Molecular Predictors of Long-Term Survival in Glioblastoma Multiforme Patients

Radiogenomics based survival prediction of small-sample glioblastoma patients by multi-task DFFSP model

In this paper, the multi-task dense-feature-fusion survival prediction (DFFSP) model is proposed to predict the three-year survival for glioblastoma (GBM) patients based on radiogenomics data. The contrast-enhanced T1-weighted (T1w) image, T2-weighted (T2w) image and copy number variation (CNV) is used as the input of the three branches of the DFFSP model. This model uses two image extraction modules consisting of residual blocks and one dense feature fusion module to make multi-scale fusion of T1w and T2w image features as backbone. Also, a gene feature extraction module is used to adaptively weight CNV fragments. Besides, a transfer learning module is introduced to solve the small sample problem and an image reconstruction module is adopted to make the model anatomy-aware under a multi-task framework. 256 sample pairs (T1w and corresponding T2w MRI slices) and 187 CNVs of 74 patients were used. The experimental results show that the proposed model can predict the three-year survival of GBM patients with the accuracy of 89.1 %, which is improved by 3.2 and 4.7 % compared with the model without genes and the model using last fusion strategy, respectively. This model could also classify the patients into high-risk and low-risk groups, which will effectively assist doctors in diagnosing GBM patients.

Proteomics of tumor and serum samples from isocitrate dehydrogenase-wildtype glioblastoma patients: is the detoxification of reactive oxygen species associated with shorter survival?

Proteomics has been little used for the identification of novel prognostic and/or therapeutic markers in isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GB). In this study, we analyzed 50 tumor and 30 serum samples from short- and long-term survivors of IDH-wildtype GB (STS and LTS, respectively) by data-independent acquisition mass spectrometry (DIA-MS)-based proteomics, with the aim of identifying such markers. DIA-MS identified 5422 and 826 normalized proteins in tumor and serum samples, respectively, with only three tumor proteins and 26 serum proteins displaying significant differential expression between the STS and LTS groups. These dysregulated proteins were principally associated with the detoxification of reactive oxygen species (ROS). In particular, GB patients in the STS group had high serum levels of malate dehydrogenase 1 (MDH1) and ribonuclease inhibitor 1 (RNH1) and low tumor levels of fatty acid-binding protein 7 (FABP7), which may have enabled them to maintain low ROS levels, counteracting the effects of the first-line treatment with radiotherapy plus concomitant and adjuvant temozolomide. A blood score built on the levels of MDH1 and RNH1 expression was found to be an independent prognostic factor for survival based on the serum proteome data for a cohort of 96 IDH-wildtype GB patients. This study highlights the utility of circulating MDH1 and RNH1 biomarkers for determining the prognosis of patients with IDH-wildtype GB. Furthermore, the pathways driven by these biomarkers, and the tumor FABP7 pathway, may constitute promising therapeutic targets for blocking ROS detoxification to overcome resistance to chemoradiotherapy in potential GB STS.

Priority-Elastic net for binary disease outcome prediction based on multi-omics data

BACKGROUND

High-dimensional omics data integration has emerged as a prominent avenue within the healthcare industry, presenting substantial potential to improve predictive models. However, the data integration process faces several challenges, including data heterogeneity, priority sequence in which data blocks are prioritized for rendering predictive information contained in multiple blocks, assessing the flow of information from one omics level to the other and multicollinearity.

METHODS

We propose the Priority-Elastic net algorithm, a hierarchical regression method extending Priority-Lasso for the binary logistic regression model by incorporating a priority order for blocks of variables while fitting Elastic-net models sequentially for each block. The fitted values from each step are then used as an offset in the subsequent step. Additionally, we considered the adaptive elastic-net penalty within our priority framework to compare the results.

RESULTS

The Priority-Elastic net and Priority-Adaptive Elastic net algorithms were evaluated on a brain tumor dataset available from The Cancer Genome Atlas (TCGA), accounting for transcriptomics, proteomics, and clinical information measured over two glioma types: Lower-grade glioma (LGG) and glioblastoma (GBM).

CONCLUSION

Our findings suggest that the Priority-Elastic net is a highly advantageous choice for a wide range of applications. It offers moderate computational complexity, flexibility in integrating prior knowledge while introducing a hierarchical modeling perspective, and, importantly, improved stability and accuracy in predictions, making it superior to the other methods discussed. This evolution marks a significant step forward in predictive modeling, offering a sophisticated tool for navigating the complexities of multi-omics datasets in pursuit of precision medicine's ultimate goal: personalized treatment optimization based on a comprehensive array of patient-specific data. This framework can be generalized to time-to-event, Cox proportional hazards regression and multicategorical outcomes. A practical implementation of this method is available upon request in R script, complete with an example to facilitate its application.

Survival analysis and associated factors of highgrade glioma patients

Introduction

High-grade gliomas are the most common primary brain tumors in adults, and they usually have a quick fatal course. Average survival is 18 months, mainly, because of tumor resistance to Stupp protocol.

Objective

To determine high-grade glioma patient survival and the effect of persuasion variables on survival.

Materials and methods

We conducted a longitudinal descriptive study in which 80 untreated recently diagnosed high-grade glioma patients participated. A survey was conducted regarding their exposure to some risk factors, degree of genetic instability in peripheral blood using micronucleus quantification on binuclear lymphocytes, micronuclei in reticulocytes and sister-chromatid exchanges in lymphocytes. In the statistical analysis, this study constructed life tables, used the Kaplan-Meier, and the log-rank test, and in the multivariate analysis, a Cox proportional hazards model was constructed.

Results

Eighty patients' clinical, demographic and lifestyle characteristics were analyzed, as well as their survival rates and the average survival time is 784 days (interquartile range: 928). Factors like age, exposure at work to polycyclic hydrocarbons and the number of sister-chromatid exchanges in lymphocytes in the first sampling was significantly survivalrelated in the multivariate analysis.

Conclusion

We determined that only three of the analyzed variables have an important effect on survival time when it comes to high-grade glioma patients.

