Article | Open | Published:

The number of key carcinogenic events can be predicted from cancer incidence


The widely accepted multiple-hit hypothesis of carcinogenesis states that cancers arise after several successive events. However, no consensus has been reached on the quantity and nature of these events, although “driver” mutations or epimutations are considered the most probable candidates. By using the largest publicly available cancer incidence statistics (20 million cases), I show that incidence of 20 most prevalent cancer types in relation to patients’ age closely follows the Erlang probability distribution (R2 = 0.9734–0.9999). The Erlang distribution describes the probability y of k independent random events occurring by the time x, but not earlier or later, with events happening on average every b time intervals. This fits well with the multiple-hit hypothesis and potentially allows to predict the number k of key carcinogenic events and the average time interval b between them, for each cancer type. Moreover, the amplitude parameter A likely predicts the maximal populational susceptibility to a given type of cancer. These parameters are estimated for 20 most common cancer types and provide numerical reference points for experimental research on cancer development.


The value of cancer incidence and mortality curves for inferring information about the underlying carcinogenic processes has long been recognized1. It has been the basis for the influential multiple-hit hypothesis of cancer development, which proposed that cancer appears after several consecutive mutations2,3,4. That prediction was based on the assumption that cancer mortality increases proportionally to the n-th power of age. However, already at that time it was known that many cancers display deceleration of mortality growth at an advanced age, which could not be explained by the power law. Many complicated equations based on multiple assumptions and empirically estimated parameters have since been proposed, attempting to model the limited growth of cancerous cells5,6,7. However, current data unequivocally show that cancer incidence not only ceases to increase with age but, for at least some cancers, decreases8,9. This behaviour cannot be explained by growth equations and has been puzzling biologists and clinicians for considerable time. The depletion of susceptible population, decreased exposure to carcinogens and conversion of cells to the proliferation-arrested, senescent phenotype have been put forward as some of the possible explanations, but none have been convincingly demonstrated (see Discussion in ref.9).

Here I propose that cancer incidence by age is, in fact, a statistical distribution of probabilities that a required number of carcinogenic events occurs by the given age, but not earlier or later. Of 16 tested continuous distributions, the best fit is observed for the gamma distribution and its special case – the Erlang distribution. Notably, these two distributions describe the probability of several independent random events occurring precisely by the given time. This takes the multiple-hit hypothesis to a new level and allows to estimate the number of key carcinogenic events and the average time interval between them, for each cancer type. Moreover, the amplitude parameter of generalized probability distributions likely predicts the maximal populational susceptibility to a given type of cancer. The Erlang distribution exhibits the excellent fit to incidence of each of 20 most prevalent cancer types, with the average R2 of 0.995. The estimated parameters suggest high heterogeneity in the carcinogenesis process and populational susceptibility amongst cancer types and provide reference points for experimental research.


The probability density function (PDF) is used to specify the probability of a random variable falling within a particular range of values. This probability is given by the integral of this variable’s PDF over that range. In the context of cancer incidence, such integral would specify the probability of a patient’s age at a cancer diagnosis falling within a particular age group. It can be seen that such PDF integrates to 1, because once a cancer is diagnosed, the patient must belong to one and only to one of the age groups. For the purpose of elucidating the underlying nature of cancer, this incidence PDF should be calculated with the assumption of indefinite 100% survival of the population, to exclude the confounding influence of mortality from a given cancer type, other cancer types, other diseases and other causes, and thus can be called mortality-independent incidence. It is important to note that it does not specify the probability to be diagnosed with cancer at a particular age (for those who survived to that age), because such function would not integrate to 1 (not every person is to develop cancer during his lifetime, especially a particular type of cancer). However, the latter probability can be derived from the former after multiplying by the maximal populational susceptibility to a given type of cancer. This parameter estimates what fraction of the population would develop a given type of cancer if the population would live indefinitely (reflecting age- and mortality-independent incidence). For an individual person, this parameter describes the maximal probability to develop a given type of cancer during his lifetime or, in other words, the probability of having a susceptible genotype and living in a permissive environment. Thus, the probability to be diagnosed with a particular cancer type at a particular age (for those who survived to that age) is the product of the maximal probability to be diagnosed with this cancer type at all during lifetime and the mortality-independent probability of the age at the cancer diagnosis falling within this age group.

