Cancer Genomics Browser Research Articles

Identification of a key glioblastoma candidate gene, FUBP3, based on weighted gene co-expression network analysis

BackgroundGlioblastoma multiforme (GBM) is the most common aggressive malignant brain tumor. However, the molecular mechanism of glioblastoma formation is still poorly understood. To identify candidate genes that may be connected to glioma growth and development, weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between gene sets and clinical characteristics. We also explored the function of the key candidate gene.MethodsTwo GBM datasets were selected from GEO Datasets. The R language was used to identify differentially expressed genes. WGCNA was performed to construct a gene co-expression network in the GEO glioblastoma samples. A custom Venn diagram website was used to find the intersecting genes. The GEPIA website was applied for survival analysis to determine the significant gene, FUBP3. OS, DSS, and PFI analyses, based on the UCSC Cancer Genomics Browser, were performed to verify the significance of FUBP3. Immunohistochemistry was performed to evaluate the expression of FUBP3 in glioblastoma and adjacent normal tissue. KEGG and GO enrichment analyses were used to reveal possible functions of FUBP3. Microenvironment analysis was used to explore the relationship between FUBP3 and immune infiltration. Immunohistochemistry was performed to verify the results of the microenvironment analysis.ResultsGSE70231 and GSE108474 were selected from GEO Datasets, then 715 and 694 differentially expressed genes (DEGs) from GSE70231 and GSE108474, respectively, were identified. We then performed weighted gene co-expression network analysis (WGCNA) and identified the most downregulated gene modules of GSE70231 and GSE108474, and 659 and 3915 module genes from GSE70231 and GSE108474, respectively, were selected. Five intersection genes (FUBP3, DAD1, CLIC1, ABR, and DNM1) were calculated by Venn diagram. FUBP3 was then identified as the only significant gene by survival analysis using the GEPIA website. OS, DSS, and PFI analyses verified the significance of FUBP3. Immunohistochemical analysis revealed FUBP3 expression in GBM and adjacent normal tissue. KEGG and GO analyses uncovered the possible function of FUBP3 in GBM. Tumor microenvironment analysis showed that FUBP3 may be connected to immune infiltration, and immunohistochemistry identified a positive correlation between immune cells (CD4 + T cells, CD8 + T cells, and macrophages) and FUBP3.ConclusionFUBP3 is associated with immune surveillance in GBM, indicating that it has a great impact on GBM development and progression. Therefore, interventions involving FUBP3 and its regulatory pathway may be a new approach for GBM treatment.

Screening and identification of key biomarkers of papillary renal cell carcinoma by bioinformatic analysis

BackgroundPapillary renal cell carcinoma (PRCC) is the most common type of renal cell carcinoma after clear cell renal cell carcinoma (ccRCC). Its pathological classification is controversial, and its molecular mechanism is poorly understood. Therefore, the identification of key genes and their biological pathways is of great significance to elucidate the molecular mechanisms of PRCC occurrence and progression.MethodsThe PRCC-related datasets GSE7023, GSE48352 and GSE15641 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and gene ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Cytoscape and STRING were used to construct the protein-protein interaction network (PPI) and perform module analysis to identify hub genes and key pathways. A heatmap of hub genes was constructed using the UCSC cancer genomics browser. Overall survival and recurrence-free survival of patients stratified by the expression levels of hub genes were analysed using Kaplan-Meier Plotter. The online database UALCAN was applied to analyse gene expression based on tissue type, stage, subtype and race.ResultsA total of 214 DEGs, specifically, 205 downregulated genes and 9 upregulated genes, were identified. The DEGs were mainly enriched in angiogenesis, kidney development, oxidation-reduction process, metabolic pathways, etc. The 17 hub genes identified were mainly enriched in the biological processes of angiogenesis, cell adhesion, platelet degranulation, and leukocyte transendothelial migration. Survival analysis showed that EGF, KDR, CXCL12, REN, PECAM1, CDH5, THY1, WT1, PLAU and DCN might be related to the carcinogenesis, metastasis or recurrence of PRCC. UALCAN analysis showed that low expression of PECAM1 and PLAU in PRCC tissues was related to stage, subtype and race.ConclusionsThe DEGs and hub genes identified in the present study provide insight into the specific molecular mechanisms of PRCC occurrence and development and may be potential molecular markers and therapeutic targets for the accurate classification and efficient diagnosis and treatment of PRCC.

