Aurora Kinase A expression predicts platinum-resistance and adverse outcome in high-grade serous ovarian carcinoma patients

High-Grade Serous Ovarian Carcinoma (HGSOC) is the predominant histotype of epithelial ovarian cancer (EOC), characterized by advanced stage at diagnosis, frequent TP53 mutation, rapid progression, and high responsiveness to platinum-based-chemotherapy. To date, standard first-line-chemotherapy in advanced EOC includes platinum salts and paclitaxel with or without bevacizumab. The major prognostic factor is the response duration from the end of the platinum-based treatment (platinum-free interval) and about 10-0 % of EOC patients bear a platinum-refractory disease or develop early resistance (platinum-free interval shorter than 6 months). On these bases, a careful selection of patients who could benefit from chemotherapy is recommended to avoid unnecessary side effects and for a better disease outcome. In this retrospective study, an immunohistochemical evaluation of Aurora Kinase A (AURKA) was performed on 41 cases of HGSOC according to platinum-status. Taking into account the number and intensity of AURKA positive cells we built a predictive score able to discriminate with high accuracy platinum-sensitive patients from platinum-resistant patients (p < 0.001). Furthermore, we observed that AURKA overexpression correlates to worse overall survival (p = 0.001; HR 0.14). We here suggest AURKA as new effective tool to predict the biological behavior of HGSOC. Particularly, our results indicate that AURKA has a role both as predictor of platinum-resistance and as prognostic factor, that deserves further investigation in prospective clinical trials. Indeed, in the era of personalized medicine, AURKA could assist the clinicians in selecting the best treatment and represent, at the same time, a promising new therapeutic target in EOC treatment

Elastofibroma dorsi: a histochemical and immunohistochemical study

Elastofibroma dorsi (ED) is considered a member of a heterogeneous group of benign fibrous (fibroblastic or myofibroblastic) soft-tissue tumors, frequently localized in the periscapular region in middle aged or older individuals. However, the pathogenesis of ED is still unclear and many authors believe that ED results from a reactive hyperproliferation of fibroblastic tissue, while others suggest that it may be a consequence of a mechanical friction. In our study, we examined 11 cases of ED using histochemical and immunohistochemical methods, in order to extend the knowledge about extracellular matrix composition and histopathogenesis of ED. From the results it appeared that stroma and interspersed spindle cells of ED were positive for both periostin and tenascin-C. Mast cells tryptase-positive were also abundant throughout the lesion. The perivascular distribution of periostin and tenascin-C, associated with the CD34 positivity, suggest that endothelial-mesenchymal transition events can account for neovascularization and production of fibroelastic tissue characteristic of elastofibroma. Our data obtained in endothelial cells cultures demonstrated that elastin production is higher when the status of confluence of the cells is low. So, we can assume that such a phenomenon is a characteristic of mesenchymal/endothelial cells CD34 positive, in which elastin production results to be inversely proportional to the vascular differentiation of cellular elements. In the light of these considerations, we think that a cancerous nature of ED is unlikely. Overall, our study report, for the first time, a detailed description of extracellular matrix composition in ED, suggesting that a mechanical strain-dependent reactivation of periostin and tenascin-C expression, as well as of elastin deposition, could be responsible for development of ED

Global and decomposition evolutionary support vector machine approaches for time series forecasting

Multi-step ahead Time Series Forecasting (TSF) is a key tool for support- ing tactical decisions (e.g., planning resources). Recently, the support vector machine emerged as a natural solution for TSF due to its nonlinear learning capabilities. This paper presents two novel Evolutionary Support Vector Machine (ESVM) methods for multi-step TSF. Both methods are based on an Estimation Distribution Algorithm (EDA) search engine that automatically performs a simultaneous variable (number of inputs) and model (hyperparameters) selection. The Global ESVM (GESVM) uses all past patterns to fit the support vector machine, while the Decomposition ESVM (DESVM) separates the series into trended and stationary effects, using a distinct ESVM to forecast each effect and then summing both predictions into a sin- gle response. Several experiments were held, using six time series. The proposed approaches were analyzed under two criteria and compared against a recent Evolu- tionary Artificial Neural Network (EANN) and two classical forecasting methods, Holt-Winters and ARIMA. Overall, the DESVM and GESVM obtained competitive and high quality results. Furthermore, both ESVM approaches consume much less computational effort when compared with EANN.The authors wish to thank Ramon Sagarna for introducing the subject of EDA. The work of P. Cortez was supported by FEDER (program COMPETE and FCT) under project FCOMP-01-0124-FEDER-022674

The heritability of beta cell function parameters in a mixed meal test design

Aims/hypothesis: We estimated the heritability of individual differences in beta cell function after a mixed meal test designed to assess a wide range of classical and model-derived beta cell function parameters. Methods: A total of 183 healthy participants (77 men), recruited from the Netherlands Twin Register, took part in a 4 h protocol, which included a mixed meal test. Participants were Dutch twin pairs and their siblings, aged 20 to 49 years. All members within a family were of the same sex. Insulin sensitivity, insulinogenic index, insulin response and postprandial glycaemia were assessed, as well as model-derived parameters of beta cell function, in particular beta cell glucose sensitivity and insulin secretion rates. Genetic modelling provided the heritability of all traits. Multivariate genetic analyses were performed to test for overlap in the genetic factors influencing beta cell function, waist circumference and insulin sensitivity. Results: Significant heritabilities were found for insulinogenic index (63%), beta cell glucose sensitivity (50%), insulin secretion during the first 2 h postprandial (42-47%) and postprandial glycaemia (43-52%). Genetic factors influencing beta cell glucose sensitivity and insulin secretion during the first 30 postprandial min showed only negligible overlap with the genetic factors that influence waist circumference and insulin sensitivity. Conclusions/interpretation: The highest heritability for postprandial beta cell function was found for the insulinogenic index, but the most specific indices of heritability of beta cell function appeared to be beta cell glucose sensitivity and the insulin secretion rate during the first 30 min after a mixed meal. © The Author(s) 2011