GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets

Fibrinogen-like protein 2 in gastrointestinal stromal tumour

Gastrointestinal stromal tumour (GIST), the most common sarcoma of the gastrointestinal tract, can be treated effectively with tyrosine kinase inhibitors, such as imatinib. Cancer immune therapy has limited efficacy, and little is known about the immune suppressive factors in GISTs. Fibrinogen‐like protein 2 (FGL2) is expressed either as a membrane‐associated protein or as a secreted soluble protein that has immune suppressive functions. We found that GISTs expressed FGL2 mRNA highly compared to other types of cancer in a large human cancer transcriptome database. GIST expressed FGL2 frequently also when studied using immunohistochemistry in two large clinical series, where 333 (78%) out of the 425 GISTs were FGL2 positive. The interstitial cells of Cajal, from which GISTs may originate, expressed FGL2. FGL2 expression was associated with small GIST size, low mitotic counts and low tumour‐infiltrating lymphocyte (TIL) counts. Patients whose GIST expressed FGL2 had better recurrence‐free survival than patients whose GIST lacked expression. Imatinib upregulated FGL2 in GIST cell lines, and the patients with FGL2‐negative GIST appeared to benefit most from long duration of adjuvant imatinib. We conclude that GISTs express FGL2 frequently and that FGL2 expression is associated with low TIL counts and favourable survival outcomes.

Cellular and molecular outcomes of glutamine supplementation in the brain of succinic semialdehyde dehydrogenase-deficient mice

Succinic semialdehyde dehydrogenase deficiency (SSADHD) manifests with low levels of glutamine in the brain, suggesting that central glutamine deficiency contributes to pathogenesis. Recently, we attempted to rescue the disease phenotype of aldh5a1 -/- mice, a murine model of SSADHD with dietary glutamine supplementation. No clinical rescue and no central glutamine improvement were observed. Here, we report the results of follow-up studies of the cellular and molecular basis of the resistance of the brain to glutamine supplementation. We first determined if the expression of genes involved in glutamine metabolism was impacted by glutamine feeding. We then searched for changes of brain histology in response to glutamine supplementation, with a focus on astrocytes, known regulators of glutamine synthesis in the brain. Glutamine supplementation significantly modified the expression of glutaminase (gls) (0.6-fold down), glutamine synthetase (glul) (1.5-fold up), and glutamine transporters (solute carrier family 7, member 5 [slc7a5], 2.5-fold up; slc38a2, 0.6-fold down). The number of GLUL-labeled cells was greater in the glutamine-supplemented group than in controls (P < .05). Reactive astrogliosis, a hallmark of brain inflammation in SSADHD, was confirmed. We observed a 2-fold stronger astrocyte staining in mutants than in wild-type controls (optical density/cell were 1.8 ± 0.08 in aldh5a1 -/- and 0.99 ± 0.06 in aldh5a1 +/+ ; P < .0001), and a 3-fold higher expression of gfap and vimentin. However, glutamine supplementation did not improve the histological and molecular signature of astrogliosis. Thus, glutamine supplementation impacts genes implicated in central glutamine homeostasis without improving reactive astrogliosis. The mechanisms underlying glutamine deficiency and its contribution to SSADHD pathogenesis remain unknown and should be the focus of future investigations.

Identification of TYMS as a promoting factor of retroperitoneal liposarcoma progression: Bioinformatics analysis and biological evidence

