Intratumor DNA methylation heterogeneity in glioblastoma: implications for DNA methylation-based classification

Spatial concordance of DNA methylation classification in diffuse glioma

Background

Intratumoral heterogeneity is a hallmark of diffuse gliomas. DNA methylation profiling is an emerging approach in the clinical classification of brain tumors. The goal of this study is to investigate the effects of intratumoral heterogeneity on classification confidence.

Methods

We used neuronavigation to acquire 133 image-guided and spatially separated stereotactic biopsy samples from 16 adult patients with a diffuse glioma (7 IDH-wildtype and 2 IDH-mutant glioblastoma, 6 diffuse astrocytoma, IDH-mutant and 1 oligodendroglioma, IDH-mutant and 1p19q codeleted), which we characterized using DNA methylation arrays. Samples were obtained from regions with and without abnormalities on contrast-enhanced T1-weighted and fluid-attenuated inversion recovery MRI. Methylation profiles were analyzed to devise a 3-dimensional reconstruction of (epi)genetic heterogeneity. Tumor purity was assessed from clonal methylation sites.

Results

Molecular aberrations indicated that tumor was found outside imaging abnormalities, underlining the infiltrative nature of this tumor and the limitations of current routine imaging modalities. We demonstrate that tumor purity is highly variable between samples and explains a substantial part of apparent epigenetic spatial heterogeneity. We observed that DNA methylation subtypes are often, but not always, conserved in space taking tumor purity and prediction accuracy into account.

Conclusion

Our results underscore the infiltrative nature of diffuse gliomas and suggest that DNA methylation subtypes are relatively concordant in this tumor type, although some heterogeneity exists.

DNA methylation profiling for molecular classification of adult diffuse lower-grade gliomas

BACKGROUND

DNA methylation profiling has facilitated and improved the classification of a wide variety of tumors of the central nervous system. In this study, we investigated the potential utility of DNA methylation profiling to achieve molecular diagnosis in adult primary diffuse lower-grade glioma (dLGG) according to WHO 2016 classification system. We also evaluated whether methylation profiling could provide improved molecular characterization and identify prognostic differences beyond the classical histological WHO grade together with IDH mutation status and 1p/19q codeletion status. All patients diagnosed with dLGG in the period 2007-2016 from the Västra Götaland region in Sweden were assessed for inclusion in the study.

RESULTS

A total of 166 dLGG cases were subjected for genome-wide DNA methylation analysis. Of these, 126 (76%) were assigned a defined diagnostic methylation class with a class prediction score ≥ 0.84 and subclass score ≥ 0.50. The assigned methylation classes were highly associated with their IDH mutation status and 1p/19q codeletion status. IDH-wildtype gliomas were further divided into subgroups with distinct molecular features.

CONCLUSION

The stratification of the patients by methylation profiling was as effective as the integrated WHO 2016 molecular reclassification at predicting the clinical outcome of the patients. Our study shows that DNA methylation profiling is a reliable and robust approach for the classification of dLGG into molecular defined subgroups, providing accurate detection of molecular markers according to WHO 2016 classification.

Brain Tumor Biobank Development for Precision Medicine: Role of the Neurosurgeon

Neuro-oncology biobanks are critical for the implementation of a precision medicine program. In this perspective, we review our first year experience of a brain tumor biobank with integrated next generation sequencing. From our experience, we describe the critical role of the neurosurgeon in diagnosis, research, and precision medicine efforts. In the first year of implementation of the biobank, 117 patients (Female: 62; Male: 55) had 125 brain tumor surgeries. 75% of patients had tumors biobanked, and 16% were of minority race/ethnicity. Tumors biobanked were as follows: diffuse gliomas (45%), brain metastases (29%), meningioma (21%), and other (5%). Among biobanked patients, 100% also had next generation sequencing. Eleven patients qualified for targeted therapy based on identification of actionable gene mutations. One patient with a hereditary cancer predisposition syndrome was also identified. An iterative quality improvement process was implemented to streamline the workflow between the operating room, pathology, and the research laboratory. Dedicated tumor bank personnel in the department of neurosurgery greatly improved standard operating procedure. Intraoperative selection and processing of tumor tissue by the neurosurgeon was integral to increasing success with cell culture assays. Currently, our institutional protocol integrates standard histopathological diagnosis, next generation sequencing, and functional assays on surgical specimens to develop precision medicine protocols for our patients. This perspective reviews the critical role of neurosurgeons in brain tumor biobank implementation and success as well as future directions for enhancing precision medicine efforts.

