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Substantial contribution of extrinsic risk factors to cancer development.

Wu S, Powers S, Zhu W, Hannun YA - Nature (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

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

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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Sensitivity analysis of different mutation rates on tLIR when the number of hits (k) required is 3Theoretical 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.
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Figure 6: Sensitivity analysis of different mutation rates on tLIR when the number of hits (k) required is 3Theoretical 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.

Mentions: Lastly, in another independent, model-driven approach to dissecting the risk contribution of the intrinsic processes, we modeled the potential lifetime cancer risk due to intrinsic stem-cell mutation errors by varying the number of hits (i.e. driver gene mutations), denoted by k, required for cancer onset. We derived the probability distribution of the propagation of driver gene mutations from one generation to the next, and subsequently established the theoretical relation between cell divisions and the degree of lifetime cancer risk due to intrinsic cell mutation errors alone, which we refer to as the theoretical lifetime intrinsic risk (tLIR). To overcome the limitation of inaccurate estimation in the reported stem cell numbers5, we calculated tLIR using both the reported stem cell number (tLIRsc) and the total tissue cell number (tLIRtt). The latter is equivalent to assuming all homeostatic tissue cells to be stem cells, representing an extreme overestimation of tissue stem cells, which consequently leads to a conservative estimation of the upper bounds in tLIR. The somatic mutation rate in tumors is estimated to be 5 × 10−10 per nucleotide site per cell division32–34. Based on this, in our initial calculation, we used an intrinsic mutation rate (r) of 1 × 10−8 per cell division, which is equivalent to approximately 20 mutable nucleotide sites for each driver gene where the driver will mutate if at least one site mutates. As shown in Figs. 4a and 4b, if only one hit (that is, mutation of one designated driver gene) is required to develop cancer, i.e. k = 1, the lifetime risk for almost all cancers is close to 100%. This confirms that one mutation is not enough for cancer onset (otherwise everyone would theoretically acquire each type of cancer). If two driver gene mutations are needed, k = 2, the modeled intrinsic risk becomes small for cancers with small total number of stem-cell divisions; however it is still very large for those with higher stem-cell divisions and even unreasonably large for some cancers by surpassing the corresponding observed total lifetime cancer risks (Adjusted Basal, COAD, Adjusted Melanoma, Small Intestine, AML and Duodenum). Therefore, it is unlikely that, at least in these cancers, two hits will suffice to induce cancer. Now, if we consider the more reasonable case where three mutations are required35, k = 3, almost all modeled intrinsic risks (both tLIRsc and tLIRtt) drops well below our earlier “intrinsic” risk lines estimated conservatively from the observed data alone (red dashed lines estimated based on observed data following the same mechanism as Fig. 3a). The lifetime risk drops even further for k = 4 and beyond. The extrinsic risks based on the tLIRsc and tLIRtt have been summarized in the Extended Data Table 4. Therefore, this modeling approach demonstrates that cancer risk due to intrinsic stem-cell mutation errors alone is low for almost all cancers that require over 2 mutations, indeed it is lower than the relatively conservative estimate based on data alone (red lines). Since the driver-gene mutation rate in stem-cell division is a key parameter, we further conducted sensitivity analyses with different rates (r = 1 × 10−10 to 1 × 10−6) to examine how this may impact the tLIR (Extended Data Fig. 2 and 3). The results show that for k = 3, when r < 1 × 10−7 (~200 sites for each driver-gene hit), almost all modeled intrinsic risks are below the observed “intrinsic” risk line (red lines); when r = 1 × 10−6 (~2000 sites for each driver-gene hit), the majority of modeled intrinsic risks are still well below the observed “intrinsic” risk lines, particularly those with small total number of divisions (Extended Data Fig. 2). For k = 4, when r < 1 × 10−6, almost all modeled intrinsic risks are below the observed “intrinsic” risk lines estimated through the data-driven approach (Extended Data Fig. 3). These sensitivity analyses demonstrate that our conclusions are highly robust, and that the attribution of intrinsic mutations to lifetime cancer risk through stem-cell divisions, particularly for those cancers with low risk, is rather small, even using widely different intrinsic mutation rates.


Substantial contribution of extrinsic risk factors to cancer development.