Retroperitoneal liposarcoma (RLPS) is one of the most common types of retroperitoneal sarcomas, and has a high recurrence rate. There is an urgent need to further explore its pathogenesis and develop more effective treatment strategies. The aim of the present study was to identify potential driver genes of RLPS through bioinformatics analysis and molecular biology to elucidate potential targets that are suitable for further analysis for the treatment of RLPS. Differentially expressed genes (DEGs) between liposarcoma and normal fatty (NF) tissues were identified based on microarray data through bioinformatics analysis, and thymidylate synthase (TYMS) was selected from the DEGs, based on high content screening (HCS). TYMS expression was evaluated in RLPS tumor tissues and cell lines. A total of 21 RLPS tissues and 10 NF frozen tissues were used for reverse transcription-quantitative PCR, and 47 RLPS formalin-fixed specimens were used for immunohistochemical analysis. The effect of TYMS downregulation on cell proliferation, apoptosis, cell cycle progression, and cell migration and invasion were evaluated using lentivirus-mediated short hairpin RNA. The underlying mechanisms of TYMS in RLPS were examined by protein microarray and verified by western blotting. A total of 855 DEGs were identified. TYMS knockdown had the most notable effect on the proliferative capacity of RLPS cells according to the HCS results. TYMS mRNA expression levels were higher in RLPS tissues compared with NF tissues (P<0.001). TYMS expression was higher in high-grade RLPS tissues compared with low-grade RLPS tissues (P=0.003). The patients with positive TYMS expression had a worse overall survival (OS) and disease-free survival (DFS) compared with the patients with negative TYMS expression (OS, P=0.024; DFS, P=0.030). The knockdown of TYMS reduced proliferation, promoted apoptosis, facilitated cell cycle progression from G₁ to S phase, and reduced cell migration and invasion of RLPS cells. Protein microarray analysis and western blotting showed that the Janus Kinase/Signal transducers and activators of transcription pathway was downregulated following TYMS knockdown. In conclusion, TYMS expression is upregulated in RLPS tissues, and downregulation of TYMS reduces RLPS progression.

aTEMPO: Pathway-Specific Temporal Anomalies for Precision Therapeutics

Dynamic processes are inherently important in disease, and identifying disease-related disruptions of normal dynamic processes can provide information about individual patients. We have previously characterized individuals' disease states via pathway-based anomalies in expression data, and we have identified disease-correlated disruption of predictable dynamic patterns by modeling a virtual time series in static data. Here we combine the two approaches, using an anomaly detection model and virtual time series to identify anomalous temporal processes in specific disease states. We demonstrate that this approach can informatively characterize individual patients, suggesting personalized therapeutic approaches.

Clinical relevance of integrin alpha 4 in gastrointestinal stromal tumours

The molecular mechanisms for the dissemination and metastasis of gastrointestinal stromal tumours (GIST) are incompletely understood. The purpose of the study was to investigate the clinical relevance of integrin alpha 4 (ITGA4) expression in GIST. GIST transcriptomes were first compared with transcriptomes of other types of cancer and histologically normal gastrointestinal tract tissue in the MediSapiens in silico database. ITGA4 was identified as an unusually highly expressed gene in GIST. Therefore, the effects of ITGA4 knock‐down and selective integrin alpha 4 beta 1 (VLA‐4) inhibitors on tumour cell proliferation and invasion were investigated in three GIST cell lines. In addition, the prognostic role of ITGA4 expression in cancer cells was investigated in a series of 147 GIST patients with immunohistochemistry. Inhibition of ITGA4‐related signalling decreased GIST cell invasion in all investigated GIST cell lines. ITGA4 protein was expressed in 62 (42.2%) of the 147 GISTs examined, and expression was significantly associated with distant metastases during the course of the disease and several adverse prognostic features. Patients whose GIST expressed strongly ITGA4 had unfavourable GIST‐specific survival and overall survival compared to patients with low or no ITGA4 expression. Taken together, ITGA4 is an important integrin in the molecular pathogenesis of GIST and may influence their clinical behaviour.

KAOS: a new automated computational method for the identification of overexpressed genes

Background

Kinase over-expression and activation as a consequence of gene amplification or gene fusion events is a well-known mechanism of tumorigenesis. The search for novel rearrangements of kinases or other druggable genes may contribute to understanding the biology of cancerogenesis, as well as lead to the identification of new candidate targets for drug discovery. However this requires the ability to query large datasets to identify rare events occurring in very small fractions (1–3 %) of different tumor subtypes. This task is different from what is normally done by conventional tools that are able to find genes differentially expressed between two experimental conditions.