The Glioblastoma Microenvironment: Morphology, Metabolism, and Molecular Signature of Glial Dynamics to Discover Metabolic Rewiring Sequence

Different functional states determine glioblastoma (GBM) heterogeneity. Brain cancer cells coexist with the glial cells in a functional syncytium based on a continuous metabolic rewiring. However, standard glioma therapies do not account for the effects of the glial cells within the tumor microenvironment. This may be a possible reason for the lack of improvements in patients with high-grade gliomas therapies. Cell metabolism and bioenergetic fitness depend on the availability of nutrients and interactions in the microenvironment. It is strictly related to the cell location in the tumor mass, proximity to blood vessels, biochemical gradients, and tumor evolution, underlying the influence of the context and the timeline in anti-tumor therapeutic approaches. Besides the cancer metabolic strategies, here we review the modifications found in the GBM-associated glia, focusing on morphological, molecular, and metabolic features. We propose to analyze the GBM metabolic rewiring processes from a systems biology perspective. We aim at defining the crosstalk between GBM and the glial cells as modules. The complex networking may be expressed by metabolic modules corresponding to the GBM growth and spreading phases. Variation in the oxidative phosphorylation (OXPHOS) rate and regulation appears to be the most important part of the metabolic and functional heterogeneity, correlating with glycolysis and response to hypoxia. Integrated metabolic modules along with molecular and morphological features could allow the identification of key factors for controlling the GBM-stroma metabolism in multi-targeted, time-dependent therapies.

Clinical validation of a novel quantitative assay for the detection of MGMT methylation in glioblastoma patients

Background

The promoter hypermethylation of the methylguanine-DNA methyltransferase gene is a frequently used biomarker in daily clinical practice as it is associated with a favorable prognosis in glioblastoma patients treated with temozolamide. Due to the absence of adequately standardized techniques, international harmonization of the MGMT methylation biomarker is still an unmet clinical need for the diagnosis and treatment of glioblastoma patients.

Results

In this study we carried out a clinical validation of a quantitative assay for MGMT methylation detection by comparing a novel quantitative MSP using double-probe (dp_qMSP) with the conventional MSP in 100 FFPE glioblastoma samples. We performed both technologies and established the best cutoff for the identification of positive-methylated samples using the quantitative data obtained from dp_qMSP. Kaplan–Meier curves and ROC time dependent curves were employed for the comparison of both methodologies.

Conclusions

We obtained similar results using both assays in the same cohort of patients, in terms of progression free survival and overall survival according to Kaplan–Meier curves. In addition, the results of ROC(t) curves showed that dp_qMSP increases the area under curve time-dependent in comparison with MSP for predicting progression free survival and overall survival over time. We concluded that dp_qMSP is an alternative methodology compatible with the results obtained with the conventional MSP. Our assay will improve the therapeutic management of glioblastoma patients, being a more sensitive and competitive alternative methodology that ensures the standardization of the MGMT-biomarker making it reliable and suitable for clinical use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01044-2.

MGMT Promoter Methylation Status Is Not Related to Histological or Radiological Features in IDH Wild-type Glioblastomas

O6-methylguanine DNA methyltransferase (MGMT) promoter methylation is an important favorable predictive marker in patients with glioblastoma (GBM). We hypothesized that MGMT status could be a surrogate marker of pretreatment tumor biology observed as histopathological and radiological features. Apart from some radiological studies aiming to noninvasively predict the MGMT status, few studies have investigated relationships between MGMT status and phenotypical tumor biology. We have therefore aimed to investigate such relationships in 85 isocitrate dehydrogenase (IDH) wild-type GBMs. MGMT status was determined by methylation-specific PCR and was assessed for associations with 22 histopathological features, immunohistochemical proliferative index and microvessel density measurements, conventional magnetic resonance imaging characteristics, preoperative speed of tumor growth, and overall survival. None of the investigated histological or radiological features were significantly associated with MGMT status. Methylated MGMT status was a significant independent predictor of improved overall survival. In conclusion, our results suggest that MGMT status is not related to the pretreatment phenotypical biology in IDH wild-type GBMs. Furthermore, our findings suggest the survival benefit of MGMT methylated GBMs is not due to an inherently less aggressive tumor biology, and that conventional magnetic resonance imaging features cannot be used to noninvasively predict the MGMT status.

Organoid Models of Glioblastoma and Their Role in Drug Discovery

Glioblastoma (GBM) is a devastating adult brain cancer with high rates of recurrence and treatment resistance. Cellular heterogeneity and extensive invasion of surrounding brain tissues are characteristic features of GBM that contribute to its intractability. Current GBM model systems do not recapitulate some of the complex features of GBM and have not produced sufficiently-effective treatments. This has cast doubt on the effectiveness of current GBM models and drug discovery paradigms. In search of alternative pre-clinical GBM models, various 3D organoid-based GBM model systems have been developed using human cells. The scalability of these systems and potential to more accurately model characteristic features of GBM, provide promising new avenues for pre-clinical GBM research and drug discovery efforts. Here, we review the current suite of organoid-GBM models, their individual strengths and weaknesses, and discuss their future applications with an emphasis on compound screening.

