Genome-wide profiling of patient-derived glioblastoma stem-like cells reveals recurrent genetic and transcriptomic signatures associated with brain tumors

对患者来源的胶质母细胞瘤干细胞的全基因组分析揭示了与脑肿瘤相关的复发性遗传和转录组特征

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作者:Elisabetta Lazzarini #, Domenico Alessandro Silvestris #, Giuseppe Benvenuto, Daniela Osti, Luigi Fattore, Rosina Paterra, Gaetano Finocchiaro, Paolo Malatesta, Antonio Daga, Alberto L Gallotti, Rossella Galli, Giuliana Pelicci, Anna Tesei, Martina Bedeschi, Roberto Pallini, Lorenza Pasqualini, Chia

Conclusion

The availability of a large set of fully molecularly characterized GCSs represents a valuable public resource to support the advancement of precision oncology for the treatment of GBM.

Methods

94 (80 I surgery/14 II surgery) and 53 (42 I surgery/11 II surgery) GSCs lines underwent whole exome and trascriptome analysis, respectively.

Purpose

Patient-derived cancer cell lines can be very useful to investigate genetic as well as epigenetic mechanisms of transformation and to test new drugs. In this multi-centric study, we performed genomic and transcriptomic characterization of a large set of patient-derived glioblastoma (GBM) stem-like cells (GSCs).

Results

Exome sequencing revealed TP53 as the main mutated gene (41/94 samples, 44%), followed by PTEN (33/94, 35%), RB1 (16/94, 17%) and NF1 (15/94, 16%), among other genes associated to brain tumors. One GSC sample bearing a BRAF p.V600E mutation showed sensitivity in vitro to a BRAF inhibitor. Gene Ontology and Reactome analysis uncovered several biological processes mostly associated to gliogenesis and glial cell differentiation, S - adenosylmethionine metabolic process, mismatch repair and methylation. Comparison of I and II surgery samples disclosed a similar distribution of mutated genes, with an overrepresentation of mutations in mismatch repair, cell cycle, p53 and methylation pathways in I surgery samples, and of mutations in receptor tyrosine kinase and MAPK signaling pathways in II surgery samples. Unsupervised hierarchical clustering of RNA-seq data produced 3 clusters characterized by distinctive sets of up-regulated genes and signaling pathways.

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