Challenging the axiom: does the occurrence of oncogenic mutations truly limit cancer development with age?


A widely accepted paradigm in cancer research holds that the development of cancers is rate limited by the occurrence of oncogenic mutations. In particular, the exponential rise in the incidence of most cancers with age is thought to reflect the time required for cells to accumulate the multiple oncogenic mutations needed to confer the cancer phenotype. Here I will argue against the axiom that the occurrence of oncogenic mutations limits cancer incidence with age, based on several observations, including that the rate of mutation accumulation is maximal during ontogeny, oncogenic mutations are frequently detected in normal tissues, the evolution of complex multicellularity was not accompanied by reductions in mutation rates, and that many oncogenic mutations have been shown to impair stem cell activity. Moreover, although evidence that has been used to support the current paradigm includes increased cancer incidence in individuals with inherited DNA repair deficiencies or exposed to mutagens, the pleotropic effects of these contexts could enhance tumorigenesis at multiple levels. I will further argue that age-dependent alteration of selection for oncogenic mutations provides a more plausible explanation for increased cancer incidence in the elderly. Although oncogenic mutations are clearly required for cancer evolution, together these observations counter the common view that age dependence of cancers is largely explained by the time required to accumulate sufficient oncogenic mutations.


The field of cancer research is dominated by the view that oncogenesis is rate limited by the incidence of oncogenic mutations, and that these mutations are typically advantageous when they occur in the right cell type. Such oncogenic mutations (including activation of proto-oncogenes and de-activation of tumor suppressor genes, whether by genetic or by epigenetic mechanisms) are thought to provide cells with various ‘Hallmarks of Cancer’, including sustained proliferative signaling and resistance to growth-suppressive and cell death signals.1 In particular, it is widely accepted that the exponential increase of cancer incidence with age reflects the time required for cells to accumulate sufficient numbers of genetic and epigenetic mutations to confer the cancer phenotype.2, 3, 4, 5, 6, 7, 8 This paradigm in part originates with the classic modeling studies of Armitage and Doll,9, 10 which showed that the incidence of cancers increases with around the sixth power of age, suggesting that the age-dependent accumulation of 6–7 oncogenic mutations is needed for cancer development. The logic is quite simple: aging→mutations (including oncogenic mutations)→cancer.

Oncogenic mutations are clearly required for cancer evolution, and increases in genetic/epigenetic diversity in somatic cells associated with aging should contribute to cancer incidence. Increased rates of genomic instability in some cancers can also help promote tumor evolution.1 However, I will argue that the axiomatic attribution of the rising incidence of cancers with age primarily to the accumulation of oncogenic mutations is insufficiently justified, stimulating the following questions: Why is aging associated with increased cancer incidence? Is the association explained by the requirement to increase the accumulation of oncogenic mutations? Or are pre-existing oncogenic mutations largely the substrate upon which age-dependent alterations in selection act? This perspective will not address whether cancer evolution in general involves or requires accelerated mutation accumulation, unless this acceleration were due to aging.

I will describe evidence to challenge the axiom that the occurrence of oncogenic mutations limits cancer incidence with age. I will argue that the age-dependent accumulation of mutations has a relatively minor role in the increased incidence of cancer with age. Instead, other aging-associated changes, such as alterations in tissues that influence the selection for oncogenic events, largely underlie the aging association of cancers.

The rate of mutation accumulation over time is maximal during ontogeny (development to maturity)

Although the commonly accepted idea is that accumulation of oncogenic mutations with age accounts for the age dependence of cancer, many, if not most, mutations appear to accumulate during ontogeny, rather than during adulthood.11, 12, 13 The maintenance of self-renewing adult tissues may require relatively few stem cell divisions. Indeed, most telomere shortening in human cells occurs before birth.14 It is estimated that any given hematopoietic stem cell (HSC) will divide on average 5–10 times through the life of an adult mouse (from maturity to 2–3 years of age),15 and yet one would surmise that the generation of each HSC in a young mouse required far larger numbers of cell divisions (counting from the one-celled zygote). Thus, it is not surprising that a substantial fraction of mutations and epigenetic changes would occur and accumulate during ontogeny,12, 13 followed by a more modest rate of accumulation during tissue maintenance post maturity (although an accelerated accumulation of mutations/genomic rearrangements in late life has been observed,16, 17 and DNA repair mechanisms, at least as assessed in cell culture, may become impaired in old age18). At least as assessed using transgenic reporters in C57BL/6 mice, accumulation of mutations (including genome rearrangements) from maturity through old age is relatively modest (2–3-fold in some tissues).12 In particular, the accumulation of mutations in the spleen is negligible from maturity through old age12, 19 (Figure 1), consistent with the very low division rates for HSC post maturity.15 As leukocytes in the spleen are derived from HSC and are relatively short-lived, they should serve as adequate proxies for the analysis of mutation accumulation in HSC and hematopoietic progenitors. As the most common malignancies that develop in old C57BL/6 mice are lymphocytic,20 the paucity of aging-dependent accumulation of mutations in the hematopoietic compartment is at odds with the mutation-centric paradigm. That the incidence of most cancers rises late in life (Figure 1), with kinetics that are quite disconnected from the time-dependent accumulation of mutations (maximal during ontogeny) in the tissues from whence these cancers arise, argues that age-dependent acquisition of oncogenic mutations is not a rate-limiting step in tumorigenesis. Indeed, Frank13 has proposed that cancers that develop in old age may in fact depend on oncogenic mutations accumulated during ontogeny.

Figure 1

Comparison of the time courses of mutation accumulation with onset of malignancies in the hematopoietic system in mice. Stylized curves represent the numbers of mutations detected in spleens (gray)12, 19 and lymphoma incidence (black)20 in C57BL/6 mice.