Wu S, Powers S, Zhu W, Hannun YA - Nature (2015)

Sensitivity analysis of different mutation rates on tLIR when the number of hits (k) required is 3Theoretical 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.
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Figure 6: Sensitivity analysis of different mutation rates on tLIR when the number of hits (k) required is 3Theoretical 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.
Mentions: Lastly, in another independent, model-driven approach to dissecting the risk contribution of the intrinsic processes, we modeled the potential lifetime cancer risk due to intrinsic stem-cell mutation errors by varying the number of hits (i.e. driver gene mutations), denoted by k, required for cancer onset. We derived the probability distribution of the propagation of driver gene mutations from one generation to the next, and subsequently established the theoretical relation between cell divisions and the degree of lifetime cancer risk due to intrinsic cell mutation errors alone, which we refer to as the theoretical lifetime intrinsic risk (tLIR). To overcome the limitation of inaccurate estimation in the reported stem cell numbers5, we calculated tLIR using both the reported stem cell number (tLIRsc) and the total tissue cell number (tLIRtt). The latter is equivalent to assuming all homeostatic tissue cells to be stem cells, representing an extreme overestimation of tissue stem cells, which consequently leads to a conservative estimation of the upper bounds in tLIR. The somatic mutation rate in tumors is estimated to be 5 × 10−10 per nucleotide site per cell division32–34. Based on this, in our initial calculation, we used an intrinsic mutation rate (r) of 1 × 10−8 per cell division, which is equivalent to approximately 20 mutable nucleotide sites for each driver gene where the driver will mutate if at least one site mutates. As shown in Figs. 4a and 4b, if only one hit (that is, mutation of one designated driver gene) is required to develop cancer, i.e. k = 1, the lifetime risk for almost all cancers is close to 100%. This confirms that one mutation is not enough for cancer onset (otherwise everyone would theoretically acquire each type of cancer). If two driver gene mutations are needed, k = 2, the modeled intrinsic risk becomes small for cancers with small total number of stem-cell divisions; however it is still very large for those with higher stem-cell divisions and even unreasonably large for some cancers by surpassing the corresponding observed total lifetime cancer risks (Adjusted Basal, COAD, Adjusted Melanoma, Small Intestine, AML and Duodenum). Therefore, it is unlikely that, at least in these cancers, two hits will suffice to induce cancer. Now, if we consider the more reasonable case where three mutations are required35, k = 3, almost all modeled intrinsic risks (both tLIRsc and tLIRtt) drops well below our earlier “intrinsic” risk lines estimated conservatively from the observed data alone (red dashed lines estimated based on observed data following the same mechanism as Fig. 3a). The lifetime risk drops even further for k = 4 and beyond. The extrinsic risks based on the tLIRsc and tLIRtt have been summarized in the Extended Data Table 4. Therefore, this modeling approach demonstrates that cancer risk due to intrinsic stem-cell mutation errors alone is low for almost all cancers that require over 2 mutations, indeed it is lower than the relatively conservative estimate based on data alone (red lines). Since the driver-gene mutation rate in stem-cell division is a key parameter, we further conducted sensitivity analyses with different rates (r = 1 × 10−10 to 1 × 10−6) to examine how this may impact the tLIR (Extended Data Fig. 2 and 3). The results show that for k = 3, when r < 1 × 10−7 (~200 sites for each driver-gene hit), almost all modeled intrinsic risks are below the observed “intrinsic” risk line (red lines); when r = 1 × 10−6 (~2000 sites for each driver-gene hit), the majority of modeled intrinsic risks are still well below the observed “intrinsic” risk lines, particularly those with small total number of divisions (Extended Data Fig. 2). For k = 4, when r < 1 × 10−6, almost all modeled intrinsic risks are below the observed “intrinsic” risk lines estimated through the data-driven approach (Extended Data Fig. 3). These sensitivity analyses demonstrate that our conclusions are highly robust, and that the attribution of intrinsic mutations to lifetime cancer risk through stem-cell divisions, particularly for those cancers with low risk, is rather small, even using widely different intrinsic mutation rates.

Bottom Line: 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.

View Article: PubMed Central - PubMed

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

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.

Show MeSH
Related in: MedlinePlus