Abstract
Tumours initiate when a population of proliferating cells accumulates a certain number and type of genetic and/or epigenetic alterations. The population dynamics of such sequential acquisition of mutations has been the topic of much investigation. The phenomenon of stochastic tunneling has been studied using a variety of computational and mathematical methods. However, the field still lacks a comprehensive analytical description since theoretical predictions of fixation times are available only for cases in which the second mutant is advantageous. Here, we study stochastic tunnelling in a Moran model. Analysing the deterministic dynamics of large populations we systematically identify the parameter regimes captured by existing approaches. Our analysis also reveals fitness landscapes and mutation rates for which finite populations are found in long-lived metastable states. These are landscapes in which the final mutant is not the most advantageous in the sequence, and resulting metastable states are a consequence of a mutation-selection balance. The escape from these states is driven by intrinsic noise, and their location affects the probability of tunnelling. Existing methods no longer apply. In these regimes it is the escape from the metastable states that is the key bottleneck. We used the so-called Wentzel–Kramers–Brillouin method to compute fixation times in these parameter regimes, successfully validated by stochastic simulations.
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Notes
- 1.
The process of repeatedly accumulating disadvantageous mutations [39], named after Hermann Joseph Muller (1890–1967).
- 2.
The minimum value of N for which our analysis is valid is dependent on the remaining model parameters, but comparisons with simulation results in the next section show it is accurate for \(N\gtrsim 100\).
- 3.
After William Rowan Hamilton (1805–1865) and the previously introduced Carl Gustav Jacob Jacobi.
References
Cancer Research UK, All cancers combined: key facts. Cancer Research UK (2014)
Cancer Research UK, Annual report and accounts 2014/15. Cancer Research UK (2015)
R.A. Weinberg, The Biology of Cancer (Garland Science, New York, 2013)
C. Nordling, A new theory on the cancer-inducing mechanism. Br. J. Cancer 7, 68 (1953)
P. Armitage, R. Doll, The age distribution of cancer and a multi-stage theory of carcinogenesis. Br. J. Cancer 8, 1 (1954)
J. Fisher, Multiple-mutation theory of carcinogenesis. Nature 181, 651 (1958)
A.G. Knudson, Mutation and cancer: statistical study of retinoblastoma. Proc. Natl. Acad. Sci. U.S.A. 68, 820 (1971)
S.H. Moolgavkar, The multistage theory of carcinogenesis and the age distribution of cancer in man. J. Natl. Cancer Inst. 61, 49 (1978)
S.H. Moolgavkar, A.G. Knudson, Mutation and cancer: a model for human carcinogenesis. J. Natl. Cancer Inst. 66, 1037 (1981)
S.H. Moolgavkar, E.G. Luebeck, Multistage carcinogenesis: population-based model for colon cancer. J. Natl. Cancer Inst. 84, 610 (1992)
L. Nunney, Lineage selection and the evolution of multistage carcinogenesis. Proc. R. Soc. Lond. B 266, 493 (1999)
R.A. Gatenby, T.L. Vincent, An evolutionary model of carcinogenesis. Cancer Res. 63, 6212 (2003)
F. Michor, Y. Iwasa, M.A. Nowak, Dynamics of cancer progression. Nat. Rev. Cancer 4, 197 (2004)
N. Beerenwinkel, T. Antal, D. Dingli, A. Traulsen, K.W. Kinzler, V.E. Velculescu, B. Vogelstein, M.A. Nowak, Genetic progression and the waiting time to cancer. PLoS Comput. Biol. 3, e225 (2007)
H. Haeno, R.L. Levine, D.G. Gilliland, F. Michor, A progenitor cell origin of myeloid malignancies. Proc. Natl. Acad. Sci. U.S.A. 106, 16616 (2009)
I. Bozic, T. Antal, H. Ohtsuki, H. Carter, D. Kim, S. Chen, R. Karchin, K.W. Kinzler, B. Vogelstein, M.A. Nowak, Accumulation of driver and passenger mutations during tumor progression. Proc. Natl. Acad. Sci. U.S.A. 107, 18545 (2010)
I. Van Leeuwen, H. Byrne, O. Jensen, J. King, Crypt dynamics and colorectal cancer: advances in mathematical modelling. Cell Prolif. 39, 157 (2006)
T. Antal, P.L. Krapivsky, M.A. Nowak, Spatial evolution of tumors with successive driver mutations. Phys. Rev. E 92, 022705 (2015)
M. Archetti, D.A. Ferraro, G. Christofori, Heterogeneity for IGF-II production maintained by public goods dynamics in neuroendocrine pancreatic cancer. Proc. Natl. Acad. Sci. U.S.A. 112, 1833 (2015)
M.A. Nowak, F. Michor, Y. Iwasa, The linear process of somatic evolution. Proc. Natl. Acad. Sci. U.S.A. 100, 14966 (2003)
N.L. Komarova, A. Sengupta, M.A. Nowak, Mutation-selection networks of cancer initiation: tumor suppressor genes and chromosomal instability. J. Theor. Biol. 223, 433 (2003)
