Abstract
AbstractBiological signal transduction networks are central to information processing and regulation of gene expression across all domains of life. Dysregulation is known to cause a wide array of diseases, including cancers. Here I introduce self-consistent signal transduction analysis, which utilizes genome-scale -omics data (specifically transcriptomics and/or proteomics) in order to predict the flow of information through these networks in an individualized manner. I apply the method to the study of endocrine therapy in breast cancer patients, and show that drugs that inhibit estrogen receptor α elicit a wide array of antitumoral effects, and that their most clinically-impactful ones are through the modulation of proliferative signals that control the genes GREB1, HK1, AKT1, MAPK1, AKT2, and NQO1. This method offers researchers a valuable tool in understanding how and why dysregulation occurs, and how perturbations to the network (such as targeted therapies) effect the network itself, and ultimately patient outcomes.
Publisher
Springer Science and Business Media LLC
Reference54 articles.
1. Schrum, A. G. & Gil, D. Robustness and specificity in signal transduction via physiologic protein interaction networks. Clin. Exp. Pharmacol. 2, S3–001 (2012).
2. Birtwistle, M. R. et al. Ligand-dependent responses of the erbb signaling network: experimental and modeling analyses. Mol. Syst. Biol. 3, 144 (2007).
3. Erdem, C. et al. A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling. Nat. Commun. 13, 3555 (2022).
4. Neves, S. R. & Iyengar, R. Modeling of signaling networks. Bioessays 24, 1110–1117 (2002).
5. Hughey, J. J., Lee, T. K. & Covert, M. W. Computational modeling of mammalian signaling networks. Wiley Interdiscip. Rev. Syst. Biol. Med. 2, 194–209 (2010).