DNA-microarray analysis of brain cancer: molecular classification for therapy

Enhancing mitosis quantification and detection in meningiomas with computational digital pathology

Mitosis is a critical criterion for meningioma grading. However, pathologists' assessment of mitoses is subject to significant inter-observer variation due to challenges in locating mitosis hotspots and accurately detecting mitotic figures. To address this issue, we leverage digital pathology and propose a computational strategy to enhance pathologists' mitosis assessment. The strategy has two components: (1) A depth-first search algorithm that quantifies the mathematically maximum mitotic count in 10 consecutive high-power fields, which can enhance the preciseness, especially in cases with borderline mitotic count. (2) Implementing a collaborative sphere to group a set of pathologists to detect mitoses under each high-power field, which can mitigate subjective random errors in mitosis detection originating from individual detection errors. By depth-first search algorithm (1) , we analyzed 19 meningioma slides and discovered that the proposed algorithm upgraded two borderline cases verified at consensus conferences. This improvement is attributed to the algorithm's ability to quantify the mitotic count more comprehensively compared to other conventional methods of counting mitoses. In implementing a collaborative sphere (2) , we evaluated the correctness of mitosis detection from grouped pathologists and/or pathology residents, where each member of the group annotated a set of 48 high-power field images for mitotic figures independently. We report that groups with sizes of three can achieve an average precision of 0.897 and sensitivity of 0.699 in mitosis detection, which is higher than an average pathologist in this study (precision: 0.750, sensitivity: 0.667). The proposed computational strategy can be integrated with artificial intelligence workflow, which envisions the future of achieving a rapid and robust mitosis assessment by interactive assisting algorithms that can ultimately benefit patient management.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

RP58 Represses Transcriptional Programs Linked to Nonneuronal Cell Identity and Glioblastoma Subtypes in Developing Neurons

How mammalian neuronal identity is progressively acquired and reinforced during development is not understood. We have previously shown that loss of RP58 (ZNF238 or ZBTB18), a BTB/POZ-zinc finger-containing transcription factor, in the mouse brain leads to microcephaly, corpus callosum agenesis, and cerebellum hypoplasia and that it is required for normal neuronal differentiation. The transcriptional programs regulated by RP58 during this process are not known. Here, we report for the first time that in embryonic mouse neocortical neurons a complex set of genes normally expressed in other cell types, such as those from mesoderm derivatives, must be actively repressed in vivo and that RP58 is a critical regulator of these repressed transcriptional programs. Importantly, gene set enrichment analysis (GSEA) analyses of these transcriptional programs indicate that repressed genes include distinct sets of genes significantly associated with glioma progression and/or pluripotency. We also demonstrate that reintroducing RP58 in glioma stem cells leads not only to aspects of neuronal differentiation but also to loss of stem cell characteristics, including loss of stem cell markers and decrease in stem cell self-renewal capacities. Thus, RP58 acts as an in vivo master guardian of the neuronal identity transcriptome, and its function may be required to prevent brain disease development, including glioma progression.

Identification of Core Genes and Screening of Potential Targets in Glioblastoma Multiforme by Integrated Bioinformatic Analysis

Glioblastoma multiforme is the most common primary intracranial malignancy, but its etiology and pathogenesis are still unclear. With the deepening of human genome research, the research of glioma subtype screening based on core molecules has become more in-depth. In the present study, we screened out differentially expressed genes (DEGs) through reanalyzing the glioblastoma multiforme (GBM) datasets GSE90598 from the Gene Expression Omnibus (GEO), the GBM dataset TCGA-GBM and the low-grade glioma (LGG) dataset TCGA-LGG from the Cancer Genome Atlas (TCGA). A total of 150 intersecting DEGs were found, of which 48 were upregulated and 102 were downregulated. These DEGs from GSE90598 dataset were enriched using the overrepresentation method, and multiple enriched gene ontology (GO) function terms were significantly correlated with neural cell signal transduction. DEGs between GBM and LGG were analyzed by gene set enrichment analysis (GSEA), and the significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in synapse signaling and oxytocin signaling pathways. Then, a protein-protein interaction (PPI) network was constructed to assess the interaction of proteins encoded by the DEGs. MCODE identified 2 modules from the PPI network. The 11 genes with the highest degrees in module 1 were designated as core molecules, namely, GABRD, KCNC1, KCNA1, SYT1, CACNG3, OPALIN, CD163, HPCAL4, ANK3, KIF5A, and MS4A6A, which were mainly enriched in ionic signaling-related pathways. Survival analysis of the GSE83300 dataset verified the significant relationship between expression levels of the 11 core genes and survival. Finally, the core molecules of GBM and the DrugBank database were assessed by a hypergeometric test to identify 10 drugs included tetrachlorodecaoxide related to cancer and neuropsychiatric diseases. Further studies are required to explore these core genes for their potentiality in diagnosis, prognosis, and targeted therapy and explain the relationship among ionic signaling-related pathways, neuropsychiatric diseases and neurological tumors.

Interactions Between Tumor Biology and Targeted Nanoplatforms for Imaging Applications

Although considerable efforts have been conducted to diagnose, improve, and treat cancer in the past few decades, existing therapeutic options are insufficient, as mortality and morbidity rates remain high. Perhaps the best hope for substantial improvement lies in early detection. Recent advances in nanotechnology are expected to increase the current understanding of tumor biology, and will allow nanomaterials to be used for targeting and imaging both in vitro and in vivo experimental models. Owing to their intrinsic physicochemical characteristics, nanostructures (NSs) are valuable tools that have received much attention in nanoimaging. Consequently, rationally designed NSs have been successfully employed in cancer imaging for targeting cancer-specific or cancer-associated molecules and pathways. This review categorizes imaging and targeting approaches according to cancer type, and also highlights some new safe approaches involving membrane-coated nanoparticles, tumor cell-derived extracellular vesicles, circulating tumor cells, cell-free DNAs, and cancer stem cells in the hope of developing more precise targeting and multifunctional nanotechnology-based imaging probes in the future.

Digital gene expression analysis might aid in the diagnosis of thyroid cancer

Background

Thyroid cancer represents approximately 90% of endocrine cancers. Difficulties in diagnosis and low inter-observer agreement are sometimes encountered, especially in the distinction between the follicular variant of papillary thyroid carcinoma (fvptc) and other follicular-patterned lesions, and can present significant challenges. In the present proof-of-concept study, we report a gene-expression assay using NanoString nCounter technology (NanoString Technologies, Seattle, WA, U.S.A.) that might aid in the differential diagnosis of thyroid neoplasms based on gene-expression signatures.

Methods

Our cohort included 29 patients with classical papillary thyroid carcinoma (ptc), 13 patients with fvptc, 14 patients with follicular thyroid carcinoma (ftc), 14 patients with follicular adenoma (fa), and 14 patients without any abnormality. We developed a 3-step classifier that shows good correlation with the pathologic diagnosis of various thyroid neoplasms. Step 1 differentiates normal from abnormal thyroid tissue; step 2 differentiates benign from malignant lesions; and step 3 differentiates the common malignant entities ptc, ftc, and fvptc.

