Feedforward and feedback regulation of the MAPK and PI3K oscillatory circuit in breast cancer

Hu, Huizhong; Goltsov, Alexey; Bown, James L.; Sims, Andrew H.; Langdon, Simon P.; Harrison, David J.; Faratian, Dana

doi:10.1016/j.cellsig.2012.09.014

Cited by 23 publications

(19 citation statements)

References 23 publications

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“…These oscillations are caused by the time delay within the negative feedback loop and also require some degree of ultrasensitivity within the cascade (Kholodenko 2000). In fact, oscillations in the MAPK/ERK pathway were recently discovered experimentally, confirming previous theoretical predictions (Nakayama et al 2008;Shankaran et al 2009;Shin et al 2009;Hu et al 2013). An intriguing systems property of protein modification cascades and even simpler signaling systems is excitable and overshoot signaling responses to transient stimuli .…”

Section: Intricate Rtk Network Dynamics Are Brought About By Feedbacksupporting

confidence: 75%

Complexity of Receptor Tyrosine Kinase Signal Processing

Volinsky

Kholodenko

2013

Cold Spring Harbor Perspectives in Biology

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Our knowledge of molecular mechanisms of receptor tyrosine kinase (RTK) signaling advances with ever-increasing pace. Yet our understanding of how the spatiotemporal dynamics of RTK signaling control specific cellular outcomes has lagged behind. Systems-centered experimental and computational approaches can help reveal how overlapping networks of signal transducers downstream of RTKs orchestrate specific cell-fate decisions. We discuss how RTK network regulatory structures, which involve the immediate posttranslational and delayed transcriptional controls by multiple feed forward and feedback loops together with pathway cross talk, adapt cells to the combinatorial variety of external cues and conditions. This intricate network circuitry endows cells with emerging capabilities for RTK signal processing and decoding. We illustrate how mathematical modeling facilitates our understanding of RTK network behaviors by unraveling specific systems properties, including bistability, oscillations, excitable responses, and generation of intricate landscapes of signaling activities.

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Section: Intricate Rtk Network Dynamics Are Brought About By Feedbacksupporting

confidence: 75%

Complexity of Receptor Tyrosine Kinase Signal Processing

Volinsky

Kholodenko

2013

Cold Spring Harbor Perspectives in Biology

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“…Comparison of the computational (open bars) and experimental (filled bars) data for pAKT ( A ) and pERK ( B ) in SKOV3 ovarian xenograft tumour after 14 days [28] and pAKT ( C ) and pERK ( D ) in MCF7 and MCF7-HER2-18 cells [57,58] treated by trastuzumab, pertuzumab, and their combination.…”

Section: Resultsmentioning

confidence: 99%

“…To validate our model we applied it to describe drug effects at the boundary HER3/HER2 composition: low (HER2 overexpression) and equimolar levels, and then compared theoretical results with experimental data. To exclude the effect of alterations in receptor composition due to treatment we considered in vitro experimental data on MCF7 and HER2 overexpressing MCF7-HER2-18 cells [57,58]. …”

Section: Resultsmentioning

confidence: 99%

“…To consider this effect more fully the model should be developed further to include the mechanisms of genetic feedback control of receptor co-expression and close signalling activation loops, and our work [58] provides the very beginnings of this development. Through the extended modelling framework it would be possible to then analyse the involvement of different outcomes of ErbB signalling, particularly pAKT and pERK, in those regulatory feedbacks.…”

Section: Resultsmentioning

confidence: 99%

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Systems Analysis of Drug-Induced Receptor Tyrosine Kinase Reprogramming Following Targeted Mono- and Combination Anti-Cancer Therapy

Goltsov

Deeni

Khalil

et al. 2014

Cells

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The receptor tyrosine kinases (RTKs) are key drivers of cancer progression and targets for drug therapy. A major challenge in anti-RTK treatment is the dependence of drug effectiveness on co-expression of multiple RTKs which defines resistance to single drug therapy. Reprogramming of the RTK network leading to alteration in RTK co-expression in response to drug intervention is a dynamic mechanism of acquired resistance to single drug therapy in many cancers. One route to overcome this resistance is combination therapy. We describe the results of a joint in silico, in vitro, and in vivo investigations on the efficacy of trastuzumab, pertuzumab and their combination to target the HER2 receptors. Computational modelling revealed that these two drugs alone and in combination differentially suppressed RTK network activation depending on RTK co-expression. Analyses of mRNA expression in SKOV3 ovarian tumour xenograft showed up-regulation of HER3 following treatment. Considering this in a computational model revealed that HER3 up-regulation reprograms RTK kinetics from HER2 homodimerisation to HER3/HER2 heterodimerisation. The results showed synergy of the trastuzumab and pertuzumab combination treatment of the HER2 overexpressing tumour can be due to an independence of the combination effect on HER3/HER2 composition when it changes due to drug-induced RTK reprogramming.

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“…Recently, Hu at al. [32] observed oscillations in MAPK and PI3K signaling cascades upon stimulation of breast cancer cells by EGF. A typical period of observed oscillations was about 20 minutes.…”

Section: Introductionmentioning

confidence: 99%

Minimal oscillating subnetwork in the Huang-Ferrell model of the MAPK cascade

et al. 2017

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Prompted by the recent growing evidence of oscillatory behavior involving MAPK cascades we present a systematic approach of analyzing models and elucidating the nature of biochemical oscillations based on reaction network theory. In particular, we formulate a minimal biochemically consistent mass action subnetwork of the Huang-Ferrell model of the MAPK signalling that provides an oscillatory response when a parameter controlling the activation of the top-tier kinase is varied. Such dynamics are either intertwined with or separated from the earlier found bistable/hysteretic behavior in this model. Using the theory of stability of stoichiometric networks, we reduce the original MAPK model, convert kinetic to convex parameters and examine those properties of the minimal subnetwork that underlie the oscillatory dynamics. We also use the methods of classification of chemical oscillatory networks to explain the rhythmic behavior in physicochemical terms, i.e., we identify of the role of individual biochemical species in positive and negative feedback loops and describe their coordinated action leading to oscillations. Our approach provides an insight into dynamics without the necessity of knowing rate coefficients and thus is useful prior the statistical evaluation of parameters.

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Feedforward and feedback regulation of the MAPK and PI3K oscillatory circuit in breast cancer

Cited by 23 publications

References 23 publications

Complexity of Receptor Tyrosine Kinase Signal Processing

Complexity of Receptor Tyrosine Kinase Signal Processing

Systems Analysis of Drug-Induced Receptor Tyrosine Kinase Reprogramming Following Targeted Mono- and Combination Anti-Cancer Therapy

Minimal oscillating subnetwork in the Huang-Ferrell model of the MAPK cascade

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