Sezione di Ematologia, Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
Gerardo Pepe
Department of Biology, University of Rome Tor Vergata, Rome, Italy
Manuela Helmer Citterich
Department of Biology, University of Rome Tor Vergata, Rome, Italy
Dimitros Mougiakakos
Health Campus for Inflammation, Immunity and Infection (GCI3), Otto-von-Guericke University of Magdeburg, Magdeburg, Germany; Department of Hematology and Oncology, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany
Health Campus for Inflammation, Immunity and Infection (GCI3), Otto-von-Guericke University of Magdeburg, Magdeburg, Germany; Department of Hematology and Oncology, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany
Thomas Fischer
Health Campus for Inflammation, Immunity and Infection (GCI3), Otto-von-Guericke University of Magdeburg, Magdeburg, Germany; Institute of Molecular and Clinical Immunology, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany
Livia Perfetto
Department of Biology, University of Rome Tor Vergata, Rome, Italy; Department of Biology, Fondazione Human Technopole, Milan, Italy
Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In the setting of acute myeloid leukemia (AML), patient-drug treatment matching fails in a subset of patients harboring atypical internal tandem duplications (ITDs) in the tyrosine kinase domain of the FLT3 gene. To address this unmet medical need, here we develop a systems-based strategy that integrates multiparametric analysis of crucial signaling pathways, and patient-specific genomic and transcriptomic data with a prior knowledge signaling network using a Boolean-based formalism. By this approach, we derive personalized predictive models describing the signaling landscape of AML FLT3-ITD positive cell lines and patients. These models enable us to derive mechanistic insight into drug resistance mechanisms and suggest novel opportunities for combinatorial treatments. Interestingly, our analysis reveals that the JNK kinase pathway plays a crucial role in the tyrosine kinase inhibitor response of FLT3-ITD cells through cell cycle regulation. Finally, our work shows that patient-specific logic models have the potential to inform precision medicine approaches.