Oncogenic mutations are frequently detected in non-diseased tissues

Oncogenic mutations are necessary, but not sufficient, for tumorigenesis. Many barriers to tumor progression exist and it is well known that multiple oncogenic mutations are required for the emergence of clinically-detectable cancers.21, 22 Still, if the incidence of oncogenic mutations were a rate-limiting step in tumorigenesis, one would not expect an abundance of oncogenic mutations in the absence of tumorigenesis. Nonetheless, cell clones with mutational or epigenetic inactivation of the PTEN or INK4A tumor suppressor genes are frequently found in histologically normal endometria and breast (respectively) of cancer-free women,23, 24 far outpacing the incidence of the corresponding cancers. Furthermore, the presence of TEL-AML1 and AML1-ETO translocations in the blood cells of newborns is 100-fold greater than the risk of the associated leukemias.25 Perhaps most surprisingly, histologically advanced microscopic tumors are detected in many tissues of adult humans,22, 26 but they appear to be mostly held in check by unknown mechanisms. In addition, even though it is thought that the incidence of chronic myeloid leukemia, which increases exponentially late in life, is limited by the occurrence of the initiating Bcr–Abl translocation,27 in-frame Bcr–Abl fusions are detected in leukocytes of 1 in 3 healthy individuals,28, 29 the vast majority of which will fortunately never develop this leukemia (despite persistence of the translocation in leukocytes for long enough to suggest an HSC origin).30 Notably, chronic myeloid leukemia in chronic phase is thought to be a simple leukemia (a myeloproliferative disorder), with Bcr–Abl as the only recurrent mutation.31 In mouse models, the expression of Bcr–Abl provides a much greater advantage to progenitor cells in an aged as compared with young hematopoietic system, leading to increased clonal expansion and leukemogenesis.32 Thus, at least in this mouse model, the occurrence of the same oncogenic mutation, Bcr–Abl, results in very different outcomes depending on the age of the target tissue.

The evolution of multicellularity has not been accompanied by decreases in DNA mutation rates

If increased acquisition of oncogenic mutations is the primary driver of oncogenesis, organisms that are more likely to acquire oncogenic mutations should be more likely to get cancer, and larger/longer-lived animals would have been expected to have evolved mechanisms to limit mutation accumulation. Thus, it might seem surprising, but mutation rates are higher in larger and more complex organisms like mammals than they are in prokaryotes, single-celled eukaryotes and simpler multicellular organisms.11 Additionally, within mammals, somatic mutation rates are even higher than those for the germline. For example, the average somatic mutation rate for humans across four tissues (1 × 10−9/site/cell division) is 17-fold higher than the germline rate, and surprisingly, several-fold higher than rates for Saccharomyces cerevisiae and Escherichia coli.11 While some studies have shown that the efficiency of DNA excision repair among mammals is proportional to lifespan and/or body size (at least for fibroblasts exposed in vitro to ultraviolet light (UV)),33, 34 these differences have not been shown to coincide with similar changes in mutation accumulation in tissues in vivo and UV-induced excision repair may be less relevant for small nocturnal mammals (in fact, recognition of UV-induced cyclobutane pyrimidine dimers is suppressed in rodent cells).35 Indeed, while the disparate methods used muddy comparisons (and highlight the need for direct measurements of mutations in mammals with age), rates of mutation accumulation in somatic tissues are similar between rodents and humans.11 Therefore, of all of the proposed evolved tumor-suppressive mechanisms that keep cancer rates sufficiently low in multicellular organisms long enough to promote reproductive success,36 improvements in DNA repair do not appear to have been harnessed during the evolution of bigger, more complex and longer-lived animals. DNA fidelity mechanisms were apparently already ‘good enough’ to limit cancer through reproductive years, and the evolution of tumor-suppressive mechanisms with increasing multicellularity did not require further refinement.

DNA repair deficiency, mutagens and cancer: complicated relationships

The increased cancer incidence associated with inherited DNA repair deficiencies or exposure to DNA-damaging agents is often cited as key support for the argument that time-dependent accumulation of oncogenic mutations is responsible for the rise of cancer rates with aging.2, 4, 6, 37 The logic seems simple: agents that increase mutation frequency also increase cancer incidence. Though easily understood, this rationale bypasses important characteristics of diseases associated with inherited DNA repair defects, particularly cancer-promoting characteristics that extend beyond increased frequencies of oncogenic mutations. For example, ATM (ataxia telangiectasia mutated) deficiency also reduces the fitness (see Box 1 for definitions) of HSC, increases reactive oxygen species, alters metabolism, promotes inflammation and decreases immune function,37, 38, 39, 40 all of which could contribute to cancer evolution at multiple levels. These pleotropic effects of ATM loss emanate both from impaired DNA repair as well as from the non-repair functions of ATM. Thus, for DNA repair deficiencies, it is difficult to assign the blame for increased cancer to a particular consequence of the genetic defect.

Moreover, an increase in mutation rates does not always confer increased cancer incidence. Heterozygous mutation of the DNA polymerase δ (at L604G and L604K) in mice increases mutation rates four- to fivefold in embryonic fibroblasts, with an even larger increase in the frequency of chromosomal aberrations (>17-fold), but without increasing the incidence of cancer.41 In particular, as the L604G/+ mice have normal lifespans and cancer incidence, increased mutation rates in these mice appear to have been uncoupled from any changes in both cancer and overall physiology (contrasting with ATM deficient mice). In contrast, heterozygous L604K mutation leads to a shorter lifespan, and cancers in L604K/+ mice show accelerated progression (but with similar tumor incidence to +/+ mice). One could conclude that L604K accelerates tumor development by either increasing genetic diversity or altering selective pressures; the absence of a similar acceleration in the L604G/+ mice would argue for the latter, although the two explanations need not be mutually exclusive. Although in vivo mutation rates have not been determined for L604K/+ and L604G/+ mice, the increases in L604 DNA polymerase δ mutant mice are likely to eclipse the modest accumulation of mutations in mice from maturity to old age.12 Thus, increasing mutation rates is apparently not sufficient for increased tumorigenesis. Still, other loss-of-function mutations in DNA polymerases do increase cancer incidence in mice,42 indicating either that there is a threshold increase in mutations required to increase cancer rates over the background or that other effects of these mutations on mouse physiology may promote the increased cancer incidence.

