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1.  Comprehensive molecular characterization of gastric adenocarcinoma 
Bass, Adam J. | Thorsson, Vesteinn | Shmulevich, Ilya | Reynolds, Sheila M. | Miller, Michael | Bernard, Brady | Hinoue, Toshinori | Laird, Peter W. | Curtis, Christina | Shen, Hui | Weisenberger, Daniel J. | Schultz, Nikolaus | Shen, Ronglai | Weinhold, Nils | Kelsen, David P. | Bowlby, Reanne | Chu, Andy | Kasaian, Katayoon | Mungall, Andrew J. | Robertson, A. Gordon | Sipahimalani, Payal | Cherniack, Andrew | Getz, Gad | Liu, Yingchun | Noble, Michael S. | Pedamallu, Chandra | Sougnez, Carrie | Taylor-Weiner, Amaro | Akbani, Rehan | Lee, Ju-Seog | Liu, Wenbin | Mills, Gordon B. | Yang, Da | Zhang, Wei | Pantazi, Angeliki | Parfenov, Michael | Gulley, Margaret | Piazuelo, M. Blanca | Schneider, Barbara G. | Kim, Jihun | Boussioutas, Alex | Sheth, Margi | Demchok, John A. | Rabkin, Charles S. | Willis, Joseph E. | Ng, Sam | Garman, Katherine | Beer, David G. | Pennathur, Arjun | Raphael, Benjamin J. | Wu, Hsin-Ta | Odze, Robert | Kim, Hark K. | Bowen, Jay | Leraas, Kristen M. | Lichtenberg, Tara M. | Weaver, Stephanie | McLellan, Michael | Wiznerowicz, Maciej | Sakai, Ryo | Getz, Gad | Sougnez, Carrie | Lawrence, Michael S. | Cibulskis, Kristian | Lichtenstein, Lee | Fisher, Sheila | Gabriel, Stacey B. | Lander, Eric S. | Ding, Li | Niu, Beifang | Ally, Adrian | Balasundaram, Miruna | Birol, Inanc | Bowlby, Reanne | Brooks, Denise | Butterfield, Yaron S. N. | Carlsen, Rebecca | Chu, Andy | Chu, Justin | Chuah, Eric | Chun, Hye-Jung E. | Clarke, Amanda | Dhalla, Noreen | Guin, Ranabir | Holt, Robert A. | Jones, Steven J.M. | Kasaian, Katayoon | Lee, Darlene | Li, Haiyan A. | Lim, Emilia | Ma, Yussanne | Marra, Marco A. | Mayo, Michael | Moore, Richard A. | Mungall, Andrew J. | Mungall, Karen L. | Nip, Ka Ming | Robertson, A. Gordon | Schein, Jacqueline E. | Sipahimalani, Payal | Tam, Angela | Thiessen, Nina | Beroukhim, Rameen | Carter, Scott L. | Cherniack, Andrew D. | Cho, Juok | Cibulskis, Kristian | DiCara, Daniel | Frazer, Scott | Fisher, Sheila | Gabriel, Stacey B. | Gehlenborg, Nils | Heiman, David I. | Jung, Joonil | Kim, Jaegil | Lander, Eric S. | Lawrence, Michael S. | Lichtenstein, Lee | Lin, Pei | Meyerson, Matthew | Ojesina, Akinyemi I. | Pedamallu, Chandra Sekhar | Saksena, Gordon | Schumacher, Steven E. | Sougnez, Carrie | Stojanov, Petar | Tabak, Barbara | Taylor-Weiner, Amaro | Voet, Doug | Rosenberg, Mara | Zack, Travis I. | Zhang, Hailei | Zou, Lihua | Protopopov, Alexei | Santoso, Netty | Parfenov, Michael | Lee, Semin | Zhang, Jianhua | Mahadeshwar, Harshad S. | Tang, Jiabin | Ren, Xiaojia | Seth, Sahil | Yang, Lixing | Xu, Andrew W. | Song, Xingzhi | Pantazi, Angeliki | Xi, Ruibin | Bristow, Christopher A. | Hadjipanayis, Angela | Seidman, Jonathan | Chin, Lynda | Park, Peter J. | Kucherlapati, Raju | Akbani, Rehan | Ling, Shiyun | Liu, Wenbin | Rao, Arvind | Weinstein, John N. | Kim, Sang-Bae | Lee, Ju-Seog | Lu, Yiling | Mills, Gordon | Laird, Peter W. | Hinoue, Toshinori | Weisenberger, Daniel J. | Bootwalla, Moiz S. | Lai, Phillip H. | Shen, Hui | Triche, Timothy | Van Den Berg, David J. | Baylin, Stephen B. | Herman, James G. | Getz, Gad | Chin, Lynda | Liu, Yingchun | Murray, Bradley A. | Noble, Michael S. | Askoy, B. Arman | Ciriello, Giovanni | Dresdner, Gideon | Gao, Jianjiong | Gross, Benjamin | Jacobsen, Anders | Lee, William | Ramirez, Ricardo | Sander, Chris | Schultz, Nikolaus | Senbabaoglu, Yasin | Sinha, Rileen | Sumer, S. Onur | Sun, Yichao | Weinhold, Nils | Thorsson, Vésteinn | Bernard, Brady | Iype, Lisa | Kramer, Roger W. | Kreisberg, Richard | Miller, Michael | Reynolds, Sheila M. | Rovira, Hector | Tasman, Natalie | Shmulevich, Ilya | Ng, Santa Cruz Sam | Haussler, David | Stuart, Josh M. | Akbani, Rehan | Ling, Shiyun | Liu, Wenbin | Rao, Arvind | Weinstein, John N. | Verhaak, Roeland G.W. | Mills, Gordon B. | Leiserson, Mark D. M. | Raphael, Benjamin J. | Wu, Hsin-Ta | Taylor, Barry S. | Black, Aaron D. | Bowen, Jay | Carney, Julie Ann | Gastier-Foster, Julie M. | Helsel, Carmen | Leraas, Kristen M. | Lichtenberg, Tara M. | McAllister, Cynthia | Ramirez, Nilsa C. | Tabler, Teresa R. | Wise, Lisa | Zmuda, Erik | Penny, Robert | Crain, Daniel | Gardner, Johanna | Lau, Kevin | Curely, Erin | Mallery, David | Morris, Scott | Paulauskis, Joseph | Shelton, Troy | Shelton, Candace | Sherman, Mark | Benz, Christopher | Lee, Jae-Hyuk | Fedosenko, Konstantin | Manikhas, Georgy | Potapova, Olga | Voronina, Olga | Belyaev, Smitry | Dolzhansky, Oleg | Rathmell, W. Kimryn | Brzezinski, Jakub | Ibbs, Matthew | Korski, Konstanty | Kycler, Witold | ŁaŸniak, Radoslaw | Leporowska, Ewa | Mackiewicz, Andrzej | Murawa, Dawid | Murawa, Pawel | Spychała, Arkadiusz | Suchorska, Wiktoria M. | Tatka, Honorata | Teresiak, Marek | Wiznerowicz, Maciej | Abdel-Misih, Raafat | Bennett, Joseph | Brown, Jennifer | Iacocca, Mary | Rabeno, Brenda | Kwon, Sun-Young | Penny, Robert | Gardner, Johanna | Kemkes, Ariane | Mallery, David | Morris, Scott | Shelton, Troy | Shelton, Candace | Curley, Erin | Alexopoulou, Iakovina | Engel, Jay | Bartlett, John | Albert, Monique | Park, Do-Youn | Dhir, Rajiv | Luketich, James | Landreneau, Rodney | Janjigian, Yelena Y. | Kelsen, David P. | Cho, Eunjung | Ladanyi, Marc | Tang, Laura | McCall, Shannon J. | Park, Young S. | Cheong, Jae-Ho | Ajani, Jaffer | Camargo, M. Constanza | Alonso, Shelley | Ayala, Brenda | Jensen, Mark A. | Pihl, Todd | Raman, Rohini | Walton, Jessica | Wan, Yunhu | Demchok, John A. | Eley, Greg | Mills Shaw, Kenna R. | Sheth, Margi | Tarnuzzer, Roy | Wang, Zhining | Yang, Liming | Zenklusen, Jean Claude | Davidsen, Tanja | Hutter, Carolyn M. | Sofia, Heidi J. | Burton, Robert | Chudamani, Sudha | Liu, Jia
Nature  2014;513(7517):202-209.
Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein–Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also knownasPD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies.
doi:10.1038/nature13480
PMCID: PMC4170219  PMID: 25079317
2.  Key nodes of a microRNA network associated with the integrated mesenchymal subtype of high-grade serous ovarian cancer 
Chinese Journal of Cancer  2015;34(1):28-40.
Metastasis is the main cause of cancer mortality. One of the initiating events of cancer metastasis of epithelial tumors is epithelial-to-mesenchymal transition (EMT), during which cells dedifferentiate from a relatively rigid cell structure/morphology to a flexible and changeable structure/morphology often associated with mesenchymal cells. The presence of EMT in human epithelial tumors is reflected by the increased expression of genes and levels of proteins that are preferentially present in mesenchymal cells. The combined presence of these genes forms the basis of mesenchymal gene signatures, which are the foundation for classifying a mesenchymal subtype of tumors. Indeed, tumor classification schemes that use clustering analysis of large genomic characterizations, like The Cancer Genome Atlas (TCGA), have defined mesenchymal subtype in a number of cancer types, such as high-grade serous ovarian cancer and glioblastoma. However, recent analyses have shown that gene expression-based classifications of mesenchymal subtypes often do not associate with poor survival. This “paradox” can be ameliorated using integrated analysis that combines multiple data types. We recently found that integrating mRNA and microRNA (miRNA) data revealed an integrated mesenchymal subtype that is consistently associated with poor survival in multiple cohorts of patients with serous ovarian cancer. This network consists of 8 major miRNAs and 214 mRNAs. Among the 8 miRNAs, 4 are known to be regulators of EMT. This review provides a summary of these 8 miRNAs, which were associated with the integrated mesenchymal subtype of serous ovarian cancer.
doi:10.5732/cjc.014.10284
PMCID: PMC4302087  PMID: 25556616
MicroRNA (miRNA); epithelial-to-mesenchymal transition (EMT); cancer; ovary; miR-506; miR-101
3.  The Somatic Genomic Landscape of Glioblastoma 
Cell  2013;155(2):462-477.
We describe the landscape of somatic genomic alterations based on multi-dimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer.
doi:10.1016/j.cell.2013.09.034
PMCID: PMC3910500  PMID: 24120142
4.  Association between BRCA2 but not BRCA1 Mutations and Beneficial Survival, Chemotherapy Sensitivity, and Gene Mutator Phenotype in Patients with Ovarian Cancer 
Context
Attempts to determine the clinical significance of BRCA1/2 mutations in ovarian cancer (OvCa) have produced conflicting results.
Objective
To determine the relationships between BRCA1/2 deficiency (i.e., mutation and promoter hypermethylation) and overall survival (OS), progression-free survival (PFS), chemotherapy response, and whole exome mutation rate in OvCa.
Design, Setting, and Patients
Observational study of multidimensional genomics and clinical data on 316 high-grade serous OvCa cases that were made public between 2009 and 2010 via The Cancer Genome Atlas project.
Main Outcome Measures
OS and PFS rates (primary outcomes) and chemotherapy response (secondary outcome).
