Halfdan Rydbeck

The Genomic HyperBrowser: inferential genomics at the sequence level

The immense increase in the generation of genomic scale data poses an unmet analytical challenge, due to a lack of established methodology with the required flexibility and power. We propose a first principled approach to statistical analysis of sequence-level genomic information. We provide a growing collection of generic biological investigations that query pairwise relations between tracks, represented as mathematical objects, along the genome. The Genomic HyperBrowser implements the approach and is available at http://hyperbrowser.uio.no.

Identification of Osteosarcoma Driver Genes by Integrative Analysis of Copy Number and Gene Expression Data

High‐grade osteosarcoma is a tumor with a complex genomic profile, occurring primarily in adolescents with a second peak at middle age. The extensive genomic alterations obscure the identification of genes driving tumorigenesis during osteosarcoma development. To identify such driver genes, we integrated DNA copy number profiles (Affymetrix SNP 6.0) of 32 diagnostic biopsies with 84 expression profiles (Illumina Human‐6 v2.0) of high‐grade osteosarcoma as compared with its putative progenitor cells, i.e., mesenchymal stem cells (n = 12) or osteoblasts (n = 3). In addition, we performed paired analyses between copy number and expression profiles of a subset of 29 patients for which both DNA and mRNA profiles were available. Integrative analyses were performed in Nexus Copy Number software and statistical language R. Paired analyses were performed on all probes detecting significantly differentially expressed genes in corresponding LIMMA analyses. For both nonpaired and paired analyses, copy number aberration frequency was set to >35%. Nonpaired and paired integrative analyses resulted in 45 and 101 genes, respectively, which were present in both analyses using different control sets. Paired analyses detected >90% of all genes found with the corresponding nonpaired analyses. Remarkably, approximately twice as many genes as found in the corresponding nonpaired analyses were detected. Affected genes were intersected with differentially expressed genes in osteosarcoma cell lines, resulting in 31 new osteosarcoma driver genes. Cell division related genes, such as MCM4 and LATS2, were overrepresented and genomic instability was predictive for metastasis‐free survival, suggesting that deregulation of the cell cycle is a driver of osteosarcomagenesis.

Integrative analysis reveals relationships of genetic and epigenetic alterations in osteosarcoma.

Background Osteosarcomas are the most common non-haematological primary malignant tumours of bone, and all conventional osteosarcomas are high-grade tumours showing complex genomic aberrations. We have integrated genome-wide genetic and epigenetic profiles from the EuroBoNeT panel of 19 human osteosarcoma cell lines based on microarray technologies. Principal Findings The cell lines showed complex patterns of DNA copy number changes, where genomic copy number gains were significantly associated with gene-rich regions and losses with gene-poor regions. By integrating the datasets, 350 genes were identified as having two types of aberrations (gain/over-expression, hypo-methylation/over-expression, loss/under-expression or hyper-methylation/under-expression) using a recurrence threshold of 6/19 (>30%) cell lines. The genes showed in general alterations in either DNA copy number or DNA methylation, both within individual samples and across the sample panel. These 350 genes are involved in embryonic skeletal system development and morphogenesis, as well as remodelling of extracellular matrix. The aberrations of three selected genes, CXCL5, DLX5 and RUNX2, were validated in five cell lines and five tumour samples using PCR techniques. Several genes were hyper-methylated and under-expressed compared to normal osteoblasts, and expression could be reactivated by demethylation using 5-Aza-2′-deoxycytidine treatment for four genes tested; AKAP12, CXCL5, EFEMP1 and IL11RA. Globally, there was as expected a significant positive association between gain and over-expression, loss and under-expression as well as hyper-methylation and under-expression, but gain was also associated with hyper-methylation and under-expression, suggesting that hyper-methylation may oppose the effects of increased copy number for detrimental genes. Conclusions Integrative analysis of genome-wide genetic and epigenetic alterations identified dependencies and relationships between DNA copy number, DNA methylation and mRNA expression in osteosarcomas, contributing to better understanding of osteosarcoma biology.

Univariate and bivariate variance component linkage analysis of a whole-genome scan for loci contributing to bone mineral density

Osteoporosis is a common condition characterized by reduced skeletal strength and increased susceptibility to fracture. The single major risk factor for osteoporosis is low bone mineral density (BMD) and strong evidence exists that genetic factors are in part responsible for an individuals BMD. A cohort of 40 multiplex Caucasian families selected through a proband with osteoporosis was genotyped for microsatellite markers spaced at an average of 10 cM, and linkage to femoral neck (FN), lumbar spine (LS) and trochanter (TR) BMD was analyzed using univariate and bivariate variance component linkage analysis. Maximum univariate multipoint lod-scores were 2.87 on chromosome 1p36 for FN BMD, 1.89 on 6q27 for TR BMD, and 2.15 on 7p15 for LS BMD. Results of bivariate linkage analysis were highly correlated with those of the univariate analysis, although generally less significant, suggesting the possibility that some of these susceptibility loci may exert pleiotropic effects on multiple skeletal sites.

The Genomic HyperBrowser: an analysis web server for genome-scale data

The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.