Results

Using our 3-step classifier approach based on selected genes, we developed an algorithm that attempts to differentiate thyroid lesions with varying levels of sensitivity and specificity. Three genes-namely SDC4, PLCD3, and NECTIN4/PVRL4-were the most informative in distinguishing normal from abnormal tissue with a sensitivity and a specificity of 100%. One gene, SDC4, was important for differentiating benign from malignant lesions with a sensitivity of 89% and a specificity of 92%. Various combinations of genes were required to classify specific thyroid neoplasms.

Conclusions

This preliminary proof-of-concept study suggests a role for nCounter technology, a digital gene expression analysis technique, as an adjunct assay for the molecular diagnosis of thyroid neoplasms.

SIRT1 and AROS suppress doxorubicin-induced apoptosis via inhibition of GSK3β activity in neuroblastoma cells

SIRT1, the best-characterized member of the sirtuin family of deacetylases, is involved in cancer, apoptosis, inflammation, and metabolism. Active regulator of SIRT1 (AROS) was the first identified direct regulator of SIRT1. An increasing number of reports have indicated that SIRT1 plays an important role in controlling brain tumors. Here, we demonstrated that depletion of SIRT1 and AROS increases doxorubicin-mediated apoptosis in human neuroblastoma SH-SY5Y cells. Glycogen synthase kinase 3β (GSK3β) promoted doxorubicin-mediated apoptosis, but this effect was abolished by overexpression of SIRT1 and AROS. Interestingly, SIRT1 and AROS interacted with GSK3β and increased inhibitory phosphorylation of GSK3β on Ser9. Finally, we determined that AROS cooperates with SIRT1 to suppress GSK3β acetylation. Taken together, our results suggest that SIRT1 and AROS inhibit GSK3β activity and provide additional insight into drug resistance in the treatment of neuroblastoma.

Adipocyte enhancer binding protein 1 (AEBP1) knockdown suppresses human glioma cell proliferation, invasion and induces early apoptosis

BACKGROUND

Glioma is the most common primary malignant tumor with poor prognosis due to the lack of understanding the mechanism underlying the disease and the early diagnosis indexs. It is necessary to identify molecular signatures for predicting the overall prognosis of glioma. Adipocyte enhancer binding protein1 (AEBP1) acts as a transcriptional repressor and plays a role in adipogenesis and smooth muscle cell differentiation. However, its role in glioma remains unclear.

MATERIALS AND METHODS

AEBP1 expression was analyzed by bioinformatics using the public database and by qPCR and western blotting in human glioma tissues. AEBP1 downregulation was performed by lipofectamine3000-mediated siRNA transfection. Cell proliferation and invasion were determined by cell counting kit-8 and transwell assays, while early cell apoptosis was determined by flow cytometry. The proteins of downstream NF-κB signaling pathway were determined by western blotting.

RESULTS

AEBP1 is highly expressed in human gliomas. Lipofectamine 3000-mediated siRNA transfection stably and efficiently suppressed AEBP1 mRNA and protein expression in human glioma cells. AEBP1 downregulation inhibited cell proliferation and invasion, but promoted early cell apoptosis. Also, AEBP1 knockdown in glioma cells decreased the expression of NF-κB1. Furthermore, the downstream of NF-κB signaling pathway, Bax, caspase-3 are increased, while MMP2 and Bcl-2 are decreased in glioma cells.

CONCLUSION

Elevated AEBP1 is positively associated with poor prognosis of glioma. AEBP1 downregulation suppressed cell proliferation and invasion, but promoted early cell apoptosis. AEBP1 downregulation suppressed the cell proliferation and invasion may by inhibiting the NF-κB signaling pathway.

EMP1 regulates cell proliferation, migration, and stemness in gliomas through PI3K-AKT signaling and CD44

Glioblastoma multiforme (GBM) is an intracranial tumor; the feature is higher malignant and poorer prognosis. The search for therapeutic targets for gliomas has always been a focus of research in the field of neurology. The unusual expression of epithelial membrane protein 1 (EMP1) has been proved in most tumors. In our study, we determined the expression level of EMP1 expression in glioma tissues. There were higher levels of EMP1 in glioma tissues-particularly GBM tissues-than those in normal brain tissues. Then we discovered that silencing EMP1 inhibited glioma cell invasion and proliferation through inhibiting the PI3K-AKT signaling pathway. Subsequently, we investigated the function of EMP1 on glioma stem cells and found that it regulates the expression of CD44 in such cells to promote stemness. Taken together, the new strategies for the treatment of glioma may be provided by these finding, thereby improving the prognosis associated with it.

The Structure of an Archaeal B-Family DNA Polymerase in Complex with a Chemically Modified Nucleotide

Archaeal B-family DNA polymerases (DNA pols) are the driving force of cutting-edge biotechnological applications like next-generation sequencing. The acceptance of chemically modified nucleotides by DNA pols is key to these technologies. Until now, no structural data have been available for these DNA pols in complex with modified substrates, which could build the basis for understanding interactions between the enzyme and the chemically modified nucleotide and for the further development of next-generation nucleotides. For the first time, we crystallized an exonuclease-deficient variant of the wild-type B-family KOD DNA pol with a modified nucleotide in a closed, ternary complex. We also crystalized the A-family DNA pol KlenTaq with the same nucleotide. The reported structural data reveal how the protein and the DNA modulate two distinct conformations of the appended moiety in the A- and B-family DNA pols and how these influence the processing of the modified nucleotide. Overall, this study provides first insight into the interplay between B-family DNA pols and relevant modified substrates.

Dynamic Magnetic Nanomixers for Improved Microarray Assays by Eliminating Diffusion Limitation

Microarrays are widely used in high-throughput analysis of DNA, protein, and small molecules. However, the majority of microarray assays need improved assay speed and sensitivity due to the slow molecular diffusion from bulk solutions to probe surfaces. Here, a new class of magnetic nanomixers in DNA and protein microarray assays is reported to eliminate the diffusion constraint through dynamic mixing. It is demonstrated that the dynamic nanomixers can improve the assay kinetics at least by a factor of 4 and 2 for DNA and protein microarray assays, respectively. By using the dynamic nanomixers, the sensitivities of detecting Escherichia coli O157:H7 DNA and prostate specific antigen increase by more than four-fold. The dynamic mixing also greatly reduces the spot-to-spot variation to below 10% across a broad concentration range, providing more accurate assay results. In comparison with existing methods, this magnetic nanomixer-based approach offers rapid turnaround, improved sensitivity, good accuracy, low cost, simple operation, and excellent compatibility with commercial microarrays.

Snapshots of a modified nucleotide moving through the confines of a DNA polymerase

Despite being evolved to process the four canonical nucleotides, DNA polymerases are known to incorporate and extend from modified nucleotides, which is the key to numerous core biotechnology applications. The structural basis for postincorporation elongation remained elusive. We successfully crystallized KlenTaq DNA polymerase in six complexes, providing high-resolution snapshots of the modification “moving” from the 3′ terminus upstream to the sixth nucleotide in the primer strand. Combining these data with quantum mechanics/molecular mechanics calculations and biochemical studies elucidates how the enzyme and the modified substrate mutually modulate their conformations without compromising the enzyme’s activity. This highlights the unexpected plasticity of the system as origin of the broad substrate properties of the DNA polymerase and guide for the design of improved systems.