DNA methylation-based age estimation in pediatric healthy tissues and brain tumors

Several DNA methylation clocks have been developed to reflect chronological age of human tissues, but most clocks have been trained on adult samples. The rapid methylome changes in children and the role of epigenetics in pediatric tumors calls for tools accurately estimating methylation age in children. We aimed to evaluate seven methylation clocks in multiple tissues from healthy children to inform future studies on the optimal clock for pediatric cohorts, and analyzed the methylation age in brain tumors. We found that clocks trained on pediatric samples were the best in all tested tissues, highlighting the need for dedicated clocks. For blood samples, the Skin and blood clock had the best correlation with chronological age, while PedBE was the most accurate for saliva and buccal samples, and Horvath for brain tissue. Horvath methylation age was accelerated in pediatric brain tumors and the acceleration was subtype-specific for atypical teratoid rhabdoid tumor (ATRT), ependymoma, medulloblastoma and glioma. The subtypes with the highest acceleration corresponded to the worst prognostic categories in ATRT, ependymoma and glioma, whereas the relationship was reversed in medulloblastoma. This suggests that methylation age has potential as a prognostic biomarker in pediatric brain tumors and should be further explored.

Immunohistochemically Characterized Intratumoral Heterogeneity Is a Prognostic Marker in Human Glioblastoma

Simple Summary

Intratumoral heterogeneity is believed to contribute to the immense therapy resistance and recurrence rate of glioblastoma. The aim of this retrospective study was to analyze the heterogeneity of 36 human glioblastoma samples on a morphological level by immunohistochemistry. We confirmed that this method is valid for heterogeneity detection. 115 Areas of Interest were labelled. By cluster analysis, we defined two subtypes (“classical” and “mesenchymal”). The results of epigenomic analyses corroborated the findings. Interestingly, patients with tumors that consisted of both subtypes (“subtype-heterogeneous”) showed a shorter overall survival compared to patients with tumor that were dominated by one subtype (“subtype-dominant”). Furthermore, the analysis of 21 corresponding pairs of primary and recurrent glioblastoma demonstrated that, additionally to an intratumoral heterogeneity, there is also a chronological heterogeneity with dominance of the mesenchymal subtype in recurrent tumors. Our study confirms the prognostic impact of intratumoral heterogeneity in glioblastoma and makes this hallmark assessable by routine diagnostics.

Abstract

Tumor heterogeneity is considered to be a hallmark of glioblastoma (GBM). Only more recently, it has become apparent that GBM is not only heterogeneous between patients (intertumoral heterogeneity) but more importantly, also within individual patients (intratumoral heterogeneity). In this study, we focused on assessing intratumoral heterogeneity. For this purpose, the heterogeneity of 38 treatment-naïve GBM was characterized by immunohistochemistry. Perceptible areas were rated for ALDH1A3, EGFR, GFAP, Iba1, Olig2, p53, and Mib1. By clustering methods, two distinct groups similar to subtypes described in literature were detected. The classical subtype featured a strong EGFR and Olig2 positivity, whereas the mesenchymal subtype displayed a strong ALDH1A3 expression and a high fraction of Iba1-positive microglia. 18 tumors exhibited both subtypes and were classified as “subtype-heterogeneous”, whereas the areas of the other tumors were all assigned to the same cluster and named “subtype-dominant”. Results of epigenomic analyses corroborated these findings. Strikingly, the subtype-heterogeneous tumors showed a clearly shorter overall survival compared to subtype-dominant tumors. Furthermore, 21 corresponding pairs of primary and recurrent GBM were compared, showing a dominance of the mesenchymal subtype in the recurrent tumors. Our study confirms the prognostic impact of intratumoral heterogeneity in GBM, and more importantly, makes this hallmark assessable by routine diagnostics.

The Effect of CDK6 Expression on DNA Methylation and DNMT3B Regulation

CDK6 is frequently overexpressed in various cancer types and functions as a positive regulator of the cell cycle and as a coregulator of gene transcription. We provide evidence that CDK6 is involved in the process of DNA methylation, at least in ALL. We observe a positive correlation of CDK6 and DNMT expression in a large number of ALL samples. ChIP-seq analysis reveals CDK6 binding to genomic regions associated with DNA methyltransferases (DNMTs). ATAC-seq shows a strong reduction in chromatin accessibility for DNMT3B in CDK6-deficient BCR-ABL ⁺ Cdk6^-/- cells, accompanied by lower levels of DNMT3B mRNA and less chromatin-bound DNMT3B, as shown by RNA-seq and chromatome analysis. Motif analysis suggests that ETS family members interact with CDK6 to regulate DNMT3B. Reduced representation bisulfite sequencing analysis uncovers reversible and cell line-specific changes in DNA methylation patterns upon CDK6 loss. The results reveal a function of CDK6 as a regulator of DNA methylation in transformed cells.

Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma

INTRODUCTION

Survival varies in patients with glioblastoma due to intratumoral heterogeneity and radiomics/imaging biomarkers have potential to demonstrate heterogeneity. The objective was to combine radiomic, semantic and clinical features to improve prediction of overall survival (OS) and O⁶-methylguanine-DNA methyltransferase (MGMT) promoter methylation status from pre-operative MRI in patients with glioblastoma.

METHODS

A retrospective study of 181 MRI studies (mean age 58 ± 13 years, mean OS 497 ± 354 days) performed in patients with histopathology-proven glioblastoma. Tumour mass, contrast-enhancement and necrosis were segmented from volumetric contrast-enhanced T1-weighted imaging (CE-T1WI). 333 radiomic features were extracted and 16 Visually Accessible Rembrandt Images (VASARI) features were evaluated by two experienced neuroradiologists. Top radiomic, VASARI and clinical features were used to build machine learning models to predict MGMT status, and all features including MGMT status were used to build Cox proportional hazards regression (Cox) and random survival forest (RSF) models for OS prediction.

