Project description
Functional tissue motifs in metastatic breast cancer
Cancer is a heterogeneous disease driven by the dynamic interplay of the tumour, immune and stromal cell compartments. Therefore, it is important to characterise disease complexity at individual level to develop and offer precision medicine solutions. The EU-funded Precision Motifs project will work towards a highly multiplexed 3D tissue imaging approach to identify functional tissue motifs in metastatic breast cancer. These motifs, identified using probabilistic network modeling, will be used to develop a novel disease classification system to apply to taking personal therapy decisions in cancer patients.
Objective
Cancer precision medicine aims to bring the right drug (or right drug combination) to the right person. To achieve this, the molecular cause of an individual’s disease must be identified and a treatment chosen that targets that cause to remedy the dysfunction. Tumors are dynamic ecosystems with high heterogeneity in tumor, immune, and stromal cell compartments. This complexity is the main obstacle to implementation of precision medicine. Consequently, approaches that rationalize and use tumor ecosystems for precision medicine are needed. To enable this, I propose to establish the concept of functional tissue motif, a multiscale network representation of multi-cellular assemblies that executes a definable set of anti-tumor or pro-tumor functional outputs. The utility of functional tissue motifs for precision medicine will be demonstrated in the context of metastatic breast cancer, which causes nearly 450,000 cancer-associated deaths each year. We will develop a highly multiplexed 3D tissue imaging approach with unprecedented throughput that provides subcellular resolution to analyze a large cohort of primary and matched metastatic tumors. We will then use viable tumor samples ex vivo to systematically perturb intra-cellular and inter-cellular network wiring using approved drugs, and the drug effects will be measured by highly multiplexed imaging. Functional tissue motifs will be identified using community and geostatistical methods and probabilistic network modeling. Analysis of functional tissue motifs in the light of the clinical data will be used to derive a novel classification system and to identify vulnerabilities in metastatic breast tumors. These vulnerabilities will be validated in follow-up experiments and in a clinical study. Given their focus on functional output, information content and versatility, functional tissue motifs promise to be a powerful concept and should enable to point to appropriate treatments, and inform development of new therapies.
Fields of science
Programme(s)
Funding Scheme
ERC-COG - Consolidator GrantHost institution
8006 Zurich
Switzerland