Results

We propose a computational method aimed at the automatic identification of genes which are selectively over-expressed in a very small fraction of samples within a specific tissue. The method does not require a healthy counterpart or a reference sample for the analysis and can be therefore applied also to transcriptional data generated from cell lines. In our implementation the tool can use gene-expression data from microarray experiments, as well as data generated by RNASeq technologies.

Conclusions

The method was implemented as a publicly available, user-friendly tool called KAOS (Kinase Automatic Outliers Search). The tool enables the automatic execution of iterative searches for the identification of extreme outliers and for the graphical visualization of the results. Filters can be applied to select the most significant outliers. The performance of the tool was evaluated using a synthetic dataset and compared to state-of-the-art tools. KAOS performs particularly well in detecting genes that are overexpressed in few samples or when an extreme outlier stands out on a high variable expression background.

To validate the method on real case studies, we used publicly available tumor cell line microarray data, and we were able to identify genes which are known to be overexpressed in specific samples, as well as novel ones.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1188-1) contains supplementary material, which is available to authorized users.

Prognostic outlier genes for enhanced prostate cancer treatment

AIM

To review the current landscape of outlier genes in the field of prostate cancer.

METHODS

A comprehensive review was performed.

RESULTS

Prostate cancer continues to be a significant worldwide health issue. In the era of personalized medicine, more emphasis is being placed on the ability to determine the timing, intensity and type of treatment, according to each patient's unique disease. Several commercial tests are available to determine the risk of aggressive prostate cancer based on genomic biomarkers and gene expression. Outlier genes represent a form of cancer classification that focuses on bimodal expression of a gene in a specific subset of patients. Outlier genes identified in prostate cancer include TMPRSS2-ERG, SPINK1, ScHLAP1, NVL, SMC4 and SQLE.

CONCLUSION

Classifying patient prostate cancers by outlier genes may allow for individualized cancer therapies and improved cancer therapy outcomes.

Outlier analysis of functional genomic profiles enriches for oncology targets and enables precision medicine

Background

Genome-scale functional genomic screens across large cell line panels provide a rich resource for discovering tumor vulnerabilities that can lead to the next generation of targeted therapies. Their data analysis typically has focused on identifying genes whose knockdown enhances response in various pre-defined genetic contexts, which are limited by biological complexities as well as the incompleteness of our knowledge. We thus introduce a complementary data mining strategy to identify genes with exceptional sensitivity in subsets, or outlier groups, of cell lines, allowing an unbiased analysis without any a priori assumption about the underlying biology of dependency.

Results

Genes with outlier features are strongly and specifically enriched with those known to be associated with cancer and relevant biological processes, despite no a priori knowledge being used to drive the analysis. Identification of exceptional responders (outliers) may not lead only to new candidates for therapeutic intervention, but also tumor indications and response biomarkers for companion precision medicine strategies. Several tumor suppressors have an outlier sensitivity pattern, supporting and generalizing the notion that tumor suppressors can play context-dependent oncogenic roles.

Conclusions

The novel application of outlier analysis described here demonstrates a systematic and data-driven analytical strategy to decipher large-scale functional genomic data for oncology target and precision medicine discoveries.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2807-y) contains supplementary material, which is available to authorized users.

