Functional Categories Associated with Clusters of Genes That Are Co-Expressed across the NCI-60 Cancer Cell Lines

The NCI-60 is a panel of 60 diverse human cancer cell lines used by the U.S. National Cancer Institute to screen compounds for anticancer activity. In the current study, gene expression levels from five platforms were integrated to yield a single composite transcriptome profile. The comprehensive and reliable nature of that dataset allows us to study gene co-expression across cancer cell lines.Hierarchical clustering revealed numerous clusters of genes in which the genes co-vary across the NCI-60. To determine functional categorization associated with each cluster, we used the Gene Ontology (GO) Consortium database and the GoMiner tool. GO maps genes to hierarchically-organized biological process categories. GoMiner can leverage GO to perform ontological analyses of gene expression studies, generating a list of significant functional categories.GoMiner analysis revealed many clusters of coregulated genes that are associated with functional groupings of GO biological process categories. Notably, those categories arising from coherent co-expression groupings reflect cancer-related themes such as adhesion, cell migration, RNA splicing, immune response and signal transduction. Thus, these clusters demonstrate transcriptional coregulation of functionally-related genes

SFSSClass: an integrated approach for miRNA based tumor classification

Background: MicroRNA (miRNA) expression profiling data has recently been found to be particularly important in cancer research and can be used as a diagnostic and prognostic tool. Current approaches of tumor classification using miRNA expression data do not integrate the experimental knowledge available in the literature. A judicious integration of such knowledge with effective miRNA and sample selection through a biclustering approach could be an important step in improving the accuracy of tumor classification. Results: In this article, a novel classification technique called SFSSClass is developed that judiciously integrates a biclustering technique SAMBA for simultaneous feature (miRNA) and sample (tissue) selection (SFSS), a cancer-miRNA network that we have developed by mining the literature of experimentally verified cancer-miRNA relationships and a classifier uncorrelated shrunken centroid (USC). SFSSClass is used for classifying multiple classes of tumors and cancer cell lines. In a part of the investigation, poorly differentiated tumors (PDT) having non diagnostic histological appearance are classified while training on more differentiated tumor (MDT) samples. The proposed method is found to outperform the best known accuracy in the literature on the experimental data sets. For example, while the best accuracy reported in the literature for classifying PDT samples is similar to 76.5%, the accuracy of SFSSClass is found to be similar to 82.3%. The advantage of incorporating biclustering integrated with the cancer-miRNA network is evident from the consistently better performance of SFSSClass (integration of SAMBA, cancer-miRNA network and USC) over USC (eg., similar to 70.5% for SFSSClass versus similar to 58.8% in classifying a set of 17 MDT samples from 9 tumor types, similar to 91.7% for SFSSClass versus similar to 75% in classifying 12 cell lines from 6 tumor types and similar to 382.3% for SFSSClass versus similar to 41.2% in classifying 17 PDT samples from 11 tumor types). Conclusion: In this article, we develop the SFSSClass algorithm which judiciously integrates a biclustering technique for simultaneous feature (miRNA) and sample (tissue) selection, the cancer-miRNA network and a classifier. The novel integration of experimental knowledge with computational tools efficiently selects relevant features that have high intra-class and low interclass similarity. The performance of the SFSSClass is found to be significantly improved with respect to the other existing approaches

Using Expression and Genotype to Predict Drug Response in Yeast

Personalized, or genomic, medicine entails tailoring pharmacological therapies according to individual genetic variation at genomic loci encoding proteins in drug-response pathways. It has been previously shown that steady-state mRNA expression can be used to predict the drug response (i.e., sensitivity or resistance) of non-genotyped mammalian cancer cell lines to chemotherapeutic agents. In a real-world setting, clinicians would have access to both steady-state expression levels of patient tissue(s) and a patient's genotypic profile, and yet the predictive power of transcripts versus markers is not well understood. We have previously shown that a collection of genotyped and expression-profiled yeast strains can provide a model for personalized medicine. Here we compare the predictive power of 6,229 steady-state mRNA transcript levels and 2,894 genotyped markers using a pattern recognition algorithm. We were able to predict with over 70% accuracy the drug sensitivity of 104 individual genotyped yeast strains derived from a cross between a laboratory strain and a wild isolate. We observe that, independently of drug mechanism of action, both transcripts and markers can accurately predict drug response. Marker-based prediction is usually more accurate than transcript-based prediction, likely reflecting the genetic determination of gene expression in this cross

Gene Expression Profiles of the NCI-60 Human Tumor Cell Lines Define Molecular Interaction Networks Governing Cell Migration Processes

Although there is extensive information on gene expression and molecular interactions in various cell types, integrating those data in a functionally coherent manner remains challenging. This study explores the premise that genes whose expression at the mRNA level is correlated over diverse cell lines are likely to function together in a network of molecular interactions. We previously derived expression-correlated gene clusters from the database of the NCI-60 human tumor cell lines and associated each cluster with function categories of the Gene Ontology (GO) database. From a cluster rich in genes associated with GO categories related to cell migration, we extracted 15 genes that were highly cross-correlated; prominent among them were RRAS, AXL, ADAM9, FN14, and integrin-beta1. We then used those 15 genes as bait to identify other correlated genes in the NCI-60 database. A survey of current literature disclosed, not only that many of the expression-correlated genes engaged in molecular interactions related to migration, invasion, and metastasis, but that highly cross-correlated subsets of those genes engaged in specific cell migration processes. We assembled this information in molecular interaction maps (MIMs) that depict networks governing 3 cell migration processes: degradation of extracellular matrix, production of transient focal complexes at the leading edge of the cell, and retraction of the rear part of the cell. Also depicted are interactions controlling the release and effects of calcium ions, which may regulate migration in a spaciotemporal manner in the cell. The MIMs and associated text comprise a detailed and integrated summary of what is currently known or surmised about the role of the expression cross-correlated genes in molecular networks governing those processes

