evaluated VEGF levels in urine specimens collected post-operatively, at the conclusion of radiation therapy (RT) and one month following RT. Urinary VEGF levels were correlated with tumor progression at one year. VEGF levels were measured by enzyme-linked immunosorbant assay in urine specimens and normalized to urinary creatinine levels. Sample size was determined based on a 50% 1-year recurrence rate. With a sensitivity and specificity of 80%, the expected 95% confidence interval was (0.69, 0.91) with 100 patients. A failure was defined as documented disease progression, recurrence or death before one year. RESULTS: 202 patients were enrolled between February-2006 and October-2007. Four patients were ineligible as they did not receive RT. Of the remaining 198 patients, 128 had all three samples collected. In this group, 35 patients (27.3%) did not progress, 89 (69.5%) had progression and 4 (3.1%) died without evidence of progression. Median VEGF levels at baseline were 52.9 pg/mg Cr (range 0.2- 15,034.4); on the last day of RT, 56.6 (range 0-2,377.1); and at one month follow-up, 70.0 (range 0.1-1813.2). In patients without progression at 1-year, both baseline VEGF level and end of RT VEGF level were lower than those of patients who progressed: 40.3 (range 0.2-350.8) vs. 59.7 (range 1.3-15,034.4) and 41.8 (range 0-356.8) vs. 69.7 (range 0-2,377.1), respectively. This did not reach statistical significance. Comparison of the change in VEGF levels between the end of RT and one month following RT, demonstrated no significant difference in the proportions of progressors or non-progressors at 1-year for either the VEGF increased or VEGF decreased groups. CONCLUSION: Urine can be collected and analyzed in a prospective, multi-institutional trial. In this study of patients with GBM a change in urinary VEGF levels between the last day of RT and the one month following RT did not predict for tumor progression by one year.