Jean C. Zenklusen

Epigenetic-Mediated Dysfunction of the Bone Morphogenetic Protein Pathway Inhibits Differentiation of Glioblastoma-Initiating Cells

Despite similarities between tumor-initiating cells with stem-like properties (TICs) and normal neural stem cells, we hypothesized that there may be differences in their differentiation potentials. We now demonstrate that both bone morphogenetic protein (BMP)-mediated and ciliary neurotrophic factor (CNTF)-mediated Jak/STAT-dependent astroglial differentiation is impaired due to EZH2-dependent epigenetic silencing of BMP receptor 1B (BMPR1B) in a subset of glioblastoma TICs. Forced expression of BMPR1B either by transgene expression or demethylation of the promoter restores their differentiation capabilities and induces loss of their tumorigenicity. We propose that deregulation of the BMP developmental pathway in a subset of glioblastoma TICs contributes to their tumorigenicity both by desensitizing TICs to normal differentiation cues and by converting otherwise cytostatic signals to proproliferative signals.

Rembrandt: Helping Personalized Medicine Become a Reality Through Integrative Translational Research

Finding better therapies for the treatment of brain tumors is hampered by the lack of consistently obtained molecular data in a large sample set and the ability to integrate biomedical data from disparate sources enabling translation of therapies from bench to bedside. Hence, a critical factor in the advancement of biomedical research and clinical translation is the ease with which data can be integrated, redistributed, and analyzed both within and across functional domains. Novel biomedical informatics infrastructure and tools are essential for developing individualized patient treatment based on the specific genomic signatures in each patients tumor. Here, we present Repository of Molecular Brain Neoplasia Data (Rembrandt), a cancer clinical genomics database and a Web-based data mining and analysis platform aimed at facilitating discovery by connecting the dots between clinical information and genomic characterization data. To date, Rembrandt contains data generated through the Glioma Molecular Diagnostic Initiative from 874 glioma specimens comprising ∼566 gene expression arrays, 834 copy number arrays, and 13,472 clinical phenotype data points. Data can be queried and visualized for a selected gene across all data platforms or for multiple genes in a selected platform. Additionally, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-anomaly pairs to facilitate the discovery of novel biomarkers and therapeutic targets. We believe that Rembrandt represents a prototype of how high-throughput genomic and clinical data can be integrated in a way that will allow expeditious and efficient translation of laboratory discoveries to the clinic. (Mol Cancer Res 2009;7(2):157–67)

Toward understanding and exploiting tumor heterogeneity

The extent of tumor heterogeneity is an emerging theme that researchers are only beginning to understand. How genetic and epigenetic heterogeneity affects tumor evolution and clinical progression is unknown. The precise nature of the environmental factors that influence this heterogeneity is also yet to be characterized. Nature Medicine, Nature Biotechnology and the Volkswagen Foundation organized a meeting focused on identifying the obstacles that need to be overcome to advance translational research in and tumor heterogeneity. Once these key questions were established, the attendees devised potential solutions. Their ideas are presented here.

Unsupervised Analysis of Transcriptomic Profiles Reveals Six Glioma Subtypes

Gliomas are the most common type of primary brain tumors in adults and a significant cause of cancer-related mortality. Defining glioma subtypes based on objective genetic and molecular signatures may allow for a more rational, patient-specific approach to therapy in the future. Classifications based on gene expression data have been attempted in the past with varying success and with only some concordance between studies, possibly due to inherent bias that can be introduced through the use of analytic methodologies that make a priori selection of genes before classification. To overcome this potential source of bias, we have applied two unsupervised machine learning methods to genome-wide gene expression profiles of 159 gliomas, thereby establishing a robust glioma classification model relying only on the molecular data. The model predicts for two major groups of gliomas (oligodendroglioma-rich and glioblastoma-rich groups) separable into six hierarchically nested subtypes. We then identified six sets of classifiers that can be used to assign any given glioma to the corresponding subtype and validated these classifiers using both internal (189 additional independent samples) and two external data sets (341 patients). Application of the classification system to the external glioma data sets allowed us to identify previously unrecognized prognostic groups within previously published data and within The Cancer Genome Atlas glioblastoma samples and the different biological pathways associated with the different glioma subtypes offering a potential clue to the pathogenesis and possibly therapeutic targets for tumors within each subtype.

Genomic changes and gene expression profiles reveal that established glioma cell lines are poorly representative of primary human gliomas.

Genetic aberrations, such as gene amplification, deletions, and loss of heterozygosity, are hallmarks of cancer and are thought to be major contributors to the neoplastic process. Established cancer cell lines have been the primary in vitro and in vivo models for cancer for more than 2 decades; however, few such cell lines have been extensively characterized at the genomic level. Here, we present a high-resolution genome-wide chromosomal alteration and gene expression analyses of five of the most commonly used glioma cell lines and compare the findings with those observed in 83 primary human gliomas. Although genomic alterations known to occur in primary tumors were identified in the cell lines, we also observed several novel recurrent aberrations in the glioma cell lines that are not frequently represented in primary tumors. Additionally, a global gene expression cluster distinct from primary tumors was identified in the glioma cell lines. Our results indicate that established cell lines are generally a poor representation of primary tumor biology, presenting a host of genomic and gene expression changes not observed in primary tissues, although some discrete features of glioma biology were conserved in the established cell lines. Refined maps of genetic alterations and transcriptional divergence from the original tumor type, such as the one presented here, may help serve as a guideline for a more biologically rational and clinically relevant selection of the most appropriate glioma model for a given experiment. (Mol Cancer Res 2008;6(1):21–30)

