Shin_2018_EGFR-PYK2-c-Met interaction network_model

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Short description
Systems modelling of the EGFR-PYK2-c-Met interaction network predicted and prioritized synergistic drug combinations for Triple-negative breast cancer
Related Publication
  • Systems modelling of the EGFR-PYK2-c-Met interaction network predicts and prioritizes synergistic drug combinations for triple-negative breast cancer.
  • Shin SY, Müller AK, Verma N, Lev S, Nguyen LK
  • PLoS computational biology , 6/ 2018 , Volume 14 , Issue 6 , pages: e1006192 , PubMed ID: 29920512
  • Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia.
  • Prediction of drug combinations that effectively target cancer cells is a critical challenge for cancer therapy, in particular for triple-negative breast cancer (TNBC), a highly aggressive breast cancer subtype with no effective targeted treatment. As signalling pathway networks critically control cancer cell behaviour, analysis of signalling network activity and crosstalk can help predict potent drug combinations and rational stratification of patients, thus bringing therapeutic and prognostic values. We have previously showed that the non-receptor tyrosine kinase PYK2 is a downstream effector of EGFR and c-Met and demonstrated their crosstalk signalling in basal-like TNBC. Here we applied a systems modelling approach and developed a mechanistic model of the integrated EGFR-PYK2-c-Met signalling network to identify and prioritize potent drug combinations for TNBC. Model predictions validated by experimental data revealed that among six potential combinations of drug pairs targeting the central nodes of the network, including EGFR, c-Met, PYK2 and STAT3, co-targeting of EGFR and PYK2 and to a lesser extent of EGFR and c-Met yielded strongest synergistic effect. Importantly, the synergy in co-targeting EGFR and PYK2 was linked to switch-like cell proliferation-associated responses. Moreover, simulations of patient-specific models using public gene expression data of TNBC patients led to predictive stratification of patients into subgroups displaying distinct susceptibility to specific drug combinations. These results suggest that mechanistic systems modelling is a powerful approach for the rational design, prediction and prioritization of potent combination therapies for individual patients, thus providing a concrete step towards personalized treatment for TNBC and other tumour types.
Submitter of the first revision: Johannes Meyer
Submitter of this revision: Johannes Meyer
Modellers: Johannes Meyer

Metadata information

is (2 statements)
BioModels Database BIOMD0000000826
BioModels Database MODEL1909270001

isDescribedBy (1 statement)
PubMed 29920512

hasTaxon (1 statement)
Taxonomy Homo sapiens

hasProperty (2 statements)

Curation status


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Model files

Shin2018.xml SBML L2V4 Representation of Shin2018 - Systems modelling of the EGFR-PYK2-c-Met interaction network 140.90 KB Preview | Download

Additional files

Shin2018.cps COPASI file of Shin2018 - Systems modelling of the EGFR-PYK2-c-Met interaction network 215.49 KB Preview | Download
Shin2018.sedml SED-ML file of Shin2018 - Systems modelling of the EGFR-PYK2-c-Met interaction network 3.86 KB Preview | Download

  • Model originally submitted by : Johannes Meyer
  • Submitted: Sep 27, 2019 1:45:39 PM
  • Last Modified: Sep 27, 2019 1:45:39 PM
  • Version: 2 public model Download this version
    • Submitted on: Sep 27, 2019 1:45:39 PM
    • Submitted by: Johannes Meyer
    • With comment: Automatically added model identifier BIOMD0000000826
: Variable used inside SBML models

Species Initial Concentration/Amount

4.67164 nmol

Protein Tyrosine Kinase
9.29922 nmol

Protein Tyrosine Phosphatase
0.49418 nmol

Protein Tyrosine Kinase
2.51016 nmol

PR:P08581 ; messenger RNA
0.0228566 nmol

E3 Ubiquitin-Protein Ligase CBL
10.4757 nmol

signal transducer and activator of transcription 3
1.17843 nmol
Reactions Rate Parameters
cMET => ; pCbl rootCompartment*(kdeg16+kc16*pCbl*rootCompartment)*cMET*rootCompartment/(Km16+cMET*rootCompartment)/rootCompartment kc16 = 1.1749; Km16 = 528.445; kdeg16 = 24.4906
PYK2 => pPYK2; pEGFR, pcMET rootCompartment*(kc9a*pEGFR*rootCompartment+kc9b*pcMET*rootCompartment/(1+EMD/Ki9))*PYK2*rootCompartment/(Km9+PYK2*rootCompartment)/rootCompartment EMD = 0.0; Km9 = 34.914; kc9a = 0.463447; kc9b = 0.988553; Ki9 = 1.65577
aPTP => rootCompartment*Vmax22*aPTP*rootCompartment/(Km22+aPTP*rootCompartment)/rootCompartment Vmax22 = 0.034914; Km22 = 46.4515
PYK2 => rootCompartment*kdeg8*PYK2*rootCompartment/rootCompartment kdeg8 = 0.0566239
pPYK2 => PYK2; aPTP rootCompartment*(Vmax10+kc10*aPTP*rootCompartment)*pPYK2*rootCompartment/(Km10+pPYK2*rootCompartment)/rootCompartment kc10 = 0.00610942; Vmax10 = 0.530884; Km10 = 9.14113
=> cMETm; pSTAT3 rootCompartment*(Vs13+Vmax13*pSTAT3*rootCompartment/(Km13+pSTAT3*rootCompartment))/rootCompartment Vs13 = 0.0937562; Vmax13 = 0.354813; Km13 = 38.7258
cMET => pcMET rootCompartment*(kc17*HGF+caHGF)*cMET*rootCompartment/(Km17+cMET*rootCompartment)/rootCompartment Km17 = 9.81748; HGF = 0.0; kc17 = 8.10961E-4; caHGF = 0.0090365
pCbl => ; aPTP rootCompartment*(Vmax20+kc20*aPTP*rootCompartment)*pCbl*rootCompartment/(Km20+pCbl*rootCompartment)/rootCompartment Km20 = 24.322; Vmax20 = 0.0483059; kc20 = 35.6451
=> pSTAT3; STAT3uStattic, pPYK2 rootCompartment*kc11*pPYK2*rootCompartment/(1+PF396/Ki3b)*((STAT3tot-pSTAT3*rootCompartment)-STAT3uStattic*rootCompartment)/(Km11+((STAT3tot-pSTAT3*rootCompartment)-STAT3uStattic*rootCompartment))/rootCompartment PF396 = 0.0; kc11 = 0.321366; STAT3tot = 144.212; Ki3b = 1.0; Km11 = 20.6063
pcMET => cMET rootCompartment*Vmax18*pcMET*rootCompartment/(Km18+pcMET*rootCompartment)/rootCompartment Km18 = 9.95405; Vmax18 = 0.0606736
Curator's comment:
(added: 27 Sep 2019, 13:45:31, updated: 27 Sep 2019, 13:45:31)
Reproduced plots of Figure 1 K-N in the original publication, control curves. Model simulated and plot produced using COPASI 4.24 (Build 197).