Laura Anderlucci

Estrogen Mediated-Activation of miR-191/425 Cluster Modulates Tumorigenicity of Breast Cancer Cells Depending on Estrogen Receptor Status

MicroRNAs (miRNAs), single-stranded non-coding RNAs, influence myriad biological processes that can contribute to cancer. Although tumor-suppressive and oncogenic functions have been characterized for some miRNAs, the majority of microRNAs have not been investigated for their ability to promote and modulate tumorigenesis. Here, we established that the miR-191/425 cluster is transcriptionally dependent on the host gene, DALRD3, and that the hormone 17β-estradiol (estrogen or E2) controls expression of both miR-191/425 and DALRD3. MiR-191/425 locus characterization revealed that the recruitment of estrogen receptor α (ERα) to the regulatory region of the miR-191/425-DALRD3 unit resulted in the accumulation of miR-191 and miR-425 and subsequent decrease in DALRD3 expression levels. We demonstrated that miR-191 protects ERα positive breast cancer cells from hormone starvation-induced apoptosis through the suppression of tumor-suppressor EGR1. Furthermore, enforced expression of the miR-191/425 cluster in aggressive breast cancer cells altered global gene expression profiles and enabled us to identify important tumor promoting genes, including SATB1, CCND2, and FSCN1, as targets of miR-191 and miR-425. Finally, in vitro and in vivo experiments demonstrated that miR-191 and miR-425 reduced proliferation, impaired tumorigenesis and metastasis, and increased expression of epithelial markers in aggressive breast cancer cells. Our data provide compelling evidence for the transcriptional regulation of the miR-191/425 cluster and for its context-specific biological determinants in breast cancers. Importantly, we demonstrated that the miR-191/425 cluster, by reducing the expression of an extensive network of genes, has a fundamental impact on cancer initiation and progression of breast cancer cells.

MDP, a database linking drug response data to genomic information, identifies dasatinib and statins as a combinatorial strategy to inhibit YAP/TAZ in cancer cells

Targeted anticancer therapies represent the most effective pharmacological strategies in terms of clinical responses. In this context, genetic alteration of several oncogenes represents an optimal predictor of response to targeted therapy. Integration of large-scale molecular and pharmacological data from cancer cell lines promises to be effective in the discovery of new genetic markers of drug sensitivity and of clinically relevant anticancer compounds. To define novel pharmacogenomic dependencies in cancer, we created the Mutations and Drugs Portal (MDP, http://mdp.unimore.it), a web accessible database that combines the cell-based NCI60 screening of more than 50,000 compounds with genomic data extracted from the Cancer Cell Line Encyclopedia and the NCI60 DTP projects. MDP can be queried for drugs active in cancer cell lines carrying mutations in specific cancer genes or for genetic markers associated to sensitivity or resistance to a given compound. As proof of performance, we interrogated MDP to identify both known and novel pharmacogenomics associations and unveiled an unpredicted combination of two FDA-approved compounds, namely statins and Dasatinib, as an effective strategy to potently inhibit YAP/TAZ in cancer cells.

Causes and timing of death during long-term follow-up after transcatheter aortic valve replacement.

