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Global computational alignment of tumor and cell line transcriptional profiles

Author

Listed:
  • Allison Warren

    (Broad Institute of MIT and Harvard)

  • Yejia Chen

    (Broad Institute of MIT and Harvard)

  • Andrew Jones

    (Broad Institute of MIT and Harvard)

  • Tsukasa Shibue

    (Broad Institute of MIT and Harvard)

  • William C. Hahn

    (Broad Institute of MIT and Harvard
    Dana Farber Cancer Institute
    Harvard Medical School)

  • Jesse S. Boehm

    (Broad Institute of MIT and Harvard)

  • Francisca Vazquez

    (Broad Institute of MIT and Harvard)

  • Aviad Tsherniak

    (Broad Institute of MIT and Harvard)

  • James M. McFarland

    (Broad Institute of MIT and Harvard)

Abstract

Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Direct comparisons of tumor and cell line transcriptional profiles are complicated by several factors, including the variable presence of normal cells in tumor samples. We thus develop an unsupervised alignment method (Celligner) and apply it to integrate several large-scale cell line and tumor RNA-Seq datasets. Although our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor similarity across cell lines. Using this approach, we identify several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and that exhibit distinct chemical and genetic dependencies. Celligner could be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell lines.

Suggested Citation

  • Allison Warren & Yejia Chen & Andrew Jones & Tsukasa Shibue & William C. Hahn & Jesse S. Boehm & Francisca Vazquez & Aviad Tsherniak & James M. McFarland, 2021. "Global computational alignment of tumor and cell line transcriptional profiles," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-020-20294-x
    DOI: 10.1038/s41467-020-20294-x
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    Cited by:

    1. Umberto Perron & Elena Grassi & Aikaterini Chatzipli & Marco Viviani & Emre Karakoc & Lucia Trastulla & Lorenzo M. Brochier & Claudio Isella & Eugenia R. Zanella & Hagen Klett & Ivan Molineris & Julia, 2024. "Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer patient-derived xenografts," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    2. Dianne Lumaquin-Yin & Emily Montal & Eleanor Johns & Arianna Baggiolini & Ting-Hsiang Huang & Yilun Ma & Charlotte LaPlante & Shruthy Suresh & Lorenz Studer & Richard M. White, 2023. "Lipid droplets are a metabolic vulnerability in melanoma," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    3. Han Jin & Cheng Zhang & Martin Zwahlen & Kalle Feilitzen & Max Karlsson & Mengnan Shi & Meng Yuan & Xiya Song & Xiangyu Li & Hong Yang & Hasan Turkez & Linn Fagerberg & Mathias Uhlén & Adil Mardinoglu, 2023. "Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    4. Min Pan & William C. Wright & Richard H. Chapple & Asif Zubair & Manbir Sandhu & Jake E. Batchelder & Brandt C. Huddle & Jonathan Low & Kaley B. Blankenship & Yingzhe Wang & Brittney Gordon & Payton A, 2021. "The chemotherapeutic CX-5461 primarily targets TOP2B and exhibits selective activity in high-risk neuroblastoma," Nature Communications, Nature, vol. 12(1), pages 1-20, December.

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