ArticlePLoS computational biology2018
Systems modelling of the EGFR-PYK2-c-Met interaction network predicts and prioritizes synergistic drug combinations for triple-negative breast cancer.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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.
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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
18 citing papers in PubMed, 36 citations in OpenAlex.
- Article
- The mechano-immunological landscape in the tumor microenvironment: From mechanical sensing to a new therapeutic paradigm.Materials today. Bio · 2026Review
- DeepDrugs: a mechanism-aware tri-linear attention framework for synergistic drug-combination prediction.Briefings in bioinformatics · 2026Article
- NEDD4 signaling: a new frontier in the diagnosis and treatment of breast and ovarian cancers.Medical oncology (Northwood, London, England) · 2025Review
- Article
- Integrative modeling uncovers p21-driven drug resistance and prioritizes therapies for PIK3CA-mutant breast cancer.NPJ precision oncology · 2024Article
- Integrative applications of network pharmacology and molecular docking: An herbal formula ameliorates H9c2 cells injury through pyroptosis.Journal of ginseng research · 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
- Mathematically Modeling the Effect of Endocrine and Cdk4/6 Inhibitor Therapies on Breast Cancer Cells.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Review
- Article
- Mouse Modeling Dissecting Macrophage-Breast Cancer Communication Uncovered Roles of PYK2 in Macrophage Recruitment and Breast Tumorigenesis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Feedback, Crosstalk and Competition: Ingredients for Emergent Non-Linear Behaviour in the PI3K/mTOR Signalling Network.International journal of molecular sciences · 2021Review
- Potentiating Therapeutic Effects of Epidermal Growth Factor Receptor Inhibition in Triple-Negative Breast Cancer.Pharmaceuticals (Basel, Switzerland) · 2021Review
- The AXL-PYK2-PKCα axis as a nexus of stemness circuits in TNBC.Life science alliance · 2021Article
- Dual inhibition of RNAi therapeutic miR-26a-5p targeting cMet and immunotherapy against EGFR in endometrial cancer treatment.Annals of translational medicine · 2021Article
- Network rewiring, adaptive resistance and combating strategies in breast cancer.Cancer drug resistance (Alhambra, Calif.) · 2019Review
- Mechanistically detailed systems biology modeling of the HGF/Met pathway in hepatocellular carcinoma.NPJ systems biology and applications · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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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 Socratic graph.