Stem cell pathways in the basal-like breast carcinoma: the role of beta-catenin and HIF-1alpha

Basal-like tumor is an aggressive breast carcinoma subtype that displays an expression signature similar to that of the basal/myoepithelial cells of the breast tissue. Basal-like carcinoma are characterized by over-expression of the Epidermal Growth Factor receptor (EGFR), high frequency of p53 mutations, cytoplasmic/nuclear localization of beta-catenin, overexpression of the Hypoxia inducible factor (HIF)-1alpha target Carbonic Anhydrase isoenzime 9 (CA9) and a gene expression pattern similar to that of normal and cancer stem cells, including the over-expression of the mammary stem cell markers CD44. In this study we investigated the role of p53, EGFR, beta-catenin and HIF-1alpha in the regulation of stem cell features and genes associated with the basal-like gene expression profile. The findings reported in this investigation indicate that p53 inactivation in ductal breast carcinoma cells leads to increased EGFR mRNA and protein levels. In our experimental model, EGFR overexpression induces beta-catenin cytoplasmatic stabilization and transcriptional activity and, by that, leads to increased aggressive features including mammosphere (MS) forming and growth capacity, invasive potential and overexpression of the mammary stem cell gene CD44. Moreover we found that EGFR/beta-catenin axis promotes hypoxia survival in breast carcinoma cells via increased CA9 expression. Indeed beta-catenin positively regulates CA9 expression upon hypoxia exposure. Interestingly we found that beta-catenin inhibits HIF-1alpha transcriptional activity. Looking for the mechanism, we found that CA9 expression is promoted by HIF-1alpha and cytoplasmatic beta-catenin further increased it post-transcriptionally, via direct mRNA binding and stabilization. These data reveal a functional beta-catenin/HIF-1alpha interplay among hallmarks of basal-like tumors and unveil a new functional role for cytoplasmic beta-catenin in the phenotype of such tumors. Therefore it can be proposed that the interplay here described among EGFR/beta-catenin and HIF-1alpha may play a role in breast cancer stem cell survival and function.

Aspetti etici e morali della Sedazione Palliativa

La sedazione palliativa come procedura terapeutica è ormai ampiamente affrontata e discussa in letteratura. A causa della delicatezza di tale procedura, molti studi affrontano le problematiche etiche ad essa relative con l’obiettivo di cercare una giustificazione morale e clinica. Questo lavoro intende affrontare la sedazione palliativa applicata ad un caso concreto. Seguendo la trama della storia di Matteo, uomo di 38 anni ricoverato in Hospice con diagnosi di cancro metastatico della mammella maschile (metastasi polmonari con incarceramento del polmone destro, metastasi diffuse a tutto il rachide, metastasi epatiche), verranno affrontate le problematiche etiche relative ad ogni fase della vicenda. In particolare i temi trattati sono i seguenti: il controllo di sé, la negoziazione della terapia e l’autodeterminazione; il principio di autonomia relazionale; l’etica del corpo; differenze tra sedazione palliativa ed eutanasia; le domande di fine vita e la spiritualità; documenti sul fine vita. Il paradigma teorico prescelto come punto di riferimento è quello dell’Etica della Cura.

Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.