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PreprintCited by 58PI3K/AKT/mTOR signaling in cancerProtein Kinase Regulation and GTPase SignalingGene Regulatory Network Analysis

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An Integrated Mechanistic Model of Pan-Cancer Driver Pathways Predicts Stochastic Proliferation and Death

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Bouhaddou, Mehdi, Barrette, Anne Marie, Koch, Rick J., DiStefano, Matthew S., Riesel, Eric A., Stern, Alan D., Santos, Luis C., Tan, Annie, Mertz, Alex, Birtwistle, Marc R.·2017·PLoS Computational Biology
DOI 10.1101/128801PMC PMC5886578

Preprint — published as 10.1371/journal.pcbi.1005985 · Paper page

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Abstract

SUMMARY: 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.

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Metadata

Authors
Bouhaddou, M., Barrette, A. M., Koch, R. J., DiStefano, M. S., Riesel, E. A., Stern, A. D., Santos, L. C., Tan, A., Mertz, A., & Birtwistle, M. R.
Affiliations
  • Icahn School of Medicine at Mount Sinai (US)
  • Clemson University (US)
Corresponding author
  • Bouhaddou, Mehdi
  • Barrette, Anne Marie
  • Koch, Rick J.
  • DiStefano, Matthew S.
  • Riesel, Eric A.
  • Stern, Alan D.
  • Tan, Annie
  • Mertz, Alex
  • Birtwistle, Marc R.
ORCID
Venue
PLoS Computational Biology
Publisher
openRxiv
Published
2017-04-19
Volume
14
Issue
3
Pages
e1005985-e1005985
ISSN
1553-734X (ISSN-L)
DOI
10.1101/128801
PMC
PMC5886578
OpenAlex
W2793626409
Cited by
58
References
1
Citations by year
2026: 5 · 2025: 4 · 2024: 3 · 2023: 8 · 2022: 6 · 2021: 10 · 2020: 5 · 2019: 10 · 2018: 6 · 2017: 1
FWCI
2.76
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
  • Clemson University
  • National Institutes of Health (NS046593)
  • National Human Genome Research Institute (U54HG008098)
  • National Institute of General Medical Sciences (T32GM062754, P50GM071558, R01GM104184)
  • National Institute of Neurological Disorders and Stroke (P30NS046593)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › PI3K/AKT/mTOR signaling in cancer
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Kinase Regulation and GTPase Signaling
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
MAPK/ERK pathway, PI3K/Akt signaling, building information modeling, Bad, epidermal growth factor, insulin, S phase entry, AKT inhibition, glioma, combination cancer therapy, multi-omics integration
MeSH terms
Algorithms, Antineoplastic Agents, Humans, Mitogens, Neoplasms, Stochastic Processes, Signal Transduction, Computational Biology, Gene Expression Profiling, Cell Line, Tumor

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