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Granulosa cell tumor (GCT) is a rare and severe form of sex-cord stromal ovarian tumor that is characterized by its long natural history and tendency to recur years after surgical ablation. Because there is no efficient curative treatment beyond surgery, ~20% of patients die of the consequences of their tumor. However, very little is known of the molecular etiology of this pathology. About 70% of GCT patients present with elevated circulating estradiol (E2). Because this hormone is known to increase tumor growth and progression in a number of cancers, we investigated the possible role of E2 in GCTs. Cell-based studies with human GCT metastases and primary tumor-derived cells, ie KGN and COV434 cells, respectively, aimed at evaluating E2 effect on cell growth, migration and invasion. Importantly, we found that E2 did not affect GCT cell growth, but that it significantly decreased the migration and matrix invasion of metastatic GCT cells. Noteworthy, our molecular studies revealed that this effect was accompanied by the inhibition through non-genomic mechanisms of extracellular signal-regulated kinase 1/2 (ERK1/2), which is constitutively activated in GCTs. By using pharmacological and RNA silencing approaches, we found that E2 action was mediated by G protein-coupled estrogen receptor 1 (GPER1) signaling pathway. Analyses of GPER1 expression on tissue microarrays from human GCTs confirmed its expression in ~90% of GCTs. Overall, our study reveals that E2 would act via non-classical pathways to prevent metastasis spreading in GCTs and also reveals GPER1 as a possible target in this disease.

The G protein-coupled receptor 30 (GPR30) has been claimed as an estrogen receptor. However, the literature reports controversial findings and the physiological function of GPR30 is not fully understood yet. Consistent with studies assigning a role of GPR30 in the cardiovascular and metabolic systems, GPR30 expression has been reported in small arterial vessels, pancreas and chief gastric cells of the stomach. Therefore, we hypothesized a role of GPR30 in the onset and progression of cardiovascular and metabolic diseases. In order to test our hypothesis, we investigated the effects of a high-fat diet on the metabolic and cardiovascular profiles of Gpr30-deficient mice (GPR30-lacZ mice). We found that GPR30-lacZ female, rather than male, mice had significant lower levels of HDL along with an increase in fat liver accumulation as compared to control mice. However, two indicators of cardiac performance assessed by echocardiography, ejection fraction and fractional shortening were both decreased in an age-dependent manner only in Gpr30-lacZ male mice. Collectively our results point to a potential role of Gpr30 in preserving lipid metabolism and cardiac function in a sex- and age-dependent fashion. (C) 2014 Elsevier B.V. All rights reserved.

The IT Future of Medicine (ITFoM, http://www.itfom.eu/ ) initiative will produce computational models of individuals to enable the prediction of their future health risks, progression of diseases and selection and efficacy of treatments while minimising side effects. To be able to move our health care system to treat patients as individuals rather than as members of larger, divergent groups, the ITFoM initiative, proposes to integrate molecular, physiological and anatomical data of every person in ‘virtual patient’ models. The establishment of such ‘virtual patient’ models is now possible due to the enormous progress in analytical techniques, particularly in the ‘-omics’ technology areas and in imaging, as well as in sensor technologies, but also due to the immense developments in the ICT field. As one of six Future and Emerging Technologies (FET) Flagship Pilot Projects funded by the European Commission, ITFoM with more than 150 academic and industrial partners from 34 countries, will foster the development in functional genomics and computer technologies to generate ‘virtual patient’ models to make them available for clinical application. The increase in the capacity of next generation sequencing systems will enable the high-throughput analysis of a large number of individuals generating huge amounts of genome, epigenome and transcriptome data, but making it feasible to apply deep sequencing in the clinic to characterise not only the patient's genome, but also individual samples, for example, from tumours. The genome profile will be integrated with proteome and metabolome information generated via new powerful chromatography, mass spectrometry and nuclear magnetic resonance techniques. The individualised model will not only enable the analysis of the current situation, but will allow the prediction of the response of the patient to different therapy options or intolerance for certain drugs.