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James F Reid

Publications and source records attributed to James F Reid.

11 recordsLinked to original sources
PubMed2006

Identification of tumor-specific molecular signatures in intracranial ependymoma and association with clinical characteristics.

PURPOSE: To delineate clinically relevant molecular signatures of intracranial ependymoma. MATERIALS AND METHODS: We analyzed 24 primary intracranial ependymomas. For genomic profiling, microarray-based comparative genomic hybridization (CGH) was used and results were validated by fluorescent in situ hybridization and loss of heterozygosity mapping. We performed gene expression profiling using microarrays, real-time quantitative reverse transcriptase polymerase chain reaction, and methylation analysis of selected genes. We applied class comparison analyses to compare both genomic and expression profiling data with clinical characteristics. RESULTS: A variable number of genomic imbalances were detected by array CGH, revealing multiple regions of recurrent gain (including 2q23, 7p21, 12p, 13q21.1, and 20p12) and loss (including 5q31, 6q26, 7q36, 15q21.1, 16q24, 17p13.3, 19p13.2, and 22q13.3). An ependymoma-specific gene expression signature was characterized by the concurrent abnormal expression of developmental and differentiation pathways, including NOTCH and sonic hedgehog signaling. We identified specific differentially imbalanced genomic clones and gene expression signatures significantly associated with tumor location, patient age at disease onset, and retrospective risk for relapse. Integrated genomic and expression profiling allowed us to identify genes of which the expression is deregulated in intracranial ependymoma, such as overexpression of the putative proto-oncogene YAP1 (located at 11q22) and downregulation of the SULT4A1 gene (at 22q13.3). CONCLUSION: The present exploratory molecular profiling study allowed us to refine previously reported intervals of genomic imbalance, to identify novel restricted regions of gain and loss, and to identify molecular signatures correlating with various clinical variables. Validation of these results on independent data sets represents the next step before translation into the clinical setting.

Adaptor Proteins, Signal Transducing↗
PubMed2006

New potential ligand-receptor signaling loops in ovarian cancer identified in multiple gene expression studies.

Based on the hypothesis that gene products involved in the same biological process would be coupled at transcriptional level, a previous study analyzed the correlation of the gene expression patterns of ligand-receptor (L-R) pairs to discover potential autocrine/paracrine signaling loops in different cancers (Graeber and Eisenberg. Nat Genet 2001; 29:295). By refining the starting database, a list of 511 L-R pairs was compiled, combined to eight data sets from a single pathology, epithelial ovarian cancer, and examined as a proof-of-principle of the statistical and biological validity of the correlation of the L-R gene expression patterns in cancer. Analysis revealed a Bonferroni-corrected significant correlation of 105 L-R pairs in at least one data set and, by systematic analysis, identified 39 more frequently correlated L-R pairs, 7 of which were already biologically confirmed. In four data sets examined for an L-R correlation associated with patient survival time, 15 L-R pairs were significantly correlated in short surviving patients in two of the data sets. Immunohistochemical analysis of one of the newly identified correlated L-R pairs (i.e., EFNB3-EPHB4) revealed the correlated expression of ephrin-B3 and EphB4 proteins in 45 of 55 epithelial ovarian tumor samples (P < 0.0001). Together, these data not only support the validity of cross-comparison analysis of gene expression data because known and expected correlations were confirmed but also point to the promise of such analysis in identifying new L-R signaling loops in cancer.

Ephrin-B3↗
PubMed2006

Regulation of lipocalin-2 gene by the cancer chemopreventive retinoid 4-HPR.

N-(4-Hydroxyphenyl)retinamide (4-HPR) is a nonclassical retinoid with cancer preventive effects in vivo and antiproliferative and apoptotic activities in vitro. Examining the transcriptional profile of human breast cancer cell lines, MCF7 and T47D, treated with 4-HPR, we identified the lipocalin member LCN2 (NGAL or 24p3) as a gene, markedly induced by the retinoid. Because of its presumed function in apoptosis, LCN2 was examined more thoroughly in response to 4-HPR. Like mRNA, the expression of LCN2 protein in MCF7 and T47D cells was highly induced in a time-dependent manner by 4-HPR, but not by its inactive metabolite 4-MPR and, to some extent, this event was linked to the free radicals normally generated by 4-HPR. All-trans retinoic acid also induced LCN2 protein, particularly in T47D cells. Ectopic LCN2 compromised cell viability, and the few MCF7 clones that survived LCN2 overexpression were less sensitive than do mock cells to 4HPR, indicating that selective pressure for survival to LCN2 confers cross-resistance to 4-HPR. Significantly, ablation of LCN2 induction by siRNA did not modify the response to 4-HPR, implying that LCN2 is not critical for apoptosis by 4-HPR. Our results indicate that 4-HPR markedly induces LCN2 expression, but this event may not represent an apoptotic response.

