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Biomedical subjects

David N Stivers

Publications and source records attributed to David N Stivers.

6 recordsLinked to original sources
PubMed2005

Molecular signatures associated with clinical outcome in patients with high-risk head-and-neck squamous cell carcinoma treated by surgery and radiation.

PURPOSE: The local-regional control rate for advanced head-and-neck squamous cell carcinoma (HNSCC) remains poor and is unpredictable for a given individual. This study examined whether gene expression patterns developed from tumors from surgicopathologic, criteria-defined, high-risk HNSCC patients could be correlated with clinical outcomes, namely, metastasis or nonrecurrent disease. METHODS AND MATERIALS: Fifteen primary tumors from patients treated with a consistent protocol of surgery followed by radiotherapy were examined. Seven of these tumors were from high-risk patients who developed distant metastasis (DM), and eight tumors were from patients with no recurrence (NR) (median follow-up, 59 months). RESULTS: Unsupervised clustering of gene expression did not separate the two groups from one another, but when supervised methodologies were applied, 205 genes discriminated the two groups. Within the DM group, genes associated with cell growth and proliferation; DNA replication, recombination, and repair; antiapoptotic pathways; cell adhesion; and angiogenesis were identified. For NR samples, discriminatory genes were associated with the onset of apoptosis. CONCLUSIONS: Our data suggest that gene expression analysis of surgically excised HNSCC tumors from patients considered at high risk for recurrence has the potential to identify individuals susceptible to metastasis on the basis of distinct gene-expression patterns. These patients would be ideal candidates for testing systemic therapy.

Carcinoma, Squamous Cell↗
PubMed2005

Classification analysis of the transcriptosome of nonlesional cultured dermal fibroblasts from systemic sclerosis patients with early disease.

OBJECTIVE: To compare the transcriptosome of early-passage nonlesional dermal fibroblasts from systemic sclerosis (SSc) patients with diffuse disease and matched normal controls in order to gain further understanding of the gene activation patterns that occur in early disease. METHODS: Total RNA was isolated from early-passage fibroblasts obtained from nonlesional skin biopsy specimens from 21 patients with diffuse SSc (disease duration <5 years in all but 1) and 18 healthy controls who were matched to the cases by age (+/-5 years), sex, and race. Array experiments were performed on a 16,659-oligonucleotide microarray utilizing a reference experimental design. Supervised methods were used to select differentially expressed genes. Quantitative polymerase chain reaction (PCR) was used to independently validate the array results. RESULTS: Of the 8,324 genes that passed filtering criteria, classification analysis revealed that <5% were differentially expressed between SSc and normal fibroblasts. Individually, differentially expressed genes included COL7A1, COL18A1 (endostatin), DAF, COMP, and VEGFB. Using the panel of genes discovered through classification analysis, a set of model predictors that achieved reasonably high predictive accuracy was developed. Analysis of 1,297 gene ontology (GO) classes revealed 35 classes that were significantly dysregulated in SSc fibroblasts. These GO classes included anchoring collagen (30934), extracellular matrix structural constituent (5201), and complement activation (6958, 6956). Validation by quantitative PCR demonstrated that 7 of 7 genes selected were concordant with the array results. CONCLUSION: Fibroblasts cultured from nonlesional skin of patients with SSc already have detectable abnormalities in a variety of genes and cellular processes, including those involved in extracellular matrix formation, fibrillogenesis, complement activation, and angiogenesis.

Dermis↗
PubMed2004

H2-Ea deficiency is a risk factor for bleomycin-induced lung fibrosis in mice.

