The Epigenetic Evolution of Glioma Is Determined by the IDH1 Mutation Status and Treatment Regimen.

Tumor adaptation or selection is thought to underlie therapy resistance in glioma. To investigate longitudinal epigenetic evolution of gliomas in response to therapeutic pressure, we performed an epigenomic analysis of 132 matched initial and recurrent tumors from patients with IDH-wildtype (IDHwt) and IDH-mutant (IDHmut) glioma. IDHwt gliomas showed a stable epigenome over time with relatively low levels of global methylation. The epigenome of IDHmut gliomas showed initial high levels of genome-wide DNA methylation that was progressively reduced to levels similar to those of IDHwt tumors. Integration of epigenomics, gene expression, and functional genomics identified HOXD13 as a master regulator of IDHmut astrocytoma evolution. Furthermore, relapse of IDHmut tumors was accompanied by histologic progression that was associated with survival, as validated in an independent cohort. Finally, the initial cell composition of the tumor microenvironment varied between IDHwt and IDHmut tumors and changed differentially following treatment, suggesting increased neoangiogenesis and T-cell infiltration upon treatment of IDHmut gliomas. This study provides one of the largest cohorts of paired longitudinal glioma samples with epigenomic, transcriptomic, and genomic profiling and suggests that treatment of IDHmut glioma is associated with epigenomic evolution toward an IDHwt-like phenotype. SIGNIFICANCE: Standard treatments are related to loss of DNA methylation in IDHmut glioma, resulting in epigenetic activation of genes associated with tumor progression and alterations in the microenvironment that resemble treatment-naïve IDHwt glioma.

Detection of diagnostic and prognostic methylation-based signatures in liquid biopsy specimens from patients with meningiomas.

Recurrence of meningiomas is unpredictable by current invasive methods based on surgically removed specimens. Identification of patients likely to recur using noninvasive approaches could inform treatment strategy, whether intervention or monitoring. In this study, we analyze the DNA methylation levels in blood (serum and plasma) and tissue samples from 155 meningioma patients, compared to other central nervous system tumor and non-tumor entities. We discover DNA methylation markers unique to meningiomas and use artificial intelligence to create accurate and universal models for identifying and predicting meningioma recurrence, using either blood or tissue samples. Here we show that liquid biopsy is a potential noninvasive and reliable tool for diagnosing and predicting outcomes in meningioma patients. This approach can improve personalized management strategies for these patients.

Advances in Central Nervous System Tumor Classification.

Historically, the classification of tumors of the central nervous system (CNS) relies on the histologic appearance of cells under a microscope; however, the molecular era of medicine has resulted in new diagnostic paradigms anchored in the intrinsic biology of disease. The 2021 World Health Organization (WHO) reformulated the classification of CNS tumors to incorporate molecular parameters, in addition to histology, to define many tumor types. A contemporary classification system with integrated molecular features aims to provide an unbiased tool to define tumor subtype, the risk of tumor progression, and even the response to certain therapeutic agents. Meningiomas are heterogeneous tumors as depicted by the current 15 distinct variants defined by histology in the 2021 WHO classification, which also incorporated the first moelcular critiera for meningioma grading: homozygous loss of CDKN2A/B and TERT promoter mutation as criteria for a WHO grade 3 meningioma. The proper classification and clinical management of meningioma patients requires a multidisciplinary approach, which in addition to the information on microscopic (histology) and macroscopic (Simpson grade and imaging), should also include molecular alterations. In this chapter, we present the most up-to-date knowledge in CNS tumor classification, particularly in meningioma, in the molecular era and how it could affect their future classification and clinical management of patients with these diseases.

Targeted sequencing of cancer-related genes reveals a recurrent TOP2A variant which affects DNA binding and coincides with global transcriptional changes in glioblastoma.

High-grade gliomas are aggressive, deadly primary brain tumors. Median survival of patients with glioblastoma (GBM, WHO grade 4) is 14 months and <10% of patients survive 2 years. Despite improved surgical strategies and forceful radiotherapy and chemotherapy, the prognosis of GBM patients is poor and did not improve over decades. We performed targeted next-generation sequencing with a custom panel of 664 cancer- and epigenetics-related genes, and searched for somatic and germline variants in 180 gliomas of different WHO grades. Herein, we focus on 135 GBM IDH-wild type samples. In parallel, mRNA sequencing was accomplished to detect transcriptomic abnormalities. We present the genomic alterations in high-grade gliomas and the associated transcriptomic patterns. Computational analyses and biochemical assays showed the influence of TOP2A variants on enzyme activities. In 4/135 IDH-wild type GBMs we found a novel, recurrent mutation in the TOP2A gene encoding topoisomerase 2A (allele frequency [AF] = 0.03, 4/135 samples). Biochemical assays with recombinant, wild type (WT) and variant proteins demonstrated stronger DNA binding and relaxation activity of the variant protein. GBM patients carrying the altered TOP2A had shorter overall survival (median OS 150 vs 500 days, P = .0018). In the GBMs with the TOP2A variant we found transcriptomic alterations consistent with splicing dysregulation. luA novel, recurrent TOP2A mutation, which was found exclusively in four GBMs, results in the TOP2A E948Q variant with altered DNA binding and relaxation activities. The deleterious TOP2A mutation resulting in transcription deregulation in GBMs may contribute to disease pathology.

