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Expression profile in hepatocellular carcinoma patient paired tissues

ABSTRACT: Expression profile in hepatocellular carcinoma patient paired tissues

PROVIDER: PRJNA656726 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR12435700.fastq.gz	Fastqsanger.gz
	SRR12435701.fastq.gz	Fastqsanger.gz
	SRR12435702.fastq.gz	Fastqsanger.gz
	SRR12435703.fastq.gz	Fastqsanger.gz
	SRR12435704.fastq.gz	Fastqsanger.gz

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Project description:Background Despite maximal therapy with surgery, chemotherapy and radiotherapy, glioblastoma multiforme (GBM) patients have a median survival of only 15 months. Almost all patients inevitably experience symptomatic tumor recurrence. A hallmark of this tumor type is the large heterogeneity between patients and within tumors itself which relate to failure of standardized tumor treatment. In this study, tissue samples of paired primary and recurrent GBM tumors were investigated to identify individual factors related to tumor progression. Methods Paired primary and recurrent GBM tumor tissues from 8 patients were investigated with a multi-omics approach using transcriptomics, proteomics and phosphoproteomics. Results In the studied patient cohort, large variations between and within patients are observed for all omics analyses. A few pathways affected at the different omics levels partly overlapped if patients are analyzed at the individual level, such as synaptogenesis (containing the SNARE complex) and cholesterol metabolism. Phosphoproteomics revealed increased STMN1(S38) phosphorylation that relates to ERBB4 signaling. A pathway tool has been developed to visualize and compare the different omics datasets per patient and showed potential therapeutic drugs, such as abobotulinumtoxina and afatinib, that target these affected pathways. Afatinib targeting ERBB4 signaling is currently in clinical trials for GBM. Conclusions Large variation on all omics levels exist between and within GBM patients. Therefore, it will be rather unlikely to find a drug treatment that would fit all patients. Instead a multi-omics approach can be used to identify affected pathways on the individual patient level and select potential treatment options.

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