Biomedical subjects

Thomas D Wu

Publications and source records attributed to Thomas D Wu.

10 recordsLinked to original sources

PubMed2006

Expression of protein elongation factor eEF1A2 predicts favorable outcome in breast cancer.

Breast cancer is the most common malignancy among North American women. The identification of factors that predict outcome is key to individualized disease management and to our understanding of breast oncogenesis. We have analyzed mRNA expression of protein elongation factor eEF1A2 in two independent breast tumor populations of size n = 345 and n = 88, respectively. We find that eEF1A2 mRNA is expressed at a low level in normal breast epithelium but is detectably expressed in approximately 50-60% of primary human breast tumors. We have derived an eEF1A2-specific antibody and measured eEF1A2 protein expression in a sample of 438 primary breast tumors annotated with 20-year survival data. We find that high levels of eEF1A2 protein are detected in 60% of primary breast tumors independent of HER-2 protein expression, tumor size, lymph node status, and estrogen receptor (ER) expression. Importantly, we find that high eEF1A2 is a significant predictor of outcome. Women whose tumor has high eEF1A2 protein expression have an increased probability of 20-year survival compared to those women whose tumor does not express substantial eEF1A2. In addition, eEF1A2 protein expression predicts increased survival probability in those breast cancer patients whose tumor is HER-2 negative or who have lymph node involvement.

Amino Acid Sequence↗

PubMed2006

Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis.

Previously undescribed prognostic subclasses of high-grade astrocytoma are identified and discovered to resemble stages in neurogenesis. One tumor class displaying neuronal lineage markers shows longer survival, while two tumor classes enriched for neural stem cell markers display equally short survival. Poor prognosis subclasses exhibit markers either of proliferation or of angiogenesis and mesenchyme. Upon recurrence, tumors frequently shift toward the mesenchymal subclass. Chromosomal locations of genes distinguishing tumor subclass parallel DNA copy number differences between subclasses. Functional relevance of tumor subtype molecular signatures is suggested by the ability of cell line signatures to predict neurosphere growth. A robust two-gene prognostic model utilizing PTEN and DLL3 expression suggests that Akt and Notch signaling are hallmarks of poor prognosis versus better prognosis gliomas, respectively.

Biomarkers, Tumor↗

PubMed2005

Identification of hepatocyte growth factor activator inhibitor-1B as a potential physiological inhibitor of prostasin.

Prostasin is a trypsin-like serine protease that is glycosylphosphatidylinositol-anchored to the epithelial cell surface, from where it can be released in a soluble form. We undertook a co-expression search using the Genesis Enterprise System Database from Gene Logic to identify prostasin inhibitors, on the assumption that prostasin and its natural inhibitors may have a similar gene expression pattern. We found the expression profile of prostasin in normal human tissues to correlate highly with hepatocyte growth factor activator inhibitor-1B (HAI-1B) and its splice variant HAI-1. Soluble HAI-1B (sHAI-1B), comprising the entire extracellular domain, formed a 1:1 complex with purified prostasin in protein binding assays and inhibited prostasin enzymatic activity with an IC(50) of 66 +/- 15 nM. Two sHAI-1B mutants with inactivated N- and C-terminal Kunitz domains (KD1 and KD2) were used to show that the interaction of sHAI-1B with prostasin is mediated by KD1. In agreement, KD1 (Thr(246)-Val(303)) alone potently inhibited prostasin activity (IC(50) = 4.7 +/- 0.5 nM). Furthermore, prostasin was isolated with two major HAI-1/1B fragments (40 and 58 kDa) from OVCAR3 cell medium, demonstrating that prostasin.HAI-1/1B complexes are formed naturally. Moreover, when prostasin and HAI-1B were co-expressed in Chinese hamster ovary cells, complexes of prostasin with HAI-1B were detected on the cell membrane as well as in the culture medium, suggesting that preformed complexes were shed from the cell surface. The identification of HAI-1B as a potential physiological regulator of prostasin function, as described herein, may further the investigation of the role of prostasin in normal physiology and cancer.

Alternative Splicing↗

PubMed2005

GMAP: a genomic mapping and alignment program for mRNA and EST sequences.

