Riku Louhimo

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/

Individual and combined effects of DNA methylation and copy number alterations on miRNA expression in breast tumors

BackgroundThe global effect of copy number and epigenetic alterations on miRNA expression in cancer is poorly understood. In the present study, we integrate genome-wide DNA methylation, copy number and miRNA expression and identify genetic mechanisms underlying miRNA dysregulation in breast cancer.ResultsWe identify 70 miRNAs whose expression was associated with alterations in copy number or methylation, or both. Among these, five miRNA families are represented. Interestingly, the members of these families are encoded on different chromosomes and are complementarily altered by gain or hypomethylation across the patients. In an independent breast cancer cohort of 123 patients, 41 of the 70 miRNAs were confirmed with respect to aberration pattern and association to expression. In vitro functional experiments were performed in breast cancer cell lines with miRNA mimics to evaluate the phenotype of the replicated miRNAs. let-7e-3p, which in tumors is found associated with hypermethylation, is shown to induce apoptosis and reduce cell viability, and low let-7e-3p expression is associated with poorer prognosis. The overexpression of three other miRNAs associated with copy number gain, miR-21-3p, miR-148b-3p and miR-151a-5p, increases proliferation of breast cancer cell lines. In addition, miR-151a-5p enhances the levels of phosphorylated AKT protein.ConclusionsOur data provide novel evidence of the mechanisms behind miRNA dysregulation in breast cancer. The study contributes to the understanding of how methylation and copy number alterations influence miRNA expression, emphasizing miRNA functionality through redundant encoding, and suggests novel miRNAs important in breast cancer.

CNAmet: an R package for integrating copy number, methylation and expression data

SUMMARY Gene copy number and DNA methylation alterations are key regulators of gene expression in cancer. Accordingly, genes that show simultaneous methylation, copy number and expression alterations are likely to have a key role in tumor progression. We have implemented a novel software package (CNAmet) for integrative analysis of high-throughput copy number, DNA methylation and gene expression data. To demonstrate the utility of CNAmet, we use copy number, DNA methylation and gene expression data from 50 glioblastoma multiforme and 188 ovarian cancer primary tumor samples. Our results reveal a synergistic effect of DNA methylation and copy number alterations on gene expression for several known oncogenes as well as novel candidate oncogenes. AVAILABILITY CNAmet R-package and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/CNAmet.

Comparative analysis of methods for identifying somatic copy number alterations from deep sequencing data

Somatic copy-number alterations (SCNAs) are an important type of structural variation affecting tumor pathogenesis. Accurate detection of genomic regions with SCNAs is crucial for cancer genomics as these regions contain likely drivers of cancer development. Deep sequencing technology provides single-nucleotide resolution genomic data and is considered one of the best measurement technologies to detect SCNAs. Although several algorithms have been developed to detect SCNAs from whole-genome and whole-exome sequencing data, their relative performance has not been studied. Here, we have compared ten SCNA detection algorithms in both simulated and primary tumor deep sequencing data. In addition, we have evaluated the applicability of exome sequencing data for SCNA detection. Our results show that (i) clear differences exist in sensitivity and specificity between the algorithms, (ii) SCNA detection algorithms are able to identify most of the complex chromosomal alterations and (iii) exome sequencing data are suitable for SCNA detection.

Comparative analysis of algorithms for integration of copy number and expression data

Chromosomal instability is a hallmark of cancer, and genes that display abnormal expression in aberrant chromosomal regions are likely to be key players in tumor progression. Identifying such driver genes reliably requires computational methods that can integrate genome-scale data from several sources. We compared the performance of ten algorithms that integrate copy-number and transcriptomics data from 15 head and neck squamous cell carcinoma cell lines, 129 lung squamous cell carcinoma primary tumors and simulated data. Our results revealed clear differences between the methods in terms of sensitivity and specificity as well as in their performance in small and large sample sizes. Results of the comparison are available at http://csbi.ltdk.helsinki.fi/cn2gealgo/.

MMP16 Mediates a Proteolytic Switch to Promote Cell-Cell Adhesion, Collagen Alignment, and Lymphatic Invasion in Melanoma

Lymphatic invasion and accumulation of continuous collagen bundles around tumor cells are associated with poor melanoma prognosis, but the underlying mechanisms and molecular determinants have remained unclear. We show here that a copy-number gain or overexpression of the membrane-type matrix metalloproteinase MMP16 (MT3-MMP) is associated with poor clinical outcome, collagen bundle assembly around tumor cell nests, and lymphatic invasion. In cultured WM852 melanoma cells derived from human melanoma metastasis, silencing of MMP16 resulted in cell-surface accumulation of the MMP16 substrate MMP14 (MT1-MMP) as well as L1CAM cell adhesion molecule, identified here as a novel MMP16 substrate. When limiting the activities of these trans-membrane protein substrates toward pericellular collagen degradation, cell junction disassembly, and blood endothelial transmigration, MMP16 supported nodular-type growth of adhesive collagen-surrounded melanoma cell nests, coincidentally steering cell collectives into lymphatic vessels. These results uncover a novel mechanism in melanoma pathogenesis, whereby restricted collagen infiltration and limited mesenchymal invasion are unexpectedly associated with the properties of the most aggressive tumors, revealing MMP16 as a putative indicator of adverse melanoma prognosis.

