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We present a systematic analysis of the effects of synchronizing a large-scale, deeply characterized, multi-omic dataset to the current human reference genome, using updated software, pipelines, and annotations. For each of 5 molecular data platforms in The Cancer Genome Atlas (TCGA)—mRNA and miRNA expression, single nucleotide variants, DNA methylation and copy number alterations—comprehensive sample, gene, and probe-level studies were performed, towards quantifying the degree of similarity between the ‘legacy’ GRCh37 (hg19) TCGA data and its GRCh38 (hg38) version as ‘harmonized’ by the Genomic Data Commons. We offer gene lists to elucidate differences that remained after controlling for confounders, and strategies to mitigate their impact on biological interpretation. Our results demonstrate that the hg19 and hg38 TCGA datasets are very highly concordant, promote informed use of either legacy or harmonized omics data, and provide a rubric that encourages similar comparisons as new data emerge and reference data evolve. Gao et al. performed a systematic analysis of the effects of synchronizing the large-scale, widely used, multi-omic dataset of The Cancer Genome Atlas to the current human reference genome. For each of the five molecular data platforms assessed, they demonstrated a very high concordance between the ‘legacy’ GRCh37 (hg19) TCGA data and its GRCh38 (hg38) version as ‘harmonized’ by the Genomic Data Commons.

Gene Regulatory Networks (GRNs) control many biological systems, but how such network coordination is shaped is still unknown. GRNs can be subdivided into basic connections that describe how the network members interact e.g. co-expression, physical interaction, co-localization, genetic influence, pathways, and shared protein domains. The important regulatory mechanisms of these networks involve miRNAs. We developed an R/Bioconductor package, namely SpidermiR, which offers an easy access to both GRNs and miRNAs to the end user, and integrates this information with differentially expressed genes obtained from The Cancer Genome Atlas. Specifically, SpidermiR allows the users to: (i) query and download GRNs and miRNAs from validated and predicted repositories; (ii) integrate miRNAs with GRNs in order to obtain miRNA–gene–gene and miRNA–protein–protein interactions, and to analyze miRNA GRNs in order to identify miRNA–gene communities; and (iii) graphically visualize the results of the analyses. These analyses can be performed through a single interface and without the need for any downloads. The full data sets are then rapidly integrated and processed locally., SCOPUS: ar.j, info:eu-repo/semantics/inPress

Background:The GDC (Genomic Data Commons) data portal provides users with data from cancer genomics studies. Recently, we developed the R/Bioconductor TCGAbiolinks package, which allows users to search, download and prepare cancer genomics data for integrative data analysis. The use of this package requires users to have advanced knowledge of R thus limiting the number of users.Results:To overcome this obstacle and improve the accessibility of the package by a wider range of users, we developed TCGAbiolinksGUI that uses shiny graphical user interface (GUI) available through the R/Bioconductor package.Conclusion:The TCGAbiolinksGUI package is freely available within the Bioconductor project at http://bioconductor.org/packages/TCGAbiolinksGUI/. Links to the GitHub repository, a demo version of the tool, a docker image and PDF/video tutorials are available at http://bit.do/TCGAbiolinksDocs.

Gene Regulatory Networks (GRNs) control many biological systems, but how such network coordination is shaped is still unknown. GRNs can be subdivided into basic connections that describe how the network members interact e.g., co-expression, physical interaction, co-localization, genetic influence, pathways, and shared protein domains. The important regulatory mechanisms of these networks involve miRNAs. We developed an R/Bioconductor package, namely SpidermiR, which offers an easy access to both GRNs and miRNAs to the end user, and integrates this information with differentially expressed genes obtained from The Cancer Genome Atlas. Specifically, SpidermiR allows the users to: (i) query and download GRNs and miRNAs from validated and predicted repositories; (ii) integrate miRNAs with GRNs in order to obtain miRNA-gene-gene and miRNA-protein-protein interactions, and to analyze miRNA GRNs in order to identify miRNA-gene communities; and (iii) graphically visualize the results of the analyses. These analyses can be performed through a single interface and without the need for any downloads. The full data sets are then rapidly integrated and processed locally.

Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). This workflow introduces the following Bioconductor packages: AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, MINET, RTCGAToolbox, TCGAbiolinks.

The GDC (Genomic Data Commons) data portal provides users with data from cancer genomics studies. Recently, we developed the R/Bioconductor TCGAbiolinks package, which allows users to search, download and prepare cancer genomics data for integrative data analysis. The use of this package requires users to have advanced knowledge of R thus limiting the number of users. To overcome this obstacle and improve the accessibility of the package by a wider range of users, we developed a graphical user interface (GUI) using Shiny available through the package TCGAbiolinksGUI. The TCGAbiolinksGUI package is freely available within the Bioconductor project at http://bioconductor.org/packages/TCGAbiolinksGUI/. Links to the GitHub repository, a demo version of the tool, a docker image and PDF/video tutorials are available from the TCGAbiolinksGUI site.

Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no comprehensive tool that provides a complete integrative analysis harnessing the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative downstream analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data and by using Roadmap and ENCODE data, we provide a workplan to identify candidate biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors : low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). This workflow introduces the following Bioconductor packages: AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, MINET, RTCGAtoolbox, TCGAbiolinks.

Vitamin D supplementation is indicated for post-menopausal women to prevent osteoporosis and lower 25(OH)2D3 or 1,25(OH)2D3 serum levels have been associated with breast cancer incidence or prognosis (metastasis). The antiproliferative effects of vitamin D are observed in breast cancer cell lines exposed to phamacological doses of calcitriol (1,25(OH)2D3, 100nM) but whether physiological doses are sufficient to produce growth inhibition in vivo is not known. The aim of our study was to investigate gene expression profile changes of breast cancer samples from patients supplemented with calcitriol, presenting an anti-proliferative effect on the tumor. Post-menopausal women diagnosed with breast cancer were instructed to take one (0.25ug/day, n=8) or two (0.50ug/day, n=8) tablets of calcitriol after tumor biopsy. Median time of supplementation was 30 days. Sixteen tumor samples were collected during biopsy (before supplementation) and breast surgery (after supplementation). Proliferation index was evaluated by tumor Ki-67 immunohistochemistry (IHC) expression in breast cancer samples before and after calcitirol supplementation and 1000 cells were counted by three observers (p < 0,001, Pearson's correlation). After establishing a counting error between observers, the cut-off value was fixed in 26.53% (percentile 60%) to define response. Patients were then categorized as responsive (a reduction of more than 26.53% on positive Ki-67 positive cells) and non-responsive (difference between samples before and after supplementation not exceeding 26.53%). Among our sixteen patients, all were categorized as responsive to calcitriol supplementation except for two. So far, gene expression profile of two patients (both categorized as responsive) has been analyzed using the U133 Plus 2.0 Affymetrix Gene Chips from 100ng of total RNA. To verify signalling pathways possibly involved in response to vitamin D exposure, additional five samples from a parallel study, in which breast cancer samples from post menopausal patients were collected at surgery and treated in vitro with a low concentration of calcitriol, 0.5nM (that can be attained with subcutaneous administration of doses of 8ug calcitriol, without hypercalcemia) for 24h, were included in the analysis. All samples had RNA hybridized to the same gene chips. Results were normalized and analyzed using RMA and Mev.TM4 softwares. CYP24A1, a target gene of vitamin D, presented a positive regulation after calcitriol supplementation in all samples analyzed. Differentially expressed genes were involved in the regulation of cell cycle [SMAD2, cyclin E, YWHAQ (14-3-3 family] and calcium signalling (HTR7, PTGER1 and PTGER2). Our results indicate that the tumor proliferation index is reduced upon calcitriol supplementation. Moreover, potentially regulated pathways in breast cancer specimens after administration of low doses of calcitriol are regulation of cell cycle and calcium signaling.Supported by FAPESP 2007/01111-0 – 2007/04799-2 Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 6128.