Results
BRCA2 mutations (29 cases) were associated with significantly better OS (adjusted hazard ratio [HR], 0.33; 95% CI, 0.16–0.69, P=0.003; 5-year OS: 61% for BRCA2 mutated vs. 25% for BRCA wild-type [wt] cases) and PFS (adjusted HR, 0.40; 95% CI, 0.22–0.74, P=0.004; 3-year PFS: 44% for BRCA2 mutated vs. 16% for BRCA wt cases), whereas neither BRCA1 mutations (37 cases) nor BRCA1 methylation (33 cases) were associated with prognosis. Moreover, BRCA2 mutations were associated with a significantly higher primary chemotherapy sensitivity rate (100% for BRCA2 mutated vs. 82% [P=0.02] and 80% [P=0.05] for BRCA wt and BRCA1 mutated cases, respectively) and longer platinum-free duration (median platinum-free duration: 18.0 months for BRCA2 mutated vs. 11.7 [P=0.02] and 12.5 [P=0.04] months for BRCA wt and BRCA1 mutated cases, respectively). Further investigation revealed that BRCA2 mutated, but not BRCA1 mutated cases, exhibited a “mutator phenotype” by containing significantly more mutations than BRCA wt cases across the whole exome (median mutation number per sample: 84 for BRCA2 mutated vs. 52 for BRCA wt cases, false-discovery rate <0.1).
Conclusions
BRCA2 mutation, but not BRCA1 deficiency, is associated with improved survival, chemotherapy response, and genome instability compared with BRCA wild-type.
doi:10.1001/jama.2011.1456
PMCID: PMC4159096  PMID: 21990299
BRCA1; BRCA2; mutations; survival; platinum-based drug response
5.  Post-transcriptional regulatory network of epithelial-to-mesenchymal and mesenchymal-to-epithelial transitions 
Epithelial-to-mesenchymal transition (EMT) and its reverse process, mesenchymal-to-epithelial transition (MET), play important roles in embryogenesis, stem cell biology, and cancer progression. EMT can be regulated by many signaling pathways and regulatory transcriptional networks. Furthermore, post-transcriptional regulatory networks regulate EMT; these networks include the long non-coding RNA (lncRNA) and microRNA (miRNA) families. Specifically, the miR-200 family, miR-101, miR-506, and several lncRNAs have been found to regulate EMT. Recent studies have illustrated that several lncRNAs are overexpressed in various cancers and that they can promote tumor metastasis by inducing EMT. MiRNA controls EMT by regulating EMT transcription factors or other EMT regulators, suggesting that lncRNAs and miRNA are novel therapeutic targets for the treatment of cancer. Further efforts have shown that non-coding-mediated EMT regulation is closely associated with epigenetic regulation through promoter methylation (e.g., miR-200 or miR-506) and protein regulation (e.g., SET8 via miR-502). The formation of gene fusions has also been found to promote EMT in prostate cancer. In this review, we discuss the post-transcriptional regulatory network that is involved in EMT and MET and how targeting EMT and MET may provide effective therapeutics for human disease.
doi:10.1186/1756-8722-7-19
PMCID: PMC3973872  PMID: 24598126
Long non-coding RNA (lncRNA); microRNA (miRNA); Epithelial-to-mesenchymal transition (EMT); Mesenchymal-to-epithelial transition (MET)
6.  Integrated analyses identify a master microRNA regulatory network for the mesenchymal subtype in serous ovarian cancer 
Cancer cell  2013;23(2):186-199.
Summary
Integrated genomic analyses revealed a miRNA-regulatory network, which further defined a robust integrated mesenchymal subtype associated with poor overall survival in 459 cases of serous ovarian cancer (OvCa) from The Cancer Genome Atlas and 560 cases from independent cohorts. Eight key miRNAs, including miR-506, miR-141 and miR-200a, were predicted to regulate 89% of the targets in this network. Follow-up functional experiments illustrate that miR-506 augmented E-cadherin expression, inhibited cell migration and invasion, and prevented TGFβ-induced epithelial-mesenchymal transition (EMT) by targeting SNAI2, a transcriptional repressor of E-cadherin. In human OvCa, miR-506 expression was correlated with decreased SNAI2 and VIM, elevated E-cadherin, and beneficial prognosis. Nanoparticle delivery of miR-506 in orthotopic OvCa mouse models led to E-cadherin induction and reduced tumor growth.
doi:10.1016/j.ccr.2012.12.020
PMCID: PMC3603369  PMID: 23410973
7.  Transcriptome and Small RNA Deep Sequencing Reveals Deregulation of miRNA Biogenesis in Human Glioma 
The Journal of pathology  2013;229(3):10.1002/path.4109.
