Anduril 2: upgraded large-scale data integration framework.

SUMMARY: Anduril is an analysis and integration framework that facilitates the design, use, parallelization and reproducibility of bioinformatics workflows. Anduril has been upgraded to use Scala for pipeline construction, which simplifies software maintenance, and facilitates design of complex pipelines. Additionally, Anduril's bioinformatics repository has been expanded with multiple components, and tutorial pipelines, for next-generation sequencing data analysis. AVAILABILITYAND IMPLEMENTATION: Freely available at http://anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comprehensive evaluation of coding region point mutations in microsatellite-unstable colorectal cancer.

Microsatellite instability (MSI) leads to accumulation of an excessive number of mutations in the genome, mostly small insertions and deletions. MSI colorectal cancers (CRCs), however, also contain more point mutations than microsatellite-stable (MSS) tumors, yet they have not been as comprehensively studied. To identify candidate driver genes affected by point mutations in MSI CRC, we ranked genes based on mutation significance while correcting for replication timing and gene expression utilizing an algorithm, MutSigCV Somatic point mutation data from the exome kit-targeted area from 24 exome-sequenced sporadic MSI CRCs and respective normals, and 12 whole-genome-sequenced sporadic MSI CRCs and respective normals were utilized. The top 73 genes were validated in 93 additional MSI CRCs. The MutSigCV ranking identified several well-established MSI CRC driver genes and provided additional evidence for previously proposed CRC candidate genes as well as shortlisted genes that have to our knowledge not been linked to CRC before. Two genes, SMARCB1 and STK38L, were also functionally scrutinized, providing evidence of a tumorigenic role, for SMARCB1 mutations in particular.

Candidate susceptibility variants for esophageal squamous cell carcinoma.

Esophageal cancer is common worldwide, and often fatal. The major histological subtype is esophageal squamous cell carcinoma (ESCC). ESCC shows familial aggregation and high heritability. Mutations in RHBDF2 cause tylosis, a very rare disorder characterized by high life-time risk of ESCC, but no other well-established predisposition genes have been identified. To identify candidate susceptibility variants for ESCC we utilized the Population Information System and the Finnish cancer registry to find study materials by clustering ESCC patients by family name at birth and municipality at birth. We collected archival tissue material and exome sequenced a total of 30 ESCC cases. We prioritized shared, deleterious and rare variants that were significantly enriched in our sample set compared to Finnish and population subset specific controls. Six variants passed filtering, the most frequent being a nonsense mutation in DNAH9 (p.Tyr1573Ter) found in four unrelated patients. DNAH9 has been reported to be frequently lost in ESCC tumors. In this study, one patient's tumor showed loss of the wild type allele of DNAH9 suggesting a tumor suppressive function. A missense variant in GKAP1 was shared by three patients, and missense variants in BAG1, NFX1, FUK, and DDOST by two each. EP300 which has previously been implicated in the genesis of ESCC had a missense variant segregating in three affected individuals in a single family. If validated in independent patient sets, these variants could serve as a tool towards prevention and early diagnosis of ESCC.

Using Semantic Similarities and csbl.go for Analyzing Microarray Data.

Cellular phenotypes result from the combined effect of multiple genes, and high-throughput techniques such as DNA microarrays and deep sequencing allow monitoring this genomic complexity. The large scale of the resulting data, however, creates challenges for interpreting results, as primary analysis often yields hundreds of genes. Gene Ontology (GO), a controlled vocabulary for gene products, enables semantic analysis of such gene sets. GO can be used to define semantic similarity between genes, which enables semantic clustering to reduce the complexity of a result set. Here, we describe how to compute semantic similarities and perform GO-based gene clustering using csbl.go, an R package for GO semantic similarity. We demonstrate the approach with expression profiles from breast cancer.

Transmembrane prostatic acid phosphatase (TMPAP) delays cells in G1 phase of the cell cycle.

