PT  - JOURNAL ARTICLE
AU  - Mehdi Bouhaddou
AU  - Anne Marie Barrette
AU  - Rick J. Koch
AU  - Matthew S. DiStefano
AU  - Eric A. Riesel
AU  - Alan D. Stern
AU  - Luis C. Santos
AU  - Annie Tan
AU  - Alex Mertz
AU  - Marc R. Birtwistle
TI  - An Integrated Mechanistic Model of Pan-Cancer Driver Pathways Predicts Stochastic Proliferation and Death
AID  - 10.1101/128801
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 128801
4099  - http://biorxiv.org/content/early/2017/04/19/128801.short
4100  - http://biorxiv.org/content/early/2017/04/19/128801.full
AB  - Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug 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 context, synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD. AKT dynamics explain S-phase entry synergy between EGF and insulin, but stochastic ERK dynamics seem to drive cell-to-cell proliferation variability, which in simulations are predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations predict MEK alteration negligibly influences transformation, consistent with clinical data. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, moving towards more rational cancer combination therapy.