RESULTS

The optimal cut-off value for MGMT promoter methylation index was 12.75%; 42 radiomic features exhibited significant differences between high and low-methylation groups. However, model performance accuracy combining radiomic, VASARI and clinical features for MGMT status prediction varied between 45 and 67%. For OS predication, the RSF model based on clinical, VASARI and CE radiomic features achieved the best performance with an average iAUC of 96.2 ± 1.7 and C-index of 90.0 ± 0.3.

CONCLUSIONS

VASARI features in combination with clinical and radiomic features from the enhancing tumour show promise for predicting OS with a high accuracy in patients with glioblastoma from pre-operative volumetric CE-T1WI.

Zinc as a plausible epigenetic modulator of glioblastoma multiforme

Glioblastoma Multiforme (GBM) is an aggressive brain tumor (WHO grade 4 astrocytoma) with unknown causes and is associated with a reduced life expectancy. The available treatment options namely radiotherapy, surgery and chemotherapy have failed to improve life expectancy. Out of the various therapeutic approaches, epigenetic therapy is one of the most studied. Epigenetic therapy is involved in the effective treatment of GBM by inhibiting DNA methyltransferase, histone deacetylation and non-coding RNA. It also promotes the expression of the tumor suppressor gene and is involved in the suppression of the oncogene. Various targets are being studied to implement proper epigenetic regulation to control GBM effectively. Zinc is one of the micronutrients which is considered to maintain epigenetic regulation by promoting the proper DNA folding, protecting genetic material from the oxidative damage and controlling the enzyme activation involved in the epigenetic regulation. Here, we are discussing the importance of zinc in regulating the epigenetic modifications and assessing its role in glioblastoma research. The discussion also highlights the importance of artificial intelligence using epigenetics for envisaging the glioma progression, diagnosis and its management.

Intratumor heterogeneity of prognostic DNA-based molecular markers in adrenocortical carcinoma

BACKGROUND

The prognosis of adrenocortical carcinoma (ACC) is heterogeneous. Genomic studies have identified ACC subgroups characterized by specific molecular alterations, including features measured at DNA level (somatic mutations, chromosome alterations, DNA methylation), which are closely associated with outcome. The aim of this study was to evaluate intratumor heterogeneity of prognostic molecular markers at the DNA level.

METHODS

Two different tissue samples (primary tumor, local recurrence or metastasis) were analyzed in 26 patients who underwent surgery for primary or recurrent ACC. DNA-related biomarkers with prognostic role were investigated in frozen and paraffin-embedded samples. Somatic mutations of p53/Rb and Wnt/β-catenin pathways were assessed using next-generation sequencing (n = 26), chromosome alteration profiles were determined using SNP arrays (n = 14) and methylation profiles were determined using four-gene bisulfite pyrosequencing (n = 12).

RESULTS

Somatic mutations for ZNRF3, TP53, CTNN1B and CDKN2A were found in 7, 6, 6 and 4 patients, respectively, with intratumor heterogeneity in 8/26 patients (31%). Chromosome alteration profiles were 'Noisy' (numerous and anarchic alterations) in 8/14 and 'Chromosomal' (extended patterns of loss of heterozygosity) in 5/14 of the study samples. For these profiles, no intratumor heterogeneity was observed. Methylation profiles were hypermethylated in 5/12 and non-hypermethylated in 7/12 of the study samples. Intratumor heterogeneity of methylation profiles was observed in 2/12 patients (17%).

CONCLUSIONS

Intratumor heterogeneity impacts DNA-related molecular markers. While somatic mutation can differ, prognostic DNA methylation and chromosome alteration profile seem rather stable and might be more robust for the prognostic assessment.

Cellular Plasticity and Tumor Microenvironment in Gliomas: The Struggle to Hit a Moving Target

Brain tumors encompass a diverse group of neoplasias arising from different cell lineages. Tumors of glial origin have been the subject of intense research because of their rapid and fatal progression. From a clinical point of view, complete surgical resection of gliomas is highly difficult. Moreover, the remaining tumor cells are resistant to traditional therapies such as radio- or chemotherapy and tumors always recur. Here we have revised the new genetic and epigenetic classification of gliomas and the description of the different transcriptional subtypes. In order to understand the progression of the different gliomas we have focused on the interaction of the plastic tumor cells with their vasculature-rich microenvironment and with their distinct immune system. We believe that a comprehensive characterization of the glioma microenvironment will shed some light into why these tumors behave differently from other cancers. Furthermore, a novel classification of gliomas that could integrate the genetic background and the cellular ecosystems could have profound implications in the efficiency of current therapies as well as in the development of new treatments.