Altered expression of oncogenic and tumor-suppressing microRNAs (miRNAs) is widely associated with tumorigenesis. However, the regulatory mechanisms underlying these alterations are poorly understood. We sought to shed light on the deregulation of miRNA biogenesis promoting the aberrant miRNA expression profiles identified in these tumors. Using sequencing technology to perform both whole-transcriptome and small RNA sequencing of glioma patient samples, we examined precursor and mature miRNAs to directly evaluate the miRNA maturation process, and interrogated expression profiles for genes involved in the major steps of miRNA biogenesis. We found that ratios of mature to precursor forms of a large number of miRNAs increased with the progression from normal brain to low-grade and then to high-grade gliomas. The expression levels of genes involved in each of the three major steps of miRNA biogenesis (nuclear processing, nucleo-cytoplasmic transport, and cytoplasmic processing) were systematically altered in glioma tissues. Survival analysis of an independent data set demonstrated that the alteration of genes involved in miRNA maturation correlates with survival in glioma patients. Direct quantification of miRNA maturation with deep sequencing demonstrated that deregulation of the miRNA biogenesis pathway is a hallmark for glioma genesis and progression.
doi:10.1002/path.4109
PMCID: PMC3857031  PMID: 23007860
microRNA; biogenesis; glioma
8.  Differing clinical impact of BRCA1 and BRCA2 mutations in serous ovarian cancer 
Pharmacogenomics  2012;13(13):1523-1535.
A key function of BRCA1 and BRCA2 is the participation in dsDNAbreak repair via homologous recombination. BRCA1 and BRCA2 mutations, which occur in most hereditary ovarian cancers (OCs) and approximately 10% of all OC cases, are associated with defects in homologous recombination and genomic instability, a phenotype termed ‘BRCAness’. The clinical effects of BRCA1 and BRCA2 mutations have commonly been analyzed together; however, it is becoming increasingly apparent that these mutations do not have the same effects in OC. Recently, three major reports highlighted the unequal clinical characteristics of OCs with BRCA1 and BRCA2 mutations. All studies demonstrated that BRCA2-mutated patients are associated with better survival and therapeutic response than BRCA1-mutated and wild-type patients with serous OC. The differing prognostic effects of the BRCA2 and BRCA1 mutations is likely due to differing roles of BRCA1 and BRCA2 in homologous recombination repair and a stronger association between the BRCA2 mutation and a hypermutator phenotype. These new findings have potentially important implications for clinical management of patients with serous OC.
doi:10.2217/pgs.12.137
PMCID: PMC3603383  PMID: 23057551
BRCA mutation; drug response; homologous recombination; ovarian cancer; PARP inhibitor; survival
9.  Information-Theoretic Analysis of the Dynamics of an Executable Biological Model 
PLoS ONE  2013;8(3):e59303.
To facilitate analysis and understanding of biological systems, large-scale data are often integrated into models using a variety of mathematical and computational approaches. Such models describe the dynamics of the biological system and can be used to study the changes in the state of the system over time. For many model classes, such as discrete or continuous dynamical systems, there exist appropriate frameworks and tools for analyzing system dynamics. However, the heterogeneous information that encodes and bridges molecular and cellular dynamics, inherent to fine-grained molecular simulation models, presents significant challenges to the study of system dynamics. In this paper, we present an algorithmic information theory based approach for the analysis and interpretation of the dynamics of such executable models of biological systems. We apply a normalized compression distance (NCD) analysis to the state representations of a model that simulates the immune decision making and immune cell behavior. We show that this analysis successfully captures the essential information in the dynamics of the system, which results from a variety of events including proliferation, differentiation, or perturbations such as gene knock-outs. We demonstrate that this approach can be used for the analysis of executable models, regardless of the modeling framework, and for making experimentally quantifiable predictions.