How can the probability to be diagnosed with cancer at a particular age (for those who survived to that age) be calculated from empirical data? It is logical to suggest that the number of newly diagnosed (during the year of observation) cancer cases in a particular age group normalized by the total number of living people in that age group would reflect this probability. In epidemiological terms, it is the crude incidence rate of a particular age group. Such accounting for the number of survivors until a particular age is very important to focus on true incidence of a given type of cancer without confounding influence of mortality from various causes. Because each person is counted only once in each age group (at his actual age during the year of observation), there is no need to normalize by the length of age intervals to derive PDF (an incidence rate for the “50 to 60 y.o.” age group would be approximately equal to an incidence rate for the single “55 y.o.” age). Likewise, when the data are pooled from several consecutive years of observation, each person that is counted several times in the denominator also has proportionally more years to develop cancer, so no adjustment is needed as well.

To test the probability hypothesis, the latest publicly available USA cancer incidence data were downloaded from the CDC WONDER database (see Methods for details and Supplementary Data 150 for original downloads). The PDFs for the general forms of the following continuous probability distributions were tested for fit with least squares non-weighted nonlinear regression analysis: beta, Cauchy, extreme value, Fisher F, gamma, Gompertz, chi-square, Levy, logistic, Maxwell, normal, Rayleigh, Student t, Wald and Weibull (see Methods for details and Supplementary Data 21 for the original project file). Only the extreme value, gamma, logistic, normal and Weibull distributions provided acceptable fits for most of cancer types. Whilst the gamma distribution has only a marginal advantage in the goodness of fit amongst five selected distributions when incidence for different genders and years of observation is combined (see Supplementary Table S1 for R2 and Supplementary Table S2 for standard deviation of the residuals), it shows systematically superior fits when gender-specific cancers are evaluated separately for each observation year (Fig. 1, see Supplementary Tables S3 and S4 for R2, Supplementary Fig. S1 for the residual plots, Supplementary Tables S5 and S6 for the standard deviation of the residuals, Supplementary Tables S7 and S8 for the Akaike Information Criterion, and Supplementary Data 50 for the original project file). Interestingly, the gamma distribution has been used before to estimate confidence intervals for age-adjusted cancer rates10,11,12.

Figure 1

Comparison of different statistical distributions with actual distributions of prostate and breast cancer incidence by age. Dots indicate actual data for 5-year age intervals, curves indicate PDFs fitted to the data. The middle age of each age group is plotted. Different colours indicate different years of observation, from 1999 to 2012. The fitting procedure was identical for all distributions. The normal distribution did not converge for prostate cancer. Prostate and breast cancers were selected due to being the highest-incidence gender-specific cancer types.

Most importantly, the gamma distribution and the Erlang distribution derived from it are the only classical continuous probability distributions that describe the cumulative waiting time for k successive random events, with the Erlang distribution differing only in counting events as integer numbers. Because these properties suit excellently to describe the waiting time for real discrete random events such as mutations, the Erlang distribution provides the opportunity to get unique insights into the carcinogenesis process. I propose that the shape parameter k of the Erlang distribution indicates the average number of key carcinogenic events that need to occur in order for a cancer to develop to a stage that can be detected during clinical screening. The scale parameter b indicates the average time interval (in years) between such events. Finally, the amplitude parameter A divided by 1000 estimates the maximal susceptibility (in percent) of a given population to a given type of cancer. This is because the area under the PDF curve is always unity, the maximal area under the cancer incidence curve is 100,000 (cases per 100,000 people), and A is used to convert probability into incidence.

To obtain these parameter values, the Erlang distribution was fitted individually to incidence of each of 20 most prevalent cancer types (Fig. 2, Table 1, see Methods for details and Supplementary Data 21 for the original project file). The goodness of fit varied from 0.9734, for thyroid cancer, to 0.9999, for pancreatic and oesophageal cancers, with the average of 0.9953. The predicted number of carcinogenic events varied from 4, for melanoma and brain cancer, to 41, for prostate cancer. The predicted average time between the events varied from 2 years, for prostate cancer, to 81 years, for melanoma. The predicted maximal populational susceptibility varied from 1%, for oesophageal, hepatic and laryngeal cancers, to 100%, for melanoma. Overall, the data predict high heterogeneity in carcinogenesis patterns.

Table 1 Estimated carcinogenesis parameters for 20 most prevalent cancer types.

