Convection, Thermal Bifurcation, and the Colors of A stars

Broad-band ultraviolet photometry from the TD-1 satellite and low dispersion spectra from the short wavelength camera of IUE have been used to investigate a long-standing proposal of Bohm-Vitense that the normal main sequence A- and early-F stars may divide into two different temperature sequences: (1) a high temperature branch (and plateau) comprised of slowly rotating convective stars, and (2) a low temperature branch populated by rapidly rotating radiative stars. We find no evidence from either dataset to support such a claim, or to confirm the existence of an "A-star gap" in the B-V color range 0.22 <= B-V <= 0.28 due to the sudden onset of convection. We do observe, nonetheless, a large scatter in the 1800--2000 A colors of the A-F stars, which amounts to ~0.65 mags at a given B-V color index. The scatter is not caused by interstellar or circumstellar reddening. A convincing case can also be made against binarity and intrinsic variability due to pulsations of delta Sct origin. We find no correlation with established chromospheric and coronal proxies of convection, and thus no demonstrable link to the possible onset of convection among the A-F stars. The scatter is not instrumental. Approximately 0.4 mags of the scatter is shown to arise from individual differences in surface gravity as well as a moderate spread (factor of ~3) in heavy metal abundance and UV line blanketing. A dispersion of ~0.25 mags remains, which has no clear and obvious explanation. The most likely cause, we believe, is a residual imprecision in our correction for the spread in metal abundances. However, the existing data do not rule out possible contributions from intrinsic stellar variability or from differential UV line blanketing effects owing to a dispersion in microturbulent velocity.Comment: 40 pages, 14 figures, 1 table, AAS LaTex, to appear in The Astrophysical Journa

Kallikrein-related peptidase 5 contributes to H3N2 influenza virus infection in human lungs

Hemagglutinin (HA) of influenza virus must be activated by proteolysis before the virus can become infectious. Previous studies indicated that HA cleavage is driven by membrane-bound or extracellular serine proteases in the respiratory tract. However, there is still uncertainty as to which proteases are critical for activating HAs of seasonal influenza A viruses (IAVs) in humans. This study focuses on human KLK1 and KLK5, 2 of the 15 serine proteases known as the kallikrein-related peptidases (KLKs). We find that their mRNA expression in primary human bronchial cells is stimulated by IAV infection. Both enzymes cleaved recombinant HA from several strains of the H1 and/or H3 virus subtype in vitro, but only KLK5 promoted the infectivity of A/Puerto Rico/8/34 (H1N1) and A/Scotland/20/74 (H3N2) virions in MDCK cells. We assessed the ability of treated viruses to initiate influenza in mice. The nasal instillation of only the KLK5-treated virus resulted in weight loss and lethal outcomes. The secretion of this protease in the human lower respiratory tract is enhanced during influenza. Moreover, we show that pretreatment of airway secretions with a KLK5-selective inhibitor significantly reduced the activation of influenza A/Scotland/20/74 virions, providing further evidence of its importance. Differently, increased KLK1 secretion appeared to be associated with the recruitment of inflammatory cells in human airways regardless of the origin of inflammation. Thus, our findings point to the involvement of KLK5 in the proteolytic activation and spread of seasonal influenza viruses in humans

Genomic approaches in the management and treatment of breast cancer

Breast cancer is the most common malignancy afflicting women from Western cultures. It has been estimated that approximately 211 000 women will be diagnosed with breast cancer in 2003 in the United States alone, and each year over 40 000 women will die of this disease. Developments in breast cancer molecular and cellular biology research have brought us closer to understanding the genetic basis of this disease. Unfortunately, this information has not yet been incorporated into the routine diagnosis and treatment of breast cancer in the clinic. Recent advancements in microarray technology hold the promise of further increasing our understanding of the complexity and heterogeneity of this disease, and providing new avenues for the prognostication and prediction of breast cancer outcomes. The most recent application of microarray genomic technologies to studying breast cancer will be the focus of this review

Heterologous Tissue Culture Expression Signature Predicts Human Breast Cancer Prognosis

BACKGROUND: Cancer patients have highly variable clinical outcomes owing to many factors, among which are genes that determine the likelihood of invasion and metastasis. This predisposition can be reflected in the gene expression pattern of the primary tumor, which may predict outcomes and guide the choice of treatment better than other clinical predictors. METHODOLOGY/PRINCIPAL FINDINGS: We developed an mRNA expression-based model that can predict prognosis/outcomes of human breast cancer patients regardless of microarray platform and patient group. Our model was developed using genes differentially expressed in mouse plasma cell tumors growing in vivo versus those growing in vitro. The prediction system was validated using published data from three cohorts of patients for whom microarray and clinical data had been compiled. The model stratified patients into four independent survival groups (BEST, GOOD, BAD, and WORST: log-rank test p = 1.7×10(−8)). CONCLUSIONS: Our model significantly improved the survival prediction over other expression-based models and permitted recognition of patients with different prognoses within the estrogen receptor-positive group and within a single pathological tumor class. Basing our predictor on a dataset that originated in a different species and a different cell type may have rendered it less sensitive to proliferation differences and endowed it with wide applicability. SIGNIFICANCE: Prognosis prediction for patients with breast cancer is currently based on histopathological typing and estrogen receptor positivity. Yet both assays define groups that are heterogeneous in survival. Gene expression profiling allows subdivision of these groups and recognition of patients whose tumors are very unlikely to be lethal and those with much grimmer outlooks, which can augment the predictive power of conventional tumor analysis and aid the clinician in choosing relaxed vs. aggressive therapy

