Genetic Instability in Cancer: Theory and Experiment

Seminars in Cancer Biology 15 (2005) 423–435

Review Genetic instability in cancer: Theory and experiment

Robert A. Beckman a,∗, Lawrence A. Loeb b

a Department of Clinical Research and Development, Hematology/Oncology, Centocor Inc., Malvern, PA 19355-1307, USA b Department of Pathology, University of Washington School of Medicine, Seattle, WA 98195-7470, USA

Abstract

Epidemiologic data and molecular biology have combined to demonstrate that multiple genetic changes may be required in carcinogenesis. Mutator mutations, defined as genetic changes which increase the rate of genetic change, including both single base changes and chromosomal instability, may accelerate this process. Key questions remain in defining the role of mutator mutations in carcinogenesis as well as in cancer therapy. Theoretical approaches, including deterministic and stochastic models, have played a significant role in hypothesis generation, experimental design, and refinement of conclusions in this field, and are expected to continue to do so in the future. © 2005 Elsevier Ltd. All rights reserved.

Keywords: Genetic instability; Mutator hypothesis; Cancer; Theory; Mathematical models

Contents

1. Introduction ...... 424 2. Theoretical approaches to genetic instability ...... 424 2.1. Objectives of theory ...... 424 2.2. Theoretical methods...... 425 3. How many genetic changes are required to produce a cancer cell? ...... 426 4. Is genetic instability essential for generating a sufﬁcient number of random mutations to produce key mutations in genes required for carcinogenesis? ...... 428 5. Does genetic instability accelerate carcinogenesis? ...... 428 6. Is the baseline level of genetic instability in wild type stem cells, from which cancers originate, “optimal” for maximizing the rate of tumor evolution? ...... 430 7. What kinds of genetic instability may contribute to carcinogenesis? ...... 430 7.1. DNA polymerases and genetic instability ...... 430 7.2. DNA repair enzymes and genetic instability ...... 431 7.3. Chromosomal instability ...... 431 7.4. Cell cycle checkpoints and genetic instability ...... 432 8. What is the role of genetic instability in relation to therapy? ...... 432 9. Could the clinical appearance of cancer be prevented by decreasing the rate of genetic change? ...... 432 10. Conclusion ...... 433 Acknowledgements ...... 433 References ...... 433

∗ Corresponding author at: 1045 Hereford Drive, Blue Bell, PA 19422, USA. Tel.: +1 610 651 7579; fax: +1 610 651 6140. E-mail addresses: [email protected] (R.A. Beckman), [email protected] (L.A. Loeb).

