Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia

Abstract

Personalized therapy is a major goal of modern oncology, as patient responses vary greatly even within a histologically defined cancer subtype. This is especially true in acute myeloid leukemia (AML), which exhibits striking heterogeneity in molecular segmentation. When calibrated to cell-specific data, executable network models can reveal subtle differences in signaling that help explain differences in drug response. Furthermore, they can suggest drug combinations to increase efficacy and combat acquired resistance. Here, we experimentally tested dynamic proteomic changes and phenotypic responses in diverse AML cell lines treated with pan-PIM kinase inhibitor and fms-related tyrosine kinase 3 (FLT3) inhibitor as single agents and in combination. We constructed cell-specific executable models of the signaling axis, connecting genetic aberrations in FLT3, tyrosine kinase 2 (TYK2), platelet-derived growth factor receptor alpha (PDGFRA), and fibroblast growth factor receptor 1 (FGFR1) to cell proliferation and apoptosis via the PIM and PI3K kinases. The models capture key differences in signaling that later enabled them to accurately predict the unique proteomic changes and phenotypic responses of each cell line. Furthermore, using cell-specific models, we tailored combination therapies to individual cell lines and successfully validated their efficacy experimentally. Specifically, we showed that cells mildly responsive to PIM inhibition exhibited increased sensitivity in combination with PIK3CA inhibition. We also used the model to infer the origin of PIM resistance engineered through prolonged drug treatment of MOLM16 cell lines and successfully validated experimentally our prediction that this resistance can be overcome with AKT1/2 inhibition.

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

“…A notable absence from the Challenge was the use of mathematical, boolean or logic based mechanistic pathway modelling approaches [25][26][27][28][29] , likely due to the intensity of model creation. The dynamic nature of mechanistic models may offer an advantage by enabling consideration of the heterogeneity that exists across even apparently 'clonal' cell line populations 21 .…”

Logic Modeling in Quantitative Systems Pharmacology

“…In particular, a number of recent studies have successfully studied logic models to investigate signaling pathways and suggest effective drug combinations which were then validated in vitro and/or in vivo. [18][19][20]45 Mathematical models calibrated using cell lines have also been proved effective in predicting clinical patient outcomes. 82 Overall, network-based models allow us to formalize this reasoning into a mechanistic computational model, and infer conclusions about a drug's effects from quantitative simulations in a principled manner.…”

“…42 It has also been used to study the immunological response to infections, 43 or to understand apoptosis given its relevance in diseases like Alzheimer's and Parkinson's. 30 These and other examples 19,44 illustrate the value of logic modeling to enhance our understanding of the systemic effect of therapies. The models provide a formal tool to quickly evaluate in silico the effect of targeting one specific component of the model or explore the effects of possible drug combinations.…”

“…16,17 Similarly, the comparison of pathway activity in different cells from the same cancer type allows investigating mechanism of resistance to drugs, which can be exploited to suggest personalized therapies. [18][19][20] If experimental data are available, optimization procedures can be used to refine the initial networks to be cell line-or context-specific.…”

Defining subpopulations of differential drug response to reveal novel target populations

“…While several methods have been proposed to identify drug response biomarkers in cell lines for precision medicine and drug repositioning 4,5,10,11 , there is a need for more objective and unsupervised approaches for identifying subpopulations with differences in drug response (differential drug response), and consequently systematically gain mechanistic insights from biomarkers. Most approaches capable of comparing multiple drugs measure the overall similarity (or correlation) based on a single response summary metric 7,12 , which permits drug repositioning based on subpopulations with similar behavior, but neglects ones that behave differently (Figure S1A).…”