Abstract
Acute myeloid leukemia (AML) is one of the most prevalent and acute blood cancers with a poor prognosis and low overall survival rate, especially in the elderly. Although several new AML markers and drug targets have been recently identified, the rate of long-term cancer eradication has not improved significantly due to the presence and drug resistance of AML cancer stem cells (CSCs). Here we develop a novel computational pipeline to analyze the transcriptomic profiles of AML cancer (stem) cells and identify novel candidate AML CSC markers and drug targets. In our novel pipeline we apply a top-down meta-analysis strategy to integrate The Cancer Genome Atlas data with CSC datasets to infer cell stemness features. As a result, a set of genes termed the "AML key CSC genes" along with all the available drugs/compounds that could target them were identified. Overall, our novel computational pipeline could retrieve known cancer drugs (Carfilzomib) and predicted novel drugs such as Zonisamide, Amitriptyline, and their targets amongst the top ranked drugs and drug targets for targeting AML. Additionally, the pipeline applied in this study could be used for the identification of CSC-specific markers, drivers and their respective targeting drugs in other cancer types.