UCSF

Although histopathological diagnosis is essential in decision of therapeutic strategy for gliomas, sometimes the tumors diagnosed in one histological entity show thoroughly different clinical courses. This phenomenon is believed to be due primarily to the presence of the genetic subgroup. In fact, relationship between treatment response and certain genetic characteristics is indicated (e.g. better chemosensitivity in glioma with losses of 1p/19q (−1p/19q)). It is highly likely that genetic classification of glioma is useful to select the adjuvant treatment. Additionally, gain of 7q (+7q) and −1p/19q are early events in 2 distinct tumor lineages, astrocytic tumors and oligodendroglial tumors, respectively, and these tumors obtain additional genetic aberration (−9p, 10q) with tumor progression. On the other hand, concerning the tumors without +7q or −1p/19q, little is known about clinically important genetic aberration. Therefore the study on such tumors could provide useful information for the prognosis prediction and the determination of treatment strategy. METHODS: We selected 39 cases of gliomas without +7q or −1p/19q from 200 adult supratentorial glioma cases surgically treated and analyzed chromosomal DNA copy number aberrations (CNAs) by comparative genomic hybridization (CGH) from 2005 to 2012. We correlated clinical features of these tumors with histological characteristics, CNAs and IDH1 status. RESULTS: The clinical course of gliomas without +7q or −1p/19q was not correlated with additional genetic aberration of -9p or 10q, which have been known as genetic markers for poor prognosis, and absence of +7q or −1p/19q was maintained at the time of recurrence. The tumors without +7q or −1p/19q showed relatively favorable prognosis although mutation of IDH1 was infrequent in these tumors (35.8 %). CONCLUSION: The gliomas without +7q or −1p/19q have clinical features distinct from the +7q and −1p/19q gliomas. Prognostic markers for each subgroups could help establish therapeutic strategy against the tumor. DNA methylation is a mechanism altering the normal state of cells implicated in many cancers. Currently the methylation status of MGMT is one of the most widely utilized clinical genetic tests performed on glioblastoma multiforme (GBM). While several global gene expression signatures have been developed, it is unclear if genome-wide DNA methylation signatures can predict prognosis in cancer. We used a computational algorithm (MethylMix) to analyze genome-wide DNA methylation in GBM data obtained from The Cancer Genome Atlas (TCGA). MethylMix identified a set of driver genes that met criteria for being both differential and functional. Differential refers to a difference in cancer methylation compared to normal tissue; functional refers to having a significant correlation with matched gene expression changes. We then used these MethylMix driver genes to build multivariate models of overall survival using linear regression and validated these models in independent data sets. Applying MethylMix and linear regression we identified a novel methylation signature predictive of overall survival, which we here define as the GLIOMETH signature. Interestingly, GLIOMETH did not include MGMT, suggesting that MGMT methylation is not essential to predict prognosis in GBM. GLIOMETH was prognostically significant even in a multivariate analysis with known prognostic covariates, including MGMT methylation. We validated GLIOMETH in two external DNA methylation data sets and two gene expression data sets, using a leveraging technique predicting methylation in terms of gene expression, showing also a significant survival correlation. Differential and functional DNA methylation is predictive of overall survival in GBM independent of known prognostic factors. We identified GLIOMETH as a DNA methylation signature that is predictive of overall survival in GBM, outperforming MGMT methylation. The GLIOMETH model validated across multiple independent DNA methylation and gene expression validation data sets demonstrating its robustness as an independent predictor of prognosis in GBM. Recent next-generation genomic studies of medulloblastoma have revealed an unexpected and overwhelming convergence of somatic alterations affecting chromatin-modifying genes. Estimates informed by next-generation sequencing implicate that at least one third of all medulloblastomas have somatic mutations in a chromatin modifier, including those targeting histone methyltransfereses, histone demethylases, and related chromatin modulators that collectively function to influence chromatin conformation associated gene expression states. These mutations occur across all four medulloblastoma subgroups although different sets of genes appear to be selectively altered in a subgroup-specific manner. Despite the abundance of evidence implicating deregulation of chromatin modifiers as a key event in medulloblastoma pathogenesis, the medulloblastoma epigenome remains largely unexplored, and studies cataloguing histone modification states on a genome-wide scale have yet to be reported. To comprehensively investigate the histone code in medulloblastoma and the consequences associated with mutations affecting histone-modifying genes, we have performed ChIP-sequencing on a set of well-characterized primary medulloblastoma specimens. Histone marks examined in this study include the six modifications mandated by the International Human Epigenome Consortium (IHEC), including H3K4me3, H3K9me3, H3K27me3, H3K27ac, H3K4me1 and H3K36me3. Chromatin isolates from primary fresh-frozen tissues representative of each medulloblastoma subgroup were immunoprecipitated with the indicated antibodies and sequenced with a HiSeq Illumina sequencer to obtain at least 10 million unique reads per ChIP experiment. Peak calling was performed using multiple publicly available tools and data integrated with existing ENCODE data for the same histone marks. Inter-subgroup comparisons of histone modification states revealed a wealth of distinguishing genomic regions that were highly correlated with alternative patterns of gene expression existing between the subgroups. Moreover, integration with existing mutational profiles demonstrated aberrant chromatin states that could be linked to underlying mutations in select chromatin modifiers. Ongoing work will focus on expanding the cohort and integration with all levels of ‘omic data.