Exploring microRNA target genes and identifying hub genes in bladder cancer based on bioinformatic analysis

BackgroundBladder cancer (BC) is the second most frequent malignancy of the urinary system. The aim of this study was to identify key microRNAs (miRNAs) and hub genes associated with BC as well as analyse their targeted relationships.MethodsAccording to the microRNA dataset GSE112264 and gene microarray dataset GSE52519, differentially expressed microRNAs (DEMs) and differentially expressed genes (DEGs) were obtained using the R limma software package. The FunRich software database was used to predict the miRNA-targeted genes. The overlapping common genes (OCGs) between miRNA-targeted genes and DEGs were screened to construct the PPI network. Then, gene ontology (GO) analysis was performed through the “cluster Profiler” and “org.Hs.eg.db” R packages. The differential expression analysis and hierarchical clustering of these hub genes were analysed through the GEPIA and UCSC Cancer Genomics Browser databases, respectively. KEGG pathway enrichment analyses of hub genes were performed through gene set enrichment analysis (GSEA).ResultsA total of 12 DEMs and 10 hub genes were identified. Differential expression analysis of the hub genes using the GEPIA database was consistent with the results for the UCSC Cancer Genomics Browser database. The results indicated that these hub genes were oncogenes, but VCL, TPM2, and TPM1 were tumour suppressor genes. The GSEA also showed that hub genes were most enriched in those pathways that were closely associated with tumour proliferation and apoptosis.ConclusionsIn this study, we built a miRNA-mRNA regulatory targeted network, which explores an understanding of the pathogenesis of cancer development and provides key evidence for novel targeted treatments for BC.

Screening and identification of hub genes in bladder cancer by bioinformatics analysis and KIF11 is a potential prognostic biomarker.

Bladder cancer (BC) is the ninth most common lethal malignancy worldwide. Great efforts have been devoted to clarify the pathogenesis of BC, but the underlying molecular mechanisms remain unclear. To screen for the genes associated with the progression and carcinogenesis of BC, three datasets were obtained from the Gene Expression Omnibus. A total of 37 tumor and 16 non-cancerous samples were analyzed to identify differentially expressed genes (DEGs). Subsequently, 141 genes were identified, including 55 upregulated and 86 downregulated genes. The protein-protein interaction network was established using the Search Tool for Retrieval of Interacting Genes database. Hub gene identification and module analysis were performed using Cytoscape software. Hierarchical clustering of hub genes was conducted using the University of California, Santa Cruz Cancer Genomics Browser. Among the hub genes, kinesin family member 11 (KIF11) was identified as one of the most significant prognostic biomarkers among all the candidates. The Kaplan Meier Plotter database was used for survival analysis of KIF11. The expression profile of KIF11 was analyzed using the ONCOMINE database. The expression levels of KIF11 in BC samples and bladder cells were measured using reverse transcription-quantitative pCR, immunohistochemistry and western blotting. In summary, KIF11 was significantly upregulated in BC and might act as a potential prognostic biomarker. The present identification of DEGs and hub genes in BC may provide novel insight for investigating the molecular mechanisms of BC.

Overexpression of RPN2 suppresses radiosensitivity of glioma cells by activating STAT3 signal transduction

BackgroundRadiation therapy is the primary method of treatment for glioblastoma (GBM). Therefore, the suppression of radioresistance in GBM cells is of enormous significance. Ribophorin II (RPN2), a protein component of an N-oligosaccharyl transferase complex, has been associated with chemotherapy drug resistance in multiple cancers, including GBM. However, it remains unclear whether this also plays a role in radiation therapy resistance in GBM.MethodsWe conducted a bioinformatic analysis of RPN2 expression using the UCSC Cancer Genomics Browser and GEPIA database and performed an immunohistochemical assessment of RPN2 expression in biopsy specimens from 34 GBM patients who had received radiation-based therapy. We also studied the expression and function of RPN2 in radiation-resistant GBM cells.ResultsWe found that RPN2 expression was upregulated in GBM tumors and correlated with poor survival. The expression of RPN2 was also higher in GBM patients with tumor recurrence, who were classified to be resistant to radiation therapy. In the radiation-resistant GBM cells, the expression of RPN2 was also higher than in the parental cells. Depletion of RPN2 in resistant cells can sensitize these cells to radiation-induced apoptosis, and overexpression of RPN2 had the reverse effect. Myeloid cell leukemia 1 (MCL1) was found to be the downstream target of RPN2, and contributed to radiation resistance in GBM cells. Furthermore, STAT3 was found to be the regulator of MCL1, which can be activated by RPN2 dysregulation.ConclusionOur study has revealed a novel function of RPN2 in radiation-resistant GBM, and has shown that MCL1 depletion or suppression could be a promising method of therapy to overcome the resistance promoted by RPN2 dysregulation.

