DNA amplifications, leading to the overexpression of oncogenes, are a cardinal feature of lung cancer and directly contribute to its pathogenesis. To uncover novel such alterations, we performed an array-based comparative genomic hybridization survey of 128 non-small cell lung cancer cell lines and tumors. Prominent among our findings, we identified recurrent high-level amplification at cytoband 22q11.21 in 3% of lung cancer specimens, with another 11% of specimens exhibiting low-level gain spanning that locus. The 22q11.21 amplicon core contained eight named genes, only four of which were overexpressed (by transcript profiling) when amplified. Among these, CRKL encodes an adaptor protein functioning in signal transduction, best known as a substrate of the BCR-ABL kinase in chronic myelogenous leukemia. RNA interference-mediated knockdown of CRKL in lung cancer cell lines with (but not without) amplification led to significantly decreased cell proliferation, cell-cycle progression, cell survival, and cell motility and invasion. In addition, overexpression of CRKL in immortalized human bronchial epithelial cells led to EGF-independent cell growth. Our findings indicate that amplification and resultant overexpression of CRKL contributes to diverse oncogenic phenotypes in lung cancer, with implications for targeted therapy, and highlighting a role of adapter proteins as primary genetic drivers of tumorigenesis.

Subclinical microemboli documented on diffusion-weighted magnetic resonance imaging (DWI) are common following carotid artery stenting (CAS) procedures despite absence of neurological symptoms. This study was to evaluate risk factors predictive of microemboli in patients undergoing protected CAS with a distal embolic protection device. All CAS patients who received pre- and postprocedural magnetic resonance imaging (MRI) evaluations for carotid interventions at a single academic institution from July 2004 to December 2008 were examined. Microemboli were defined by new hyperintensities on postoperative DWI with corresponding decreased diffusion. Risk factors including patient demographics, medical comorbidities, clinical symptoms, lesion morphologies, and perioperative information were examined, and logistic regression analyses were utilized to determine predictors of CAS-related microemboli. A total of 204 patients underwent carotid interventions (76 CAS and 128 carotid endarterectomies) during the study period; 167 of them, including 67 CAS patients, received both preoperative and postoperative MRIs. Among those who underwent protected CAS, the incidence of microemboli was 46.3% despite a relative low incidence of associated neurological symptoms (2.9%). Univariate and multivariate regression analyses showed that date of procedure (odds ratio [OR] 30.6 and p = 0.019) and preoperative transient ischemic attack symptoms (OR 9.24 and p = 0.009) were independent predictors of developing postoperative changes on DWI in the ipsilateral hemisphere, and age >76 years was predictive of having new lesions on DWI in the contralateral hemisphere (OR 6.11 and p = 0.026). Our study underscores that certain risk factors are significantly associated with CAS-related microemboli and that physician experience and patient selection are essential in improving outcome of CAS procedures.

OBJECTIVE:

The external carotid artery (ECA) is an important collateral pathway for cerebral blood flow. Carotid artery stenting (CAS) typically crosses the ECA, while carotid endarterectomy (CEA) includes deliberate ECA plaque removal. The purpose of the present study was to compare the long-term patency of the ECA following CAS and CEA as determined by carotid duplex ultrasound.

METHODS:

Duplex ultrasounds and hospital records were reviewed for consecutive patients undergoing CAS between February 2002 and April 2008, and were compared with those undergoing CEA in the same time period. Preoperative and postoperative ECA peak systolic velocities were normalized to the common carotid artery (CCA) as ECA/CCA ratios. A significant (80% or greater) ECA stenosis was defined as an ECA/CCA ratio of 4.0. A change of ratio by more than 1 was defined as significant. Data were analyzed using Student’s t test and χ2 analysis.

RESULTS:

A total of 86 CAS procedures in 83 patients were performed (81 men, mean age 69.9 years). Among them, 38.4% of patients had previous CEA, 9.6% of whom had contralateral internal carotid artery occlusion. Sixty-seven CAS and 65 CEA patients with complete duplex data in the same time period were included in the analyses. There was no difference in the incidence of severe ECA stenosis on preoperative ultrasound evaluations. During a mean follow-up of 34 months (range four to 78 months), three postprocedure ECA occlusions were found in the CAS group. The likelihood of severe stenosis or occlusion following CAS was 28.3%, compared with 11% following CEA (P<0.025). However, 62% of CEA patients and 57% of CAS patients had no significant change in ECA status. Reduction in the patient’s degree of ECA stenosis was observed in 9.4% of CAS versus 26.6% of CEA patients. Overall, immediate postoperative ratios of both groups were slightly improved, but there was a trend of more disease progression in the CAS group during follow-up.