Analogous concerns can be raised for associations between exposure to DNA-damaging agents and cancer, as these agents (and the resulting DNA damage) similarly cause pleotropic effects (reduced progenitor cell fitness, increased inflammation, increased cell turnover, decreased immune function, etc.).43 Thus, the extent to which radiation, chemotherapy treatments and other mutagenic exposures increase cancer rates by inducing oncogenic mutations cannot currently be determined.

Fitness, selection and cancer evolution: an alternative model

For organismal evolution, natural selection works on heritable diversity, and major periods of speciation (such as the Cambrian Explosion) were likely due to altered environmental selection pressures rather than increases in mutation rates. Analogously, numerous investigators have stressed the importance of the microenvironment in cancer development and the critical role of altered selection.21, 22, 44, 45, 46, 47, 48, 49 Dramatic changes in tissue microenvironments occur with age, including stromal changes and increased inflammation.50, 51 These age-dependent changes should substantially alter adaptive landscapes (relationships between genotype and cellular fitness; Box 1), which describe how mutational changes can be adaptive, maladaptive or neutral in a context-dependent fashion. Alterations in adaptive landscapes in old age should promote selection for particular oncogenic mutations from within the standing genetic/epigenetic variation (Figure 2), whether it arose from endogenous (oxidative damage, replicative errors, and so on) or exogenous (exposure to environmental carcinogens) insults. Indeed, recent studies indicate that the frequency of cells with clonally-expanded genomic rearrangements increases substantially after 50–60 years of age in humans, correlating with cancer risk, which could reflect alterations in the adaptive landscape, increased rates of genomic alterations and/or decreased stem cell polyclonality.52, 53, 54 Notably, in another study, five of the six detected clonally-expanded chromosomal abnormalities were present in both bladder and blood, suggesting an early embryonic origin of the events.55 Mutations that arise during ontogeny (or anytime after) but were neither adaptive nor maladaptive at the time may be adaptive in the new landscape, thereby conferring a selective advantage and promoting clonal expansion.36, 48 Context-dependent selection leading to expansion of the oncogenically mutated clone would then greatly increase the likelihood of acquisition of secondary oncogenic mutations in cells that harbor an initiating lesion. Moreover, some of these oncogenic events selected for by the age- or carcinogen-altered adaptive landscapes could then contribute to increased genomic instability, providing more fuel for selection to act upon. While new mutations that accumulate with age should increase the cellular variation subject to selection, this alternative model does not depend on age-dependent accumulation of mutations to explain increased cancer incidence in old age.

Figure 2

Selection-centric model. This model posits that aging is largely associated with cancer due to alterations in selection for oncogenic mutations. The weight of the arrow reflects the proposed contribution to cancer incidence.

To understand cancer evolution, we should consider why large and long-lived multicellular organisms like ourselves are so good at not getting cancer.36 For example, what tumor-suppressive mechanism could allow for mammals as diverse as mice and blue whales to largely avoid cancer through their reproductive years (Peto’s Paradox)? As argued above, the commonly accepted view that cancer incidence is rate-limited by the occurrence of oncogenic mutations does not appear to be consistent with the common presence of oncogenic mutations in normal tissues, with the most rapid accumulation of mutations during ontogeny, and with the lack of reductions in somatic mutation rates during the evolution of complex multicellularity. We have proposed that cancer avoidance through reproductive years is dependent on the same basic principle that governs the avoidance of other hallmarks of aging: investments are made in tissue maintenance to the extent that provides the best return in terms of reproductive success. Thus, we have argued that the maintenance of tissue stem and progenitor cell fitness is inherently tumor suppressive, as high cellular fitness should disfavor selection for phenotype-altering somatic mutations (see DeGregori36 and Marusyk and DeGregori48 for a full description of this ‘Adaptive Oncogenesis’ model). Of course, other mechanisms, such as alterations in how telomeres are maintained,56, 57 could also contribute to similar tumor suppression through reproductive years for species with hugely different sizes and lifespans.

If we again consider HSC, given that hematopoietic malignancies are common in mice and that HSC are the best-characterized stem cells, it is striking that mutations defined as oncogenic (activation of an oncogenic pathway, either by tumor suppressor gene deletion or by oncogene expression) typically exhibit a common phenotype in HSC: loss of self-renewal (Table 1). For this table, I have assembled all published reports that I could find that describe oncogenic mutations engineered in mouse HSC under reasonably physiological contexts (that is, in young unperturbed bone marrow at steady state). It is notable that even mutations, such as in PTEN,58, 59 which increase proliferation (leading to initial expansion of short-term progenitors), impair HSC maintenance. In fact, a common effect of oncogenic mutations in HSC is to increase cell cycling,60 which likely contributes to loss of self-renewal: HSC maintenance necessitates an appropriate level of quiescence. Thus, we would expect that these mutations, should they occur in an individual HSC, would lead to clonal exhaustion by differentiation. Finally, it is notable that many of these mutations have been shown to be advantageous in vitro. For example, β-catenin activation increases HSC self-renewal and expansion in vitro.61 Animals did not evolve stem cells that would be well adapted to in vitro culture, and certain oncogenic events can be adaptive under such stressful conditions. The studies summarized in Table 1 provide support for the model that stem cells occupy a local fitness peak on the adaptive landscape, such that changes in phenotypic parameters will be rarely advantageous and typically disadvantageous (Figure 3).

