ErbB-2 expression and its association with other biological parameters of breast cancer among Indian women

Overexpression of the epidermal growth factor receptor family genes, which include ErbB-1, 2, 3 and 4, has been implicated in a number of cancers. We have studied the extent of ErbB-2 overexpression among Indian women with sporadic breast cancer. Methods: Immmunohistochemistry and genomic polymerase chain reaction (PCR) were used to study the ErbB2 overexpression. ErbB2 status was correlated with other clinico-pathological parameters, including patient survival. Results: ErbB-2 overexpression was detected in 43.2% (159/368) of the cases by immunohistochemistry. For a sub-set of patients (n = 55) for whom total DNA was available, ErbB-2 gene amplification was detected in 25.5% (14/55) of the cases by genomic PCR. While the ErbB2 overexpression was significantly higher in patients with lymphnode (χ2 = 12.06, P≤ 0.001), larger tumor size (χ2 = 8.22, P = 0.042) and ductal carcinoma (χ2 = 15.42, P ≤ 0.001), it was lower in patients with disease-free survival (χ2 = 22.13, P ≤ 0.001). Survival analysis on a sub-set of patients for whom survival data were available (n = 179) revealed that ErbB-2 status (χ2 =25.94, P ≤ 0.001), lymphnode status (χ2 = 12.68, P ≤ 0.001), distant metastasis (χ2 = 19.49, P ≤ 0.001) and stage of the disease (χ2 = 28.04, P ≤0.001) were markers of poor prognosis. Conclusions: ErbB-2 overexpression was significantly greater compared with the Western literature, but comparable to other Indian studies. Significant correlation was found between ErbB-2 status and lymphnode status, tumor size and ductal carcinoma. ErbB-2 status, lymph node status, distant metastasis and stage of the disease were found to be prognostic indicators.

Generalized survival in step fluctuations

The properties of the generalized survival probability, that is, the probability of not crossing an arbitrary location R during relaxation, have been investigated experimentally (via scanning tunneling microscope observations) and numerically. The results confirm that the generalized survival probability decays exponentially with a time constant tau(s)(R). The distance dependence of the time constant is shown to be tau(s)(R)=tau(s0)exp[-R/w(T)], where w(2)(T) is the material-dependent mean-squared width of the step fluctuations. The result reveals the dependence on the physical parameters of the system inherent in the prior prediction of the time constant scaling with R/L-alpha, with L the system size and alpha the roughness exponent. The survival behavior is also analyzed using a contrasting concept, the generalized inside survival S-in(t,R), which involves fluctuations to an arbitrary location R further from the average. Numerical simulations of the inside survival probability also show an exponential time dependence, and the extracted time constant empirically shows (R/w)(lambda) behavior, with lambda varying over 0.6 to 0.8 as the sampling conditions are changed. The experimental data show similar behavior, and can be well fit with lambda=1.0 for T=300 K, and 0.5

Spatial survival probability for one-dimensional fluctuating interfaces in the steady state

We report numerical and analytic results for the spatial survival probability for fluctuating one-dimensional interfaces with Edwards-Wilkinson or Kardar-Parisi-Zhang dynamics in the steady state. Our numerical results are obtained from analysis of steady-state profiles generated by integrating a spatially discretized form of the Edwards-Wilkinson equation to long times. We show that the survival probability exhibits scaling behavior in its dependence on the system size and the "sampling interval" used in the measurement for both "steady-state" and "finite" initial conditions. Analytic results for the scaling functions are obtained from a path-integral treatment of a formulation of the problem in terms of one-dimensional Brownian motion. A "deterministic approximation" is used to obtain closed-form expressions for survival probabilities from the formally exact analytic treatment. The resulting approximate analytic results provide a fairly good description of the numerical data.

A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n = 222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio HR] = 2.4; 95% CI = 1.4-3.8; p < 0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR = 1.7; 95% CI = 1.1-2.8; p = 0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR = 2.0; 95% CI = 1.4-2.8; p < 0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR = 1.120; 95% CI = 1.04-1.20; p = 0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy.