CONCLUSION:

CAS is associated with a higher incidence of post-procedure ECA stenosis. Despite the absence of neurological symptoms, a trend toward late disease progression of ECA following CAS warrants long-term evaluation.

The development of the Functional Genomics Investigation Ontology (FuGO) is a collaborative, international effort that will provide a resource for annotating functional genomics investigations, including the study design, protocols and instrumentation used, the data generated and the types of analysis performed on the data. FuGO will contain both terms that are universal to all functional genomics investigations and those that are domain specific. In this way, the ontology will serve as the “semantic glue” to provide a common understanding of data from across these disparate data sources. In addition, FuGO will reference out to existing mature ontologies to avoid the need to duplicate these resources, and will do so in such a way as to enable their ease of use in annotation. This project is in the early stages of development; the paper will describe efforts to initiate the project, the scope and organization of the project, the work accomplished to date, and the challenges encountered, as well as future plans.

Background

Breast cancer cell lines have been used widely to investigate breast cancer pathobiology and new therapies. Breast cancer is a molecularly heterogeneous disease, and it is important to understand how well and which cell lines best model that diversity. In particular, microarray studies have identified molecular subtypes–luminal A, luminal B, ERBB2-associated, basal-like and normal-like–with characteristic gene-expression patterns and underlying DNA copy number alterations (CNAs). Here, we studied a collection of breast cancer cell lines to catalog molecular profiles and to assess their relation to breast cancer subtypes.

Methods

Whole-genome DNA microarrays were used to profile gene expression and CNAs in a collection of 52 widely-used breast cancer cell lines, and comparisons were made to existing profiles of primary breast tumors. Hierarchical clustering was used to identify gene-expression subtypes, and Gene Set Enrichment Analysis (GSEA) to discover biological features of those subtypes. Genomic and transcriptional profiles were integrated to discover within high-amplitude CNAs candidate cancer genes with coordinately altered gene copy number and expression.

Findings

Transcriptional profiling of breast cancer cell lines identified one luminal and two basal-like (A and B) subtypes. Luminal lines displayed an estrogen receptor (ER) signature and resembled luminal-A/B tumors, basal-A lines were associated with ETS-pathway and BRCA1 signatures and resembled basal-like tumors, and basal-B lines displayed mesenchymal and stem/progenitor-cell characteristics. Compared to tumors, cell lines exhibited similar patterns of CNA, but an overall higher complexity of CNA (genetically simple luminal-A tumors were not represented), and only partial conservation of subtype-specific CNAs. We identified 80 high-level DNA amplifications and 13 multi-copy deletions, and the resident genes with concomitantly altered gene-expression, highlighting known and novel candidate breast cancer genes.

Conclusions

Overall, breast cancer cell lines were genetically more complex than tumors, but retained expression patterns with relevance to the luminal-basal subtype distinction. The compendium of molecular profiles defines cell lines suitable for investigations of subtype-specific pathobiology, cancer stem cell biology, biomarkers and therapies, and provides a resource for discovery of new breast cancer genes.

Pancreatobiliary cancers have among the highest mortality rates of any cancer type. Discovering the full spectrum of molecular genetic alterations may suggest new avenues for therapy. To catalogue genomic alterations, we carried out array-based genomic profiling of 31 exocrine pancreatic cancers and 6 distal bile duct cancers, expanded as xenografts to enrich the tumor cell fraction. We identified numerous focal DNA amplifications and deletions, including in 19% of pancreatobiliary cases gain at cytoband 18q11.2, a locus uncommonly amplified in other tumor types. The smallest shared amplification at 18q11.2 included GATA6, a transcriptional regulator previously linked to normal pancreas development. When amplified, GATA6 was overexpressed at both the mRNA and protein levels, and strong immunostaining was observed in 25 of 54 (46%) primary pancreatic cancers compared to 0 of 33 normal pancreas specimens surveyed. GATA6 expression in xenografts was associated with specific microarray gene-expression patterns, enriched for GATA binding sites and mitochondrial oxidative phosphorylation activity. siRNA mediated knockdown of GATA6 in pancreatic cancer cell lines with amplification led to reduced cell proliferation, cell cycle progression, and colony formation. Our findings indicate that GATA6 amplification and overexpression contribute to the oncogenic phenotypes of pancreatic cancer cells, and identify GATA6 as a candidate lineage-specific oncogene in pancreatobiliary cancer, with implications for novel treatment strategies.

