Learning to predict relapse in invasive ductal carcinomas based on the subcellular localization of junctional proteins

Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature

Developing predictive modeling frameworks of potential cytotoxicity of engineered nanoparticles is critical for environmental and health risk analysis. The complexity and the heterogeneity of available data on potential risks of nanoparticles, in addition to interdependency of relevant influential attributes, makes it challenging to develop a generalization of nanoparticle toxicity behavior. Lack of systematic approaches to investigate these risks further adds uncertainties and variability to the body of literature and limits generalizability of existing studies. Here, we developed a rigorous approach for assembling published evidence on cytotoxicity of several organic and inorganic nanoparticles and unraveled hidden relationships that were not targeted in the original publications. We used a machine learning approach that employs decision trees together with feature selection algorithms ( e.g., Gain ratio) to analyze a set of published nanoparticle cytotoxicity sample data (2896 samples). The specific studies were selected because they specified nanoparticle-, cell-, and screening method-related attributes. The resultant decision-tree classifiers are sufficiently simple, accurate, and with high prediction power and should be widely applicable to a spectrum of nanoparticle cytotoxicity settings. Among several influential attributes, we show that the cytotoxicity of nanoparticles is primarily predicted from the nanoparticle material chemistry, followed by nanoparticle concentration and size, cell type, and cytotoxicity screening indicator. Overall, our study indicates that following rigorous and transparent methodological experimental approaches, in parallel to continuous addition to this data set developed using our approach, will offer higher predictive power and accuracy and uncover hidden relationships. Results obtained in this study help focus future studies to develop nanoparticles that are safe by design.

Regulation of subcellular distribution and oncogenic potential of nucleophosmin by plakoglobin

Nucleophosmin (NPM) is a nucleolar phosphoprotein that is involved in many cellular processes and has both oncogenic and growth suppressing activities. NPM is localized primarily in nucleoli but shuttles between the nucleus and the cytoplasm, and sustained cytoplasmic distribution contributes to its tumor promoting activities. Plakoglobin (PG, γ-catenin) is a homolog of β-catenin with dual adhesive and signaling functions. These proteins interact with cadherins and mediate adhesion, while their signaling activities are regulated by association with various intracellular partners. Despite these similarities, β-catenin has a well-defined oncogenic activity, whereas PG acts as a tumor/metastasis suppressor through unknown mechanisms. Comparison of the proteomic profiles of carcinoma cell lines with low- or no PG expression with their PG-expressing transfectants has identified NPM as being upregulated upon PG expression. Here, we examined NPM subcellular distribution and in vitro tumorigenesis/metastasis in the highly invasive and very low PG expressing MDA-MB-231 (MDA-231) breast cancer cells and their transfectants expressing increased PG (MDA-231-PG) or NPM shRNA (MDA-231-NPM-KD) or both (MDA-231-NPM-KD+PG). Increased PG expression increased the levels of nucleolar NPM and coimmunoprecipitation studies showed that NPM interacts with PG. PG expression or NPM knockdown decreased the growth rate of MDA-231 cells substantially and this reduction was decreased further in MDA-231-NPM-KD+PG cells. In in vitro tumorigenesis/metastasis assays, MDA-231-PG cells showed substantially lower and MDA-231-NPM-KD cells substantially higher invasiveness relative to the MDA-231 parental cells, and the co-expression of PG and NPM shRNA led to even further reduction of the invasiveness of MDA-231-PG cells. Furthermore, examination of the levels and localization of PG and NPM in primary biopsies of metastatic infiltrating ductal carcinomas revealed coordinated expression of PG and NPM. Together, the data suggest that PG may regulate NPM subcellular distribution, which may potentially change the function of the NPM protein from oncogenic to tumor suppression.