Predictive Model to Identify the Long Time Survivor in Patients with Glioblastoma: A Cohort Study Integrating Machine Learning Algorithms

We aimed to develop and validate a predictive model for identifying long-term survivors (LTS) among glioblastoma (GB) patients, defined as those with an overall survival (OS) of more than 3 years. A total of 293 GB patients from CGGA and 169 from TCGA database were assigned to training and validation cohort, respectively. The differences in expression of immune checkpoint genes (ICGs) and immune infiltration landscape were compared between LTS and short time survivor (STS) (OS<1.5 years). The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were used to identify the genes differentially expressed between LTS and STS. Three different machine learning algorithms were employed to select the predictive genes from the overlapping region of DEGs and WGCNA to construct the nomogram. The comparison between LTS and STS revealed that STS exhibited an immune-resistant status, with higher expression of ICGs (P<0.05) and greater infiltration of immune suppression cells compared to LTS (P<0.05). Four genes, namely, OSMR, FMOD, CXCL14, and TIMP1, were identified and incorporated into the nomogram, which possessed good potential in predicting LTS probability among GB patients both in the training (C-index, 0.791; 0.772-0.817) and validation cohort (C-index, 0.770; 0.751-0.806). STS was found to be more likely to exhibit an immune-cold phenotype. The identified predictive genes were used to construct the nomogram with potential to identify LTS among GB patients.

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies

BACKGROUND

DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis.

METHODS

We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively.

RESULTS

Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques.

CONCLUSIONS

There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.

Cellular and molecular features related to exceptional therapy response and extreme long-term survival in glioblastoma

Glioblastoma Multiforme (GBM) remains the most common malignant primary brain tumor with a dismal prognosis that rarely exceeds beyond 2 years despite extensive therapy, which consists of maximal safe surgical resection, radiotherapy, and/or chemotherapy. Recently, it has become clear that GBM is not one homogeneous entity and that both intra-and intertumoral heterogeneity contributes significantly to differences in tumoral behavior which may consequently be responsible for differences in survival. Strikingly and in spite of its dismal prognosis, small fractions of GBM patients seem to display extremely long survival, defined as surviving over 10 years after diagnosis, compared to the large majority of patients. Although the underlying mechanisms for this peculiarity remain largely unknown, emerging data suggest that still poorly characterized both cellular and molecular factors of the tumor microenvironment and their interplay probably play an important role. We hereby give an extensive overview of what is yet known about these cellular and molecular features shaping extreme long survival in GBM.

Metabolic remodeling of pyrimidine synthesis pathway and serine synthesis pathway in human glioblastoma

Glioblastoma is the most common brain tumor with dismal outcomes in adults. Metabolic remodeling is now widely acknowledged as a hallmark of cancer cells, but glioblastoma-specific metabolic pathways remain unclear. Here we show, using a large-scale targeted proteomics platform and integrated molecular pathway-level analysis tool, that the de novo pyrimidine synthesis pathway and serine synthesis pathway (SSP) are the major enriched pathways in vivo for patients with glioblastoma. Among the enzymes associated with nucleotide synthesis, RRM1 and NME1 are significantly upregulated in glioblastoma. In the SSP, SHMT2 and PSPH are upregulated but the upstream enzyme PSAT1 is downregulated in glioblastoma. Kaplan–Meier curves of overall survival for the GSE16011 and The Cancer Genome Atlas datasets revealed that high SSP activity correlated with poor outcome. Enzymes relating to the pyrimidine synthesis pathway and SSP might offer therapeutic targets for new glioblastoma treatments.

Molecular and Circulating Biomarkers in Patients with Glioblastoma

Glioblastoma is the most aggressive malignant tumor of the central nervous system with a low survival rate. The difficulty of obtaining this tumor material represents a major limitation, making the real-time monitoring of tumor progression difficult, especially in the events of recurrence or resistance to treatment. The identification of characteristic biomarkers is indispensable for an accurate diagnosis, the rigorous follow-up of patients, and the development of new personalized treatments. Liquid biopsy, as a minimally invasive procedure, holds promise in this regard. The purpose of this paper is to summarize the current literature regarding the identification of molecular and circulating glioblastoma biomarkers and the importance of their integration as a valuable tool to improve patient care.

Chromatin structure predicts survival in glioma patients

The pathological changes in epigenetics and gene regulation that accompany the progression of low-grade to high-grade gliomas are under-studied. The authors use a large set of paired atac-seq and RNA-seq data from surgically resected glioma specimens to infer gene regulatory relationships in glioma. Thirty-eight glioma patient samples underwent atac-seq sequencing and 16 samples underwent additional RNA-seq analysis. Using an atac-seq/RNA-seq correlation matrix, atac-seq peaks were paired with genes based on high correlation values (|r²| > 0.6). Samples clustered by IDH1 status but not by grade. Surprisingly there was a trend for IDH1 mutant samples to have more peaks. The majority of peaks are positively correlated with survival and positively correlated with gene expression. Constructing a model of the top six atac-seq peaks created a highly accurate survival prediction model (r² = 0.68). Four of these peaks were still significant after controlling for age, grade, pathology, IDH1 status and gender. Grade II, III, and IV (primary) samples have similar transcription factors and gene modules. However, grade IV (recurrent) samples have strikingly few peaks. Patient-derived glioma cultures showed decreased peak counts following radiation indicating that this may be radiation-induced. This study supports the notion that IDH1 mutant and IDH1 wildtype gliomas have different epigenetic landscapes and that accessible chromatin sites mapped by atac-seq peaks tend to be positively correlated with expression. The data in this study leads to a new model of treatment response wherein glioma cells respond to radiation therapy by closing open regions of DNA.

Prognostic risk stratification of gliomas using deep learning in digital pathology images

Background

Evaluation of tumor-tissue images stained with hematoxylin and eosin (H&E) is pivotal in diagnosis, yet only a fraction of the rich phenotypic information is considered for clinical care. Here, we propose a survival deep learning (SDL) framework to extract this information to predict glioma survival.

Methods

Digitized whole slide images were downloaded from The Cancer Genome Atlas (TCGA) for 766 diffuse glioma patients, including isocitrate dehydrogenase (IDH)-mutant/1p19q-codeleted oligodendroglioma, IDH-mutant/1p19q-intact astrocytoma, and IDH-wildtype astrocytoma/glioblastoma. Our SDL framework employs a residual convolutional neural network with a survival model to predict patient risk from H&E-stained whole-slide images. We used statistical sampling techniques and randomized the transformation of images to address challenges in learning from histology images. The SDL risk score was evaluated in traditional and recursive partitioning (RPA) survival models.

