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Spatiotemporal modeling of chemoresistance evolution in breast tumors uncovers dependencies on SLC38A7 and SLC46A1

ABSTRACT: Spatiotemporal modeling of chemoresistance evolution in breast tumors uncovers dependencies on SLC38A7 and SLC46A1

PROVIDER: PRJNA892139 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR21975900_1.fastq.gz	Fastqsanger.gz
	SRR21975900_2.fastq.gz	Fastqsanger.gz
	SRR21975901_1.fastq.gz	Fastqsanger.gz
	SRR21975901_2.fastq.gz	Fastqsanger.gz
	SRR21975902_1.fastq.gz	Fastqsanger.gz

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Project description:Chemoresistance is a major clinical challenge in management of glioblastoma (GBM) Temozolomide (TMZ) is the chemotherapeutic drug of choice for GBM; however, the therapeutic effect of TMZ is limited due to the development of resistance. Recapitulating GBM chemoresistance in a controlled environment is thus essential in understanding the mechanism of chemoresistance. Herein, we present a hybrid microphysiological model of chemoresistant GBM-on-a-chip (HGoC) by directly co-culturing TMZ-resistant GBM spheroids with healthy neurons to mimic the microenvironment of both the tumor and the surrounding healthy tissue. We characterized the model with proteomics, lipidomics, and secretome assays. The results showed that our artificial model recapitulated the molecular signatures of recurrent GBM in humans. Both showed alterations in vesicular transport and cholesterol pathways, mitotic quiescence, and a switch in metabolism to oxidative phosphorylation associated with a transition from mesenchymal to amoeboid. This is the first report to unravel the interplay of all these molecular changes as a mechanism of chemoresistance in glioblastoma. Moreover, we have shown that acquisition of resistance increases invasiveness and the presence of neurons decreases this property. 1. Seyfoori, A., et al., Self-filling microwell arrays (SFMAs) for tumor spheroid formation. Lab Chip, 2018. 18(22): p. 3516-3528. 2. Amereh, M., et al., In-Silico Modeling of Tumor Spheroid Formation and Growth. Micromachines (Basel), 2021. 12(7). 3. Senko, M.W., et al., Novel Parallelized Quadrupole/Linear Ion Trap/Orbitrap Tribrid Mass Spectrometer Improving Proteome Coverage and Peptide Identification Rates. Analytical Chemistry, 2013. 85(24): p. 11710-11714. 4. Zhou, G., et al., NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res, 2019. 47(W1): p. W234-w241. 5. Xia, J., E.E. Gill, and R.E. Hancock, NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc, 2015. 10(6): p. 823-44. 6. Breuer, K., et al., InnateDB: systems biology of innate immunity and beyond--recent updates and continuing curation. Nucleic Acids Res, 2013. 41(Database issue): p. D1228-33.

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Spatiotemporal modeling of chemoresistance evolution in breast tumors uncovers dependencies on SLC38A7 and SLC46A1

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