Jeremy Miller

Identifying disease-specific genes based on their topological significance in protein networks

BackgroundThe identification of key target nodes within complex molecular networks remains a common objective in scientific research. The results of pathway analyses are usually sets of fairly complex networks or functional processes that are deemed relevant to the condition represented by the molecular profile. To be useful in a research or clinical laboratory, the results need to be translated to the level of testable hypotheses about individual genes and proteins within the condition of interest.ResultsIn this paper we describe novel computational methodology capable of predicting key regulatory genes and proteins in disease- and condition-specific biological networks. The algorithm builds shortest path network connecting condition-specific genes (e.g. differentially expressed genes) using global database of protein interactions from MetaCore. We evaluate the number of all paths traversing each node in the shortest path network in relation to the total number of paths going via the same node in the global network. Using these numbers and the relative size of the initial data set, we determine the statistical significance of the network connectivity provided through each node. We applied this method to gene expression data from psoriasis patients and identified many confirmed biological targets of psoriasis and suggested several new targets. Using predicted regulatory nodes we were able to reconstruct disease pathways that are in excellent agreement with the current knowledge on the pathogenesis of psoriasis.ConclusionThe systematic and automated approach described in this paper is readily applicable to uncovering high-quality therapeutic targets, and holds great promise for developing network-based combinational treatment strategies for a wide range of diseases.

Glycogene Expression Alterations Associated with Pancreatic Cancer Epithelial-Mesenchymal Transition in Complementary Model Systems

Background The ability to selectively detect and target cancer cells that have undergone an epithelial-mesenchymal transition (EMT) may lead to improved methods to treat cancers such as pancreatic cancer. The remodeling of cellular glycosylation previously has been associated with cell differentiation and may represent a valuable class of molecular targets for EMT. Methodology/Principal Findings As a first step toward investigating the nature of glycosylation alterations in EMT, we characterized the expression of glycan-related genes in three in-vitro model systems that each represented a complementary aspect of pancreatic cancer EMT. These models included: 1) TGFβ-induced EMT, which provided a look at the active transition between states; 2) a panel of 22 pancreatic cancer cell lines, which represented terminal differentiation states of either epithelial-like or mesenchymal-like; and 3) actively-migrating and stationary cells, which provided a look at the mechanism of migration. We analyzed expression data from a list of 587 genes involved in glycosylation (biosynthesis, sugar transport, glycan-binding, etc.) or EMT. Glycogenes were altered at a higher prevalence than all other genes in the first two models (p<0.05 and <0.005, respectively) but not in the migration model. Several functional themes were shared between the induced-EMT model and the cell line panel, including alterations to matrix components and proteoglycans, the sulfation of glycosaminoglycans; mannose receptor family members; initiation of O-glycosylation; and certain forms of sialylation. Protein-level changes were confirmed by Western blot for the mannose receptor MRC2 and the O-glycosylation enzyme GALNT3, and cell-surface sulfation changes were confirmed using Alcian Blue staining. Conclusions/Significance Alterations to glycogenes are a major component of cancer EMT and are characterized by changes to matrix components, the sulfation of GAGs, mannose receptors, O-glycosylation, and specific sialylated structures. These results provide leads for targeting aggressive and drug resistant forms of pancreatic cancer cells.