doi:10.1371/journal.pone.0059303
PMCID: PMC3602105  PMID: 23527156
10.  Integrated Analysis of Gene Expression and Tumor Nuclear Image Profiles Associated with Chemotherapy Response in Serous Ovarian Carcinoma 
PLoS ONE  2012;7(5):e36383.
Background
Small sample sizes used in previous studies result in a lack of overlap between the reported gene signatures for prediction of chemotherapy response. Although morphologic features, especially tumor nuclear morphology, are important for cancer grading, little research has been reported on quantitatively correlating cellular morphology with chemotherapy response, especially in a large data set. In this study, we have used a large population of patients to identify molecular and morphologic signatures associated with chemotherapy response in serous ovarian carcinoma.
Methodology/Principal Findings
A gene expression model that predicts response to chemotherapy is developed and validated using a large-scale data set consisting of 493 samples from The Cancer Genome Atlas (TCGA) and 244 samples from an Australian report. An identified 227-gene signature achieves an overall predictive accuracy of greater than 85% with a sensitivity of approximately 95% and specificity of approximately 70%. The gene signature significantly distinguishes between patients with unfavorable versus favorable prognosis, when applied to either an independent data set (P = 0.04) or an external validation set (P<0.0001). In parallel, we present the production of a tumor nuclear image profile generated from 253 sample slides by characterizing patients with nuclear features (such as size, elongation, and roundness) in incremental bins, and we identify a morphologic signature that demonstrates a strong association with chemotherapy response in serous ovarian carcinoma.
Conclusions
A gene signature discovered on a large data set provides robustness in accurately predicting chemotherapy response in serous ovarian carcinoma. The combination of the molecular and morphologic signatures yields a new understanding of potential mechanisms involved in drug resistance.
doi:10.1371/journal.pone.0036383
PMCID: PMC3348145  PMID: 22590536
11.  Using cell fate attractors to uncover transcriptional regulation of HL60 neutrophil differentiation 
BMC Systems Biology  2009;3:20.
Background
The process of cellular differentiation is governed by complex dynamical biomolecular networks consisting of a multitude of genes and their products acting in concert to determine a particular cell fate. Thus, a systems level view is necessary for understanding how a cell coordinates this process and for developing effective therapeutic strategies to treat diseases, such as cancer, in which differentiation plays a significant role. Theoretical considerations and recent experimental evidence support the view that cell fates are high dimensional attractor states of the underlying molecular networks. The temporal behavior of the network states progressing toward different cell fate attractors has the potential to elucidate the underlying molecular mechanisms governing differentiation.
Results
Using the HL60 multipotent promyelocytic leukemia cell line, we performed experiments that ultimately led to two different cell fate attractors by two treatments of varying dosage and duration of the differentiation agent all-trans-retinoic acid (ATRA). The dosage and duration combinations of the two treatments were chosen by means of flow cytometric measurements of CD11b, a well-known early differentiation marker, such that they generated two intermediate populations that were poised at the apparently same stage of differentiation. However, the population of one treatment proceeded toward the terminally differentiated neutrophil attractor while that of the other treatment reverted back toward the undifferentiated promyelocytic attractor. We monitored the gene expression changes in the two populations after their respective treatments over a period of five days and identified a set of genes that diverged in their expression, a subset of which promotes neutrophil differentiation while the other represses cell cycle progression. By employing promoter based transcription factor binding site analysis, we found enrichment in the set of divergent genes, of transcription factors functionally linked to tumor progression, cell cycle, and development.