Haploinsufficiency of NFKBIA reshapes the epigenome antipodal to the IDH mutation and imparts disease fate in diffuse gliomas.

Genetic alterations help predict the clinical behavior of diffuse gliomas, but some variability remains uncorrelated. Here, we demonstrate that haploinsufficient deletions of chromatin-bound tumor suppressor NFKB inhibitor alpha (NFKBIA) display distinct patterns of occurrence in relation to other genetic markers and are disproportionately present at recurrence. NFKBIA haploinsufficiency is associated with unfavorable patient outcomes, independent of genetic and clinicopathologic predictors. NFKBIA deletions reshape the DNA and histone methylome antipodal to the IDH mutation and induce a transcriptome landscape partly reminiscent of H3K27M mutant pediatric gliomas. In IDH mutant gliomas, NFKBIA deletions are common in tumors with a clinical course similar to that of IDH wild-type tumors. An externally validated nomogram model for estimating individual patient survival in IDH mutant gliomas confirms that NFKBIA deletions predict comparatively brief survival. Thus, NFKBIA haploinsufficiency aligns with distinct epigenome changes, portends a poor prognosis, and should be incorporated into models predicting the disease fate of diffuse gliomas.

Noninvasive approaches to detect methylation-based markers to monitor gliomas.

In this review, we summarize the current approaches used to detect glioma tissue-derived DNA methylation markers in liquid biopsy specimens with the aim to diagnose, prognosticate and potentially track treatment response and evolution of patients with gliomas.

Molecular landscape of IDH-wild type, pTERT-wild type adult glioblastomas.

Telomerase reverse transcriptase (TERT) promoter (pTERT) mutation has often been described as a late event in gliomagenesis and it has been suggested as a prognostic biomarker in gliomas other than 1p19q codeleted tumors. However, the characteristics of isocitrate dehydrogenase (IDH) wild type (wt) (IDHwt), pTERTwt glioblastomas are not well known. We recruited 72 adult IDHwt, pTERTwt glioblastomas and performed methylation profiling, targeted sequencing, and fluorescence in situ hybridization (FISH) for TERT structural rearrangement and ALT (alternative lengthening of telomeres). There was no significant difference in overall survival (OS) between our cohort and a the Cancer Genome Atlas (TCGA) cohort of IDHwt, pTERT mutant (mut) glioblastomas, suggesting that pTERT mutation on its own is not a prognostic factor among IDHwt glioblastomas. Epigenetically, the tumors clustered into classic-like (11%), mesenchymal-like (32%), and LGm6-glioblastoma (GBM) (57%), the latter far exceeding the corresponding proportion seen in the TCGA cohort of IDHwt, pTERTmut glioblastomas. LGm6-GBM-clustered tumors were enriched for platelet derived growth factor receptor alpha (PDGFRA) amplification or mutation (p = 0.008), and contained far fewer epidermal growth factor receptor (EGFR) amplification (p < 0.01), 10p loss (p = 0.001) and 10q loss (p < 0.001) compared with cases not clustered to this group. LGm6-GBM cases predominantly showed ALT (p = 0.038). In the whole cohort, only 35% cases showed EGFR amplification and no case showed combined chromosome +7/-10. Since the cases were already pTERTwt, so the three molecular properties of EGFR amplification, +7/-10, and pTERT mutation may not cover all IDHwt glioblastomas. Instead, EGFR and PDGFRA amplifications covered 67% and together with their mutations covered 71% of cases of this cohort. Homozygous deletion of cyclin dependent kinase inhibitor 2A (CDKN2A)/B was associated with a worse OS (p = 0.031) and was an independent prognosticator in multivariate analysis (p = 0.032). In conclusion, adult IDHwt, pTERTwt glioblastomas show epigenetic clustering different from IDHwt, pTERTmut glioblastomas, and IDHwt glioblastomas which are pTERTwt may however not show EGFR amplification or +7/-10 in a significant proportion of cases. CDKN2A/B deletion is a poor prognostic biomarker in this group.