3-Bromofascaplysin is a prospective chemical compound for developing new chemotherapy agents in glioblastoma treatment

Glioblastoma (GB) is one of the most aggressive human brain tumors. The prognosis is unfavorable, its treatment is relatively ineffective, and the median survival is about 15months. Medication development with new chemical compounds is one of the ways to solve the problem of current treatment inefficiency. This study is focused on the group of chemical substances, based on pentacyclic system of 12H-pyrido[1,2-a:3,4-b]diindole, and the most well-known part of this group is fascaplysin, first extracted from the sponge Fascaplysinopsis spp. We have synthesized a series of the following fascaplysin derivatives: 7-phenylfascaplysin, 3-chlorofascaplysin, 3-bromofascaplysin, 9-bromofascaplysin. The paper is aimed at analyzing the cytotoxic effect of these compounds on GB cells.

MATERIALS AND METHODS

The study used rat glioma C6 cell line (ATCC®; cat no CCL-107), U-87MG cell line (ATCC; cat no. HTB-14™) and human glioblastoma T98-G cells (ATCC® CRL-1690™). Cell culture method, experimental pharmacological trials and γ-radiation in vitro, as well as flow cytofluorometry were used in the study.

RESULTS

Cytotoxic effect of the tested compounds is stronger than the effect of unsubstituted fascaplysin, and appears to be dose-dependent and time-dependent. 3-bromofascaplysin is more efficient for cancer cells elimination, and by the end of the experiment the amount of living cancer cells in G₀ phase remained at its lowest. Cytotoxic effect of 3-bromofascaplysin on glioblastoma T98-G cells is inferior to that of TMZ, and in case of preliminary radiation treatment of cancer cells with 48Gy the effect of the compound matches the TMZ treatment results.

CONCLUSION

3-Bromofascaplysin is a prospective chemical compound for development of new anti-cancer chemotherapeutic agents.

The clinical significance of the T2-FLAIR mismatch sign in grade II and III gliomas: a population-based study

Background

The T2-FLAIR mismatch sign is an imaging finding highly suggestive of isocitrate dehydrogenase mutated (IDH-mut) 1p19q non-codeleted (non-codel) gliomas (astrocytomas). In previous studies, it has shown excellent specificity but limited sensitivity for IDH-mut astrocytomas. Whether the mismatch sign is a marker of a clinically relevant subtype of IDH-mut astrocytomas is unknown.

Methods

We included histopathologically verified supratentorial lower-grade gliomas (LGG) WHO grade II-III retrospectively during the period 2010–2016. In the period 2017–2018, patients with suspected LGG radiologically were prospectively included, and in this cohort other diagnoses than glioma could occur. Clinical, radiological and molecular data were collected. For clinical evaluation we included all patients with IDH-mut astrocytomas. In the 2010–2016 cohort DNA methylation analysis with Infinium MethylationEPIC BeadChip (Illumina) was performed for patients with an IDH-mut astrocytoma with available tissue. We aimed to examine the association of the T2-FLAIR mismatch sign with clinical factors and outcomes. Additionally, we evaluated the diagnostic reliability of the mismatch sign and its relation to methylation profiles.

Results

Out of 215 patients with LGG, 135 had known IDH-mutation and 1p19q codeletion status. Fifty patients had an IDH-mut astrocytoma and 12 of these (24.0%) showed a mismatch sign. The sensitivity and specificity of the mismatch sign for IDH-mut detection were 26.4 and 97.6%, respectively. There were no differences between patients with an IDH-mut astrocytoma with or without mismatch sign when grouped according to T2-FLAIR mismatch sign with respect to baseline characteristics, clinical outcomes and methylation profiles. The overall interrater agreement between neuroradiologist and clinical neurosurgeons for the T2-FLAIR mismatch sign was significant when all 215 MRI examination assessed (κ = 0.77, p < 0.001, N = 215).

Conclusion

The T2-FLAIR mismatch sign in patients with an IDH-mut astrocytoma is not associated with clinical presentation or outcome. It seems unlikely that the IDH-mut astrocytomas with mismatch sign represent a specific subentity. Finally, we have validated that the T2-FLAIR mismatch sign is a reliable and specific marker of IDH-mut astrocytomas.

Next Generation Sequencing and Machine Learning Technologies Are Painting the Epigenetic Portrait of Glioblastoma

Even with a rare occurrence of only 1.35% of cancer cases in the United States of America, brain tumors are considered as one of the most lethal malignancies. The most aggressive and invasive type of brain tumor, glioblastoma, accounts for 60–70% of all gliomas and presents with life expectancy of only 12–18 months. Despite trimodal treatment and advances in diagnostic and therapeutic methods, there are no significant changes in patient outcome. Our understanding of glioblastoma was significantly improved with the introduction of next generation sequencing technologies. This led to the identification of different genetic and molecular subtypes, which greatly improve glioblastoma diagnosis. Still, because of the poor life expectancy, novel diagnostic, and treatment methods are broadly explored. Epigenetic modifications like methylation and changes in histone acetylation are such examples. Recently, in addition to genetic and molecular characteristics, epigenetic profiling of glioblastomas is also used for sample classification. Further advancement of next generation sequencing technologies is expected to identify in detail the epigenetic signature of glioblastoma that can open up new therapeutic opportunities for glioblastoma patients. This should be complemented with the use of computational power i.e., machine and deep learning algorithms for objective diagnostics and design of individualized therapies. Using a combination of phenotypic, genotypic, and epigenetic parameters in glioblastoma diagnostics will bring us closer to precision medicine where therapies will be tailored to suit the genetic profile and epigenetic signature of the tumor, which will grant longer life expectancy and better quality of life. Still, a number of obstacles including potential bias, availability of data for minorities in heterogeneous populations, data protection, and validation and independent testing of the learning algorithms have to be overcome on the way.