DNA polymerases have evolved to process the four canonical nucleotides accurately. Nevertheless, these enzymes are also known to process modified nucleotides, which is the key to numerous core biotechnology applications. Processing of modified nucleotides includes incorporation of the modified nucleotide and postincorporation elongation to proceed with the synthesis of the nascent DNA strand. The structural basis for postincorporation elongation is currently unknown. We addressed this issue and successfully crystallized KlenTaq DNA polymerase in six closed ternary complexes containing the enzyme, the modified DNA substrate, and the incoming nucleotide. Each structure shows a high-resolution snapshot of the elongation of a modified primer, where the modification “moves” from the 3′-primer terminus upstream to the sixth nucleotide in the primer strand. Combining these data with quantum mechanics/molecular mechanics calculations and biochemical studies elucidates how the enzyme and the modified substrate mutually modulate their conformations without compromising the enzyme’s activity significantly. The study highlights the plasticity of the system as origin of the broad substrate properties of DNA polymerases and facilitates the design of improved systems.

Downregulation of miR-137 and miR-6500-3p promotes cell proliferation in pediatric high-grade gliomas

Pediatric high-grade gliomas (pHGGs) are aggressive brain tumors affecting children, and outcomes have remained dismal, even with access to new multimodal therapies. In this study, we compared the miRNomes and transcriptomes of pediatric low- (pLGGs) and high-grade gliomas (pHGGs) using small RNA sequencing (smRNA-Seq) and gene expression microarray, respectively. Through integrated bioinformatics analyses and experimental validation, we identified miR-137 and miR-6500-3p as significantly downregulated in pHGGs. miR-137 or miR-6500-3p overexpression reduced cell proliferation in two pHGG cell lines, SF188 and UW479. CENPE, KIF14 and NCAPG levels were significantly higher in pHGGs than pLGGs, and were direct targets of miR-137 or miR-6500-3p. Furthermore, knockdown of CENPE, KIF14 or NCAPG combined with temozolomide treatment resulted in a combined suppressive effect on pHGG cell proliferation. In summary, our results identify novel mRNA/miRNA interactions that contribute to pediatric glioma malignancy and represent potential targets for the development of new therapeutic strategies.

Integrating RNA sequencing into neuro-oncology practice

Malignant tumors of the central nervous system (CNS) cause substantial morbidity and mortality, yet efforts to optimize chemo- and radiotherapy have largely failed to improve dismal prognoses. Over the past decade, RNA sequencing (RNA-seq) has emerged as a powerful tool to comprehensively characterize the transcriptome of CNS tumor cells in one high-throughput step, leading to improved understanding of CNS tumor biology and suggesting new routes for targeted therapies. RNA-seq has been instrumental in improving the diagnostic classification of brain tumors, characterizing oncogenic fusion genes, and shedding light on intratumor heterogeneity. Currently, RNA-seq is beginning to be incorporated into regular neuro-oncology practice in the form of precision neuro-oncology programs, which use information from tumor sequencing to guide implementation of personalized targeted therapies. These programs show great promise in improving patient outcomes for tumors where single agent trials have been ineffective. As RNA-seq is a relatively new technique, many further applications yielding new advances in CNS tumor research and management are expected in the coming years.

A sequential Monte Carlo approach to gene expression deconvolution

High-throughput gene expression data are often obtained from pure or complex (heterogeneous) biological samples. In the latter case, data obtained are a mixture of different cell types and the heterogeneity imposes some difficulties in the analysis of such data. In order to make conclusions on gene expresssion data obtained from heterogeneous samples, methods such as microdissection and flow cytometry have been employed to physically separate the constituting cell types. However, these manual approaches are time consuming when measuring the responses of multiple cell types simultaneously. In addition, exposed samples, on many occasions, end up being contaminated with external perturbations and this may result in an altered yield of molecular content. In this paper, we model the heterogeneous gene expression data using a Bayesian framework, treating the cell type proportions and the cell-type specific expressions as the parameters of the model. Specifically, we present a novel sequential Monte Carlo (SMC) sampler for estimating the model parameters by approximating their posterior distributions with a set of weighted samples. The SMC framework is a robust and efficient approach where we construct a sequence of artificial target (posterior) distributions on spaces of increasing dimensions which admit the distributions of interest as marginals. The proposed algorithm is evaluated on simulated datasets and publicly available real datasets, including Affymetrix oligonucleotide arrays and national center for biotechnology information (NCBI) gene expression omnibus (GEO), with varying number of cell types. The results obtained on all datasets show a superior performance with an improved accuracy in the estimation of cell type proportions and the cell-type specific expressions, and in addition, more accurate identification of differentially expressed genes when compared to other widely known methods for blind decomposition of heterogeneous gene expression data such as Dsection and the nonnegative matrix factorization (NMF) algorithms. MATLAB implementation of the proposed SMC algorithm is available to download at https://github.com/moyanre/smcgenedeconv.git.

Znf179 induces differentiation and growth arrest of human primary glioblastoma multiforme in a p53-dependent cell cycle pathway

Malignant glioblastoma multiforme (GBM) is an aggressive brain tumor with strong local invasive growth and a poor prognosis. One probable way to manipulate GBM cells toward a less invasive status is to reprogram the most malignant GBM cells to a more differentiated and less oncogenic phenotype. Herein, we identified a novel role of a RING finger protein Znf179 in gliomagenesis. Znf179 overexpression induced differentiation of primary GBM cells, which were accompanied with elevated glial fibrillary acidic protein (GFAP) expression through up-regulating several cell-cycle-related factors, p53, p21, and p27, and allowed the cell-cycle arrest in the G₀/G₁ phase. In addition, Znf179 was highly correlated with the prognosis and survival rates of glioma patients. The expression levels of Znf179 was relatively lower in glioma patients compared to normal people, and glioma patients with lower expression levels of Znf179 mRNA had poorer prognosis and lower survival rates. In conclusion, we provide novel insight that Znf179 can reprogram GBM cells into a more-differentiated phenotype and prevent the progression of gliomas to a more-malignant state through p53-mediated cell-cycle signaling pathways. Understanding the molecular mechanism of Znf179 in gliomagenesis could help predict prognostic consequences, and targeting Znf179 could be a potential biomarker for glioma progression.

