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Ilya Shmulevich

Publications and source records attributed to Ilya Shmulevich.

At least 19 recordsLinked to original sources
PubMed2006

Pathway alterations during glioma progression revealed by reverse phase protein lysate arrays.

The progression of gliomas has been extensively studied at the genomic level using cDNA microarrays. However, systematic examinations at the protein translational and post-translational levels are far more limited. We constructed a glioma protein lysate array from 82 different primary glioma tissues, and surveyed the expression and phosphorylation of 46 different proteins involved in signaling pathways of cell proliferation, cell survival, apoptosis, angiogenesis, and cell invasion. An analysis algorithm was employed to robustly estimate the protein expressions in these samples. When ranked by their discriminating power to separate 37 glioblastomas (high-grade gliomas) from 45 lower-grade gliomas, the following 12 proteins were identified as the most powerful discriminators: IBalpha, EGFRpTyr845, AKTpThr308, phosphatidylinositol 3-kinase (PI3K), BadpSer136, insulin-like growth factor binding protein (IGFBP) 2, IGFBP5, matrix metalloproteinase 9 (MMP9), vascular endothelial growth factor (VEGF), phosphorylated retinoblastoma protein (pRB), Bcl-2, and c-Abl. Clustering analysis showed a close link between PI3K and AKTpThr308, IGFBP5 and IGFBP2, and IBalpha and EGFRpTyr845. Another cluster includes MMP9, Bcl-2, VEGF, and pRB. These clustering patterns may suggest functional relationships, which warrant further investigation. The marked association of phosphorylation of AKT at Thr308, but not Ser473, with glioblastoma suggests a specific event of PI3K pathway activation in glioma progression.

Adult↗
PubMed2006

Unsupervised analysis uncovers changes in histopathologic diagnosis in supervised genomic studies.

Human gastrointestinal stromal tumors (GIST) have recently emerged as a distinct mesenchymal tumor type that has a unique phenotype characterized by a gain of function mutations in c-kit. In contrast, leiomyosarcomas (LMS) of the gastrointestinal tract or retroperitoneum, which were previously classified together with GISTs as gastrointestinal sarcomas, have much less frequent mutations of c-kit. We performed microarray analyses to gain a comprehensive understanding of the difference between the two types of soft-tissue sarcomas at the level of gene expression. Microarray experiments were performed on 30 GISTs and 30 LMSs that were collected at the time of surgical resection. These tumors were categorized based on the histopathologic diagnosis recorded in our institutional database. Prior to our search for genes that are differentially expressed between these two types of cancers, we first carried out an unsupervised analysis using multidimensional scaling (MDS) to determine whether the two groups have marked overall differences in gene expression. Initially, the MDS did not reveal a good separation between the two groups. We then re-reviewed the histopathology of these tumors and realized that some of the cases included in our study were acquired 10 years ago when the diagnosis of gastrointestinal sarcoma was made according to histopathologic criteria alone without immunohistochemistry for c-kit. An experienced pathologist reviewed all of the specimens and this revealed that a number of the GIST cases were classified as LMS in the clinical database. Correction of the histopathologic diagnosis and relabeling of the samples resulted in a much more pronounced separation of GIST and LMS in the MDS analysis. This study underscores the need to re-review histopathology as reclassification occurs. While updating the clinical database may be desired, this is usually impractical. For molecular studies that use archival samples, it is critical to have the archival samples re-reviewed by a pathologist. Further, unsupervised analysis often proves to be a critical quality control step in identifying structural problems that may exist. Finally, MDS analysis further supports that GIST is a distinct type of sarcoma.

Biomarkers, Tumor↗
PubMed2005

Analysis of angiogenesis using in vitro experiments and stochastic growth models.

The global properties of vascular networks grown with an in vitro angiogenesis assay are compared quantitatively, using automated image analysis, with the global properties of networks obtained with discrete, stochastic growth models. The model classes that are investigated are invasion percolation and diffusion limited aggregation. By matching global properties to experimental data, one can infer which model classes and parameters are most reflective of angiogenesis in experimental cells. This sheds light on large-scale emergent properties of angiogenesis from a systems perspective. It is found that invasion percolation is better than diffusion limited aggregation at matching experimental data. We also present evidence that the distribution of the lengths of real tubule complexes follows a power law.