Genomic events associated with progression of pleural malignant mesothelioma

Pleural malignant mesothelioma (MM) is an aggressive cancer with a very long latency and a very short median survival. Little is known about the genetic events that trigger MM and their relation to poor outcome. The goal of our study was to characterize major genomic gains and losses associated with MM origin and progression and assess their clinical significance. We performed Representative Oligonucleotide Microarray Analysis (ROMA) on DNA isolated from tumors of 22 patients who recurred at variable interval with the disease after surgery. The total number of copy number alterations (CNA) and frequent imbalances for patients with short time (<12 months from surgery) and long time to recurrence were recorded and mapped using the Analysis of Copy Errors algorithm. We report a profound increase in CNA in the short‐time recurrence group with most chromosomes affected, which can be explained by chromosomal instability associated with MM. Deletions in chromosomes 22q12.2, 19q13.32 and 17p13.1 appeared to be the most frequent events (55‐74%) shared between MM patients followed by deletions in 1p, 9p, 9q, 4p, 3p and gains in 5p, 18q, 8q and 17q (23‐55%). Deletions in 9p21.3 encompassing CDKN2A/ARF and CDKN2B were characterized as specific for the short‐term recurrence group. Analysis of the minimal common areas of frequent gains and losses identified candidate genes that may be involved in different stages of MM: OSM (22q12.2), FUS1 and PL6 (3p21.3), DNAJA1 (9p21.1) and CDH2 (18q11.2‐q12.3). Imbalances seen by ROMA were confirmed by Affymetrix genome analysis in a subset of samples.

CIT: identification of differentially expressed clusters of genes from microarray data.

Cluster Identification Tool (CIT) is a microarray analysis program that identifies differentially expressed genes. Following division of experimental samples based on a parameter of interest, CIT uses a statistical discrimination metric and permutation analysis to identify clusters of genes or individual genes that best differentiate between the experimental groups. CIT integrates with the freely available CLUSTER and TREEVIEW programs to form a more complete microarray analysis package.

The application of protein microarrays to serum diagnostics: Prostate cancer as a test case

Reliable and specific serum disease markers have great value as non-invasive, rapid and inexpensive assays. The discovery of new disease markers is particularly necessary for diseases that are difficult to detect or diagnose at an early and curable stage. For example, the early detection of pancreatic cancer and the differentiation of malignant from benign disease are extremely difficult using current imaging and cytological methods. Improved screening tools would permit the avoidance of unnecessary pancreaticoduodenectomies and allow the opportunity to perform the procedure at a curative stage [1]. The challenge to the discovery of new serum markers lies in the difficulties of highthroughput detection and quantitation of proteins. A new tool that is potentially well suited to meet this challenge is the protein microarray. The feasibility of accurate, sensitive and specific protein microarray detection of multiple proteins in a serum background was recently demonstrated [2], and efforts are now underway to apply this technology to marker discovery. The technology as described by Haab et al. [2] was built upon the existing DNA microarray platforms that are present in many labs (see http://cmgm.stanford.edu/pbrown/), making the method practical and easy to implement.

Platelet-derived Growth Factor Stimulates Src-dependent mRNA Stabilization of Specific Early Genes in Fibroblasts

The Src family of protein-tyrosine kinases (SFKs) participates in a variety of signal transduction pathways, including promotion of cell growth, prevention of apoptosis, and regulation of cell interactions and motility. In particular, SFKs are required for the mitogenic response to platelet-derived growth factor (PDGF). However, it is not clear whether there is a discrete SFK-specific pathway leading to enhanced gene expression or whether SFKs act to generally enhance PDGF-stimulated gene expression. To examine this, we treated quiescent NIH3T3 cells with PDGF in the presence or absence of small molecule inhibitors of SFKs, phosphatidylinositol 3-kinase (PI3K), and MEK1/2. Global patterns of gene expression were analyzed by using Affymetrix Gene-Chip arrays, and data were validated by using reverse transcription-PCR and ribonuclease protection assay. We identified a discrete set of immediate early genes induced by PDGF and inhibited in the presence of the SFK-selective inhibitor SU6656. A subset of these SFK-dependent genes was induced by PDGF even in the presence of the MEK1/2 inhibitor U0126 or the PI3K inhibitor LY294002. By using ribonuclease protection assays and nuclear run-off assays, we further determined that PDGF did not stimulate the rate of transcription of these SFK-dependent immediate early genes but rather promoted mRNA stabilization. Our data suggest that PDGF regulates gene expression through an SFK-specific pathway that is distinct from the Ras-MAPK and PI3K pathways, and that SFKs signal gene expression by enhancing mRNA stability.