Predicting MGMT Promoter Methylation of Glioblastoma from Dynamic Susceptibility Contrast Perfusion: A Radiomic Approach

BACKGROUND AND PURPOSE

This study aims to investigate whether radiomic quantitative image features (IFs) from perfusion dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) retain sufficient strength to predict O6-methylguanine-DNA methyltransferase promoter methylation (MGMT_pm) in newly diagnosed glioblastoma (GB) patients.

METHODS

We retrospectively reviewed the perfusion DSC-MRI of 59 patients with GB. Patients were classified into three groups: (1) unmethylated if MGMT_pm ≤ 9% (UM); (2) intermediate-methylated if MGMT_pm ranged between 10% and 29% (IM); (3) methylated if MGMT_pm ≥ 30% (M). A total of 92 quantitative IFs were obtained from relative cerebral blood volume and relative cerebral blood flow maps. The Mann-Whitney U-test was applied to assess whether there were statistical differences in IFs between patient groups. Those IFs showing significant difference between two patient groups were termed relevant IFs (rIFs). rIFs were uploaded to a machine learning model to predict the MGMT_pm.

RESULTS

No rIFs were found between UM and IM groups. Fourteen rIFs were found among UM-M, IM-M, and (UM + IM)-M groups. We built a multilayer perceptron deep learning model that classified patients as belonging to UM + IM and M group. The model performed well with 75% sensitivity, 85% specificity, and an area under the receiver-operating curve of .84.

CONCLUSION

rIFs from perfusion DSC-MRI are potential biomarkers in GBs with a ≥30% MGMT_pm. Otherwise, unmethylated and intermediate-methylated GBs lack of rIFs. Five of 14 rIFs show sufficient strength to build an accurate prediction model of MGMT_pm.

Dual Regulation of Histone Methylation by mTOR Complexes Controls Glioblastoma Tumor Cell Growth via EZH2 and SAM

Epigenetic regulation known for DNA methylation and histone modification is critical for securing proper gene expression and chromosomal function, and its aberration induces various pathologic conditions including cancer. Trimethylation of histone H3 on lysine 27 (H3K27me3) is known to suppress various genes related to cancer cell survival and the level of H3K27me3 may have an influence on tumor progression and malignancy. However, it remains unclear how histone methylation is regulated in response to genetic mutation and microenvironmental cues to facilitate the cancer cell survival. Here, we report a novel mechanism of the specific regulation of H3K27me3 by cooperatively two mTOR complexes, mTORC1 and mTORC2 in human glioblastoma (GBM). Integrated analyses revealed that mTORC1 upregulates the protein expression of enhancer of zeste homolog 2, a main component of polycomb repressive complex 2 which is known as H3K27-specific methyltransferase. The other mTOR complex, mTORC2, regulates production of S-adenosylmethionine, an essential substrate for histone methylation. This cooperative regulation causes H3K27 hypermethylation which subsequently promotes tumor cell survival both in vitro and in vivo xenografted mouse tumor model. These results indicate that activated mTORC1 and mTORC2 complexes cooperatively contribute to tumor progression through specific epigenetic regulation, nominating them as an exploitable therapeutic target against cancer. IMPLICATIONS: A dynamic regulation of histone methylation by mTOR complexes promotes tumor growth in human GBM, but at the same time could be exploitable as a novel therapeutic target against this deadly tumor.

Lessons Learned in Using Laser Interstitial Thermal Therapy for Treatment of Brain Tumors: A Case Series of 238 Patients from a Single Institution

BACKGROUND

Laser interstitial thermal therapy (LITT) is a novel, minimally invasive alternative to craniotomy, and as with any new technology, comes with a learning curve.

OBJECTIVE

We present our experience detailing the evolution of this technology in our practice in one of the largest patient cohorts to date regarding LITT in neuro-oncology.

METHODS

We reviewed 238 consecutive patients with brain tumor treated with LITT at our institution. Data on patient, surgery and tumor characteristics, and follow-up were collected. Patients were categorized into 2 cohorts: early (<2014, 100 patients) and recent (>2015, 138 patients). Median follow-up for the entire cohort was 8.4 months.

RESULTS

The indications for LITT included gliomas (70.2%), radiation necrosis (21.0%), and metastasis (8.8%). Patient demographics stayed consistent between the 2 cohorts, with the exception of age (early, 54.3; recent, 58.4; P = 0.04). Operative time (6.6 vs. 3.5; P < 0.001) and number of trajectories (53.1% vs. 77.9% with 1 trajectory; P < 0.001) also decreased in the recent cohort. There was a significant decrease in permanent motor deficits over time (15.5 vs. 4.4%; P = 0.005) and 30-day mortality (4.1% vs. 1.5%) also decreased (not statistically significant) in the recent cohort. In terms of clinical outcomes, poor preoperative Karnofsky Performance Status (≤70) were significantly correlated with increased permanent deficits (P = 0.001) and decreased overall survival (P < 0.001 for all time points).