B. Werner, D. Dingli, A. Traulsen, A deterministic model for the occurrence and dynamics of multiple mutations in hierarchically organized tissues. J. R. Soc. Interface 10, 20130349 (2013)
T. Antal, P. Krapivsky, Exact solution of a two-type branching process: models of tumor progression. J. Stat. Mech. 2011, P08018 (2011)
Y. Iwasa, F. Michor, M.A. Nowak, Stochastic tunnels in evolutionary dynamics. Genetics 166, 1571 (2004)
M.A. Nowak, F. Michor, N.L. Komarova, Y. Iwasa, Evolutionary dynamics of tumor suppressor gene inactivation. Proc. Natl. Acad. Sci. U.S.A. 101, 10635 (2004)
P. Ashcroft, F. Michor, T. Galla, Stochastic tunneling and metastable states during the somatic evolution of cancer. Genetics 199, 1213 (2015)
Y. Iwasa, F. Michor, N.L. Komarova, M.A. Nowak, Population genetics of tumor supressor genes. J. Theor. Biol. 233, 15 (2005)
F. Michor, Y. Iwasa, Dynamics of metastasis suppressor gene inactivation. J. Theor. Biol. 241, 676 (2006)
S.R. Proulx, The rate of multi-step evolution in Moran and Wright-Fisher populations. Theor. Popul. Biol. 80, 197 (2011)
H. Haeno, Y.E. Maruvka, Y. Iwasa, F. Michor, Stochastic tunneling of two mutations in a population of cancer cells. PLoS ONE 8, e65724 (2013)
J.F. Crow, M. Kimura, An Introduction to Population Genetics Theory (Harper and Row, New York, 1970)
O. Gottesman, B. Meerson, Multiple extinction routes in stochastic population models. Phys. Rev. E 85, 021140 (2012)
O.A. van Herwaarden, J. Grasman, Stochastic epidemics: major outbreaks and the duration of the endemic period. J. Math. Biol. 33, 581 (1995)
A. Kamenev, B. Meerson, Extinction of an infectious disease: a large fluctuation in a nonequilibrium system. Phys. Rev. E 77, 061107 (2008)
M.I. Dykman, I.B. Schwartz, A.S. Landsman, Disease extinction in the presence of random vaccination. Phys. Rev. Lett. 101, 078101 (2008)
A.J. Black, A.J. McKane, WKB calculation of an epidemic outbreak distribution. J. Stat. Mech. 2011, P12006 (2011)
L. Billings, L. Mier-Y-Teran-Romero, B. Lindley, I.B. Schwartz, Intervention-based stochastic disease eradication. PLoS ONE 8, e70211 (2013)
A. Altland, A. Fischer, J. Krug, I.G. Szendro, Rare events in population genetics: stochastic tunneling in a two-locus model with recombination. Phys. Rev. Lett. 106, 088101 (2011)
H.J. Muller, The relation of recombination to mutational advance. Mut. Res. 1, 2 (1964)
J.J. Metzger, S. Eule, Distribution of the fittest individuals and the rate of Muller’s ratchet in a model with overlapping generations. PLoS Comput. Biol. 9, e1003303 (2013)
P.A.P. Moran, The Statistical Processes of Evolutionary Theory (Clarendon Press, Oxford UK, 1962)
J. Ma, A. Ratan, B.J. Raney, B.B. Suh, W. Miller, D. Haussler, The infinite sites model of genome evolution. Proc. Natl. Acad. Sci. U.S.A. 105, 14254 (2008)
T.A. Kunkel, K. Bebenek, DNA replication fidelity. Annu. Rev. Biochem. 69, 497 (2000)
D.M. Weinreich, L. Chao, Rapid evolutionary escape by large populations from local fitness peaks is likely in nature. Evolution 59, 1175 (2005)
D.B. Weissman, M.M. Desai, D.S. Fisher, M.W. Feldman, The rate at which asexual populations cross fitness valleys. Theor. Popul. Biol. 75, 286 (2009)
D.B. Weissman, M.W. Feldman, D.S. Fisher, The rate of fitness-valley crossing in sexual populations. Genetics 186, 1389 (2010)
M. Lynch, Scaling expectations for the time to establishment of complex adaptations. Proc. Natl. Acad. Sci. U.S.A. 107, 16577 (2010)
W.J. Ewens, Mathematical Population Genetics I. Theoretical Introduction (Springer, New York, 2004)
T. Antal, I. Scheuring, Fixation of strategies for an evolutionary game in finite populations. Bull. Math. Biol. 68, 1923 (2006)
M. Mobilia, Oscillatory dynamics in rock-paper-scissors games with mutations. J. Theor. Biol. 264, 1 (2010)
R.A. Fisher, The Genetical Theory of Natural Selection (Clarendon Press, Oxford UK, 1930)
C.M. Bender, S.A. Orszag, Advanced Mathematical Methods for Scientists and Engineers (Springer, New York, 1999)
M. Assaf, B. Meerson, Extinction of metastable stochastic populations. Phys. Rev. E 81, 021116 (2010)
L.D. Landau, E.M. Lifshitz, Mechanics, vol. 1 (Pergamon Press, Oxford UK, 1976)
I. Lohmar, B. Meerson, Switching between phenotypes and population extinction. Phys. Rev. E 84, 051901 (2011)
M. Heymann, E. Vanden-Eijnden, The geometric minimum action method: a least action principle on the space of curves. Comm. Pure Appl. Math. 61, 1052 (2008)
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Ashcroft, P. (2016). Metastable States in a Model of Cancer Initiation. In: The Statistical Physics of Fixation and Equilibration in Individual-Based Models. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-41213-9_5
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