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David Botstein

Publications and source records attributed to David Botstein.

81 records · Page 5Linked to original sources
PubMed2002

Expression of cytokeratins 17 and 5 identifies a group of breast carcinomas with poor clinical outcome.

While several prognostic factors have been identified in breast carcinoma, the clinical outcome remains hard to predict for individual patients. Better predictive markers are needed to help guide difficult treatment decisions. In a previous study of 78 breast carcinoma specimens, we noted an association between poor clinical outcome and the expression of cytokeratin 17 and/or cytokeratin 5 mRNAs. Here we describe the results of immunohistochemistry studies using monoclonal antibodies against these markers to analyze more than 600 paraffin-embedded breast tumors in tissue microarrays. We found that expression of cytokeratin 17 and/or cytokeratin 5/6 in tumor cells was associated with a poor clinical outcome. Moreover, multivariate analysis showed that in node-negative breast carcinoma, expression of these cytokeratins was a prognostic factor independent of tumor size and tumor grade.

Animals↗
PubMed2002

Challenges in developing a molecular characterization of cancer.

DNA microarrays are widely used to measure gene expression across thousands of genes in parallel. Recently, considerable efforts have been made to utilize this technology to improve our understanding of cancer and to identify novel diagnostic markers and therapeutic targets. Here, we detail some of the challenges in developing a molecular characterization of cancer and in translating these new discoveries towards clinical utility.

Gene Expression Profiling↗
PubMed2002

Systematic structure-function analysis of the small GTPase Arf1 in yeast.

Members of the ADP-ribosylation factor (Arf) family of small GTPases are implicated in vesicle traffic in the secretory pathway, although their precise function remains unclear. We generated a series of 23 clustered charge-to-alanine mutations in the Arf1 protein of Saccharomyces cerevisiae to determine the portions of this protein important for its function in cells. These mutants display a number of phenotypes, including conditional lethality at high or low temperature, defects in glycosylation of invertase, dominant lethality, fluoride sensitivity, and synthetic lethality with the arf2 null mutation. All mutations were mapped onto the available crystal structures for Arf1p: Arf1p bound to GDP, to GTP, and complexed with the regulatory proteins ArfGEF and ArfGAP. From this systematic structure-function analysis we demonstrate that all essential mutations studied map to one hemisphere of the protein and provide strong evidence in support of the proposed ArfGEF contact site on Arf1p but minimal evidence in support of the proposed ArfGAP-binding site. In addition, we describe the isolation of a spatially distant intragenic suppressor of a dominant lethal mutation in the guanine nucleotide-binding region of Arf1p.

ADP-Ribosylation Factor 1↗
PubMed2002

Gene expression patterns in human liver cancers.

Hepatocellular carcinoma (HCC) is a leading cause of death worldwide. Using cDNA microarrays to characterize patterns of gene expression in HCC, we found consistent differences between the expression patterns in HCC compared with those seen in nontumor liver tissues. The expression patterns in HCC were also readily distinguished from those associated with tumors metastatic to liver. The global gene expression patterns intrinsic to each tumor were sufficiently distinctive that multiple tumor nodules from the same patient could usually be recognized and distinguished from all the others in the large sample set on the basis of their gene expression patterns alone. The distinctive gene expression patterns are characteristic of the tumors and not the patient; the expression programs seen in clonally independent tumor nodules in the same patient were no more similar than those in tumors from different patients. Moreover, clonally related tumor masses that showed distinct expression profiles were also distinguished by genotypic differences. Some features of the gene expression patterns were associated with specific phenotypic and genotypic characteristics of the tumors, including growth rate, vascular invasion, and p53 overexpression.

Carcinoma, Hepatocellular↗
PubMed2002

Identification of genes periodically expressed in the human cell cycle and their expression in tumors.

The genome-wide program of gene expression during the cell division cycle in a human cancer cell line (HeLa) was characterized using cDNA microarrays. Transcripts of >850 genes showed periodic variation during the cell cycle. Hierarchical clustering of the expression patterns revealed coexpressed groups of previously well-characterized genes involved in essential cell cycle processes such as DNA replication, chromosome segregation, and cell adhesion along with genes of uncharacterized function. Most of the genes whose expression had previously been reported to correlate with the proliferative state of tumors were found herein also to be periodically expressed during the HeLa cell cycle. However, some of the genes periodically expressed in the HeLa cell cycle do not have a consistent correlation with tumor proliferation. Cell cycle-regulated transcripts of genes involved in fundamental processes such as DNA replication and chromosome segregation seem to be more highly expressed in proliferative tumors simply because they contain more cycling cells. The data in this report provide a comprehensive catalog of cell cycle regulated genes that can serve as a starting point for functional discovery. The full dataset is available at http://genome-www.stanford.edu/Human-CellCycle/HeLa/.

Cell Cycle↗
PubMed2002

Nonparametric methods for identifying differentially expressed genes in microarray data.

MOTIVATION: Gene expression experiments provide a fast and systematic way to identify disease markers relevant to clinical care. In this study, we address the problem of robust identification of differentially expressed genes from microarray data. Differentially expressed genes, or discriminator genes, are genes with significantly different expression in two user-defined groups of microarray experiments. We compare three model-free approaches: (1). nonparametric t-test, (2). Wilcoxon (or Mann-Whitney) rank sum test, and (3). a heuristic method based on high Pearson correlation to a perfectly differentiating gene ('ideal discriminator method'). We systematically assess the performance of each method based on simulated and biological data under varying noise levels and p-value cutoffs. RESULTS: All methods exhibit very low false positive rates and identify a large fraction of the differentially expressed genes in simulated data sets with noise level similar to that of actual data. Overall, the rank sum test appears most conservative, which may be advantageous when the computationally identified genes need to be tested biologically. However, if a more inclusive list of markers is desired, a higher p-value cutoff or the nonparametric t-test may be appropriate. When applied to data from lung tumor and lymphoma data sets, the methods identify biologically relevant differentially expressed genes that allow clear separation of groups in question. Thus the methods described and evaluated here provide a convenient and robust way to identify differentially expressed genes for further biological and clinical analysis.

Carcinoma, Squamous Cell↗
PubMed2002

Expression array technology in the diagnosis and treatment of breast cancer.

The most common group of cancers among American women involves malignancies of the breast. Breast cancer is a complex disease, involving several different types of tissues and specific cells with various functions, that is categorized into many distinct subtypes. Microarray analysis has recently revealed that different biological subtypes of breast cancer are accompanied by differences in their specific gene expression profile. Because breast tissue (and breast cancer) is heterogeneous, microarray analysis may provide clinicians with a better understanding of how to treat each specific case. Thus, microarray analysis may translate basic research data into more confident diagnoses, specifically designed treatment regimens geared to each patient's needs, and better clinical prognoses.

Breast Neoplasms↗