Receptor tyrosine kinases fall into distinct classes based on their inferred signaling networks

• Published in: Science signaling Vol. 6; no. 284; p. ra58
• Main Authors: Wagner, Joel P, Wolf-Yadlin, Alejandro, Sevecka, Mark, Grenier, Jennifer K, Root, David E, Lauffenburger, Douglas A, MacBeath, Gavin
• Format: Journal Article
• Language: English
• Published: United States 16.07.2013
• Subjects: Cell Line, Tumor; Gene Expression Regulation, Enzymologic - physiology; HEK293 Cells; Humans; Models, Biological; Receptor Protein-Tyrosine Kinases - genetics; Receptor Protein-Tyrosine Kinases - metabolism; Signal Transduction - physiology
• ISSN: 1937-9145
• DOI: 10.1126/scisignal.2003994

Although many anticancer drugs that target receptor tyrosine kinases (RTKs) provide clinical benefit, their long-term use is limited by resistance that is often attributed to increased abundance or activation of another RTK that compensates for the inhibited receptor. To uncover common and unique features in the signaling networks of RTKs, we measured time-dependent signaling in six isogenic cell lines, each expressing a different RTK as downstream proteins were systematically perturbed by RNA interference. Network models inferred from the data revealed a conserved set of signaling pathways and RTK-specific features that grouped the RTKs into three distinct classes: (i) an EGFR/FGFR1/c-Met class constituting epidermal growth factor receptor, fibroblast growth factor receptor 1, and the hepatocyte growth factor receptor c-Met; (ii) an IGF-1R/NTRK2 class constituting insulin-like growth factor 1 receptor and neurotrophic tyrosine receptor kinase 2; and (iii) a PDGFRβ class constituting platelet-derived growth factor receptor β. Analysis of cancer cell line data showed that many RTKs of the same class were coexpressed and that increased abundance of an RTK or its cognate ligand frequently correlated with resistance to a drug targeting another RTK of the same class. In contrast, abundance of an RTK or ligand of one class generally did not affect sensitivity to a drug targeting an RTK of a different class. Thus, classifying RTKs by their inferred networks and then therapeutically targeting multiple receptors within a class may delay or prevent the onset of resistance.