Animals↗
PubMed2005

Eukaryotic cells are dynamically ordered or critical but not chaotic.

Two important theoretical approaches have been developed to generically characterize the relationship between the structure and function of large genetic networks: the continuous approach, based on reaction-kinetics differential equations, and the Boolean approach, based on difference equations and discrete logical rules. These two approaches do not always coincide in their predictions for the same system. Nonetheless, both of them predict that the highly nonlinear dynamics exhibited by genetic regulatory systems can be characterized into two broad regimes, to wit, an ordered regime where the system is robust against perturbations, and a chaotic regime where the system is extremely sensitive to perturbations. It has been a plausible and long-standing hypothesis that genomic regulatory networks of real cells operate in the ordered regime or at the border between order and chaos. This hypothesis is indirectly supported by the robustness and stability observed in the phenotypic traits of living organisms under genetic perturbations. However, there has been no systematic study to determine whether the gene-expression patterns of real cells are compatible with the dynamically ordered regimes predicted by theoretical models. Using the Boolean approach, here we show what we believe to be the first direct evidence that the underlying genetic network of HeLa cells appears to operate either in the ordered regime or at the border between order and chaos but does not appear to be chaotic.

Gene Expression Regulation↗
PubMed2005

Insulin-like growth factor binding protein 2 promotes ovarian cancer cell invasion.

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.

Cell Line, Tumor↗
PubMed2005

Robust estimation of protein expression ratios with lysate microarray technology.

MOTIVATION: The protein lysate microarray is a developing proteomic technology for measuring protein expression levels in a large number of biological samples simultaneously. A challenge for accurate quantification is the relatively narrow dynamic range associated with the commonly used chromogenic signal detection system. To facilitate accurate measurement of the relative expression levels, each sample is serially diluted and each diluted version is spotted on a nitrocellulose-coated slide in triplicate. Thus, each sample yields multiple measurements in different dynamic ranges of the detection system. This study aims to develop suitable algorithms that yield accurate representations of the relative expression levels in different samples from multiple data points. RESULTS: We evaluated two algorithms for estimating relative protein expression in different samples on the lysate microarray by means of a cross-validation procedure. For this purpose as well as for quality control we designed a 1440-spot lysate microarray containing 80 identical samples of purified bovine serum albumin, printed in triplicate with six 2-fold dilutions. Our analysis showed that the algorithm based on a robust least squares estimator provided the most accurate quantification of the protein lysate microarray data. We also demonstrated our methods by estimating relative expression levels of p53 and p21 in either p53(+/+) or p53(-/-) HCT116 colon cancer cells after two drug treatments and their combinations on another lysate microarray. AVAILABILITY: http://www.cs.tut.fi/~mirceanc/lysate_array_bioinformatics.htm

Algorithms↗
PubMed2005

Noise in a Small Genetic Circuit that Undergoes Bifurcation.

Based on the consideration of Boolean dynamics, it has been hypothesized that cell types may correspond to alternative attractors of a gene regulatory network. Recent stochastic Boolean network analysis, however, raised the important question concerning the stability of such attractors. In this paper a detailed numerical analysis is performed within the framework of Langevin dynamics. While the present results confirm that the noise is indeed an important dynamical element, the cell type as represented by attractors can still be a viable hypothesis. It is found that the stability of an attractor depends on the strength of noise related to the distance of the system to the bifurcation point and it can be exponentially stable depending on biological parameters.

Journal Article↗
PubMed2005

Robust quantification of in vitro angiogenesis through image analysis.

An automated image analysis method for quantification of in vitro angiogenesis is presented. The method is designed for in vitro angiogenesis assays that are based on co-culturing endothelial cells with fibroblasts. Such assays are used in many current studies in which anti-angiogenic agents for the treatment of cancer are being sought. This search requires accurate quantification of the stimulatory and inhibitory effects of the different agents. The quantification method gives lengths and sizes of the tubule complexes as well as the numbers of junctions in each of them. The method is tested with a set of test images obtained with a commercially available in vitro angiogenesis assay. The results correctly indicate the inhibitory effect of suramin and the stimulatory effect of vascular endothelial growth factor. Moreover, the image analysis method is shown to be robust against variations in illumination. We have implemented a software package that utilizes the methods. The software as well as a set of test images are available at http://www.cs.tut.fi/sgn/csb/angioquant/.

