Saija Haapa-Paananen

Systematic bioinformatic analysis of expression levels of 17,330 human genes across 9,783 samples from 175 types of healthy and pathological tissues

Our knowledge on tissue- and disease-specific functions of human genes is rather limited and highly context-specific. Here, we have developed a method for the comparison of mRNA expression levels of most human genes across 9,783 Affymetrix gene expression array experiments representing 43 normal human tissue types, 68 cancer types, and 64 other diseases. This database of gene expression patterns in normal human tissues and pathological conditions covers 113 million datapoints and is available from the GeneSapiens website.

Protein lysate microarray analysis to identify microRNAs regulating estrogen receptor signaling in breast cancer cell lines.

Predicting the impact of microRNAs (miRNAs) on target proteins is challenging because of their different regulatory effects at the transcriptional and translational levels. In this study, we applied a novel protein lysate microarray (LMA) technology to systematically monitor for target protein levels after high-throughput transfections of 319 pre-miRs into breast cancer cells. We identified 21 miRNAs that downregulated the estrogen receptor-α (ERα), as validated by western blotting and quantitative real time–PCR, and by demonstrating the inhibition of estrogen-stimulated cell growth. Five potent ERα-regulating miRNAs, miR-18a, miR-18b, miR-193b, miR-206 and miR-302c, were confirmed to directly target ERα in 3′-untranslated region reporter assays. The gene expression signature that they repressed highly overlapped with that of a small interfering RNA against ERα, and across all the signatures tested, was most closely associated with the repression of known estrogen-induced genes. Furthermore, miR-18a and miR-18b showed higher levels of expression in ERα-negative as compared with ERα-positive clinical tumors. In summary, we present systematic and direct functional evidence of miRNAs inhibiting ERα signaling in breast cancer, and demonstrate the high-throughput LMA technology as a novel, powerful technique in determining the relative impact of various miRNAs on key target proteins and associated cellular processes and pathways.

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

BackgroundCoordinated 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.MethodsWe 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.ResultsWe 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.ConclusionsOur 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/

Identification of target genes in laryngeal squamous cell carcinoma by high-resolution copy number and gene expression microarray analyses

Molecular mechanisms contributing to initiation and progression of head and neck squamous cell carcinoma are still poorly known. Numerous genetic alterations have been described, but molecular consequences of such alterations in most cases remain unclear. Here, we performed an integrated high-resolution microarray analysis of gene copy number and expression in 20 laryngeal cancer cell lines and primary tumors. Our aim was to identify genetic alterations that play a key role in disease pathogenesis and pinpoint genes whose expression is directly impacted by these events. Integration of DNA level data from array-based comparative genomic hybridization with RNA level information from oligonucleotide microarrays was achieved with custom-developed bioinformatic methods. High-level amplifications had a clear impact on gene expression. Across the genome, overexpression of 739 genes could be attributed to gene amplification events in cell lines, with 325 genes showing the same phenomenon in primary tumors including FADD and PPFIA1 at 11q13. The analysis of gene ontology and pathway distributions further pinpointed genes that may identify potential targets of therapeutic intervention. Our data highlight genes that may be critically important to laryngeal cancer progression and offer potential therapeutic targets.

Interactions between Notch- and hypoxia-induced transcriptomes in embryonic stem cells.

Interaction between key signaling mechanisms is important to generate the diversity in signaling output required for proper control of cellular differentiation and function, although the molecular manifestations of such cross-talk are only partially understood. Notch signaling and the cellular response to hypoxia intersect at different points in the signaling cascades, and in this report we analyze the consequences of this cross-talk at the transcriptome level. Mouse ES cells were subjected to various combinations of hypoxia and/or activated Notch signaling, and the transcriptome changes could be grouped into different categories, reflecting various modes of hypoxia and Notch signaling integration. Two principal categories of novel Notch- and hypoxia-induced genes were identified: (i) a larger set of Notch or hypoxic target genes which were induced by one pathway and not significantly affected by the activity status of the other pathway and (ii) a smaller set of genes co-regulated by Notch and hypoxia. In the latter category, we identified genes that were induced by hypoxia and the expression of which was enhanced by active Notch signaling and another group of genes that were induced by Notch and hypoxia independently. Several of the hypoxia- and Notch-induced genes were found to be upregulated in various forms of cancer. Identification of genes co-regulated by the two pathways may provide a molecular platform to better understand the intersection between the two signaling cascades in normal development and cancer.

Functional Profiling of Precursor MicroRNAs Identifies MicroRNAs Essential for Glioma Proliferation

Cancer initiation and progression involve microRNAs that can function like tumor suppressors and oncogenes. The functional significance of most miRNAs is currently unknown. To determine systematically which microRNAs are essential for glioma growth, we screened a precursor microRNA library in three human glioblastoma and one astroglial cell line model systems. The most prominent and consistent cell proliferation–reducing hits were validated in secondary screening with an additional apoptosis endpoint. The functional screening data were integrated in the miRNA expression data to find underexpressed true functional tumor suppressor miRNAs. In addition, we used miRNA-target gene predictions and combined siRNA functional screening data to find the most probable miRNA-target gene pairs with a similar functional effect on proliferation. Nine novel functional miRNAs (hsa-miR-129, -136, -145, -155, -181b, -342-5p, -342-3p, -376a/b) in GBM cell lines were validated for their importance in glioma cell growth, and similar effects for six target genes (ROCK1, RHOA, MET, CSF1R, EIF2AK1, FGF7) of these miRNAs were shown functionally. The clinical significance of the functional hits was validated in miRNA expression data from the TCGA glioblastoma multiforme (GBM) tumor cohort. Five tumor suppressor miRNAs (hsa-miR-136, -145, -342, -129, -376a) showed significant underexpression in clinical GBM tumor samples from the TCGA GBM cohort further supporting the role of these miRNAs in vivo. Most importantly, higher hsa-miR-145 expression in GBM tumors yielded significantly better survival (p<0.005) in a subset of patients thus validating it as a genuine tumor suppressor miRNA. This systematic functional profiling provides important new knowledge about functionally relevant miRNAs in GBM biology and may offer new targets for treating glioma.