Author Summary

Pancreatic cancer is a devastating disease, having among the lowest survival rates of any cancer. A better understanding of the molecular basis of pancreatic cancer may lead to improved rationale therapies. We report here the discovery of amplification (i.e. extra copies) of the GATA6 gene in many human pancreatic cancers. GATA6 is a regulator of gene expression and functions in the development of the normal pancreas. Our findings indicate that its amplification and aberrant overexpression contribute to pancreatic cancer development. GATA6 joins a growing list of cancer genes with key roles in normal human development but pathogenic roles in cancer when aberrantly expressed. Our discovery of GATA6 amplification provides a new foothold into understanding the pathogenic mechanisms underlying pancreatic cancer, and suggests new strategies for therapy by targeting GATA6 or the genes it regulates.

PharmGKB is a knowledge base that captures the relationships between drugs, diseases/phenotypes and genes involved in pharmacokinetics (PK) and pharmacodynamics (PD). This information includes literature annotations, primary data sets, PK and PD pathways, and expert-generated summaries of PK/PD relationships between drugs, diseases/phenotypes and genes. PharmGKB's website is designed to effectively disseminate knowledge to meet the needs of our users. PharmGKB currently has literature annotations documenting the relationship of over 500 drugs, 450 diseases and 600 variant genes. In order to meet the needs of whole genome studies, PharmGKB has added new functionalities, including browsing the variant display by chromosome and cytogenetic locations, allowing the user to view variants not located within a gene. We have developed new infrastructure for handling whole genome data, including increased methods for quality control and tools for comparison across other data sources, such as dbSNP, JSNP and HapMap data. PharmGKB has also added functionality to accept, store, display and query high throughput SNP array data. These changes allow us to capture more structured information on phenotypes for better cataloging and comparison of data. PharmGKB is available at www.pharmgkb.org

The Stanford Microarray Database (SMD; ) is a research tool and archive that allows hundreds of researchers worldwide to store, annotate, analyze and share data generated by microarray technology. SMD supports most major microarray platforms, and is MIAME-supportive and can export or import MAGE-ML. The primary mission of SMD is to be a research tool that supports researchers from the point of data generation to data publication and dissemination, but it also provides unrestricted access to analysis tools and public data from 300 publications. In addition to supporting ongoing research, SMD makes its source code fully and freely available to others under an Open Source license, enabling other groups to create a local installation of SMD. In this article, we describe several data analysis tools implemented in SMD and we discuss features of our software release.

Abstract

Pancreatic cancer, the fourth leading cause of cancer death in the United States, is frequently associated with the amplification and deletion of specific oncogenes and tumor-suppressor genes (TSGs), respectively. To identify such novel alterations and to discover the underlying genes, we performed comparative genomic hybridization on a set of 22 human pancreatic cancer cell lines, using cDNA microarrays measuring ∼26,000 human genes (thereby providing an average mapping resolution of <60 kb). To define the subset of amplified and deleted genes with correspondingly altered expression, we also profiled mRNA levels in parallel using the same cDNA microarray platform. In total, we identified 14 high-level amplifications (38–4934 kb in size) and 15 homozygous deletions (46–725 kb). We discovered novel localized amplicons, suggesting previously unrecognized candidate oncogenes at 6p21, 7q21 (SMURF1, TRRAP), 11q22 (BIRC2, BIRC3), 12p12, 14q24 (TGFB3), 17q12, and 19q13. Likewise, we identified novel polymerase chain reaction-validated homozygous deletions indicating new candidate TSGs at 6q25, 8p23, 8p22 (TUSC3), 9q33 (TNC, TNFSF15), 10q22, 10q24 (CHUK), 11p15 (DKK3), 16q23, 18q23, 21q22 (PRDM15, ANKRD3), and Xp11. Our findings suggest candidate genes and pathways, which may contribute to the development or progression of pancreatic cancer.

