Family history of cancer in benign brain tumor subtypes versus gliomas

TumorDetNet: A unified deep learning model for brain tumor detection and classification

Accurate diagnosis of the brain tumor type at an earlier stage is crucial for the treatment process and helps to save the lives of a large number of people worldwide. Because they are non-invasive and spare patients from having an unpleasant biopsy, magnetic resonance imaging (MRI) scans are frequently employed to identify tumors. The manual identification of tumors is difficult and requires considerable time due to the large number of three-dimensional images that an MRI scan of one patient's brain produces from various angles. Moreover, the variations in location, size, and shape of the brain tumor also make it challenging to detect and classify different types of tumors. Thus, computer-aided diagnostics (CAD) systems have been proposed for the detection of brain tumors. In this paper, we proposed a novel unified end-to-end deep learning model named TumorDetNet for brain tumor detection and classification. Our TumorDetNet framework employs 48 convolution layers with leaky ReLU (LReLU) and ReLU activation functions to compute the most distinctive deep feature maps. Moreover, average pooling and a dropout layer are also used to learn distinctive patterns and reduce overfitting. Finally, one fully connected and a softmax layer are employed to detect and classify the brain tumor into multiple types. We assessed the performance of our method on six standard Kaggle brain tumor MRI datasets for brain tumor detection and classification into (malignant and benign), and (glioma, pituitary, and meningioma). Our model successfully identified brain tumors with remarkable accuracy of 99.83%, classified benign and malignant brain tumors with an ideal accuracy of 100%, and meningiomas, pituitary, and gliomas tumors with an accuracy of 99.27%. These outcomes demonstrate the potency of the suggested methodology for the reliable identification and categorization of brain tumors.

Epidemiology and survival of non-malignant and malignant meningiomas in middle-aged females, 2004-2018

Background

The incidence of meningioma is disparate to sex: meningiomas are more common in women than in men, especially in middle-aged women. Understanding the epidemiology and survival of middle-aged women with meningiomas would help estimate their public health impacts and optimize risk stratification.

Methods

Data on middle-aged (35-54 years) female patients with meningiomas between 2004 and 2018 were obtained from the SEER database. Age-adjusted incidence rates per 100 000 population-years were calculated. Kaplan-Meier and multivariate Cox proportional hazard models were utilized in the overall survival (OS) analysis.

Results

Data from 18302 female patients with meningioma were analyzed. The distribution of patients increased with age. Most patients were White and non-Hispanic, according to race and ethnicity, respectively. Over the past 15 years, non-malignant meningiomas have shown an increasing trend; however, malignant meningiomas have shown an opposite trend. Older age, Black population, and large non-malignant meningiomas tend to have worse prognoses. Surgical resection improves OS, and the extent of resection is a critical prognostic factor.

Conclusions

This study observed an increase in non-malignant meningiomas and a decrease in the incidence of malignant meningiomas in middle-aged females. The prognosis deteriorated with age, in Black people, and with large tumor size. Additionally, the extent of tumor excision was found to be a significant prognostic factor.

A SCARE-compliant case report of recurrent meningioma in a 75-year-old patient after 10 years of surgical resection

Meningiomas are intracranial extracerebral tumors derived from arachnoid cells of the neural crest. They represent ∼20% of primary intracranial tumors and are seen as more common in elderly patients and women. Recurrence of meningioma can be observed during the early years after surgical treatment, but their occurrence within 10 years is rare.

Case presentation

In this report, the authors discuss a case of a 75-year-old patient with a recurrence of a frontal meningioma after 10 years of successful surgical resection. Our patient was a female who presented amnesia and memory lapses associated with several weeks of progressive heaviness of the lower limbs accompanied by speech heaviness, intense headaches, asthenia, consciousness disorder, and tonic-clonic convulsive seizures for 10 days. The patient had previously been treated for a benign meningioma by surgical excision. Imaging was performed, and recurrent frontal meningioma was retained as a final diagnosis. The patient underwent a successful total resection of her frontal tumor.

Clinical discussion

Recurrent tumors after complete surgical removal of meningiomas are rare and may be associated with microscopic residues. The more radical the surgery, the lower the risk of observing a recurrence. Adjuvant radiotherapy can be proposed, but the evidence is still lacking. Careful follow-up of all patients with or without complete surgical resection is therefore recommended.