Glycogen Synthase Kinase-3 Inhibition Induces Glioma Cell Death through c-MYC, Nuclear Factor-κB, and Glucose Regulation

Glycogen synthase kinase 3 (GSK3), a serine/threonine kinase, is involved in diverse cellular processes ranging from nutrient and energy homeostasis to proliferation and apoptosis. Its role in glioblastoma multiforme has yet to be elucidated. We identified GSK3 as a regulator of glioblastoma multiforme cell survival using microarray analysis and small-molecule and genetic inhibitors of GSK3 activity. Various molecular and genetic approaches were then used to dissect out the molecular mechanisms responsible for GSK3 inhibition-induced cytotoxicity. We show that multiple small molecular inhibitors of GSK3 activity and genetic down-regulation of GSK3alpha/beta significantly inhibit glioma cell survival and clonogenicity. The potency of the cytotoxic effects is directly correlated with decreased enzyme activity-activating phosphorylation of GSK3alpha/beta Y276/Y216 and with increased enzyme activity inhibitory phosphorylation of GSK3alpha S21. Inhibition of GSK3 activity results in c-MYC activation, leading to the induction of Bax, Bim, DR4/DR5, and tumor necrosis factor-related apoptosis-inducing ligand expression and subsequent cytotoxicity. Additionally, down-regulation of GSK3 activity results in alteration of intracellular glucose metabolism resulting in dissociation of hexokinase II from the outer mitochondrial membrane with subsequent mitochondrial destabilization. Finally, inhibition of GSK3 activity causes a dramatic decrease in intracellular nuclear factor-kappaB activity. Inhibition of GSK3 activity results in c-MYC-dependent glioma cell death through multiple mechanisms, all of which converge on the apoptotic pathways. GSK3 may therefore be an important therapeutic target for gliomas. Future studies will further define the optimal combinations of GSK3 inhibitors and cytotoxic agents for use in gliomas and other cancers.

High-resolution Global Genomic Survey of 178 Gliomas Reveals Novel Regions of Copy Number Alteration and Allelic Imbalances

Primary brain tumors are the fourth leading cause of cancer mortality in adults under the age of 54 years and the leading cause of cancer mortality in children in the United States. Therapy for the most common type of primary brain tumors, gliomas, remains suboptimal. The development of new and more effective treatments will likely require a better understanding of the biology of these tumors. Here, we show that use of the high-density 100K single-nucleotide polymorphism arrays in a large number of primary tumor samples allows for a much higher resolution survey of the glioma genome than has been previously reported in any tumor type. We not only confirmed alterations in genomic areas previously reported to be affected in gliomas, but we also refined the location of those sites and uncovered multiple, previously unknown regions that are affected by copy number alterations (amplifications, homozygous and heterozygous deletions) as well as allelic imbalances (loss of heterozygosity/gene conversions). The wealth of genomic data produced may allow for the development of a more rational molecular classification of gliomas and serve as an important starting point in the search for new molecular therapeutic targets.

Mutational and functional analyses reveal that ST7 is a highly conserved tumor-suppressor gene on human chromosome 7q31.

Loss of heterozygosity (LOH) of markers on human chromosome 7q31 is frequently encountered in a variety of human neoplasias, indicating the presence of a tumor-suppressor gene (TSG). By a combination of microcell-fusion and deletion-mapping studies, we previously established that this TSG resides within a critical region flanked by the genetic markers D7S522 and D7S677. Using a positional cloning strategy and aided by the availability of near-complete sequence of this genomic interval, we have identified a TSG within 7q31, named ST7 (for suppression of tumorigenicity 7; this same gene was recently reported in another context and called RAY1). ST7 is ubiquitously expressed in human tissues. Analysis of a series of cell lines derived from breast tumors and primary colon carcinomas revealed the presence of mutations in ST7. Introduction of the ST7 cDNA into the prostate-cancer–derived cell line PC3 had no effect on the in vitro proliferation of the cells, but abrogated their in vivo tumorigenicity. Our data indicate that ST7 is a TSG within chromosome 7q31 and may have an important role in the development of some types of human cancer.

Prediction of Associations between microRNAs and Gene Expression in Glioma Biology

Despite progress in the determination of miR interactions, their regulatory role in cancer is only beginning to be unraveled. Utilizing gene expression data from 27 glioblastoma samples we found that the mere knowledge of physical interactions between specific mRNAs and miRs can be used to determine associated regulatory interactions, allowing us to identify 626 associated interactions, involving 128 miRs that putatively modulate the expression of 246 mRNAs. Experimentally determining the expression of miRs, we found an over-representation of over(under)-expressed miRs with various predicted mRNA target sequences. Such significantly associated miRs that putatively bind over-expressed genes strongly tend to have binding sites nearby the 3′UTR of the corresponding mRNAs, suggesting that the presence of the miRs near the translation stop site may be a factor in their regulatory ability. Our analysis predicted a significant association between miR-128 and the protein kinase WEE1, which we subsequently validated experimentally by showing that the over-expression of the naturally under-expressed miR-128 in glioma cells resulted in the inhibition of WEE1 in glioblastoma cells.

Towards patient-based cancer therapeutics

A new approach to the discovery of cancer therapeutics is emerging that begins with the cancer patient. Genomic analysis of primary tumors is providing an unprecedented molecular characterization of the disease. The next step requires relating the genetic features of cancers to acquired gene and pathway dependencies and identifying small-molecule therapeutics that target them.