Pulmonary fibrosis is a significant complication in cancer patients when treated by radiation, e.g., thoracic malignant diseases, or chemotherapeutic agents. Bleomycin is one of the primary drugs used to treat testicular cancer, but the incidence of significant pulmonary fibrosis limits the dose. It is known that susceptibility to bleomycin-induced pulmonary fibrosis is a heritable trait controlled by multiple genes, none of which, however, are yet known. In this study, we used expression profiling and genetic analysis in mouse models of bleomycin-induced pulmonary fibrosis and identified MHC class II antigen Ealpha (H2-Ea) as a risk factor for this disease. We found that a loss-of-function deletion in the H2-Ea gene was linked to susceptibility. A functional test of H2-Ea in transgenic mice showed 100% survival in the transgenic mice compared with 53% in C57BL/10J mice and significantly decreased pulmonary fibrosis from 16.42% (C57BL/10J) to 5.76% (transgenic; P = 1.20e(-8)). These results show that H2-Ea expression protects mice from bleomycin-induced pulmonary fibrosis, which implicates H2-Ea as a candidate susceptibility gene for pulmonary fibrosis.

Animals↗
PubMed2002

Identifying and quantifying sources of variation in microarray data using high-density cDNA membrane arrays.

Microarray experiments involve many steps, including spotting cDNA, extracting RNA, labeling targets, hybridizing, scanning, and analyzing images. Each step introduces variability, confounding our ability to obtain accurate estimates of the biological differences between samples. We ran repeated experiments using high-density cDNA microarray membranes (Research Genetics Human GeneFilters Microarrays Version I) and 33P-labeled targets. Total RNA was extracted from a Burkitt lymphoma cell line (GA-10). We estimated the components of variation coming from: (1) image analysis, (2) exposure time to PhosphorImager screens, (3) differences in membranes, (4) reuse of membranes, and (5) differences in targets prepared from two independent RNA extractions. Variation was assessed qualitatively using a clustering algorithm and quantitatively using a version of ANOVA adapted to multivariate microarray data. The largest contribution to variation came from reusing membranes, which contributed 38% of the total variation. Differences in membranes and in exposure time each contributed about 10%. Differences in target preparations contributed less than 5%. The effect of image quantification was negligible. Much of the effect from reusing membranes was attributable to increasing levels of background radiation and can be reduced by using membranes at most four times. The effects of exposure time, which were partly attributable to variation in the scanning process, can be minimized by using the same exposure time for all experiments.

Algorithms↗
PubMedDate not supplied

Global gene expression changes during neoadjuvant chemotherapy for human breast cancer.

PURPOSE: The purpose of this study was to analyze global gene expression changes in serial tumor core biopsy specimens taken during neoadjuvant chemotherapy for primary breast cancer. PATIENTS AND METHODS: Core biopsy specimens from tumors were obtained before treatment and 24 and/or 48 hours after treatment from 21 women who were beginning chemotherapy for breast cancer. RNA was extracted, and radiolabeled complementary DNA was synthesized. The complementary DNA probes were hybridized to high-density microarray membranes that contained more than 25,000 human sequence clones. Hierarchical cluster analysis was used to compare the degree of similarity between expression profiles. RESULTS: Twenty-five (45%) of the 56 available core specimens yielded sufficient quantity and quality RNA for microarray analysis. Microarray profiles were performed only on samples from patients with pretreatment and posttreatment specimens, resulting in serial data sets for five patients (14 specimens). The serial samples from individual patients clustered more closely than the samples taken from different patients. Analyses of the variance of individual gene expression showed that there were significantly fewer genes with fivefold differences in expression in an individual tumor at different times (average, 359 genes) versus pretreatment samples of different tumors (average, 732 genes). Patients with a good pathological response to treatment had gene patterns that clustered distinctly from those of poor responders. Significant transcriptional response occurred in all patients during therapy. Surprisingly, all patients had different genes change after chemotherapy, with no single gene having a significant expression change in all five patients. DISCUSSION: This is the first report to show global gene expression changes during chemotherapy in a human solid tumor. Comprehensive gene expression profiles of more than 25,000 genes can be obtained from core biopsy specimens. A remarkable diversity in transcriptional response was observed for individual cases. Further data are needed to determine whether gene profiling can predict response to chemotherapy.

Analysis of Variance↗