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

Pediatric meningiomas in The Netherlands 1974–2010: a descriptive epidemiological case study

The purpose of this study was to review the epidemiology and the clinical, radiological, pathological, and follow-up data of all surgically treated pediatric meningiomas during the last 35 years in The Netherlands. Patients were identified in the Pathological and Anatomical Nationwide Computerized Archive database, the nationwide network and registry of histopathology and cytopathology in The Netherlands. Pediatric patients of 18 years or younger at first operation in 1974-2009 with the diagnosis meningioma were included. Clinical records, follow-up data, radiological findings, operative reports, and pathological examinations were reviewed. In total, 72 patients (39 boys) were identified. The incidence of operated meningiomas in the Dutch pediatric population is 1:1,767,715 children per year. Median age at diagnosis was 13 years (range 0-18 years). Raised intracranial pressure and seizures were the most frequent signs at presentation. Thirteen (18 %) patients had neurofibromatosis type 2 (NF2). Fifty-three (74 %) patients had a meningioma World Health Organization grade I. Total resection was achieved in 35 of 64 patients. Fifteen patients received radiotherapy postoperatively. Mean follow-up was 4.8 years (range 0-27.8 years). Three patients died as a direct result of their meningioma within 3 years. Four patients with NF2 died as a result of multiple tumors. Nineteen patients had disease progression, requiring additional treatment. Meningiomas are extremely rare in the pediatric population; 25 % of all described meningiomas show biological aggressive behavior in terms of disease progression, requiring additional treatment. The 5-year survival is 83.9 %, suggesting that the biological behavior of pediatric menigiomas is more aggressive than that of its adult counterpart

Text-derived concept profiles support assessment of DNA microarray data for acute myeloid leukemia and for androgen receptor stimulation

BACKGROUND: High-throughput experiments, such as with DNA microarrays, typically result in hundreds of genes potentially relevant to the process under study, rendering the interpretation of these experiments problematic. Here, we propose and evaluate an approach to find functional associations between large numbers of genes and other biomedical concepts from free-text literature. For each gene, a profile of related concepts is constructed that summarizes the context in which the gene is mentioned in literature. We assign a weight to each concept in the profile based on a likelihood ratio measure. Gene concept profiles can then be clustered to find related genes and other concepts. RESULTS: The experimental validation was done in two steps. We first applied our method on a controlled test set. After this proved to be successful the datasets from two DNA microarray experiments were analyzed in the same way and the results were evaluated by domain experts. The first dataset was a gene-expression profile that characterizes the cancer cells of a group of acute myeloid leukemia patients. For this group of patients the biological background of the cancer cells is largely unknown. Using our methodology we found an association of these cells to monocytes, which agreed with other experimental evidence. The second data set consisted of differentially expressed genes following androgen receptor stimulation in a prostate cancer cell line. Based on the analysis we put forward a hypothesis about the biological processes induced in these studied cells: secretory lysosomes are involved in the production of prostatic fluid and their development and/or secretion are androgen-regulated processes. CONCLUSION: Our method can be used to analyze DNA microarray datasets based on information explicitly and implicitly available in the literature. We provide a publicly available tool, dubbed Anni, for this purpose

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

Two Different EVI1 Expressing Poor-Risk AML Subgroups with Distinct Epigenetic Signatures Uncovered by Genome Wide DNA Methylation Profiling