Algorithms↗
PubMed2005

Molecular voting for glioma classification reflecting heterogeneity in the continuum of cancer progression.

Gliomas, the most common brain tumors, are generally categorized into two lineages (astrocytic and oligodendrocytic) and further classified as low-grade (astrocytoma and oligodendroglioma), mid-grade (anaplastic astrocytoma and anaplastic oligodendroglioma), and high-grade (glioblastoma multiforme) based on morphological features. A strict classification scheme has limitations because a specific glioma can be at any stage of the continuum of cancer progression and may contain mixed features. Thus, a more comprehensive classification based on molecular signatures may reflect the biological nature of specific tumors more accurately. In this study, we used microarray technology to profile the gene expression of 49 human brain tumors and applied the k-nearest neighbor algorithm for classification. We first trained the classification gene set with 19 of the most typical glioma cases and selected a set of genes that provide the lowest cross-validation classification error with k=5. We then applied this gene set to the 30 remaining cases, including several that do not belong to gliomas such as atypical meningioma. The results showed that not only does the algorithm correctly classify most of the gliomas, but the detailed voting results also provide more subtle information regarding the molecular similarities to neighboring classes. For atypical meningioma, the voting was equally split among the four classes, indicating a difficulty in placement of meningioma into the four classes of gliomas. Thus, the actual voting results, which are typically used only to decide the winning class label in k-nearest neighbor algorithms, provide a useful method for gaining deeper insight into the stage of a tumor in the continuum of cancer development.

Algorithms↗
PubMed2004

Activities and sensitivities in boolean network models.

We study how the notions of importance of variables in Boolean functions as well as the sensitivities of the functions to changes in these variables impact the dynamical behavior of Boolean networks. The activity of a variable captures its influence on the output of the function and is a measure of that variable's importance. The average sensitivity of a Boolean function captures the smoothness of the function and is related to its internal homogeneity. In a random Boolean network, we show that the expected average sensitivity determines the well-known critical transition curve. We also discuss canalizing functions and the fact that the canalizing variables enjoy higher importance, as measured by their activities, than the noncanalizing variables. Finally, we demonstrate the important role of the average sensitivity in determining the dynamical behavior of a Boolean network.

Logic↗
PubMed2004

Differential gene and protein expression in primary breast malignancies and their lymph node metastases as revealed by combined cDNA microarray and tissue microarray analysis.

BACKGROUND: Metastatic disease is a major adverse prognostic factor in breast carcinoma. Lymph node metastases often represent the first step in the metastatic process. METHODS: To gain insight into the molecular events that underlie breast carcinoma metastasis, the authors compared gene expression profiles, obtained by cDNA microarray analysis, of nine matched primary tumors and metastases after screening for enrichment of tumor cells. Statistical analysis identified genes that are expressed at elevated or decreased levels in metastases relative to the corresponding primary tumors. Multidimensional scaling analysis indicated that in terms of expression levels, primary tumors were tightly clustered, whereas metastases exhibited a greater spread; this finding points to the more heterogeneous nature of metastases. Among the differentially expressed entities were the invasion- and tissue modeling-related genes IGFBP5, fibronectin, and MMP2; the cell cycle regulatory gene cyclin D1; other genes, such as enolase 2; and an expressed sequence tag similar to angiopoietin 1. To validate and extend these initial findings, the authors constructed a tissue microarray consisting of 100 primary malignancies paired with their lymph node metastases. Antibodies for the IGFBP-5, fibronectin, MMP-2, cyclin D1, and MDM-2 proteins were used to stain tissue array sections. RESULTS: Consistent with microarray data, statistically significant overexpression of IGFBP-5, down-regulation of cyclin D1, and unchanged MDM-2 levels were observed in metastatic tumor cells. Nonetheless, although fibronectin and MMP2 mRNA expression levels were decreased in many metastasis specimens, expression levels of the corresponding proteins in the extracellular matrix were elevated in most metastases. Decreased expression of fibronectin and MMP2 in lymph node metastases was further confirmed by real-time polymerase chain reaction assays performed on five additional specimen pairs. CONCLUSIONS: The results of the current study suggest that extracellular matrix protein expression and nuclear gene expression are associated via a negative-feedback regulatory mechanism. Therefore, gene expression profiling and tissue array validation should be combined to elucidate molecular events associated with the metastatic process.