Conclusion

This case illustrates the importance of suspecting recurrence of meningioma in adult patients after successful surgical excision, even after 10 years of free disease. Clinicians should be aware of long-term meningioma recurrence in this population, and imaging is key for a positive diagnosis.

National and subnational burden of brain and central nervous system cancers in Iran, 1990-2019: Results from the global burden of disease study 2019

INTRODUCTION

Central nervous system cancers (CNS cancers) impose a significant burden upon healthcare systems worldwide. Currently, the lack of a comprehensive study to assess various epidemiological indexes of CNS cancers on national and subnational scales in Iran can hamper healthcare planning and resource allocation in this regard. This study aims to fill this gap by providing estimates of CNS cancer epidemiological measures on national and subnational levels in Iran from 1990 to 2019.

MATERIALS AND METHODS

This study is a part of Global Burden of Disease (GBD) 2019 that contains epidemiological measures including prevalence, incidence, mortality, Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLDs), and Years of Life Lost (YLLs) of CNS cancers. Age standardization was utilized for comparing different provinces.

RESULTS

In 2019, 5811 (95% Uncertainty Interval: 2942-7046) national new cases and 3494 (1751-4173) deaths due to CNS cancers were reported. National age-standardized incidence (ASIR), deaths (ASDR), and DALYs rates were 7.3 (3.7-8.8), 4.6 (2.3-5.5), and 156.4 (82.0-187.0) per 100,000 in 2019, respectively. Subnational results revealed that ASDR and ASIR have increased in the past 30 years in all provinces. Although incidence rates have increased in all age groups and genders since 1990, death rates have remained the same for most age groups and genders except for young patients aged under 15, where a decrease in mortality and YLLs can be observed.

CONCLUSION

The incidence, deaths, and DALYs of CNS cancers increased at national and subnational levels. These findings should be considered for planning and resource allocation.

Convection-Enhanced Delivery of Antiangiogenic Drugs and Liposomal Cytotoxic Drugs to Heterogeneous Brain Tumor for Combination Therapy

Simple Summary

Although developed anticancer drugs have shown desirable effects in preclinical trials, the clinical efficacy of chemotherapy against brain cancer remains disappointing. One of the important obstacles is the highly heterogeneous environment in tumors. This study aims to evaluate the performance of an emerging treatment using antiangiogenic and cytotoxic drugs. Our mathematical modelling confirms the advantage of this combination therapy in homogenizing the intratumoral environment for better drug delivery outcomes. In addition, the effects of local microvasculature and cell density on this therapy are also discussed. The results would contribute to the development of more effective treatments for brain cancer.

Abstract

Although convection-enhanced delivery can successfully bypass the blood-brain barrier, its clinical performance remains disappointing. This is primarily attributed to the heterogeneous intratumoral environment, particularly the tumor microvasculature. This study investigates the combined convection-enhanced delivery of antiangiogenic drugs and liposomal cytotoxic drugs in a heterogeneous brain tumor environment using a transport-based mathematical model. The patient-specific 3D brain tumor geometry and the tumor’s heterogeneous tissue properties, including microvascular density, porosity and cell density, are extracted from dynamic contrast-enhanced magnetic resonance imaging data. Results show that antiangiogenic drugs can effectively reduce the tumor microvascular density. This change in tissue structure would inhibit the fluid loss from the blood to prevent drug concentration from dilution, and also reduce the drug loss by blood drainage. The comparisons between different dosing regimens demonstrate that the co-infusion of liposomal cytotoxic drugs and antiangiogenic drugs has the advantages of homogenizing drug distribution, increasing drug accumulation, and enlarging the volume where tumor cells can be effectively killed. The delivery outcomes are susceptible to the location of the infusion site. This combination treatment can be improved by infusing drugs at higher microvascular density sites. In contrast, infusion at a site with high cell density would lower the treatment effectiveness of the whole brain tumor. Results obtained from this study can deepen the understanding of this combination therapy and provide a reference for treatment design and optimization that can further improve survival and patient quality of life.