Acute-Phase Proteins↗
PubMed2006

Molecular predictors of response and outcome in ovarian cancer.

A major problem in clinical management of patients with epithelial ovarian cancer (EOC) is the largely unpredictable response to first-line treatment and the occurrence of relapse after complete initial response, associated with broad cross-resistance to even structurally dissimilar drugs. During tumor development and progression, multiple genic alterations take place that might contribute specifically to the treatment response and eventually impact on disease outcome. One area of intense research is the identification of molecular markers to accurately assess the prognosis of EOC patients and to define innovative therapeutic strategies. A large survey of recent published data indicates the need to revisit traditional molecular markers with respect to their contribution to the assessment of overall survival in selected populations. Furthermore, recent technological developments that enable simultaneous measurement of many parameters ("omic" approaches) hold the promise of identifying new molecular prognostic and predictive markers.

Algorithms↗
PubMed2006

Transcriptional network dynamics in macrophage activation.

Transcriptional regulatory networks govern cell differentiation and the cellular response to external stimuli. However, mammalian model systems have not yet been accessible for network analysis. Here, we present a genome-wide network analysis of the transcriptional regulation underlying the mouse macrophage response to bacterial lipopolysaccharide (LPS). Key to uncovering the network structure is our combination of time-series cap analysis of gene expression with in silico prediction of transcription factor binding sites. By integrating microarray and qPCR time-series expression data with a promoter analysis, we find dynamic subnetworks that describe how signaling pathways change dynamically during the progress of the macrophage LPS response, thus defining regulatory modules characteristic of the inflammatory response. In particular, our integrative analysis enabled us to suggest novel roles for the transcription factors ATF-3 and NRF-2 during the inflammatory response. We believe that our system approach presented here is applicable to understanding cellular differentiation in higher eukaryotes.

Algorithms↗
PubMed2006

Complex Loci in human and mouse genomes.

Mammalian genomes harbor a larger than expected number of complex loci, in which multiple genes are coupled by shared transcribed regions in antisense orientation and/or by bidirectional core promoters. To determine the incidence, functional significance, and evolutionary context of mammalian complex loci, we identified and characterized 5,248 cis-antisense pairs, 1,638 bidirectional promoters, and 1,153 chains of multiple cis-antisense and/or bidirectionally promoted pairs from 36,606 mouse transcriptional units (TUs), along with 6,141 cis-antisense pairs, 2,113 bidirectional promoters, and 1,480 chains from 42,887 human TUs. In both human and mouse, 25% of TUs resided in cis-antisense pairs, only 17% of which were conserved between the two organisms, indicating frequent species specificity of antisense gene arrangements. A sampling approach indicated that over 40% of all TUs might actually be in cis-antisense pairs, and that only a minority of these arrangements are likely to be conserved between human and mouse. Bidirectional promoters were characterized by variable transcriptional start sites and an identifiable midpoint at which overall sequence composition changed strand and the direction of transcriptional initiation switched. In microarray data covering a wide range of mouse tissues, genes in cis-antisense and bidirectionally promoted arrangement showed a higher probability of being coordinately expressed than random pairs of genes. In a case study on homeotic loci, we observed extensive transcription of nonconserved sequences on the noncoding strand, implying that the presence rather than the sequence of these transcripts is of functional importance. Complex loci are ubiquitous, host numerous nonconserved gene structures and lineage-specific exonification events, and may have a cis-regulatory impact on the member genes.

Animals↗
PubMed2005

Limits of predictive models using microarray data for breast cancer clinical treatment outcome.