REG4 Is Highly Expressed in Mucinous Ovarian Cancer: A Potential Novel Serum Biomarker

Preoperative diagnostics of ovarian neoplasms rely on ultrasound imaging and the serum biomarkers CA125 and HE4. However, these markers may be elevated in non-neoplastic conditions and may fail to identify most non-serous epithelial cancer subtypes. The objective of this study was to identify histotype-specific serum biomarkers for mucinous ovarian cancer. The candidate genes with mucinous histotype specific expression profile were identified from publicly available gene-expression databases and further in silico data mining was performed utilizing the MediSapiens database. Candidate biomarker validation was done using qRT-PCR, western blotting and immunohistochemical staining of tumor tissue microarrays. The expression level of the candidate gene in serum was compared to the serum CA125 and HE4 levels in a patient cohort of prospectively collected advanced ovarian cancer. Database searches identified REG4 as a potential biomarker with specificity for the mucinous ovarian cancer subtype. The specific expression within epithelial ovarian tumors was further confirmed by mRNA analysis. Immunohistochemical staining of ovarian tumor tissue arrays showed distinctive cytoplasmic expression pattern only in mucinous carcinomas and suggested differential expression between benign and malignant mucinous neoplasms. Finally, an ELISA based serum biomarker assay demonstrated increased expression only in patients with mucinous ovarian cancer. This study identifies REG4 as a potential serum biomarker for histotype-specific detection of mucinous ovarian cancer and suggests serum REG4 measurement as a non-invasive diagnostic tool for postoperative follow-up of patients with mucinous ovarian cancer.

CSAX: Characterizing Systematic Anomalies in eXpression Data

Methods for translating gene expression signatures into clinically relevant information have typically relied upon having many samples from patients with similar molecular phenotypes. Here, we address the question of what can be done when it is relatively easy to obtain healthy patient samples, but when abnormalities corresponding to disease states may be rare and one-of-a-kind. The associated computational challenge, anomaly detection, is a well-studied machine-learning problem. However, due to the dimensionality and variability of expression data, existing methods based on feature space analysis or individual anomalously expressed genes are insufficient. We present a novel approach, CSAX, that identifies pathways in an individual sample in which the normal expression relationships are disrupted. To evaluate our approach, we have compiled and released a compendium of public expression data sets, reformulated to create a test bed for anomaly detection. We demonstrate the accuracy of CSAX on the data sets in our compendium, compare it to other leading methods, and show that CSAX aids in both identifying anomalies and explaining their underlying biology. We describe an approach to characterizing the difficulty of specific expression anomaly detection tasks. We then illustrate CSAX's value in two developmental case studies. Confirming prior hypotheses, CSAX highlights disruption of platelet activation pathways in a neonate with retinopathy of prematurity and identifies, for the first time, dysregulated oxidative stress response in second trimester amniotic fluid of fetuses with obese mothers. Our approach provides an important step toward identification of individual disease patterns in the era of precision medicine.

ZODET: software for the identification, analysis and visualisation of outlier genes in microarray expression data

SUMMARY

Complex human diseases can show significant heterogeneity between patients with the same phenotypic disorder. An outlier detection strategy was developed to identify variants at the level of gene transcription that are of potential biological and phenotypic importance. Here we describe a graphical software package (z-score outlier detection (ZODET)) that enables identification and visualisation of gross abnormalities in gene expression (outliers) in individuals, using whole genome microarray data. Mean and standard deviation of expression in a healthy control cohort is used to detect both over and under-expressed probes in individual test subjects. We compared the potential of ZODET to detect outlier genes in gene expression datasets with a previously described statistical method, gene tissue index (GTI), using a simulated expression dataset and a publicly available monocyte-derived macrophage microarray dataset. Taken together, these results support ZODET as a novel approach to identify outlier genes of potential pathogenic relevance in complex human diseases. The algorithm is implemented using R packages and Java.

AVAILABILITY

The software is freely available from http://www.ucl.ac.uk/medicine/molecular-medicine/publications/microarray-outlier-analysis.

Analysis considerations for utilizing RNA-Seq to characterize the brain transcriptome

RNA-Seq allows one to examine only gene expression as well as expression of noncoding RNAs, alternative splicing, and allele-specific expression. With this increased sensitivity and dynamic range, there are computational and statistical considerations that need to be contemplated, which are highly dependent on the biological question being asked. We highlight these to provide an overview of their importance and the impact they can have on downstream interpretation of the brain transcriptome.