BACKGROUND Transcatheter aortic valve replacement (TAVR) is an effective therapeutic option for patients with severe aortic stenosis at high risk for surgery. Identification of causes of death after TAVR may help improve patient selection and outcome. METHODS We enrolled 874 consecutive patients who underwent TAVR at 3 centers using all approved bioprostheses and different access routes. Clinical outcomes during follow-up were defined according to the Valve Academic Research Consortium 2 definitions. Causes of deaths were carefully investigated. RESULTS Mean logistic European System for Cardiac Operative Risk Evaluation was 23.5% ± 15.3%; Society of Thoracic Surgery score, 9.0% ± 8.2%. The Corevalve (Medtronic, Minneapolis, MN) was used in 41.3%; the Edwards Sapien (Edwards Lifesciences Inc., Irvine, CA) in 57.3%. Vascular access was transfemoral in 75.7%. In-hospital mortality was 5.0%. Cumulative mortality rates at 1 to 3 years were 12.4%, 23.4%, and 31.5%, respectively. Landmark analysis showed a significantly higher incidence of cardiovascular (CV) death in the first 6 months of follow-up and a significantly higher incidence of non-CV death thereafter. At Cox regression analysis, the independent predictors of in-hospital mortality were acute kidney injury grades 2 to 3 (hazard ratio [HR] 3.41) life-threatening bleeding (HR 4.26), major bleeding (HR 4.61), and myocardial infarction (HR 3.89). The independent predictors of postdischarge mortality were chronic obstructive pulmonary disease (HR 1.48), left ventricular ejection fraction at discharge (HR 0.98), and glomerular filtration rate <30 mL/min per 1.73 m(2) (HR 1.64). CONCLUSIONS Around a third of patients treated with TAVR in daily practice die within the first 3 years of follow-up. Early mortality is predominantly CV, whereas late mortality is mainly non-CV, and it is often due to preexisting comorbidity.

Covariance pattern mixture models for the analysis of multivariate heterogeneous longitudinal data

We propose a novel approach for modeling multivariate longitudinal data in the presence of unobserved heterogeneity for the analysis of the Health and Retirement Study (HRS) data. Our proposal can be cast within the framework of linear mixed models with discrete individual random intercepts; however, differently from the standard formulation, the proposed Covariance Pattern Mixture Model (CPMM) does not require the usual local independence assumption. The model is thus able to simultaneously model the heterogeneity, the association among the responses and the temporal dependence structure. We focus on the investigation of temporal patterns related to the cognitive functioning in retired American respondents. In particular, we aim to understand whether it can be affected by some individual socio-economical characteristics and whether it is possible to identify some homogenous groups of respondents that share a similar cognitive profile. An accurate description of the detected groups allows government policy interventions to be opportunely addressed. Results identify three homogenous clusters of individuals with specific cognitive functioning, consistent with the class conditional distribution of the covariates. The flexibility of CPMM allows for a different contribution of each regressor on the responses according to group membership. In so doing, the identified groups receive a global and accurate phenomenological characterization.

The Clustering of Categorical Data: A Comparison of a Model-based and a Distance-based Approach

For clustering multivariate categorical data, a latent class model-based approach (LCC) with local independence is compared with a distance-based approach, namely partitioning around medoids (PAM). A comprehensive simulation study was evaluated by both a model-based as well as a distance-based criterion. LCC was better according to the model-based criterion and PAM was sometimes better according to the distance-based criterion. However, LCC had an overall good and sometimes better distance-based performance as PAM, although this was not the case in a real data set on tribal art items.

Relationship between sleep time preference of adolescents and their parents

This study aimed to explore the relationship between sleep time preference of adolescents and their parents, considering gender separately. To this end, 912 adolescents (501 females) from 10 to 17 years were administered the Morningness–Eveningness Questionnaire for Children and Adolescents and their parents filled in the Morningness–Eveningness Questionnaire. To assess sleep time preference, the ideal midpoint of sleep was computed. A mathematical function was developed in order to correct the evolution of the ideal midpoint of sleep over time. The actual values were adjusted on the basis of those provided by the function for a specific age and gender and then reclassified into a new ordinal variable, with five categories, according to the interval where difference between actual and predicted values lies. This variable is informative as to the degree and magnitude of the ideal midpoint of sleep dispersion regarding/compared to the average value for a certain age and sex, represented by estimated function. The correlations between actual values of the ideal midpoint of sleep of adolescents and actual values of their parents, separately for gender of both, were not significant. The “corrected” ideal midpoint of sleep of daughters resulted significantly correlated with those “corrected” of mothers and fathers; furthermore, these correlation values were significantly higher than those detected between daughters and unrelated parents, highlighting the fact that such a privileged relationship between daughters and both parents is not due to chance.