To evaluate reproducibility and robustness of parameter estimation, the gamma distribution was fitted to incidence of prostate cancer separately for each observation year (Fig. 1, Table 2, see Supplementary Data 50 for the original project file). The gamma distribution was selected instead of the Erlang distribution to show precise estimates for the number of carcinogenic events. Prostate cancer was selected due to the highest incidence, the highly efficient screening procedure, the highest estimated number of carcinogenic events and the dramatic variation in incidence between the years of observation. Whilst the maximal populational susceptibility dropped from 32% in 1999 to 17% in 2012 (by 47%), which is explained largely by the official 2008 and 2011/2012 recommendations against screening13,14,15, the average time between events varied from 1.6 to 2.0 years (±11%) and the estimated number of carcinogenic events varied from 38 to 45 (±8%). The upward trend in the number of carcinogenic events may be readily explained by the detection of cancer at later stages in the absence of early screening13,14,15. The variation before the 2008 recommendation was only from 38 to 41 events (±3.8%). Such robustness in the estimation of the number of carcinogenic events for a given cancer type highlights its fundamental nature and thus lends further support to the multiple-hit hypothesis of carcinogenesis.

Table 2 Robustness of carcinogenesis parameter estimation for prostate cancer.


I have shown that cancer incidence by age is best approximated by the Erlang distribution. In most general sense, the Erlang distribution is the sum of k independent exponentially distributed random variables, each of which has the mean of b. As in the case of cancer incidence by age the argument x corresponds to time, and the exponential distribution describes the time between events in a Poisson process, i.e. a process in which events occur independently at a constant average rate, the Erlang distribution describes the cumulative waiting time for k successive random events. Therefore, the only assumption that is required for a mechanistic interpretation is that key carcinogenic events occur independently at a constant average rate. This assumption does not appear unreasonable, as mutations in individual genes are indeed random events that occur independently of each other usually at a constant average rate defined by the levels of gamma and UV radiation, reactive oxygen species and carcinogenic substances, and the rates of DNA replication and repair. While these parameters may vary during the adult lifespan, they do not appear to substantially change in a monotonic and unidirectional fashion, and instead fluctuate around an average.

The DNA replication rate may increase during uncontrolled tumour growth. However, it happens at the last stage (terminal clonal expansion) after most, if not all, key mutations have occurred16. The key mutations may accumulate silently. Indeed, accumulation of mutations and realization of their potential are two different processes that may occur relatively independently. Mutations can stay dormant for a long time, as exemplified by latent driver mutations that exert their effects only upon the occurrence of another mutation or other favourable condition17. Another possibility is that a mutation can occur in a gene responsible for DNA replication, DNA repair or antioxidant activity, thus increasing the overall mutation rate. However, mutations in such “mutator” genes are found in only about 15–20% of tumour samples18,19, and thus are not an essential feature of carcinogenesis20 and cannot have a major influence on cancer incidence statistics. In fact, given the excellent fit of the Erlang distribution to the actual data, this assumption of mutation randomness can be viewed as the prediction, i.e. that previous mutations do not substantially affect the appearance rate of subsequent mutations.

The progression from one carcinogenesis stage to the other is usually assumed to be mediated by “driver” mutations in crucial genes, which give the mutated cell some growth advantage, apoptosis resistance or other oncogenic properties, as opposed to inconsequential “passenger” mutations21. Many algorithms have been suggested for identification of driver mutations22, indicating that no universally accepted criteria exist. Moreover, whilst hundreds of potential driver mutations have been identified in various tumours, they need not be all present in the same tumour specimen, as many of them are redundant or even mutually exclusive, e.g. when the affected proteins are components of the same signalling pathway23. Thus, each tumour is expected to have only a sample of all possible driver mutations. Another aspect to consider is that while one mutation is usually sufficient to activate an oncogene, two mutations are typically required to inactivate both alleles of a tumour suppressor gene. Therefore, the number of carcinogenetic events predicted by the Erlang distribution should be translated not into the number of mutated driver genes, but rather into the number of driver mutations.

When cancer drivers are searched for in tumour genomes, most studies focus on nonsynonymous point mutations24. This gives relatively low numbers of driver mutations, in the range from one to eight (Fig. 3 in ref.24). However, it has been recently shown that synonymous25 and noncoding26 mutations also can act as carcinogenesis drivers. Moreover, there are many more types of genetic alterations that can possibly contribute to cancer progression. They include indels27, homozygous deletions28, inversions29, tandem duplications30, amplifications31, intra- and inter-chromosomal translocations32 (often resulting in gene fusions33), as well as chromosomal arm-level and whole-level copy-number alterations34, and chromothripsis35. Additionally, epigenetic alterations (epimutations) are a whole new level of potential cancer drivers36,37.

