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Transcriptome analysis of TP53-R248L overexpressed 19NS patient-derived glioblastoma cells

ABSTRACT: This study demonstrates the role of TP53 gain-of-function (GOF) mutation on glioblastoma progression. However, little is known about the transcriptional alteration upon TP53 GOF mutation. In this study, we found that TP53 GOF mutation (R248L mutation) promotes the enrichment of multiple gene signatures related to inflammation and chemotaxis. Particularly, through in vitro and in vivo approaches, we verified that TP53 GOF mutation enhances NFKB signaling which in turn up-regulates CCL2 and TNFA. Taken together, these results suggest the function of TP53 GOF mutation driving aggressiveness of malignant brain tumor.

ORGANISM(S): Homo sapiens

PROVIDER: GSE101980 | GEO | 2019/02/26

REPOSITORIES: GEO

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Project description:Background: Although TP53 gain-of-function (GOF) mutations promote cancer survival, its effect on EGFR-TKI efficacy remains unclear. We established EGFR-mutant lung cancer cell lines expressing various TP53 genotypes using CRISPR-Cas9 technology and found that TP53-GOF mutant cells develop an early resistance to EGFR-TKI osimertinib.The goal of this study is to elucidate the mechanisms underlying resistance to osimertinib treatment in TP53 GOF mutations through comprehensive gene analysis using RNA-seq with next-generation sequencing (NGS). Methods: Total RNA was isolated from PC-9 cells overexpressing TP53 R248Q mutation (PC9/p53R248Q: TP53 GOF mutation) and PC-9 cells overexpressing empty vector plasmid (PC9/p53EV: TP53 null) treated with DMSO or osimertinib for 24hours, using the RNeasy Micro Kit , in accordance with the manufacturer’s instructions. RNA samples were quantified by NanoDrop-2000 spectrophotometer, and the quality was confirmed with a 2200 TapeStation. rRNA was removed using MGI Easy rRNA Depletion Kit according to manufacturer's instructions followed by library construction using MGIEasy RNA Directional Library Prep Set (MGI). MGI DNBseq-G400 FAST was used to perform the amplicons deep sequencing following the standard operation protocol. The sequence format was 150bp pair read for all samples. All sequencing reads were trimmed low-quality bases and adapters with Trimmomatic (v.0.38) , and RNA sequencing reads were mapped to hg38 using HISAT2 software . Raw counts for each gene were estimated in each sample using RSEM version 1.3.0 and Bowtie 2. Calculation of the log fold-change (log FC) and p-value were performed using edgeR. Results: We explored the functions of specifically upregulated genes in TP53 GOF mutation after osimertinib treatment by KEGG pathway-enrichment analysis and found that the cytokine-cytokine receptor interaction was the most significantly altered pathway. Hallmark pathway analysis identified the TNF-α/NF-κB pathway was significantly enriched. Furthermore, TRRUST analysis showed enhanced activity of transcription factors especially RELA (p65) and NF-kB1. Conclusions: TP53 GOF mutaion induces osimertinib resistance by activating TNF-α/NF-κB pathway.

Project description:Background: Gene expression profiling of breast carcinomas has increased our understanding of the heterogeneous biology of this disease and promises to impact clinical care. The aim of this study was to evaluate the prognostic value of gene expression-based classification along with established prognostic markers and mutation status of the TP53 gene, in a group of breast cancer patients with long-term (12-16 years) follow-up. Methods: The clinical and histopathological parameters of 200 breast cancer patients were studied for their effects on clinical outcome using univariate/multivariate Cox regression. The prognostic impact of mutations in the TP53 gene, identified using TTGE and sequencing, was also evaluated. Eighty of the samples were analyzed for gene expression using 42K cDNA microarrays and the patients were assigned to five previously defined molecular expression groups. The strength of the gene expression based classification versus standard markers was evaluated by adding this variable to the Cox regression model used to analyze all samples. Results: Both univariate and multivariate analysis showed that TP53 mutation status, tumor size and lymph node status were the strongest predictors of breast cancer survival for the whole group of patients. Analyses of the patients with gene expression data showed that TP53 mutation status, gene expression based classification, tumor size and lymph node status were significant predictors of survival. The TP53 mutation status showed strong association with the ?basal-like? and ?ERBB2+? gene expression subgroups, and tumors with mutation had a characteristic gene expression pattern. Conclusions: TP53 mutation status and gene-expression based groups are important survival markers of breast cancer, and these molecular markers may provide prognostic information that complements clinical variables. The study adds experience and knowledge to an ongoing characterization and classification of the disease. Experiment set consisting of 80 primary breast carcinomas collected at Ulleval University Hospital (ULL-samples), Oslo, Norway from 1990-94, and one normal sample from breast reduction surgery.

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Transcriptome analysis of TP53-R248L overexpressed 19NS patient-derived glioblastoma cells

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