RNA Sequencing of Hepatobiliary Cancer Cell Lines: Data and Applications to Mutational and Transcriptomic Profiling.

exome mutations gallbladder cancer hepatobiliary cancer transcriptomics

Journal

Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829

Informations de publication

Date de publication:
03 Sep 2020
Historique:
received: 04 07 2020
revised: 15 08 2020
accepted: 19 08 2020
entrez: 9 9 2020
pubmed: 10 9 2020
medline: 10 9 2020
Statut: epublish

Résumé

Cancer cell lines allow the identification of clinically relevant alterations and the prediction of drug response. However, sequencing data for hepatobiliary cancer cell lines in general, and particularly gallbladder cancer (GBC), are sparse. Here, we apply RNA sequencing to characterize 10 GBC, eight hepatocellular carcinoma, and five cholangiocarcinoma (CCA) cell lines. RNA extraction, quality control, library preparation, sequencing, and pre-processing of sequencing data were implemented using state-of-the-art techniques. Public data from the MSK-IMPACT database and a large cohort of Japanese biliary tract cancer patients were used to illustrate the usage of the released data. The total number of exonic mutations varied from 7207 for the cell line NOZ to 9760 for HuCCT1. Researchers planning experiments that require TP53 mutations could use the cell lines NOZ, OCUG-1, SNU308, or YoMi. Mz-Cha-1 showed mutations in ATM, SNU308 presented SMAD4 mutations, and the only investigated cell line that showed ARID1A mutations was GB-d1. SNU478 was the cell line with the global gene expression pattern most similar to GBC, intrahepatic CCA, and extrahepatic CCA. EGFR, KMT2D, and KMT2C generally presented a higher expression in the investigated cell lines than in Japanese primary GBC tumors. We provide the scientific community with detailed mutation and gene expression data, together with three showcase applications, with the aim of facilitating the design of future in vitro cell culture assays for research on hepatobiliary cancer.

Identifiants

pubmed: 32899426
pii: cancers12092510
doi: 10.3390/cancers12092510
pmc: PMC7565451
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Horizon 2020 Framework Programme
ID : 825741
Organisme : Bundesministerium für Bildung und Forschung
ID : 01DN15021
Organisme : Wilhelm Sander-Stiftung
ID : 2015.111.1
Organisme : Olympia Morata Programme
ID : F.206830
Organisme : Stiftelsen för Strategisk Forskning
ID : RIF14-0081

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Auteurs

Dominique Scherer (D)

Institute of Medical Biometry and Informatics, University of Heidelberg, 69120 Heidelberg, Germany.

Marcela Dávila Dávila López (MD)

Bioinformatics Core Facility, University of Gothenburg, 40530 Gothenburg, Sweden.

Benjamin Goeppert (B)

Institute of Pathology, Heidelberg University Hospital, 69120 Heidelberg, Germany.

Sanna Abrahamsson (S)

Bioinformatics Core Facility, University of Gothenburg, 40530 Gothenburg, Sweden.

Rosa González González Silos (RG)

Institute of Medical Biometry and Informatics, University of Heidelberg, 69120 Heidelberg, Germany.

Igor Nova (I)

Institute of Medical Biometry and Informatics, University of Heidelberg, 69120 Heidelberg, Germany.

Katherine Marcelain (K)

Department of Basic and Clinical Oncology, Faculty of Medicine, Universidad ode Chile, Santiago 8380000, Chile.

Juan C Roa (JC)

Department of Pathology, Faculty of Medicine, Millennium Institute of Immunology and Immunotherapy, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile.

David Ibberson (D)

Deep Sequencing Core Facility, CellNetworks Excellence Cluster, University of Heidelberg, 69120 Heidelberg, Germany.

Sinan U Umu (SU)

Department of Research, Cancer Registry of Norway, 0379 Oslo, Norway.

Trine Ballestad Rounge (TB)

Department of Research, Cancer Registry of Norway, 0379 Oslo, Norway.
Department of Informatics, University of Oslo, 0373 Oslo, Norway.

Stephanie Roessler (S)

Institute of Pathology, Heidelberg University Hospital, 69120 Heidelberg, Germany.

Justo Lorenzo Bermejo (JL)

Institute of Medical Biometry and Informatics, University of Heidelberg, 69120 Heidelberg, Germany.

Classifications MeSH