Conclusion
Since many of the transcription factors identified by this approach are also known to be implicated in hematopoietic differentiation and leukemia, this study points to the utility of incorporating a dynamical systems level view into a computational analysis framework for elucidating transcriptional mechanisms regulating differentiation.
doi:10.1186/1752-0509-3-20
PMCID: PMC2652435  PMID: 19222862
12.  Insulin-like growth factor binding protein 2 promotes ovarian cancer cell invasion 
Molecular Cancer  2005;4:7.
Background
Insulin-like growth factor binding protein 2 (IGFBP2) is overexpressed in ovarian malignant tissues and in the serum and cystic fluid of ovarian cancer patients, suggesting an important role of IGFBP2 in the biology of ovarian cancer. The purpose of this study was to assess the role of increased IGFBP2 in ovarian cancer cells.
Results
Using western blotting and tissue microarray analyses, we showed that IGFBP2 was frequently overexpressed in ovarian carcinomas compared with normal ovarian tissues. Furthermore, IGFBP2 was significantly overexpressed in invasive serous ovarian carcinomas compared with borderline serous ovarian tumors. To test whether increased IGFBP2 contributes to the highly invasive nature of ovarian cancer cells, we generated IGFBP2-overexpressing cells from an SKOV3 ovarian cancer cell line, which has a very low level of endogenous IGFBP2. A Matrigel invasion assay showed that these IGFBP2-overexpressing cells were more invasive than the control cells. We then designed small interference RNA (siRNA) molecules that attenuated IGFBP2 expression in PA-1 ovarian cancer cells, which have a high level of endogenous IGFBP2. The Matrigel invasion assay showed that the attenuation of IGFBP2 expression indeed decreased the invasiveness of PA-1 cells.
Conclusions
We therefore showed that IGFBP2 enhances the invasion capacity of ovarian cancer cells. Blockage of IGFBP2 may thus constitute a viable strategy for targeted cancer therapy.
doi:10.1186/1476-4598-4-7
PMCID: PMC549074  PMID: 15686601
13.  Steady-State Analysis of Genetic Regulatory Networks Modelled by Probabilistic Boolean Networks 
Probabilistic Boolean networks (PBNs) have recently been introduced as a promising class of models of genetic regulatory networks. The dynamic behaviour of PBNs can be analysed in the context of Markov chains. A key goal is the determination of the steady-state (long-run) behaviour of a PBN by analysing the corresponding Markov chain. This allows one to compute the long-term influence of a gene on another gene or determine the long-term joint probabilistic behaviour of a few selected genes. Because matrix-based methods quickly become prohibitive for large sizes of networks, we propose the use of Monte Carlo methods. However, the rate of convergence to the stationary distribution becomes a central issue. We discuss several approaches for determining the number of iterations necessary to achieve convergence of the Markov chain corresponding to a PBN. Using a recently introduced method based on the theory of two-state Markov chains, we illustrate the approach on a sub-network designed from human glioma gene expression data and determine the joint steadystate probabilities for several groups of genes.
doi:10.1002/cfg.342
PMCID: PMC2447305  PMID: 18629023
14.  Data extraction from composite oligonucleotide microarrays 
Nucleic Acids Research  2003;31(7):e36.
Microarray or DNA chip technology is revolutionizing biology by empowering researchers in the collection of broad-scope gene information. It is well known that microarray-based measurements exhibit a substantial amount of variability due to a number of possible sources, ranging from hybridization conditions to image capture and analysis. In order to make reliable inferences and carry out quantitative analysis with microarray data, it is generally advisable to have more than one measurement of each gene. The availability of both between-array and within-array replicate measurements is essential for this purpose. Although statistical considerations call for increasing the number of replicates of both types, the latter is particularly challenging in practice due to a number of limiting factors, especially for in-house spotting facilities. We propose a novel approach to design so-called composite microarrays, which allow more replicates to be obtained without increasing the number of printed spots.
PMCID: PMC152821  PMID: 12655024

Results 1-14 (14)