Application of a DNp73-IGF1R signaling-based systems model for the prediction of metastasis probability and therapy responses in malignant melanoma

Our group provided first functional evidence that oncogenic DNp73 variants are crucial to initiate BRAF/NRAS/p53mut-independent melanoma invasion via the EPLIN-IGF1R/AKT axis, and identified a multilevel DNp73-governed regulatory network that specifically controls malignant progression. This opens up completely new possibilities for the treatment of aggressive melanoma. Due to the DNp73-network complexity, holding multiple overlapping nonlinear regulatory motifs, we apply a computational systems model to determine molecular fingerprints allowing patient-stratified therapies and the discovery of novel drug targets. For this purpose, we developed a mathematical model that allows to consider the interpatient heterogeneity for metastasis-targeted strategiesto generate molecular signatures reflecting early metastatic spread and drug resistance. Based on validated simulated signatures, we will design multimodal patient-tailored therapies using CRISPR-Cas9 knockout/activation strategies, in vivo LNAs, miR-mimics and clinically applied kinase inhibitors, which will be tested in established mouse metastasis models. The overall goal of this project is to provide DNp73-driven biomarker constellations that predict metastasis initiation and resistance probabilities, and to develop novel individual treatment paradigms.

This project is funded by the German Cancer Aid (Deutsche Krebshilfe) 70112353.