A new validated mathematical model of the Wnt signalling pathway predicts effective combinational therapy by sFRP and Dkk

Abstract

The Wnt signalling pathway controls cell proliferation and differentiation, and its deregulation is implicated in different diseases including cancer. Learning how to manipulate this pathway could substantially contribute to the development of therapies. We developed a mathematical model describing the initial sequence of events in the Wnt pathway, from ligand binding to β-catenin accumulation, and the effects of inhibitors, such as sFRPs (secreted Frizzled-related proteins) and Dkk (Dickkopf). Model parameters were retrieved from experimental data reported previously. The model was retrospectively validated by accurately predicting the effects of Wnt3a and sFRP1 on β-catenin levels in two independent published experiments (R(2) between 0.63 and 0.91). Prospective validation was obtained by testing the model's accuracy in predicting the effect of Dkk1 on Wnt-induced β-catenin accumulation (R(2)≈0.94). Model simulations under different combinations of sFRP1 and Dkk1 predicted a clear synergistic effect of these two inhibitors on β-catenin accumulation, which may point towards a new treatment avenue. Our model allows precise calculation of the effect of inhibitors applied alone or in combination, and provides a flexible framework for identifying potential targets for intervention in the Wnt signalling pathway.

MicroRNA-744 promotes prostate cancer progression through aberrantly activating Wnt/β-catenin signaling

“…Until now, increasing evidences have demonstrated that multiple negative modulators could antagonize Wnt/β-catenin signaling through recruiting different mechanisms. For example, secreted Frizzled-related proteins (SFRPs) and Wnt inhibitory factor-1 (WIF-1) are able to directly bind Wnt proteins and block Wnt/ β-catenin pathway, and Dickkopf1 (DKK1) negatively regulates Wnt signaling via interacting with Wnt co-receptors LRP5/LRP6 whereas GSK3β displays its suppression on Wnt/β-catenin pathway through phosphorylating β-catenin, leads to the proteasome proteolytic degradation of β-catenin [28, 33, 34]. Moreover, nuclear transcriptionalsuppressors including transducin-like enhancer of split 3 (TLE3) inhibit the transcriptional activity of LEF/TCF [35].…”

Computational systems biology approaches to anti-angiogenic cancer therapeutics

“…Antagonists of Wnt signaling include secreted Frizzled-related protein (sFRP), Wnt inhibitory factor 1 (WIF-1), and Dickkopf (Dkk). Mathematical models have been used to investigate the effect of inhibiting Wnt via its antagonists and, in one example, modeling was applied to investigate Wnt inhibition via Wnt3a, sFRP, and Dkk [74]. The effect of inhibiting Wnt signaling was quantified by estimating the accumulation of β-catenin, which leads to the transcription of various genes involved in EC proliferation, migration, and other processes.…”

“…Additionally, the model simulated the effect of Dkk alone and in combination with sFRP. Interestingly, Dkk and sFRP were predicted to have a synergistic effect on inhibiting β-catenin accumulation [74], and this work could lead to a new anti-angiogenic treatment strategy.…”

Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway

“…Computational methods have been developed for predicting drug combination effects from gene expression profiles of drug-treated samples1112131415 and simulation of drug-targeted signaling161718192021 and metabolic22232425 pathways. In particular, simulation of drug-targeted pathways is potentially useful for predicting CIs1726, as demonstrated by the successful applications of the chemogenomic profile based models2728 and the statistically-inferenced network models2930 for the prediction of synergistic effects of drug combinations.…”

“…These also provide useful knowledge for developing drug or drug combination targeted mathematical models for a number of pathways targeted by drugs and drug combinations (e.g. EGFR-ERK3132333435, apoptosis3637, NFκB1617, Wnt19 and disease-relevant metabolic22232425 pathways).…”