Table 1 Oncogenic mutations typically impair HSC maintenance
Figure 3

The Goldilocks Rule for stem cells. (a) Young healthy stem cells are proposed to possess parameters (cell cycle, differentiation, interactions with the niche, etc.) that are near optimal (‘just right’) for maintenance as a stem cells. Stem cells are presumed to occupy a local fitness peak on the adaptive landscape; cancer cells in the same tissue could occupy a higher peak, but transitions to this peak would require passage through lower fitness states on the landscape (see DeGregori36). Thus, acquisition of a single oncogenic mutation would typically be disadvantageous, by changing parameters from their optimum (see Table 1). (b) For old or damaged stem cells, parameters are suboptimal or abnormal, and the stem cells no longer possess optimal or near optimal fitness. Changes in parameters could result from both cell-autonomous events (damage to the stem cells) or from non-cell-autonomous changes (such as degradation of the niche or systemic changes). These changes in the stem cell pool can lead to selection for oncogenic events that are adaptive to this context.

While the Adaptive Oncogenesis model posits that oncogenic mutations should rarely be advantageous within young, fit stem cell pools, there are potential exceptions. First, c-CBL−/− mice exhibit increased numbers of HSC, and these HSC exhibit increased cycling and greater reconstitution potential in competitive bone marrow transplantation experiments62 (Table 1). C-CBL is an E3 ligase that downregulates tyrosine kinase signaling. Gain-of-function mutations and translocation of c-CBL are implicated in several cancers including myeloid neoplasms,63, 64 and c-CBL−/− mice exhibit tissue hyperplasia.65 It will be interesting to determine if mutation of c-Cbl in an isolated HSC indeed proves advantageous in a young healthy mouse (as opposed to a mouse deficient in c-CBL in all tissues). Second, the induction of a KRASG12D mutation in mice leads to competitive expansion of the hematopoietic clones (including HSC) expressing activated K-Ras, despite a dramatic loss of functional HSC numbers.66 As noted by the authors, conditional activation of KRASG12D occurs in many (if not all) tissues, and thus K-RasG12D expression in non-hematopoietic tissues could alter the microenvironment for HSC (and thus the adaptive landscape), which could also explain the reductions in HSC numbers. Another possible exception is for Bcr–Abl. Reynaud et al. showed that activation of Bcr–Abl expression in unperturbed mice results in reduced HSC numbers, apparently by increased differentiation to more committed myeloid progenitors,67 which nicely supports our model that young unperturbed HSC favor the status quo (the youthful phenotype). These results are also consistent with previous studies which indicate that Bcr–Abl promotes differentiation of human HSC, inhibiting self-renewal,68, 69 and that selection for Bcr–Abl is context dependent.32, 70 However, in the Reynaud et al. study, following transplantation into irradiated recipient mice, Bcr–Abl provides a competitive advantage to HSC.67 Irradiation clearly alters the bone marrow microenvironment (and thus the stem cell niche), which could impact upon the adaptive landscape and promote selection for Bcr–Abl mutation. Of course, the alternative explanation is that some oncogenic mutations can be advantageous even in young healthy HSC pools, and it is the small size of this stem cell pool (together with other hurdles to tumorigenesis, such as the need for multiple oncogenic mutations) combined with the inability of these oncogenes to initiate cancer in more committed hematopoietic progenitors that sufficiently limits leukemias initiated by these oncogenes until older ages.

Just as high cellular fitness should prevent the fixation of phenotype-altering mutations, the converse should also be true: reductions in progenitor cell fitness with aging or other insults should increase selection for oncogenic mutations adaptive to the particular context (Figure 3). For example, while INK4A (encodes the p16 cyclin-dependent kinase inhibitor) mutation reduces the self-renewal of young HSC (Table 1), p16 loss actually increases the self-renewal of old HSC (which exhibit self-renewal defects),71 and thus we would expect that p16 loss would be adaptive in old HSC pools. Similarly, while loss of p53 does not provide an advantage within young healthy hematopoietic pools, p53 mutation is potently selected for within HSC and more committed progenitor pools following irradiation of mice.72, 73 Finally, we have shown that Bcr–Abl is adaptive in old hematopoietic progenitor pools, but not young, by restoring kinase signaling pathways that are reduced in old progenitors.32 Thus, just as maintenance of fit stem cell pools should be tumor suppressive by disfavoring phenotype-altering mutations, reductions in the fitness of stem cell pools (such as during aging or following irradiation) should increase selection for particular oncogenic mutations adaptive to the altered context.


The current paradigm that the occurrence of oncogenic mutations with age is rate limiting for cancer development has provided a framework for a large body of cancer research, particularly for the field of carcinogenesis. I have raised questions to challenge this paradigm: if cancer were rate-limited by the occurrence of oncogenic mutations with age: Why would cancers increase exponentially late in life given that mutation accumulation rates are maximal during ontogeny? Would we expect the frequency of oncogenic mutations in tissues to far outpace the rates of corresponding cancers? Why have mutation rates not decreased during the evolution of larger and longer-lived species? Why would oncogenic mutations impair stem cell maintenance? In addition, I have argued that the cause-and-effect relationships between inherited DNA repair deficiencies (or mutagen exposure), oncogenic mutations and cancer incidence are far from established. There are important implications for a revised understanding of the relationships between aging, carcinogens and cancer incidence. From a practical standpoint, perhaps we should be more concerned about how aging, environmental exposures, and therapies impact on the overall tissue landscape, especially given that prevention of changes in adaptive landscapes is probably more feasible than the prevention of the occurrence of mutations. Whereas limiting the incidence of cancers through maintenance of healthier tissues might turn out to be a reasonable preventative approach, meaningful developments in this area will necessitate challenging the current dogma.


  1. 1

    Hanahan D, Weinberg RA . Hallmarks of cancer: the next generation. Cell 2011; 144: 646–674.

    CAS  Article  Google Scholar 

  2. 2

    Kennedy SR, Loeb LA, Herr AJ . Somatic mutations in aging, cancer and neurodegeneration. Mech Ageing Dev 2012; 133: 118–126.

    CAS  Google Scholar 

  3. 3

    Vogelstein B, Kinzler KW . Cancer genes and the pathways they control. Nat Med 2004; 10: 789–799.

