Obiettivo 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. Campo scientifico scienze mediche e della salutemedicina clinicaoncologiatumore al seno Programma(i) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Argomento(i) ERC-2017-COG - ERC Consolidator Grant Invito a presentare proposte ERC-2017-COG Vedi altri progetti per questo bando Meccanismo di finanziamento ERC-COG - Consolidator Grant Coordinatore UNIVERSITA COMMERCIALE LUIGI BOCCONI Contribution nette de l'UE € 455 977,50 Indirizzo Via sarfatti 25 20136 Milano Italia Mostra sulla mappa Regione Nord-Ovest Lombardia Milano Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Altri finanziamenti € 0,00 Beneficiari (2) Classifica in ordine alfabetico Classifica per Contributo netto dell'UE Espandi tutto Riduci tutto UNIVERSITA COMMERCIALE LUIGI BOCCONI Italia Contribution nette de l'UE € 455 977,50 Indirizzo Via sarfatti 25 20136 Milano Mostra sulla mappa Regione Nord-Ovest Lombardia Milano Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Altri finanziamenti € 0,00 THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD Regno Unito Contribution nette de l'UE € 1 540 347,50 Indirizzo Wellington square university offices OX1 2JD Oxford Mostra sulla mappa Regione South East (England) Berkshire, Buckinghamshire and Oxfordshire Oxfordshire Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Altri finanziamenti € 0,00