It is likely that many of these alterations contribute to progression of each cancer type. Moreover, different cancer types and subtypes require different proportions of these alterations38, e.g. some cancers are driven mostly by point mutations, some by amplifications, yet some by gene fusions. Interestingly, the total number of important alterations per tumour ranged from 0 to 40 (Fig. 2c in ref.38), which corresponds to the range of event numbers predicted by the Erlang distribution. Therefore, the number of carcinogenic events per tumour predicted by the current theory is most likely the sum of driver alterations of several different types. Astonishingly, the recent massive omics study of 333 primary prostate carcinomas by The Cancer Genome Atlas Research Network has found only a single or no alterations in up to 26% of tumour samples19. In extreme case, this may mean that the true nature of carcinogenesis drivers is still not known.

Figure 2

The Erlang distribution approximates cancer incidence by age for 20 most prevalent cancer types. Dots indicate actual data for 5-year age intervals, curves indicate the PDF of the Erlang distribution fitted to the data (see Table 1 for R2 and estimated parameters). The middle age of each age group is plotted. Cancer types are arranged in the order of decreasing incidence.

Most data that were used in this study represent combined cancer cases, e.g. acute and chronic, lymphocytic, myeloid and monocytic leukaemias were combined into Leukaemias. The resulting curve is necessary different in shape, position and amplitude from the curves of individual leukaemia subtypes. Hence, the estimated parameters are also different and reflect only the average. When the exact number of carcinogenic alterations is required, it is necessary to analyse the data for a particular cancer subtype and also for a particular gender and race. Such data are readily accessible at the CDC WONDER portal.

Another factor that influences the results is the stage at which cancer is diagnosed. Cancer types that are diagnosed at early stages, e.g. due to highly developed screening programs, will likely undergo fewer carcinogenic transformations by the time of the first diagnosis than cancers that are difficult to diagnose early. Thus, the current theory predicts the average number of carcinogenic events that happen by the time of diagnosis and not by the time of appearance of the first malignant cell or the time of full cancer development. Therefore, improvements in diagnostics will likely lead to decreases in the estimated numbers of carcinogenic events. A curious counterexample with abandonment of efficient screening that led to an increase in the number of carcinogenic events has been provided in the Results section.

Overall, the theory and methodology presented here allow to generate testable predictions about the carcinogenesis process in any cancer subtype for which reliable incidence statistics is available. Thus, they may help to define the subtype-specific cancer drivers, by providing numerical reference points. Also, the estimated maximal populational susceptibility may help to predict the allele frequencies of driver genes. Finally, these findings provide additional support to the multiple-hit theory of carcinogenesis.


Data acquisition

United States Cancer Statistics Public Information Data: Incidence 1999–2012 were downloaded via Centers for Disease Control and Prevention Wide-ranging OnLine Data for Epidemiologic Research (CDC WONDER) online database ( The United States Cancer Statistics (USCS) are the official federal statistics on cancer incidence from registries having high-quality data for 50 states and the District of Columbia. Data are provided by The Centers for Disease Control and Prevention National Program of Cancer Registries (NPCR) and The National Cancer Institute Surveillance, Epidemiology and End Results (SEER) program. Results were grouped by 5-year Age Groups, Crude Rates were selected as output, and all other settings were kept at default values. Crude Rates are expressed as the number of cases reported each calendar year per 100,000 population. A single person with more than one primary cancer verified by a medical doctor is counted as a case report for each type of primary cancer reported. The population estimates for the denominators of incidence rates are a slight modification of the annual time series of July 1 county population estimates (by age, sex, race, and Hispanic origin) aggregated to the state or metropolitan area level and produced by the Population Estimates Program of the U.S. Bureau of the Census (Census Bureau) with support from the National Cancer Institute (NCI) through an interagency agreement. These estimates are considered to reflect the average population of a defined geographic area for a calendar year. The data were downloaded separately for each cancer type, upon its selection in the Leading Cancer Sites tab. The original txt downloads are available as Supplementary Data 120. For the parameter estimation robustness test, the data for prostate and breast cancers were additionally downloaded separately for each year of observation. These txt downloads are available as Supplementary Data 2249.

