TY - JOUR
T1 - Feasibility of phosphoproteomics to uncover oncogenic signalling in secreted extracellular vesicles using glioblastoma-EGFRVIII cells as a model
AU - Bijnsdorp, Irene V.
AU - Schelfhorst, Tim
AU - Luinenburg, Mark
AU - Rolfs, Frank
AU - Piersma, Sander R.
AU - de Haas, Richard R.
AU - Pham, Thang V.
AU - Jimenez, Connie R.
PY - 2021/2/10
Y1 - 2021/2/10
N2 - Cancer cells secrete extracellular vesicles (EVs) that contain molecular information, including proteins and RNA. Oncogenic signalling can be transferred via the cargo of EVs to recipient cells and may influence the behaviour of neighbouring cells or cells at a distance. This cargo may contain cancer drivers, such as EGFR, and also phosphorylated (activated) components of oncogenic signalling cascades. Till date, the cancer EV phosphoproteome has not been studied in great detail. In the present study, we used U87 and U87EGFRvIII cells as a model to explore EV oncogenic signalling components in comparison to the cellular profile. EVs were isolated using the VN96 ME-kit and subjected to LC-MS/MS based phosphoproteomics and dedicated bioinformatics. Expression of (phosphorylated)-EGFR was highly increased in EGFRvIII overexpressing cells and their secreted EVs. The increased phosphorylated proteins in both cells and EVs were associated with activated components of the EGFR-signalling cascade and included EGFR, AKT2, MAPK8, SMG1, MAP3K7, DYRK1A, RPS6KA3 and PAK4 kinases. In conclusion, EVs harbour oncogenic signalling networks including multiple activated kinases including EGFR, AKT and mTOR. Significance: Extracellular vesicles (EVs) are biomarker treasure troves and are widely studied for their biomarker content in cancer. However, little research has been done on the phosphorylated protein profile within cancer EVs. In the current study, we demonstrate that EVs that are secreted by U87-EGFRvIII mutant glioblastoma cells contain high levels of oncogenic signalling networks. These networks contain multiple activated (phosphorylated) kinases, including EGFR, MAPK, AKT and mTOR.
AB - Cancer cells secrete extracellular vesicles (EVs) that contain molecular information, including proteins and RNA. Oncogenic signalling can be transferred via the cargo of EVs to recipient cells and may influence the behaviour of neighbouring cells or cells at a distance. This cargo may contain cancer drivers, such as EGFR, and also phosphorylated (activated) components of oncogenic signalling cascades. Till date, the cancer EV phosphoproteome has not been studied in great detail. In the present study, we used U87 and U87EGFRvIII cells as a model to explore EV oncogenic signalling components in comparison to the cellular profile. EVs were isolated using the VN96 ME-kit and subjected to LC-MS/MS based phosphoproteomics and dedicated bioinformatics. Expression of (phosphorylated)-EGFR was highly increased in EGFRvIII overexpressing cells and their secreted EVs. The increased phosphorylated proteins in both cells and EVs were associated with activated components of the EGFR-signalling cascade and included EGFR, AKT2, MAPK8, SMG1, MAP3K7, DYRK1A, RPS6KA3 and PAK4 kinases. In conclusion, EVs harbour oncogenic signalling networks including multiple activated kinases including EGFR, AKT and mTOR. Significance: Extracellular vesicles (EVs) are biomarker treasure troves and are widely studied for their biomarker content in cancer. However, little research has been done on the phosphorylated protein profile within cancer EVs. In the current study, we demonstrate that EVs that are secreted by U87-EGFRvIII mutant glioblastoma cells contain high levels of oncogenic signalling networks. These networks contain multiple activated (phosphorylated) kinases, including EGFR, MAPK, AKT and mTOR.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85098469192&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/33307249
U2 - https://doi.org/10.1016/j.jprot.2020.104076
DO - https://doi.org/10.1016/j.jprot.2020.104076
M3 - Article
C2 - 33307249
SN - 1874-3919
VL - 232
JO - Journal of Proteomics
JF - Journal of Proteomics
M1 - 104076
ER -