gene expression profiling of endocrine tumors by microarray analysis

&NA; In recent years, high‐throughput technologies such as microarrays have allowed comprehensive genetic profiling of a variety of diseases. Many types of tumors, including those of endocrine origin, have been subjected to microarray profiling. To the delight of most scientists, not only can the analysis of thousands of genes expressed in the tumors glean invaluable information regarding tumor biology, but one can also correlate the data with clinical parameters including diagnosis, prognosis, and drug response. In addition, some of these genes will likely serve as novel targets for future therapeutic intervention. We review here some of these technologies with a focus on their application to tumors of endocrine origin. We believe that these technologies, most of which are still evolving, will become mainstay research tools for biomedical research including the diagnosis and treatment of endocrine tumors. Curr Opin Endocrinol Diabetes 10:162–167

Abstract 3311: Glycosylation alterations associated with pancreatic cancer EMT probed using gene expression in three model systems

The lethal nature of pancreatic cancer is related to its propensity to disseminate at early stages and its resistance to chemotherapeutics. Cancer cells that have undergone epithelial-mesenchymal transition acquire these traits and may play an important role in pancreatic cancer progression. The identification of the defining molecular features and functional drivers of pancreatic cancer EMT may lead to new methods to control cancer progression. Particular glycosylation patterns are specifically associated with cellular differentiation and activation and play functional roles in cell migration and signal transduction. Therefore we hypothesized that pancreatic cancer EMT is characterized by specific glycosylation alterations that play functional roles in cancer cell differentiation or migration. As a first step toward investigating that hypothesis, we characterized the expression of glycan-related genes in three model systems of pancreatic cancer EMT. We used whole-genome microarrays to study gene expression in: 1) TGF-beta-induced EMT in two different cell lines; 2) a panel of 23 pancreatic cancer cell lines characterized as either epithelial-like or mesenchymal-like; and 3) actively-migrating and stationary cells from two different cell lines. We compiled a list of 585 genes involved in glycosylation (biosynthesis, sugar transport, glycan-binding, etc.) or EMT. Using this list, we found that each model system displayed both elevations and reductions with high significance in multiple glycan-related genes. The percentage of glycan-related genes showing changes exceeded the percentage of overall genes showing changes in each case, suggesting an important role of glycan alterations in EMT. To narrow the list of candidate genes for further study, we looked at the overlap in altered genes between the model systems. ST6GALNAC4, an enzyme that transfers sialic acid to particular presentations of the N-acetyl galactosamine monosaccharide, was elevated in the pancreatic cancer cell lines of all model systems. Glycan structures similar to those synthesized by ST6GALNAC4 are commonly associated with cancer. Genes involved in the sulfation and de-sulfation of glycosaminoglycans were frequently altered, as well as members of the mannose receptor family, each of which suggests routes of modulating extracellular interactions. These results support the involvement of glycan alterations in cancer EMT and suggest targets for the further study of mechanistic drivers of pancreatic cancer progression. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3311.

Abstract LB-247: Regulation signatures consistently detect activity of EGFR pathway in different cell lines

Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC In this work we introduce a concept of “regulation signatures” which aims to overcome shortcomings of existing methods of molecular diagnostics. In this approach we utilize our recently developed methodology for identifying key regulatory proteins in biological networks. The procedure works by combining high-throughput molecular data from individual biological samples with the knowledge-base on the global network of protein interactions. The algorithm performs scoring of signaling proteins in the global network to identify those that most likely represent key regulators responsible for observed downstream changes in gene and protein expression. Using these scores we construct “regulation signatures” - sets of proteins that characterize core cell signaling activity associated with a condition of interest. In the present study we illustrate this approach by building a signature for the EGFR pathway activation using gene-expression profiles from different cell lines stimulated with EGF. We demonstrate that our algorithm consistently predicts activity of the EGFR pathway despite significant variation in gene expression profiles across different samples. We also illustrate that this signature can detect signaling patterns associated with constitutive RAS activity. We discuss how regulation signatures could be applied to develop more accurate, robust and mechanistically relevant methods for predicting drug sensitivity, disease progression, and other clinical end-points. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr LB-247.