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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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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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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.
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