Data selection and analysis

For analysis, the data were imported into GraphPad Prism 5. The following age groups were selected: “15–19 years”, “20–24 years”, “25–29 years”, “30–34 years”, “35–39 years”, “40–44 years”, “45–49 years”, “50–54 years”, “55–59 years”, “60–64 years “, “65–69 years”, “70–74 years”, “75–79 years” and “80–84 years”. Prior age groups were excluded due to unreliably low incidence rates, and “85+ years” was excluded due to the undefined age interval. The middle age of each age group was used as the x value, e.g. 17.5 for the “15–19 years” age group. Data were analysed with Nonlinear regression. The following User-defined equations were created for the statistical distributions:

Extreme value:

Y = A*(exp(−((x−t)/b)))*(exp(−exp(−((x−t)/b))))/b


Y = A*(x^(k−1))*(exp(−x/b))/((b^k)*gamma(k))


Y = A*(exp((x−t)/b))/(b*((1 + exp((x−t)/b))^2))


Y = A*(exp(−0.5*(((x−t)/b)^2)))/(b*((2*pi)^0.5))


Y = A*(k/(b^k))*(x^(k−1))*exp(−((x/b)^k))

The parameter A was constrained to “Must be between zero and 100000.0”, parameter t to “Must be between zero and 150.0”, parameters b and k to “Must be greater than 0.0”. “Initial values, to be fit” for all parameters were set to 1.0. All other settings were kept at default values, e.g. Least squares fit and No weighting.

For the Erlang distribution, the parameter k for each cancer type was estimated by the fitting of the Gamma distribution, rounded to the nearest integer and used as “Constant equal to” in the second round of the Gamma distribution fitting, which provided the final results.

The original pzfx project file with data, analyses and graphs is available as Supplementary Data 21. The pzfx project file for the parameter estimation robustness test is available as Supplementary Data 50.

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Hornsby, C., Page, K. M. & Tomlinson, I. P. What can we learn from the population incidence of cancer? Armitage and Doll revisited. Lancet Oncol. 8, 1030–1038 (2007).

  2. 2.

    Nordling, C. O. A new theory on cancer-inducing mechanism. Br. J. Canc. 7, 68–72 (1953).

  3. 3.

    Armitage, P. & Doll, R. The age distribution of cancer and a multi-stage theory of carcinogenesis. Br. J. Canc. 91, 1983–1989 (2004).

  4. 4.

    Knudson, A. G. Two genetic hits (more or less) to cancer. Nat. Rev. Canc. 1, 157–162 (2001).

  5. 5.

    Armitage, P. & Doll, R. A two-stage theory of carcinogenesis in relation to the age distribution of human cancer. Br. J. Canc. 11, 161–169 (1957).

  6. 6.

    Fisher, J. C. Multiple-mutation theory of carcinogenesis. Nature 181, 651–652 (1958).

  7. 7.

    Little, M. P. & Wright, E. G. A stochastic carcinogenesis model incorporating genomic instability fitted to colon cancer data. Math. Biosci. 183, 111–134 (2003).

  8. 8.

    Saltzstein, S. L., Behling, C. A. & Baergen, R. N. Features of cancer in nonagenarians and centenarians. J. Am. Geriatr. Soc. 46, 994–998 (1998).

  9. 9.

    Harding, C., Pompei, F. & Wilson, R. Peak and decline in cancer incidence, mortality, and prevalence at old ages. Cancer 118, 1371–1386 (2012).

  10. 10.

    Fay, M. P. & Feuer, E. J. Confidence intervals for directly standardized rates: a method based on the gamma distribution. Stat. Med. 16, 791–801 (1997).

  11. 11.

    Tiwari, R. C., Clegg, L. X. & Zou, Z. Efficient interval estimation for age-adjusted cancer rates. Stat. Methods Med. Res. 15, 547–569 (2006).

  12. 12.

    Zhang, S. et al. Confidence intervals for ranks of age-adjusted rates across states or counties. Stat. Med. 33, 1853–1866 (2014).

  13. 13.

    Howard, D. H. Declines in prostate cancer incidence after changes in screening recommendations. Arch. Intern. Med. 172, 1267–1268 (2012).

  14. 14.

