Tumour heterogeneity and the evolutionary trade-offs of cancer


Tumours vary in gene expression programmes and genetic alterations. Understanding this diversity and its biological meaning requires a theoretical framework, which could in turn guide the development of more accurate prognosis and therapy. Here, we review the theory of multi-task evolution of cancer, which is based upon the premise that tumours evolve in the host and face selection trade-offs between multiple biological functions. This theory can help identify the major biological tasks that cancer cells perform and the trade-offs between these tasks. It introduces the concept of specialist tumours, which focus on one task, and generalist tumours, which perform several tasks. Specialist tumours are suggested to be sensitive to therapy targeting their main task. Driver mutations tune gene expression towards specific tasks in a tissue-dependent manner and thus help to determine whether a tumour is specialist or generalist. We discuss potential applications of the theory of multi-task evolution to interpret the spatial organization of tumours and intratumour heterogeneity.

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Fig. 1: Evolutionary trade-offs constrain optimal traits (gene expression, morphology) to the geometry of polyhedra.
Fig. 2: ParTI infers tasks and trade-offs from data.
Fig. 3: Spatial gradients and maximization of combined tissue performance creates continua in gene expression.


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The authors thank R. Shouval and members of U.A. and J. Joyce’s labs for feedback on the manuscript. This work was supported by the Minerva foundation. U.A. is the incumbent of the Abisch-Frenkel Professorial Chair and acknowledges funding by BSF-NSF-NIH-CRCNS. J.H. acknowledges the support of the Swiss National Science Foundation (#177868), the Swedish Research Council and the Swedish Cancer Society.

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Correspondence to Jean Hausser or Uri Alon.

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Nature Reviews Cancer thanks A. Anderson, T. Graham and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Pareto Task Inference software package, Matlab implementation: https://www.weizmann.ac.il/mcb/UriAlon/download/ParTI

An implementation of Pareto Task Inference for the R software: https://github.com/vitkl/ParetoTI

The IMAXT Grand Challenge: https://www.cruk.cam.ac.uk/research-groups/imaxt-laboratory/the-project

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Hausser, J., Alon, U. Tumour heterogeneity and the evolutionary trade-offs of cancer. Nat Rev Cancer 20, 247–257 (2020). https://doi.org/10.1038/s41568-020-0241-6

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