Glioma progression is shaped by genetic evolution and microenvironment interactions.

The factors driving therapy resistance in diffuse glioma remain poorly understood. To identify treatment-associated cellular and genetic changes, we analyzed RNA and/or DNA sequencing data from the temporally separated tumor pairs of 304 adult patients with isocitrate dehydrogenase (IDH)-wild-type and IDH-mutant glioma. Tumors recurred in distinct manners that were dependent on IDH mutation status and attributable to changes in histological feature composition, somatic alterations, and microenvironment interactions. Hypermutation and acquired CDKN2A deletions were associated with an increase in proliferating neoplastic cells at recurrence in both glioma subtypes, reflecting active tumor growth. IDH-wild-type tumors were more invasive at recurrence, and their neoplastic cells exhibited increased expression of neuronal signaling programs that reflected a possible role for neuronal interactions in promoting glioma progression. Mesenchymal transition was associated with the presence of a myeloid cell state defined by specific ligand-receptor interactions with neoplastic cells. Collectively, these recurrence-associated phenotypes represent potential targets to alter disease progression.

Detection of tumor-specific DNA methylation markers in the blood of patients with pituitary neuroendocrine tumors.

BACKGROUND: DNA methylation abnormalities are pervasive in pituitary neuroendocrine tumors (PitNETs). The feasibility to detect methylome alterations in circulating cell-free DNA (cfDNA) has been reported for several central nervous system (CNS) tumors but not across PitNETs. The aim of the study was to use the liquid biopsy (LB) approach to detect PitNET-specific methylation signatures to differentiate these tumors from other sellar diseases. METHODS: We profiled the cfDNA methylome (EPIC array) of 59 serum and 41 plasma LB specimens from patients with PitNETs and other CNS diseases (sellar tumors and other pituitary non-neoplastic diseases, lower-grade gliomas, and skull-base meningiomas) or nontumor conditions, grouped as non-PitNET. RESULTS: Our results indicated that despite quantitative and qualitative differences between serum and plasma cfDNA composition, both sources of LB showed that patients with PitNETs presented a distinct methylome landscape compared to non-PitNETs. In addition, LB methylomes captured epigenetic features reported in PitNET tissue and provided information about cell-type composition. Using LB-derived PitNETs-specific signatures as input to develop machine-learning predictive models, we generated scores that distinguished PitNETs from non-PitNETs conditions, including sellar tumor and non-neoplastic pituitary diseases, with accuracies above ~93% in independent cohort sets. CONCLUSIONS: Our results underpin the potential application of methylation-based LB profiling as a noninvasive approach to identify clinically relevant epigenetic markers to diagnose and potentially impact the prognostication and management of patients with PitNETs.

Predicting master transcription factors from pan-cancer expression data.

Critical developmental "master transcription factors" (MTFs) can be subverted during tumorigenesis to control oncogenic transcriptional programs. Current approaches to identifying MTFs rely on ChIP-seq data, which is unavailable for many cancers. We developed the CaCTS (Cancer Core Transcription factor Specificity) algorithm to prioritize candidate MTFs using pan-cancer RNA sequencing data. CaCTS identified candidate MTFs across 34 tumor types and 140 subtypes including predictions for cancer types/subtypes for which MTFs are unknown, including e.g. PAX8, SOX17, and MECOM as candidates in ovarian cancer (OvCa). In OvCa cells, consistent with known MTF properties, these factors are required for viability, lie proximal to superenhancers, co-occupy regulatory elements globally, co-bind loci encoding OvCa biomarkers, and are sensitive to pharmacologic inhibition of transcription. Our predictions of MTFs, especially for tumor types with limited understanding of transcriptional drivers, pave the way to therapeutic targeting of MTFs in a broad spectrum of cancers.

Expression and regulatory roles of lncRNAs in G-CIMP-low vs G-CIMP-high Glioma: an in-silico analysis.