Coordinate Hyperactivation of Notch1 and Ras/MAPK Pathways Correlates with Poor Patient Survival: Novel Therapeutic Strategy for Aggressive Breast Cancers

Aberrant activation of Notch and Ras pathways has been detected in breast cancers. A synergy between these two pathways has also been shown in breast cell transformation in culture. Yet, the clinical relevance of Notch-Ras cooperation in breast cancer progression remains unexplored. In this study, we show that coordinate hyperactivation of Notch1 and Ras/MAPK pathways in breast cancer patient specimens, as assessed by IHC for cleaved Notch1 and pErk1/2, respectively, correlated with early relapse to vital organs and poor overall survival. Interestingly, majority of such Notch1 (high)Erk(high) cases encompassed the highly aggressive triple-negative breast cancers (TNBC), and were enriched in stem cell markers. We further show that combinatorial inhibition of Notch1 and Ras/MAPK pathways, using a novel mAb against Notch1 and a MEK inhibitor, respectively, led to a significant reduction in proliferation and survival of breast cancer cells compared with individual inhibition. Combined inhibition also abrogated sphere-forming potential, and depleted the putative cancer stem-like cell subpopulation. Most importantly, combinatorial inhibition of Notch1 and Ras/MAPK pathways completely blocked tumor growth in a panel of breast cancer xenografts, including the TNBCs. Thus, our study identifies coordinate hyperactivation of Notch1 and Ras/MAPK pathways as novel biomarkers for poor breast cancer outcome. Furthermore, based on our preclinical data, we propose combinatorial targeting of these two pathways as a treatment strategy for highly aggressive breast cancers, particularly the TNBCs that currently lack any targeted therapeutic module. (C) 2014 AACR.

A Mycobacterial Phosphoribosyltransferase Promotes Bacillary Survival by Inhibiting Oxidative Stress and Autophagy Pathways in Macrophages and Zebrafish

Mycobacterium tuberculosis employs various strategies to modulate host immune responses to facilitate its persistence in macrophages. The M. tuberculosis cell wall contains numerous glycoproteins with unknown roles in pathogenesis. Here, by using Concanavalin A and LC-MS analysis, we identified a novel mannosylated glycoprotein phosphoribosyltransferase, encoded by Rv3242c from M. tuberculosis cell walls. Homology modeling, bioinformatic analyses, and an assay of phosphoribosyltransferase activity in Mycobacterium smegmatis expressing recombinant Rv3242c (MsmRv3242c) confirmed the mass spectrometry data. Using Mycobacterium marinum-zebrafish and the surrogate MsmRv3242c infection models, we proved that phosphoribosyltransferase is involved in mycobacterial virulence. Histological and infection assays showed that the M. marinum mimG mutant, an Rv3242c orthologue in a pathogenic M. marinum strain, was strongly attenuated in adult zebrafish and also survived less in macrophages. In contrast, infection with wild type and the complemented Delta mimG: Rv3242c M. marinum strains showed prominent pathological features, such as severe emaciation, skin lesions, hemorrhaging, and more zebrafish death. Similarly, recombinant Msm Rv3242c bacteria showed increased invasion in non-phagocytic epithelial cells and longer intracellular survival in macrophages as compared with wild type and vector control M. smegmatis strains. Further mechanistic studies revealed that the Rv3242c- and mimG-mediated enhancement of intramacrophagic survival was due to inhibition of autophagy, reactive oxygen species, and reduced activities of superoxide dismutase and catalase enzymes. Infection with MsmRv3242c also activated the MAPK pathway, NF-kappa B, and inflammatory cytokines. In summary, we show that a novel mycobacterial mannosylated phosphoribosyltransferase acts as a virulence and immunomodulatory factor, suggesting that it may constitute a novel target for antimycobacterial drugs.

PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach

Online Social Networks (OSNs) facilitate to create and spread information easily and rapidly, influencing others to participate and propagandize. This work proposes a novel method of profiling Influential Blogger (IB) based on the activities performed on one's blog documents who influences various other bloggers in Social Blog Network (SBN). After constructing a social blogging site, a SBN is analyzed with appropriate parameters to get the Influential Blog Power (IBP) of each blogger in the network and demonstrate that profiling IB is adequate and accurate. The proposed Profiling Influential Blogger (PIB) Algorithm survival rate of IB is high and stable. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).