MOTIVATION: We introduce GMAP, a standalone program for mapping and aligning cDNA sequences to a genome. The program maps and aligns a single sequence with minimal startup time and memory requirements, and provides fast batch processing of large sequence sets. The program generates accurate gene structures, even in the presence of substantial polymorphisms and sequence errors, without using probabilistic splice site models. Methodology underlying the program includes a minimal sampling strategy for genomic mapping, oligomer chaining for approximate alignment, sandwich DP for splice site detection, and microexon identification with statistical significance testing. RESULTS: On a set of human messenger RNAs with random mutations at a 1 and 3% rate, GMAP identified all splice sites accurately in over 99.3% of the sequences, which was one-tenth the error rate of existing programs. On a large set of human expressed sequence tags, GMAP provided higher-quality alignments more often than blat did. On a set of Arabidopsis cDNAs, GMAP performed comparably with GeneSeqer. In these experiments, GMAP demonstrated a several-fold increase in speed over existing programs. AVAILABILITY: Source code for gmap and associated programs is available at http://www.gene.com/share/gmap SUPPLEMENTARY INFORMATION: http://www.gene.com/share/gmap.

Algorithms↗

PubMed2004

GEPIS--quantitative gene expression profiling in normal and cancer tissues.

MOTIVATION: Expression profiling in diverse tissues is fundamental to understanding gene function as well as therapeutic target identification. The vast collection of expressed sequence tags (ESTs) and the associated tissue source information provides an attractive opportunity for studying gene expression. RESULTS: To facilitate EST-based expression analysis, we developed GEPIS (gene expression profiling in silico), a tool that integrates EST and tissue source information to compute gene expression patterns in a large panel of normal and tumor samples. We found EST-based expression patterns to be consistent with published papers as well as our own experimental results. We also built a GEPIS Regional Atlas that depicts expression characteristics of all genes in a selected genomic region. This program can be adapted for large-scale screening for genes with desirable expression patterns, as illustrated by our large-scale mining for tissue- and tumor-specific genes. AVAILABILITY: The email server version of the GEPIS application is freely available at http://share.gene.com/share/gepis. An interactive version of GEPIS will soon be freely available at http://www.cgl.ucsf.edu/Research/genentech/gepis/. The source code, modules, data and gene lists can be downloaded at http://share.gene.com/share/gepis.

Algorithms↗

PubMed2003

Genome-wide identification of chromosomal regions of increased tumor expression by transcriptome analysis.

Genes up-regulated in tumor cells provide attractive anticancer therapeutic targets. Although the general underlying mechanism for the increased expression in tumors is unknown, tumor-specific up-regulation of some genes can be attributed to aberrant DNA amplification, a phenomenon common to many tumors. Using a computational method, we constructed a general transcriptome map with the human genomic sequences and expressed sequence tags in the public database. The transcriptome map revealed nonrandom chromosomal regions (termed region of increased tumor expression) where clusters of genes exhibited increased expression in the 10 tumor tissue types tested. These genomic regions often correspond to experimentally verified tumor amplicons. Our large-scale transcriptome analysis led to identification of many additional chromosomal regions with increased tumor expression, regions that represent potential tumor amplicons.

Chromosomes, Human↗

PubMed2003

Extended spectrum of HIV-1 reverse transcriptase mutations in patients receiving multiple nucleoside analog inhibitors.

OBJECTIVE: To characterize reverse transcriptase (RT) mutations by their association with extent of nucleoside RT inhibitor (NRTI) therapy. To identify mutational clusters in RT sequences from persons receiving multiple NRTI. DESIGN: A total of 1210 RT sequences from persons with known antiretroviral therapy were analyzed: 641 new sequences were performed at Stanford University Hospital; 569 were previously published. METHODS: Chi-square tests and logistic regression were done to identify associations between mutations and NRTI therapy. Correlation studies were done to identify mutational clusters. The Benjamini-Hochberg procedure was used to correct for multiple comparisons. RESULTS: Mutations at 26 positions were significantly associated with NRTI including 17 known resistance mutations (positions 41, 44, 62, 65, 67, 69, 70, 74, 75, 77, 116, 118, 151, 184, 210, 215, 219) and nine previously unreported mutations (positions 20, 39, 43, 203, 208, 218, 221, 223, 228). The nine new mutations correlated linearly with number of NRTI; 777 out of 817 (95%) instances occurred with known drug resistance mutations. Positions 203, 208, 218, 221, 223, and 228 were conserved in untreated persons; positions 20, 39, and 43 were polymorphic. Most NRTI-associated mutations clustered into three groups: (i) 62, 65, 75, 77, 115, 116, 151; (ii) 41, 43, 44, 118, 208, 210, 215, 223; (iii) 67, 69, 70, 218, 219, 228. CONCLUSIONS: Mutations at nine previously unreported positions are associated with NRTI therapy. These mutations are probably accessory because they occur almost exclusively with known drug resistance mutations. Most NRTI mutations group into one of three clusters, although several (e.g., M184V) occur in multiple mutational contexts.