BACKGROUND: Prostate adenocarcinoma is the most common form of prostate cancer. We have previously shown in a murine model that prostatic acid phosphatase (PAP) deficiency leads to increased cell proliferation and development of prostate adenocarcinoma. The association between PAP and prostate cancer has been reported. Indeed, high PAP enzymatic activity is detected in the serum of patients with metastatic disease while its expression is reduced in prostate cancer tissue. However, the molecular mechanisms behind the onset of the disease remains poorly understood. We previously identified a novel transmembrane prostatic acid phosphatase (TMPAP) isoform, which interacts with snapin. TMPAP is expressed on the plasma membrane, as well as endosomal/lysosomal and exosomal membrane vesicles by means of a tyrosine-based lysosomal targeting motif (Yxxϕ). METHODS: We used stable overexpression of the secreted isoform (SPAP) and TMPAP in LNCaP cells, live cell imaging, microarray and qRT-PCR analyses, and fluid phase uptake of HRP and transferrin. RESULTS: Our results indicate that the stable overexpression of TMPAP, but not SPAP in LNCaP cells reduces cell growth while increasing endo/exocytosis and cell size. Specifically, cells overexpressing TMPAP accumulate in the G1 phase of the cell cycle, and show altered gene expression profile. CONCLUSIONS: Our data suggests that TMPAP may function as a non-canonical tumor suppressor by delaying cell growth in G1 phase of the cell cycle.

Molecular characterization of subcutaneous panniculitis-like T-cell lymphoma reveals upregulation of immunosuppression- and autoimmunity-associated genes.

BACKGROUND: Subcutaneous panniculitis-like T cell lymphomas represent a rare and difficult to diagnose entity of cutaneous T cell lymphomas. SPTL affects predominantly young adults and presents with multifocal subcutaneous nodules and frequently associated autoimmune features. The pathogenesis of SPTL is not completely understood. METHODS: The aim of this study was to unravel molecular pathways critical to the SPTL pathogenesis. Therefore, we analyzed 23 skin samples from 20 newly diagnosed SPTL patients and relevant control samples of adipose and non-malignant panniculitis tissue by using gene expression microarray, quantitative PCR, and two-colour immunohistochemistry. RESULTS: Interestingly, indoleamine 2,3-dioxygenase (IDO-1), an immunotolerance-inducing enzyme, was among the most highly overexpressed genes in all comparisons. The expression of Th1-specific cytokines, known to be associated with autoimmune inflammation (i.e. IFNG, CXCR3, CXCL9, CXCL10, CXCL11, and CCL5), were also significantly increased. Confirmed using immunohistochemistry, the morphologically malignant lymphocytes expressed CXCR3 and CXCL9. IDO-1 expression was found both in some morphologically malignant lymphocytes rimming the adipocytes and in surrounding CD11c(-) CD68(-) cells but not in CD11c(+) dendritic cells in the microenvironment. The proportion of FoxP3+ cells in SPTL exceeded that in the benign panniculitis samples. CONCLUSIONS: Our results indicate that the up regulation of the tolerogenic IDO-1 together with the up regulation of IFNG, CXCR3 ligands, and CCL5 are features of SPTL lesions. We anticipate that the IFNG-inducible IDO-1 expression contributes to the formation of an immunosuppressive microenvironment, favorable for the malignant T cells. This study provides a relevant molecular basis for further studies exploring novel therapeutic means for subcutaneous T cell lymphoma.

Transmembrane prostatic acid phosphatase (TMPAP) interacts with snapin and deficient mice develop prostate adenocarcinoma.