Evaluation of high-resolution microarray platforms for genomic profiling of bone tumours

BackgroundSeveral high-density oligonucleotide microarray platforms are available for genome-wide single nucleotide polymorphism (SNP) detection and microarray-based comparative genomic hybridisation (array CGH), which may be used to detect copy number aberrations in human tumours. As part of the EuroBoNeT network of excellence for research on bone tumours (eurobonet.eu), we have evaluated four different commercial high-resolution microarray platforms in order to identify the most appropriate technology for mapping DNA copy number aberrations in such tumours.FindingsDNA from two different cytogenetically well-characterized bone sarcoma cell lines, representing a simple and a complex karyotype, respectively, was tested in duplicate on four high-resolution microarray platforms; Affymetrix Genome-Wide Human SNP Array 6.0, Agilent Human Genome CGH 244A, Illumina HumanExon510s-duo and Nimblegen HG18 CGH 385 k WG tiling v1.0. The data was analysed using the platform-specific analysis software, as well as a platform-independent analysis algorithm. DNA copy number was measured at six specific chromosomes or chromosomal regions, and compared with the expected ratio based on available cytogenetic information. All platforms performed well in terms of reproducibility and were able to delimit and score small amplifications and deletions at similar resolution, but Agilent microarrays showed better linearity and dynamic range. The platform-specific analysis software provided with each platform identified in general correct copy numbers, whereas using a platform-independent analysis algorithm, correct copy numbers were determined mainly for Agilent and Affymetrix microarrays.ConclusionsAll platforms performed reasonably well, but Agilent microarrays showed better dynamic range, and like Affymetrix microarrays performed well with the platform-independent analysis software, implying more robust data. Bone tumours like osteosarcomas are heterogeneous tumours with complex karyotypes that may be difficult to interpret, and it is of importance to be able to well separate the copy number levels and detect copy number changes in subpopulations. Taking all this into consideration, the Agilent and Affymetrix microarray platforms were found to be a better choice for mapping DNA copy numbers in bone tumours, the latter having the advantage of also providing heterozygosity information.

The differential disease regulome

BackgroundTranscription factors in disease-relevant pathways represent potential drug targets, by impacting a distinct set of pathways that may be modulated through gene regulation. The influence of transcription factors is typically studied on a per disease basis, and no current resources provide a global overview of the relations between transcription factors and disease. Furthermore, existing pipelines for related large-scale analysis are tailored for particular sources of input data, and there is a need for generic methodology for integrating complementary sources of genomic information.ResultsWe here present a large-scale analysis of multiple diseases versus multiple transcription factors, with a global map of over-and under-representation of 446 transcription factors in 1010 diseases. This map, referred to as the differential disease regulome, provides a first global statistical overview of the complex interrelationships between diseases, genes and controlling elements. The map is visualized using the Google map engine, due to its very large size, and provides a range of detailed information in a dynamic presentation format.The analysis is achieved through a novel methodology that performs a pairwise, genome-wide comparison on the cartesian product of two distinct sets of annotation tracks, e.g. all combinations of one disease and one TF.The methodology was also used to extend with maps using alternative data sets related to transcription and disease, as well as data sets related to Gene Ontology classification and histone modifications. We provide a web-based interface that allows users to generate other custom maps, which could be based on precisely specified subsets of transcription factors and diseases, or, in general, on any categorical genome annotation tracks as they are improved or become available.ConclusionWe have created a first resource that provides a global overview of the complex relations between transcription factors and disease. As the accuracy of the disease regulome depends mainly on the quality of the input data, forthcoming ChIP-seq based binding data for many TFs will provide improved maps. We further believe our approach to genome analysis could allow an advance from the current typical situation of one-time integrative efforts to reproducible and upgradable integrative analysis. The differential disease regulome and its associated methodology is available at http://hyperbrowser.uio.no.

Abstract 5128: Identification of osteosarcoma driver genes by integrative analysis of copy number and gene expression data

Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL High-grade osteosarcoma is a tumor with a complex genomic profile, occurring primarily in adolescents. The extensive genomic alterations obscure the identification of genes driving tumorigenesis in osteosarcoma progenitor cells. In order to identify such driver genes, we integrated DNA copy number profiles (Affymetrix SNP 6.0) of 32 diagnostic biopsies with 84 expression profiles (Illumina Human-6 v2.0) of osteosarcoma as compared with its putative progenitor cells, i.e. mesenchymal stem cells (n=12) or osteoblasts (n=3). In addition, we performed paired analyses between copy number and expression profiles of a subset of 29 patients for which we had both DNA and mRNA profiles. Integrative analyses were performed in Nexus Copy Number Software and statistical language R. Paired analyses were performed on all probes which were significantly differentially expressed in corresponding LIMMA analyses. For both non-paired and paired analyses, copy number aberration frequency was set to >35%. Non-paired integrative analyses resulted in 45 genes that were present in both analyses using different control sets, i.e. MSCs and osteoblasts. 101 genes overlapped between the two paired integrative analyses. Paired analyses detected >90% of all genes found with the corresponding non-paired analyses. Remarkably, approximately twice as many genes as found in the corresponding non-paired analyses were detected. Affected genes were compared with differential expression in osteosarcoma cell lines. An overrepresentation of altered cell division related genes was found, such as MCM4 and LATS2, suggesting that deregulation of the cell cycle is an initial driver of osteosarcomagenesis. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5128. doi:1538-7445.AM2012-5128