ArticleScientific reports2016
Identification of Potential Drug Targets in Cancer Signaling Pathways using Stochastic Logical Models.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 27 citations in OpenAlex.
- Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma.Journal of translational medicine · 2025Article
- Role of ZNF143 and Its Association with Gene Expression Patterns, Noncoding Mutations, and the Immune System in Human Breast Cancer.Life (Basel, Switzerland) · 2022Article
- In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model.Biomolecules · 2022Article
- Conformational perturbation, allosteric modulation of cellular signaling pathways, and disease in P23H rhodopsin.Scientific reports · 2020Article
- NOTCH2/NOTCH3/DLL3/MAML1/ADAM17 signaling network is associated with ovarian cancer.Oncology letters · 2019Article
- Computational discovery of dynamic cell line specific Boolean networks from multiplex time-course data.PLoS computational biology · 2018Article
- Article
- Potential candidate treatment agents for targeting of cholangiocarcinoma identified by gene expression profile analysis.Biomedical reports · 2018Article
- Boolean network modeling in systems pharmacology.Journal of pharmacokinetics and pharmacodynamics · 2018Review
- Hierarchical closeness-based properties reveal cancer survivability and biomarker genes in molecular signaling networks.PloS one · 2018Article
- Article
- Towards targeted combinatorial therapy design for the treatment of castration-resistant prostate cancer.BMC bioinformatics · 2017Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The investigation of vulnerable components in a signaling pathway can contribute to development of drug therapy addressing aberrations in that pathway. Here, an original signaling pathway is derived from the published literature on breast cancer models. New stochastic logical models are then developed to analyze the vulnerability of the components in multiple signalling sub-pathways involved in this signaling cascade. The computational results are consistent with the experimental results, where the selected proteins were silenced using specific siRNAs and the viability of the cells were analyzed 72 hours after silencing. The genes elF4E and NFkB are found to have nearly no effect on the relative cell viability and the genes JAK2, Stat3, S6K, JUN, FOS, Myc, and Mcl1 are effective candidates to influence the relative cell growth. The vulnerabilities of some targets such as Myc and S6K are found to vary significantly depending on the weights of the sub-pathways; this will be indicative of the chosen target to require customization for therapy. When these targets are utilized, the response of breast cancers from different patients will be highly variable because of the known heterogeneities in signaling pathways among the patients. The targets whose vulnerabilities are invariably high might be more universally acceptable targets.
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