Results

The SDL risk score demonstrated substantial univariate prognostic power (median concordance index of 0.79 [se: 0.01]). After adjusting for age and World Health Organization 2016 subtype, the SDL risk score was significantly associated with overall survival (OS; hazard ratio = 2.45; 95% CI: 2.01 to 3.00). Four distinct survival risk groups were characterized by RPA based on SDL risk score, IDH status, and age with markedly different median OS ranging from 1.03 years to 14.14 years.

Conclusions

The present study highlights the independent prognostic power of the SDL risk score for objective and accurate prediction of glioma outcomes. Further, we show that the RPA delineation of patient-specific risk scores and clinical prognostic factors can successfully demarcate the OS of glioma patients.

Machine Learning-Based Individualized Survival Prediction Model for Total Knee Replacement in Osteoarthritis: Data From the Osteoarthritis Initiative

OBJECTIVE

By using machine learning, our study aimed to build a model to predict risk and time to total knee replacement (TKR) of an osteoarthritic knee.

METHODS

Features were from the Osteoarthritis Initiative (OAI) cohort at baseline. Using the lasso method for variable selection in the Cox regression model, we identified the 10 most important characteristics among 1,107 features. The prognostic power of the selected features was assessed by the Kaplan-Meier method and applied to 7 machine learning methods: Cox, DeepSurv, random forests algorithm, linear/kernel support vector machine (SVM), and linear/neural multi-task logistic regression models. As some of the 10 first-found features included similar radiographic measurements, we further looked at using the least number of features without compromising the accuracy of the model. Prediction performance was assessed by the concordance index, Brier score, and time-dependent area under the curve (AUC).

RESULTS

Ten features were identified and included radiographs, bone marrow lesions of the medial condyle on magnetic resonance imaging, hyaluronic acid injection, performance measure, medical history, and knee-related symptoms. The methodologies Cox, DeepSurv, and linear SVM demonstrated the highest accuracy (concordance index scores of 0.85, Brier score of 0.02, and an AUC of 0.87). DeepSurv was chosen to build the prediction model to estimate the time to TKR for a given knee. Moreover, we were able to decrease the features to only 3 and maintain the high accuracy (concordance index of 0.85, Brier score of 0.02, and AUC of 0.86), which included bone marrow lesions, Kellgren/Lawrence grade, and knee-related symptoms, to predict risk and time of a TKR event.

CONCLUSION

For the first time, we developed a model using the OAI cohort to predict with high accuracy if a given osteoarthritic knee would require TKR, when a TKR would be required, and who would likely progress fast toward this event.

Anticancer Potential of Temozolomide-Loaded Eudragit-Chitosan Coated Selenium Nanoparticles: In Vitro Evaluation of Cytotoxicity, Apoptosis and Gene Regulation

Resistance to temozolomide (TMZ) is the main cause of death in glioblastoma multiforme (GBM). The use of nanocarriers for drug delivery applications is one of the known approaches to overcome drug resistance. This study aimed to investigate the possible effect of selenium-chitosan nanoparticles loaded with TMZ on the efficacy of TMZ on the expression of MGMT, E2F6, and RELA genes and the rate of apoptosis in the C6 cell line. Selenium nanoparticles (SNPs) were loaded with TMZ and then they were coated by Eudragit® RS100 (Eud) and chitosan (CS) to prepare Se@TMZ/Eud-Cs. Physicochemical properties were determined by scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDAX), thermal gravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), and dynamic light scattering (DLS) methods. Se@TMZ/Eud-Cs was evaluated for loading and release of TMZ by spectrophotometric method. Subsequently, SNPs loaded with curcumin (as a fluorophore) were analyzed for in vitro uptake by C6 cells. Cytotoxicity and apoptosis assay were measured by MTT assay and Annexin-PI methods. Finally, real-time PCR was utilized to determine the expression of MGMT, E2F6, and RELA genes. Se@TMZ/Eud-Cs was prepared with an average size of 200 nm as confirmed by the DLS and microscopical methods. Se@TMZ/Eud-Cs presented 82.77 ± 5.30 loading efficiency with slow and pH-sensitive release kinetics. SNPs loaded with curcumin showed a better uptake performance by C6 cells compared with free curcumin (p-value < 0.01). Coated nanoparticles loaded with TMZ showed higher cytotoxicity, apoptosis (p-value < 0.0001), and down-regulation of MGMT, E2F6, and RELA and lower IC50 value (p-value < 0.0001) than free TMZ and control (p-value < 0.0001) groups. Using Cs as a targeting agent in Se@TMZ/Eud-Cs system improved the possibility for targeted drug delivery to C6 cells. This drug delivery system enhanced the apoptosis rate and decreased the expression of genes related to TMZ resistance. In conclusion, Se@TMZ/Eud-Cs may be an option for the enhancement of TMZ efficiency in GBM treatment.

Molecular Correlates of Long Survival in IDH-Wildtype Glioblastoma Cohorts

IDH-wildtype glioblastoma is a relatively common malignant brain tumor in adults. These patients generally have dismal prognoses, although outliers with long survival have been noted in the literature. Recently, it has been reported that many histologically lower-grade IDH-wildtype astrocytomas have a similar clinical outcome to grade IV tumors, suggesting they may represent early or undersampled glioblastomas. cIMPACT-NOW 3 guidelines now recommend upgrading IDH-wildtype astrocytomas with certain molecular criteria (EGFR amplifications, chromosome 7 gain/10 loss, and/or TERT promoter mutations), establishing the concept of a "molecular grade IV" astrocytoma. In this report, we apply these cIMPACT-NOW 3 criteria to 2 independent glioblastoma cohorts, totaling 393 public database and institutional glioblastoma cases: 89 cases without any of the cIMPACT-NOW 3 criteria (GBM-C0) and 304 cases with one or more criteria (GBM-C1-3). In the GBM-C0 groups, there was a trend toward longer recurrence-free survival (median 12-17 vs 6-10 months), significantly longer overall survival (median 32-41 vs 15-18 months), younger age at initial diagnosis, and lower overall mutation burden compared to the GBM-C1-3 cohorts. These data suggest that while histologic features may not be ideal indicators of patient survival in IDH-wildtype astrocytomas, these 3 molecular features may also be important prognostic factors in IDH-wildtype glioblastoma.