Comparison of methods to identify aberrant expression patterns in individual patients: augmenting our toolkit for precision medicine

BACKGROUND

Patient-specific aberrant expression patterns in conjunction with functional screening assays can guide elucidation of the cancer genome architecture and identification of therapeutic targets. Since most statistical methods for expression analysis are focused on differences between experimental groups, the performance of approaches for patient-specific expression analyses are currently less well characterized. A comparison of methods for the identification of genes that are dysregulated relative to a single sample in a given set of experimental samples, to our knowledge, has not been performed.

METHODS

We systematically evaluated several methods including variations on the nearest neighbor based outlying degree method, as well as the Zscore and a robust variant for their suitability to detect patient-specific events. The methods were assessed using both simulations and expression data from a cohort of pediatric acute B lymphoblastic leukemia patients.

RESULTS

We first assessed power and false discovery rates using simulations and found that even under optimal conditions, high effect sizes (>4 unit differences) were necessary to have acceptable power for any method (>0.9) though high false discovery rates (>0.1) were pervasive across simulation conditions. Next we introduced a technical factor into the simulation and found that performance was reduced for all methods and that using weights with the outlying degree could provide performance gains depending on the number of samples and genes affected by the technical factor. In our use case that highlights the integration of functional assays and aberrant expression in a patient cohort (the identification of gene dysregulation events associated with the targets from a siRNA screen), we demonstrated that both the outlying degree and the Zscore can successfully identify genes dysregulated in one patient sample. However, only the outlying degree can identify genes dysregulated across several patient samples.

CONCLUSION

Our results show that outlying degree methods may be a useful alternative to the Zscore or Rscore in a personalized medicine context especially in small to medium sized (between 10 and 50 samples) expression datasets with moderate to high sample-to-sample variability. From these results we provide guidelines for detection of aberrant expression in a precision medicine context.

Illuminating radiogenomic characteristics of glioblastoma multiforme through integration of MR imaging, messenger RNA expression, and DNA copy number variation

PURPOSE

To perform a multilevel radiogenomics study to elucidate the glioblastoma multiforme (GBM) magnetic resonance (MR) imaging radiogenomic signatures resulting from changes in messenger RNA (mRNA) expression and DNA copy number variation (CNV).

MATERIALS AND METHODS

Radiogenomic analysis was performed at MR imaging in 23 patients with GBM in this retrospective institutional review board-approved HIPAA-compliant study. Six MR imaging features-contrast enhancement, necrosis, contrast-to-necrosis ratio, infiltrative versus edematous T2 abnormality, mass effect, and subventricular zone (SVZ) involvement-were independently evaluated and correlated with matched genomic profiles (global mRNA expression and DNA copy number profiles) in a significant manner that also accounted for multiple hypothesis testing by using gene set enrichment analysis (GSEA), resampling statistics, and analysis of variance to gain further insight into the radiogenomic signatures in patients with GBM.

RESULTS

GSEA was used to identify various oncogenic pathways with MR imaging features. Correlations between 34 gene loci were identified that showed concordant variations in gene dose and mRNA expression, resulting in an MR imaging, mRNA, and CNV radiogenomic association map for GBM. A few of the identified gene-to-trait associations include association of the contrast-to-necrosis ratio with KLK3 and RUNX3; association of SVZ involvement with Ras oncogene family members, such as RAP2A, and the metabolic enzyme TYMS; and association of vasogenic edema with the oncogene FOXP1 and PIK3IP1, which is a member of the PI3K signaling network.

CONCLUSION

Construction of an MR imaging, mRNA, and CNV radiogenomic association map has led to identification of MR traits that are associated with some known high-grade glioma biomarkers and association with genomic biomarkers that have been identified for other malignancies but not GBM. Thus, the traits and genes identified on this map highlight new candidate radiogenomic biomarkers for further evaluation in future studies.

