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Network slicing is a central feature in 5G and beyond systems to allow operators to customize their networks for different applications and customers. With network slicing, different logical networks, i.e. network slices, with specific functional and performance requirements can be created over the same physical network. A key challenge associated with the exploitation of the network slicing feature is how to efficiently allocate underlying network resources, especially radio resources, to cope with the spatio-temporal traffic variability while ensuring that network slices can be provisioned and assured within the boundaries of Service Level Agreements / Service Level Specifications (SLAs/SLSs) with customers. In this field, the use of artificial intelligence, and, specifically, Machine Learning (ML) techniques, has arisen as a promising approach to cater for the complexity of resource allocation optimization among network slices. This paper tackles the description of a feasible implementation framework for deploying ML-assisted solutions for cross-slice radio resource optimization that builds upon the work conducted by 3GPP and O-RAN Alliance. On this basis, the paper also describes and evaluates an ML-assisted solution that uses a Multi-Agent Reinforcement Learning (MARL) approach based on the Deep Q-Network (DQN) technique and fits within the presented implementation framework. ; This work has been supported by the Spanish Research Council and FEDER funds under SONAR 5G grant (ref. TEC2017-82651-R) and by the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia under grant 2019FI_B1 00102. ; Peer Reviewed ; Postprint (published version)

Metastasic breast cancer is the leading cause of death by malignancy in women worldwide. Tumor metastasis is a multistep process encompassing local invasion of cancer cells at primary tumor site, intravasation into the blood vessel, survival in systemic circulation, and extravasation across the endothelium to metastasize at a secondary site. However, only a small percentage of circulating cancer cells initiate metastatic colonies. This fact, together with the inaccessibility and structural complexity of target tissues has hampered the study of the later steps in cancer metastasis. In addition, most data are derived from in vivo models where critical steps such as intravasation/ extravasation of human cancer cells are mediated by murine endothelial cells. Here, we developed a new mouse model to study the molecular and cellular mechanisms underlying late steps of the metastatic cascade. We have shown that a network of functional human blood vessels can be formed by co-implantation of human endothelial cells and mesenchymal cells, embedded within a reconstituted basement membrane-like matrix and inoculated subcutaneously into immunodeficient mice. The ability of circulating cancer cells to colonize these human vascularized organoids was next assessed in an orthotopic model of human breast cancer by bioluminescent imaging, molecular techniques and immunohistological analysis. We demonstrate that disseminated human breast cancer cells efficiently colonize organoids containing a functional microvessel network composed of human endothelial cells, connected to the mouse circulatory system. Human breast cancer cells could be clearly detected at different stages of the metastatic process: initial arrest in the human microvasculature, extravasation, and growth into avascular micrometastases. This new mouse model may help us to map the extravasation process with unprecedented detail, opening the way for the identification of relevant targets for therapeutic intervention ; This study was supported by grants from the Ministerio de Ciencia e Innovación (BIO2008-03233), Ministerio de Economía y Competitividad (BIO2011-22738), the Comunidad de Madrid (S2010/BMD-2312) and the European Union [SUDOE-FEDER (IMMUNONETSOE1/P1/E014)] to LAV; by grant PS09/00227 from the Fondo de Investigación Sanitaria to L.S; by grants PS09/01296, RD09/0076/00101 and RD06/0020/0109 from the Fondo de Investigación Sanitaria to FR and by grant AP86442011 from Fundación Mutua Madrileña to FR. RFP was supported by a predoctoral fellowship from the Ministerio de Economía y Competitividad (BES-2009- 027649), VAC by a predoctoral fellowship from the Gobierno Vasco (BFI07.132), PSV by a predoctoral fellowship from the Fundación Investigación Biomedica Hospital Universitario Puerta de Hierro, and DSM by a Comunidad de Madrid/Fondo Social Europeo Training Grant (FPI-000531). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript