Evidence map›Paper›PMID 32899426›Full record

ArticleCancers2020

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

Dominique Scherer, Marcela Dávila Dávila López, Benjamin Goeppert, Sanna Abrahamsson, Rosa González González Silos, Igor Nova, Katherine Marcelain, Juan C Roa, David Ibberson, Sinan U Umu and 3 more

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 20% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 6 institutions in 4 countries.

Dominique SchererInstitute of Medical Biometry and Informatics, University of Heidelberg, 69120 Heidelberg, Germany.ORCID 0000-0003-4430-296X
Marcela Dávila Dávila LópezBioinformatics Core Facility, University of Gothenburg, 40530 Gothenburg, Sweden.
Benjamin GoeppertInstitute of Pathology, Heidelberg University Hospital, 69120 Heidelberg, Germany.
Sanna AbrahamssonBioinformatics Core Facility, University of Gothenburg, 40530 Gothenburg, Sweden.
Rosa González González SilosInstitute of Medical Biometry and Informatics, University of Heidelberg, 69120 Heidelberg, Germany.
Igor NovaInstitute of Medical Biometry and Informatics, University of Heidelberg, 69120 Heidelberg, Germany.
Katherine MarcelainDepartment of Basic and Clinical Oncology, Faculty of Medicine, Universidad ode Chile, Santiago 8380000, Chile.
Juan C RoaDepartment of Pathology, Faculty of Medicine, Millennium Institute of Immunology and Immunotherapy, Pontificia Universidad Católica de Chile, Santiago 8330024, Chile.
David IbbersonDeep Sequencing Core Facility, CellNetworks Excellence Cluster, University of Heidelberg, 69120 Heidelberg, Germany.
Sinan U UmuDepartment of Research, Cancer Registry of Norway, 0379 Oslo, Norway.
Trine Ballestad RoungeDepartment of Research, Cancer Registry of Norway, 0379 Oslo, Norway.
Stephanie RoesslerInstitute of Pathology, Heidelberg University Hospital, 69120 Heidelberg, Germany.
Justo Lorenzo BermejoInstitute of Medical Biometry and Informatics, University of Heidelberg, 69120 Heidelberg, Germany.
Heidelberg University · DEUniversity of Gothenburg · SECancer Registry of Norway · NOPontificia Universidad Católica de Chile · CLUniversity of Chile · CLUniversity of Oslo · NO

Funding

Bundesministerium für Bildung und Forschung 01DN15021Horizon 2020 Framework Programme 825741Olympia Morata Programme F.206830Stiftelsen för Strategisk Forskning RIF14-0081Wilhelm Sander-Stiftung 2015.111.1
6 · The paper itself

Abstract

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.

Indexed as

exome mutationsgallbladder cancerhepatobiliary cancertranscriptomics

Identifiers

PMID32899426
PMCPMC7565451
OpenAlexW3081948230

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.