The molecular mechanisms underlying prostate carcinogenesis are poorly understood. Prostatic acid phosphatase (PAP), a prostatic epithelial secretion marker, has been linked to prostate cancer since the 1930's. However, the contribution of PAP to the disease remains controversial. We have previously cloned and described two isoforms of this protein, a secretory (sPAP) and a transmembrane type-I (TMPAP). The goal in this work was to understand the physiological function of TMPAP in the prostate. We conducted histological, ultra-structural and genome-wide analyses of the prostate of our PAP-deficient mouse model (PAP(-/-)) with C57BL/6J background. The PAP(-/-) mouse prostate showed the development of slow-growing non-metastatic prostate adenocarcinoma. In order to find out the mechanism behind, we identified PAP-interacting proteins byyeast two-hybrid assays and a clear result was obtained for the interaction of PAP with snapin, a SNARE-associated protein which binds Snap25 facilitating the vesicular membrane fusion process. We confirmed this interaction by co-localization studies in TMPAP-transfected LNCaP cells (TMPAP/LNCaP cells) and in vivo FRET analyses in transient transfected LNCaP cells. The differential gene expression analyses revealed the dysregulation of the same genes known to be related to synaptic vesicular traffic. Both TMPAP and snapin were detected in isolated exosomes. Our results suggest that TMPAP is involved in endo-/exocytosis and disturbed vesicular traffic is a hallmark of prostate adenocarcinoma.

Integrative analysis of deep sequencing data identifies estrogen receptor early response genes and links ATAD3B to poor survival in breast cancer.

Identification of responsive genes to an extra-cellular cue enables characterization of pathophysiologically crucial biological processes. Deep sequencing technologies provide a powerful means to identify responsive genes, which creates a need for computational methods able to analyze dynamic and multi-level deep sequencing data. To answer this need we introduce here a data-driven algorithm, SPINLONG, which is designed to search for genes that match the user-defined hypotheses or models. SPINLONG is applicable to various experimental setups measuring several molecular markers in parallel. To demonstrate the SPINLONG approach, we analyzed ChIP-seq data reporting PolII, estrogen receptor α (ERα), H3K4me3 and H2A.Z occupancy at five time points in the MCF-7 breast cancer cell line after estradiol stimulus. We obtained 777 ERa early responsive genes and compared the biological functions of the genes having ERα binding within 20 kb of the transcription start site (TSS) to genes without such binding site. Our results show that the non-genomic action of ERα via the MAPK pathway, instead of direct ERa binding, may be responsible for early cell responses to ERα activation. Our results also indicate that the ERα responsive genes triggered by the genomic pathway are transcribed faster than those without ERα binding sites. The survival analysis of the 777 ERα responsive genes with 150 primary breast cancer tumors and in two independent validation cohorts indicated the ATAD3B gene, which does not have ERα binding site within 20 kb of its TSS, to be significantly associated with poor patient survival.

Genomic region operation kit for flexible processing of deep sequencing data.

Computational analysis of data produced in deep sequencing (DS) experiments is challenging due to large data volumes and requirements for flexible analysis approaches. Here, we present a mathematical formalism based on set algebra for frequently performed operations in DS data analysis to facilitate translation of biomedical research questions to language amenable for computational analysis. With the help of this formalism, we implemented the Genomic Region Operation Kit (GROK), which supports various DS-related operations such as preprocessing, filtering, file conversion, and sample comparison. GROK provides high-level interfaces for R, Python, Lua, and command line, as well as an extension C++ API. It supports major genomic file formats and allows storing custom genomic regions in efficient data structures such as red-black trees and SQL databases. To demonstrate the utility of GROK, we have characterized the roles of two major transcription factors (TFs) in prostate cancer using data from 10 DS experiments. GROK is freely available with a user guide from >http://csbi.ltdk.helsinki.fi/grok/.

Neuron navigator 3 alterations in nervous system tumors associate with tumor malignancy grade and prognosis.

