Circulating microRNA analysis in a prospective co-clinical trial identifies biomarker of response and mechanisms of resistance to regorafenib in colorectal cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE248663 | GEO | 2024/01/25
REPOSITORIES: GEO
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Project description:Developing effective model systems to evaluate new potential chemotherapeutic reagents is critical. Three-dimensional cell cultures, such as spheroids, aid in bridging the gap between commonly used monolayer cell cultures and significantly more complex and expensive animal models. Spheroid model systems contain pathophysiological and chemical gradients similar to an in vivo tumor, which ultimately form distinct cellular subpopulations. In the spatial SILAC model, labels are pulsed into the spheroid at discrete time windows during development. These media pulses result in labeled proteins in distinct regions of the spheroid, which allows for tracing of quantitative proteomic changes to be correlated to different cellular subregions. In this study, we use the Spatial SILAC model to evaluate the proteomic response of a colon carcinoma spheroid to the multikinase inhibitor Regorafenib. We determined regorafenib to be an effective kinase inhibitor which significantly altered whole spheroid proliferation and resulted in increased apoptosis when the signal was averaged for all cells in the culture. However, when regorafenib treatment is more closely examined among the cellular subpopulations, drastic differences are observed for how the drug impacted the proteome of the necrotic spheroid core and the proliferating outer regions. Whole spheroid and outer region analysis shows that regorafenib treatment inhibited critical pathways such as mTOR signaling, ERK/MAPK signaling, and colorectal cancer metastasis signaling. However, analysis of the core shows that regorafenib had an entirely different effect, including upregulation of MAPK1 and KRAS, possibly indicating drug resistance within these late apoptotic cells. Ultimately, these combinatory studies can be used to further understanding of drug metabolism is different cellular subpopulations and provide valuable information to ultimately improve the accuracy of therapeutic testing.
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