Objetivo The occurrence of therapeutic resistance is a major cause for the small effect on overall survival showed by targeted cancer therapies. Whilst experimental strategies to evaluate available treatments have been faced by an ever increasing number of possible combinations, computational approaches have been challenged by the lack of a framework able to model the multiple interactions encompassed by the three major factors affecting therapeutic resistance: selection of resistant clones, adaptability of gene signalling networks, and a protective and hypoxic tumour microenvironment.Here I propose a novel modelling framework, Agent-Based Modelling of Gene Networks, which brings together powerful computational modelling techniques and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict therapeutic resistance and guide effective treatment selection.Using triple negative breast cancer (TNBC) as a testing case (15% of breast cancers, lacks validated), I propose to: 1. Develop a computational model of the TNBC tumour microenvironment using in-vitro and in-vivo, including patient-derived, models and data from clinical samples. 2. Validate the ability of the model to predict driver genes conferring a survival advantage to cancer cells in a hypoxic microenvironment. 3. Predict combinations of druggable targets to tackle TNBC therapeutic resistance. 4. Select most effective drug combinations and validate pre-clinically.This project will deliver pre-clinically validated drug combinations, new therapeutic targets and a virtual environment to study individual tumours and predict therapeutic resistance. Complementing and empowering experimental models and assays, microC will offer a new powerful tool for diagnosis and therapy. Ámbito científico ciencias médicas y de la saludmedicina clínicaoncologíacáncer de mama Programa(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Tema(s) ERC-2017-COG - ERC Consolidator Grant Convocatoria de propuestas ERC-2017-COG Consulte otros proyectos de esta convocatoria Régimen de financiación ERC-COG - Consolidator Grant Coordinador UNIVERSITA COMMERCIALE LUIGI BOCCONI Aportación neta de la UEn € 455 977,50 Dirección Via sarfatti 25 20136 Milano Italia Ver en el mapa Región Nord-Ovest Lombardia Milano Tipo de actividad Higher or Secondary Education Establishments Enlaces Contactar con la organización Opens in new window Sitio web Opens in new window Participación en los programas de I+D de la UE Opens in new window Red de colaboración de HORIZON Opens in new window Otras fuentes de financiación € 0,00 Beneficiarios (2) Ordenar alfabéticamente Ordenar por aportación neta de la UE Ampliar todo Contraer todo UNIVERSITA COMMERCIALE LUIGI BOCCONI Italia Aportación neta de la UEn € 455 977,50 Dirección Via sarfatti 25 20136 Milano Ver en el mapa Región Nord-Ovest Lombardia Milano Tipo de actividad Higher or Secondary Education Establishments Enlaces Contactar con la organización Opens in new window Sitio web Opens in new window Participación en los programas de I+D de la UE Opens in new window Red de colaboración de HORIZON Opens in new window Otras fuentes de financiación € 0,00 THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD Reino Unido Aportación neta de la UEn € 1 540 347,50 Dirección Wellington square university offices OX1 2JD Oxford Ver en el mapa Región South East (England) Berkshire, Buckinghamshire and Oxfordshire Oxfordshire Tipo de actividad Higher or Secondary Education Establishments Enlaces Contactar con la organización Opens in new window Sitio web Opens in new window Participación en los programas de I+D de la UE Opens in new window Red de colaboración de HORIZON Opens in new window Otras fuentes de financiación € 0,00