Analysis of Factors Associated with Long-Term Survival in Patients with Glioblastoma

BACKGROUND

Some patients with glioblastoma multiforme (GBM) survive 3-5 years (or longer) after diagnosis. The goal of this study was to identify differences between the long-term survivors (LTS) and those who had a shorter overall survival (non-LTS groups).

METHODS

This study was a retrospective analysis of prospectively maintained surgical databases. All patients who underwent safe maximal resection for GBM were included. Demographic, clinical, radiologic, and pathologic data were obtained from electronic medical records. Values of the biomarkers of systemic inflammation were computed from the preoperative hemogram reports. Patients with an overall survival (OS) ≥36 months were defined as the LTS group and were compared with the non-LTS groups (OS<36 months).

RESULTS

Patients in the LTS group were younger, had a better baseline performance status, and were more likely to have undergone near- or gross-total resection. LTS was associated with lower Ki67 labeling, MGMT methylation, IDH mutation, and lack of p53 overexpression. Several novel findings were generated by this study. A longer pretreatment duration of symptoms was associated with a longer OS. Higher pretreatment levels of the absolute neutrophil count, neutrophil-lymphocyte ratio, monocyte-lymphocyte ratio, derived neutrophil-lymphocyte ratio and systemic index of inflammation, and lower levels of the absolute eosinophil count and eosinophil-lymphocyte ratio all correlated with a shorter OS.

CONCLUSIONS

Several differences were identified between the LTS and non-LTS groups. These differences will likely be incorporated into future prognostic models. They may also aid in differentiation between recurrent disease and treatment-related changes.

Radiotherapy for glioblastoma in the elderly: A protocol for systematic review and meta-analysis

BACKGROUND

Glioblastoma is an aggressive form of brain cancer with significant morbidity and mortality. This study aims to determine the radiotherapy for treatment of elderly people with diagnosed glioblastoma.

METHOD

This study adheres to the Preferred Reporting Items for Systematic Reviews and Meta-analysis for Protocols. Chinese electronic Database (CBM, Wanfang, and CNKI) and international electronic databases (PubMed, Embase, Cochrane Library, and Web of Science) will be searched for all relevant published articles, with no restrictions on the year of publication or language. Study selection, data collection, and assessment of study bias will be conducted independently by a pair of independent reviewers. The Cochrane Risk of bias (ROB) tool will be used for the risk of bias assessment. The Grading of Recommendations Assessment Development and Evaluation (GRADE) system will be used to assess the quality of evidence. The statistical analysis of this meta-analysis will be calculated by Review manager version 5.3.

RESULTS

The results of this study will be published in a peer-reviewed journal.

CONCLUSION

The findings of this review will to provide high-level evidence in terms of the benefits and harms of radiotherapy in people with glioblastoma to provide meaningful conclusions for clinical practice and further research.

TRIAL REGISTRATION

This study protocol was registered in open Science framework (OSF), (Registration DOI: 10.17605/OSF.IO/A6BCS).

Identification of New Genetic Clusters in Glioblastoma Multiforme: EGFR Status and ADD3 Losses Influence Prognosis

Glioblastoma multiforme (GB) is one of the most aggressive tumors. Despite continuous efforts to improve its clinical management, there is still no strategy to avoid a rapid and fatal outcome. EGFR amplification is the most characteristic alteration of these tumors. Although effective therapy against it has not yet been found in GB, it may be central to classifying patients. We investigated somatic-copy number alterations (SCNA) by multiplex ligation-dependent probe amplification in a series of 137 GB, together with the detection of EGFRvIII and FISH analysis for EGFR amplification. Publicly available data from 604 patients were used as a validation cohort. We found statistical associations between EGFR amplification and/or EGFRvIII, and SCNA in CDKN2A, MSH6, MTAP and ADD3. Interestingly, we found that both EGFRvIII and losses on ADD3 were independent markers of bad prognosis (p = 0.028 and 0.014, respectively). Finally, we got an unsupervised hierarchical classification that differentiated three clusters of patients based on their genetic alterations. It offered a landscape of EGFR co-alterations that may improve the comprehension of the mechanisms underlying GB aggressiveness. Our findings can help in defining different genetic profiles, which is necessary to develop new and different approaches in the management of our patients.

A validated integrated clinical and molecular glioblastoma long-term survival-predictive nomogram

Background

Glioblastoma (GBM) is the most common primary malignant brain tumor in adulthood. Despite multimodality treatments, including maximal safe resection followed by irradiation and chemotherapy, the median overall survival times range from 14 to 16 months. However, a small subset of GBM patients live beyond 5 years and are thus considered long-term survivors.

Methods

A retrospective analysis of the clinical, radiographic, and molecular features of patients with newly diagnosed primary GBM who underwent treatment at The University of Texas MD Anderson Cancer Center was conducted. Eighty patients had sufficient quantity and quality of tissue available for next-generation sequencing and immunohistochemical analysis. Factors associated with survival time were identified using proportional odds ordinal regression. We constructed a survival-predictive nomogram using a forward stepwise model that we subsequently validated using The Cancer Genome Atlas.

Results

Univariate analysis revealed 3 pivotal genetic alterations associated with GBM survival: both high tumor mutational burden (P = .0055) and PTEN mutations (P = .0235) negatively impacted survival, whereas IDH1 mutations positively impacted survival (P < .0001). Clinical factors significantly associated with GBM survival included age (P < .0001), preoperative Karnofsky Performance Scale score (P = .0001), sex (P = .0164), and clinical trial participation (P < .0001). Higher preoperative T1-enhancing volume (P = .0497) was associated with shorter survival. The ratio of TI-enhancing to nonenhancing disease (T1/T2 ratio) also significantly impacted survival (P = .0022).

Conclusions

Our newly devised long-term survival-predictive nomogram based on clinical and genomic data can be used to advise patients regarding their potential outcomes and account for confounding factors in nonrandomized clinical trials.

Checkpoint inhibitor immunotherapy for glioblastoma: current progress, challenges and future outlook

INTRODUCTION

Despite maximal surgical resection and chemoradiation, glioblastoma (GBM) continues to be associated with significant morbidity and mortality. Novel therapeutic strategies are urgently needed. Given success in treating multiple other forms of cancer, checkpoint inhibitor immunotherapy remains foremost amongst novel therapeutic strategies that are currently under investigation.