Incidence and Survival of Benign, Borderline, and Malignant Meningioma Patients in the United States from 2004 to 2018

Meningioma is the most common primary central nervous system tumor, and its incidence is increasing. A systematic epidemiological and clinical analysis is required to better estimate its public health impact and understand its prognostic factors. Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2018 for all types of meningiomas without an age restriction. Age-adjusted incidence rates (IRs) and 95% confidence intervals were estimated according to sex, age, race, ethnicity, and tumor location. Kaplan-Meier analysis and multivariate Cox proportional hazard models were used to analyze the overall survival (OS). The competing risk regression model of Fine-Gray was used to analyze cause-specific survival. Data from a total of 109,660 meningioma patients were analyzed. A majority of patients were older than 60 years, and only 0.41% of patients were 0-19 years. The meningioma IRs were higher in females, Black, and non-Hispanic patients than in males, White, and Hispanic patients, respectively, and IRs increased with age. The ratio of IRs for females to males was 2.1 and also increased with age, peaking at 3.6 in the 45-49-year-old group. Older and male patients with all types of meningiomas, Black patients with benign and borderline meningiomas, and patients with larger borderline and malignant meningiomas showed poorer prognosis. For all meningioma types, surgical resection improved survival. The reported incidence rates and survival trends covered all demographics and subtypes of meningiomas. Older age, male sex, Black race, and tumor size may be important prognostic factors for meningioma cases, and tumor resection can substantially improve survival among meningioma patients.

ARMC5 is part of an RPB1-specific ubiquitin ligase implicated in adrenal hyperplasia

ARMC5 is implicated in several pathological conditions, but its function remains unknown. We have previously identified CUL3 and RPB1 (the largest subunit of RNA polymerase II (Pol II) as potential ARMC5-interacting proteins. Here, we show that ARMC5, CUL3 and RBX1 form an active E3 ligase complex specific for RPB1. ARMC5, CUL3, and RBX1 formed an active E3 specific for RPB1. Armc5 deletion caused a significant reduction in RPB1 ubiquitination and an increase in an accumulation of RPB1, and hence an enlarged Pol II pool in normal tissues and organs. The compromised RPB1 degradation did not cause generalized Pol II stalling nor depressed transcription in the adrenal glands but did result in dysregulation of a subset of genes, with most upregulated. We found RPB1 to be highly expressed in the adrenal nodules from patients with primary bilateral macronodular adrenal hyperplasia (PBMAH) harboring germline ARMC5 mutations. Mutant ARMC5 had altered binding with RPB1. In summary, we discovered that wildtype ARMC5 was part of a novel RPB1-specific E3. ARMC5 mutations resulted in an enlarged Pol II pool, which dysregulated a subset of effector genes. Such an enlarged Pol II pool and gene dysregulation was correlated to adrenal hyperplasia in humans and KO mice.

Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data

The precise initial characterization of contrast-enhancing brain tumors has significant consequences for clinical outcomes. Various novel neuroimaging methods have been developed to increase the specificity of conventional magnetic resonance imaging (cMRI) but also the increased complexity of data analysis. Artificial intelligence offers new options to manage this challenge in clinical settings. Here, we investigated whether multiclass machine learning (ML) algorithms applied to a high-dimensional panel of radiomic features from advanced MRI (advMRI) and physiological MRI (phyMRI; thus, radiophysiomics) could reliably classify contrast-enhancing brain tumors. The recently developed phyMRI technique enables the quantitative assessment of microvascular architecture, neovascularization, oxygen metabolism, and tissue hypoxia. A training cohort of 167 patients suffering from one of the five most common brain tumor entities (glioblastoma, anaplastic glioma, meningioma, primary CNS lymphoma, or brain metastasis), combined with nine common ML algorithms, was used to develop overall 135 classifiers. Multiclass classification performance was investigated using tenfold cross-validation and an independent test cohort. Adaptive boosting and random forest in combination with advMRI and phyMRI data were superior to human reading in accuracy (0.875 vs. 0.850), precision (0.862 vs. 0.798), F-score (0.774 vs. 0.740), AUROC (0.886 vs. 0.813), and classification error (5 vs. 6). The radiologists, however, showed a higher sensitivity (0.767 vs. 0.750) and specificity (0.925 vs. 0.902). We demonstrated that ML-based radiophysiomics could be helpful in the clinical routine diagnosis of contrast-enhancing brain tumors; however, a high expenditure of time and work for data preprocessing requires the inclusion of deep neural networks.

Overexpression of Hepatocyte growth factor and its soluble receptor (s-cMet) in the serum of patients with different grades of meningioma

OBJECTIVES

Meningiomas are the most common primary intracranial tumor. Hepatocyte growth factor (HGF) and its receptor, cMet, were shown to be involved in meningioma. This study was aimed to determine the concentration of HGF and soluble cMet (s-cMet) in the serum of patients with different grades of meningioma.