Classification of ductal carcinoma in situ by gene expression profiling

INTRODUCTION: Ductal carcinoma in situ (DCIS) is characterised by the intraductal proliferation of malignant epithelial cells. Several histological classification systems have been developed, but assessing the histological type/grade of DCIS lesions is still challenging, making treatment decisions based on these features difficult. To obtain insight in the molecular basis of the development of different types of DCIS and its progression to invasive breast cancer, we have studied differences in gene expression between different types of DCIS and between DCIS and invasive breast carcinomas. METHODS: Gene expression profiling using microarray analysis has been performed on 40 in situ and 40 invasive breast cancer cases. RESULTS: DCIS cases were classified as well- (n = 6), intermediately (n = 18), and poorly (n = 14) differentiated type. Of the 40 invasive breast cancer samples, five samples were grade I, 11 samples were grade II, and 24 samples were grade III. Using two-dimensional hierarchical clustering, the basal-like type, ERB-B2 type, and the luminal-type tumours originally described for invasive breast cancer could also be identified in DCIS. CONCLUSION: Using supervised classification, we identified a gene expression classifier of 35 genes, which differed between DCIS and invasive breast cancer; a classifier of 43 genes could be identified separating between well- and poorly differentiated DCIS samples

Detection of gene pathways with predictive power for breast cancer prognosis

<p>Abstract</p> <p>Background</p> <p>Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent predictive power for prognosis. Gene profiling studies have been conducted to search for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed.</p> <p>Results</p> <p>The new method advances beyond existing alternatives along the following aspects. First, it can assess the predictive power of gene pathways, whereas existing methods tend to focus on model fitting accuracy only. Second, it can account for the joint effects of multiple genes in a pathway, whereas existing methods tend to focus on the marginal effects of genes. Third, it can accommodate multiple heterogeneous datasets, whereas existing methods analyze a single dataset only. We analyze four breast cancer prognosis studies and identify 97 pathways with significant predictive power for prognosis. Important pathways missed by alternative methods are identified.</p> <p>Conclusions</p> <p>The proposed method provides a useful alternative to existing pathway analysis methods. Identified pathways can provide further insights into breast cancer prognosis.</p

Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling Strategies

High-throughput post-genomic studies are now routinely and promisingly investigated in biological and biomedical research. The main statistical approach to select genes differentially expressed between two groups is to apply a t-test, which is subject of criticism in the literature. Numerous alternatives have been developed based on different and innovative variance modeling strategies. However, a critical issue is that selecting a different test usually leads to a different gene list. In this context and given the current tendency to apply the t-test, identifying the most efficient approach in practice remains crucial. To provide elements to answer, we conduct a comparison of eight tests representative of variance modeling strategies in gene expression data: Welch's t-test, ANOVA [1], Wilcoxon's test, SAM [2], RVM [3], limma [4], VarMixt [5] and SMVar [6]. Our comparison process relies on four steps (gene list analysis, simulations, spike-in data and re-sampling) to formulate comprehensive and robust conclusions about test performance, in terms of statistical power, false-positive rate, execution time and ease of use. Our results raise concerns about the ability of some methods to control the expected number of false positives at a desirable level. Besides, two tests (limma and VarMixt) show significant improvement compared to the t-test, in particular to deal with small sample sizes. In addition limma presents several practical advantages, so we advocate its application to analyze gene expression data

BRCA1-deficient mammary tumor cells are dependent on EZH2 expression and sensitive to Polycomb Repressive Complex 2-inhibitor 3-deazaneplanocin A

10.1186/bcr2354Breast Cancer Research114R6

Gene expression signatures of morphologically normal breast tissue identify basal-like tumors

INTRODUCTION: The role of the cellular microenvironment in breast tumorigenesis has become an important research area. However, little is known about gene expression in histologically normal tissue adjacent to breast tumor, if this is influenced by the tumor, and how this compares with non-tumor-bearing breast tissue. METHODS: To address this, we have generated gene expression profiles of morphologically normal epithelial and stromal tissue, isolated using laser capture microdissection, from patients with breast cancer or undergoing breast reduction mammoplasty (n = 44). RESULTS: Based on this data, we determined that morphologically normal epithelium and stroma exhibited distinct expression profiles, but molecular signatures that distinguished breast reduction tissue from tumor-adjacent normal tissue were absent. Stroma isolated from morphologically normal ducts adjacent to tumor tissue contained two distinct expression profiles that correlated with stromal cellularity, and shared similarities with soft tissue tumors with favorable outcome. Adjacent normal epithelium and stroma from breast cancer patients showed no significant association between expression profiles and standard clinical characteristics, but did cluster ER/PR/HER2-negative breast cancers with basal-like subtype expression profiles with poor prognosis. CONCLUSION: Our data reveal that morphologically normal tissue adjacent to breast carcinomas has not undergone significant gene expression changes when compared to breast reduction tissue, and provide an important gene expression dataset for comparative studies of tumor expression profiles