Project description
Analysis of combinatorial treatment for breast cancer
Breast cancer is the leading cause of cancer-related death in women. Breast cancer is classified into well-recognised molecular subtypes. Despite established molecular classification of tumour subtypes, only some patients benefit from administering drug combinations, which is an indication of tumour heterogeneity. The EU-funded RESCUER project aims to develop a new approach and identify mechanisms of resistance at systems level, exploring how the treatment is affected by patient- and tumour-specific conditions. The project will integrate longitudinal multidimensional data from ongoing clinical trials and novel systems approaches, which combine subcellular/cellular and organ-level in silico models to discover molecular signatures of resistance and predict patient response to combinatorial therapies. This new knowledge will be used to identify already approved drugs with a high curative potential of new personalised drug combinations.
Objective
Breast Cancer (BC) is the first cause of cancer-related death in women worldwide. Breast cancer is classified into well-recognized molecular subtypes. Despite solid pre-clinical evidence, only some patients benefit from administering drug combinations, an indication that patient and tumor heterogeneity is still present in the current stratification. Out of the numerous possible combinations of approved drugs, only a few have been actually tried, and the choice of tested combinations has been to some degree arbitrary. This proposal seeks to develop new approaches and identify mechanisms of treatment resistance at systems level, exploring how the effectiveness of specific targeted therapies applied in different clinical trials is affected by patient- and tumor-specific conditions. For this purpose, the project will gather and integrate longitudinal multidimensional data from ongoing clinical trials and newly generated --omics using systems approaches, which combine sub-cellular/cellular and/or organ level in-silico models and network analysis to build computational frameworks able to discover molecular signatures of resistance and predict patient response to combinatorial therapies. We aim to identify the physiological characteristics of non-responders vs. responders from existing and newly generated multi-omic data and biological samples from in-vivo and ex-vivo clinical studies of specific subtypes of BC patients treated with combination therapy. This new knowledge will be used to investigate the curative potential of new personalized drugs combinations. The overreaching goal is to develop computer “xenograft model” as a cost-efficient and better alternative in terms of ethics, availability to everyone, and animal use. The framework will include optimization algorithms to identify combinations of approved drugs with a high probability to work on individual or thin strata of patients. The project is endowed with a “legal” framework addressing ethical aspects
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RIA - Research and Innovation actionCoordinator
0313 Oslo
Norway