Base Sequence↗

PubMed2003

Mutation patterns and structural correlates in human immunodeficiency virus type 1 protease following different protease inhibitor treatments.

Although many human immunodeficiency virus type 1 (HIV-1)-infected persons are treated with multiple protease inhibitors in combination or in succession, mutation patterns of protease isolates from these persons have not been characterized. We collected and analyzed 2,244 subtype B HIV-1 isolates from 1,919 persons with different protease inhibitor experiences: 1,004 isolates from untreated persons, 637 isolates from persons who received one protease inhibitor, and 603 isolates from persons receiving two or more protease inhibitors. The median number of protease mutations per isolate increased from 4 in untreated persons to 12 in persons who had received four or more protease inhibitors. Mutations at 45 of the 99 amino acid positions in the protease-including 22 not previously associated with drug resistance-were significantly associated with protease inhibitor treatment. Mutations at 17 of the remaining 99 positions were polymorphic but not associated with drug treatment. Pairs and clusters of correlated (covarying) mutations were significantly more likely to occur in treated than in untreated persons: 115 versus 23 pairs and 30 versus 2 clusters, respectively. Of the 115 statistically significant pairs of covarying residues in the treated isolates, 59 were within 8 A of each other-many more than would be expected by chance. In summary, nearly one-half of HIV-1 protease positions are under selective drug pressure, including many residues not previously associated with drug resistance. Structural factors appear to be responsible for the high frequency of covariation among many of the protease residues. The presence of mutational clusters provides insight into the complex mutational patterns required for HIV-1 protease inhibitor resistance.

Drug Resistance, Viral↗

PubMed2002

In silico data filtering to identify new angiogenesis targets from a large in vitro gene profiling data set.

The objective of this study was to use gene expression data from well-defined cell culture models, in combination with expression data from diagnostic samples of human diseased tissues, to identify potential therapeutic targets and markers of disease. Using Affymetrix oligonucleotide array technology, we identified a common profile of genes upregulated during endothelial morphogenesis into tubelike structures in three in vitro models of angiogenesis. Rigorous data selection criteria were used to identify a list of over 1,000 genes whose expression was increased more than twofold over baseline at either 4, 8, 24, 40 or 50 h. To further refine and prioritize this list, we used standard bioinformatic algorithms to identify potential transmembrane and secreted proteins. We then overlapped this gene set with genes upregulated in colon tumors vs. normal colon, resulting in a subset of 128 genes in common with our endothelial list. We removed from this list those genes expressed in 6 different colon tumor lines, resulting in a list of 24 putative, vascular-specific angiogenesis-associated genes. Three genes, gp34, stanniocalcin-1 (STC-1), and GA733-1, were expressed at levels 10-fold or more in colon tumors compared with normal mucosa. We validated the vascular-specific expression of one of these genes, STC-1, by in situ hybridization. The ability to combine in vitro and in vivo data sets should permit one to identify putative angiogenesis target genes in various tumors, chronic inflammation, and other disorders where therapeutic manipulation of angiogenesis is a desirable treatment modality.

Animals↗

PubMed2002

Large-scale analysis of gene expression profiles.

The accumulation of DNA microarray data has now made it possible to use gene expression profiles to analyse expression data. A gene expression profile contains the expression data for a given gene over various samples, and can be contrasted with an expression signature, which contains the expression data for a single sample. Gene expression profiles are most revealing when samples are grouped appropriately, either by standard clinical or pathological categories or by categories discovered through cluster analysis techniques. Expression profiles can exist at various levels of abstraction, yielding information across various tissues or across diseases within a particular tissue. Hypothesis tests may be applied to expression profiles on a large scale to identify candidate genes of interest.

Cluster Analysis↗