    CAS  Google Scholar 

  4. 4

    Weinberg RA . The Biology of Cancer, Chapter 11. Garland Science: New York, 2007.

    Google Scholar 

  5. 5

    Serrano M, Blasco MA . Cancer and ageing: convergent and divergent mechanisms. Nat Rev Mol Cell Biol 2007; 8: 715–722.

    CAS  Google Scholar 

  6. 6

    Hoeijmakers JH . DNA damage, aging, and cancer. N Engl J Med. 2009; 361: 1475–1485.

    CAS  Google Scholar 

  7. 7

    Peto R, Roe FJ, Lee PN, Levy L, Clack J . Cancer and ageing in mice and men. Br J Cancer 1975; 32: 411–426.

    CAS  Google Scholar 

  8. 8

    Nowell PC . The clonal evolution of tumor cell populations. Science 1976; 194: 23–28.

    CAS  Google Scholar 

  9. 9

    Armitage P, Doll R . The age distribution of cancer and a multi-stage theory of carcinogenesis. Br J Cancer 1954; 8: 1–12.

    CAS  Google Scholar 

  10. 10

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

    CAS  Google Scholar 

  11. 11

    Lynch M . Evolution of the mutation rate. Trends Genet. 2010; 26: 345–352.

    CAS  Google Scholar 

  12. 12

    Vijg J, Busuttil RA, Bahar R, Dolle ME . Aging and genome maintenance. Ann NY Acad Sci 2005; 1055: 35–47.

    CAS  Google Scholar 

  13. 13

    Frank SA . Evolution in health and medicine Sackler colloquium: Somatic evolutionary genomics: mutations during development cause highly variable genetic mosaicism with risk of cancer and neurodegeneration. Proc Natl Acad Sci USA. 2010; 107 (Suppl 1): 1725–1730.

    CAS  Google Scholar 

  14. 14

    Weng NP, Hathcock KS, Hodes RJ . Regulation of telomere length and telomerase in T and B cells: a mechanism for maintaining replicative potential. Immunity 1998; 9: 151–157.

    CAS  Google Scholar 

  15. 15

    Wilson A, Laurenti E, Oser G, van der Wath RC, Blanco-Bose W, Jaworski M et al. Hematopoietic stem cells reversibly switch from dormancy to self-renewal during homeostasis and repair. Cell 2008; 135: 1118–1129.

    CAS  Google Scholar 

  16. 16

    Li W, Vijg J . Measuring genome instability in aging—a mini-review. Gerontology 2012; 58: 129–138.

    CAS  Google Scholar 

  17. 17

    Dolle ME, Giese H, Hopkins CL, Martus HJ, Hausdorff JM, Vijg J . Rapid accumulation of genome rearrangements in liver but not in brain of old mice. Nat Genet 1997; 17: 431–434.

    CAS  Google Scholar 

  18. 18

    Freitas AA, de Magalhaes JP . A review and appraisal of the DNA damage theory of ageing. Mutat Res 2011; 728: 12–22.

    CAS  Google Scholar 

  19. 19

    Giese H, Snyder WK, van Oostrom C, van Steeg H, Dolle ME, Vijg J . Age-related mutation accumulation at a lacZ reporter locus in normal and tumor tissues of Trp53-deficient mice. Mutat Res 2002; 514: 153–163.

    CAS  Google Scholar 

  20. 20

    Frith CH, Ward JM, Chandra M . The morphology, immunohistochemistry, and incidence of hematopoietic neoplasms in mice and rats. Toxicol Pathol 1993; 21: 206–218.

    CAS  Google Scholar 

  21. 21

    Gatenby RA, Gillies RJ . A microenvironmental model of carcinogenesis. Nat Rev Cancer 2008; 8: 56–61.

    CAS  Google Scholar 

  22. 22

    Greaves M, Maley CC . Clonal evolution in cancer. Nature 2012; 481: 306–313.

    CAS  Google Scholar 

  23. 23

    Mutter GL, Ince TA, Baak JP, Kust GA, Zhou XP, Eng C . Molecular identification of latent precancers in histologically normal endometrium. Cancer Res 2001; 61: 4311–4314.

    CAS  Google Scholar 

  24. 24

    Crawford YG, Gauthier ML, Joubel A, Mantei K, Kozakiewicz K, Afshari CA et al. Histologically normal human mammary epithelia with silenced p16(INK4a) overexpress COX-2, promoting a premalignant program. Cancer Cell 2004; 5: 263–273.

    CAS  Google Scholar 

  25. 25

    Greaves MF, Wiemels J . Origins of chromosome translocations in childhood Leukemia. Nat Rev Cancer 2003; 3: 1–10.

    Google Scholar 

  26. 26

    Naumov GN, Akslen LA, Folkman J . Role of angiogenesis in human tumor dormancy: animal models of the angiogenic switch. Cell Cycle 2006; 5: 1779–1787.

    CAS  Google Scholar 

  27. 27

    Vickers M . Estimation of the number of mutations necessary to cause chronic myeloid leukaemia from epidemiological data. British Journal of Haematology 1996; 94: 1–4.

    CAS  Google Scholar 

  28. 28

    Bose S, Deininger M, Gora-Tybor J, Goldman JM, Melo JV . The presence of typical and atypical BCR-ABL fusion genes in leukocytes of normal individuals: biologic significance and implications for the assessment of minimal residual disease. Blood 1998; 92: 3362–3367.

    CAS  Google Scholar 

  29. 29

    Biernaux C, Loos M, Sels A, Huez G, Stryckmans P . Detection of major bcr-abl gene expression at a very low level in blood cells of some healthy individuals. Blood 1995; 86: 3118–3122.

    CAS  Google Scholar 

  30. 30

    Matioli GT . BCR-ABL insufficiency for the transformation of human stem cells into CML. Med Hypotheses 2002; 59: 588–589.

