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Substantial contribution of extrinsic risk factors to cancer development

Nature volume 529, pages 43–47 (2016)Cite this article

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Abstract

Recent research has highlighted a strong correlation between tissue-specific cancer risk and the lifetime number of tissue-specific stem-cell divisions. Whether such correlation implies a high unavoidable intrinsic cancer risk has become a key public health debate with the dissemination of the ‘bad luck’ hypothesis. Here we provide evidence that intrinsic risk factors contribute only modestly (less than ~10–30% of lifetime risk) to cancer development. First, we demonstrate that the correlation between stem-cell division and cancer risk does not distinguish between the effects of intrinsic and extrinsic factors. We then show that intrinsic risk is better estimated by the lower bound risk controlling for total stem-cell divisions. Finally, we show that the rates of endogenous mutation accumulation by intrinsic processes are not sufficient to account for the observed cancer risks. Collectively, we conclude that cancer risk is heavily influenced by extrinsic factors. These results are important for strategizing cancer prevention, research and public health.

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Figure 1: Schematic showing how intrinsic processes and extrinsic factors relate to cancer risks through stem-cell division.
Figure 2: Correlation analysis of stem-cell division and cancer risk does not distinguish contribution of extrinsic versus intrinsic factors to cancer risk.
Figure 3: Estimation of the proportion of lifetime cancer risk that is not due entirely to ‘bad luck’.
Figure 4: Theoretical lifetime intrinsic risks (tLIR) for cancers based on different number of hits (k) required for cancer onset.

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Acknowledgements

We thank L. Obeid for constructive comments. This work was supported in part by NCI grants 97132 and 168409 and Stony Brook NYSTEM award C026716.

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Authors and Affiliations

  1. Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, 11794, New York, USA

    Song Wu, Scott Powers & Wei Zhu

  2. Stony Brook Cancer Center, Stony Brook University, Health Sciences Center, Stony Brook, 11794, New York, USA

    Song Wu, Scott Powers, Wei Zhu & Yusuf A. Hannun

  3. Department of Pathology, Stony Brook University, Health Sciences Center, Stony Brook, 11794, New York, USA

    Scott Powers & Yusuf A. Hannun

  4. Department of Medicine, Stony Brook University, Health Sciences Center, Stony Brook, 11794, New York, USA

    Yusuf A. Hannun

  5. Department of Biochemistry, Stony Brook University, Health Sciences Center, Stony Brook, 11794, New York, USA

    Yusuf A. Hannun

Authors
  1. Song Wu
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  2. Scott Powers
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  4. Yusuf A. Hannun
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Contributions

Y.A.H. formulated the hypothesis. S.W. and Y.A.H. designed the research. S.W. and W.Z. performed mathematical and statistical analysis. S.W., S.P., W.Z. and Y.A.H. performed research. S.W., S.P., W.Z. and Y.A.H. wrote the paper.

Corresponding authors

Correspondence to Song Wu or Yusuf A. Hannun.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Examples of increased cancer incidence trends from 1973–2012 in SEER data.

The cancer types include melanoma, thyroid cancer, kidney cancer, liver cancer, small intestine cancer, testicular cancer, non-Hodgkin lymphoma (NHL), anal and anorectal cancer and thymus cancer. The horizontal dashed lines indicate the historical minimal incidence. The vertical solid lines indicate the most recent year. The numbers represent the minimal percentage of extrinsic risk. The cervix uteri cancer, gallbladder cancer and oesophageal cancer are examples with declining or consistent incidence trend. The incidence rate is per 100,000 people.

Extended Data Figure 2 Sensitivity analysis of different mutation rates on tLIR when the number of hits (k) required is 3.

a, b, Theoretical intrinsic lifetime risks (tLIR) for cancers have been calculated based on five different mutation rates: r = 1 × 10−10, 1 × 10−9, 1 × 10−8, 1 × 10−7, 1 × 10−6. The red dashed lines are the ‘intrinsic’ risk lines based on the observed data following the same estimation mechanism as the intrinsic risk line in Fig. 3a. The green (a) and blue (b) dashed lines are the ‘intrinsic’ risk lines estimated based on total reported stem-cell numbers and total homeostatic tissue cells, respectively.

Extended Data Figure 3 Sensitivity analysis of different mutation rates on tLIR when the number of hits (k) required is 4.

a, b, Theoretical intrinsic lifetime risks (tLIR) for cancers have been calculated based on five different mutation rates: r = 1 × 10−10, 1 × 10−9, 1 × 10−8, 1 × 10−7, 1 × 10−6. The red dashed lines are the ‘intrinsic’ risk lines based on the observed data following the same estimation mechanism as the intrinsic risk line in Fig. 3a. The green (a) and blue (b) dashed lines are the ‘intrinsic’ risk lines estimated based on total reported stem-cell numbers and total homeostatic tissue cells, respectively.

Extended Data Figure 4 Intrinsic cancer risk modelling.

Part 1 of 2: propagation diagram of driver gene mutation states between generations in one stem cell, from which the stem-cell mutation transition probabilities from one generation to the next are computed.

Extended Data Figure 5 Intrinsic cancer risk modelling.

Part 2 of 2: schema of stem-cell divisions and driver gene mutations, from which the theoretical lifetime intrinsic risks (tLIR) for cancer due to k driver gene mutations are computed. Each coloured circle represents the mutation of a new driver gene in the given stem cell (yellow, first mutation; green, second mutation; red, third mutation). If the mutation of 3 designated driver genes would induce a cancerous stem cell (k = 3), then this diagram shows a cancer occurrence as the second stem cell in the last generation (generation n) that has accumulated all 3 driver gene mutations.

Extended Data Table 1 Robustness analysis on total stem-cell divisions and cell divisions estimates in Fig. 3
Full size table
Extended Data Table 2 Epidemiological studies on the extrinsic risks of various cancers
Full size table
Extended Data Table 3 Percentages of intrinsic versus extrinsic MS with known and unknown causes in different cancer types
Full size table
Extended Data Table 4 Percentages of extrinsic risks based on the reported stem-cell estimates and total homeostatic tissue cells, as shown in Fig. 4
Full size table

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Wu, S., Powers, S., Zhu, W. et al. Substantial contribution of extrinsic risk factors to cancer development. Nature 529, 43–47 (2016). https://doi.org/10.1038/nature16166

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