Abstract Although the clinical outcome for patients with acute myeloid leukemia (AML) has improved over the years, failure to maintain complete remission remains a major problem with current standard treatments. The development of individually tailored and patient-specific therapy could potentially significantly improve therapeutic efficacy. In particular we are interested in better understanding the biological features associated with aberrant expression of the EVI1 oncogene, which we previously showed is associated with a poor prognosis. Two different EVI1 transcripts have been identified, i.e. a short form (E) and a long form called MDS1-EVI1 (ME) encoding respectively, a 140 kDa and 170 kDa protein. In EVI1 positive AMLs a distinction can be made between patients that express both EVI1 transcripts (E+/ME+) and cases that express the short form solely (E+), since the latter group is exclusively associated with 3q26 chromosomal abnormalities. EVI1 is a nuclear zinc-finger transcriptional repressor oncoprotein that is known to interact with several epigenetic regulators, e.g. HDACs, CtBPs, histone methyl transferases and MBD3. Since EVI1 presumably mediates its effects through aberrant transcriptional repression, we hypothesize that its aberrant expression results in aberrant epigenetic programming of leukemia cells, which might provide an opportunity for epigenetic-targeted therapy in these patients. In order to determine whether EVI1 over-expressing (EVI1+) AMLs display aberrant epigenetic programming we performed HELP (HpaII tiny fragment enrichment by ligation-mediated PCR) DNA methylation assays in 26 EVI1+ AMLs and 8 CD34+ normal bone marrow controls (NBM). Our HELP assay measured the abundance of DNA methylation at ~50,000 CpG sites covering ~13,000 promoter regions. Single locus validation assays using Sequenom Epityping showed that HELP was >95% accurate in quantifying CpG methylation. We found that unsupervised analysis using hierarchical clustering (Pearson correlation distance with Ward’s clustering method) readily separated the EVI1+ AMLs from NBMs. Supervised analysis comparing EVI1+ to NBM identified 303 promoter sequences as being differently methylated (P1.5). Remarkably, 80% of these genes were hypermethylated in EVI1+ patients, while only 20% of genes were hypomethylated. The hypermethylated profile included genes associated with cell death (Caspase-2, MAD1L1) and cell cycle (TNF, JARID1B). The 26 EVI1+ leukemias further segregated into two distinct subgroups in unsupervised analysis: one cluster (n=14) was highly enriched for E+ AML cases carrying 3q26 abnormalities (n=7) while the other one (n=12) mainly harbored the E+/ME+ AMLs (n=10). Supervised analysis of these two EVI1+ clusters revealed that the 3q26-enriched group featured 122-gene signature (P1.5) consisting entirely of hypermethylated genes. When each of the individual EVI1 clusters was independently compared to the NBM samples using supervised analysis we found that the 3q26-enriched group contained a significantly more methylated gene signature containing 429 hypermethylated and 47 hypomethylated HpaII fragments (P1.5). Pathway analysis of the promoter regions differentially methylated in the 3q26-enriched AML group included genes involved in protein degradation and cellular response to therapeutics. In contrast, the E+/ME+ enriched group showed a more balanced distribution of differential methylation when compared to the NBMs (226 hypermethylated and 158 hypomethylated genes). Taken together, our data show that EVI1 overexpression is associated with specific alterations in epigenetic programming vs. normal CD34+ cells. Even more remarkably, we showed that EVI1+ AMLs form two epigenetically distinct AML subtypes. Specifically, the 3q26 subgroup, short EVI1+ isoform AMLs display marked hypermethylation vs. the MDS1-EVI1 expressing patients, involving aberrant methylation of different pathways. This shows that the two forms of EVI1+ AMLs become aberrantly programmed in different ways and are biologically distinct entities, and further suggest distinct mechanisms of action for the different EVI1 isoforms. The marked hypermethylation profile of the short EVI1 isoform AMLs suggests that these patients might benefit from treatment with DNA methyltransferase inhibitors

Epigenetic Signatures Identify New Clinically Relevant Subtypes and Define Gene Regulatory Patterns in Patients with Acute Myeloid Leukemia (AML)

