Unveiling the signaling network of FLT3-ITD AML improves drug sensitivity prediction

• DOI: 10.7554/elife.90532.1
• Publication Date: 2023-10-24T15:35:35Z
• Authors: Sara Latini; Veronica Venafra; Giorgia Massacci; Valeria Bica; Simone Graziosi; Giusj Monia Pugliese; Marta Iannuccelli; Filippo Frioni; Gessica Minnella; John Donald Marra; Patrizia Chiusolo; Gerardo Pepe; Manuela Helmer-Ci...; Dimitrios Mougiak...; Martin Boettcher; Thomas Fischer; Livia Perfetto; Francesca Sacco
• Keywords: 0301 basic medicine; 570; QH301-705.5; MAP Kinase Signaling System; Science; Boolean model; acute myeloid leukemia; cancer biology; cancer therapy; computational biology; drug resistance; human; mouse; systems biology; tyrosine kinase; Drug Resistance; 610; acute myeloid leukemia; Cell Line; 03 medical and health sciences; computational biology; Humans; human; Biology (General); mouse; cancer biology; Cancer Biology; drug resistance; Settore BIO/18; Settore BIO/11; Q; R; tyrosine kinase; Boolean model; systems biology; 3. Good health; Leukemia, Myeloid, Acute; fms-Like Tyrosine Kinase 3; cancer therapy; Medicine; Signal Transduction

Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In setting acute myeloid leukemia (AML), patient-drug treatment matching fails a subset patients harboring atypical internal tandem duplications (ITDs) tyrosine kinase domain FLT3 gene. To address this unmet medical need, here we develop systems-based strategy that integrates multiparametric analysis crucial signaling pathways, and transcriptomic data with prior-knowledge network using Boolean-based formalism. By approach, derive predictive models describing landscape AML FLT3-ITD positive cell lines patients. These enable us to mechanistic insight into drug resistance mechanisms suggest novel opportunities for combinatorial treatments. Interestingly, our reveals JNK pathway plays role inhibitor response cells through cycle regulation. Finally, work shows logic have potential inform precision medicine approaches.