Biomedical subjects

Timothy Triche

Publications and source records attributed to Timothy Triche.

4 recordsLinked to original sources

PubMed2005

Expression profiles of osteosarcoma that can predict response to chemotherapy.

Osteosarcoma is the most common malignant bone tumor in children. After initial diagnosis is made with a biopsy, treatment consists of preoperative chemotherapy followed by definitive surgery and postoperative chemotherapy. The degree of tumor necrosis in response to preoperative chemotherapy is a reliable prognostic factor and is used to guide the choice of postoperative chemotherapy. Patients with tumors, which reveal > or = 90% necrosis (good responders), have a much better prognosis than those with < 90% necrosis (poor responders). Despite previous attempts to improve the outcome of poor responders by modifying the postoperative chemotherapy, their prognosis remains poor. Therefore, there is a need to predict at the time of diagnosis patients' response to preoperative chemotherapy. This will provide the basis for developing potentially effective therapy that can be given at the outset for those who are likely to have a poor response. Here, we report the analysis of 34 pediatric osteosarcoma samples by expression profiling. Using parametric two-sample t test, we identified 45 genes that discriminate between good and poor responders (P < 0.005) in 20 definitive surgery samples. A support vector machine classifier was built using these predictor genes and was tested for its ability to classify initial biopsy samples. Five of six initial biopsy samples that had corresponding definitive surgery samples in the training set were classified correctly (83%; confidence interval, 36%, 100%). When this classifier was used to predict eight independent initial biopsy samples, there was 100% accuracy (confidence interval, 63%, 100%). Many of the predictor genes are implicated in bone development, drug resistance, and tumorigenesis.

Adolescent↗

PubMed2004

Sensitivity and specificity of five abundance estimators for high-density oligonucleotide microarrays.

MOTIVATION: A number of algorithms have been proposed for the processing of feature-level data from high-density oligonucleotide microarrays to give estimates of transcript abundance. Performance in the common task of detecting differential expression between samples can be quantified by the statistical concepts of sensitivity and specificity, and represented by the use of receiver operating characteristic curves. These have been previously presented for small numbers of genes known to be differentially present in spiked-in samples. We present here a study of performance over a large number (thousands) of transcripts for which there is strong evidence of differential expression, with corresponding false positive rates controlled by comparisons between replicates. RESULTS: The straight-line regression analysis of a mixture series with replicates by five estimation algorithms produces a consensus set of 4462 transcripts with differential expression of agreed direction and high significance (p < 0.01) according to all algorithms. The more difficult task of two-sample tests between adjacent mixture levels produces performance curves of fraction true positive detected against significance level. Performance varies significantly between algorithms: at the p < 0.01 level, the detection rate varies between 41 and 66%. A control using comparisons between replicates at the same levels indicates that the tests produce empirical false positive rates closely matching the nominal p-values.

Algorithms↗

PubMed2002

Infectomics: genomics and proteomics of microbial infections.

The completion of genomic sequences is the greatest triumph of molecular reductionism since the discovery of the DNA double helix in 1953. However, the utility of reductionism is becoming limited and holistic approaches, including theories and techniques, are desperately needed in the postgenomic era. In the field of infectious diseases there is an urgent need for global approaches that can efficiently, precisely and integratively study structural and functional genomics and proteomics of microbial infections (infectomics). The combination of new (e.g. DNA and protein microarrays) and traditional approaches (e.g. cloning, PCR, gene knockout and knockin, and antisense) will help overcome the challenges we are facing today. We assume that the global phenotypic changes (infectomes) in microbes and their host during infections are encoded by the genomes of microbial pathogens and their hosts, expressed in certain environmental conditions devoted to specific microbe-host interactions. Global drug responses (pharmacomes) in microbes and their host can be detected by genomic and proteomic approaches. Genome-wide approaches to genotyping and phenotyping or expression profiling will eventually lead to global dissection of microbial pathogenesis, efficient and rapid diagnosis of infectious diseases, and the development of novel strategies to control infections. The key fundamental issue of infectious diseases is how to globally and integratively understand the interactions between microbial pathogens and their hosts by using infectomics. In this review, we focus on the events that are considered important in infectomics.

Animals↗

PubMed2002

Genetic determinants of coxsackievirus B3 pathogenesis.

The development of high throughput genomic and bioinformatic analysis tools, coupled with established molecular techniques, has allowed new insights into the pathogenesis of infectious diseases. In humans, coxasackievirus B3 (CVB3) is the primary etiological agent of viral myocarditis, an inflammatory disease process involving the heart muscle. Early host cellular survival and apoptotic mechanisms during viral infections, as well as immune events, affect myocarditis progression and outcome. Therefore, our laboratory has been keenly interested in infectomics, defined here as the transcriptional events of both virus and host. We first elucidated up- or downregulated transcriptional activities in CVB3-infected hearts by mRNA differential display. Further characterization of these regulated genes including Nip21, IP10, and IGTPase, and study of their role in CVB3-infection are underway. In further dissection of the stages of myocarditis-peak viremia, inflammatory infiltration and tissue repair-we used cDNA microarrays to probe differential gene expression in the myocardium following virus infection. Following virus infection, there are global decreases in metabolic and mitochondrial genes, increases in signaling genes and distinctive patterns in other functional groups. To establish early gene expression profiles in infected cells by themselves, we also used oligonucleotide arrays in an in vitro model of CVB3 infection. Notably, we have found increased expression of transcription factors c-fos and c-jun down-stream of extracellular signal-related kinase, a pathway which is crucial for virus replication and pathogenesis. Our investigations based on gene profiling following CVB3 infection have thus far been fruitful in providing new experimental leads. High throughput genetic analysis has allowed us to simultaneously try on greater than 12,000 potential genetic "glass slippers." Our in vitro experimental plan has enabled us to chart prominent patterns of gene expression, analyzed by novel bioinformatic approaches, and to separate varied and potentially significant gene expression events.

Computational Biology↗