Integrated proteomics and genomics analysis reveals a novel mesenchymal to epithelial reverting transition in leiomyosarcoma through regulation of slug.

Leiomyosarcoma is one of the most common mesenchymal tumors. Proteomics profiling analysis by reverse-phase protein lysate array surprisingly revealed that expression of the epithelial marker E-cadherin (encoded by CDH1) was significantly elevated in a subset of leiomyosarcomas. In contrast, E-cadherin was rarely expressed in the gastrointestinal stromal tumors, another major mesenchymal tumor type. We further sought to 1) validate this finding, 2) determine whether there is a mesenchymal to epithelial reverting transition (MErT) in leiomyosarcoma, and if so 3) elucidate the regulatory mechanism responsible for this MErT. Our data showed that the epithelial cell markers E-cadherin, epithelial membrane antigen, cytokeratin AE1/AE3, and pan-cytokeratin were often detected immunohistochemically in leiomyosarcoma tumor cells on tissue microarray. Interestingly, the E-cadherin protein expression was correlated with better survival in leiomyosarcoma patients. Whole genome microarray was used for transcriptomics analysis, and the epithelial gene expression signature was also associated with better survival. Bioinformatics analysis of transcriptome data showed an inverse correlation between E-cadherin and E-cadherin repressor Slug (SNAI2) expression in leiomyosarcoma, and this inverse correlation was validated on tissue microarray by immunohistochemical staining of E-cadherin and Slug. Knockdown of Slug expression in SK-LMS-1 leiomyosarcoma cells by siRNA significantly increased E-cadherin; decreased the mesenchymal markers vimentin and N-cadherin (encoded by CDH2); and significantly decreased cell proliferation, invasion, and migration. An increase in Slug expression by pCMV6-XL5-Slug transfection decreased E-cadherin and increased vimentin and N-cadherin. Thus, MErT, which is mediated through regulation of Slug, is a clinically significant phenotype in leiomyosarcoma.

Compression of annotated nucleotide sequences.

This article introduces an algorithm for the lossless compression of DNA files, which contain annotation text besides the nucleotide sequence. First a grammar is specifically designed to capture the regularities of the annotation text. A revertible transformation uses the grammar rules in order to equivalently represent the original file as a collection of parsed segments and a sequence of decisions made by the grammar parser. This decomposition enables the efficient use of state-of-the-art encoders for processing the parsed segments. The output size of the decision-making process of the grammar is optimized by extending the states to account for high-order Markovian dependencies. The practical implementation of the algorithm achieves a significant improvement when compared to the general-purpose methods currently used for DNA files.

On the linear programming bound for linear Lee codes.

Based on an invariance-type property of the Lee-compositions of a linear Lee code, additional equality constraints can be introduced to the linear programming problem of linear Lee codes. In this paper, we formulate this property in terms of an action of the multiplicative group of the field [Formula: see text] on the set of Lee-compositions. We show some useful properties of certain sums of Lee-numbers, which are the eigenvalues of the Lee association scheme, appearing in the linear programming problem of linear Lee codes. Using the additional equality constraints, we formulate the linear programming problem of linear Lee codes in a very compact form, leading to a fast execution, which allows to efficiently compute the bounds for large parameter values of the linear codes.

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.

Fast iterative gene clustering based on information theoretic criteria for selecting the cluster structure.

Grouping of genes into clusters according to their expression levels is important for deriving biological information, e.g., on gene functions based on microarray and other related analyses. The paper introduces the selection of the number of clusters based on the minimum description length (MDL) principle for the selection of the number of clusters in gene expression data. The main feature of the new method is the ability to evaluate in a fast way the number of clusters according to the sound MDL principle, without exhaustive evaluations over all possible partitions of the gene set. The estimation method can be used in conjunction with various clustering algorithms. A recent clustering algorithm using principal component analysis, the "gene shaving" (GS) procedure, can be modified to make use of the new MDL estimation method, replacing the Gap statistics originally used in GS algorithm. The resulting clustering algorithm is shown to perform better than GS-Gap and CEM (classification expectation maximization), in the simulations using artificial data. The proposed method is applied to B-cell differentiation data, and the resulting clusters are compared with those found by self-organizing maps (SOM).

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.

Using contexts and R-R interval estimation in lossless ECG compression.