Breast Neoplasms↗
PubMed2004

Growing genetic regulatory networks from seed genes.

MOTIVATION: A number of models have been proposed for genetic regulatory networks. In principle, a network may contain any number of genes, so long as data are available to make inferences about their relationships. Nevertheless, there are two important reasons why the size of a constructed network should be limited. Computationally and mathematically, it is more feasible to model and simulate a network with a small number of genes. In addition, it is more likely that a small set of genes maintains a specific core regulatory mechanism. RESULTS: Subnetworks are constructed in the context of a directed graph by beginning with a seed consisting of one or more genes believed to participate in a viable subnetwork. Functionalities and regulatory relationships among seed genes may be partially known or they may simply be of interest. Given the seed, we iteratively adjoin new genes in a manner that enhances subnetwork autonomy. The algorithm is applied using both the coefficient of determination and the Boolean-function influence among genes, and it is illustrated using a glioma gene-expression dataset. AVAILABILITY: Software for the seed-growing algorithm will be available at the website for Probabilistic Boolean Networks: http://www2.mdanderson.org/app/ilya/PBN/PBN.htm

Algorithms↗
PubMed2004

Pathway analysis of informative genes from microarray data reveals that metabolism and signal transduction genes distinguish different subtypes of lymphomas.

Recent clinicopathological studies identified a unique subgroup of diffuse large B-cell lymphoma (DLBCL) that expresses CD5 on the cell surface. This 'de novo CD5+ DLBCL' comprises 10% of all DLBCL and has a poorer prognosis than CD5- DLBCL. Comparison of gene expression profiles between de novo CD5+ DLBCLs and CD5- DLBCLs shows that de novo CD5+ DLBCL expresses high levels of integrin beta1 in tumor cells and CD36 in the vascular cells. On the other hand, comparison between mantle cell lymphomas (MCLs) and DLBCLs expectedly identified cyclin D1 as a top feature gene. To gain insight into the molecular pathway differences among the three types of lymphoma, we evaluated the functional categories of groups of genes important for the discrimination among the three groups. We first selected 280 (from 2,142) genes, according to their individual discriminatory power. We then used the gene-shaving clustering algorithm and identified 22 clusters of genes. Of the 22 clusters, six were highly correlated with the class labels of the patients and the top three clusters accounted for the major difference among the three lymphoma subtypes. A multidimensional scaling (MDS) analysis using the average genes from the top three clusters separated the three lymphoma subtypes quite well. The functions of the genes in the top three gene clusters showed a significant enrichment of metabolism and signal transduction. To further examine whether genes of particular functions reflect more faithfully the difference between the subtypes of lymphomas, we separated the 280 informative genes into six different functional groups and performed MDS analysis using each of the gene groups. Four of the gene-function groups (metabolism, signal transduction pathway, transcriptional factors, cell adhesion and migration), separated the three lymphoma subtypes well, whereas apoptosis genes and cell cycle genes did not result in good separation.

Algorithms↗
PubMed2004

Distinguishing key biological pathways between primary breast cancers and their lymph node metastases by gene function-based clustering analysis.