METHODS

Ninety serum samples from different grades of meningioma patients (42 cases of grade I, 28 grade II, 20 grade III) and 51 controls were included in this study. The serum total protein concentration (TPC) was measured by a Bio-Rad protein assay and serum concentration of HGF and s-cMet by enzyme linked immunosorbent assay (ELISA).

RESULTS

No significant change in the serum TPC of patients was seen as compared to controls. We also showed that serum HGF and s-cMet concentration in meningioma patients was higher than in controls. The results showed that starting from grades I to III meningioma, a significant increase in HGF and s-cMet serum concentration was observed (HGF; 380 ± 57.69, 430.27 ± 48.72, 596.36 ± 104.49 pg/ml, respectively, as compared to controls which was 327.72 ± 49.68 pg/ml and for s-cMet was 274.45 ± 45.05, 314.81 ± 38.71, 433.54 ± 51.81 ng/ml, respectively, as compared to controls which was 213.72 ± 29.13 ng/ml). The results showed that a high concentration of HGF and s-cMet is associated with advanced grades of meningioma.

CONCLUSION

It is concluded that HGF and s-cMet serum levels increased in meningioma patients and their concentration was significantly higher in more advanced grades of the disease. It is also suggested that HGF/s-cMet might be involved in the progression of meningioma.

Metabolic Tumor Microenvironment Characterization of Contrast Enhancing Brain Tumors Using Physiologic MRI

The tumor microenvironment is a critical regulator of cancer development and progression as well as treatment response and resistance in brain neoplasms. The available techniques for investigation, however, are not well suited for noninvasive in vivo characterization in humans. A total of 120 patients (59 females; 61 males) with newly diagnosed contrast-enhancing brain tumors (64 glioblastoma, 20 brain metastases, 15 primary central nervous system (CNS) lymphomas (PCNSLs), and 21 meningiomas) were examined with a previously established physiological MRI protocol including quantitative blood-oxygen-level-dependent imaging and vascular architecture mapping. Six MRI biomarker maps for oxygen metabolism and neovascularization were fused for classification of five different tumor microenvironments: glycolysis, oxidative phosphorylation (OxPhos), hypoxia with/without neovascularization, and necrosis. Glioblastoma showed the highest metabolic heterogeneity followed by brain metastasis with a glycolysis-to-OxPhos ratio of approximately 2:1 in both tumor entities. In addition, glioblastoma revealed a significant higher percentage of hypoxia (24%) compared to all three other brain tumor entities: brain metastasis (7%; p < 0.001), PCNSL (8%; p = 0.001), and meningioma (8%; p = 0.003). A more aggressive biological brain tumor behavior was associated with a higher percentage of hypoxia and necrosis and a lower percentage of remaining vital tumor tissue and aerobic glycolysis. The proportion of oxidative phosphorylation, however, was rather similar (17–26%) for all four brain tumor entities. Tumor microenvironment (TME) mapping provides insights into neurobiological differences of contrast-enhancing brain tumors and deserves further clinical cancer research attention. Although there is a long roadmap ahead, TME mapping may become useful in order to develop new diagnostic and therapeutic approaches.

A global, regional, and national survey on burden and Quality of Care Index (QCI) of brain and other central nervous system cancers; global burden of disease systematic analysis 1990-2017

Primary brain and other central nervous system (CNS) cancers cause major burdens. In this study, we introduced a measure named the Quality of Care Index (QCI), which indirectly evaluates the quality of care given to patients with this group of cancers. Here we aimed to compare different geographic and socioeconomic patterns of CNS cancer care according to the novel measure introduced. In this regard, we acquired age-standardized primary epidemiologic measures were acquired from the Global Burden of Disease (GBD) study 1990-2017. The primary measures were combined to make four secondary indices which all of them indirectly show the quality of care given to patients. Principal Component Analysis (PCA) method was utilized to calculate the essential component named QCI. Further analyses were made based on QCI to assess the quality of care globally, regionally, and nationally (with a scale of 0-100 which higher values represent better quality of care). For 2017, the global calculated QCI was 55.0. QCI showed a desirable condition in higher socio-demographic index (SDI) quintiles. Oppositely, low SDI quintile countries (7.7) had critically worse care quality. Western Pacific Region with the highest (76.9) and African Region with the lowest QCIs (9.9) were the two WHO regions extremes. Singapore was the country with the maximum QCI of 100, followed by Japan (99.9) and South Korea (98.9). In contrast, Swaziland (2.5), Lesotho (3.5), and Vanuatu (3.9) were countries with the worse condition. While the quality of care for most regions was desirable, regions with economic constraints showed to have poor quality of care and require enforcements toward this lethal diagnosis.