    CAS  Google Scholar 

  31. 31

    Mullighan CG, Miller CB, Radtke I, Phillips LA, Dalton J, Ma J et al. BCR-ABL1 lymphoblastic leukaemia is characterized by the deletion of Ikaros. Nature 2008.

  32. 32

    Henry CJ, Marusyk A, Zaberezhnyy V, Adane B, DeGregori J . Declining lymphoid progenitor fitness promotes aging-associated leukemogenesis. Proc Natl Acad Sci USA 2010; 107: 21713–21718.

    CAS  Google Scholar 

  33. 33

    Cortopassi GA, Wang E . There is substantial agreement among interspecies estimates of DNA repair activity. Mech Ageing Dev 1996; 91: 211–218.

    CAS  Google Scholar 

  34. 34

    Promislow DE . DNA repair and the evolution of longevity: a critical analysis. J Theor Biol 1994; 170: 291–300.

    CAS  Google Scholar 

  35. 35

    Tang JY, Hwang BJ, Ford JM, Hanawalt PC, Chu G . Xeroderma pigmentosum p48 gene enhances global genomic repair and suppresses UV-induced mutagenesis. Mol Cell 2000; 5: 737–744.

    CAS  Google Scholar 

  36. 36

    DeGregori J . Evolved tumor suppression: why are we so good at not getting cancer? Cancer Res 2011; 71: 3739–3744.

    CAS  Google Scholar 

  37. 37

    Thompson LH, Schild D . Recombinational DNA repair and human disease. Mutat Res 2002; 509: 49–78.

    CAS  Google Scholar 

  38. 38

    Bagley J, Cortes ML, Breakefield XO, Iacomini J . Bone marrow transplantation restores immune system function and prevents lymphoma in Atm-deficient mice. Blood 2004; 104: 572–578.

    CAS  Google Scholar 

  39. 39

    Bensimon A, Aebersold R, Shiloh Y . Beyond ATM: the protein kinase landscape of the DNA damage response. FEBS Lett 2011; 585: 1625–1639.

    CAS  Google Scholar 

  40. 40

    Westbrook AM, Schiestl RH . Atm-deficient mice exhibit increased sensitivity to dextran sulfate sodium-induced colitis characterized by elevated DNA damage and persistent immune activation. Cancer Res 2010; 70: 1875–1884.

    CAS  Google Scholar 

  41. 41

    Venkatesan RN, Treuting PM, Fuller ED, Goldsby RE, Norwood TH, Gooley TA et al. Mutation at the polymerase active site of mouse DNA polymerase delta increases genomic instability and accelerates tumorigenesis. Mol Cell Biol 2007; 27: 7669–7682.

    CAS  Google Scholar 

  42. 42

    Preston BD, Albertson TM, Herr AJ . DNA replication fidelity and cancer. Semin Cancer Biol 2010; 20: 281–293.

    CAS  Google Scholar 

  43. 43

    Fleenor CJ, Marusyk A, DeGregori J . Ionizing radiation and hematopoietic malignancies: altering the adaptive landscape. Cell Cycle 2010; 9: 3005–3011.

    CAS  Google Scholar 

  44. 44

    Laconi E, Doratiotto S, Vineis P . The microenvironments of multistage carcinogenesis. Semin Cancer Biol 2008; 18: 322–329.

    CAS  Google Scholar 

  45. 45

    Bagby GC, Fleischman AG . The stem cell fitness landscape and pathways of molecular leukemogenesis. Front Biosci (Schol Ed) 2011; 3: 487–500.

    Google Scholar 

  46. 46

    Bissell MJ, Hines WC . Why don’t we get more cancer? A proposed role of the microenvironment in restraining cancer progression. Nat Med 2011; 17: 320–329.

    CAS  Google Scholar 

  47. 47

    Blagosklonny MV . Carcinogenesis, cancer therapy and chemoprevention. Cell Death Differ 2005; 12: 592–602.

    CAS  Google Scholar 

  48. 48

    Marusyk A, DeGregori J . Declining cellular fitness with age promotes cancer initiation by selecting for adaptive oncogenic mutations. Biochim Biophys Acta 2008; 1785: 1–11.

    CAS  Google Scholar 

  49. 49

    Sieber OM, Tomlinson SR, Tomlinson IP . Tissue cell and stage specificity of (epi)mutations in cancers. Nat Rev Cancer 2005; 5: 649–655.

    CAS  Google Scholar 

  50. 50

    Henry CJ, Marusyk A, DeGregori J . Aging-associated changes in hematopoiesis and leukemogenesis: what's the connection? Aging (Albany NY) 2011; 3: 643–656.

    CAS  Google Scholar 

  51. 51

    Rando TA . Stem cells, ageing and the quest for immortality. Nature 2006; 441: 1080–1086.

    CAS  Google Scholar 

  52. 52

    Laurie CC, Laurie CA, Rice K, Doheny KF, Zelnick LR, McHugh CP et al. Detectable clonal mosaicism from birth to old age and its relationship to cancer. Nat Genet 2012; 44: 642–650.

    CAS  Google Scholar 

  53. 53

    Jacobs KB, Yeager M, Zhou W, Wacholder S, Wang Z, Rodriguez-Santiago B et al. Detectable clonal mosaicism and its relationship to aging and cancer. Nat Genet 2012; 44: 651–658.

    CAS  Google Scholar 

  54. 54

    Forsberg LA, Rasi C, Razzaghian HR, Pakalapati G, Waite L, Thilbeault KS et al. Age-related somatic structural changes in the nuclear genome of human blood cells. Am J Hum Genet 2012; 90: 217–228.

    CAS  Google Scholar 

  55. 55

    Rodriguez-Santiago B, Malats N, Rothman N, Armengol L, Garcia-Closas M, Kogevinas M et al. Mosaic uniparental disomies and aneuploidies as large structural variants of the human genome. Am J Hum Genet 2010; 87: 129–138.