Identification of Flap endonuclease 1 as a potential core gene in hepatocellular carcinoma by integrated bioinformatics analysis.

Hepatocellular carcinoma (HCC) is a common yet deadly form of malignant cancer. However, the specific mechanisms involved in HCC diagnosis have not yet fully elucidated. Herein, we screened four publically available Gene Expression Omnibus (GEO) expression profiles (GSE14520, GSE29721, GSE45267 and GSE60502), and used them to identify 409 differentially expressed genes (DEGs), including 142 and 267 up- and down-regulated genes, respectively. The DAVID database was used to look for functionally enriched pathways among DEGs, and the STRING database and Cytoscape platform were used to generate a protein-protein interaction (PPI) network for these DEGs. The cytoHubba plug-in was utilized to detect 185 hub genes, and three key clustering modules were constructed with the MCODE plug-in. Gene functional enrichment analyses of these three key clustering modules were further performed, and nine core genes including BIRC5, DLGAP5, DTL, FEN1, KIAA0101, KIF4A, MCM2, MKI67, and RFC4, were identified in the most critical cluster. Subsequently, the hierarchical clustering and expression of core genes in TCGA liver cancer tissues were analyzed using the UCSC Cancer Genomics Browser, and whether elevated core gene expression was linked to a poor prognosis in HCC patients was assessed using the GEPIA database. The PPI of the nine core genes revealed an interaction between FEN1, MCM2, RFC4, and BIRC5. Furthermore, the expression of FEN1 was positively correlated with that of three other core genes in TCGA liver cancer tissues. FEN1 expression in HCC and other tumor types was assessed with the FIREBROWSE and ONCOMINE databases, and results were verified in HCC samples and hepatoma cells. FEN1 levels were also positively correlated with tumor size, distant metastasis and vascular invasion. In conclusion, we identified nine core genes associated with HCC development, offering novel insight into HCC progression. In particular, the aberrantly elevated FEN1 may represent a potential biomarker for HCC diagnosis and treatment.

Identifying hepatocellular carcinoma-related hub genes by bioinformatics analysis and CYP2C8 is a potential prognostic biomarker

Hepatocellular carcinoma (HCC) is one type of the most common malignancies. However, the underlying molecular mechanisms involved in the development of HCC remain unknown. To identify the candidate genes in the progression of HCC, gene expression profiles GSE14520, GSE54236, GSE57957 and GSE64041 were downloaded from the Gene Expression Omnibus database (GEO). A total of 405 tumor and 399 para-carcinoma samples from patients with HCC were examined to identify the differentially expressed genes (DEGs), followed by function enrichment analyses including Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. A total of 78 DEGs were screened, including 62 downregulated genes and 16 upregulated genes. Subsequently, the protein-protein interaction network (PPI) was constructed using the Search Tool for Retrieval of Interacting Genes (STRING) database. The module analysis and Hub genes validation were performed using Cytoscape software. Hierarchical clustering of hub genes was evaluated using UCSC Cancer Genomics Browser. Survival analyses of Hub genes were performed using Kaplan Meier Plotter database. Genes specifically expressed in the liver were analyzed using GENEVESTIGATOR database. CYP2C8 was identified as one of the most promising molecules among all the candidate genes. The expression profile of CYP2C8 in HCC was analyzed using ONCOMINE and UALCAN database. The expression levels of CYP2C8 in HCC samples and hepatoma cells were verified using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry analysis. In summary, DEGs and hub genes were identified in the present study, which provides novel insight on the development of HCC. CYP2C8 was downregulated in HCC and could be a potential prognostic biomarker.