Pathway-based outlier method reveals heterogeneous genomic structure of autism in blood transcriptome

Background

Decades of research strongly suggest that the genetic etiology of autism spectrum disorders (ASDs) is heterogeneous. However, most published studies focus on group differences between cases and controls. In contrast, we hypothesized that the heterogeneity of the disorder could be characterized by identifying pathways for which individuals are outliers rather than pathways representative of shared group differences of the ASD diagnosis.

Methods

Two previously published blood gene expression data sets – the Translational Genetics Research Institute (TGen) dataset (70 cases and 60 unrelated controls) and the Simons Simplex Consortium (Simons) dataset (221 probands and 191 unaffected family members) – were analyzed. All individuals of each dataset were projected to biological pathways, and each sample’s Mahalanobis distance from a pooled centroid was calculated to compare the number of case and control outliers for each pathway.

Results

Analysis of a set of blood gene expression profiles from 70 ASD and 60 unrelated controls revealed three pathways whose outliers were significantly overrepresented in the ASD cases: neuron development including axonogenesis and neurite development (29% of ASD, 3% of control), nitric oxide signaling (29%, 3%), and skeletal development (27%, 3%). Overall, 50% of cases and 8% of controls were outliers in one of these three pathways, which could not be identified using group comparison or gene-level outlier methods. In an independently collected data set consisting of 221 ASD and 191 unaffected family members, outliers in the neurogenesis pathway were heavily biased towards cases (20.8% of ASD, 12.0% of control). Interestingly, neurogenesis outliers were more common among unaffected family members (Simons) than unrelated controls (TGen), but the statistical significance of this effect was marginal (Chi squared P < 0.09).

Conclusions

Unlike group difference approaches, our analysis identified the samples within the case and control groups that manifested each expression signal, and showed that outlier groups were distinct for each implicated pathway. Moreover, our results suggest that by seeking heterogeneity, pathway-based outlier analysis can reveal expression signals that are not apparent when considering only shared group differences.

A variational Bayes beta mixture model for feature selection in DNA methylation studies

An increasing number of studies are using beadarrays to measure DNA methylation on a genome-wide basis. The purpose is to identify novel biomarkers in a wide range of complex genetic diseases including cancer. A common difficulty encountered in these studies is distinguishing true biomarkers from false positives. While statistical methods aimed at improving the feature selection step have been developed for gene expression, relatively few methods have been adapted to DNA methylation data, which is naturally beta-distributed. Here we explore and propose an innovative application of a recently developed variational Bayesian beta-mixture model (VBBMM) to the feature selection problem in the context of DNA methylation data generated from a highly popular beadarray technology. We demonstrate that VBBMM offers significant improvements in inference and feature selection in this type of data compared to an Expectation-Maximization (EM) algorithm, at a significantly reduced computational cost. We further demonstrate the added value of VBBMM as a feature selection and prioritization step in the context of identifying prognostic markers in breast cancer. A variational Bayesian approach to feature selection of DNA methylation profiles should thus be of value to any study undergoing large-scale DNA methylation profiling in search of novel biomarkers.

Systemic analysis of gene expression profiles identifies ErbB3 as a potential drug target in pediatric alveolar rhabdomyosarcoma

Pediatric sarcomas, including rhabdomyosarcomas, Ewing's sarcoma, and osteosarcoma, are aggressive tumors with poor survival rates. To overcome problems associated with nonselectivity of the current therapeutic approaches, targeted therapeutics have been developed. Currently, an increasing number of such drugs are used for treating malignancies of adult patients but little is known about their effects in pediatric patients. We analyzed expression of 24 clinically approved target genes in a wide variety of pediatric normal and malignant tissues using a novel high-throughput systems biology approach. Analysis of the Genesapiens database of human transcriptomes demonstrated statistically significant up-regulation of VEGFC and EPHA2 in Ewing's sarcoma, and ERBB3 in alveolar rhabdomyosarcomas. In silico data for ERBB3 was validated by demonstrating ErbB3 protein expression in pediatric rhabdomyosarcoma in vitro and in vivo. ERBB3 overexpression promoted whereas ERBB3-targeted siRNA suppressed rhabdomyosarcoma cell gowth, indicating a functional role for ErbB3 signaling in rhabdomyosarcoma. These data suggest that drugs targeting ErbB3, EphA2 or VEGF-C could be further tested as therapeutic targets for pediatric sarcomas.