AREAS COVERED

Through a systematic review of both published literature and the latest preliminary data available from ongoing clinical studies, we provide an up-to-date discussion on the immune system in the CNS, a detailed mechanistic evaluation of checkpoint biology in the CNS along with evidence for disruption of these pathways in GBM, and a summary of available preclinical and clinical data for checkpoint blockade in GBM. We also include a discussion of novel, emerging targets for checkpoint blockade which may play an important role in GBM immunotherapy.

EXPERT OPINION

Evidence indicates that while clinical success of checkpoint blockade for the treatment of GBM has been limited to date, through improved preclinical models, optimization in the context of standard of care therapies, assay standardization and harmonization, and combinatorial approaches which may include novel targets for checkpoint blockade, checkpoint inhibitor immunotherapy may yield a safe and effective therapeutic option for the treatment of GBM.

Beyond IDH-Mutation: Emerging Molecular Diagnostic and Prognostic Features in Adult Diffuse Gliomas

Diffuse gliomas are among the most common adult central nervous system tumors with an annual incidence of more than 16,000 cases in the United States. Until very recently, the diagnosis of these tumors was based solely on morphologic features, however, with the publication of the WHO Classification of Tumours of the Central Nervous System, revised 4th edition in 2016, certain molecular features are now included in the official diagnostic and grading system. One of the most significant of these changes has been the division of adult astrocytomas into IDH-wildtype and IDH-mutant categories in addition to histologic grade as part of the main-line diagnosis, although a great deal of heterogeneity in the clinical outcome still remains to be explained within these categories. Since then, numerous groups have been working to identify additional biomarkers and prognostic factors in diffuse gliomas to help further stratify these tumors in hopes of producing a more complete grading system, as well as understanding the underlying biology that results in differing outcomes. The field of neuro-oncology is currently in the midst of a "molecular revolution" in which increasing emphasis is being placed on genetic and epigenetic features driving current diagnostic, prognostic, and predictive considerations. In this review, we focus on recent advances in adult diffuse glioma biomarkers and prognostic factors and summarize the state of the field.

Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data

Background

Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges to applying conventional survival analysis.

Results

We propose a novel biologically interpretable pathway-based sparse deep neural network, named Cox-PASNet, which integrates high-dimensional gene expression data and clinical data on a simple neural network architecture for survival analysis. Cox-PASNet is biologically interpretable where nodes in the neural network correspond to biological genes and pathways, while capturing the nonlinear and hierarchical effects of biological pathways associated with cancer patient survival. We also propose a heuristic optimization solution to train Cox-PASNet with HDLSS data. Cox-PASNet was intensively evaluated by comparing the predictive performance of current state-of-the-art methods on glioblastoma multiforme (GBM) and ovarian serous cystadenocarcinoma (OV) cancer. In the experiments, Cox-PASNet showed out-performance, compared to the benchmarking methods. Moreover, the neural network architecture of Cox-PASNet was biologically interpreted, and several significant prognostic factors of genes and biological pathways were identified.

Conclusions

Cox-PASNet models biological mechanisms in the neural network by incorporating biological pathway databases and sparse coding. The neural network of Cox-PASNet can identify nonlinear and hierarchical associations of genomic and clinical data to cancer patient survival. The open-source code of Cox-PASNet in PyTorch implemented for training, evaluation, and model interpretation is available at: https://github.com/DataX-JieHao/Cox-PASNet.

Molecular profile of long-term survivors of glioblastoma: A scoping review of the literature

Molecular aberrations of malignancy are becoming widely recognized as important predictive and prognostic markers for treatment response and survival in oncology and have been linked to the discovery of novel treatment targets. This area of research in glioblastoma continues to evolve. The aim of this scoping review was to document the hallmark molecular characteristics of long-term survivors of glioblastoma. MEDLINE, Scopus and EMBASE were searched with core concepts: (1) glioblastoma, (2) long-term survivor and (3) molecular OR mutation. A thematic analysis was undertaken of the 18 included studies. Four main classes of characteristics were obtained: IDH mutation, MGMT methylation, other known characteristics and novel discoveries. While MGMT methylation or the combination with IDH mutation are suggested to be hallmark characteristics, there remains enough uncertainty to suggest further factors may be involved, such as CD34 expression. Further research is required to accurately describe hallmark molecular characteristics of long-term survivors to assist in defining these patients at diagnosis, preventing treatment complications and discovering novel treatments.

Pathway-based deep clustering for molecular subtyping of cancer

Cancer is a genetic disease comprising multiple subtypes that have distinct molecular characteristics and clinical features. Cancer subtyping helps in improving personalized treatment and making decision, as different cancer subtypes respond differently to the treatment. The increasing availability of cancer related genomic data provides the opportunity to identify molecular subtypes. Several unsupervised machine learning techniques have been applied on molecular data of the tumor samples to identify cancer subtypes that are genetically and clinically distinct. However, most clustering methods often fail to efficiently cluster patients due to the challenges imposed by high-throughput genomic data and its non-linearity. In this paper, we propose a pathway-based deep clustering method (PACL) for molecular subtyping of cancer, which incorporates gene expression and biological pathway database to group patients into cancer subtypes. The main contribution of our model is to discover high-level representations of biological data by learning complex hierarchical and nonlinear effects of pathways. We compared the performance of our model with a number of benchmark clustering methods that recently have been proposed in cancer subtypes. We assessed the hypothesis that clusters (subtypes) may be associated to different survivals by logrank tests. PACL showed the lowest p-value of the logrank test against the benchmark methods. It demonstrates the patient groups clustered by PACL may correspond to subtypes which are significantly associated with distinct survival distributions. Moreover, PACL provides a solution to comprehensively identify subtypes and interpret the model in the biological pathway level. The open-source software of PACL in PyTorch is publicly available at https://github.com/tmallava/PACL.

