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.

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.

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.

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.

Metabolic reprogramming associated with aggressiveness occurs in the G-CIMP-high molecular subtypes of IDH1mut lower grade gliomas.

BACKGROUND: Early detection of increased aggressiveness of brain tumors is a major challenge in the field of neuro-oncology because of the inability of traditional imaging to uncover it. Isocitrate dehydrogenase (IDH)-mutant gliomas represent an ideal model system to study the molecular mechanisms associated with tumorigenicity because they appear indolent and non-glycolytic initially, but eventually a subset progresses toward secondary glioblastoma with a Warburg-like phenotype. The mechanisms and molecular features associated with this transformation are poorly understood. METHODS: We employed model systems for IDH1 mutant (IDH1mut) gliomas with different growth and proliferation rates in vivo and in vitro. We described the metabolome, transcriptome, and epigenome of these models in order to understand the link between their metabolism and the tumor biology. To verify whether this metabolic reprogramming occurs in the clinic, we analyzed data from The Cancer Genome Atlas. RESULTS: We reveal that the aggressive glioma models have lost DNA methylation in the promoters of glycolytic enzymes, especially lactate dehydrogenase A (LDHA), and have increased mRNA and metabolite levels compared with the indolent model. We find that the acquisition of the high glycolytic phenotype occurs at the glioma cytosine-phosphate-guanine island methylator phenotype (G-CIMP)-high molecular subtype in patients and is associated with the worst outcome. CONCLUSION: We propose very early monitoring of lactate levels as a biomarker of metabolic reprogramming and tumor aggressiveness.

A non-functional galanin receptor-2 in a multiple sclerosis patient.

Multiple Sclerosis (MS) is an inflammatory neurodegenerative disease that affects approximately 2.5 million people globally. Even though the etiology of MS remains unknown, it is accepted that it involves a combination of genetic alterations and environmental factors. Here, after performing whole exome sequencing, we found a MS patient harboring a rare and homozygous single nucleotide variant (SNV; rs61745847) of the G-protein coupled receptor (GPCR) galanin-receptor 2 (GALR2) that alters an important amino acid in the TM6 molecular toggle switch region (W249L). Nuclear magnetic resonance imaging showed that the hypothalamus (an area rich in GALR2) of this patient exhibited an important volumetric reduction leading to an enlarged third ventricle. Ex vivo experiments with patient-derived blood cells (AKT phosphorylation), as well as studies in recombinant cell lines expressing the human GALR2 (calcium mobilization and NFAT mediated gene transcription), showed that galanin (GAL) was unable to stimulate cell signaling in cells expressing the variant GALR2 allele. Live cell confocal microscopy showed that the GALR2 mutant receptor was primarily localized to intracellular endosomes. We conclude that the W249L SNV is likely to abrogate GAL-mediated signaling through GALR2 due to the spontaneous internalization of this receptor in this patient. Although this homozygous SNV was rare in our MS cohort (1:262 cases), our findings raise the potential importance of impaired neuroregenerative pathways in the pathogenesis of MS, warrant future studies into the relevance of the GAL/GALR2 axis in MS and further suggest the activation of GALR2 as a potential therapeutic route for this disease.

A Distinct DNA Methylation Shift in a Subset of Glioma CpG Island Methylator Phenotypes during Tumor Recurrence.

Glioma diagnosis is based on histomorphology and grading; however, such classification does not have predictive clinical outcome after glioblastomas have developed. To date, no bona fide biomarkers that significantly translate into a survival benefit to glioblastoma patients have been identified. We previously reported that the IDH mutant G-CIMP-high subtype would be a predecessor to the G-CIMP-low subtype. Here, we performed a comprehensive DNA methylation longitudinal analysis of diffuse gliomas from 77 patients (200 tumors) to enlighten the epigenome-based malignant transformation of initially lower-grade gliomas. Intra-subtype heterogeneity among G-CIMP-high primary tumors allowed us to identify predictive biomarkers for assessing the risk of malignant recurrence at early stages of disease. G-CIMP-low recurrence appeared in 9.5% of all gliomas, and these resembled IDH-wild-type primary glioblastoma. G-CIMP-low recurrence can be characterized by distinct epigenetic changes at candidate functional tissue enhancers with AP-1/SOX binding elements, mesenchymal stem cell-like epigenomic phenotype, and genomic instability. Molecular abnormalities of longitudinal G-CIMP offer possibilities to defy glioblastoma progression.

RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes.

We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target gene to determine the optimal set of TFs associated with it. Our proposed framework allows to incorporate a mechanistic active biding network based on cis-regulatory motif analysis. We evaluate our regularization framework in conjunction with two non-linear ML techniques, namely gradient boosting machines (GBM) and random-forests (GENIE), resulting in a regularized feature selection based method specifically called RGBM and RGENIE respectively. RGBM has been used to identify the main transcription factors that are causally involved as master regulators of the gene expression signature activated in the FGFR3-TACC3-positive glioblastoma. Here, we illustrate that RGBM identifies the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators characterizing the difference between G-CIMP-high and G-CIMP-low subtypes and between PA-like and LGm6-GBM, thus providing a clue to the yet undetermined nature of the transcriptional events among these subtypes.

Glioma CpG island methylator phenotype (G-CIMP): biological and clinical implications.

Gliomas are a heterogeneous group of brain tumors with distinct biological and clinical properties. Despite advances in surgical techniques and clinical regimens, treatment of high-grade glioma remains challenging and carries dismal rates of therapeutic success and overall survival. Challenges include the molecular complexity of gliomas, as well as inconsistencies in histopathological grading, resulting in an inaccurate prediction of disease progression and failure in the use of standard therapy. The updated 2016 World Health Organization (WHO) classification of tumors of the central nervous system reflects a refinement of tumor diagnostics by integrating the genotypic and phenotypic features, thereby narrowing the defined subgroups. The new classification recommends molecular diagnosis of isocitrate dehydrogenase (IDH) mutational status in gliomas. IDH-mutant gliomas manifest the cytosine-phosphate-guanine (CpG) island methylator phenotype (G-CIMP). Notably, the recent identification of clinically relevant subsets of G-CIMP tumors (G-CIMP-high and G-CIMP-low) provides a further refinement in glioma classification that is independent of grade and histology. This scheme may be useful for predicting patient outcome and may be translated into effective therapeutic strategies tailored to each patient. In this review, we highlight the evolution of our understanding of the G-CIMP subsets and how recent advances in characterizing the genome and epigenome of gliomas may influence future basic and translational research.

SIRT1 regulates Mxd1 during malignant melanoma progression.

In a murine melanoma model, malignant transformation promoted by a sustained stress condition was causally related to increased levels of reactive oxygen species resulting in DNA damage and massive epigenetic alterations. Since the chromatin modifier Sirtuin-1 (SIRT1) is a protein attracted to double-stranded DNA break (DSB) sites and can recruit other components of the epigenetic machinery, we aimed to define the role of SIRT1 in melanomagenesis through our melanoma model. The DNA damage marker, γH2AX was found increased in melanocytes after 24 hours of deadhesion, accompanied by increased SIRT1 expression and decreased levels of its target, H4K16ac. Moreover, SIRT1 started to be associated to DNMT3B during the stress condition, and this complex was maintained along malignant progression. Mxd1 was identified by ChIP-seq among the DNA sequences differentially associated with SIRT1 during deadhesion and was shown to be a common target of both, SIRT1 and DNMT3B. In addition, Mxd1 was found downregulated from pre-malignant melanocytes to metastatic melanoma cells. Treatment with DNMT inhibitor 5AzaCdR reversed the Mxd1 expression. Sirt1 stable silencing increased Mxd1 mRNA expression and led to down-regulation of MYC targets, such as Cdkn1a, Bcl2 and Psen2, whose upregulation is associated with human melanoma aggressiveness and poor prognosis. We demonstrated a novel role of the stress responsive protein SIRT1 in malignant transformation of melanocytes associated with deadhesion. Mxd1 was identified as a new SIRT1 target gene. SIRT1 promoted Mxd1 silencing, which led to increased activity of MYC oncogene contributing to melanoma progression.

Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma.

Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole-genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wild-type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes.

TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.