COMP Gene Coexpresses With EMT Genes and Is Associated With Poor Survival in Colon Cancer Patients

BackgroundAbout 1.2 million new cases of colon cancer (CC) and 0.6 million deaths are reported every year, establishing CC as an important contributor to worldwide cancer morbidity and mortality. Although the overall incidence and mortality of CC have declined over the past 3 decades, the number of early-onset colon cancer ([EOCC], patients <50 y old) continues to rise alarmingly. These young patients are often diagnosed at a more advanced stage and tend to have poor survival. Our recently published data showed that the cartilage oligomeric matrix protein (COMP) is overexpressed in early-onset colon cancer patients. COMP is also reported in several cancers to coexpress with epithelial-mesenchymal transition (EMT) transcription factors. Given the role of EMT in cancer metastasis and cell invasion, we assessed the correlation between COMP gene expression and EMT gene expression in CC, and COMP's relationship to patient survival. MethodsmRNA expression of COMP was compared to that of EMT markers using the UCSC Cancer Genomics Browser. Survival analysis was performed using the UCSC Xena Browser for cancer genomics. ResultsExpression analysis revealed coexpression of COMP with the EMT markers CDH2, FN1, VIM, TWIST1, TWIST2, SNAI1, SNAI2, ZEB1, ZEB2, POSTN, MMP2, MMP9, and COL1A1. Samples that were more mesenchymal had higher expression levels of COMP and EMT markers, thus suggesting a potential role of COMP in EMT. Patients with increased COMP expression presented with poorer overall survival compared to patients with no change or reduced COMP expression (P = 0.02). ConclusionsThese findings reveal COMP as a potential biomarker for CC especially in more aggressive CC and CC in young patients, with a likely role in EMT during tumor metastasis and invasion, and a contributing factor to patient survival.

MPTH-39. SEX-SPECIFIC ANALYSIS OF GBM TRANSCRIPTOMES REVEALS NEW PROGNOSTICALLY IMPORTANT MOLECULAR SUBTYPES

Molecular subtyping of GBM has advanced basic, translational and clinical neuro-oncology. As there are well-recognized sex differences in GBM incidence and outcome, we hypothesized that analysis of merged male and female datasets had previously masked important molecular features of GBM. To test this hypothesis we applied a JIVE algorithm to decompose male and female complete GBM transcriptome datasets (TCGA) accessed through the UCSC Cancer Genomics Browser, to joint and sex-specific components. We sought to determine whether there are shared or distinct GBM molecular subtypes in males and females. Using consensus hierarchical clustering independently on the sex-specific component from the JIVE, we defined five male and five female transcriptome clusters. Most clusters were a mix of samples previously assigned to the four Verhaak subtypes. While driver events were shared between the male and female clusters, they could be distinguished by their expression profiles, pathway analysis, and by their survival. Most notable were the differences between male and female subsets with the best survival. In female GBM, IDH1 mutation alone defined a single cluster with superior survival. Two male subtypes exhibited better survival, and this could not be completely explained by enrichment for IDH mutant tumors. Pathway analyses of genes differentiating a male or female cluster versus the others indicated that the male clusters with better survival were enriched with expression of collagen genes and extracellular matrix components. No similar phenotype was observed in the female subsets. The correlation between the sex-specific GBM clusters and survival was validated in further analyses of the GSE13041 and 16011 datasets. Together these data indicate that important elements of GBM biology and outcome can be discovered through sex-specific analyses and that distinctions between the biology of male and female GBM may need to be incorporated into precision approaches to treatment.

Abstract A1-32: The Stanford-TCGA portal: An interactive web/mobile interface for exploring the clinical phenotypic relevance of specific cancer drivers

Abstract The Stanford-TCGA Portal (http://genomeportal.stanford.edu/pan-tcga) enables users to easily navigate cancer genomic/proteomic data of cancer patients with their clinical information from the Cancer Genome Atlas (TCGA) project. As of 2014 January, the TCGA processed thousands of samples from over 20 cancer types and the data is publically available. This huge data set can offer many valuable insights about cancer research. However, exploring data from TCGA remains a challenge, particularly for researchers and others who lack formal bioinformatics training. With the Stanford-TCGA Portal, anyone can use a personal device such as laptops or smart phones to explore the TCGA data in regards to clinically relevant questions. For example, “What genes are associated with advanced breast cancer?”, “What is the difference in the frequency of TP53 mutations between diffuse and intestinal gastric cancers?”, or “What is the frequency of copy number deletion of APC for samples with/without PIK3CA mutations?”. While several websites such as cBio portal or UCSC cancer genomics browser was developed in order to make TCGA data more accessible to a community, there are few interactive features that enable querying of clinically-relevant phenotypic associations with drivers and modifier genes. The Stanford-TCGA portal allows users to navigate TCGA data in three different ways; i) search for clinically relevant genes/micro RNAs (miRs)/proteins by names, cancer types or clinical parameters, ii) profile genomic/proteomic changes by clinical parameters in a cancer type, or iii) test two-hit hypothesis. Thus, one can address specific questions regarding the association of cancers drivers with clinical parameters and outcome. We identify clinically relevant genes/miRs/proteins by our rigorous statistical analysis, which has been previously published, from 17 cancer types with 19 clinical parameters such as clinical stage or smoking history. Basically, elastic-net method estimates an optimal multiple linear regularized regression of the clinical parameters on the space of genomic/proteomic features. This analysis identified the set of top gene predictors of each clinical parameter in each cancer type respectively. Users can easily access the lists of these identified genes through the Stanford-TCGA Portal. In summary, the Stanford-TCGA Portal enables the cancer research community and others to fully utilize TCGA data. It provides simple yet clinically relevant information for web and mobile interface, thus one can examine queries and test hypothesis regarding genomic/proteomic alterations in cancers from any time and place. This will be an important step towards translation of genomic/proteomic data into clinics. Citation Format: HoJoon Lee, Jennifer Palm, Hanlee Ji. The Stanford-TCGA portal: An interactive web/mobile interface for exploring the clinical phenotypic relevance of specific cancer drivers. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-32.