Integrative analysis of DNA methylation suggests down-regulation of oncogenic pathways and reduced somatic mutation rates in survival outliers of glioblastoma

The study of survival outliers of glioblastoma can provide important clues on gliomagenesis as well as on the ways to alter clinical course of this almost uniformly lethal cancer type. However, there has been little consensus on genetic and epigenetic signatures of the long-term survival outliers of glioblastoma. Although the two classical molecular markers of glioblastoma including isocitrate dehydrogenase 1 or 2 (IDH1/2) mutation and O6-methylguanine DNA methyltransferase (MGMT) promoter methylation are associated with overall survival rate of glioblastoma patients, they are not specific to the survival outliers. In this study, we compared the two groups of survival outliers of glioblastoma with IDH wild-type, consisting of the glioblastoma patients who lived longer than 3 years (n = 17) and the patients who lived less than 1 year (n = 12) in terms of genome-wide DNA methylation profile. Statistical analyses were performed to identify differentially methylated sites between the two groups. Functional implication of DNA methylation patterns specific to long-term survivors of glioblastoma were investigated by comprehensive enrichment analyses with genomic and epigenomic features. We found that the genome of long-term survivors of glioblastoma is differentially methylated relative to short-term survivor patients depending on CpG density: hypermethylation near CpG islands (CGIs) and hypomethylation far from CGIs. Interestingly, these two patterns are associated with distinct oncogenic aspects in gliomagenesis. In the long-term survival glioblastoma-specific sites distant from CGI, somatic mutations of glioblastoma are enriched with higher DNA methylation, suggesting that the hypomethylation in long-term survival glioblastoma can contribute to reduce the rate of somatic mutation. On the other hand, the hypermethylation near CGIs associates with transcriptional downregulation of genes involved in cancer progression pathways. Using independent cohorts of IDH1/2- wild type glioblastoma, we also showed that these two patterns of DNA methylation can be used as molecular markers of long-term survival glioblastoma. Our results provide extended understanding of DNA methylation, especially of DNA hypomethylation, in cancer genome and reveal clinical importance of DNA methylation pattern as prognostic markers of glioblastoma.

Genome-Wide Analysis of Glioblastoma Patients with Unexpectedly Long Survival

Glioblastoma (GBM), representing WHO grade IV astrocytoma, is a relatively common primary brain tumor in adults with an exceptionally dismal prognosis. With an incidence rate of over 10 000 cases in the United States annually, the median survival rate ranges from 10-15 months in IDH1/2-wildtype tumors and 24-31 months in IDH1/2-mutant tumors, with further variation depending on factors such as age, MGMT methylation status, and treatment regimen. We present a cohort of 4 patients, aged 37-60 at initial diagnosis, with IDH1-mutant GBMs that were associated with unusually long survival intervals after the initial diagnosis, currently ranging from 90 to 154 months (all still alive). We applied genome-wide profiling with a methylation array (Illumina EPIC Array 850k) and a next-generation sequencing panel to screen for genetic and epigenetic alterations in these tumors. All 4 tumors demonstrated methylation patterns and genomic alterations consistent with GBM. Three out of four cases showed focal amplification of the CCND2 gene or gain of the region on 12p that included CCND2, suggesting that this may be a favorable prognostic factor in GBM. As this study has a limited sample size, further evaluation of patients with similar favorable outcome is warranted to validate these findings.

Identification of potential biomarkers related to glioma survival by gene expression profile analysis

BACKGROUND

Recent studies have proposed several gene signatures as biomarkers for different grades of gliomas from various perspectives. However, most of these genes can only be used appropriately for patients with specific grades of gliomas.

METHODS

In this study, we aimed to identify survival-relevant genes shared between glioblastoma multiforme (GBM) and lower-grade glioma (LGG), which could be used as potential biomarkers to classify patients into different risk groups. Cox proportional hazard regression model (Cox model) was used to extract relative genes, and effectiveness of genes was estimated against random forest regression. Finally, risk models were constructed with logistic regression.

RESULTS

We identified 104 key genes that were shared between GBM and LGG, which could be significantly correlated with patients' survival based on next-generation sequencing data obtained from The Cancer Genome Atlas for gene expression analysis. The effectiveness of these genes in the survival prediction of GBM and LGG was evaluated, and the average receiver operating characteristic curve (ROC) area under the curve values ranged from 0.7 to 0.8. Gene set enrichment analysis revealed that these genes were involved in eight significant pathways and 23 molecular functions. Moreover, the expressions of ten (CTSZ, EFEMP2, ITGA5, KDELR2, MDK, MICALL2, MAP 2 K3, PLAUR, SERPINE1, and SOCS3) of these genes were significantly higher in GBM than in LGG, and comparing their expression levels to those of the proposed control genes (TBP, IPO8, and SDHA) could have the potential capability to classify patients into high- and low- risk groups, which differ significantly in the overall survival. Signatures of candidate genes were validated, by multiple microarray datasets from Gene Expression Omnibus, to increase the robustness of using these potential prognostic factors. In both the GBM and LGG cohort study, most of the patients in the high-risk group had the IDH1 wild-type gene, and those in the low-risk group had IDH1 mutations. Moreover, most of the high-risk patients with LGG possessed a 1p/19q-noncodeletion.

CONCLUSION

In this study, we identified survival relevant genes which were shared between GBM and LGG, and those enabled to classify patients into high- and low-risk groups based on expression level analysis. Both the risk groups could be correlated with the well-known genetic variants, thus suggesting their potential prognostic value in clinical application.

PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data

Background

Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis may be caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data.

Results

In this study, we propose a Pathway-Associated Sparse Deep Neural Network (PASNet) that not only predicts patients’ prognoses but also describes complex biological processes regarding biological pathways for prognosis. PASNet models a multilayered, hierarchical biological system of genes and pathways to predict clinical outcomes by leveraging deep learning. The sparse solution of PASNet provides the capability of model interpretability that most conventional fully-connected neural networks lack. We applied PASNet for long-term survival prediction in Glioblastoma multiforme (GBM), which is a primary brain cancer that shows poor prognostic performance. The predictive performance of PASNet was evaluated with multiple cross-validation experiments. PASNet showed a higher Area Under the Curve (AUC) and F1-score than previous long-term survival prediction classifiers, and the significance of PASNet’s performance was assessed by Wilcoxon signed-rank test. Furthermore, the biological pathways, found in PASNet, were referred to as significant pathways in GBM in previous biology and medicine research.

Conclusions

PASNet can describe the different biological systems of clinical outcomes for prognostic prediction as well as predicting prognosis more accurately than the current state-of-the-art methods. PASNet is the first pathway-based deep neural network that represents hierarchical representations of genes and pathways and their nonlinear effects, to the best of our knowledge. Additionally, PASNet would be promising due to its flexible model representation and interpretability, embodying the strengths of deep learning. The open-source code of PASNet is available at https://github.com/DataX-JieHao/PASNet.

