Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types

K. Yu, Been-Lon Chen, D. Aran, J. Charalel, C. Yau, D. M. Wolf, L. J. ‘t Veer, A. J. Butte, T. Goldstein and M. Sirota ()
Additional contact information
K. Yu: University of California San Francisco
D. Aran: University of California San Francisco
J. Charalel: Stanford University
C. Yau: Buck Institute for Research on Aging
D. M. Wolf: University of California, San Francisco
L. J. ‘t Veer: University of California, San Francisco
A. J. Butte: University of California San Francisco
T. Goldstein: University of California San Francisco
M. Sirota: University of California San Francisco

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: Abstract Cancer cell lines are a cornerstone of cancer research but previous studies have shown that not all cell lines are equal in their ability to model primary tumors. Here we present a comprehensive pan-cancer analysis utilizing transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 tumor types. We perform correlation analysis and gene set enrichment analysis to understand the differences between cell lines and primary tumors. Additionally, we classify cell lines into tumor subtypes in 9 tumor types. We present our pancreatic cancer results as a case study and find that the commonly used cell line MIA PaCa-2 is transcriptionally unrepresentative of primary pancreatic adenocarcinomas. Lastly, we propose a new cell line panel, the TCGA-110-CL, for pan-cancer studies. This study provides a resource to help researchers select more representative cell line models.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-11415-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-11415-2

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-11415-2

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().