Multi-institutional noninvasive in vivo characterization of IDH, 1p/19q, and EGFRvIII in glioma using neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk)

Background

Gliomas represent a biologically heterogeneous group of primary brain tumors with uncontrolled cellular proliferation and diffuse infiltration that renders them almost incurable, thereby leading to a grim prognosis. Recent comprehensive genomic profiling has greatly elucidated the molecular hallmarks of gliomas, including the mutations in isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2), loss of chromosomes 1p and 19q (1p/19q), and epidermal growth factor receptor variant III (EGFRvIII). Detection of these molecular alterations is based on ex vivo analysis of surgically resected tissue specimen that sometimes is not adequate for testing and/or does not capture the spatial tumor heterogeneity of the neoplasm.

Methods

We developed a method for noninvasive detection of radiogenomic markers of IDH both in lower-grade gliomas (WHO grade II and III tumors) and glioblastoma (WHO grade IV), 1p/19q in IDH-mutant lower-grade gliomas, and EGFRvIII in glioblastoma. Preoperative MRIs of 473 glioma patients from 3 of the studies participating in the ReSPOND consortium (collection I: Hospital of the University of Pennsylvania [HUP: n = 248], collection II: The Cancer Imaging Archive [TCIA; n = 192], and collection III: Ohio Brain Tumor Study [OBTS, n = 33]) were collected. Neuro-Cancer Imaging Phenomics Toolkit (neuro-CaPTk), a modular platform available for cancer imaging analytics and machine learning, was leveraged to extract histogram, shape, anatomical, and texture features from delineated tumor subregions and to integrate these features using support vector machine to generate models predictive of IDH, 1p/19q, and EGFRvIII. The models were validated using 3 configurations: (1) 70-30% training-testing splits or 10-fold cross-validation within individual collections, (2) 70-30% training-testing splits within merged collections, and (3) training on one collection and testing on another.

Results

These models achieved a classification accuracy of 86.74% (HUP), 85.45% (TCIA), and 75.15% (TCIA) in identifying EGFRvIII, IDH, and 1p/19q, respectively, in configuration I. The model, when applied on combined data in configuration II, yielded a classification success rate of 82.50% in predicting IDH mutation (HUP + TCIA + OBTS). The model when trained on TCIA dataset yielded classification accuracy of 84.88% in predicting IDH in HUP dataset.

Conclusions

Using machine learning algorithms, high accuracy was achieved in the prediction of IDH, 1p/19q, and EGFRvIII mutation. Neuro-CaPTk encompasses all the pipelines required to replicate these analyses in multi-institutional settings and could also be used for other radio(geno)mic analyses.

Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma

PURPOSE

To construct a multi-institutional radiomic model that supports upfront prediction of progression-free survival (PFS) and recurrence pattern (RP) in patients diagnosed with glioblastoma multiforme (GBM) at the time of initial diagnosis.

PATIENTS AND METHODS

We retrospectively identified data for patients with newly diagnosed GBM from two institutions (institution 1, n = 65; institution 2, n = 15) who underwent gross total resection followed by standard adjuvant chemoradiation therapy, with pathologically confirmed recurrence, sufficient follow-up magnetic resonance imaging (MRI) scans to reliably determine PFS, and available presurgical multiparametric MRI (MP-MRI). The advanced software suite Cancer Imaging Phenomics Toolkit (CaPTk) was leveraged to analyze standard clinical brain MP-MRI scans. A rich set of imaging features was extracted from the MP-MRI scans acquired before the initial resection and was integrated into two distinct imaging signatures for predicting mean shorter or longer PFS and near or distant RP. The predictive signatures for PFS and RP were evaluated on the basis of different classification schemes: single-institutional analysis, multi-institutional analysis with random partitioning of the data into discovery and replication cohorts, and multi-institutional assessment with data from institution 1 as the discovery cohort and data from institution 2 as the replication cohort.

RESULTS

These predictors achieved cross-validated classification performance (ie, area under the receiver operating characteristic curve) of 0.88 (single-institution analysis) and 0.82 to 0.83 (multi-institution analysis) for prediction of PFS and 0.88 (single-institution analysis) and 0.56 to 0.71 (multi-institution analysis) for prediction of RP.