Abstract A1-46: Using Medbook Workbench to create evidence streams to guide medical decisions

Abstract Background: Clinical datasets typically have small sample size limiting the ability to generate inference with strong statistical significance. By combining: 1) existing datasets; 2)integrated analysis of different data types 3) curated pathways and 4) state of the art machine learning classifiers into one analysis pipeline, clinicians and researchers may be able to combine multiple observations to spot a pattern in an individual patient sample to base a critical decision about therapy. However, few tools exist to enable a diverse set of analytical results to be coherently pooled and disseminated to a research team. New web-based modalities are urgently needed to meet the demands of new genomics-based oncology use cases. Methods: Building on the success of MSKCC cBioPortal and UCSC Cancer Genomics Browser, an easy to use workbench allows users to generate new datasets by integrating these types of data: Import public datasets from NCBI,Wrangle data into standard format,Import clinical from OnCore,RNA Seq analysis and mutation analysis,Unsupervised clustering analysis,Sharing Data with collaborators in a secure manner,Differential gene expression analysis,Pathway enrichment analysis,Training classifiers to recognize events on existing datasets,Applying classifiers to new datasets to infer molecular events. We have created a new Medical Information System called MedBook to give a context for integrating multiple observations about patients and their biopsies into a unified social network. Data can be shared with clinicians and collaborators in a secure manner. Borrowing terminology from systems such as FaceBook and Google Plus, we describe an organization of collaborative analyses as evidence “streams” that allow sharing, annotation, and nucleation points for further analyses. Streams are composed of “evidence cards” that encapsulate figures and/or tables. Discussion threads allow interpretation and commenting on findings associated with each evidence card. Results: We demonstrate the utility of the stream concept using a gene expression based signature that predicts small cell disease in castration resistant prostate cancer. Importantly, the stream concept allows 1) redefinition of the signature to incorporate additional patient samples and clinical definitions on-the-fly, 2) the application of the signature to query samples, 3) viewing the predictions in cBioPortal to assist clinicians judgment of samples in the context of other relevant genomics events, 3) extend the results by leveraging additional bioinformatics apps like the Medbook Workbench and observation Deck 4) the recording of the provenance, and 5) creates a focal point for deliberation about patients, signatures, genes, mutations, pathways, and clinical inferences. Conclusions: Prior to MedBook, there was a cognitive gap between individual doctor observations and large research paper driven cohort meta-analysis. MedBook is able to fill the gap by facilitating the online integration necessary for data analysis in a dynamic clinical environment. Citation Format: Robert Baertsch, Chris Wong, Jack Youngren, Josh Stuart, Eric Small, Ted Goldstein. Using Medbook Workbench to create evidence streams to guide medical decisions. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-46.

