Literature DB >> 32937114

Assessment of Genetic Drift in Large Pharmacogenomic Studies.

Rene Quevedo1, Petr Smirnov1, Denis Tkachuk2, Chantal Ho2, Nehme El-Hachem3, Zhaleh Safikhani1, Trevor J Pugh4, Benjamin Haibe-Kains5.   

Abstract

Genomic instability affects the reproducibility of experiments that rely on cancer cell lines. However, measuring the genomic integrity of these cells throughout a study is a costly endeavor that is commonly forgone. Here, we validate the identity of cancer cell lines in three pharmacogenomic studies and screen for genetic drift within and between datasets. Using SNP data from these datasets encompassing 1,497 unique cell lines and 63 unique pharmacological compounds, we show that genetic drift is widely prevalent in almost all cell lines with a median of 4.5%-6.1% of the total genome size drifted between any two isogenic cell lines. This study highlights the need for molecular profiling of cell lines to minimize the effects of passaging or misidentification in biomedical studies. We developed the CCLid web application, available at www.cclid.ca, to allow users to screen the genomic profiles of their cell lines against these datasets. A record of this paper's transparent peer review process is included in the Supplemental Information.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  SNP array; aneuploidy; cell lines; chromosomal instability; gene expression; genetic drift; genotyping; karyotype; pharmacogenomics

Year:  2020        PMID: 32937114     DOI: 10.1016/j.cels.2020.08.012

Source DB:  PubMed          Journal:  Cell Syst        ISSN: 2405-4712            Impact factor:   10.304


  3 in total

Review 1.  Computational estimation of quality and clinical relevance of cancer cell lines.

Authors:  Lucia Trastulla; Javad Noorbakhsh; Francisca Vazquez; James McFarland; Francesco Iorio
Journal:  Mol Syst Biol       Date:  2022-07       Impact factor: 13.068

Review 2.  Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models.

Authors:  Hossein Sharifi-Noghabi; Soheil Jahangiri-Tazehkand; Petr Smirnov; Casey Hon; Anthony Mammoliti; Sisira Kadambat Nair; Arvind Singh Mer; Martin Ester; Benjamin Haibe-Kains
Journal:  Brief Bioinform       Date:  2021-11-05       Impact factor: 13.994

Review 3.  Progress towards non-small-cell lung cancer models that represent clinical evolutionary trajectories.

Authors:  Robert E Hynds; Kristopher K Frese; David R Pearce; Eva Grönroos; Caroline Dive; Charles Swanton
Journal:  Open Biol       Date:  2021-01-13       Impact factor: 6.411

  3 in total

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