    Jemal, A. et al. Prostate Cancer Incidence and PSA Testing Patterns in Relation to USPSTF Screening Recommendations. JAMA 314, 2054–2061 (2015).

  15. 15.

    Jemal, A. et al. Prostate Cancer Incidence Rates 2 Years After the US Preventive Services Task Force Recommendations Against Screening. JAMA Oncol. 2, 1657–1660 (2016).

  16. 16.

    Tsao, J. L. et al. Genetic reconstruction of individual colorectal tumor histories. Proc. Natl. Acad. Sci. USA 97, 1236–1241 (2000).

  17. 17.

    Nussinov, R. & Tsai, C. J. ‘Latent drivers’ expand the cancer mutational landscape. Curr. Opin. Struct. Biol. 32, 25–32 (2015).

  18. 18.

    Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).

  19. 19.

    Cancer Genome Atlas Research Network. The Molecular Taxonomy of Primary Prostate Cancer. Cell 163, 1011–1025 (2015).

  20. 20.

    Bodmer, W., Bielas, J. H. & Beckman, R. A. Genetic instability is not a requirement for tumor development. Cancer Res. 68, 3558–3560 (2008).

  21. 21.

    Pon, J. R. & Marra, M. A. Driver and passenger mutations in cancer. Annu. Rev. Pathol. 10, 25–50 (2015).

  22. 22.

    Raphael, B. J., Dobson, J. R., Oesper, L. & Vandin, F. Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine. Genome Med. 6, 5 (2014).

  23. 23.

    Vandin, F., Upfal, E. & Raphael, B. J. De novo discovery of mutated driver pathways in cancer. Genome Res. 22, 375–385 (2012).

  24. 24.

    Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

  25. 25.

    Supek, F., Minana, B., Valcarcel, J., Gabaldon, T. & Lehner, B. Synonymous mutations frequently act as driver mutations in human cancers. Cell 156, 1324–1335 (2014).

  26. 26.

    Weinhold, N., Jacobsen, A., Schultz, N., Sander, C. & Lee, W. Genome-wide analysis of noncoding regulatory mutations in cancer. Nat. Genet. 46, 1160–1165 (2014).

  27. 27.

    Ye, K. et al. Systematic discovery of complex insertions and deletions in human cancers. Nat. Med. 22, 97–104 (2016).

  28. 28.

    Cox, C. et al. A survey of homozygous deletions in human cancer genomes. Proc. Natl. Acad. Sci. USA 102, 4542–4547 (2005).

  29. 29.

    Inaki, K. & Liu, E. T. Structural mutations in cancer: mechanistic and functional insights. Trends Genet. 28, 550–559 (2012).

  30. 30.

    Menghi, F. et al. The tandem duplicator phenotype as a distinct genomic configuration in cancer. Proc. Natl. Acad. Sci. USA 113, E2373–2382 (2016).

  31. 31.

    Albertson, D. G. Gene amplification in cancer. Trends Genet. 22, 447–455 (2006).

  32. 32.

    Bunting, S. F. & Nussenzweig, A. End-joining, translocations and cancer. Nat. Rev. Cancer 13, 443–454 (2013).

  33. 33.

    Mertens, F., Johansson, B., Fioretos, T. & Mitelman, F. The emerging complexity of gene fusions in cancer. Nat. Rev. Cancer 15, 371–381 (2015).

  34. 34.

    Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).

  35. 35.

    Stephens, P. J. et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 27–40 (2011).

  36. 36.

    Hitchins, M. P. Constitutional epimutation as a mechanism for cancer causality and heritability? Nat. Rev. Cancer 15, 625–634 (2015).

  37. 37.

    Roy, D. M., Walsh, L. A. & Chan, T. A. Driver mutations of cancer epigenomes. Protein Cell 5, 265–296 (2014).

  38. 38.

    Ciriello, G. et al. Emerging landscape of oncogenic signatures across human cancers. Nat. Genet. 45, 1127–1133 (2013).

Download references


The author is grateful to Prof. Andrey V. Brailov for the stimulating discussion and especially for the suggestion to test the robustness of parameter estimation.

Author information

A.V.B. conceived of and performed the analysis and wrote the manuscript.

Competing Interests

The author declares that he has no competing interests.

Correspondence to Aleksey V. Belikov.

Electronic supplementary material

  1. Supplementary Information

  2. Supplementary Data 1-50

Rights and permissions

Creative Commons BY

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit

About this article


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.