RT Journal Article
SR Electronic
T1 Predictive data-driven modeling of C-terminal tyrosine function in the EGFR signaling network
JF Life Science Alliance
JO Life Sci. Alliance
FD Life Science Alliance LLC
SP e202201466
DO 10.26508/lsa.202201466
VO 6
IS 8
A1 Jacqueline S Gerritsen
A1 Joseph S Faraguna
A1 Rudy Bonavia
A1 Frank B Furnari
A1 Forest M White
YR 2023
UL https://www.life-science-alliance.org/content/6/8/e202201466.abstract
AB The epidermal growth factor receptor (EGFR) has been studied extensively because of its critical role in cellular signaling and association with disease. Previous models have elucidated interactions between EGFR and downstream adaptor proteins or showed phenotypes affected by EGFR. However, the link between specific EGFR phosphorylation sites and phenotypic outcomes is still poorly understood. Here, we employed a suite of isogenic cell lines expressing site-specific mutations at each of the EGFR C-terminal phosphorylation sites to interrogate their role in the signaling network and cell biological response to stimulation. Our results demonstrate the resilience of the EGFR network, which was largely similar even in the context of multiple Y-to-F mutations in the EGFR C-terminal tail, while also revealing nodes in the network that have not previously been linked to EGFR signaling. Our data-driven model highlights the signaling network nodes associated with distinct EGF-driven cell responses, including migration, proliferation, and receptor trafficking. Application of this same approach to less-studied RTKs should provide a plethora of novel associations that should lead to an improved understanding of these signaling networks.