CONCLUSIONS

We observed improvement in operative efficiency and permanent deficits over time and also patients with poor preoperative Karnofsky Performance Status achieved suboptimal outcomes with LITT. As many other treatment modalities, patient selection is important in this procedure.

Advancements in predicting outcomes in patients with glioma: a surgical perspective

Introduction: Diffuse glioma is a challenging neurosurgical entity. Although surgery does not provide a cure, it may greatly influence survival, brain function, and quality of life. Surgical treatment is by nature highly personalized and outcome prediction is very complex. To engage and succeed in this balancing act it is important to make best use of the information available to the neurosurgeon.Areas covered: This narrative review provides an update on advancements in predicting outcomes in patients with glioma that are relevant to neurosurgeons.Expert opinion: The classical 'gut feeling' is notoriously unreliable and better prediction strategies for patients with glioma are warranted. There are numerous tools readily available for the neurosurgeon in predicting tumor biology and survival. Predicting extent of resection, functional outcome, and quality of life remains difficult. Although machine-learning approaches are currently not readily available in daily clinical practice, there are several ongoing efforts with the use of big data sets that are likely to create new prediction models and refine the existing models.

Tracking intratumoral heterogeneity in glioblastoma via regularized classification of single-cell RNA-Seq data

Background

Understanding cellular and molecular heterogeneity in glioblastoma (GBM), the most common and aggressive primary brain malignancy, is a crucial step towards the development of effective therapies. Besides the inter-patient variability, the presence of multiple cell populations within tumors calls for the need to develop modeling strategies able to extract the molecular signatures driving tumor evolution and treatment failure. With the advances in single-cell RNA Sequencing (scRNA-Seq), tumors can now be dissected at the cell level, unveiling information from their life history to their clinical implications.

Results

We propose a classification setting based on GBM scRNA-Seq data, through sparse logistic regression, where different cell populations (neoplastic and normal cells) are taken as classes. The goal is to identify gene features discriminating between the classes, but also those shared by different neoplastic clones. The latter will be approached via the network-based twiner regularizer to identify gene signatures shared by neoplastic cells from the tumor core and infiltrating neoplastic cells originated from the tumor periphery, as putative disease biomarkers to target multiple neoplastic clones. Our analysis is supported by the literature through the identification of several known molecular players in GBM. Moreover, the relevance of the selected genes was confirmed by their significance in the survival outcomes in bulk GBM RNA-Seq data, as well as their association with several Gene Ontology (GO) biological process terms.

Conclusions

We presented a methodology intended to identify genes discriminating between GBM clones, but also those playing a similar role in different GBM neoplastic clones (including migrating cells), therefore potential targets for therapy research. Our results contribute to a deeper understanding on the genetic features behind GBM, by disclosing novel therapeutic directions accounting for GBM heterogeneity.

Ex vivo ultrasonic samples of human brain tumors in the molecular era

Background

Gliomas are composed of multiple clones of tumor cells. This intratumor heterogeneity contributes to the ability of gliomas to resist treatment. It is vital that gliomas are fully characterized at a molecular level when a diagnosis is made to maximize treatment effectiveness.

Methods

We collected ultrasonic tissue fragments during glioma surgery. Large tissue fragments were separated in the operating theater and bathed continuously in oxygenated artificial cerebrospinal fluid to keep them alive. The ex vivo tissue fragments were transferred to a laboratory and incubated in 5-aminolevulinic acid (5-ALA). 5-ALA is metabolized to Protoporphyrin IX (PpIX), which accumulates in glioma cells and makes them fluorescent. The molecular and neuropathological features of the PpIX fluorescent ultrasonic tissue fragments were studied.

Results

We show that PpIX fluorescence can rapidly identify tissue fragments infiltrated by glioma in the laboratory. Ultrasonic tissue fragments from the tumor core provided molecular and neuropathological information about the glioma that was comparable to the surgical biopsy. We characterized the heterogeneity within individual gliomas by studying ultrasonic tissue fragments from different parts of the tumor. We found that gliomas exhibit a power relationship between cellular proliferation and tumor infiltration. Tissue fragments that deviate from this relationship may contain foci of more malignant glioma. The methylation status of the O ⁶-methylguanine DNA methyltransferase gene promoter varied within each glioma.

Conclusions

Ex vivo ultrasonic tissue fragments can be rapidly screened for glioma infiltration. They offer a viable platform to characterize heterogeneity within individual gliomas, thereby enhancing their diagnosis and treatment.

