A Systems Biology- and Machine Learning-Based Study to Unravel Potential Therapeutic Mechanisms of Midostaurin as a Multitarget Therapy on FLT3-Mutated AML

Abstract

Acute myeloid leukemia (AML), a hematologic malignancy that results in bone marrow failure, is the most common acute leukemia in adults. The presence of FMS-related tyrosine kinase 3 (FLT3) mutations is associated with a poor prognosis, making the evaluation of FLT3-inhibitors an imperative goal in clinical trials. Midostaurin was the first FLT3-inhibitor approved by the FDA and EMA for the treatment of FLT3-mutated AML, and it showed a significant improvement in overall survival for newly diagnosed patients treated with midostaurin, in combination with standard chemotherapy (RATIFY study). The main interest of midostaurin has been the FLT3-specific inhibition, but little is known about its role as a multikinase inhibitor and whether it may be used in relapse and maintenance therapy. Here, we used systems biology- and machine learning-based approaches to deepen the potential benefits of the multitarget activity of midostaurin and to better understand its anti-leukemic effect on FLT3-mutated AML. The resulting in silico study revealed that the multikinase activity of midostaurin may play a role in the treatment’s efficacy. Additionally, we propose a series of molecular mechanisms that support a potential benefit of midostaurin as a maintenance therapy in FLT3-mutated AML, by regulating the microenvironment. The obtained results are backed up using independent gene expression data.