The paper presents a new lossless ECG compression scheme. The short-term predictor and the coder use conditioning on a small number of contexts. The long-term prediction is based on an algorithm for R-R interval estimation. Several QRS detection algorithms are investigated to select a low complexity and reliable detection algorithm. The coding of prediction residuals uses primarily the Golomb-Rice (GR) codes, but, to improve the coding results, escape codes GR-ESC are used in some contexts for a limited number of samples. Experimental results indicate the good overall performance of the lossless ECG compression algorithms (reducing the storage needs from 12 to about 3-4 bits per sample). The scheme consistently outperforms other waveform or general purpose coding algorithms.

Context coding of depth map images under the piecewise-constant image model representation.

This paper introduces an efficient method for lossless compression of depth map images, using the representation of a depth image in terms of three entities: 1) the crack-edges; 2) the constant depth regions enclosed by them; and 3) the depth value over each region. The starting representation is identical with that used in a very efficient coder for palette images, the piecewise-constant image model coding, but the techniques used for coding the elements of the representation are more advanced and especially suitable for the type of redundancy present in depth images. Initially, the vertical and horizontal crack-edges separating the constant depth regions are transmitted by 2D context coding using optimally pruned context trees. Both the encoder and decoder can reconstruct the regions of constant depth from the transmitted crack-edge image. The depth value in a given region is encoded using the depth values of the neighboring regions already encoded, exploiting the natural smoothness of the depth variation, and the mutual exclusiveness of the values in neighboring regions. The encoding method is suitable for lossless compression of depth images, obtaining compression of about 10-65 times, and additionally can be used as the entropy coding stage for lossy depth compression.

Obituary: professor paul dan cristea.

Paul Dan Cristea, professor of Electrical Engineering and Computer Science at 'Politehnica' University of Bucharest died on 17 April 2013, following several years of bravely battling a perfidious illness.

Inference of gene regulatory networks based on a universal minimum description length.

The Boolean network paradigm is a simple and effective way to interpret genomic systems, but discovering the structure of these networks remains a difficult task. The minimum description length (MDL) principle has already been used for inferring genetic regulatory networks from time-series expression data and has proven useful for recovering the directed connections in Boolean networks. However, the existing method uses an ad hoc measure of description length that necessitates a tuning parameter for artificially balancing the model and error costs and, as a result, directly conflicts with the MDL principle's implied universality. In order to surpass this difficulty, we propose a novel MDL-based method in which the description length is a theoretical measure derived from a universal normalized maximum likelihood model. The search space is reduced by applying an implementable analogue of Kolmogorov's structure function. The performance of the proposed method is demonstrated on random synthetic networks, for which it is shown to improve upon previously published network inference algorithms with respect to both speed and accuracy. Finally, it is applied to time-series Drosophila gene expression measurements.

Analysis of signaling pathways in 90 cancer cell lines by protein lysate array.

UNLABELLED: Multiple signal transduction pathways play a crucial role in cancer development, progression, and response to different therapies. An important issue is whether common signal transduction pathways are ubiquitously altered in all cancer types and some unique pathways are involved in different cancer types. Another important issue is whether and how transduction signaling molecules are heterogeneously expressed and activated in different cancer cells within and between cancer cell types. METHODS: To gain insight into these issues, we assembled a protein lysate array with 90 different cell lines of 12 different cell types. Each sample is diluted 2-fold six times, and samples from the dilution series were printed three times on the array. We then measured the expression levels and phosphorylation status of 52 different signaling proteins with specific antibodies and carried out statistical hierarchical clustering analysis. RESULTS: The most significant finding based on the cluster analysis was that the cell lines did not group based on tumor types, suggesting that the signaling pathways studied were commonly activated in most of the tumor types cultured in vitro. As expected, related proteins associated with specific signaling pathways clustered together, and analysis of the 30 most differentially expressed proteins revealed the PI3-K signaling pathway was upregulated in several different tumor types and the VEGF-angiogenesis pathway was downregulated in hematopoetic cancers. Another important observation, with clinical implications was that EGFR was the most heterogeneous among all the cell lines. We also observed signaling pathways unique to specific types of cancers such as the inverse relationship between p16ink and Rb, and the EGFR mediated pathway activation characteristic of pancreatic cancers. CONCLUSIONS: Using reverse phase lysate array analysis in this study, we were able to determine potential relationships and signaling pathways, both common and unique, to different types of cancer using cell lines in vitro. This data could be utilized for mining information related to an individual cancer of interest and combined with morphological and genomic profiles would help in creating a combination of expression markers and/or functional signaling maps for specific cancer diagnosis and therapy.

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.

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