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A metastasis map of human cancer cell lines

Author

Listed:
  • Xin Jin

    (Broad Institute of MIT and Harvard)

  • Zelalem Demere

    (Broad Institute of MIT and Harvard)

  • Karthik Nair

    (Broad Institute of MIT and Harvard)

  • Ahmed Ali

    (Broad Institute of MIT and Harvard
    Massachusetts Institute of Technology)

  • Gino B. Ferraro

    (Massachusetts General Hospital)

  • Ted Natoli

    (Broad Institute of MIT and Harvard)

  • Amy Deik

    (Broad Institute of MIT and Harvard)

  • Lia Petronio

    (Broad Institute of MIT and Harvard)

  • Andrew A. Tang

    (Broad Institute of MIT and Harvard)

  • Cong Zhu

    (Broad Institute of MIT and Harvard)

  • Li Wang

    (Broad Institute of MIT and Harvard)

  • Danny Rosenberg

    (Broad Institute of MIT and Harvard)

  • Vamsi Mangena

    (Massachusetts Institute of Technology)

  • Jennifer Roth

    (Broad Institute of MIT and Harvard)

  • Kwanghun Chung

    (Broad Institute of MIT and Harvard
    Massachusetts Institute of Technology)

  • Rakesh K. Jain

    (Massachusetts General Hospital
    Harvard Medical School)

  • Clary B. Clish

    (Broad Institute of MIT and Harvard)

  • Matthew G. Heiden

    (Broad Institute of MIT and Harvard
    Massachusetts Institute of Technology
    Dana-Farber Cancer Institute)

  • Todd R. Golub

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

Abstract

Most deaths from cancer are explained by metastasis, and yet large-scale metastasis research has been impractical owing to the complexity of in vivo models. Here we introduce an in vivo barcoding strategy that is capable of determining the metastatic potential of human cancer cell lines in mouse xenografts at scale. We validated the robustness, scalability and reproducibility of the method and applied it to 500 cell lines1,2 spanning 21 types of solid tumour. We created a first-generation metastasis map (MetMap) that reveals organ-specific patterns of metastasis, enabling these patterns to be associated with clinical and genomic features. We demonstrate the utility of MetMap by investigating the molecular basis of breast cancers capable of metastasizing to the brain—a principal cause of death in patients with this type of cancer. Breast cancers capable of metastasizing to the brain showed evidence of altered lipid metabolism. Perturbation of lipid metabolism in these cells curbed brain metastasis development, suggesting a therapeutic strategy to combat the disease and demonstrating the utility of MetMap as a resource to support metastasis research.

Suggested Citation

  • Xin Jin & Zelalem Demere & Karthik Nair & Ahmed Ali & Gino B. Ferraro & Ted Natoli & Amy Deik & Lia Petronio & Andrew A. Tang & Cong Zhu & Li Wang & Danny Rosenberg & Vamsi Mangena & Jennifer Roth & K, 2020. "A metastasis map of human cancer cell lines," Nature, Nature, vol. 588(7837), pages 331-336, December.
    • Handle: RePEc:nat:nature:v:588:y:2020:i:7837:d:10.1038_s41586-020-2969-2
    DOI: 10.1038/s41586-020-2969-2
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    Citations

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    Cited by:

    1. Guidantonio Malagoli Tagliazucchi & Anna J. Wiecek & Eloise Withnell & Maria Secrier, 2023. "Genomic and microenvironmental heterogeneity shaping epithelial-to-mesenchymal trajectories in cancer," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    2. Iqbal Mahmud & Guimei Tian & Jia Wang & Tarun E. Hutchinson & Brandon J. Kim & Nikee Awasthee & Seth Hale & Chengcheng Meng & Allison Moore & Liming Zhao & Jessica E. Lewis & Aaron Waddell & Shangtao , 2023. "DAXX drives de novo lipogenesis and contributes to tumorigenesis," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    3. Qiuchen Guo & Milos Spasic & Adam G. Maynard & Gregory J. Goreczny & Amanuel Bizuayehu & Jessica F. Olive & Peter Galen & Sandra S. McAllister, 2022. "Clonal barcoding with qPCR detection enables live cell functional analyses for cancer research," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    4. Zhu, Guang & Lin, Zhenhua, 2021. "Commentary on statistical mechanical models of cancer," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    5. C. Megan Young & Laurent Beziaud & Pierre Dessen & Angela Madurga Alonso & Albert Santamaria-Martínez & Joerg Huelsken, 2023. "Metabolic dependencies of metastasis-initiating cells in female breast cancer," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

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