CONCLUSION

Imaging signatures of presurgical MP-MRI scans reveal relatively high predictability of time and location of GBM recurrence, subject to the patients receiving standard first-line chemoradiation therapy. Through its graphical user interface, CaPTk offers easy accessibility to advanced computational algorithms for deriving imaging signatures predictive of clinical outcome and could similarly be used for a variety of radiomic and radiogenomic analyses.

Association of metabolic syndrome with glioblastoma: a retrospective cohort study and review

Background

Metabolic syndrome is identified as a risk factor for the development of several systemic cancers, but its frequency among patients with glioblastoma and its association with clinical outcomes have yet to be determined. The aim of this study was to investigate metabolic syndrome as a risk factor for and affecting survival in glioblastoma patients.

Methods

A retrospective cohort study, consisting of patients with diagnoses at a single institution between 2007 and 2013, was conducted. Clinical records were reviewed, and clinical and laboratory data pertaining to 5 metabolic criteria were extrapolated. Overall survival was determined by time from initial surgical diagnosis to date of death or last follow-up.

Results

The frequency of metabolic syndrome among patients diagnosed with glioblastoma was slightly greater than the frequency of metabolic syndrome among the general population. Within a subset of patients (n = 91) receiving the full schedule of concurrent radiation and temozolomide and adjuvant temozolomide, median overall survival was significantly shorter for patients with metabolic syndrome compared with those without. In addition, the presence of all 5 elements of the metabolic syndrome resulted in significantly decreased median survival in these patients.

Conclusions

We identified the metabolic syndrome at a slightly higher frequency in patients with diagnosed glioblastoma compared with the general population. In addition, metabolic syndrome with each of its individual components is associated with an overall worse prognosis in patients receiving the standard schedule of radiation and temozolomide after adjustment for age.

Proteins inform survival-based differences in patients with glioblastoma

BACKGROUND

Improving the care of patients with glioblastoma (GB) requires accurate and reliable predictors of patient prognosis. Unfortunately, while protein markers are an effective readout of cellular function, proteomics has been underutilized in GB prognostic marker discovery.

METHODS

For this study, GB patients were prospectively recruited and proteomics discovery using liquid chromatography-mass spectrometry analysis (LC-MS/MS) was performed for 27 patients including 13 short-term survivors (STS) (≤10 months) and 14 long-term survivors (LTS) (≥18 months).

RESULTS

Proteomics discovery identified 11 941 peptides in 2495 unique proteins, with 469 proteins exhibiting significant dysregulation when comparing STS to LTS. We verified the differential abundance of 67 out of these 469 proteins in a small previously published independent dataset. Proteins involved in axon guidance were upregulated in STS compared to LTS, while those involved in p53 signaling were upregulated in LTS. We also assessed the correlation between LS MS/MS data with RNAseq data from the same discovery patients and found a low correlation between protein abundance and mRNA expression. Finally, using LC-MS/MS on a set of 18 samples from 6 patients, we quantified the intratumoral heterogeneity of more than 2256 proteins in the multisample dataset.

CONCLUSIONS

These proteomic datasets and noted protein variations present a beneficial resource for better predicting patient outcome and investigating potential therapeutic targets.

Sex is an important prognostic factor for glioblastoma but not for nonglioblastoma

BACKGROUND

Glioblastoma (GBM) is the most common and most malignant glioma. Nonglioblastoma (non-GBM) gliomas (WHO Grades II and III) are invasive and also often fatal. The goal of this study is to determine whether sex differences exist in glioma survival.

METHODS

Data were obtained from the National Cancer Database (NCDB) for years 2010 to 2014. GBM (WHO Grade IV; N = 2073) and non-GBM (WHO Grades II and III; N = 2963) were defined using the histology grouping of the Central Brain Tumor Registry of the United States. Non-GBM was divided into oligodendrogliomas/mixed gliomas and astrocytomas. Sex differences in survival were analyzed using Kaplan-Meier and multivariable Cox proportional hazards models adjusted for known prognostic variables.

RESULTS

There was a female survival advantage in patients with GBM both in the unadjusted (P = .048) and adjusted (P = .003) models. Unadjusted, median survival was 20.1 months (95% CI: 18.7-21.3 months) for women and 17.8 months (95% CI: 16.9-18.7 months) for men. Adjusted, median survival was 20.4 months (95% CI: 18.9-21.6 months) for women and 17.5 months (95% CI: 16.7-18.3 months) for men. When stratifying by age group (18-55 vs 56+ years at diagnosis), this female survival advantage appeared only in the older group, adjusting for covariates (P = .017). Women (44.1%) had a higher proportion of methylated MGMT (O6-methylguanine-DNA methyltransferase) than men (38.4%). No sex differences were found for non-GBM.