Whole-Genome Multi-omic Study of Survival in Patients with Glioblastoma Multiforme

Glioblastoma multiforme (GBM) has been recognized as the most lethal type of malignant brain tumor. Despite efforts of the medical and research community, patients' survival remains extremely low. Multi-omic profiles (including DNA sequence, methylation and gene expression) provide rich information about the tumor. These profiles are likely to reveal processes that may be predictive of patient survival. However, the integration of multi-omic profiles, which are high dimensional and heterogeneous in nature, poses great challenges. The goal of this work was to develop models for prediction of survival of GBM patients that can integrate clinical information and multi-omic profiles, using multi-layered Bayesian regressions. We apply the methodology to data from GBM patients from The Cancer Genome Atlas (TCGA, n = 501) to evaluate whether integrating multi-omic profiles (SNP-genotypes, methylation, copy number variants and gene expression) with clinical information (demographics as well as treatments) leads to an improved ability to predict patient survival. The proposed Bayesian models were used to estimate the proportion of variance explained by clinical covariates and omics and to evaluate prediction accuracy in cross validation (using the area under the Receiver Operating Characteristic curve, AUC). Among clinical and demographic covariates, age (AUC = 0.664) and the use of temozolomide (AUC = 0.606) were the most predictive of survival. Among omics, methylation (AUC = 0.623) and gene expression (AUC = 0.593) were more predictive than either SNP (AUC = 0.539) or CNV (AUC = 0.547). While there was a clear association between age and methylation, the integration of age, the use of temozolomide, and either gene expression or methylation led to a substantial increase in AUC in cross-validaton (AUC = 0.718). Finally, among the genes whose methylation was higher in aging brains, we observed a higher enrichment of these genes being also differentially methylated in cancer.

Intravenously-injected gold nanoparticles (AuNPs) access intracerebral F98 rat gliomas better than AuNPs infused directly into the tumor site by convection enhanced delivery

BACKGROUND

Intravenously (IV)-injected gold nanoparticles (AuNPs) powerfully enhance the efficacy of X-ray therapy of tumors including advanced gliomas. However, pharmacokinetic issues, such as slow tissue clearance and skin discoloration, may impede clinical translation. The direct infusion of AuNPs into the tumor might be an alternative mode of delivery.

MATERIALS AND METHODS

Using the advanced, invasive, and difficult-to-treat F98 rat glioma model, we have studied the biodistribution of the AuNPs in the tumor and surrounding brain after either IV injection or direct intratumoral infusion by convection-enhanced delivery using light microscopy immunofluorescence and direct gold visualization.

RESULTS

IV-injected AuNPs localize more specifically to intracerebral tumor cells, both in the main tumor mass and in the migrated tumor cells as well as the tumor edema, than do the directly infused AuNPs. Although some of the directly infused AuNPs do access the main tumor region, such access is largely restricted.

CONCLUSION

These data suggest that IV-injected AuNPs are likely to have a greater therapeutic benefit when combined with radiation therapy than after the direct infusion of AuNPs.

The Survival Advantage of "Supratotal" Resection of Glioblastoma Using Selective Cortical Mapping and the Subpial Technique

BACKGROUND

A substantial body of evidence suggests that cytoreductive surgery is a prerequisite to prolonging survival in patients with glioblastoma (GBM).

OBJECTIVE

To evaluate the safety and impact of "supratotal" resections beyond the zone of enhancement seen on magnetic resonance imaging scans, using a subpial technique.

METHODS

We retrospectively evaluated 86 consecutive patients with primary GBM, managed by the senior author, using a subpial resection technique with or without carmustine (BCNU) wafer implantation. Multivariate Cox proportional hazards regression was used to analyze clinical, radiological, and outcome variables. Overall impacts of extent of resection (EOR) and BCNU wafer placement were compared using Kaplan-Meier survival analysis.

RESULTS

Mean patient age was 56 years. The median OS for the group was 18.1 months. Median OS for patients undergoing gross total, near-total, and subtotal resection were 54, 16.5, and 13.2 months, respectively. Patients undergoing near-total resection ( P = .05) or gross total resection ( P < .01) experienced statistically significant longer survival time than patients undergoing subtotal resection as well as patients undergoing ≥95% EOR ( P < .01) when compared to <95% EOR. The addition of BCNU wafers had no survival advantage.

CONCLUSIONS

The subpial technique extends the resection beyond the contrast enhancement and is associated with an overall survival beyond that seen in similar series where resection of the enhancement portion is performed. The effect of supratotal resection on survival exceeded the effects of age, Karnofsky performance score, and tumor volume. A prospective study would help to quantify the impact of the subpial technique on quality of life and survival as compared to a traditional resection limited to the enhancing tumor.

Breaching barriers in glioblastoma. Part I: Molecular pathways and novel treatment approaches

Glioblastoma multiforme (GBM) is the most common primary brain tumour, and the most aggressive in nature. The prognosis for patients with GBM remains poor, with a median survival time of only 1-2 years. The treatment failure relies on the development of resistance by tumour cells and the difficulty of ensuring that drugs effectively cross the dual blood brain barrier/blood brain tumour barrier. The advanced molecular and genetic knowledge has allowed to identify the mechanisms responsible for temozolomide resistance, which represents the standard of care in GBM, along with surgical resection and radiotherapy. Such resistance has motivated the researchers to investigate new avenues for GBM treatment intended to improve patient survival. In this review, we provide an overview of major obstacles to effective treatment of GBM, encompassing biological barriers, cancer stem cells, DNA repair mechanisms, deregulated signalling pathways and autophagy. New insights and potential therapy approaches for GBM are also discussed, emphasizing localized chemotherapy delivered directly to the brain, immunotherapy, gene therapy and nanoparticle-mediated brain drug delivery.

The interventional effect of new drugs combined with the Stupp protocol on glioblastoma: A network meta-analysis

OBJECTIVE

New therapeutic agents in combination with the standard Stupp protocol (a protocol about the temozolomide combined with radiotherapy treatment with glioblastoma was research by Stupp R in 2005) were assessed to evaluate whether they were superior to the Stupp protocol alone, to determine the optimum treatment regimen for patients with newly diagnosed glioblastoma.

PATIENTS AND METHODS

We implemented a search strategy to identify studies in the following databases: PubMed, Cochrane Library, EMBASE, CNKI, CBM, Wanfang, and VIP, and assessed the quality of extracted data from the trials included. Statistical software was used to perform network meta-analysis.