Copy number changes or reduced expression of the Neuron navigator 3 (NAV3) gene occurs in neuroblastomas and malignancies of epithelial or lymphoid origin. To elucidate whether NAV3 has a role in the tumorigenesis of nervous system tumors in general, we studied central and peripheral nervous system tumors for NAV3 copy number changes. In search for common tumorigenic denominators, we analyzed 113 central and peripheral nervous system tumors, including glial tumors (grades I-IV gliomas), medulloblastomas, and neuroblastomas. NAV3 copy number changes were studied by fluorescence in situ hybridization and correlated to survival analyses. To identify target genes of NAV3 deletion, NAV3 was silenced by siRNA in glioblastoma cell lines and gene expression profiles were analyzed by Agilent 4×44k dual-color microarrays. Selected upregulations were confirmed by immunohistochemistry and quantitative polymerase chain reaction. We found NAV3 amplifications to dominate in neuronally differentiated tumors, whereas glial tumors showed almost equal proportions of NAV3 deletion and amplification. However, Grade IV gliomas had more frequent NAV3 deletions than grades I-III gliomas. Silencing of NAV3 in glioma cell lines led to the upregulation of receptor genes associated with gonadotropin-releasing hormone and Jak-Stat signaling pathways. Kaplan-Meier analysis of the entire clinical tumor material showed association between NAV3 amplifications and favorable prognosis, as well as NAV3 deletions and unfavorable prognosis. With Cox regression model, a hazard ratio of 0.51 was observed for NAV3 amplifications and 1.36 for NAV3 deletions. We conclude that NAV3 may be a potential new prognostic biomarker and a potential therapeutic target.

FoxA1 specifies unique androgen and glucocorticoid receptor binding events in prostate cancer cells.

The forkhead protein FoxA1 has functions other than a pioneer factor, in that its depletion brings about a significant redistribution in the androgen receptor (AR) and glucocorticoid receptor (GR) cistromes. In this study, we found a novel function for FoxA1 in defining the cell-type specificity of AR- and GR-binding events in a distinct fashion, namely, for AR in LNCaP-1F5 cells and for GR in VCaP cells. We also found different, cell-type and receptor-specific compilations of cis-elements enriched adjacent to the AR- and GR-binding sites. The AR pathway is central in prostate cancer biology, but the role of GR is poorly known. We find that AR and GR cistromes and transcription programs exhibit significant overlap, and GR regulates a large number of genes considered to be AR pathway-specific. This raises questions about the role of GR in maintaining the AR pathway under androgen-deprived conditions in castration-resistant prostate cancer patients. However, in the presence of androgen, ligand-occupied GR acts as a partial antiandrogen and attenuates the AR-dependent transcription program. .

NAV3 copy number changes and target genes in basal and squamous cell cancers.

The neuron navigator 3 (NAV3) gene on chromosome 12q21 encodes a microtubule plus end tracking protein and belongs to the navigator family of cytoskeletal regulators. Loss of heterozygosity on 12q has previously been suggested to be associated with poor prognosis in cancers of epithelial origin. In this study, we characterized copy number changes of NAV3 in 24 basal cell cancers (BCCs), eight squamous cell cancers (SCCs) and eight non-malignant inflammatory skin lesions by fluorescent in situ hybridization. To identify genes affected by NAV3, we used oligo siRNA gene silencing and gene microarrays to analyse gene expression profiles at several time points post-transfection in primary human keratinocytes. We found NAV3 copy number loss and decreased protein expression in 21% of the BCCs and 25% of the SCCs. In the nodular/superficial BCC subgroup, low-level NAV3 amplification was also observed. NAV3 aberrations were independent of the known chromosome 6 amplifications in BCC. Chromosome 12 polysomy was detected in 33% and 25% of the invasive type of BCC and SCC, respectively. Silencing of NAV3 in primary human keratinocytes revealed 22 differentially expressed genes, mostly related to inflammation. The most relevant of these were validated with qPCR or immunohistochemistry. This pilot study suggests that NAV3 is a novel cancer-associated gene that contributes to the pathogenesis of a subgroup of BCC and SCC.

Dual role of FoxA1 in androgen receptor binding to chromatin, androgen signalling and prostate cancer.