CONCLUSIONS

Using the NCDB data, there was a statistically significant female survival advantage in GBM, but not in non-GBM.

Integrated genomic analysis of survival outliers in glioblastoma

Background

To elucidate molecular features associated with disproportionate survival of glioblastoma (GB) patients, we conducted deep genomic comparative analysis of a cohort of patients receiving standard therapy (surgery plus concurrent radiation and temozolomide); "GB outliers" were identified: long-term survivor of 33 months (LTS; n = 8) versus short-term survivor of 7 months (STS; n = 10).

Methods

We implemented exome, RNA, whole genome sequencing, and DNA methylation for collection of deep genomic data from STS and LTS GB patients.

Results

LTS GB showed frequent chromosomal gains in 4q12 (platelet derived growth factor receptor alpha and KIT) and 12q14.1 (cyclin-dependent kinase 4), and deletion in 19q13.33 (BAX, branched chain amino-acid transaminase 2, and cluster of differentiation 33). STS GB showed frequent deletion in 9p11.2 (forkhead box D4-like 2 and aquaporin 7 pseudogene 3) and 22q11.21 (Hypermethylated In Cancer 2). LTS GB showed 2-fold more frequent copy number deletions compared with STS GB. Gene expression differences showed the STS cohort with altered transcriptional regulators: activation of signal transducer and activator of transcription (STAT)5a/b, nuclear factor-kappaB (NF-κB), and interferon-gamma (IFNG), and inhibition of mitogen-activated protein kinase (MAPK1), extracellular signal-regulated kinase (ERK)1/2, and estrogen receptor (ESR)1. Expression-based biological concepts prominent in the STS cohort include metabolic processes, anaphase-promoting complex degradation, and immune processes associated with major histocompatibility complex class I antigen presentation; the LTS cohort features genes related to development, morphogenesis, and the mammalian target of rapamycin signaling pathway. Whole genome methylation analyses showed that a methylation signature of 89 probes distinctly separates LTS from STS GB tumors.

Conclusion

We posit that genomic instability is associated with longer survival of GB (possibly with vulnerability to standard therapy); conversely, genomic and epigenetic signatures may identify patients where up-front entry into alternative, targeted regimens would be a preferred, more efficacious management.

International Differences in Treatment and Clinical Outcomes for High Grade Glioma

BACKGROUND

High grade gliomas are the most common type of malignant brain tumor, and despite their rarity, cause significant morbidity and mortality. This study aimed to compare the treatment patterns of high grade glioma to examine survival patterns in patients who receive specific treatments between cohorts in Ohio and Taiwan.

METHOD

Patients aged 18 years and older at age of diagnosis with World Health Organization (WHO) grade III or IV astrocytoma from 2007-2012 were selected from the Ohio Brain Tumor Study and the Taiwan Cancer Registry. The treatment information was derived from medical chart reviews in Ohio and National Health Insurance Research Data in Taiwan. Treatment examined included surgical procedure (brain biopsy and/or resection), radiotherapy (radiation and/or radiosurgery), and alkylating chemotherapy. Kaplan-Meier and parametric survival models were used to examine the effect of treatment on survival, adjusted for age, sex, and comorbidities.

RESULTS

294 patients in Ohio and 1,097 patients in Taiwan met the inclusion criteria. 70.3% patients in Ohio and 51.4% in Taiwan received surgical resection, followed by concurrent chemoradiation. Patients who received this treatment had the highest survival rate, with a 1-year survival rate of 72.8% in Ohio and 73.4% in Taiwan. Patients who did not receive surgical resection, followed by concurrent chemoradiation had an increased risk of death (hazard ratio of 5.03 [95% confidence interval (CI): 3.61-7.02] in Ohio and 1.49 [95% CI: 1.31-1.71] in Taiwan) after adjustment for age, sex, and comorbidities.

CONCLUSION

Surgical resection followed by concurrent chemoradiation was associated with higher survival rate of patients with high grade glioma in both Ohio and Taiwan; however, one-third of patients in Ohio and half in Taiwan did not receive this treatment.