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Comment
. 2021 Oct 11;39(10):1311-1313.
doi: 10.1016/j.ccell.2021.09.002. Epub 2021 Sep 30.

Evolution's cartographer: Mapping the fitness landscape in cancer

Calum Gabbutt  1 , Trevor A Graham  2
Affiliations
Comment

Evolution's cartographer: Mapping the fitness landscape in cancer

Calum Gabbutt et al. Cancer Cell. .

Abstract

Cancer treatment effectiveness could be improved if it were possible to accurately anticipate the response of the tumor to treatment. Writing in Nature, Salehi et al. combine single-cell genomics and mathematical modeling to measure cancer subclone fitness and use these measurements to accurately predict the future trajectory of cancer evolution.

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Conflict of interest statement

Declaration of interests The authors declare no competing interests.

Figures

Figure
Figure. Measuring clonal composition and inferring fitness to predict cancer evolution.
Salehi and colleagues used single-cell genome sequencing to measure the size of subclones within cell cultures and PDX models. Clone sizes were then inputted into a population genetics mathematical model to infer clone-specific fitness coefficients. The same mathematical model could then be run-forwards in time to make predictions about future evolution.
See this image and copyright information in PMC

Comment on

  • Clonal fitness inferred from time-series modelling of single-cell cancer genomes.
    Salehi S, Kabeer F, Ceglia N, Andronescu M, Williams MJ, Campbell KR, Masud T, Wang B, Biele J, Brimhall J, Gee D, Lee H, Ting J, Zhang AW, Tran H, O'Flanagan C, Dorri F, Rusk N, de Algara TR, Lee SR, Cheng BYC, Eirew P, Kono T, Pham J, Grewal D, Lai D, Moore R, Mungall AJ, Marra MA; IMAXT Consortium; McPherson A, Bouchard-Côté A, Aparicio S, Shah SP. Salehi S, et al. Nature. 2021 Jul;595(7868):585-590. doi: 10.1038/s41586-021-03648-3. Epub 2021 Jun 23. Nature. 2021. PMID: 34163070 Free PMC article.

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