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, gamma H2AX 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.FAPESP [2011/0166-38, 2011/12306-1, 2014/13663-0, 2015/07925-5, 2016/06488-3]DAAD [PKZ A/12/79134]FAPESP/BAYLAT [2012/51300-7]Univ Fed Sao Paulo UNIFESP, Dept Pharmacol, Ontogeny & Epigenet Lab, Sao Paulo, SP, BrazilUniv Sao Paulo, Ribeirao Preto Med Sch, Dept Genet, Ribeirao Preto, SP, BrazilFriedrich Alexander Univ Erlangen Nurnberg FAU, Inst Pathol, Expt Tumorpathol, Erlangen, GermanyFriedrich Alexander Univ Erlangen Nurnberg FAU, Dept Pediat & Adolescent Med, Erlangen, GermanyUniv Fed Sao Paulo UNIFESP, Dept Pharmacol, Ontogeny & Epigenet Lab, Sao Paulo, SP, BrazilFAPESP [2011/0166-38, 2011/12306-1, 2014/13663-0, 2015/07925-5, 2016/06488-3]DAAD [PKZ A/12/79134]FAPESP/BAYLAT [2012/51300-7]Web of Scienc

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

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

Depletion of 5-hydroxymethylcytosine in aggressive G-CIMP subtype

5-hydroxymethylcytosine (5hmC) is an oxidation product of 5-methylcytosine, a reaction potentially mediated by the Tet family of enzymes. Levels of 5hmC were reported to be lower in glioblastoma and because gliomas carrying an IDH1/2 mutation (high or low grade) manifest a CpG island methylator phenotype (G-CIMP), we decided to investigate 5hmC levels in G-CIMP subtypes, G-CIMP-high and G-CIMP-low, due to their distinct clinical outcome, independent of histological grade. We generated genome-wide maps of 5hmC for G-CIMP-low (n=4) and G-CIMP-high (n=6) samples by hMeDIP-seq (∼ 46M reads per sample). We also have additional whole-genome bisulfite sequencing (WGBS) data for G-CIMPlow (n=1), G-CIMP-high (n=2) and non-tumor brain (n=2) samples. When we compared hMeDIP-seq with WGBS, we found a positive correlation between DNA hypomethylation and depletion of 5hmC in G-CIMPlow. As reported in previous studies, the highest concentration of 5hmC is within gene bodies (75% vs. 25% in intergenic regions). However, we observed an unbalanced level of 5hmC in G-CIMP subtypes (68,397 5hmC peaks lost in G-CIMP-low vs. 2,554 gained, FDR \u3c 0.05). G-CIMP-high has an abundant number of 5hmC peaks, whereas G-CIMP-low seems to have poor 5hmC density in the same regions. We observed G-CIMP-low may arise from G-CIMP-high during tumor recurrence, and we suggest that loss of 5hmC within G-CIMP tumors lead to a more aggressive phenotype. Interestly, 85% of peaks associated with loss of 5hmC overlap regions with loss of intergenic enhancer activity in G-CIMP-low, as defined by H3K27ac peaks. Furthermore, 22% of genes with intronic loss of 5hmC in G-CIMP-low samples are downregulated (FDR \u3c 0.05). We did not find any distinct genomic alterations associated with G-CIMP-low nor did we observe differential expression of genes from the Tet family, we suggest that a yet to be determined alternative mechanism may be driving an aberrant loss of 5hmC in G-CIMP-low

Deep Learning Classification of Neuro-Oncology Medical Documents

Introduction Precision medicine and big data for cancer discovery requires well curated indexed critical health care data, however to date limited resources exist that successfully parse out unstructured clinical data in neuro-oncology. Current practice relies on time consuming manual extraction by researchers or clinicians resulting in data inconsistency and limitation in data set volume. Rule-based natural language processing algorithms could be used for simple consistent text, but medical documents are created longitudinally by multiple people across long periods of time resulting in inconsistencies and semantic heterogeneity that render rule-based techniques insufficient. Methods We applied a deep learning text classification method to multiple clinical document categories including clinical pathology reports and a text based clinical database spanning 17 years of clinical narratives with approximately 4000 unique cases. For this study we identified clinically relevant molecular criteria for glioma outlined in the WHO 2016 CNS classification of tumors including IDH mutation, MGMT methylation, and 1p19q co-deletion status. Using a convolutional neural network with two densely connected layers of 30 rectified linear nodes we were able to classify patients into their respected molecular cohort with an accuracy of 98%. Conclusion Parsing of unstructured text based clinical narratives and pathology reports using convolutional neural networks is a promising method to extract heterogeneous molecular data in neuro-oncology for large scale data analysis

Bioinformatic Method to Define Epigenetically Regulated Enhancer Elements Associated with Cancer

BACKGROUND: Several mechanisms involved in gene regulation are altered in cancer. Cataloging these alterations can lead to a better understanding of tumorigenesis. In addition, the alterations can be used to classify patients with similar clinical features and thereby lead to better targeted treatment. Epigenetics (e.g. DNA methylation) is the process by which cells define gene regulation and aberrant DNA methylation patterns have been observed in many cancer types. Alterations in non-promoter (intergenic regions) have been shown to be tightly associated with functional genomic elements such as enhancers or transcription factor binding. In order to identify altered candidate functional elements associated with specific gene or pathways, we developed a method to integrate enhancer, DNA methylation and gene expression data, using tumor and non-tumor data. METHOD: Using epigenome-wide platform (Illumina 850K), CpG probes were separated into promoters and intergenic regions. The intragenic CpGs were further filtered by overlapping with known functional enhancer database from multiple studies. The nearest genes to each CpG enhancer is further stratified based on differential gene expression. Each CpG/gene pair is classified as methylated or unmethylated by sample, using a 50% methylation cutoff. The mean expression of the methylated samples is calculated and pairs with lower than the bottom 10% (1.28 standard deviation) of the mean expression in the unmethylated group of samples are selected. Finally, CpG/gene pairs with at least 75% of the methylated samples have expression values lower than the mean expression in the unmethylated group of samples are classified as epigenetically silenced. CpG/gene pairs unmethylated and upregulated are called as epigenetically active. By separating samples into different epigenetically deregulated states (silenced or active), we can further characterize each sample by evaluating the association or enrichment for specific clinical features such as outcome, treatment, age at diagnosis, etc. RESULTS: As a proof of concept, we applied our method across the TCGA PanCan cohort to identify potential enhancers regulating genes encoding subunits of the SWI/SNF protein complex. Our method was able to detect several deregulated enhancers associated with SWI/SNF genes specifically altered in each tumor type, independent of mutation. We validated the results using Hi-C data from primary cancer cell lines

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

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

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