Comparison of Gene Expression Profile Between Tumor Tissue and Adjacent Non-tumor Tissue in Patients with Gastric Gastrointestinal Stromal Tumor (GIST)

Gastrointestinal stromal tumors (GISTs) are defined as spindle cell and/or epithelioid tumors originated from interstitial Cajal cells or precursors in the digestive tract. This study was conducted to identify genes differing in expression between the gastric tumors and the adjacent non-cancerous mucosas in patients with primary gastric GIST. The gene expression profile was determined by using oligonucleotide-based DNA microarrays and further validated by quantitative real-time PCR. The Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis was performed to predict signaling pathways involved in gastric GIST. Our data showed that the expression levels of 957 genes (RAB39B, member RAS oncogene family; VCAN, versican; etc.) were higher and that of 526 genes (CXCL14, chemokine C-X-C motif ligand 14; MTUS1, microtubule-associated tumor suppressor 1; etc.) were lower in the gastric tumor tissues as compared with normal gastric tissues. Results from KEGG pathway analysis revealed that the differentially expressed genes were enriched into 16 signaling transduction pathways, including Hedeghog and Wnt signaling pathways. Our study may provide basis for identification of novel biomarkers associated with primary gastric GIST pathogenesis and for exploration of underlying mechanisms involved in this gastric sarcoma.

Phospholipase C Beta 1: a Candidate Signature Gene for Proneural Subtype High-Grade Glioma

Phospholipase C beta 1 (PLCβ1) expresses in gliomas and cultured glial cells, but its expression is barely detectable in normal glial cells. We analyzed data from Gene Expression Omnibus (GEO-GDSxxx), The Cancer Genome Atlas (TCGA), and the Repository for Molecular Brain Neoplasia Data (REMBRANDT) to explore the potential role of PLCβ1 as a biomarker in high-grade glioma (HGG). PLCβ1 expression is significantly higher in grade III gliomas than that in grade IV gliomas from GDS1815 (n = 24 vs. 76), GDS1962 (n = 19 vs. 81), and GDS1975 (n = 26 vs. 59). In GDS1815, PLCβ1 expression correlates with several known proneural (PN) signature genes; its expression from PN subtype (n = 15) is significantly higher than that from mesenchymal (Mes) subtype (n = 33) HGG. In GDS1962, PLCβ1 expression is the highest in nontumor brain tissue (n = 23) and is significantly higher than its expression in grade II gliomas [astrocytomas (n = 7) and oligodendrogliomas (n = 37)]. A Kaplan-Meier survival curve from a REMBRANDT cohort demonstrates that glioma patients with intermediate PLCβ1 expression (n = 103) survived significantly longer than PLCβ1 downregulated (2X) groups (n = 226). From TCGA data, PLCβ1 RNA-Seq signal inversely correlates with the pathological grades, and PLCβ1 expression in PN (n = 8) is of significantly higher levels than that in Mes (n = 8) subtypes of glioblastoma. The top 50 % of PLCβ1 expression subgroup (n = 294) of gliomas (grades II to IV merged) survived significantly longer than the low 50 percentile of the PLCβ1 expression subgroup (n = 293). p values are less than 0.05 for all these analyses. We conclude that PLCβ1 is a candidate signature gene for PN subtype HGG, and its expression inversely correlates with glioma pathological grade and is a potential prognostic factor.

Platform-Independent Genome-Wide Pattern of DNA Copy-Number Alterations Predicting Astrocytoma Survival and Response to Treatment Revealed by the GSVD Formulated as a Comparative Spectral Decomposition

We use the generalized singular value decomposition (GSVD), formulated as a comparative spectral decomposition, to model patient-matched grades III and II, i.e., lower-grade astrocytoma (LGA) brain tumor and normal DNA copy-number profiles. A genome-wide tumor-exclusive pattern of DNA copy-number alterations (CNAs) is revealed, encompassed in that previously uncovered in glioblastoma (GBM), i.e., grade IV astrocytoma, where GBM-specific CNAs encode for enhanced opportunities for transformation and proliferation via growth and developmental signaling pathways in GBM relative to LGA. The GSVD separates the LGA pattern from other sources of biological and experimental variation, common to both, or exclusive to one of the tumor and normal datasets. We find, first, and computationally validate, that the LGA pattern is correlated with a patient's survival and response to treatment. Second, the GBM pattern identifies among the LGA patients a subtype, statistically indistinguishable from that among the GBM patients, where the CNA genotype is correlated with an approximately one-year survival phenotype. Third, cross-platform classification of the Affymetrix-measured LGA and GBM profiles by using the Agilent-derived GBM pattern shows that the GBM pattern is a platform-independent predictor of astrocytoma outcome. Statistically, the pattern is a better predictor (corresponding to greater median survival time difference, proportional hazard ratio, and concordance index) than the patient's age and the tumor's grade, which are the best indicators of astrocytoma currently in clinical use, and laboratory tests. The pattern is also statistically independent of these indicators, and, combined with either one, is an even better predictor of astrocytoma outcome. Recurring DNA CNAs have been observed in astrocytoma tumors' genomes for decades, however, copy-number subtypes that are predictive of patients' outcomes were not identified before. This is despite the growing number of datasets recording different aspects of the disease, and due to an existing fundamental need for mathematical frameworks that can simultaneously find similarities and dissimilarities across the datasets. This illustrates the ability of comparative spectral decompositions to find what other methods miss.

Generation of Microtumors Using 3D Human Biogel Culture System and Patient-derived Glioblastoma Cells for Kinomic Profiling and Drug Response Testing

The use of patient-derived xenografts for modeling cancers has provided important insight into cancer biology and drug responsiveness. However, they are time consuming, expensive, and labor intensive. To overcome these obstacles, many research groups have turned to spheroid cultures of cancer cells. While useful, tumor spheroids or aggregates do not replicate cell-matrix interactions as found in vivo. As such, three-dimensional (3D) culture approaches utilizing an extracellular matrix scaffold provide a more realistic model system for investigation. Starting from subcutaneous or intracranial xenografts, tumor tissue is dissociated into a single cell suspension akin to cancer stem cell neurospheres. These cells are then embedded into a human-derived extracellular matrix, 3D human biogel, to generate a large number of microtumors. Interestingly, microtumors can be cultured for about a month with high viability and can be used for drug response testing using standard cytotoxicity assays such as 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and live cell imaging using Calcein-AM. Moreover, they can be analyzed via immunohistochemistry or harvested for molecular profiling, such as array-based high-throughput kinomic profiling, which is detailed here as well. 3D microtumors, thus, represent a versatile high-throughput model system that can more closely replicate in vivo tumor biology than traditional approaches.

Chimeric adeno-associated virus and bacteriophage: a potential targeted gene therapy vector for malignant glioma

The incipient development of gene therapy for cancer has fuelled its progression from bench to bedside in mere decades. Of all malignancies that exist, gliomas are the largest class of brain tumors, and are renowned for their aggressiveness and resistance to therapy. In order for gene therapy to achieve clinical success, a multitude of barriers ranging from glioma tumor physiology to vector biology must be overcome. Many viral gene delivery systems have been subjected to clinical investigation; however, with highly limited success. In this review, the current progress and challenges of gene therapy for malignant glioma are discussed. Moreover, we highlight the hybrid adeno-associated virus and bacteriophage vector as a potential candidate for targeted gene delivery to brain tumors.