Abstract B1-07: Using the UCSC Xena Platform to integrate, visualize, and analyze your own data in the context of large external genomic datasets

Abstract With the advent of cancer genome analysis, there is an enormous need for an integrative computational approach to understand the functional impact of the genomic aberrations that drive and characterize cancers. This requires mechanisms to aggregate and visualize both public and investigator-generated data on cancer genomes, transcriptomes, epigenomes and more. Extending the UCSC Cancer Genomics Browser, we are developing the UCSC Xena platform to achieve this. UCSC's Xena is a data server-based platform that stores functional genomics data and serves them in response to data requests in real-time and with minimal informatics overhead. Examples of these data requests include data visualization, integration and further downstream analysis. Xena can easily be installed on a laptop, or on servers behind a firewall. The UCSC Xena server provides access to TCGA open access data, with 526 datasets from 31 different TCGA cancer types. Types of hosted datasets include copy number, somatic mutation, DNA methylation, gene and exon expression, protein expression, PARADIGM pathway inference, and phenotype data. Our automated pipeline updates TCGA data periodically, ensuring we are visualizing the most recent data available. Additionally, our pipeline ingests TCGA phenotype data and attempts to assign more readable feature names and values. We further derive overall and recurrence free survival from TCGA phenotype data, allowing users to perform survival analysis. We are extending the UCSC Cancer Genomics Browser to access and visualize data hosted across multiple Xena servers while maintaining data privacy. This functionality allows viewing and interpretation of one's own data (e.g. stored on a private Xena) in the context of a large collection of cancer genomics datasets (e.g. TCGA data stored at UCSC). The outcome is a platform for researchers to store and analyze their datasets in an interoperable manner. We are integrating Xena with other tools such as MuPIT (enables visualization of somatic mutations on three-dimensional protein structures), and with Galaxy to allow integration with other bioinformatics tools such as Trinity (via Galaxy, RNA-seq data analysis to identify coding and non-coding transcripts, score them for aberrations, and quantify their expression). Integrating these tools provides researchers with a workflow with strong analysis and visualization capabilities, and brings sophisticated computational analyses within the reach of non-computational scientists. Citation Format: Jingchun Zhu, Brian Craft, Mary Goldman, Melissa Cline, Mark Diekhans, David Haussler. Using the UCSC Xena Platform to integrate, visualize, and analyze your own data in the context of large external genomic datasets. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-07.

Oncogenomic portals for the visualization and analysis of genome-wide cancer data.

Somatically acquired genomic alterations that drive oncogenic cellular processes are of great scientific and clinical interest. Since the initiation of large-scale cancer genomic projects (e.g., the Cancer Genome Project, The Cancer Genome Atlas, and the International Cancer Genome Consortium cancer genome projects), a number of web-based portals have been created to facilitate access to multidimensional oncogenomic data and assist with the interpretation of the data. The portals provide the visualization of small-size mutations, copy number variations, methylation, and gene/protein expression data that can be correlated with the available clinical, epidemiological, and molecular features. Additionally, the portals enable to analyze the gathered data with the use of various user-friendly statistical tools. Herein, we present a highly illustrated review of seven portals, i.e., Tumorscape, UCSC Cancer Genomics Browser, ICGC Data Portal, COSMIC, cBioPortal, IntOGen, and BioProfiling.de. All of the selected portals are user-friendly and can be exploited by scientists from different cancer-associated fields, including those without bioinformatics background. It is expected that the use of the portals will contribute to a better understanding of cancer molecular etiology and will ultimately accelerate the translation of genomic knowledge into clinical practice.

Abstract LB-212: Treehouse Childhood Cancer Project: a resource for sharing and multiple cohort analysis of pediatric cancer genomics data

