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Inconsistency in large pharmacogenomic studies

Author

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  • Benjamin Haibe-Kains

    (Institut de Recherches Cliniques de Montréal, University of Montreal, Montreal, Quebec, Canada
    Ontario Cancer Institute, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2M9, Canada)

  • Nehme El-Hachem

    (Institut de Recherches Cliniques de Montréal, University of Montreal, Montreal, Quebec, Canada)

  • Nicolai Juul Birkbak

    (Center for Biological Sequence Analysis, Technical University of Denmark, 2800 Kgs, Lyngby, Denmark)

  • Andrew C. Jin

    (Beth Israel Deaconess Medical Center and Harvard Medical School)

  • Andrew H. Beck

    (Beth Israel Deaconess Medical Center and Harvard Medical School)

  • Hugo J. W. L. Aerts

    (Dana-Farber Cancer Institute
    Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School
    Maastricht University, Maastricht 6200 MD, The Netherlands)

  • John Quackenbush

    (Dana-Farber Cancer Institute
    Dana-Farber Cancer Institute)

Abstract

This Analysis compares two large-scale pharmacogenomic data sets that catalogued the sensitivity of a large number of cancer cell lines to approved and potential drugs, and finds that whereas the gene expression data are largely concordant between the two studies, the reported drug sensitivity measures and subsequently their association with genomic features are highly discordant.

Suggested Citation

  • Benjamin Haibe-Kains & Nehme El-Hachem & Nicolai Juul Birkbak & Andrew C. Jin & Andrew H. Beck & Hugo J. W. L. Aerts & John Quackenbush, 2013. "Inconsistency in large pharmacogenomic studies," Nature, Nature, vol. 504(7480), pages 389-393, December.
    • Handle: RePEc:nat:nature:v:504:y:2013:i:7480:d:10.1038_nature12831
    DOI: 10.1038/nature12831
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    Citations

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    Cited by:

    1. Paul Geeleher & Nancy Cox & R Stephanie Huang, 2014. "pRRophetic: An R Package for Prediction of Clinical Chemotherapeutic Response from Tumor Gene Expression Levels," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-3, September.
    2. Jurica Levatić & Marina Salvadores & Francisco Fuster-Tormo & Fran Supek, 2022. "Mutational signatures are markers of drug sensitivity of cancer cells," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    3. Naiqian Zhang & Haiyun Wang & Yun Fang & Jun Wang & Xiaoqi Zheng & X Shirley Liu, 2015. "Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-18, September.
    4. David A Knowles & Gina Bouchard & Sylvia Plevritis, 2019. "Sparse discriminative latent characteristics for predicting cancer drug sensitivity from genomic features," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-18, May.
    5. Adrià Fernández-Torras & Miquel Duran-Frigola & Martino Bertoni & Martina Locatelli & Patrick Aloy, 2022. "Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    6. Xiao-Song Wang & Sanghoon Lee & Han Zhang & Gong Tang & Yue Wang, 2022. "An integral genomic signature approach for tailored cancer therapy using genome-wide sequencing data," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    7. Elisabeth Prince & Jennifer Cruickshank & Wail Ba-Alawi & Kelsey Hodgson & Jillian Haight & Chantal Tobin & Andrew Wakeman & Alona Avoulov & Valentina Topolskaia & Mitchell J. Elliott & Alison P. McGu, 2022. "Biomimetic hydrogel supports initiation and growth of patient-derived breast tumor organoids," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    8. Caitlin E. Mills & Kartik Subramanian & Marc Hafner & Mario Niepel & Luca Gerosa & Mirra Chung & Chiara Victor & Benjamin Gaudio & Clarence Yapp & Ajit J. Nirmal & Nicholas Clark & Peter K. Sorger, 2022. "Multiplexed and reproducible high content screening of live and fixed cells using Dye Drop," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    9. Nina Kusch & Andreas Schuppert, 2020. "Two-step multi-omics modelling of drug sensitivity in cancer cell lines to identify driving mechanisms," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.

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