Different pediatric brain tumors are associated with different gene expression profiling

Malignant brain tumors are the most common pediatric solid tumors and are the leading cause of death from childhood cancers. These tumors include several histologic subtypes. Due to the particular properties of brain tumors, such as growth and division, examination of brain tumors and the analysis of results are not simple. Up to date there is a dearth of useful biomarkers that have been validated and clinically implemented for pediatric brain tumors. In order to identify the new genetic alterations we recognized, using microarray dataset, chitinases as new potential biomarkers of CNS tumors. The modulation of chitinases was confirmed also in the different histologic subtypes. Our study revealed that distinct patterns of chitinases expression characterize the diverse histological subtypes. In addition evaluating other lisosomal enzymes such as glycosidases and proteases we found that NEU4, CTBS and GBA2 belonging to glycosidases family and CTSC, CTSK and CTSF belonging to proteases family were differently modulated. Future investigations are needed to be performed before some of these enzymes could finally be used as biomarkers of specific types of CNS neoplasms.

Screening of differentially expressed genes associated with human glioblastoma and functional analysis using a DNA microarray

Glioblastoma multiforme (GBM) is the most malignant type of human glioma, and has a poor prognosis. Screening differentially expressed genes (DEGs) in brain tumor samples and normal brain samples is of importance for identifying GBM and to design specific-targeting drugs. The transcriptional profile of GSE30563, containing three genechips of brain tumor samples and three genechips of normal brain samples, was downloaded from Gene Expression Omnibus to identify the DEGs. The differences in the expression of the DEGs in the two different samples were compared through hierarchical biclustering. The co-expression coefficient of the DEGs was calculated using the information from COXPRESdb, the network of the DEGs was constructed and functional enrichment and pathway analysis were performed. Finally, the transcription factors of important DEGs were predicted. A total of 1,006 DEGs, including 368 upregulated and 638 downregulated DEGs, were identified. A close correlation was demonstrated between six important genes, associated with immune response, HLA-DQB1, HLA-DRB1, HLA-DPA1, HLA-B, HLA-DMA and HLA-DRA, and the immune response. Allograft rejection was selected as the most significant pathway. A total of 17 transcription factors, including nuclear factor (NF)-κB and NF-κB1, and their binding sites containing these six DEGs, were also identified. The DEGs, including major histocompatibility complex (MHC) class II, DQβ1, MHC class II, DRβ1, MHC class IB, MHC class II, DMα, MHC class II, DPα1, MHC class II, DRα, may provide novel targets for the diagnosis and treatment of GBM. The transcription factors of these six genes and their binding sites may also provide evidence and direction for identifying target-specific drugs.

Neuroimaging and genetic influence in treating brain neoplasms

The current treatment of glioblastoma patients based on surgery, radiation, and chemotherapy has achieved modest improvement in progression-free survival. In this direction, personalized treatment is the next achievement for better patient management and increased overall survival. Genetic characterization of high-grade gliomas by MR imaging is the goal in neuroimaging. The main genetic alterations described in these neoplasms, implications in patient treatment, and prognosis are reviewed. MR imaging features and novel techniques are correlated with the main genetic aspects of such tumors. Posttreatment phenomena, such as pseudoprogression and pseudoresponse, are analyzed in association with the genetic expression of these tumors.

Ranking candidate genes of esophageal squamous cell carcinomas based on differentially expressed genes and the topological properties of the co-expression network

Background

The aim of this study was to identify the candidate genes of esophageal squamous cell carcinoma (ESCC).

Methods

Gene expression profiling of 17 ESCC samples and 17 adjacent normal samples, GSE20347, was downloaded from Gene Expression Omnibus database. The raw data were preprocessed, and the differentially expressed genes (DEGs) between ESCC and normal samples were identified by using SAM software (false discovery rate <0.001). Then, the co-expression network of DEGs was constructed based on Pearson’s correlation test (r-value ≥0.8). Furthermore, the topological properties of the co-expression network were analyzed through NetworkAnalyzer (default settings) of Cytoscape. The expression fold changes of DEGs and topological properties were utilized to identify the candidate genes of ESCC (Cri_n score >4), which were further analyzed based on DAVID functional enrichment analysis (P-value <0.05).

Results

A total of 1,063 DEGs were identified, including 490 up-regulated and 573 down-regulated DEGs. Then, the co-expression network of DEGs was constructed, containing 999 nodes and 46,323 edges. Based on the expression fold changes of DEGs and the topological properties of the co-expression network, DEGs were ranked, and the top 24 genes were candidate genes of ESCC, such as CRISP3, EREG, CXCR2, and CRNN. Furthermore, the 24 genes were significantly enriched in bio-functions regarding cell differentiation, glucan biosynthetic process and immune response.

Conclusion

The present study suggested that CRISP3, EREG, CXCR2, and CRNN might be causative genes of ESCC, and play vital roles in the development of ESCC. However, further experimental studies are needed to confirm our results.

Electronic supplementary material

The online version of this article (doi:10.1186/s40001-014-0052-x) contains supplementary material, which is available to authorized users.

Towards developing biomarkers for glioblastoma multiforme: a proteomics view

Glioblastoma multiforme (GBM) is one of the most aggressive and lethal forms of the primary brain tumors. With predominance of tumor heterogeneity and emergence of new subtypes, new approaches are needed to develop tissue-based markers for tumor typing or circulatory markers to serve as blood-based assays. Multi-omics data integration for GBM tissues would offer new insights on the molecular view of GBM pathogenesis useful to identify biomarker panels. On the other hand, mapping differentially expressed tissue proteins for their secretory potential through bioinformatics analysis or analysis of the tumor cell secretome or tumor exosomes would enhance our understanding of the tumor microenvironment and prospects for targeting circulatory biomarkers. In this review, the authors first present potential biomarker candidates for GBM that have been reported and then focus on plausible pipelines for multi-omic data integration to identify additional, high-confidence molecular panels for clinical applications in GBM.

Cytoplasmic TRADD confers a worse prognosis in glioblastoma

Tumor necrosis factor receptor 1 (TNFR1)-associated death domain protein (TRADD) is an important adaptor in TNFR1 signaling and has an essential role in nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) activation and survival signaling. Increased expression of TRADD is sufficient to activate NF-κB. Recent studies have highlighted the importance of NF-κB activation as a key pathogenic mechanism in glioblastoma multiforme (GBM), the most common primary malignant brain tumor in adults.We examined the expression of TRADD by immunohistochemistry (IHC) and find that TRADD is commonly expressed at high levels in GBM and is detected in both cytoplasmic and nuclear distribution. Cytoplasmic IHC TRADD scoring is significantly associated with worse progression-free survival (PFS) both in univariate and multivariate analysis but is not associated with overall survival (n = 43 GBMs). PFS is a marker for responsiveness to treatment. We propose that TRADD-mediated NF-κB activation confers chemoresistance and thus a worse PFS in GBM. Consistent with the effect on PFS, silencing TRADD in glioma cells results in decreased NF-κB activity, decreased proliferation of cells, and increased sensitivity to temozolomide. TRADD expression is common in glioma-initiating cells. Importantly, silencing TRADD in GBM-initiating stem cell cultures results in decreased viability of stem cells, suggesting that TRADD may be required for maintenance of GBM stem cell populations. Thus, our study suggests that increased expression of cytoplasmic TRADD is both an important biomarker and a key driver of NF-κB activation in GBM and supports an oncogenic role for TRADD in GBM.

Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq

BACKGROUND

High-throughput sequencing is gradually replacing microarrays as the preferred method for studying mRNA expression levels, providing nucleotide resolution and accurately measuring absolute expression levels of almost any transcript, known or novel. However, existing microarray data from clinical, pharmaceutical, and academic settings represent valuable and often underappreciated resources, and methods for assessing and improving the quality of these data are lacking.

RESULTS

To quantitatively assess the quality of microarray probes, we directly compare RNA-Seq to Agilent microarrays by processing 231 unique samples from the Allen Human Brain Atlas using RNA-Seq. Both techniques provide highly consistent, highly reproducible gene expression measurements in adult human brain, with RNA-Seq slightly outperforming microarray results overall. We show that RNA-Seq can be used as ground truth to assess the reliability of most microarray probes, remove probes with off-target effects, and scale probe intensities to match the expression levels identified by RNA-Seq. These sequencing scaled microarray intensities (SSMIs) provide more reliable, quantitative estimates of absolute expression levels for many genes when compared with unscaled intensities. Finally, we validate this result in two human cell lines, showing that linear scaling factors can be applied across experiments using the same microarray platform.

CONCLUSIONS

Microarrays provide consistent, reproducible gene expression measurements, which are improved using RNA-Seq as ground truth. We expect that our strategy could be used to improve probe quality for many data sets from major existing repositories.

Malignant gliomas: strategies to increase the effectiveness of targeted molecular treatment

Recently, there has been increasing interest in the use of targeted molecular agents for the treatment of malignant gliomas. These agents are generally well tolerated but have demonstrated only modest activity. In this article, the current status of targeted molecular agents for malignant gliomas will be reviewed and strategies to improve their effectiveness will be discussed.

Immunohistochemical classification of primary and secondary glioblastomas

Background

Glioblastomas may develop de novo (primary glioblastomas, P-GBLs) or through progression from lower-grade astrocytomas (secondary glioblastomas, S-GBLs). The aim of this study was to compare the immunohistochemical classification of glioblastomas with clinically determined P-GBLs and S-GBLs to identify the best combination of antibodies for immunohistochemical classification.

Methods

We evaluated the immunohistochemical expression of epidermal growth factor receptor (EGFR), p53, and isocitrate dehydrogenase 1 (IDH-1) in 150 glioblastoma cases.

Results

According to clinical history, the glioblastomas analyzed in this study consisted of 146 P-GBLs and 4 S-GBLs. Immunohistochemical expression of EGFR, p53, and IDH-1 was observed in 62.6%, 49.3%, and 11.1%, respectively. Immunohistochemical profiles of EGFR(+)/p53(-), IDH-1(-)/EGFR(+)/p53(-), and EGFR(-)/p53(+) were noted in 41.3%, 40.2%, and 28.7%, respectively. Expression of IDH-1 and EGFR(-)/p53(+) was positively correlated with young age. The typical immunohistochemical features of S-GBLs comprised IDH-1(+)/EGFR(-)/p53(+), and were noted in 3.6% of clinically P-GBLs. The combination of IDH-1(-) or EGFR(+) was the best set of immunohistochemical stains for identifying P-GBLs, whereas the combination of IDH-1(+) and EGFR(-) was best for identifying S-GBLs.

Conclusions

We recommend a combination of IDH-1 and EGFR for immunohistochemical classification of glioblastomas. We expect our results to be useful for determining treatment strategies for glioblastoma patients.

Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers

Many studies have been carried out for early diagnosis of complex diseases by finding accurate and robust biomarkers specific to respective diseases. In particular, recent rapid advance of high-throughput technologies provides unprecedented rich information to characterize various disease genotypes and phenotypes in a global and also dynamical manner, which significantly accelerates the study of biomarkers from both theoretical and clinical perspectives. Traditionally, molecular biomarkers that distinguish disease samples from normal samples are widely adopted in clinical practices due to their ease of data measurement. However, many of them suffer from low coverage and high false-positive rates or high false-negative rates, which seriously limit their further clinical applications. To overcome those difficulties, network biomarkers (or module biomarkers) attract much attention and also achieve better performance because a network (or subnetwork) is considered to be a more robust form to characterize diseases than individual molecules. But, both molecular biomarkers and network biomarkers mainly distinguish disease samples from normal samples, and they generally cannot ensure to identify predisease samples due to their static nature, thereby lacking ability to early diagnosis. Based on nonlinear dynamical theory and complex network theory, a new concept of dynamical network biomarkers (DNBs, or a dynamical network of biomarkers) has been developed, which is different from traditional static approaches, and the DNB is able to distinguish a predisease state from normal and disease states by even a small number of samples, and therefore has great potential to achieve "real" early diagnosis of complex diseases. In this paper, we comprehensively review the recent advances and developments on molecular biomarkers, network biomarkers, and DNBs in particular, focusing on the biomarkers for early diagnosis of complex diseases considering a small number of samples and high-throughput data (or big data). Detailed comparisons of various types of biomarkers as well as their applications are also discussed.

Data Mining and Meta-Analysis on DNA Microarray Data

Microarray technology enables high-throughput parallel gene expression analysis, and use has grown exponentially thanks to the development of a variety of applications for expression, genetics and epigenetic studies. A wealth of data is now available from public repositories, providing unprecedented opportunities for meta-analysis approaches, which could generate new biological information, unrelated to the original scope of individual studies. This study provides a guideline for identification of biological significance of the statistically-selected differentially-expressed genes derived from gene expression arrays as well as to suggest further analysis pathways. The authors review the prerequisites for data-mining and meta-analysis, summarize the conceptual methods to derive biological information from microarray data and suggest software for each category of data mining or meta-analysis.

Inference of gene regulatory networks from genome-wide knockout fitness data

Motivation: Genome-wide fitness is an emerging type of high-throughput biological data generated for individual organisms by creating libraries of knockouts, subjecting them to broad ranges of environmental conditions, and measuring the resulting clone-specific fitnesses. Since fitness is an organism-scale measure of gene regulatory network behaviour, it may offer certain advantages when insights into such phenotypical and functional features are of primary interest over individual gene expression. Previous works have shown that genome-wide fitness data can be used to uncover novel gene regulatory interactions, when compared with results of more conventional gene expression analysis. Yet, to date, few algorithms have been proposed for systematically using genome-wide mutant fitness data for gene regulatory network inference.

Results: In this article, we describe a model and propose an inference algorithm for using fitness data from knockout libraries to identify underlying gene regulatory networks. Unlike most prior methods, the presented approach captures not only structural, but also dynamical and non-linear nature of biomolecular systems involved. A state–space model with non-linear basis is used for dynamically describing gene regulatory networks. Network structure is then elucidated by estimating unknown model parameters. Unscented Kalman filter is used to cope with the non-linearities introduced in the model, which also enables the algorithm to run in on-line mode for practical use. Here, we demonstrate that the algorithm provides satisfying results for both synthetic data as well as empirical measurements of GAL network in yeast Saccharomyces cerevisiae and TyrR–LiuR network in bacteria Shewanella oneidensis.

