ArticlePLoS computational biology2018
A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 35 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
35 citing papers in PubMed, 57 citations in OpenAlex.
- Review
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- A comprehensive review of computational cell cycle models in guiding cancer treatment strategies.NPJ systems biology and applications · 2024Review
- Predicting anti-cancer drug combination responses with a temporal cell state network model.PLoS computational biology · 2023Article
- Cell state-directed therapy - epigenetic modulation of gene transcription demonstrated with a quantitative systems pharmacology model of temozolomide.CPT: pharmacometrics & systems pharmacology · 2023Article
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- MEMMAL: A tool for expanding large-scale mechanistic models with machine learned associations and big datasets.Frontiers in systems biology · 2023Article
- SynDISCO: A Mechanistic Modeling-Based Framework for Predictive Prioritization of Synergistic Drug Combinations Targeting Cell Signalling Networks.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format.Bioinformatics advances · 2023Article
- Relating individual cell division events to single-cell ERK and Akt activity time courses.Scientific reports · 2022Article
- Systems biology of angiogenesis signaling: Computational models and omics.WIREs mechanisms of disease · 2022Review
- A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling.Nature communications · 2022Article
- OBIF: an omics-based interaction framework to reveal molecular drivers of synergy.NAR genomics and bioinformatics · 2022Article
- Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.Biophysics reviews · 2022Review
- Mini-batch optimization enables training of ODE models on large-scale datasets.Nature communications · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.