American Association for Cancer Research
00085472can161578-sup-167311_1_supp_3733015_hhrp1w.xlsx (1.42 MB)

Supplementary Tables 6 through 14 from Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia

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posted on 2023-03-31, 01:07 authored by Dana Silverbush, Shaun Grosskurth, Dennis Wang, Francoise Powell, Berthold Gottgens, Jonathan Dry, Jasmin Fisher

The following tables are supplied in the excel file due to their sizes: Table S6. Phosphosite Overlap. PhosphoScan mass-spectrometry in MOLM16 cells after 3 hour treatment with AZD1208 Table S7. Literature Entries. Full literature entries. Table S8. Model Target Functions. Target functions for AML general model and cell specific calibrated AML model. Table S9. AML genotype state. Nodes settings for model initialization for each cell-specific state. Table S10. Immediate downstream effect of modelled AML genotypes. Direct modelled effect of each genotype. Table S11. Cell-specific AML network model replicates response to treatments reported in external publications. Summary of experimental conclusions compared to model predictions. Table S12. Combination strategy. showing predicted activity for combinations of PI3K inhibitor, AKT inhibitor, MEK inhibitor, and AKT inhibitor with the PIM inhibitor AZD1208 across the 4 AML cell lines. Table S13. MOLM16_Res_DNAseq. Whole exome DNA-seq for PIM inhibition resistant populations of MOLM16 cell line. Table S14. BIOGRID Interactions. BIOGRID interactions between putative resistant deriving genes to the RAS/PI3K and/or the AKT/MTOR signaling pathways.



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. Cancer Res; 77(4); 827–38. ©2016 AACR.

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