Analysis of renal cancer cell lines from two major resources enables genomics-guided cell line selection

Rileen Sinha (), Andrew G. Winer, Michael Chevinsky, Christopher Jakubowski, Ying-Bei Chen, Yiyu Dong, Satish K. Tickoo, Victor E. Reuter, Paul Russo, Jonathan A. Coleman, Chris Sander, James J. Hsieh and A. Ari Hakimi
Additional contact information
Rileen Sinha: Memorial Sloan-Kettering Cancer Center
Andrew G. Winer: Urology Service, Memorial Sloan-Kettering Cancer Center
Michael Chevinsky: Urology Service, Memorial Sloan-Kettering Cancer Center
Christopher Jakubowski: Urology Service, Memorial Sloan-Kettering Cancer Center
Ying-Bei Chen: Memorial Sloan-Kettering Cancer Center
Yiyu Dong: Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center
Satish K. Tickoo: Memorial Sloan-Kettering Cancer Center
Victor E. Reuter: Memorial Sloan-Kettering Cancer Center
Paul Russo: Urology Service, Memorial Sloan-Kettering Cancer Center
Jonathan A. Coleman: Urology Service, Memorial Sloan-Kettering Cancer Center
Chris Sander: cBio Center, Dana-Farber Cancer Institute and Compbio Collaboratory, Harvard Medical School
James J. Hsieh: Molecular Oncology, Siteman Cancer Center, Washington University
A. Ari Hakimi: Memorial Sloan-Kettering Cancer Center

Nature Communications, 2017, vol. 8, issue 1, 1-10

Abstract: Abstract The utility of cancer cell lines is affected by the similarity to endogenous tumour cells. Here we compare genomic data from 65 kidney-derived cell lines from the Cancer Cell Line Encyclopedia and the COSMIC Cell Lines Project to three renal cancer subtypes from The Cancer Genome Atlas: clear cell renal cell carcinoma (ccRCC, also known as kidney renal clear cell carcinoma), papillary (pRCC, also known as kidney papillary) and chromophobe (chRCC, also known as kidney chromophobe) renal cell carcinoma. Clustering copy number alterations shows that most cell lines resemble ccRCC, a few (including some often used as models of ccRCC) resemble pRCC, and none resemble chRCC. Human ccRCC tumours clustering with cell lines display clinical and genomic features of more aggressive disease, suggesting that cell lines best represent aggressive tumours. We stratify mutations and copy number alterations for important kidney cancer genes by the consistency between databases, and classify cell lines into established gene expression-based indolent and aggressive subtypes. Our results could aid investigators in analysing appropriate renal cancer cell lines.

Date: 2017
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DOI: 10.1038/ncomms15165

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