Consensus-Phenotype Integration of Transcriptomic and Metabolomic Data Implies a Role for Metabolism in the Chemosensitivity of Tumour Cells

Using transcriptomic and metabolomic measurements from the NCI60 cell line panel, together with a novel approach to integration of molecular profile data, we show that the biochemical pathways associated with tumour cell chemosensitivity to platinum-based drugs are highly coincident, i.e. they describe a consensus phenotype. Direct integration of metabolome and transcriptome data at the point of pathway analysis improved the detection of consensus pathways by 76%, and revealed associations between platinum sensitivity and several metabolic pathways that were not visible from transcriptome analysis alone. These pathways included the TCA cycle and pyruvate metabolism, lipoprotein uptake and nucleotide synthesis by both salvage and de novo pathways. Extending the approach across a wide panel of chemotherapeutics, we confirmed the specificity of the metabolic pathway associations to platinum sensitivity. We conclude that metabolic phenotyping could play a role in predicting response to platinum chemotherapy and that consensus-phenotype integration of molecular profiling data is a powerful and versatile tool for both biomarker discovery and for exploring the complex relationships between biological pathways and drug response

Anti-cancer potential of MAPK pathway inhibition in paragangliomas-effect of different statins on mouse pheochromocytoma cells.

To date, malignant pheochromocytomas and paragangliomas (PHEOs/PGLs) cannot be effectively cured and thus novel treatment strategies are urgently needed. Lovastatin has been shown to effectively induce apoptosis in mouse PHEO cells (MPC) and the more aggressive mouse tumor tissue-derived cells (MTT), which was accompanied by decreased phosphorylation of mitogen-activated kinase (MAPK) pathway players. The MAPK pathway plays a role in numerous aggressive tumors and has been associated with a subgroup of PHEOs/PGLs, including K-RAS-, RET-, and NF1-mutated tumors. Our aim was to establish whether MAPK signaling may also play a role in aggressive, succinate dehydrogenase (SDH) B mutation-derived PHEOs/PGLs. Expression profiling and western blot analysis indicated that specific aspects of MAPK-signaling are active in SDHB PHEOs/PGLs, suggesting that inhibition by statin treatment could be beneficial. Moreover, we aimed to assess whether the anti-proliferative effect of lovastatin on MPC and MTT differed from that exerted by fluvastatin, simvastatin, atorvastatin, pravastatin, or rosuvastatin. Simvastatin and fluvastatin decreased cell proliferation most effectively and the more aggressive MTT cells appeared more sensitive in this respect. Inhibition of MAPK1 and 3 phosphorylation following treatment with fluvastatin, simvastatin, and lovastatin was confirmed by western blot. Increased levels of CASP-3 and PARP cleavage confirmed induction of apoptosis following the treatment. At a concentration low enough not to affect cell proliferation, spontaneous migration of MPC and MTT was significantly inhibited within 24 hours of treatment. In conclusion, lipophilic statins may present a promising therapeutic option for treatment of aggressive human paragangliomas by inducing apoptosis and inhibiting tumor spread

SPARC: a matricellular regulator of tumorigenesis

Although many clinical studies have found a correlation of SPARC expression with malignant progression and patient survival, the mechanisms for SPARC function in tumorigenesis and metastasis remain elusive. The activity of SPARC is context- and cell-type-dependent, which is highlighted by the fact that SPARC has shown seemingly contradictory effects on tumor progression in both clinical correlative studies and in animal models. The capacity of SPARC to dictate tumorigenic phenotype has been attributed to its effects on the bioavailability and signaling of integrins and growth factors/chemokines. These molecular pathways contribute to many physiological events affecting malignant progression, including extracellular matrix remodeling, angiogenesis, immune modulation and metastasis. Given that SPARC is credited with such varied activities, this review presents a comprehensive account of the divergent effects of SPARC in human cancers and mouse models, as well as a description of the potential mechanisms by which SPARC mediates these effects. We aim to provide insight into how a matricellular protein such as SPARC might generate paradoxical, yet relevant, tumor outcomes in order to unify an apparently incongruent collection of scientific literature

Over-elongation of centrioles in cancer promotes centriole amplification and chromosome missegregation

G.M. and A.G. were funded by the FCT-Harvard Medical School Program Portugal grant (HMSP-CT/SAU-ICT/0075/2009) and individual FCT post-doctoral fellowships (SFRH/BPD/98439/2013 and SFRH/BPD/82420/2011, respectively). The M.B-D. laboratory is supported by IGC, an EMBO installation grant, ERC grant ERC-2010-StG-261344, FCT grants (FCT Investigator to M.B-D., POCI-01-0145-FEDER-016390 and PTDC/BIM-ONC/6858/2014) and a FCT-Harvard Medical School Program Portugal grant (HMSP-CT/SAU-ICT/0075/2009)