SNPExpress: integrated visualization of genome-wide genotypes, copy numbers and gene expression levels

Background: Accurate analyses of comprehensive genome-wide SNP genotyping and gene expression data sets is challenging for many researchers. In fact, obtaining an integrated view of both large scale SNP genotyping and gene expression is currently complicated since only a limited number of appropriate software tools are available. Results: We present SNPExpress, a software tool to accurately analyze Affymetrix and Illumina SNP genotype calls, copy numbers, polymorphic copy number variations (CNVs) and Affymetrix gene expression in a combinatorial and efficient way. In addition, SNPExpress allows concurrent interpretation of these items with Hidden-Markov Model (HMM) inferred Loss-of-Heterozygosity (LOH)- and copy number regions. Conclusion: The combined analyses with the easily accessible software tool SNPExpress will not only facilitate the recognition of recurrent genetic lesions, but also the identification of critical pathogenic genes

HeatMapper: powerful combined visualization of gene expression profile correlations, genotypes, phenotypes and sample characteristics

BACKGROUND: Accurate interpretation of data obtained by unsupervised analysis of large scale expression profiling studies is currently frequently performed by visually combining sample-gene heatmaps and sample characteristics. This method is not optimal for comparing individual samples or groups of samples. Here, we describe an approach to visually integrate the results of unsupervised and supervised cluster analysis using a correlation plot and additional sample metadata. RESULTS: We have developed a tool called the HeatMapper that provides such visualizations in a dynamic and flexible manner and is available from . CONCLUSION: The HeatMapper allows an accessible and comprehensive visualization of the results of gene expression profiling and cluster analysis

Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1

The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM). We describe a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of EGFR, NF1, and PDGFRA/IDH1 each define the Classical, Mesenchymal, and Proneural subtypes, respectively. Gene signatures of normal brain cell types show a strong relationship between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype, with the greatest benefit in the Classical subtype and no benefit in the Proneural subtype. We provide a framework that unifies transcriptomic and genomic dimensions for GBM molecular stratification with important implications for future studies

SNPExpress: integrated visualization of genome-wide genotypes, copy numbers and gene expression levels

Abstract Background Accurate analyses of comprehensive genome-wide SNP genotyping and gene expression data sets is challenging for many researchers. In fact, obtaining an integrated view of both large scale SNP genotyping and gene expression is currently complicated since only a limited number of appropriate software tools are available. Results We present SNPExpress, a software tool to accurately analyze Affymetrix and Illumina SNP genotype calls, copy numbers, polymorphic copy number variations (CNVs) and Affymetrix gene expression in a combinatorial and efficient way. In addition, SNPExpress allows concurrent interpretation of these items with Hidden-Markov Model (HMM) inferred Loss-of-Heterozygosity (LOH)- and copy number regions. Conclusion The combined analyses with the easily accessible software tool SNPExpress will not only facilitate the recognition of recurrent genetic lesions, but also the identification of critical pathogenic genes.</p

Suppression of lung adenocarcinoma progression by Nkx2-1.

Despite the high prevalence and poor outcome of patients with metastatic lung cancer the mechanisms of tumour progression and metastasis remain largely uncharacterized. Here we modelled human lung adenocarcinoma, which frequently harbours activating point mutations in KRAS and inactivation of the p53 pathway, using conditional alleles in mice. Lentiviral-mediated somatic activation of oncogenic Kras and deletion of p53 in the lung epithelial cells of Kras(LSL-G12D/+);p53(flox/flox) mice initiates lung adenocarcinoma development. Although tumours are initiated synchronously by defined genetic alterations, only a subset becomes malignant, indicating that disease progression requires additional alterations. Identification of the lentiviral integration sites allowed us to distinguish metastatic from non-metastatic tumours and determine the gene expression alterations that distinguish these tumour types. Cross-species analysis identified the NK2-related homeobox transcription factor Nkx2-1 (also called Ttf-1 or Titf1) as a candidate suppressor of malignant progression. In this mouse model, Nkx2-1 negativity is pathognomonic of high-grade poorly differentiated tumours. Gain- and loss-of-function experiments in cells derived from metastatic and non-metastatic tumours demonstrated that Nkx2-1 controls tumour differentiation and limits metastatic potential in vivo. Interrogation of Nkx2-1-regulated genes, analysis of tumours at defined developmental stages, and functional complementation experiments indicate that Nkx2-1 constrains tumours in part by repressing the embryonically restricted chromatin regulator Hmga2. Whereas focal amplification of NKX2-1 in a fraction of human lung adenocarcinomas has focused attention on its oncogenic function, our data specifically link Nkx2-1 downregulation to loss of differentiation, enhanced tumour seeding ability and increased metastatic proclivity. Thus, the oncogenic and suppressive functions of Nkx2-1 in the same tumour type substantiate its role as a dual function lineage factor

Discordant inheritance of chromosomal and extrachromosomal DNA elements contributes to dynamic disease evolution in glioblastoma

To understand how genomic heterogeneity of glioblastoma (GBM) contributes to poor therapy response, we performed DNA and RNA sequencing on GBM samples and the neurospheres and orthotopic xenograft models derived from them. We used the resulting dataset to show that somatic driver alterations including single-nucleotide variants, focal DNA alterations and oncogene amplification on extrachromosomal DNA (ecDNA) elements were in majority propagated from tumor to model systems. In several instances, ecDNAs and chromosomal alterations demonstrated divergent inheritance patterns and clonal selection dynamics during cell culture and xenografting. We infer that ecDNA was unevenly inherited by offspring cells, a characteristic that affects the oncogenic potential of cells with more or fewer ecDNAs. Longitudinal patient tumor profiling found that oncogenic ecDNAs are frequently retained throughout the course of disease. Our analysis shows that extrachromosomal elements allow rapid increase of genomic heterogeneity during GBM evolution, independently of chromosomal DNA alterations