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T Hernandez-Boussard

Publications and source records attributed to T Hernandez-Boussard.

5 recordsLinked to original sources
PubMed2006

Combined microarray analysis of small cell lung cancer reveals altered apoptotic balance and distinct expression signatures of MYC family gene amplification.

DNA amplifications and deletions frequently contribute to the development and progression of lung cancer. To identify such novel alterations in small cell lung cancer (SCLC), we performed comparative genomic hybridization on a set of 24 SCLC cell lines, using cDNA microarrays representing approximately 22,000 human genes (providing an average mapping resolution of <70 kb). We identified localized DNA amplifications corresponding to oncogenes known to be amplified in SCLC, including MYC (8q24), MYCN (2p24) and MYCL1 (1p34). Additional highly localized DNA amplifications suggested candidate oncogenes not previously identified as amplified in SCLC, including the antiapoptotic genes TNFRSF4 (1p36), DAD1 (14q11), BCL2L1 (20q11) and BCL2L2 (14q11). Likewise, newly discovered PCR-validated homozygous deletions suggested candidate tumor-suppressor genes, including the proapoptotic genes MAPK10 (4q21) and TNFRSF6 (10q23). To characterize the effect of DNA amplification on gene expression patterns, we performed expression profiling using the same microarray platform. Among our findings, we identified sets of genes whose expression correlated with MYC, MYCN or MYCL1 amplification, with surprisingly little overlap among gene sets. While both MYC and MYCN amplification were associated with increased and decreased expression of known MYC upregulated and downregulated targets, respectively, MYCL1 amplification was associated only with the latter. Our findings support a role of altered apoptotic balance in the pathogenesis of SCLC, and suggest that MYC family genes might affect oncogenesis through distinct sets of targets, in particular implicating the importance of transcriptional repression.

Apoptosis↗
PubMed2001

The Stanford Microarray Database.

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.

Animals↗
PubMed1999

IARC p53 mutation database: a relational database to compile and analyze p53 mutations in human tumors and cell lines. International Agency for Research on Cancer.

The tumor suppressor p53 gene is the most frequently mutated gene in human cancer. To date, more than 10,000 mutations have been described in the literature, and these data are available in various electronic formats on the World Wide Web. Here we describe the structure and format of the different p53 datasets maintained and curated at the International Agency for Research on Cancer (IARC) in Lyon, France. These include p53 somatic mutations (more than 10,000 entries), p53 germline mutations (144 entries), and p53 polymorphisms (13 entries), with the somatic mutations organized into a relational database using AccessTM. The main features of these datasets are (1) controlled entry with standardized format and restricted vocabulary, (2) inclusion of annotations on individual characteristics and exposures, and (3) a classification of pathologies based on the International Classification of Diseases for Oncology (ICD-O). In addition, several interfaces have been developed to analyze the data in order to produce mutation spectra, codon analyses, or visualization of the mutation with the tertiary structure of the protein. All datasets and tools for analysis are available at http://www.iarc.fr/p53/homepage.

Databases, Factual↗
PubMed1999

Sources of bias in the detection and reporting of p53 mutations in human cancer: analysis of the IARC p53 mutation database.

p53 gene encodes a transcription factor with tumor suppressive properties and to date, somatic mutation of this gene is the most common genetic event in human cancer. A relational database has been developed to facilitate the retrieval and analysis of these mutations at the International Agency for Research on Cancer (IARC) and it currently contains information on over 8000 individual tumors and cell lines. Many factors may influence the detection and reporting of mutations, including selection of tumor samples, study design, choice of methods, and quality control. There is also concern that several biases may affect the way data appear in the literature. Minimizing these biases is an essential methodological issue in the development of mutation data-bases. In this paper, we review and discuss these main sources of bias and make recommendations to authors in order to minimize bias in mutation detection and reporting.

Bias↗