    CAS  Google Scholar 

  56. 56

    Gomes NM, Ryder OA, Houck ML, Charter SJ, Walker W, Forsyth NR et al. Comparative biology of mammalian telomeres: hypotheses on ancestral states and the roles of telomeres in longevity determination. Aging Cell 2011; 10: 761–768.

    CAS  Google Scholar 

  57. 57

    Gorbunova V, Seluanov A . Coevolution of telomerase activity and body mass in mammals: from mice to beavers. Mech Ageing Dev 2009; 130: 3–9.

    CAS  Google Scholar 

  58. 58

    Yilmaz OH, Valdez R, Theisen BK, Guo W, Ferguson DO, Wu H et al. Pten dependence distinguishes haematopoietic stem cells from leukaemia-initiating cells. Nature 2006; 441: 475–482.

    CAS  Google Scholar 

  59. 59

    Zhang J, Grindley JC, Yin T, Jayasinghe S, He XC, Ross JT et al. PTEN maintains haematopoietic stem cells and acts in lineage choice and leukaemia prevention. Nature 2006; 441: 518–522.

    CAS  Google Scholar 

  60. 60

    Wilson A, Laurenti E, Trumpp A . Balancing dormant and self-renewing hematopoietic stem cells. Current Opinion in Genetics & Development 2009; 19: 461–468.

    CAS  Google Scholar 

  61. 61

    Reya T, Duncan AW, Ailles L, Domen J, Scherer DC, Willert K et al. A role for Wnt signalling in self-renewal of haematopoietic stem cells. Nature 2003; 423: 409–414.

    CAS  Google Scholar 

  62. 62

    Rathinam C, Thien CB, Langdon WY, Gu H, Flavell RA . The E3 ubiquitin ligase c-Cbl restricts development and functions of hematopoietic stem cells. Genes Dev 2008; 22: 992–997.

    CAS  Google Scholar 

  63. 63

    Ogawa S, Shih LY, Suzuki T, Otsu M, Nakauchi H, Koeffler HP et al. Deregulated intracellular signaling by mutated c-CBL in myeloid neoplasms. Clin Cancer Res 2010; 16: 3825–3831.

    CAS  Google Scholar 

  64. 64

    Rathinam C, Thien CB, Flavell RA, Langdon WY . Myeloid leukemia development in c-Cbl RING finger mutant mice is dependent on FLT3 signaling. Cancer Cell 2010; 18: 341–352.

    CAS  Google Scholar 

  65. 65

    Murphy MA, Schnall RG, Venter DJ, Barnett L, Bertoncello I, Thien CB et al. Tissue hyperplasia and enhanced T-cell signalling via ZAP-70 in c-Cbl-deficient mice. Mol Cell Biol 1998; 18: 4872–4882.

    CAS  Google Scholar 

  66. 66

    Sabnis AJ, Cheung LS, Dail M, Kang HC, Santaguida M, Hermiston ML et al. Oncogenic Kras initiates leukemia in hematopoietic stem cells. PLoS Biol 2009; 7: e59.

    Google Scholar 

  67. 67

    Reynaud D, Pietras E, Barry-Holson K, Mir A, Binnewies M, Jeanne M et al. IL-6 controls leukemic multipotent progenitor cell fate and contributes to chronic myelogenous leukemia development. Cancer Cell 2011; 20: 661–673.

    CAS  Google Scholar 

  68. 68

    Holyoake TL, Jiang X, Drummond MW, Eaves AC, Eaves CJ . Elucidating critical mechanisms of deregulated stem cell turnover in the chronic phase of chronic myeloid leukemia. Leukemia 2002; 16: 549–558.

    CAS  Google Scholar 

  69. 69

    Schemionek M, Elling C, Steidl U, Baumer N, Hamilton A, Spieker T et al. BCR-ABL enhances differentiation of long-term repopulating hematopoietic stem cells. Blood 2010; 115: 3185–3195.

    CAS  Google Scholar 

  70. 70

    Bilousova G, Marusyk A, Porter CC, Cardiff RD, DeGregori J . Impaired DNA replication within progenitor cell pools promotes leukemogenesis. PLoS Biology 2005; 3: e401.

    Google Scholar 

  71. 71

    Janzen V, Forkert R, Fleming HE, Saito Y, Waring MT, Dombkowski DM et al. Stem-cell ageing modified by the cyclin-dependent kinase inhibitor p16INK4a. Nature 2006; 443: 421–426.

    CAS  Google Scholar 

  72. 72

    Bondar T, Medzhitov R . p53-mediated hematopoietic stem and progenitor cell competition. Cell Stem Cell 2010; 6: 309–322.

    CAS  Google Scholar 

  73. 73

    Marusyk A, Porter CC, Zaberezhnyy V, DeGregori J . Irradiation selects for p53-deficient hematopoietic progenitors. PLoS Biol 2010; 8: e1000324.

    Google Scholar 

  74. 74

    Daria D, Filippi MD, Knudsen ES, Faccio R, Li Z, Kalfa T et al. The retinoblastoma tumor suppressor is a critical intrinsic regulator for hematopoietic stem and progenitor cells under stress. Blood 2008; 111: 1894–1902.

    CAS  Google Scholar 

  75. 75

    Walkley CR, Shea JM, Sims NA, Purton LE, Orkin SH . Rb regulates interactions between hematopoietic stem cells and their bone marrow microenvironment. Cell 2007; 129: 1081–1095.

    CAS  Google Scholar 

  76. 76

    Spike BT, Dirlam A, Dibling BC, Marvin J, Williams BO, Jacks T et al. The Rb tumor suppressor is required for stress erythropoiesis. EMBO J. 2004; 23: 4319–4329.

    CAS  Google Scholar 

  77. 77

    Cheng T, Rodrigues N, Shen H, Yang Y, Dombkowski D, Sykes M et al. Hematopoietic stem cell quiescence maintained by p21cip1/waf1. Science 2000; 287: 1804–1808.

