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Bridging the gap between cancer cell line models and tumours using gene expression data

British Journal of Cancer volume 125, pages 311–312 (2021)Cite this article

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Summary

Cancer cell line models are a cornerstone of cancer research, yet our understanding of how well they represent the molecular features of patient tumours remains limited. Our recent work provides a computational approach to systematically compare large gene expression datasets to better understand which cell lines most closely resemble each tumour type, as well as identify potential gaps in our current cancer models.

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References

  1. Ghandi, M., Huang, F. W., Jané-Valbuena, J., Kryukov, G. V., Lo, C. C., McDonald, E. R. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

    Article  CAS  Google Scholar 

  2. Boehm, J. S., Garnett, M. J., Adams, D. J., Francies, H. E., Golub, T. R., Hahn, W. C. et al. Cancer research needs a better map. Nature 589, 514–516 (2021).

    Article  CAS  Google Scholar 

  3. Tseng, Y.-Y. & Boehm, J. S. From cell lines to living biosensors: new opportunities to prioritize cancer dependencies using ex vivo tumor cultures. Curr Opin. Genet. Dev. 54, 33–40 (2019).

    Article  CAS  Google Scholar 

  4. Najgebauer, H., Yang, M., Francies, H. E., Pacini, C., Stronach, E. A., Garnett, M. J. et al. CELLector: genomics-guided selection of cancer in vitro models. Cell Syst. 10, 424–432 (2020).

    Article  CAS  Google Scholar 

  5. Tsherniak, A., Vazquez, F., Montgomery, P. G., Weir, B. A., Kryukov, G., Cowley, G. S. et al. Defining a cancer dependency map. Cell 170, 564–576 (2017).

    Article  CAS  Google Scholar 

  6. Corsello, S. M., Nagari, R. T., Spangler, R. D., Rossen, J., Kocak, M., Bryan, J. G. et al. Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling. Nat. Cancer 1, 235–248 (2020).

    Article  Google Scholar 

  7. Marisa, L., de, Reyniès, Duval, A., Selves, J., Gaub, M. P., Vescovo, L. et al. Gene expression classification of colon cancer into molecular subtypes: characterization, validation, and prognostic value. PLoS Med 10, e1001453 (2013).

    Article  CAS  Google Scholar 

  8. Warren, A., Chen, Y., Jones, A., Shibue, T., Hahn, W. C., Boehm, J. S. et al. Global computational alignment of tumor and cell line transcriptional profiles. Nat Commun 12, 22 (2021).

    Article  CAS  Google Scholar 

  9. Abid, A., Zhang, M. J., Bagaria, V. K. & Zou, J. Exploring patterns enriched in a dataset with contrastive principal component analysis. Nat. Commun. 9, 2134 (2018).

    Article  Google Scholar 

  10. Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol 36, 421–427 (2018).

    Article  CAS  Google Scholar 

  11. Rozenblatt-Rosen, O., Regev, A., Oberdoerffer, P., Nawy, T., Hupalowska, A., Rood, J. E. et al. The Human Tumor Atlas Network: charting tumor transitions across space and time at single-cell resolution. Cell 181, 236–249 (2020).

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Javad Noorbakhsh, Francisca Vazquez & James M. McFarland

Authors
  1. Javad Noorbakhsh
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  2. Francisca Vazquez
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  3. James M. McFarland
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Contributions

J.N., F.V. and J.M.M. drafted and revised the paper.

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Correspondence to James M. McFarland.

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Competing interests

F.V. receives research support from Novo Ventures. All authors were partially funded by the Cancer Dependency Map Consortium, but no consortium member was involved in or influenced the study.

Funding information

This work was supported by the Cancer Dependency Map Consortium.

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Noorbakhsh, J., Vazquez, F. & McFarland, J.M. Bridging the gap between cancer cell line models and tumours using gene expression data. Br J Cancer 125, 311–312 (2021). https://doi.org/10.1038/s41416-021-01359-0

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  • DOI: https://doi.org/10.1038/s41416-021-01359-0

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