Literature DB >> 33643466

Pre-existing Cell States Control Heterogeneity of Both EGFR and CXCR4 Signaling.

Phillip C Spinosa1, Patrick C Kinnunen1, Brock A Humphries2, Gary D Luker2,3,4, Kathryn E Luker2,5, Jennifer J Linderman1,3.   

Abstract

INTRODUCTION: CXCR4 and epidermal growth factor receptor (EGFR) represent two major families of receptors, G-protein coupled receptors and receptor tyrosine kinases, with central functions in cancer. While utilizing different upstream signaling molecules, both CXCR4 and EGFR activate kinases ERK and Akt, although single-cell activation of these kinases is markedly heterogeneous. One hypothesis regarding the origin of signaling heterogeneity proposes that intercellular variations arise from differences in pre-existing intracellular states set by extrinsic noise. While pre-existing cell states vary among cells, each pre-existing state defines deterministic signaling outputs to downstream effectors. Understanding causes of signaling heterogeneity will inform treatment of cancers with drugs targeting drivers of oncogenic signaling.
METHODS: We built a single-cell computational model to predict Akt and ERK responses to CXCR4- and EGFR-mediated stimulation. We investigated signaling heterogeneity through these receptors and tested model predictions using quantitative, live-cell time-lapse imaging.
RESULTS: We show that the pre-existing cell state predicts single-cell signaling through both CXCR4 and EGFR. Computational modeling reveals that the same set of pre-existing cell states explains signaling heterogeneity through both EGFR and CXCR4 at multiple doses of ligands and in two different breast cancer cell lines. The model also predicts how phosphatidylinositol-3-kinase (PI3K) targeted therapies potentiate ERK signaling in certain breast cancer cells and that low level, combined inhibition of MEK and PI3K ablates potentiated ERK signaling.
CONCLUSIONS: Our data demonstrate that a conserved motif exists for EGFR and CXCR4 signaling and suggest potential clinical utility of the computational model to optimize therapy. © Biomedical Engineering Society 2020.

Entities:  

Keywords:  Computational modeling; Heterogeneity; Kinase; Molecular imaging; Single cell

Year:  2020        PMID: 33643466      PMCID: PMC7878609          DOI: 10.1007/s12195-020-00640-1

Source DB:  PubMed          Journal:  Cell Mol Bioeng        ISSN: 1865-5025            Impact factor:   2.321


  57 in total

1.  CXCR4, but not CXCR7, discriminates metastatic behavior in non-small cell lung cancer cells.

Authors:  Young H Choi; Marie D Burdick; Brett A Strieter; Borna Mehrad; Robert M Strieter
Journal:  Mol Cancer Res       Date:  2013-09-11       Impact factor: 5.852

2.  On the nature of low- and high-affinity EGF receptors on living cells.

Authors:  Ferruh Ozcan; Peter Klein; Mark A Lemmon; Irit Lax; Joseph Schlessinger
Journal:  Proc Natl Acad Sci U S A       Date:  2006-03-29       Impact factor: 11.205

Review 3.  Role of CXCL12 and CXCR4 in normal cerebellar development and medulloblastoma.

Authors:  Patricia Midori Murobushi Ozawa; Carolina Batista Ariza; Cintya Mayumi Ishibashi; Thiago Cezar Fujita; Bruna Karina Banin-Hirata; Julie Massayo Maeda Oda; Maria Angelica Ehara Watanabe
Journal:  Int J Cancer       Date:  2014-12-01       Impact factor: 7.396

4.  Live-cell measurements of kinase activity in single cells using translocation reporters.

Authors:  Takamasa Kudo; Stevan Jeknić; Derek N Macklin; Sajia Akhter; Jacob J Hughey; Sergi Regot; Markus W Covert
Journal:  Nat Protoc       Date:  2017-12-21       Impact factor: 13.491

Review 5.  mTOR Signaling in Growth, Metabolism, and Disease.

Authors:  Robert A Saxton; David M Sabatini
Journal:  Cell       Date:  2017-03-09       Impact factor: 41.582

6.  ADAM-mediated amphiregulin shedding and EGFR transactivation.

Authors:  S Kasina; P A Scherle; C L Hall; J A Macoska
Journal:  Cell Prolif       Date:  2009-09-07       Impact factor: 6.831

7.  High-sensitivity measurements of multiple kinase activities in live single cells.

Authors:  Sergi Regot; Jacob J Hughey; Bryce T Bajar; Silvia Carrasco; Markus W Covert
Journal:  Cell       Date:  2014-06-19       Impact factor: 41.582

8.  Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters.

Authors:  Simon van Mourik; Cajo Ter Braak; Hans Stigter; Jaap Molenaar
Journal:  PeerJ       Date:  2014-06-17       Impact factor: 2.984

Review 9.  Emerging functions of the EGFR in cancer.

Authors:  Sara Sigismund; Daniele Avanzato; Letizia Lanzetti
Journal:  Mol Oncol       Date:  2017-11-27       Impact factor: 6.603

Review 10.  Resistance to mTORC1 Inhibitors in Cancer Therapy: From Kinase Mutations to Intratumoral Heterogeneity of Kinase Activity.

Authors:  Seraina Faes; Nicolas Demartines; Olivier Dormond
Journal:  Oxid Med Cell Longev       Date:  2017-02-09       Impact factor: 6.543

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