Pathway sensitivity analysis for detecting pro‐proliferation activities of oncogenes and tumor suppressors of epidermal growth factor receptor‐extracellular signal‐regulated protein kinase pathway at altered protein levels

Hu, Li; Ung, Choong Yong; Xiao, Hua; Liu, Xiang Hui; Li, Bao Wen; Low, Boon Chuan; Chen, Yu Zong

doi:10.1002/cncr.24485

Cited by 7 publications

(5 citation statements)

References 101 publications

(110 reference statements)

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“…24 A common method of modeling biological pathways is to formulate coupled ordinary differential equations (ODEs). [25][26][27][28][29][30][31][32][33][34][35][36] However, to formulate and solve ODE models, both the network structure and parameter estimates are required. Yeast two-hybrid (Y2H), [37][38][39][40] fluorescence resonance energy transfer (FRET) 41 or chromatin immunoprecipitation (ChIP)-DNA microarray techniques [42][43][44][45] have all been used to identify network interactions.…”

Section: Insight Innovation Integrationmentioning

confidence: 99%

Modeling and analysis of retinoic acid induced differentiation of uncommitted precursor cells

et al. 2011

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Manipulation of differentiation programs has therapeutic potential in a spectrum of human cancers and neurodegenerative disorders. In this study, we integrated computational and experimental methods to unravel the response of a lineage uncommitted precursor cell-line, HL-60, to Retinoic Acid (RA). HL-60 is a human myeloblastic leukemia cell-line used extensively to study human differentiation programs. Initially, we focused on the role of the BLR1 receptor in RA-induced differentiation and G1/0-arrest in HL-60. BLR1, a putative G protein-coupled receptor expressed following RA exposure, is required for RA-induced cell-cycle arrest and differentiation and causes persistent MAPK signaling. A mathematical model of RA-induced cell-cycle arrest and differentiation was formulated and tested against BLR1 wild-type (wt) knock-out and knock-in HL-60 cell-lines with and without RA. The current model described the dynamics of 729 proteins and protein complexes interconnected by 1356 interactions. An ensemble strategy was used to compensate for uncertain model parameters. The ensemble of HL-60 models recapitulated the positive feedback between BLR1 and MAPK signaling. The ensemble of models also correctly predicted Rb and p47phox regulation and the correlation between p21-CDK4-cyclin D formation and G1/0-arrest following exposure to RA. Finally, we investigated the robustness of the HL-60 network architecture to structural perturbations and generated experimentally testable hypotheses for future study. Taken together, the model presented here was a first step toward a systematic framework for analysis of programmed differentiation. These studies also demonstrated that mechanistic network modeling can help prioritize experimental directions by generating falsifiable hypotheses despite uncertainty.

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Section: Insight Innovation Integrationmentioning

confidence: 99%

Modeling and analysis of retinoic acid induced differentiation of uncommitted precursor cells

et al. 2011

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“…Our mathematical model, was derived from the conventional models313233343538 and drug-target binding kinetics34 with the parameters fitted to the median IC50 values of potent inhibitors. Variation of the IC50 from 5 nM to 50 nM led to comparable degree of variations in the predicted GI50 values (9–95 nM, 19–195 nM and 10–99 nM for EGFR, BRaf and MEK inhibitor respectively).…”

Section: Resultsmentioning

confidence: 99%

“…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).…”

mentioning

confidence: 99%

“…First, several kinases in this pathway have been targeted by individual inhibitor drugs and drug combinations with available experimental drug response and CI data394041424344. Secondly, it is one of the pathways with well-established mathematical models313233343538, ideal for developing and testing drug-targeted pathway mathematical models. The kinase inhibitor drugs included in our mathematical model are EGFR, BRaf and MEK inhibitors, which together with their combinations have been clinically used or tested for the treatment of melanoma, colon, gastric, pancreatic, non-small-cell-lung-cancer (NSCLC) and other cancers394041424344.…”

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confidence: 99%

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

Huang

Jiang

Chen

2017

Sci Rep

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Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.

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“…For example, dynamical modeling helped uncover the importance of phosphorylation events and effects due to gain or loss of protein-protein interactions in establishing novel pathway crosstalk. Using mass action law, our previous studies showed that dynamical modeling is a powerful approach to identify the “Achilles heel” of a cancer network, or genes sensitive to perturbations that may alter disease outcome [ 186 ]. Dynamical modeling may be applied to neuroblastomas, once key regulatory modules have been discerned via network-based modeling.…”

Section: Modeling Neuroblastoma-derived Big Datamentioning

confidence: 99%

Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology

Salazar

Balczewski

Ung

et al. 2016

IJMS

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Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring “big data” applications in pediatric oncology. Computational strategies derived from big data science–network- and machine learning-based modeling and drug repositioning—hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which “big data” and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.

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Pathway sensitivity analysis for detecting pro‐proliferation activities of oncogenes and tumor suppressors of epidermal growth factor receptor‐extracellular signal‐regulated protein kinase pathway at altered protein levels

Cited by 7 publications

References 101 publications

Modeling and analysis of retinoic acid induced differentiation of uncommitted precursor cells

Modeling and analysis of retinoic acid induced differentiation of uncommitted precursor cells

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

Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology

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