In order to identify key biological pathways that can distinguish between primary breast cancers and their lymph node metastases, we employed gene expression profiling together with gene function-based clustering analysis. We first acquired gene expression profiles of 9 matched primary tumors and the corresponding metastases that contained at least 75% of tumor cells. Then, we applied a clustering algorithm to the preprocessed data. In order to focus on the most informative genes, we ranked all the genes individually based on their abilities to separate the primary breast tumor and metastases samples. Further, we separated these genes into six functional groups according to the Stanford SOURCE database: 'cell cycle,' 'apoptosis,' 'metabolism,' 'cell adhesion and migration,' 'signal transduction,' and 'transcriptional factor and DNA binding molecules.' Unsupervised clustering analysis using all of the 2,303 genes on the microarrays was not able to separate the primary and metastases samples. Clustering analysis using the most informative genes revealed that primary tumors were more tightly clustered, whereas the metastases samples were relatively heterogeneous. The clustering analysis with the genes belonging to different functional groups showed that different functional gene sets varied in their abilities to separate primary tumors and their metastases. Marked separations were found with genes involved in metabolism, signal transduction, cell cycle, and transcriptional factor and DNA binding molecules. In contrast, apoptosis and cell adhesion and migration genes did not provide a clear separation of the two groups of samples. These results suggest that metastatic cells have different metabolism and signal transduction activities, regulated by transcriptional events, from the primary tumor cells. The results also suggest that the altered cell adhesion and migration potentials that are required for tumors to metastasize already exist in the primary tumors as a whole.

Biomarkers, Tumor↗
PubMed2003

The role of certain Post classes in Boolean network models of genetic networks.

A topic of great interest and debate concerns the source of order and remarkable robustness observed in genetic regulatory networks. The study of the generic properties of Boolean networks has proven to be useful for gaining insight into such phenomena. The main focus, as regards ordered behavior in networks, has been on canalizing functions, internal homogeneity or bias, and network connectivity. Here we examine the role that certain classes of Boolean functions that are closed under composition play in the emergence of order in Boolean networks. The closure property implies that any gene at any number of steps in the future is guaranteed to be governed by a function from the same class. By means of Derrida curves on random Boolean networks and percolation simulations on square lattices, we demonstrate that networks constructed from functions belonging to these classes have a tendency toward ordered behavior. Thus they are not overly sensitive to initial conditions, and damage does not readily spread throughout the network. In addition, the considered classes are significantly larger than the class of canalizing functions as the connectivity increases. The functions in these classes exhibit the same kind of preference toward biased functions as do canalizing functions, meaning that functions from this class are likely to be biased. Finally, functions from this class have a natural way of ensuring robustness against noise and perturbations, thus representing plausible evolutionarily selected candidates for regulatory rules in genetic networks.

Computational Biology↗
PubMed2003

Data extraction from composite oligonucleotide microarrays.

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.

Gene Expression Profiling↗
PubMed2003

Microarray reveals differences in both tumors and vascular specific gene expression in de novo CD5+ and CD5- diffuse large B-cell lymphomas.

Malignant lymphoma is a heterogeneous disease with different clinical features. Among diffuse large B-cell lymphomas (DLBCLs), a unique subtype has been identified recently based on cell surface marker CD5 and clinicopathological features. These de novo CD5(+) DLBCLs account for approximately 10% of all of the DLBCLs and have poorer prognosis. To additionally understand this subtype of DLBCLs at the molecular level and to find genes that are differentially expressed in de novo CD5(+) DLBCLs, CD5(-) DLBCLs, and mantle cell lymphomas, which also have poor prognosis, we performed gene expression profiling using cDNA microarray technology. Data from a total of 9 samples of CD5(-) DLBCLs, 11 samples of de novo CD5(+) DLBCLs, and 10 samples of mantle cell lymphomas were acquired. A series of genes were identified that distinguish these three types of lymphomas. Among DLBCL cases, integrin beta1 and/or CD36 adhesion molecules were overexpressed in most cases of CD5(+) DLBCL. An immunohistochemical confirmation study revealed that integrin beta1 was expressed on lymphoma cells, which may account for the high extranodal involvement and poor prognosis of CD5(+) DLBCLs. In contrast, CD36 was overexpressed on vascular endothelia in CD5(+) DLBCLs, although there was no difference in vascularity detected by von Wilbrand factor antibody between CD5(+) and CD5(-) DLBCLs. Those results suggest that CD5(+) and CD5(-) DLBCLs have different gene expression signatures in both tumor cells and their vascular systems.

Aged↗