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Hepatocellular carcinoma cell lines retain the genomic and transcriptomic landscapes of primary human cancers
Hepatocellular carcinoma (HCC) cell lines are useful in vitro models for the study of primary HCCs. Because cell lines acquire additional mutations in culture, it is important to understand to what extent HCC cell lines retain the genetic landscapes of primary HCCs. Most HCC cell lines were establis...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4895220/ https://www.ncbi.nlm.nih.gov/pubmed/27273737 http://dx.doi.org/10.1038/srep27411 |
Sumario: | Hepatocellular carcinoma (HCC) cell lines are useful in vitro models for the study of primary HCCs. Because cell lines acquire additional mutations in culture, it is important to understand to what extent HCC cell lines retain the genetic landscapes of primary HCCs. Most HCC cell lines were established during the last century, precluding comparison between cell lines and primary cancers. In this study, 9 Chinese HCC cell lines with matched patient-derived cells at low passages (PDCs) were established in the defined culture condition. Whole genome analyses of 4 HCC cell lines showed that genomic mutation landscapes, including mutations, copy number alterations (CNAs) and HBV integrations, were highly stable during cell line establishment. Importantly, genetic alterations in cancer drivers and druggable genes were reserved in cell lines. HCC cell lines also retained gene expression patterns of primary HCCs during in vitro culture. Finally, sequential analysis of HCC cell lines and PDCs at different passages revealed their comparable and stable genomic and transcriptomic levels if maintained within proper passages. These results show that HCC cell lines largely retain the genomic and transcriptomic landscapes of primary HCCs, thus laying the rationale for testing HCC cell lines as preclinical models in precision medicine. |
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