    CAS  Google Scholar 

  78. 78

    Ito K, Hirao A, Arai F, Matsuoka S, Takubo K, Hamaguchi I et al. Regulation of oxidative stress by ATM is required for self-renewal of haematopoietic stem cells. Nature 2004; 431: 997–1002.

    CAS  Google Scholar 

  79. 79

    Huang J, Zhang Y, Bersenev A, O'Brien WT, Tong W, Emerson SG et al. Pivotal role for glycogen synthase kinase-3 in hematopoietic stem cell homeostasis in mice. J Clin Invest 2009; 119: 3519–3529.

    CAS  Google Scholar 

  80. 80

    Qian Z, Chen L, Fernald AA, Williams BO, Le Beau MM . A critical role for Apc in hematopoietic stem and progenitor cell survival. J Exp Med 2008; 205: 2163–2175.

    CAS  Google Scholar 

  81. 81

    Chen C, Liu Y, Liu R, Ikenoue T, Guan KL, Zheng P . TSC-mTOR maintains quiescence and function of hematopoietic stem cells by repressing mitochondrial biogenesis and reactive oxygen species. J Exp Med 2008; 205: 2397–2408.

    CAS  Google Scholar 

  82. 82

    Gan B, Sahin E, Jiang S, Sanchez-Aguilera A, Scott KL, Chin L et al. mTORC1-dependent and -independent regulation of stem cell renewal, differentiation, and mobilization. Proc Natl Acad Sci USA 2008; 105: 19384–19389.

    CAS  Google Scholar 

  83. 83

    Gan B, Hu J, Jiang S, Liu Y, Sahin E, Zhuang L et al. Lkb1 regulates quiescence and metabolic homeostasis of haematopoietic stem cells. Nature 2010; 468: 701–704.

    CAS  Google Scholar 

  84. 84

    Min IM, Pietramaggiori G, Kim FS, Passegue E, Stevenson KE, Wagers AJ . The transcription factor EGR1 controls both the proliferation and localization of hematopoietic stem cells. Cell Stem Cell 2008; 2: 380–391.

    CAS  Google Scholar 

  85. 85

    Thompson BJ, Jankovic V, Gao J, Buonamici S, Vest A, Lee JM et al. Control of hematopoietic stem cell quiescence by the E3 ubiquitin ligase Fbw7. J Exp Med 2008; 205: 1395–1408.

    CAS  Google Scholar 

  86. 86

    Matsuoka S, Oike Y, Onoyama I, Iwama A, Arai F, Takubo K et al. Fbxw7 acts as a critical fail-safe against premature loss of hematopoietic stem cells and development of T-ALL. Genes Dev 2008; 22: 986–991.

    CAS  Google Scholar 

  87. 87

    Maillard I, Chen YX, Friedman A, Yang Y, Tubbs AT, Shestova O et al. Menin regulates the function of hematopoietic stem cells and lymphoid progenitors. Blood 2009; 113: 1661–1669.

    CAS  Google Scholar 

  88. 88

    Papathanasiou P, Attema JL, Karsunky H, Hosen N, Sontani Y, Hoyne GF et al. Self-renewal of the long-term reconstituting subset of hematopoietic stem cells is regulated by Ikaros. Stem Cells 2009; 27: 3082–3092.

    CAS  Google Scholar 

  89. 89

    Nichogiannopoulou A, Trevisan M, Neben S, Friedrich C, Georgopoulos K . Defects in hemopoietic stem cell activity in Ikaros mutant mice. J Exp Med 1999; 190: 1201–1214.

    CAS  Google Scholar 

  90. 90

    Papathanasiou P, Perkins AC, Cobb BS, Ferrini R, Sridharan R, Hoyne GF et al. Widespread failure of hematolymphoid differentiation caused by a recessive niche-filling allele of the Ikaros transcription factor. Immunity 2003; 19: 131–144.

    CAS  Google Scholar 

  91. 91

    Perry JM, He XC, Sugimura R, Grindley JC, Haug JS, Ding S et al. Cooperation between both Wnt/{beta}-catenin and PTEN/PI3K/Akt signaling promotes primitive hematopoietic stem cell self-renewal and expansion. Genes Dev 2011; 25: 1928–1942.

    CAS  Google Scholar 

  92. 92

    Kirstetter P, Anderson K, Porse BT, Jacobsen SE, Nerlov C . Activation of the canonical Wnt pathway leads to loss of hematopoietic stem cell repopulation and multilineage differentiation block. Nat Immunol 2006; 7: 1048–1056.

    CAS  Google Scholar 

  93. 93

    Scheller M, Huelsken J, Rosenbauer F, Taketo MM, Birchmeier W, Tenen DG et al. Hematopoietic stem cell and multilineage defects generated by constitutive beta-catenin activation. Nat Immunol 2006; 7: 1037–1047.

    CAS  Google Scholar 

  94. 94

    Wilson A, Murphy MJ, Oskarsson T, Kaloulis K, Bettess MD, Oser GM et al. c-Myc controls the balance between hematopoietic stem cell self-renewal and differentiation. Genes Dev 2004; 18: 2747–2763.

    CAS  Google Scholar 

  95. 95

    Campbell TB, Basu S, Hangoc G, Tao W, Broxmeyer HE . Overexpression of Rheb2 enhances mouse hematopoietic progenitor cell growth while impairing stem cell repopulation. Blood 2009; 114: 3392–3401.

    CAS  Google Scholar 

Download references


These studies were supported by grants from the National Institutes of Health (R01-CA157850) and the Leukemia Lymphoma Society. I thank Robert Sclafani, Michael Weil, Andriy Marusyk, Ruth Hershberg, Andrew Thorburn and members of my laboratory for their critical comments and suggestions.

Author information



Corresponding author

Correspondence to J DeGregori.

Ethics declarations

Competing interests

The author declares no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

DeGregori, J. Challenging the axiom: does the occurrence of oncogenic mutations truly limit cancer development with age?. Oncogene 32, 1869–1875 (2013).

Download citation


  • mutation
  • evolution
  • aging

Further reading