The role of interleuchin 6 (IL-6) in the promotion of an aggressive and stem cell-like phenotype in human breast cancer cells and in stem/progenitor cells expanded in vitro as mammospheres

High serum levels of Interleukin-6 (IL-6) correlate with poor outcome in breast cancer patients. However no data are available on the relationship between IL-6 and stem/progenitor cells which may fuel the genesis of breast cancer in vivo. Herein, we address this issue in mammospheres (MS), multi-cellular structures enriched in stem/progenitor cells of the mammary gland, and also in MCF-7 breast cancer cells. We show that MS from node invasive breast carcinoma tissues express IL-6 mRNA at higher levels than MS from matched non-neoplastic mammary glands. We find that IL-6 mRNA is detectable only in basal-like breast carcinoma tissues, an aggressive variant showing stem cell features. Our results reveal that IL-6 triggers a Notch-3-dependent up-regulation of the Notch ligand Jagged-1, whose interaction with Notch-3 promotes the growth of MS and MCF-7 derived spheroids. Moreover, IL-6 induces a Notch-3-dependent up-regulation of the carbonic anhydrase IX gene, which promotes a hypoxia-resistant/invasive phenotype in MCF-7 cells and MS. Finally, an autocrine IL-6 loop relies upon Notch-3 activity to sustain the aggressive features of MCF-7-derived hypoxia-selected cells. In conclusion, our data support the hypothesis that IL-6 induces malignant features in Notch-3 expressing, stem/progenitor cells from human ductal breast carcinoma and normal mammary gland.

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.

Development of machine learning methods for multi-modal biomarkers detection and integration

In medicine, innovation depends on a better knowledge of the human body mechanism, which represents a complex system of multi-scale constituents. Unraveling the complexity underneath diseases proves to be challenging. A deep understanding of the inner workings comes with dealing with many heterogeneous information. Exploring the molecular status and the organization of genes, proteins, metabolites provides insights on what is driving a disease, from aggressiveness to curability. Molecular constituents, however, are only the building blocks of the human body and cannot currently tell the whole story of diseases. This is why nowadays attention is growing towards the contemporary exploitation of multi-scale information. Holistic methods are then drawing interest to address the problem of integrating heterogeneous data. The heterogeneity may derive from the diversity across data types and from the diversity within diseases. Here, four studies conducted data integration using customly designed workflows that implement novel methods and views to tackle the heterogeneous characterization of diseases. The first study devoted to determine shared gene regulatory signatures for onco-hematology and it showed partial co-regulation across blood-related diseases. The second study focused on Acute Myeloid Leukemia and refined the unsupervised integration of genomic alterations, which turned out to better resemble clinical practice. In the third study, network integration for artherosclerosis demonstrated, as a proof of concept, the impact of network intelligibility when it comes to model heterogeneous data, which showed to accelerate the identification of new potential pharmaceutical targets. Lastly, the fourth study introduced a new method to integrate multiple data types in a unique latent heterogeneous-representation that facilitated the selection of important data types to predict the tumour stage of invasive ductal carcinoma. The results of these four studies laid the groundwork to ease the detection of new biomarkers ultimately beneficial to medical practice and to the ever-growing field of Personalized Medicine.