Analysis of functional tissue motifs for precision medicine in metastatic breast cancer

Projektbeschreibung

Funktionelle Gewebemotive bei metastasierendem Brustkrebs

Krebs ist eine heterogene Erkrankung, die durch die dynamische Wechselwirkung von Tumor-, Immun- und Stromazellkompartimenten hervorgerufen wird. Daher ist es wichtig, die Erkrankungskomplexität auf individueller Ebene zu charakterisieren, um präzisionsmedizinische Lösungen entwickeln und bereitstellen zu können. Das EU-finanzierte Projekt Precision Motifs wird einen Ansatz zur hoch multiplexen 3D-Gewebebildgebung erarbeiten, um funktionelle Gewebemotive bei metastasierendem Brustkrebs zu identifizieren. Diese funktionellen Gewebemotive aus der Modellierung probabilistischer Netze werden zur Entwicklung eines neuen Systems für Krankheitsklassifizierung dienen, das individuelle Behandlungsentscheidungen bei Krebskranken erleichtert.

Ziel

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.