Analysis of signaling network rewiring in FLT3-ITD driven AML

Cancer cells rewire their signaling pathways to sustain growth and proliferation. Targeted therapies are designed to hit crucial signaling molecules, but their efficacy is often mined by drug resistance mechanisms (Min and Lee, 2022). Tyrosine kinase inhibitor therapy fails in acute myeloid leukemia patients harboring the non-canonical ITD mutation in the catalytic domain of the tyrosine receptor FLT3 (FLT3 ITD-TKD patients) (Rücker et al., 2022). As cancer is mainly a signaling disease, it is crucial to elucidate how the rewiring of pathways is implicated in drug resistance. Here, we propose a systemic approach that combines literature derived prior-knowledge networks (PKN) with multiparametric experimental data to model FLT3ITD downstream signaling. Through the optimization process, we trained the PKN into cellspecific logic models. Then, we used the models as frameworks to in silico predict putative co-treatments reverting TKI resistance in FLT3 ITD-TKD cells. We highlighted JNK as a target to sensibilize resistant cells to TKI treatment. Indeed, we demonstrate how pairing the pharmacological inhibition of JNK with FLT3 triggers apoptosis in TKI resistant cells. Moreover, we show how JNK plays a role in the modulation of cell cycle progression in FLT3 ITD-TKD cells, through the phosphorylation of CDK1, a known player in TKI resistance (Massacci et al., 2022). In conclusion, our systemic strategy led to mutationspecific predictive models that could be used as new tools to infer novel therapeutic approaches for FLT3ITD+ AML patients.