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Biomedical subjects

Joseph T Chang

Publications and source records attributed to Joseph T Chang.

10 recordsLinked to original sources
PubMed2006

[18F]fluorodeoxyglucose positron emission tomography is more sensitive than skeletal scintigraphy for detecting bone metastasis in endemic nasopharyngeal carcinoma at initial staging.

PURPOSE: Bone metastasis occurs frequently in patients with endemic nasopharyngeal carcinoma (NPC). The main objective of this study is to evaluate positron emission tomography (PET) using fluorine-18-labeled fluorodeoxyglucose ([18F]FDG) and conventional skeletal scintigraphy (SS) for detecting bone metastasis at initial staging. Auxiliary objectives are to identify risk factors for bone metastasis and features associated with poor survival in patients with bone metastasis. PATIENTS AND METHODS: Patients with endemic NPC before initiation of treatment were enrolled. PET and SS were performed at initial staging and compared using McNemar's paired-sample test. Bone metastasis was considered to be present if there was any reliable evidence identified within 1 year after primary diagnosis. Multiple logistic regression and Cox's proportional hazards models were used for auxiliary objectives. RESULTS: Thirty (15%) of 202 eligible patients were found to have bone metastasis. [18F]FDG PET was found to be more sensitive than SS in the patient-based analysis (P = .006) and in the region-based analysis at the spine (P = .001). Advanced N stage was the only significant risk factor (P < .0001), and the coexistence of hepatic metastasis was a prognosticator of poor survival (P = .017). The survival was not significantly better for patients with bone metastasis undetected at primary staging than for those with initially detectable bone metastasis (P = .620). CONCLUSION: [18F]FDG PET is more sensitive than SS for detecting bone metastasis in endemic NPC at initial staging, whereas SS can be considered as supplementary in this setting.

Adult↗
PubMed2006

A signal-to-noise analysis of phylogeny estimation by neighbor-joining: Insufficiency of polynomial length sequences.

Phylogeny reconstruction is the process of inferring evolutionary relationships from molecular sequences, and methods that are expected to accurately reconstruct trees from sequences of reasonable length are highly desirable. To formalize this concept, the property of fast-convergence has been introduced to describe phylogeny reconstruction methods that, with high probability, recover the true tree from sequences that grow polynomially in the number of taxa n. While provably fast-converging methods have been developed, the neighbor-joining (NJ) algorithm of Saitou and Nei remains one of the most popular methods used in practice. This algorithm is known to converge for sequences that are exponential in n, but no lower bound for its convergence rate has been established. To address this theoretical question, we analyze the performance of the NJ algorithm on a type of phylogeny known as a 'caterpillar tree'. We find that, for sequences of polynomial length in the number of taxa n, the variability of the NJ criterion is sufficiently high that the algorithm is likely to fail even in the first step of the phylogeny reconstruction process, regardless of the degree of polynomial considered. This result demonstrates that, for general n-taxa trees, the exponential bound cannot be improved.

Algorithms↗
PubMed2005

Identification of differentially expressed genes in oral squamous cell carcinoma (OSCC): overexpression of NPM, CDK1 and NDRG1 and underexpression of CHES1.

To identify cellular genes that could potentially serve as predictive molecular markers for human oral cancer, we employed differential display analysis to compare the gene expression profiles between oral squamous cell carcinoma (OSCC) and histopathologically normal epithelium tissues. Comparative real-time RT-PCR was used to confirm the gene expression in 52 OSCC patients, and a 2-fold difference was defined as over- or underexpression. A total of 7 genes were identified: NPM, CDK1, NDRG1, HMGCR, EF1A, NAC and CHES1. In the cancer tissues, NPM, CDK1 and NDRG1 were significantly overexpressed (an average of 7.6-, 17.2- and 12.9-fold, respectively), and CHES1 was underexpressed (15-fold). The frequencies of the differential expression were 40, 56, 67 and 46%, respectively in NPM, CDK1, NDRG1 and CHES1. In Western blot analysis, the protein expressions of NPM, CDK1 and NDRG1 were also increased in the cancer tissues, consistent with the mRNA expression results. To further evaluate clinicopathological associations in these genes, Pearson chi-square analysis was employed. Levels of CDK1 and NDRG1 were associated with poorly differentiated tumors (p = 0.043 and 0.023), suggesting that these genes participate in the mechanism of tumor transformation. Expressions of CDK1 and NDRG1, and CDK1 and CHES1 were mutually statistically correlated (p = 0.001 and 0.014), indicating that these genes share a very close regulatory relationship or interact synergistically in oncogenesis. In conclusion, we identified 7 genes that are differentially expressed in OSCC, and we provide the first evidence that NPM, CDK1 and NDRG1 are overexpressed and CHES1 is underexpressed in oral cancer. These results serve as a fundamental base for employing these genes in future clinical applications.