RESULTS

The use of novel therapeutic agents in combination with the Stupp protocol were all shown to be superior than the Stupp protocol alone for the treatment of newly diagnosed glioblastoma, ranked as follows: cilengitide 2000mg/5/week, bevacizumab in combination with irinotecan, nimotuzumab, bevacizumab, cilengitide 2000mg/2/week, cytokine-induced killer cell immunotherapy, and the Stupp protocol. In terms of serious adverse effects, the intervention group showed a 29% increase in the incidence of adverse events compared with the control group (patients treated only with Stupp protocol) with a statistically significant difference (RR=1.29; 95%CI 1.17-1.43; P<0.001). The most common adverse events were thrombocytopenia, lymphopenia, neutropenia, pneumonia, nausea, and vomiting, none of which were significantly different between the groups except for neutropenia, pneumonia, and embolism.

CONCLUSIONS

All intervention drugs evaluated in our study were superior to the Stupp protocol alone when used in combination with it. However, we could not conclusively confirm whether cilengitide 2000mg/5/week was the optimum regime, as only one trial using this protocol was included in our study.

In search of druggable targets for GBM amino acid metabolism

BACKGROUND

Amino acid (AA) pathways may contain druggable targets for glioblastoma (GBM). Literature reviews and GBM database ( http://r2.amc.nl ) analyses were carried out to screen for such targets among 95 AA related enzymes.

METHODS

First, we identified the genes that were differentially expressed in GBMs (3 datasets) compared to non-GBM brain tissues (5 datasets), or were associated with survival differences. Further, protein expression for these enzymes was also analyzed in high grade gliomas (HGGs) (proteinatlas.org). Finally, AA enzyme and gene expression were compared among the 4 TCGA (The Cancer Genome Atlas) subtypes of GBMs.

RESULTS

We detected differences in enzymes involved in glutamate and urea cycle metabolism in GBM. For example, expression levels of BCAT1 (branched chain amino acid transferase 1) and ASL (argininosuccinate lyase) were high, but ASS1 (argininosuccinate synthase 1) was low in GBM. Proneural and neural TCGA subtypes had low expression of all three. High expression of all three correlated with worse outcome. ASL and ASS1 protein levels were mostly undetected in high grade gliomas, whereas BCAT1 was high. GSS (glutathione synthetase) was not differentially expressed, but higher levels were linked to poor progression free survival. ASPA (aspartoacylase) and GOT1 (glutamic-oxaloacetic transaminase 1) had lower expression in GBM (associated with poor outcomes). All three GABA related genes -- glutamate decarboxylase 1 (GAD1) and 2 (GAD2) and 4-aminobutyrate aminotransferase (ABAT) -- were lower in mesenchymal tumors, which in contrast showed higher IDO1 (indoleamine 2, 3-dioxygenase 1) and TDO2 (tryptophan 2, 3-diaxygenase). Expression of PRODH (proline dehydrogenase), a putative tumor suppressor, was lower in GBM. Higher levels predicted poor survival.

CONCLUSIONS

Several AA-metabolizing enzymes that are higher in GBM, are also linked to poor outcome (such as BCAT1), which makes them potential targets for therapeutic inhibition. Moreover, existing drugs that deplete asparagine and arginine may be effective against brain tumors, and should be studied in conjunction with chemotherapy. Last, AA metabolism is heterogeneous in TCGA subtypes of GBM (as well as medulloblastomas and other pediatric tumors), which may translate to variable responses to AA targeted therapies.

A METHYLATION-TO-EXPRESSION FEATURE MODEL FOR GENERATING ACCURATE PROGNOSTIC RISK SCORES AND IDENTIFYING DISEASE TARGETS IN CLEAR CELL KIDNEY CANCER

Many researchers now have available multiple high-dimensional molecular and clinical datasets when studying a disease. As we enter this multi-omic era of data analysis, new approaches that combine different levels of data (e.g. at the genomic and epigenomic levels) are required to fully capitalize on this opportunity. In this work, we outline a new approach to multi-omic data integration, which combines molecular and clinical predictors as part of a single analysis to create a prognostic risk score for clear cell renal cell carcinoma. The approach integrates data in multiple ways and yet creates models that are relatively straightforward to interpret and with a high level of performance. Furthermore, the proposed process of data integration captures relationships in the data that represent highly disease-relevant functions.

A Comparative Study of the Molecular Characteristics of Familial Gliomas and Other Cancers

BACKGROUND

Familial cancers are those that co-occur among first-degree relatives without showing Mendelian patterns of inheritance.

MATERIALS AND METHODS

In this analysis, we compare the genomic characteristics of familial and sporadic cancers, with a focus on low-grade gliomas (LGGs) using sequence and expression data from the Cancer Genome Atlas.

RESULTS

Familial cancers show similar genomic and molecular biomarker profiles to sporadic cancers, consistent with the similarity in their clinical features. There are no statistically significant differences among somatic mutation, copy number variant, or gene expression patterns between familial and sporadic cancers; methylation profiles are the only class of molecular data to show significant differences.

CONCLUSION

Familial cancers are likely driven by multiple, individually weak contributions to familiality (i.e. large numbers of alleles and/or shared environmental risks). Consequently, these risk factors tend to be obscured by stronger confounding variables such as clinical or molecular variation among cancer subtypes.

Aquaporins and Brain Tumors

Brain primary tumors are among the most diverse and complex human cancers, and they are normally classified on the basis of the cell-type and/or the grade of malignancy (the most malignant being glioblastoma multiforme (GBM), grade IV). Glioma cells are able to migrate throughout the brain and to stimulate angiogenesis, by inducing brain capillary endothelial cell proliferation. This in turn causes loss of tight junctions and fragility of the blood–brain barrier, which becomes leaky. As a consequence, the most serious clinical complication of glioblastoma is the vasogenic brain edema. Both glioma cell migration and edema have been correlated with modification of the expression/localization of different isoforms of aquaporins (AQPs), a family of water channels, some of which are also involved in the transport of other small molecules, such as glycerol and urea. In this review, we discuss relationships among expression/localization of AQPs and brain tumors/edema, also focusing on the possible role of these molecules as both diagnostic biomarkers of cancer progression, and therapeutic targets. Finally, we will discuss the possibility that AQPs, together with other cancer promoting factors, can be exchanged among brain cells via extracellular vesicles (EVs).