Data from microarray studies have been used to develop predictive models for treatment outcome in breast cancer, such as a recently proposed predictive model for antiestrogen response after tamoxifen treatment that was based on the expression ratio of two genes. We attempted to validate this model on an independent cohort of 58 patients with resectable estrogen receptor-positive breast cancer. We measured expression of the genes HOXB13 and IL17BR with real time-quantitative polymerase chain reaction and assessed the association between their expression and outcome by use of univariate logistic regression, area under the receiver-operating-characteristic curve (AUC), a two-sample t test, and a Mann-Whitney test. We also applied standard supervised methods to the original microarray dataset and to another independent dataset from similar patients to estimate the classification accuracy obtainable by using more than two genes in a microarray-based predictive model. We could not validate the performance of the two-gene predictor on our cohort of samples (relation between outcome and the following genes estimated by logistic regression: for HOXB13, odds ratio [OR] = 1.04, 95% confidence interval [CI] = 0.92 to 1.16, P = .54; for IL17BR, OR = 0.69, 95% CI = 0.40 to 1.20, P = .18; and for HOXB13/IL17BR, OR = 1.30, 95% CI = 0.88 to 1.93, P = .18). Similar results were obtained with the AUC, a two-sample two-sided t test, and a Mann-Whitney test. In addition, estimates of classification accuracies applied to two independent microarray datasets highlighted the poor performance of treatment-response predictive models that can be achieved with the sample sizes of patients and informative genes to date.

Adult↗
PubMed2004

Gene expression profiling of advanced ovarian cancer: characterization of a molecular signature involving fibroblast growth factor 2.

Epithelial ovarian cancer (EOC) is the gynecological disease with the highest death rate. We applied an automatic class discovery procedure based on gene expression profiling to stages III-IV tumors to search for molecular signatures associated with the biological properties and progression of EOC. Using a complementary DNA microarray containing 4451 cancer-related, sequence-verified features, we identified a subset of EOC characterized by the expression of numerous genes related to the extracellular matrix (ECM) and its remodeling, along with elements of the fibroblast growth factor 2 (FGF2) signaling pathway. A total of 10 genes were validated by quantitative real-time polymerase chain reaction, and coexpression of FGF2 and fibroblast growth factor receptor 4 in tumor cells was revealed by immunohistochemistry, confirming the reliability of gene expression by cDNA microarray. Since the functional relationships among these genes clearly suggested involvement of the identified molecular signature in processes related to epithelial-stromal interactions and/or epithelial-mesenchymal cellular plasticity, we applied supervised learning analysis on ovarian-derived cell lines showing distinct cellular phenotypes in culture. This procedure enabled construction of a gene classifier able to discriminate mesenchymal-like from epithelial-like cells. Genes overexpressed in mesenchymal-like cells proved to match the FGF2 signaling and ECM molecular signature, as identified by unsupervised class discovery on advanced tumor samples. In vitro functional analysis of the cell plasticity classifier was carried out using two isogenic and immortalized cell lines derived from ovarian surface epithelium and displaying mesenchymal and epithelial morphology, respectively. The results indicated the autocrine, but not intracrine stimulation of mesenchymal conversion and cohort/scatter migration of cells by FGF2, suggesting a central role for FGF2 signaling in the maintenance of cellular plasticity of ovary-derived cells throughout the carcinogenesis process. These findings raise mechanistic hypotheses on EOC pathogenesis and progression that might provide a rational underpinning for new therapeutic modalities.

Cell Line, Tumor↗
PubMed2003

Automated DNA chip annotation tables at IFOM: the importance of synchronisation and cross-referencing of sequence databases.

The increasing popularity of DNA chip technology for the study of gene expression is producing, for each experiment, a sizable quantity of numerical data to analyse and an accompanying large number of gene identifiers that should be associated with the relevant biological annotation. We describe here a website at IFOM (FIRC Institute of Molecular Oncology) where we release regularly updated annotation tables for the most used Affymetrix oligonucleotide DNA chips and for the whole Research Genetics 46K clone collection for cDNA arrays. These tables are synchronised with every new release of the mouse and human UniGene databases (NCBI; National Center for Biotechnology Information), allowing fast and easy preliminary annotation of DNA array experiments. We also report some comparative evidence about the importance of biological database synchronisation and cross-references in the process of generating annotation tables for DNA chips.

Abstracting and Indexing↗
PubMedDate not supplied

LNCIB human full-length cDNAs collection: towards a better comprehension of the human transcriptome.

LNCIB has been producing a variety of human full-length-enriched, normalized and subtracted cDNA libraries from various cell lines and tissues in different developmental stages by using the CAP-Trapper method. By sequencing 23000 clones of these libraries we identified a pool of about 5800 good quality unique cDNAs. After BLAST analysis on Human RefSeq/Unigene databases, 1717 of these sequences remained with no or poor annotation. We show that cross-species comparative BLAST resulted as a valid tool for the annotation of orthologous genes.

Databases, Factual↗