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Genomic evolution of cancer models: perils and opportunities

Nature Reviews Cancer (2018) | Download Citation

Abstract

Cancer research relies on model systems, which reflect the biology of actual human tumours to only a certain extent. One important feature of human cancer is its intra-tumour genomic heterogeneity and instability. However, the extent of such genomic instability in cancer models has received limited attention in research. Here, we review the state of knowledge of genomic instability of cancer models and discuss its biological origins and implications for basic research and for cancer precision medicine. We discuss strategies to cope with such genomic evolution and evaluate both the perils and the emerging opportunities associated with it.

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Acknowledgements

The authors thank I. Fung for her assistance with designing and preparing the figures. This work was supported by the Human Frontiers Science Program (U.B.-D.), the Howard Hughes Medical Institute (T.R.G.), the US National Institutes of Health (R01 CA188228; R.B.), the Gray Matters Brain Cancer Foundation (R.B.), the Bridge Project (R.B.), a Broad Institute SPARC award (R.B.) and a Broad Institute BroadNext10 grant (U.B.-D.).

Reviewer information

Nature Reviews Cancer thanks J. Gray, R. Kimple and other anonymous reviewer(s) for their contribution to the peer review of this work.

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Affiliations

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

    • Uri Ben-David
    • , Rameen Beroukhim
    •  & Todd R. Golub
  2. Dana-Farber Cancer Institute, Boston, MA, USA

    • Rameen Beroukhim
    •  & Todd R. Golub
  3. Harvard Medical School, Boston, MA, USA

    • Rameen Beroukhim
    •  & Todd R. Golub
  4. Brigham and Women’s Hospital, Boston, MA, USA

    • Rameen Beroukhim
  5. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    • Todd R. Golub

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Contributions

All authors researched data for the article, contributed to discussion of the content and wrote, reviewed and edited the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Uri Ben-David or Rameen Beroukhim or Todd R. Golub.

Glossary

Clonal dynamics

Changes in the relative abundance of tumour subclones throughout model propagation.

Copy number effect

In CRISPR screens, copy number changes result in a gene-independent anti-proliferative effect of Cas9-mediated DNA cleavage, confounding the measurement of gene essentiality. This effect can be corrected computationally using genome-wide copy number measurements.

Established cell lines

(ECLs). Models generated as patient-derived cell lines, followed by prolonged culture propagation. These models are not assumed to represent the specific tumours from which they were derived.

Founder effect

Genetic diversity that results when a cell population is descended from a small number of original cells.

Genetically engineered mouse models

(GEMMs). Models generated by genetically manipulating mice using genetic alterations that characterize human tumours.

Genetic drift

Stochastic changes in the clonal composition of the cancer cell population owing to chance disappearance and/or expansion of particular subclones.

Genetic selection

Directional changes in the clonal composition of the cancer cell population owing to growth advantage and/or disadvantage of particular subclones.

Ongoing genomic instability

Generation of de novo genetic alterations throughout model propagation, contributing to the genomic evolution of the model.

Patient-derived cell lines

(PDCLs). Models generated by the transferring of tumour cells into a 2D plastic dish using culture conditions that enable cells to proliferate.

Patient-derived organoids

(PDOs). Models generated by the embedment of tumour (or normal) cells into a 3D matrix using culture conditions that mimic the in vivo tumour niche.

Patient-derived xenografts

(PDXs). Models generated by the direct engraftment of resected human tumours into immune-deficient mice, followed by their serial transplantation between mice.

Pre-existing heterogeneity

Genetic diversity within the original tumour that contributes to the genomic evolution of the model.

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https://doi.org/10.1038/s41568-018-0095-3