Supplementary Figures 1 through 6 and Tables 1 through 5 from Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia

Figure S1. A schematic representation for the method of transforming the log2 linear RPPA values to a 5 point categorical scale suitable for BMA modeling. Figure S2. Differential AML signaling responses to single agent PIM inhibition with AZD1208 and combination with FLT3 inhibitor AC220 Figure S3. Synergic combinations of drugs are sought to increase sensitivity. Figure S4. Susceptibility identified in AZD1208 Resistant Cells. Figure S5. Generation of a Boolean model to predict phosphorylation events, responses to single treatment and synergistic combinations of treatments. Figure S6. Generation of an AND/OR model to predict phosphorylation events, responses to single treatment and synergistic combinations of treatments. Table S1. Categorized RPPA. RPPA results for MOLM16, MV411, KG1A and EOL1 cells, categorized to 5 point scale from 0-4 Table S2. PhosphoScan MassSpec. LC-MS/MS phosphorylation proteomic in MOLM16 cells treated with 2 uM AZD1208 for 3 hours Table S3. QMN RPPA. Quadrant median normalized (QMN) calculated for RPPA protein levels for MOLM16, MV411, KG1A and EOL1 cells. Table S4. RPPA Stats. Calling statistically significant total protein and phosphorylation changes (determined by log2 QMN differences greater than or equal to 0.5 and Wilcoxon Rank Sum Tests p-values less than or equal to 0.1) Table S5. Exome. Whole exome DNA sequencing