Availability: MATLAB code and datasets are available to download at http://www.duke.edu/∼lw174/Fitness.zip and http://genomics.lbl.gov/supplemental/fitness-bioinf/

Contact:wangx@ee.columbia.edu or mssamoilov@lbl.gov

Supplementary information:Supplementary data are available at Bioinformatics online

Candidate pathways for promoting differentiation or quiescence of oligodendrocyte progenitor-like cells in glioma

Platelet-derived growth factor receptor alpha-positive oligodendrocyte progenitor cells (OPC) located within the mature central nervous system may remain quiescent, proliferate, or differentiate into oligodendrocytes. Human glioblastoma multiforme tumors often contain rapidly proliferating oligodendrocyte lineage transcription factor 2 (Olig2)-positive cells that resemble OPCs. In this study, we sought to identify candidate pathways that promote OPC differentiation or quiescence rather than proliferation. Gene expression profiling conducted in both normal murine OPCs and highly proliferative Olig2-positive glioma cells identified all the transcripts associated with the highly proliferative state of these cells and showed that among the various cell types found within the brain, Olig2-positive tumor cells are most similar to OPCs. We then subtracted OPC transcripts found in tumor samples from those found in normal brain samples and identified 28 OPC transcripts as candidates for promoting differentiation or quiescence. Systematic analysis of human glioma data revealed that these genes have similar expression profiles in human tumors and were significantly enriched in genomic deletions, suggesting an antiproliferative role. Treatment of primary murine glioblastoma cells with agonists of one candidate gene, Gpr17, resulted in a decreased number of neurospheres. Together, our findings show that comparison of the molecular phenotype of progenitor cells in tumors to the equivalent cells in the normal brain represents a novel approach for the identification of targeted therapies.

Aptamer-based polymerase chain reaction for ultrasensitive cell detection

A new system was developed for sensitive and selective detection of tumor cells taking advantage of cell-attached aptamers amplified by PCR and output signals amplified by cationic conjugated polymers.

Long non-coding RNA expression profiles predict clinical phenotypes in glioma

Glioma is the commonest form of primary brain tumor in adults with varying malignancy grades and histological subtypes. Long non-coding RNAs (lncRNAs) are a novel class of non-protein-coding transcripts that have been shown to play important roles in cancer development. To discover novel tumor-related lncRNAs and determine their associations with glioma subtypes, we first applied a lncRNA classification pipeline to identify 1970 lncRNAs that were represented on Affymetrix HG-U133 Plus 2.0 array. We then analyzed the lncRNA expression patterns in a set of previously published glioma gene expression profiles of 268 clinical specimens, and identified sets of lncRNAs that were unique to different histological subtypes (astrocytic versus oligodendroglial tumors) and malignancy grades. These lncRNAs signatures were then subject to validation in another non-overlapping, independent data set that contained 157 glioma samples. This is the first reported study that correlates lncRNA expression profiles with malignancy grade and histological differentiation in human gliomas. Our findings indicate the potential roles of lncRNAs in the biogenesis, development and differentiation of gliomas, and provide an important platform for future studies.

Deregulation of ion channel and transporter encoding genes in pediatric gliomas

Brain tumors, including the majority gliomas, are the leading cause of cancer-related death in children. World Health Organization has divided pediatric brain tumors into different grades and, based upon cDNA microarray data identifying gene expression profiles (GEPs), it has become evident in the last decade that the various grades involve different types of genetic alterations. However, it is not known whether ion channel and transporter genes, intimately involved in brain functioning, are associated with such GEPs. We determined the GEPs in an available cohort of 10 pediatric brain tumors initially by comparing the data obtained from four primary tumor samples and corresponding short-term cultures. The correspondence between the two types of samples was statistically significant. We then performed bioinformatic analyses on those samples (a total of nine) which corresponded to tumors of glial origin, either tissues or cell cultures, depending on the best "RNA integrity number." We used R software to evaluate the genes which were differentially expressed (DE) in gliomas compared with normal brain. Applying a p-value below 0.01 and fold change ≥4, led to identification of 2284 DE genes. Through a Functional Annotation Analysis (FAA) using the NIH-DAVID software, the DE genes turned out to be associated mainly with: immune/inflammatory response, cell proliferation and survival, cell adhesion and motility, neuronal phenotype, and ion transport. We have shown that GEPs of pediatric brain tumors can be studied using either primary tumor samples or short-term cultures with similar results. From FAA, we concluded that, among DE genes, pediatric gliomas show a strong deregulation of genes related to ion channels and transporters.

Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme

Background

Despite recent discoveries of new molecular targets and pathways, the search for an effective therapy for Glioblastoma Multiforme (GBM) continues. A newly emerged field, radiogenomics, links gene expression profiles with MRI phenotypes. MRI-FLAIR is a noninvasive diagnostic modality and was previously found to correlate with cellular invasion in GBM. Thus, our radiogenomic screen has the potential to reveal novel molecular determinants of invasion. Here, we present the first comprehensive radiogenomic analysis using quantitative MRI volumetrics and large-scale gene- and microRNA expression profiling in GBM.

Methods

Based on The Cancer Genome Atlas (TCGA), discovery and validation sets with gene, microRNA, and quantitative MR-imaging data were created. Top concordant genes and microRNAs correlated with high FLAIR volumes from both sets were further characterized by Kaplan Meier survival statistics, microRNA-gene correlation analyses, and GBM molecular subtype-specific distribution.

Results

The top upregulated gene in both the discovery (4 fold) and validation (11 fold) sets was PERIOSTIN (POSTN). The top downregulated microRNA in both sets was miR-219, which is predicted to bind to POSTN. Kaplan Meier analysis demonstrated that above median expression of POSTN resulted in significantly decreased survival and shorter time to disease progression (P<0.001). High POSTN and low miR-219 expression were significantly associated with the mesenchymal GBM subtype (P<0.0001).

Conclusion

Here, we propose a novel diagnostic method to screen for molecular cancer subtypes and genomic correlates of cellular invasion. Our findings also have potential therapeutic significance since successful molecular inhibition of invasion will improve therapy and patient survival in GBM.

Gene therapy and targeted toxins for glioma

The most common primary brain tumor in adults is glioblastoma. These tumors are highly invasive and aggressive with a mean survival time of 15-18 months from diagnosis to death. Current treatment modalities are unable to significantly prolong survival in patients diagnosed with glioblastoma. As such, glioma is an attractive target for developing novel therapeutic approaches utilizing gene therapy. This review will examine the available preclinical models for glioma including xenographs, syngeneic and genetic models. Several promising therapeutic targets are currently being pursued in pre-clinical investigations. These targets will be reviewed by mechanism of action, i.e., conditional cytotoxic, targeted toxins, oncolytic viruses, tumor suppressors/oncogenes, and immune stimulatory approaches. Preclinical gene therapy paradigms aim to determine which strategies will provide rapid tumor regression and long-term protection from recurrence. While a wide range of potential targets are being investigated preclinically, only the most efficacious are further transitioned into clinical trial paradigms. Clinical trials reported to date are summarized including results from conditionally cytotoxic, targeted toxins, oncolytic viruses and oncogene targeting approaches. Clinical trial results have not been as robust as preclinical models predicted; this could be due to the limitations of the GBM models employed. Once this is addressed, and we develop effective gene therapies in models that better replicate the clinical scenario, gene therapy will provide a powerful approach to treat and manage brain tumors.