Abstract Deep sequencing of adult and pediatric tumors revealed that different cancers share common genetic mutations. Aside from sequence mutation, gene expression, copy number, and epigenetic mechanisms contribute to tumorigenesis, and integrating this information may reveal more aberrant signaling pathways than analysis of mutations alone. Significantly, agents targeting specific pathways may be effective against multiple malignancies, regardless of the mechanisms of pathway deregulation. These observations suggest that pediatric cancer patients may benefit from targeted therapies developed for adults. Since the development of pediatric-cancer-specific therapies is hindered by the limited involvement of pharmaceutical companies and small patient cohorts, repositioning drugs designed for adult tumors remains the fastest and most effective way to bring new treatment options to pediatric cancer patients While pediatric tumors have been characterized by genome-wide technologies, the data from these studies are typically under-utilized beyond the initial single cohort, single data type analyses. Consequently, we still lack a comprehensive picture of the molecular pathways that contribute to pediatric cancer in each patient, especially those that can be targeted in the clinic. Integrating multiple datasets is essential for assembling large enough patient cohorts to achieve an understanding of cancer-driving molecular aberrations in individual patients. The Treehouse Childhood Cancer Project consolidates gene expression, mutation and copy number datasets under the UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu), and currently contains data from over 1000 pediatric tumors from TARGET and other studies. Treehouse enables mining these data alongside the data from adult cancers studied by The Cancer Genome Atlas consortium (TCGA). This is accomplished using bioinformatics tools developed for the TCGA Pan-Cancer Analysis Working Group and aimed at identifying situations where a subset of pediatric tumors may be driven by similar molecular pathways as adult tumors. We have assembled a consortium of researchers who plan to both contribute data to the Treehouse platform and apply Treehouse data in their analyses. These include John Maris (Children's Hospital of Philadelphia), Michael Taylor (Hospital for Sick Children, Toronto), Poul Sorensen (University of British Columbia), Timothy Triche (Children's Hospital Los Angeles), Soheil Meshinchi (Fred Hutchinson Cancer Research Center), Doug Hawkins (Seattle Children's Hospital), Javed Khan (NIH Center for Cancer Research), Ching Lao (Texas Children's Hospital), Leonard Sender (UC Irvine, Children's Hospital of Orange County), Alejandro Sweet-Cordero (Stanford School of Medicine), and D.W. Parsons (Baylor College of Medicine). In this submission, we demonstrate the utility of the Treehouse resource by analyzing the neuroblastoma TARGET cohort in the context of adult TCGA cancers. This work presents a proof of concept that cross-cancer multiple cohort analysis can lead to new insights into pediatric malignancies. Citation Format: Olena Morozova, Yulia Newton, Melissa Cline, Jingchun Zhu, Katrina Learned, Josh Stuart, Sofie Salama, Robert Arceci, David Haussler. Treehouse Childhood Cancer Project: a resource for sharing and multiple cohort analysis of pediatric cancer genomics data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-212. doi:10.1158/1538-7445.AM2015-LB-212

The long non-coding RNA HNF1A-AS1 regulates proliferation and metastasis in lung adenocarcinoma.

Long noncoding RNAs (lncRNAs) have emerged as key regulators of tumor development and progression. The lncRNA HNF1A-antisense 1 (HNF1A-AS1) is a 2455-bp transcript on chromosome 12 with a potential oncogenic role in esophageal adenocarcinoma. Nevertheless, current understanding of the involvement of HNF1A-AS1 in lung adenocarcinoma tumorigenesis remains limited. In this study, we analyzed the roles of HNF1A-AS1 in 40 lung adenocarcinoma tissues and five lung cancer cell lines. Our results showed that HNF1A-AS1 was significantly up-regulated in lung adenocarcinoma tissues compared with corresponding non-tumor tissues, and its expression level was significantly correlated with TNM stage, tumor size, and lymph node metastasis. The UCSC Cancer Genomics Browser's Kaplan-Meier plot suggested that patients in the high HNF1A-AS1 expression subgroup experienced worse overall survival compared to the low expression subgroup. Moreover, HNF1A-AS1 was determined to promote tumor proliferation and metastasis, both in vitro and in vivo, by regulating cyclin D1, E-cadherin, N-cadherin and β-catenin expression. In addition, the binding of HNF1A-AS1 to DNMT1 may explain its regulation of E-cadherin. In conclusions, we demonstrated that increased HNF1A-AS1 expression could regulate cell proliferation and metastasis and identified it as a poor prognostic biomarker in lung adenocarcinoma.

The UCSC Cancer Genomics Browser: update 2015.

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu/) is a web-based application that integrates relevant data, analysis and visualization, allowing users to easily discover and share their research observations. Users can explore the relationship between genomic alterations and phenotypes by visualizing various -omic data alongside clinical and phenotypic features, such as age, subtype classifications and genomic biomarkers. The Cancer Genomics Browser currently hosts 575 public datasets from genome-wide analyses of over 227 000 samples, including datasets from TCGA, CCLE, Connectivity Map and TARGET. Users can download and upload clinical data, generate Kaplan–Meier plots dynamically, export data directly to Galaxy for analysis, plus generate URL bookmarks of specific views of the data to share with others.

