Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic Targets.
David J WootenMelat GebruHong-Gang WangReka AlbertPublished in: Journal of personalized medicine (2021)
FLT3-mutant acute myeloid leukemia (AML) is an aggressive form of leukemia with poor prognosis. Treatment with FLT3 inhibitors frequently produces a clinical response, but the disease nevertheless often recurs. Recent studies have revealed system-wide gene expression changes in FLT3-mutant AML cell lines in response to drug treatment. Here we sought a systems-level understanding of how these cells mediate these drug-induced changes. Using RNAseq data from AML cells with an internal tandem duplication FLT3 mutation (FLT3-ITD) under six drug treatment conditions including quizartinib and dexamethasone, we identified seven distinct gene programs representing diverse biological processes involved in AML drug-induced changes. Based on the literature knowledge about genes from these modules, along with public gene regulatory network databases, we constructed a network of FLT3-ITD AML. Applying the BooleaBayes algorithm to this network and the RNAseq data, we created a probabilistic, data-driven dynamical model of acquired resistance to these drugs. Analysis of this model reveals several interventions that may disrupt targeted parts of the system-wide drug response. We anticipate co-targeting these points may result in synergistic treatments that can overcome resistance and prevent eventual recurrence.
Keyphrases
- acute myeloid leukemia
- drug induced
- liver injury
- allogeneic hematopoietic stem cell transplantation
- poor prognosis
- gene expression
- induced apoptosis
- adverse drug
- healthcare
- cancer therapy
- electronic health record
- long non coding rna
- big data
- systematic review
- genome wide
- combination therapy
- oxidative stress
- deep learning
- mental health
- drug delivery
- cell proliferation
- physical activity
- acute lymphoblastic leukemia
- bone marrow
- signaling pathway
- network analysis
- genome wide identification
- tyrosine kinase
- data analysis