Adult↗
PubMed2004

Modelling the recent common ancestry of all living humans.

If a common ancestor of all living humans is defined as an individual who is a genealogical ancestor of all present-day people, the most recent common ancestor (MRCA) for a randomly mating population would have lived in the very recent past. However, the random mating model ignores essential aspects of population substructure, such as the tendency of individuals to choose mates from the same social group, and the relative isolation of geographically separated groups. Here we show that recent common ancestors also emerge from two models incorporating substantial population substructure. One model, designed for simplicity and theoretical insight, yields explicit mathematical results through a probabilistic analysis. A more elaborate second model, designed to capture historical population dynamics in a more realistic way, is analysed computationally through Monte Carlo simulations. These analyses suggest that the genealogies of all living humans overlap in remarkable ways in the recent past. In particular, the MRCA of all present-day humans lived just a few thousand years ago in these models. Moreover, among all individuals living more than just a few thousand years earlier than the MRCA, each present-day human has exactly the same set of genealogical ancestors.

Computer Simulation↗
PubMed2004

Lineage specificity of gene expression patterns.

The hematopoietic system offers many advantages as a model for understanding general aspects of lineage choice and specification. Using oligonucleotide microarrays, we compared gene expression patterns of multiple purified hematopoietic cell populations, including neutrophils, monocytes, macrophages, resting, centrocytic, and centroblastic B lymphocytes, dendritic cells, and hematopoietic stem cells. Some of these cells were studied under both resting and stimulated conditions. We studied the collective behavior of subsets of genes derived from the Biocarta database of functional pathways, hand-tuned groupings of genes into broad functional categories based on the Gene Ontology database, and the metabolic pathways in the Kyoto Encyclopedia of Genes and Genomes database. Principal component analysis revealed strikingly pervasive differences in relative levels of gene expression among cell lineages that involve most of the subsets examined. These results indicate that many processes in these cells behave differently in different lineages. Much of the variation among lineages was captured by the first few principal components. Principal components biplots were found to provide a convenient visual display of the contributions of the various genes within the subsets in lineage discrimination. Moreover, by applying tree-constructing methodologies borrowed from phylogenetics to the expression data from differentiated cells and stem cells, we reconstructed a tree of relationships that resembled the established hematopoietic program of lineage development. Thus, the mRNA expression data implicitly contained information about developmental relationships among cell types.

Cell Lineage↗
PubMed2004

cDNA microarray analysis of invasive and tumorigenic phenotypes in a breast cancer model.

The fms oncogene encodes the macrophage colony-stimulating factor receptor (CSF1R), a transmembrane tyrosine kinase receptor, which is abnormally expressed in breast cancer. Transfection of wild-type CSF1R into HC11 mammary epithelial cells (HC11-CSF1R) renders the transfectants capable of in vitro local invasion and in vivo tumorigenesis. Transfection with CSF1R mutated to express phe at the tyr-721 autophosphorylation site (HC11-CSF1R-721) creates a phenotype that lacks metastastic competence but maintains local invasiveness. Conversely, HC11 cells transfected with CSF1R mutated at tyr-807 (HC11-CSF1R-807) retain their metastatic competence, but are not locally invasive. Our aims were to determine which genes were differentially expressed with transfection of HC11 with wild-type CSF1R, and to determine the effect of mutation at the autophosphorylation sites on gene expression, using 4.6 K cDNA microarrays. Complementary DNA from HC11, HC11-CSF1R-721 and HC11-CSF1R-807 were each hybridized together with HC11-CSF1R on individual arrays. A principal component spectral method combined with prenormalization procedures was used for sample clustering. Differentially expressed genes were identified by the analysis of variance. Confirmation by Northern blotting was performed for MAP kinase phosphatase-1, WDNM1 (extracellular proteinase inhibitor), Trop 2 (tumor-associated calcium signal transducer-2), procollagen type IV alpha, secretory leukoprotease inhibitor, prenylated snare protein Ykt6, ceruloplasmin and chaperonin 10. Many of these genes have not previously been associated with tumor invasion and metastasis. We have successfully identified genes that can be linked to the invasive phenotypes or to tumorigenesis. These genes provide a basis for further studies of metastatic progression and local invasiveness, and can be evaluated as therapeutic targets.