High androgen receptor (AR) level in primary tumour predicts increased prostate cancer-specific mortality. However, the mechanisms that regulate AR function in prostate cancer are poorly known. We report here a new paradigm for the forkhead protein FoxA1 action in androgen signalling. Besides pioneering the AR pathway, FoxA1 depletion elicited extensive redistribution of AR-binding sites (ARBs) on LNCaP-1F5 cell chromatin that was commensurate with changes in androgen-dependent gene expression signature. We identified three distinct classes of ARBs and androgen-responsive genes: (i) independent of FoxA1, (ii) pioneered by FoxA1 and (iii) masked by FoxA1 and functional upon FoxA1 depletion. FoxA1 depletion also reprogrammed AR binding in VCaP cells, and glucocorticoid receptor binding and glucocorticoid-dependent signalling in LNCaP-1F5 cells. Importantly, FoxA1 protein level in primary prostate tumour had significant association to disease outcome; high FoxA1 level was associated with poor prognosis, whereas low FoxA1 level, even in the presence of high AR expression, predicted good prognosis. The role of FoxA1 in androgen signalling and prostate cancer is distinctly different from that in oestrogen signalling and breast cancer.

Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme.

BACKGROUND: Coordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualization are needed. METHODS: We introduce a novel data integration framework, Anduril, for translating fragmented large-scale data into testable predictions. The Anduril framework allows rapid integration of heterogeneous data with state-of-the-art computational methods and existing knowledge in bio-databases. Anduril automatically generates thorough summary reports and a website that shows the most relevant features of each gene at a glance, allows sorting of data based on different parameters, and provides direct links to more detailed data on genes, transcripts or genomic regions. Anduril is open-source; all methods and documentation are freely available. RESULTS: We have integrated multidimensional molecular and clinical data from 338 subjects having glioblastoma multiforme, one of the deadliest and most poorly understood cancers, using Anduril. The central objective of our approach is to identify genetic loci and genes that have significant survival effect. Our results suggest several novel genetic alterations linked to glioblastoma multiforme progression and, more specifically, reveal Moesin as a novel glioblastoma multiforme-associated gene that has a strong survival effect and whose depletion in vitro significantly inhibited cell proliferation. All analysis results are available as a comprehensive website. CONCLUSIONS: Our results demonstrate that integrated analysis and visualization of multidimensional and heterogeneous data by Anduril enables drawing conclusions on functional consequences of large-scale molecular data. Many of the identified genetic loci and genes having significant survival effect have not been reported earlier in the context of glioblastoma multiforme. Thus, in addition to generally applicable novel methodology, our results provide several glioblastoma multiforme candidate genes for further studies.Anduril is available at http://csbi.ltdk.helsinki.fi/anduril/The glioblastoma multiforme analysis results are available at http://csbi.ltdk.helsinki.fi/anduril/tcga-gbm/

Integrated network analysis platform for protein-protein interactions.

There is an increasing demand for network analysis of protein-protein interactions (PPIs). We introduce a web-based protein interaction network analysis platform (PINA), which integrates PPI data from six databases and provides network construction, filtering, analysis and visualization tools. We demonstrated the advantages of PINA by analyzing two human PPI networks; our results suggested a link between LKB1 and TGFbeta signaling, and revealed possible competitive interactors of p53 and c-Jun.

Fast gene ontology based clustering for microarray experiments.

BACKGROUND: Analysis of a microarray experiment often results in a list of hundreds of disease-associated genes. In order to suggest common biological processes and functions for these genes, Gene Ontology annotations with statistical testing are widely used. However, these analyses can produce a very large number of significantly altered biological processes. Thus, it is often challenging to interpret GO results and identify novel testable biological hypotheses. RESULTS: We present fast software for advanced gene annotation using semantic similarity for Gene Ontology terms combined with clustering and heat map visualisation. The methodology allows rapid identification of genes sharing the same Gene Ontology cluster. CONCLUSION: Our R based semantic similarity open-source package has a speed advantage of over 2000-fold compared to existing implementations. From the resulting hierarchical clustering dendrogram genes sharing a GO term can be identified, and their differences in the gene expression patterns can be seen from the heat map. These methods facilitate advanced annotation of genes resulting from data analysis.