The Stanford Microarray Database (SMD) (http://smd.stanford.edu) is a research tool for hundreds of Stanford researchers and their collaborators. In addition, SMD functions as a resource for the entire biological research community by providing unrestricted access to microarray data published by SMD users and by disseminating its source code. In addition to storing GenePix (Axon Instruments) and ScanAlyze output from spotted microarrays, SMD has recently added the ability to store, retrieve, display and analyze the complete raw data produced by several additional microarray platforms and image analysis software packages, so that we can also now accept data from Affymetrix GeneChips (MAS5/GCOS or dChip), Agilent Catalog or Custom arrays (using Agilent's Feature Extraction software) or data created by SpotReader (Niles Scientific). We have implemented software that allows us to accept MAGE-ML documents from array manufacturers and to submit MIAME-compliant data in MAGE-ML format directly to ArrayExpress and GEO, greatly increasing the ease with which data from SMD can be published adhering to accepted standards and also increasing the accessibility of published microarray data to the general public. We have introduced a new tool to facilitate data sharing among our users, so that datasets can be shared during, before or after the completion of data analysis. The latest version of the source code for the complete database package was released in November 2004 (http://smd.stanford.edu/download/), allowing researchers around the world to deploy their own installations of SMD.

Background

Microarray-based comparative genome hybridization experiments generate data that can be mapped onto the genome. These data are interpreted more easily when represented graphically in a genomic context.

Results

We have developed Caryoscope, which is an open source Java application for visualizing microarray data from array comparative genome hybridization experiments in a genomic context. Caryoscope can read General Feature Format files (GFF files), as well as comma- and tab-delimited files, that define the genomic positions of the microarray reporters for which data are obtained. The microarray data can be browsed using an interactive, zoomable interface, which helps users identify regions of chromosomal deletion or amplification. The graphical representation of the data can be exported in a number of graphic formats, including publication-quality formats such as PostScript.

Conclusion

Caryoscope is a useful tool that can aid in the visualization, exploration and interpretation of microarray data in a genomic context.

Given that gene duplication is a major driving force of evolutionary change and the key mechanism underlying the emergence of new genes and biological processes, this study sought to use a novel genome-wide approach to identify genes that have undergone lineage-specific duplications or contractions among several hominoid lineages. Interspecies cDNA array-based comparative genomic hybridization was used to individually compare copy number variation for 39,711 cDNAs, representing 29,619 human genes, across five hominoid species, including human. We identified 1,005 genes, either as isolated genes or in clusters positionally biased toward rearrangement-prone genomic regions, that produced relative hybridization signals unique to one or more of the hominoid lineages. Measured as a function of the evolutionary age of each lineage, genes showing copy number expansions were most pronounced in human (134) and include a number of genes thought to be involved in the structure and function of the brain. This work represents, to our knowledge, the first genome-wide gene-based survey of gene duplication across hominoid species. The genes identified here likely represent a significant majority of the major gene copy number changes that have occurred over the past 15 million years of human and great ape evolution and are likely to underlie some of the key phenotypic characteristics that distinguish these species.

This genome-wide analysis reports the major lineage-specific gene copy number changes that have occurred over the past 15 million years of human and great ape evolution

We used DNA microarrays to characterize the global gene expression patterns in surface epithelial cancers of the ovary. We identified groups of genes that distinguished the clear cell subtype from other ovarian carcinomas, grade I and II from grade III serous papillary carcinomas, and ovarian from breast carcinomas. Six clear cell carcinomas were distinguished from 36 other ovarian carcinomas (predominantly serous papillary) based on their gene expression patterns. The differences may yield insights into the worse prognosis and therapeutic resistance associated with clear cell carcinomas. A comparison of the gene expression patterns in the ovarian cancers to published data of gene expression in breast cancers revealed a large number of differentially expressed genes. We identified a group of 62 genes that correctly classified all 125 breast and ovarian cancer specimens. Among the best discriminators more highly expressed in the ovarian carcinomas were PAX8 (paired box gene 8), mesothelin, and ephrin-B1 (EFNB1). Although estrogen receptor was expressed in both the ovarian and breast cancers, genes that are coregulated with the estrogen receptor in breast cancers, including GATA-3, LIV-1, and X-box binding protein 1, did not show a similar pattern of coexpression in the ovarian cancers.

The Stanford Microarray Database (SMD; http://genome-www.stanford.edu/microarray/) serves as a microarray research database for Stanford investigators and their collaborators. In addition, SMD functions as a resource for the entire scientific community, by making freely available all of its source code and providing full public access to data published by SMD users, along with many tools to explore and analyze those data. SMD currently provides public access to data from 3500 microarrays, including data from 85 publications, and this total is increasing rapidly. In this article, we describe some of SMD's newer tools for accessing public data, assessing data quality and for data analysis.