GTI: A Novel Algorithm for Identifying Outlier Gene Expression Profiles from Integrated Microarray Datasets

Background Meta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type (‘outlier genes’), a hallmark of potential oncogenes. Methodology A new statistical method (the gene tissue index, GTI) was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29) of these genes, and 17 of these 19 genes (90%) showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target. Conclusions/Significance Taken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is implemented in an R package (Text S1).

Integrative omics reveals MYCN as a global suppressor of cellular signalling and enables network-based therapeutic target discovery in neuroblastoma

Despite intensive study, many mysteries remain about the MYCN oncogenes functions. Here we focus on MYCNs role in neuroblastoma, the most common extracranial childhood cancer. MYCN gene amplification occurs in 20% of cases, but other recurrent somatic mutations are rare. This scarcity of tractable targets has hampered efforts to develop new therapeutic options. We employed a multi-level omics approach to examine MYCN functioning and identify novel therapeutic targets for this largely un-druggable oncogene. We used systems medicine based computational network reconstruction and analysis to integrate a range of omic techniques: sequencing-based transcriptomics, genome-wide chromatin immunoprecipitation, siRNA screening and interaction proteomics, revealing that MYCN controls highly connected networks, with MYCN primarily supressing the activity of network components. MYCNs oncogenic functions are likely independent of its classical heterodimerisation partner, MAX. In particular, MYCN controls its own protein interaction network by transcriptionally regulating its binding partners. Our network-based approach identified vulnerable therapeutically targetable nodes that function as critical regulators or effectors of MYCN in neuroblastoma. These were validated by siRNA knockdown screens, functional studies and patient data. We identified β-estradiol and MAPK/ERK as having functional cross-talk with MYCN and being novel targetable vulnerabilities of MYCN-amplified neuroblastoma. These results reveal surprising differences between the functioning of endogenous, overexpressed and amplified MYCN, and rationalise how different MYCN dosages can orchestrate cell fate decisions and cancerous outcomes. Importantly, this work describes a systems-level approach to systematically uncovering network based vulnerabilities and therapeutic targets for multifactorial diseases by integrating disparate omic data types.

HES6 gene is selectively overexpressed in glioma and represents an important transcriptional regulator of glioma proliferation

Malignant glioma is the most common brain tumor with 16 000 new cases diagnosed annually in the United States. We performed a systematic large-scale transcriptomics data mining study of 9783 tissue samples from the GeneSapiens database to systematically identify genes that are most glioma-specific. We searched for genes that were highly expressed in 322 glioblastoma multiforme tissue samples and 66 anaplastic astrocytomas as compared with 425 samples from histologically normal central nervous system. Transcription cofactor HES6 (hairy and enhancer of split 6) emerged as the most glioma-specific gene. Immunostaining of a tissue microarray showed HES6 expression in 335 (98.8%) out of the 339 glioma samples. HES6 was expressed in endothelial cells of the normal brain and glioma tissue. Recurrent grade 2 astrocytomas and grade 2 or 3 oligodendrogliomas showed higher levels of HES6 immunoreactivity than the corresponding primary tumors. High HES6 mRNA expression correlated with the proneural subtype that generally has a favorable outcome but is prone to recur. Functional studies suggested an important role for HES6 in supporting survival of glioma cells, as evidenced by reduction of cancer cell proliferation and migration after HES6 silencing. The biological role and consequences of HES6 silencing and overexpression was explored with genome-wide analyses, which implicated a role for HES6 in p53, c-myc and nuclear factor-κB transcriptional networks. We conclude that HES6 is important for glioma cell proliferation and migration, and may have a role in angiogenesis.

Wnt signalling is a bi-directional vulnerability of cancer cells

Wnt signalling is involved in the formation, metastasis and relapse of a wide array of cancers. However, there is ongoing debate as to whether activation or inhibition of the pathway holds the most promise as a therapeutic treatment for cancer, with conflicting evidence from a variety of tumour types. We show that Wnt/β-catenin signalling is a bi-directional vulnerability of neuroblastoma, malignant melanoma and colorectal cancer, with hyper-activation or repression of the pathway both representing a promising therapeutic strategy, even within the same cancer type. Hyper-activation directs cancer cells to undergo apoptosis, even in cells oncogenically driven by β-catenin. Wnt inhibition blocks proliferation of cancer cells and promotes neuroblastoma differentiation. Wnt and retinoic acid co-treatments synergise, representing a promising combination treatment for MYCN-amplified neuroblastoma. Additionally, we report novel cross-talks between MYCN and β-catenin signalling, which repress normal β-catenin mediated transcriptional regulation. A β-catenin target gene signature could predict patient outcome, as could the expression level of its DNA binding partners, the TCF/LEFs. This β-catenin signature provides a tool to identify neuroblastoma patients likely to benefit from Wnt-directed therapy. Taken together, we show that Wnt/β-catenin signalling is a bi-directional vulnerability of a number of cancer entities, and potentially a more broadly conserved feature of malignant cells.