Animals↗
PubMed2003

ExpressYourself: A modular platform for processing and visualizing microarray data.

DNA microarrays are widely used in biological research; by analyzing differential hybridization on a single microarray slide, one can detect changes in mRNA expression levels, increases in DNA copy numbers and the location of transcription factor binding sites on a genomic scale. Having performed the experiments, the major challenge is to process large, noisy datasets in order to identify the specific array elements that are significantly differentially hybridized. This normally requires aggregating different, often incompatible programs into a multi-step pipeline. Here we present ExpressYourself, a fully integrated platform for processing microarray data. In completely automated fashion, it will correct the background array signal, normalize the Cy5 and Cy3 signals, score levels of differential hybridization, combine the results of replicate experiments, filter problematic regions of the array and assess the quality of individual and replicate experiments. ExpressYourself is designed with a highly modular architecture so various types of microarray analysis algorithms can readily be incorporated as they are developed; for example, the system currently implements several normalization methods, including those that simultaneously consider signal intensity and slide location. The processed data are presented using a web-based graphical interface to facilitate comparison with the original images of the array slides. In particular, Express Yourself is able to regenerate images of the original microarray after applying various steps of processing, which greatly facilities identification of position-specific artifacts. The program is freely available for use at http://bioinfo.mbb.yale.edu/expressyourself.

Algorithms↗
PubMed2003

Spectral biclustering of microarray data: coclustering genes and conditions.

Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find "marker genes" that are differentially expressed in particular sets of "conditions." We have developed a method that simultaneously clusters genes and conditions, finding distinctive "checkerboard" patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data).

Breast Neoplasms↗
PubMed2002

A reverse transcription comparative real-time PCR method for quantitative detection of angiogenic growth factors in head and neck cancer patients.

OBJECTIVES: Head and neck cancer is one of the ten most frequent cancers in the world. The angiogenic growth factors VEGF, PDGF and bFGF play a role in cancer aggressiveness. We developed a sensitive method to quantify the gene expression of these factors in the tissues of head and neck cancer patients. DESIGN AND METHODS: All assays were performed using real-time RT-PCR, which yields a value (Ct) denoting the threshold cycle of PCR amplification at which product is first detected by fluorescence. The Ct is dependent on the quantity of the target molecule in the sample. To control for variation in RNA quantity and quality, we used 18S ribosome RNA as an internal control to calculate a relative Ct for the target molecules of interest, VEGF, PDGF and bFGF. A serially diluted positive control sample was analyzed by linear regression to determine the sensitivity and linearity of the assay. Paired normal and cancerous tissue samples from 115 head and neck cancer patients were assayed to ascertain the relative levels of the growth factors. RESULTS: The CVs of within-run and between-run assays for VEGF, PDGF and bFGF were all less than 3%. The correlation coefficient of the RNA concentrations and Ct values were 0.9987, 0.9977, and 0.9996 respectively for VEGF, PDGF and bFGF. The assay was sensitive to as little as 10(-3) ng of RNA. All three growth factors were significantly increased in tumor tissue as compared to normal tissue. VEGF, PDGF and bFGF levels were elevated in 71.3%, 58.2% and 54.0% of cancerous tissue samples, with average levels of over-expression of 35.1, 24.6 and 13. sixfold, respectively. CONCLUSION: This method provides sensitive, quantitative, high-throughput analysis for direct comparison of gene expression levels between samples, while adjusting for factors that may influence quantity determination. It should be applicable to molecules other than angiogenic growth factors, as well.

Endothelial Growth Factors↗