The explosion in the number of functional genomic datasets generated with tools such as DNA microarrays has created a critical need for resources that facilitate the interpretation of large-scale biological data. SOURCE is a web-based database that brings together information from a broad range of resources, and provides it in manner particularly useful for genome-scale analyses. SOURCE's GeneReports include aliases, chromosomal location, functional descriptions, GeneOntology annotations, gene expression data, and links to external databases. We curate published microarray gene expression datasets and allow users to rapidly identify sets of co-regulated genes across a variety of tissues and a large number of conditions using a simple and intuitive interface. SOURCE provides content both in gene and cDNA clone-centric pages, and thus simplifies analysis of datasets generated using cDNA microarrays. SOURCE is continuously updated and contains the most recent and accurate information available for human, mouse, and rat genes. By allowing dynamic linking to individual gene or clone reports, SOURCE facilitates browsing of large genomic datasets. Finally, SOURCEs batch interface allows rapid extraction of data for thousands of genes or clones at once and thus facilitates statistical analyses such as assessing the enrichment of functional attributes within clusters of genes. SOURCE is available at http://source.stanford.edu.

The Stanford Microarray Database (SMD) stores raw and normalized data from microarray experiments, and provides web interfaces for researchers to retrieve, analyze and visualize their data. The two immediate goals for SMD are to serve as a storage site for microarray data from ongoing research at Stanford University, and to facilitate the public dissemination of that data once published, or released by the researcher. Of paramount importance is the connection of microarray data with the biological data that pertains to the DNA deposited on the microarray (genes, clones etc.). SMD makes use of many public resources to connect expression information to the relevant biology, including SGD [Ball,C.A., Dolinski,K., Dwight,S.S., Harris,M.A., Issel-Tarver,L., Kasarskis,A., Scafe,C.R., Sherlock,G., Binkley,G., Jin,H. et al. (2000) Nucleic Acids Res., 28, 77–80], YPD and WormPD [Costanzo,M.C., Hogan,J.D., Cusick,M.E., Davis,B.P., Fancher,A.M., Hodges,P.E., Kondu,P., Lengieza,C., Lew-Smith,J.E., Lingner,C. et al. (2000) Nucleic Acids Res., 28, 73–76], Unigene [Wheeler,D.L., Chappey,C., Lash,A.E., Leipe,D.D., Madden,T.L., Schuler,G.D., Tatusova,T.A. and Rapp,B.A. (2000) Nucleic Acids Res., 28, 10–14], dbEST [Boguski,M.S., Lowe,T.M. and Tolstoshev,C.M. (1993) Nature Genet., 4, 332–333] and SWISS-PROT [Bairoch,A. and Apweiler,R. (2000) Nucleic Acids Res., 28, 45–48] and can be accessed at http://genome-www.stanford.edu/microarray.

Many soft tissue tumors recapitulate features of normal connective tissue. We hypothesize that different types of fibroblastic tumors are representative of different populations of fibroblastic cells or different activation states of these cells. We examined two tumors with fibroblastic features, solitary fibrous tumor (SFT) and desmoid-type fibromatosis (DTF), by DNA microarray analysis and found that they have very different expression profiles, including significant differences in their patterns of expression of extracellular matrix genes and growth factors. Using immunohistochemistry and in situ hybridization on a tissue microarray, we found that genes specific for these two tumors have mutually specific expression in the stroma of nonneoplastic tissues. We defined a set of 786 gene spots whose pattern of expression distinguishes SFT from DTF. In an analysis of DNA microarray gene expression data from 295 previously published breast carcinomas, we found that expression of this gene set defined two groups of breast carcinomas with significant differences in overall survival. One of the groups had a favorable outcome and was defined by the expression of DTF genes. The other group of tumors had a poor prognosis and showed variable expression of genes enriched for SFT type. Our findings suggest that the host stromal response varies significantly among carcinomas and that gene expression patterns characteristic of soft tissue tumors can be used to discover new markers for normal connective tissue cells.

The authors used two different fibroblastic tumors to identify markers expressed by normal stroma cells (the supporting framework of body tissues and/or tumors), and show that different stromal backgrounds exist in different breast carcinomas.