Specific glioblastoma multiforme prognostic-subtype distinctions based on DNA methylation patterns

DNA methylation is an important regulator of gene expression, and plays a significant role in carcinogenesis in the brain. Here, we explored specific prognosis-subtypes based on DNA methylation status using 138 Glioblastoma Multiforme (GBM) samples from The Cancer Genome Atlas (TCGA) database. The methylation profiles of 11,637 CpG sites that significantly correlated with survival in the training set were employed for consensus clustering. We identified three GBM molecular subtypes, and their survival curves were distinct from each other. Furthermore, ten feature CpG sites were obtained on conducting a weighted gene co-expression network analysis (WGCNA) of the CpG sites. We were able to classify the samples into high- and low-methylation groups, and classified the prognosis information of the samples after cluster analysis of the training set samples using the hierarchical clustering algorithm. Similar results were obtained in the test set and clinical GBM specimens. Finally, we found that a positive relationship existed between methylation level and sensitivity to temozolomide (or radiotherapy) or anti-migration ability of GBM cells. Taken together, these results suggest that the model constructed in this study could help explain the heterogeneity of previous molecular subgroups in GBM and can provide guidance to clinicians regarding the prognosis of GBM.

Integrative analysis of DNA methylation and gene expression to identify key epigenetic genes in glioblastoma

Glioblastoma (GBM) ranks the most common and aggressive primary brain malignant tumor worldwide. However, the survival rates of patients remain very poor. Therefore, molecular oncology of GBM are urgently needed. In this study, we performed an integrative analysis of DNA methylation and gene expression to identify key epigenetic genes in GBM. The methylation and gene expression of GBM patients in The Cancer Genome Atlas (TCGA) database were downloaded. After data preprocessing, we identified 4,881 differentially expressed genes (DEGs) between tumor and normal samples, including 1,111 upregulated and 3,770 downregulated genes. Then, we randomly separated all samples into training set (n = 69) and testing set (n = 69). We next obtained 11,269 survival-methylation sites by univariate and multivariate Cox regression analyses. In the correlation analysis, we defined 198 low promoter methylation with high gene expression as epigenetically induced (EI) genes and 111 high promoter methylation with low gene expression as epigenetically suppressed (ES) genes. Key markers including C1orf61 and FAM50B were selected with a Pearson correlation coefficient greater than 0.75. Further, we chose the 20 CpG methylation sites of above two genes in unsupervised clustering analysis using the Euclidean distance. We found that the prognosis of the hypomethylated group was significantly better than that in the hypermethylated group (log-rank test p-value = 0.011). Based on the validation in the TCGA testing set and GEO dataset, we validated the prognostic value of our signature (p-value = 0.02 in TCGA and 0.012 in GEO). In conclusion, our findings provided predictive and prognostic value as methylation-based biomarkers for the diagnosis and treatment of GBM.

Advances in Targeting the Epidermal Growth Factor Receptor Pathway by Synthetic Products and Its Regulation by Epigenetic Modulators As a Therapy for Glioblastoma

Glioma is the most common primary tumor of the nervous system, and approximately 50% of patients exhibit the most aggressive form of the cancer, glioblastoma. The biological function of epidermal growth factor receptor (EGFR) in tumorigenesis and progression has been established in various types of cancers, since it is overexpressed, mutated, or dysregulated. Its overexpression has been shown to be associated with enhanced metastatic potential in glioblastoma, with EGFR at the top of a downstream signaling cascade that controls basic functional properties of glioblastoma cells such as survival, cell proliferation, and migration. Thus, EGFR is considered as an important therapeutic target in glioblastoma. Many anti-EGFR therapies have been investigated both in vivo and in vitro, making their way to clinical studies. However, in clinical trials, the potential efficacy of anti-EGFR therapies is low, primarily because of chemoresistance. Currently, a range of epigenetic drugs including histone deacetylase (HDAC) inhibitors, DNA methylation and histone inhibitors, microRNA, and different types of EGFR inhibitor molecules are being actively investigated in glioblastoma patients as therapeutic strategies. Here, we describe recent knowledge on the signaling pathways mediated by EGFR/EGFR variant III (EGFRvIII) with regard to current therapeutic strategies to target EGFR/EGFRvIII amplified glioblastoma.

Epigenetic Classifiers for Precision Diagnosis of Brain Tumors

DNA methylation profiling has proven to be a powerful analytical tool, which can accurately identify the tissue of origin of a wide range of benign and malignant neoplasms. Using microarray-based profiling and supervised machine learning algorithms, we and other groups have recently unraveled DNA methylation signatures capable of aiding the histomolecular diagnosis of different tumor types. We have explored the methylomes of metastatic brain tumors from patients with lung cancer, breast cancer, and cutaneous melanoma and primary brain neoplasms to build epigenetic classifiers. Our brain metastasis methylation (BrainMETH) classifier has the ability to determine the type of brain tumor, the origin of the metastases, and the clinical-therapeutic subtype for patients with breast cancer brain metastases. To facilitate the translation of these epigenetic classifiers into clinical practice, we selected and validated the most informative genomic regions utilizing quantitative methylation-specific polymerase chain reaction (qMSP). We believe that the refinement, expansion, integration, and clinical validation of BrainMETH and other recently developed epigenetic classifiers will significantly contribute to the development of more comprehensive and accurate systems for the personalized management of patients with brain metastases.