Detecting cancer outlier genes with potential rearrangement using gene expression data and biological networks

Gene alterations are a major component of the landscape of tumor genomes. To assess the significance of these alterations in the development of prostate cancer, it is necessary to identify these alterations and analyze them from systems biology perspective. Here, we present a new method (EigFusion) for predicting outlier genes with potential gene rearrangement. EigFusion demonstrated excellent performance in identifying outlier genes with potential rearrangement by testing it to synthetic and real data to evaluate performance. EigFusion was able to identify previously unrecognized genes such as FABP5 and KCNH8 and confirmed their association with primary and metastatic prostate samples while confirmed the metastatic specificity for other genes such as PAH, TOP2A, and SPINK1. We performed protein network based approaches to analyze the network context of potential rearranged genes. Functional gene rearrangement Modules are constructed by integrating functional protein networks. Rearranged genes showed to be highly connected to well-known altered genes in cancer such as AR, RB1, MYC, and BRCA1. Finally, using clinical outcome data of prostate cancer patients, potential rearranged genes demonstrated significant association with prostate cancer specific death.

HES6 gene is selectively overexpressed in glioma and represents an important transcriptional regulator of glioma proliferation

Malignant glioma is the most common brain tumor with 16,000 new cases diagnosed annually in the United States. We performed a systematic large-scale transcriptomics data mining study of 9783 tissue samples from the GeneSapiens database to systematically identify genes that are most glioma-specific. We searched for genes that were highly expressed in 322 glioblastoma multiforme tissue samples and 66 anaplastic astrocytomas as compared with 425 samples from histologically normal central nervous system. Transcription cofactor HES6 (hairy and enhancer of split 6) emerged as the most glioma-specific gene. Immunostaining of a tissue microarray showed HES6 expression in 335 (98.8%) out of the 339 glioma samples. HES6 was expressed in endothelial cells of the normal brain and glioma tissue. Recurrent grade 2 astrocytomas and grade 2 or 3 oligodendrogliomas showed higher levels of HES6 immunoreactivity than the corresponding primary tumors. High HES6 mRNA expression correlated with the proneural subtype that generally has a favorable outcome but is prone to recur. Functional studies suggested an important role for HES6 in supporting survival of glioma cells, as evidenced by reduction of cancer cell proliferation and migration after HES6 silencing. The biological role and consequences of HES6 silencing and overexpression was explored with genome-wide analyses, which implicated a role for HES6 in p53, c-myc and nuclear factor-κB transcriptional networks. We conclude that HES6 is important for glioma cell proliferation and migration, and may have a role in angiogenesis.

A cell spot microarray method for production of high density siRNA transfection microarrays

BACKGROUND

High-throughput RNAi screening is widely applied in biological research, but remains expensive, infrastructure-intensive and conversion of many assays to HTS applications in microplate format is not feasible.

RESULTS

Here, we describe the optimization of a miniaturized cell spot microarray (CSMA) method, which facilitates utilization of the transfection microarray technique for disparate RNAi analyses. To promote rapid adaptation of the method, the concept has been tested with a panel of 92 adherent cell types, including primary human cells. We demonstrate the method in the systematic screening of 492 GPCR coding genes for impact on growth and survival of cultured human prostate cancer cells.

CONCLUSIONS

The CSMA method facilitates reproducible preparation of highly parallel cell microarrays for large-scale gene knockdown analyses. This will be critical towards expanding the cell based functional genetic screens to include more RNAi constructs, allow combinatorial RNAi analyses, multi-parametric phenotypic readouts or comparative analysis of many different cell types.