Abstract AML is heterogeneous group of diseases with variable clinical outcomes. While cytogenetics, molecular markers and gene expression profiling can help to classify these patients, they still cannot fully explain the biology and clinical outcomes of the disease. Epigenetic gene deregulation is a hallmark of cancer and our preliminary data suggest that epigenetic signatures are critical determinants of cellular phenotype in AML. Therefore, we hypothesized that aberrant epigenetic regulation of genes would provide critical insight into the biological complexity of AML and identify new and clinically relevant disease subtypes. We studied genome wide DNA methylation in a cohort of 295 patients from HOVON multicenter clinical trials using the HELP assay, which measures with >95% accuracy the abundance of DNA methylation at ~50,000 CpG sites covering ~13,000 promoter regions. Median follow-up was 18.2 months (range=0.1–214.5); median age: 48.1 years (range=15.8–75). Unsupervised analysis using hierarchical clustering (Pearson correlation distance with Ward’s clustering method) segregated the AMLs into 16 well-defined epigenetic clusters. Cluster 1 consisted 100% of patients with acute promyelocytic leukemia (n=6); 100% of cluster 4 harbored CEBPA mutations (n=14); 21/23 patients in cluster 6 carried an inv(16); clusters 7 and 9 were enriched for cases carrying the NPM1 mutation (#7: 80% NPM1+ and #9: 96%) and cluster 12 was enriched for t(8;21) AMLs (18/23). Most of the clusters however define previously unknown biological entities. Next we used a supervised analysis and identified the differentially methylated genes and gene networks that define each cluster, which revealed previously unknown biological differences among these patients. Moreover, Kaplan-Meier survival analysis revealed significant differences in event-free survival (EFS) and overall survival (OS) for the 8 clusters that consisted of >20 patients (clusters 5, 6, 7, 8, 9, 11, 12, and 14), which includes clusters that represent previously unidentified AML subtypes. The inv(16) and t(8;21) containing clusters (i.e. #6 and #12) demonstrated a 2-year EFS of 48% and 58%, respectively, compared to 2-year EFS ranging from 19%–44% for all other clusters (p=0.002 by log-rank test) and a 2-year OS of 70% and 61%, respectively, compared to 2-year OS ranging from 25%–50% for all other clusters (p=0.008 by log-rank test). After adjustment for age, cytogenetic risk, NPM1 mutation, and FLT3-itd status in a multivariate cox proportional hazards regression model, differences in EFS and OS remained between clusters i.e. multivariate analysis (utilizing cluster 12 as reference) showed that clusters 9, 5, 8 and 11 demonstrated hazard ratios for poor events of 3.2 (95% CI=1.0, 10.6; p=0.06), 3.2 (95% CI=1.1, 9.1; p=0.03), 3.4 (95% CI=1.2, 9.8; p=0.03) and 3.6 (95% CI=1.2, 10.3; p=0.02), respectively. Similarly, clusters 9, 8 and 11 demonstrated hazard ratios for mortality of 4.7 (95% CI=1.1, 19.8; p=0.03), 4.1 (95% CI=1.1, 15.2; p=0.03) and 3.7 (95% CI=1.0, 13.6; p=0.05), respectively. Interestingly none of these clusters could be entirely explained by any of the known molecular or cytogenetic markers. Clusters 9 and 5 consisted mainly of cases with normal karyotypes, while #8 and #11 grouped cases with a variety of karyotypes. Furthermore, cluster 9 was associated with a worse outcome despite the fact that 24/25 cases were NPM1+, only 11 of which also presented the poor risk association with FLT3-itd. An analysis restricted to the 125 cases with normal karyotype (NK-AML) segregated them into 2 main clusters, one enriched for NPM1+ cases (81.9%) and the other not (29.6% NPM1+) (Fisher exact test: p-value <2.6e-9). 13/14 cases with CEBPA mutations grouped in the NPM1 cluster, while the 52 FLT3-itd cases were equally distributed among the two clusters. A supervised analysis of NPM1+ NK-AMLs vs. cases without the mutation revealed a 167-gene signature of genes that were uniformly hypermethylated in NK-AML that did not carry the NPM1 mutation, suggesting that non-NPM1 NK-AML patients display common features indicative of a new AML subtype. These data show that rigorous analysis of epigenetic gene regulation in AML identifies novel and biologically relevant subgroups of AML with prognostic significance, and establishes the capture of epigenetic signatures as a new paradigm to improve understanding of disease pathogenesis and clinical behavior