Abstract A33: Exploring pediatric cancer genomics with the UCSC Cancer Genomics Browser

Abstract The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu) is a set of web-based tools to display, investigate and analyze cancer genomics data and associated clinical data. Experimental quantities such as gene expression levels, copy number variation and somatic mutations are displayed next to clinical features such as age of onset and cancer subtype. Users can interactively group or sort data by clinical features to dynamically explore how genomic aberrations relate to clinical outcomes. Integrated Kaplan–Meier plots help investigators assess how clinical or genomic values impact long-term survival. The browser currently hosts data from 144 cancer studies including TCGA, CCLE and LINCS. It can display data from multiple studies at once, facilitating cross-cancer comparisons. We are currently interested in hosting new pediatric cancer datasets, and can offer either public or protected access as appropriate. Figure 1 illustrates the power of this approach. It compares patterns of somatic mutations in pediatric high-risk neuroblastoma, a cancer of the peripheral nervous system (Pugh, Morozova et al, Nature Genetics 2013) to those in two adult cancers of the central nervous system: lower grade glioma (LGG, TCGA) and glioblastoma multiforme (GBM, TCGA), and contrasts the age of the patient with frequency of mutations in the Alpha Thalassemia/Mental Retardation Syndrome X-linked (ATRX) gene. The data for all three cancers is sorted by the age of the patient, with yellow being the oldest. The age ranges overlap: the oldest neuroblastoma patients are 16, while the youngest LGG and GBM patients are 14 and 21 respectively. While the three cancers show distinct patterns of somatic mutations, all three show frequent mutations in the ATRX gene in older teens or young adults. This suggests that these cancers may share age-related subtypes with similar genomic signatures. These subtypes may indicate the age-dependent importance of ATRX in the development of both central and peripheral nervous systems, and may ultimately highlight common therapeutic avenues for the two groups of diseases. This data set can be explored at the UCSC Cancer Genomics Browser at https://genome-cancer.ucsc.edu/proj/site/hgHeatmap/#?bookmark=pc Citation Format: Melissa Cline, Olena Morozova, Teresa Swatloski, Brian Craft, Mary Goldman, David Haussler, Jingchun Zhu. Exploring pediatric cancer genomics with the UCSC Cancer Genomics Browser. [abstract]. In: Proceedings of the AACR Special Conference on Pediatric Cancer at the Crossroads: Translating Discovery into Improved Outcomes; Nov 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2013;74(20 Suppl):Abstract nr A33.

Exploring TCGA Pan-Cancer Data at the UCSC Cancer Genomics Browser

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu) offers interactive visualization and exploration of TCGA genomic, phenotypic, and clinical data, as produced by the Cancer Genome Atlas Research Network. Researchers can explore the impact of genomic alterations on phenotypes by visualizing gene and protein expression, copy number, DNA methylation, somatic mutation and pathway inference data alongside clinical features, Pan-Cancer subtype classifications and genomic biomarkers. Integrated Kaplan–Meier survival analysis helps investigators to assess survival stratification by any of the information.

Visualizing multidimensional cancer genomics data

Cancer genomics projects employ high-throughput technologies to identify the complete catalog of somatic alterations that characterize the genome, transcriptome and epigenome of cohorts of tumor samples. Examples include projects carried out by the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). A crucial step in the extraction of knowledge from the data is the exploration by experts of the different alterations, as well as the multiple relationships between them. To that end, the use of intuitive visualization tools that can integrate different types of alterations with clinical data is essential to the field of cancer genomics. Here, we review effective and common visualization techniques for exploring oncogenomics data and discuss a selection of tools that allow researchers to effectively visualize multidimensional oncogenomics datasets. The review covers visualization methods employed by tools such as Circos, Gitools, the Integrative Genomics Viewer, Cytoscape, Savant Genome Browser, StratomeX and platforms such as cBio Cancer Genomics Portal, IntOGen, the UCSC Cancer Genomics Browser, the Regulome Explorer and the Cancer Genome Workbench.

The UCSC Cancer Genomics Browser: update 2013

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu/) is a set of web-based tools to display, investigate and analyse cancer genomics data and its associated clinical information. The browser provides whole-genome to base-pair level views of several different types of genomics data, including some next-generation sequencing platforms. The ability to view multiple datasets together allows users to make comparisons across different data and cancer types. Biological pathways, collections of genes, genomic or clinical information can be used to sort, aggregate and zoom into a group of samples. We currently display an expanding set of data from various sources, including 201 datasets from 22 TCGA (The Cancer Genome Atlas) cancers as well as data from Cancer Cell Line Encyclopedia and Stand Up To Cancer. New features include a completely redesigned user interface with an interactive tutorial and updated documentation. We have also added data downloads, additional clinical heatmap features, and an updated Tumor Image Browser based on Google Maps. New security features allow authenticated users access to private datasets hosted by several different consortia through the public website.