BACKGROUND: Clinically relevant glioma subtypes, such as the glioma-CpG island methylator phenotype (G-CIMP), have been defined by epigenetics. In this study, the role of long non-coding RNAs in association with the poor-prognosis G-CMIP-low phenotype and the good-prognosis G-CMIP-high phenotype was investigated. Functional associations of lncRNAs with mRNAs and miRNAs were examined to hypothesize influencing factors of the aggressive phenotype. METHODS: RNA-seq data on 250 samples from TCGA's Pan-Glioma study, quantified for lncRNA and mRNAs (GENCODE v28), were analyzed for differential expression between G-CIMP-low and G-CIMP-high phenotypes. Functional interpretation of the differential lncRNAs was performed by Ingenuity Pathway Analysis. Spearman rank order correlation estimates between lncRNA, miRNA, and mRNA nominated differential lncRNA with a likely miRNA sponge function. RESULTS: We identified 4371 differentially expressed features (mRNA = 3705; lncRNA = 666; FDR ≤ 5%). From these, the protein-coding gene TP53 was identified as an upstream regulator of differential lncRNAs PANDAR and PVT1 (p = 0.0237) and enrichment was detected in the "development of carcinoma" (p = 0.0176). Two lncRNAs (HCG11, PART1) were positively correlated with 342 mRNAs, and their correlation estimates diminish after adjusting for either of the target miRNAs: hsa-miR-490-3p, hsa-miR-129-5p. This suggests a likely sponge function for HCG11 and PART1. CONCLUSIONS: These findings identify differential lncRNAs with oncogenic features that are associated with G-CIMP phenotypes. Further investigation with controlled experiments is needed to confirm the molecular relationships.

Prognostic significance of genome-wide DNA methylation profiles within the randomized, phase 3, EORTC CATNON trial on non-1p/19q deleted anaplastic glioma.

BACKGROUND: Survival in patients with IDH1/2-mutant (mt) anaplastic astrocytomas is highly variable. We have used the prospective phase 3 CATNON trial to identify molecular factors related to outcome in IDH1/2mt anaplastic astrocytoma patients. METHODS: The CATNON trial randomized 751 adult patients with newly diagnosed 1p/19q non-codeleted anaplastic glioma to 59.4 Gy radiotherapy +/- concurrent and/or adjuvant temozolomide. The presence of necrosis and/or microvascular proliferation was scored at central pathology review. Infinium MethylationEPIC BeadChip arrays were used for genome-wide DNA methylation analysis and the determination of copy number variations (CNV). Two DNA methylation-based tumor classifiers were used for risk stratification. Next-generation sequencing (NGS) was performed using 1 of the 2 glioma-tailored NGS panels. The primary endpoint was overall survival measured from the date of randomization. RESULTS: Full analysis (genome-wide DNA methylation and NGS) was successfully performed on 654 tumors. Of these, 432 tumors were IDH1/2mt anaplastic astrocytomas. Both epigenetic classifiers identified poor prognosis patients that partially overlapped. A predictive prognostic Cox proportional hazard model identified that independent prognostic factors for IDH1/2mt anaplastic astrocytoma patients included; age, mini-mental state examination score, treatment with concurrent and/or adjuvant temozolomide, the epigenetic classifiers, PDGFRA amplification, CDKN2A/B homozygous deletion, PI3K mutations, and total CNV load. Independent recursive partitioning analysis highlights the importance of these factors for patient prognostication. CONCLUSION: Both clinical and molecular factors identify IDH1/2mt anaplastic astrocytoma patients with worse outcome. These results will further refine the current WHO criteria for glioma classification.

Molecular landscape of IDH-mutant primary astrocytoma Grade IV/glioblastomas.

WHO 2016 classified glioblastomas into IDH-mutant and IDH-wildtype with the former having a better prognosis but there was no study on IDH-mutant primary glioblastomas only, as previous series included secondary glioblastomas. We recruited a series of 67 IDH-mutant primary glioblastomas/astrocytoma IV without a prior low-grade astrocytoma and examined them using DNA-methylation profiling, targeted sequencing, RNA sequencing and TERT promoter sequencing, and correlated the molecular findings with clinical parameters. The median OS of 39.4 months of 64 cases and PFS of 25.9 months of 57 cases were better than the survival data of IDH-wildtype glioblastomas and IDH-mutant secondary glioblastomas retrieved from datasets. The molecular features often seen in glioblastomas, such as EGFR amplification, combined +7/-10, and TERT promoter mutations were only observed in 6/53 (11.3%), 4/53 (7.5%), and 2/67 (3.0%) cases, respectively, and gene fusions were found only in two cases. The main mechanism for telomere maintenance appeared to be alternative lengthening of telomeres as ATRX mutation was found in 34/53 (64.2%) cases. In t-SNE analyses of DNA-methylation profiles, with an exceptional of one case, a majority of our cases clustered to IDH-mutant high-grade astrocytoma subclass (40/53; 75.5%) and the rest to IDH-mutant astrocytoma subclass (12/53; 22.6%). The latter was also enriched with G-CIMP high cases (12/12; 100%). G-CIMP-high status and MGMT promoter methylation were independent good prognosticators for OS (p = 0.022 and p = 0.002, respectively) and TP53 mutation was an independent poor prognosticator (p = 0.013) when correlated with other clinical parameters. Homozygous deletion of CDKN2A/B was not correlated with OS (p = 0.197) and PFS (p = 0.278). PDGFRA amplification or mutation was found in 16/59 (27.1%) of cases and was correlated with G-CIMP-low status (p = 0.010). Aside from the three well-known pathways of pathogenesis in glioblastomas, chromatin modifying and mismatch repair pathways were common aberrations (88.7% and 20.8%, respectively), the former due to high frequency of ATRX involvement. We conclude that IDH-mutant primary glioblastomas have better prognosis than secondary glioblastomas and have major molecular differences from other commoner glioblastomas. G-CIMP subgroups, MGMT promoter methylation, and TP53 mutation are useful prognostic adjuncts.