A molecular screening approach to identify and characterize inhibitors of glioblastoma stem cells

Glioblastoma (GBM) is among the most lethal of all cancers. GBM consist of a heterogeneous population of tumor cells among which a tumor-initiating and treatment-resistant subpopulation, here termed GBM stem cells, have been identified as primary therapeutic targets. Here, we describe a high-throughput small molecule screening approach that enables the identification and characterization of chemical compounds that are effective against GBM stem cells. The paradigm uses a tissue culture model to enrich for GBM stem cells derived from human GBM resections and combines a phenotype-based screen with gene target-specific screens for compound identification. We used 31,624 small molecules from 7 chemical libraries that we characterized and ranked based on their effect on a panel of GBM stem cell-enriched cultures and their effect on the expression of a module of genes whose expression negatively correlates with clinical outcome: MELK, ASPM, TOP2A, and FOXM1b. Of the 11 compounds meeting criteria for exerting differential effects across cell types used, 4 compounds showed selectivity by inhibiting multiple GBM stem cells-enriched cultures compared with nonenriched cultures: emetine, n-arachidonoyl dopamine, n-oleoyldopamine (OLDA), and n-palmitoyl dopamine. ChemBridge compounds #5560509 and #5256360 inhibited the expression of the 4 mitotic module genes. OLDA, emetine, and compounds #5560509 and #5256360 were chosen for more detailed study and inhibited GBM stem cells in self-renewal assays in vitro and in a xenograft model in vivo. These studies show that our screening strategy provides potential candidates and a blueprint for lead compound identification in larger scale screens or screens involving other cancer types.

Microarray analysis in a cell death resistant glioma cell line to identify signaling pathways and novel genes controlling resistance and malignancy

Glioblastoma multiforme (GBM) is a lethal type of cancer mainly resistant to radio- and chemotherapy. Since the tumor suppressor p53 functions as a transcription factor regulating the expression of genes involved in growth inhibition, DNA repair and apoptosis, we previously assessed whether specific differences in the modulation of gene expression are responsible for the anti-tumor properties of a dominant positive p53, chimeric tumor suppressor (CTS)-1. CTS-1 is based on the sequence of p53 and designed to resist various mechanisms of inactivation which limit the activity of p53. To identify CTS-1-regulated cell death-inducing genes, we generated a CTS-1-resistant glioma cell line (229R). We used Affymetrix whole-genome microarray expression analysis to analyze alterations in gene expression and identified a variety of CTS-1 regulated genes involved in cancer-linked processes. 313 genes were differentially expressed in Adeno-CTS-1 (Ad-CTS-1)-infected and 700 genes in uninfected 229R cells compared to matching parental cells. Ingenuity Pathway Analysis (IPA) determined a variety of differentially expressed genes in Ad-CTS-1-infected cells that were members of the intracellular networks with central tumor-involved players such as nuclear factor kappa B (NF-κB), protein kinase B (PKB/AKT) or transforming growth factor beta (TGF-β). Differentially regulated genes include secreted factors as well as intracellular proteins and transcription factors regulating not only cell death, but also processes such as tumor cell motility and immunity. This work gives an overview of the pathways differentially regulated in the resistant versus parental glioma cells and might be helpful to identify candidate genes which could serve as targets to develop novel glioma specific therapy strategies.

DNA methylation-mediated silencing of nonsteroidal anti-inflammatory drug-activated gene (NAG-1/GDF15) in glioma cell lines

Nonsteroidal anti-inflammatory drug-activated gene, NAG-1, a transforming growth factor-β member, is involved in tumor progression and development. The association between NAG-1 expression and development and progression of glioma has not been well defined. Glioblastoma cell lines have lower basal expression of NAG-1 than other gliomas and normal astrocytes. Most primary human gliomas have very low levels of NAG-1 expression. NAG-1 basal expression appeared to inversely correlate with tumor grade in glioma. Aberrant promoter hypermethylation is a common mechanism for silencing of tumor suppressor genes in cancer cells. In glioblastoma cell lines, NAG-1 expression was increased by the demethylating agent, 5-aza-2'-deoxycytidine. To investigate whether the NAG-1 gene was silenced by hypermethylation in glioblastoma, we examined DNA methylation status using genomic bisulfite sequencing. The NAG-1 promoter was densely methylated in several glioblastoma cell lines as well as in primary oligodendroglioma tumor samples, which have low basal expression of NAG-1. DNA methylation at two specific sites (-53 and +55 CpG sites) in the NAG-1 promoter was strongly associated with low NAG-1 expression. The methylation of the NAG-1 promoter at the -53 site blocks Egr-1 binding and thereby suppresses Nag-1 induction. Treatment of cells with low basal NAG-1 expression with NAG-1 inducer also did not increase NAG-1. Incubation with a demethylation chemical increased Nag-1 basal expression and subsequent incubation with a NAG-1 inducer increased NAG-1 expression. We concluded from these data that methylation of specific promoter sequences causes transcriptional silencing of the NAG-1 locus in glioma and may ultimately contribute to tumor progression.

Genome-wide comparison of paired fresh frozen and formalin-fixed paraffin-embedded gliomas by custom BAC and oligonucleotide array comparative genomic hybridization: facilitating analysis of archival gliomas

Array comparative genomic hybridization (aCGH) is a powerful tool for detecting DNA copy number alterations (CNA). Because diffuse malignant gliomas are often sampled by small biopsies, formalin-fixed paraffin-embedded (FFPE) blocks are often the only tissue available for genetic analysis; FFPE tissues are also needed to study the intratumoral heterogeneity that characterizes these neoplasms. In this paper, we present a combination of evaluations and technical advances that provide strong support for the ready use of oligonucleotide aCGH on FFPE diffuse gliomas. We first compared aCGH using bacterial artificial chromosome (BAC) arrays in 45 paired frozen and FFPE gliomas, and demonstrate a high concordance rate between FFPE and frozen DNA in an individual clone-level analysis of sensitivity and specificity, assuring that under certain array conditions, frozen and FFPE DNA can perform nearly identically. However, because oligonucleotide arrays offer advantages to BAC arrays in genomic coverage and practical availability, we next developed a method of labeling DNA from FFPE tissue that allows efficient hybridization to oligonucleotide arrays. To demonstrate utility in FFPE tissues, we applied this approach to biphasic anaplastic oligoastrocytomas and demonstrate CNA differences between DNA obtained from the two components. Therefore, BAC and oligonucleotide aCGH can be sensitive and specific tools for detecting CNAs in FFPE DNA, and novel labeling techniques enable the routine use of oligonucleotide arrays for FFPE DNA. In combination, these advances should facilitate genome-wide analysis of rare, small and/or histologically heterogeneous gliomas from FFPE tissues.