Deregulation of COMMD1 Is Associated with Poor Prognosis in Diffuse Large B-cell Lymphoma

Background Despite improved survival for the patients with diffuse large B-cell lymphoma (DLBCL), the prognosis after relapse is poor. The aim was to identify molecular events that contribute to relapse and treatment resistance in DLBCL. Methods We analysed 51 prospectively collected pretreatment tumour samples from clinically high risk patients treated in a Nordic phase II study with dose-dense chemoimmunotherapy and central nervous system prophylaxis with high resolution array comparative genomic hybridization (aCGH) and gene expression microarrays. Major finding was validated at the protein level immunohistochemically in a trial specific tissue microarray series of 70, and in an independent validation series of 146 patients. Results We identified 31 genes whose expression changes were strongly associated with copy number aberrations. In addition, gains of chromosomes 2p15 and 18q12.2 were associated with unfavourable survival. The 2p15 aberration harboured COMMD1 gene, whose expression had a significant adverse prognostic impact on survival. Immunohistochemical analysis of COMMD1 expression in two series confirmed the association of COMMD1 expression with poor prognosis. Conclusion COMMD1 is a potential novel prognostic factor in DLBCLs. The results highlight the value of integrated comprehensive analysis to identify prognostic markers and genetic driver events not previously implicated in DLBCL. Trial Registration ClinicalTrials.gov NCT01502982

Identification of several potential chromatin binding sites of HOXB7 and its downstream target genes in breast cancer

HOXB7 encodes a transcription factor that is overexpressed in a number of cancers and encompasses many oncogenic functions. Previous results have shown it to promote cell proliferation, angiogenesis, epithelial–mesenchymal transition, DNA repair and cell survival. Because of its role in many cancers and tumorigenic processes, HOXB7 has been suggested to be a potential drug target. However, HOXB7 binding sites on chromatin and its targets are poorly known. The aim of our study was to identify HOXB7 binding sites on breast cancer cell chromatin and to delineate direct target genes located nearby these binding sites. We found 1,504 HOXB7 chromatin binding sites in BT‐474 breast cancer cell line that overexpresses HOXB7. Seventeen selected binding sites were validated by ChIP‐qPCR in several breast cancer cell lines. Furthermore, we analyzed expression of a large number of genes located nearby HOXB7 binding sites and found several new direct targets, such as CTNND2 and SCGB1D2. Identification of HOXB7 chromatin binding sites and target genes is essential to understand better the role of HOXB7 in breast cancer and mechanisms by which it regulates tumorigenic processes.

Data integration to prioritize drugs using genomics and curated data

BackgroundGenomic alterations affecting drug target proteins occur in several tumor types and are prime candidates for patient-specific tailored treatments. Increasingly, patients likely to benefit from targeted cancer therapy are selected based on molecular alterations. The selection of a precision therapy benefiting most patients is challenging but can be enhanced with integration of multiple types of molecular data. Data integration approaches for drug prioritization have successfully integrated diverse molecular data but do not take full advantage of existing data and literature.ResultsWe have built a knowledge-base which connects data from public databases with molecular results from over 2200 tumors, signaling pathways and drug-target databases. Moreover, we have developed a data mining algorithm to effectively utilize this heterogeneous knowledge-base. Our algorithm is designed to facilitate retargeting of existing drugs by stratifying samples and prioritizing drug targets. We analyzed 797 primary tumors from The Cancer Genome Atlas breast and ovarian cancer cohorts using our framework. FGFR, CDK and HER2 inhibitors were prioritized in breast and ovarian data sets. Estrogen receptor positive breast tumors showed potential sensitivity to targeted inhibitors of FGFR due to activation of FGFR3.ConclusionsOur results suggest that computational sample stratification selects potentially sensitive samples for targeted therapies and can aid in precision medicine drug repositioning. Source code is available from http://csblcanges.fimm.fi/GOPredict/.

DNA methylation signature (SAM40) identifies subgroups of the Luminal A breast cancer samples with distinct survival

Breast cancer patients with Luminal A disease generally have a good prognosis, but among this patient group are patients with good prognosis that are currently overtreated with adjuvant chemotherapy, and also patients that have a bad prognosis and should be given more aggressive treatment. There is no available method for subclassification of this patient group. Here we present a DNA methylation signature (SAM40) that segregates Luminal A patients based on prognosis, and identify one good prognosis group and one bad prognosis group. The prognostic impact of SAM40 was validated in four independent patient cohorts. Being able to subdivide the Luminal A patients may give the two-sided benefit of identifying one subgroup that may benefit from a more aggressive treatment than what is given today, and importantly, identifying a subgroup that may benefit from less treatment.