DNA methylation-based signatures classify sporadic pituitary tumors according to clinicopathological features.

BACKGROUND: Distinct genome-wide methylation patterns cluster pituitary neuroendocrine tumors (PitNETs) into molecular groups associated with specific clinicopathological features. Here we aim to identify, characterize, and validate methylation signatures that objectively classify PitNET into clinicopathological groups. METHODS: Combining in-house and publicly available data, we conducted an analysis of the methylome profile of a comprehensive cohort of 177 tumors (Panpit cohort) and 20 nontumor specimens from the pituitary gland. We also retrieved methylome data from an independent PitNET cohort (N = 86) to validate our findings. RESULTS: We identified three methylation clusters associated with adenohypophyseal cell lineages and functional status using an unsupervised approach. Differentially methylated probes (DMP) significantly distinguished the Panpit clusters and accurately assigned the samples of the validation cohort to their corresponding lineage and functional subtypes memberships. The DMPs were annotated in regulatory regions enriched with enhancer elements, associated with pathways and genes involved in pituitary cell identity, function, tumorigenesis, and invasiveness. Some DMPs correlated with genes with prognostic and therapeutic values in other intra- or extracranial tumors. CONCLUSIONS: We identified and validated methylation signatures, mainly annotated in enhancer regions that distinguished PitNETs by distinct adenohypophyseal cell lineages and functional status. These signatures provide the groundwork to develop an unbiased approach to classifying PitNETs according to the most recent classification recommended by the 2017 WHO and to explore their biological and clinical relevance in these tumors.

A serum-based DNA methylation assay provides accurate detection of glioma.

BACKGROUND: The detection of somatic mutations in cell-free DNA (cfDNA) from liquid biopsy has emerged as a noninvasive tool to monitor the follow-up of cancer patients. However, the significance of cfDNA clinical utility remains uncertain in patients with brain tumors, primarily because of the limited sensitivity cfDNA has to detect real tumor-specific somatic mutations. This unresolved challenge has prevented accurate follow-up of glioma patients with noninvasive approaches. METHODS: Genome-wide DNA methylation profiling of tumor tissue and serum cfDNA of glioma patients. RESULTS: Here, we developed a noninvasive approach to profile the DNA methylation status in the serum of patients with gliomas and identified a cfDNA-derived methylation signature that is associated with the presence of gliomas and related immune features. By testing the signature in an independent discovery and validation cohorts, we developed and verified a score metric (the "glioma-epigenetic liquid biopsy score" or GeLB) that optimally distinguished patients with or without glioma (sensitivity: 100%, specificity: 97.78%). Furthermore, we found that changes in GeLB score reflected clinicopathological changes during surveillance (eg, progression, pseudoprogression, and response to standard or experimental treatment). CONCLUSIONS: Our results suggest that the GeLB score can be used as a complementary approach to diagnose and follow up patients with glioma.

Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?

The neurological ICU (neuro ICU) often suffers from significant limitations due to scarce resource availability for their neurocritical care patients. Neuro ICU patients require frequent neurological evaluations, continuous monitoring of various physiological parameters, frequent imaging, and routine lab testing. This amasses large amounts of data specific to each patient. Neuro ICU teams are often overburdened by the resulting complexity of data for each patient. Machine Learning algorithms (ML), are uniquely capable of interpreting high-dimensional datasets that are too difficult for humans to comprehend. Therefore, the application of ML in the neuro ICU could alleviate the burden of analyzing big datasets for each patient. This review serves to (1) briefly summarize ML and compare the different types of MLs, (2) review recent ML applications to improve neuro ICU management and (3) describe the future implications of ML to neuro ICU management.