A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens

Abstract

Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug and drug combination sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this MCF10A cell context, simulations suggest that synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD, which is supported by prior experimental studies. AKT dynamics explain S-phase entry synergy between EGF and insulin, but simulations suggest that stochastic ERK, and not AKT, dynamics seem to drive cell-to-cell proliferation variability, which in simulations is predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations suggest MEK alteration negligibly influences transformation, consistent with clinical data. Tailoring the model to an alternate cell expression and mutation context, a glioma cell line, allows prediction of increased sensitivity of cell death to AKT inhibition. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, providing a framework for designing more rational cancer combination therapy.

Predicting In Vivo Efficacy from In Vitro Data: Quantitative Systems Pharmacology Modeling for an Epigenetic Modifier Drug in Cancer

“…Pharmacokinetic/pharmacodynamic (PK/PD) models are commonly used to establish quantitative relationships among dose, exposure, and efficacy . These models, primarily systems of ordinary differential equations, are trained on sets of experimental data, and then used to predict preclinical or clinical outcomes.…”

Development of Fangjiomics for Systems Elucidation of Synergistic Mechanism Underlying Combination Therapy

“… The GSI between addictive and synergistic drugs treated-group (JA + UA) was 0.57 and 0.81,, respectively; lower than JA,UA treated-groups(0.68) and JA,BA treated-groups(0.91), indicating that the gene expression variation of the synergy effect is greater than that of the additive effect. Pathway-based A mechanistic pan-cancer pathway model based on chemical kinetics approach [ 40 ] A systematic model assembled with six created“submodels” (the RTK-Ras-Raf pathway, the PI3K-AKT-mTOR pathway, cell cycle pathways, and p53-DNA repair pathways, apoptosis pathways, and gene expression and degradation processes.) Genomics; transcriptomics; proteomics Capturing the regulation of stochastic proliferation and death by pan-cancer driver pathways.…”

Network Inference from Perturbation Time Course Data

“…crosstalk) between the ERK and AKT pathways, two broadly important mammalian signaling pathways regulating cell proliferation, death, migration and differentiation (Manning and Toker, 2017;Shaul and Seger, 2007). We used In-Cell ELISA to measure the dynamic response of ERK and AKT phosphorylation in serum-and growth-factor starved MCF10A cells to an Epidermal Growth Factor (EGF) and Insulin combination stimulus, which robustly activates both ERK and AKT over ~hour time scales to drive the cell cycle (Bouhaddou et al, 2018). We did this in the presence of vehicle (Fig.…”