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An analysis of genetic heterogeneity in untreated cancers

Nature Reviews Cancer volume 19, pages 639–650 (2019)Cite this article

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Abstract

Genetic intratumoural heterogeneity is a natural consequence of imperfect DNA replication. Any two randomly selected cells, whether normal or cancerous, are therefore genetically different. Here, we review the different forms of genetic heterogeneity in cancer and re-analyse the extent of genetic heterogeneity within seven types of untreated epithelial cancers, with particular regard to its clinical relevance. We find that the homogeneity of predicted functional mutations in driver genes is the rule rather than the exception. In primary tumours with multiple samples, 97% of driver-gene mutations in 38 patients were homogeneous. Moreover, among metastases from the same primary tumour, 100% of the driver mutations in 17 patients were homogeneous. With a single biopsy of a primary tumour in 14 patients, the likelihood of missing a functional driver-gene mutation that was present in all metastases was 2.6%. Furthermore, all functional driver-gene mutations detected in these 14 primary tumours were present among all their metastases. Finally, we found that individual metastatic lesions responded concordantly to targeted therapies in 91% of 44 patients. These analyses indicate that the cells within the primary tumours that gave rise to metastases are genetically homogeneous with respect to functional driver-gene mutations, and we suggest that future efforts to develop combination therapies have the potential to be curative.

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Fig. 1: Clonal sweeps give rise to driver-gene mutation homogeneity.
Fig. 2: Three forms of heterogeneity can exist within a single patient.
Fig. 3: The majority of primary tumours are surgically resectable at the time of diagnosis.
Fig. 4: Intratumoural heterogeneity in untreated primary tumours and among metastases.
Fig. 5: Subclonal driver-gene mutations did not lead to worse patient outcomes in patients with non-small-cell lung carcinomas.
Fig. 6: Lesions of individual patients respond similarly to targeted therapy.

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Acknowledgements

The authors thank all authors of the original publications for enabling this re-analysis by sharing their sequencing data — in particular, A. Bass, D. Brown, C. Sotiriou, C. Curtis, W. Gibson, E. Hoivik, M. Cmero, C. Hovens, T.-M. Kim, S.-H. Lee, M. Ryser, S. Shah, D. Shibata, M. Stachler, R. Sun and A. Zhang. This study was supported by the US National Institutes of Health grants R00 CA229991 (J.G.R.), CA179991 (C.A.I.-D.), F31 CA180682 (A.P.M.-M.), T32 CA160001-06 (A.P.M.-M.) and CA43460 (B.V.), as well as by the Lustgarten Foundation for Pancreatic Cancer Research, The Sol Goldman Pancreatic Cancer Research Center, the Virginia and D. K. Ludwig Fund for Cancer Research, an Erwin Schrödinger fellowship (J.G.R.; Austrian Science Fund FWF J-3996), a Landry Cancer Biology fellowship (J.M.G.) and the Office of Naval Research grant N00014-16-1-2914.

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Authors and Affiliations

  1. Canary Center for Cancer Early Detection, Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, USA

    Johannes G. Reiter

  2. The Sidney Kimmel Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Marina Baretti, Nilofer S. Azad, Kenneth W. Kinzler & Bert Vogelstein

  3. Program for Evolutionary Dynamics, Harvard University, Cambridge, MA, USA

    Jeffrey M. Gerold & Martin A. Nowak

  4. The David M. Rubenstein Center for Pancreatic Cancer Research, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Alvin P. Makohon-Moore & Christine A. Iacobuzio-Donahue

  5. University of California, San Francisco, San Francisco, CA, USA

    Adil Daud

  6. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Christine A. Iacobuzio-Donahue

  7. The Ludwig Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Kenneth W. Kinzler & Bert Vogelstein

  8. The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Kenneth W. Kinzler & Bert Vogelstein

  9. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA

    Martin A. Nowak

  10. Department of Mathematics, Harvard University, Cambridge, MA, USA

    Martin A. Nowak

  11. Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Bert Vogelstein

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  1. Johannes G. Reiter
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Contributions

All authors researched data for this article. J.G.R., M.B., J.M.G., A.P.M.-M., C.A.I.-D., N.S.A., K.W.K., M.A.N. and B.V. substantially contributed to discussion of the content. J.G.R., M.B., N.S.A., M.A.N. and B.V. wrote the article. All authors reviewed and/or edited the manuscript before submission.

Corresponding authors

Correspondence to Johannes G. Reiter, Martin A. Nowak or Bert Vogelstein.

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Competing interests

K.W.K. and B.V. are founders of Personal Genome Diagnostics and Thrive, as well as advisers to Sysmex, Eisai, CAGE and Neophore. B.V. is also an adviser to Nexus. These companies and others have licensed technologies related to the work described in this paper from Johns Hopkins University. Some of these licences are associated with equity or royalty payments to K.W.K. and B.V. The terms of these arrangements are being managed by Johns Hopkins University in accordance with its conflict-of-interest policies. The other authors declare no competing interests.

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

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Supplementary Information

Glossary

Subclonal

Mutations present in only a subset of the tumour’s cells. They are sometimes described as ‘branched’ because they occur on a branch of the tree when the evolutionary trajectory of the tumour is assessed.

Clonal

Mutations present in virtually all cells of the tumour. They are also called ‘truncal’ because they are in the trunk of the tumour evolutionary tree.

Clonal sweep

A mechanism through which a subpopulation sweeps through a tumour and drives all other competing subpopulations to extinction.

Simpson index

An index denoting the probability that two randomly chosen cancer cells would belong to the same subclone.

Shannon index

An index describing the uncertainty of predicting the subclone of a randomly chosen cancer cell.

Jaccard similarity coefficient

A measure defined as the ratio of the number of shared mutations to all mutations in two samples.

Selective bottlenecks

The scenario in which a decrease of the tumour size (for example, due to therapy) leads to a decrease in genetic diversity and an increase in the prevalence of some subclones in the tumour.

Response evaluation criteria in solid tumours

(RECIST). A standardized measure of solid tumour response to a therapy.

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Reiter, J.G., Baretti, M., Gerold, J.M. et al. An analysis of genetic heterogeneity in untreated cancers. Nat Rev Cancer 19, 639–650 (2019). https://doi.org/10.1038/s41568-019-0185-x

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