1. Introduction if a loss of function of one copy of the gene product also suppresses the function of the gene product of the wild type The increase of cancer incidence with age has long been copy. Often this is the case when a gene product functions in taken as evidence that there are multiple rate limiting steps a multimeric fashion. in the development of cancer [1–3], and these steps have The importance of recessive tumor suppressor genes in been assumed to involve genetic and/or epigenetic changes. carcinogenesis was predicted by Knudson, utilizing a statisti- In order for cells to become tumorigenic in laboratory mod- cal model of familial and sporadic retinoblastoma incidence, els, at least six cellular phenotypes must be altered [4], which prior to the molecular identification of any specific recessive implies derangement of a corresponding number of metabolic oncogene [15]. Now the study of recessive oncogenes is a pathways [5]. Selective expansion of altered clones may major field in oncology [16]. This classic paper demonstrates occur at any time during the process [6]. Thus, carcinogene- the ability of theory to substantially influence the thinking of sis may be viewed as an evolutionary process involving both experimental cancer researchers. genetic change and selection. Many significant questions remain concerning genetic If multiple genetic changes are required for carcinogene- instability in cancer, each of which may be amenable to thesis, anything which increases the rate of genetic change may oretical analysis: accelerate the process [7]. Loeb [8] postulated that muta- 1. How many genetic changes are required to produce a can- tions leading to greater genetic instability were required to cer cell? explain the accumulation of the multiple cancer-associated 2. Is genetic instability essential for generating a sufficient mutations within a cellular lineage at the incidence rates number of random mutations to produce key mutations in observed within a human lifetime. These mutations, which genes required for carcinogenesis? increase the inherent rate of genetic change, are referred to 3. Does genetic instability accelerate carcinogenesis? as mutator mutations, cell lineages harboring them are called 4. Is the baseline level of genetic instability in wild type mutator clones, and cancer cells are said to exhibit a mutator stem cells, from which cancers originate, “optimal” for phenotype [9]. Mutator mutations and genetic instability are maximizing the rate of tumor evolution? generalized concepts, referring not just to mutations which 5. What kinds of genetic instability may contribute to car- lead to enhanced rate of single nucleotide substitutions, but cinogenesis? also to mutations that enhance changes in the number of 6. What is the role of genetic instability in relation to ther- repeats in repetitive DNA sequences, termed microsatellite apy? instability (MIN) [10,11]; mutations that enhance the rate 7. Could the clinical appearance of cancer be prevented by of chromosome loss, gain, or translocation, termed chromo- decreasing the rate of genetic change? somal instability (CIN) [12]; and mutations which interfere with cell cycle checkpoints related to DNA damage [13].In Answers to these questions are gradually emerging, in part summary, mutations in a mutator gene can result in increased due to a particularly fruitful interaction between theory and changes in DNA nucleotide sequence throughout the genome. experiment. This article reviews genetic instability in cancer However, other authors have pointed out that if early genetic and focuses on the role of theory and experiment in addressing changes confer a survival advantage on premalignant clones, these questions. their progeny will increase in number (clonal expansion of premalignant clones), potentially increasing the frequency of appearance of mutations in the expanded clones and thus 2. Theoretical approaches to genetic instability accounting for the required number of genetic changes within a cancer without invoking a mutator phenotype [14]. The role 2.1. Objectives of theory of mutator mutations and whether they are essential for carcinogenesis should be amenable to mathematical analysis. Albert Einstein summed up several objectives of theory Genetic changes which are required in carcinogenesis succinctly: “Only theory can tell us what to measure and are divided into two very broad classes: those which are how to interpret it.” It should be noted that theories can be dominant, requiring alteration of only one gene copy to either descriptive or mathematical. However, mathematics is contribute to a premalignant or malignant phenotype, and an important tool which enables theoreticians to rigorously those which are recessive, requiring alteration of both gene state and evaluate theories as well as to confirm or refute copies to contribute to a premalignant or malignant pheno- theories through analysis of experimental results. type. Genetic changes in dominant cancer-associated genes The potential areas in which theory can contribute to sci- typically promote cancer through gain of function, whereas entific inquiry are shown in Fig. 1. Theory can contribute to genetic changes in recessive cancer-associated genes typi- the generation of hypotheses, as Knudson’s analysis of the cally promote carcinogenesis through loss of function, and incidence of retinoblastoma led to a hypothesis concerning hence the involved genes are often referred to as tumor sup- the importance of recessive cancer genes (see Section 3). A pressors. Dominant oncogenic mutations can occur in tumor detailed theoretical analysis will often lead to the identifica- suppressor genes if the mutation is dominant negative, i.e. tion of key parameters which frame or answer questions of R.A. Beckman, L.A. Loeb / Seminars in Cancer Biology 15 (2005) 423–435 425

qualitative agreement with experiment. However, a series of experiments designed with the aid of theory, and subsequent analysis of the results, demonstrated that the majority of these mechanisms were not in detailed agreement with experiment (see Section 3) [18].

2.2. Theoretical methods

Theoretical models fall into two very broad classes: deterministic and stochastic models. Deterministic models attempt to model or predict the average behavior of systems according to precise rules. In contrast, stochastic models describe the probability of very specific behaviors of individuals rather Fig. 1. Role of theoretical approaches in scientific inquiry. Theoretical than average behavior of the population. Newtonian physics methods can contribute substantially at every step, except the critical data is deterministic; quantum mechanics is stochastic. generation step. The value of theoretical contributions depends on experi- Deterministic models often use differential equations to mental verification. exactly describe the changes in average properties of a system over a very brief period of time. A group of interdepen- interest. For example, Nowak et al. calculated the conditions dent differential equations can be used to describe interacting required for a chromosomal instability mutation to be an ini- properties of a complex system. These equations, represent- tial event in colorectal cancer pathogenesis (see Section 7) ing short time intervals, can be integrated, i.e. summed, over [17]. Both the generation of biologically important hypothe- longer time periods of interest. For example, one can sum the ses and the identification of key parameters that affect them changes in a cell lineage that may occur in one cell generation can delineate the prioritization of experimental efforts. These over a human lifetime. These techniques can be applied over insights can also affect experimental design. For example, broad ranges of phenomena since they require only under- based on a careful theoretical analysis of biochemical mech- standing of average system properties. The results can be anisms of ensuring the accuracy of DNA replication, exper- smooth or continuous because they represent average prop- iments were designed to distinguish among closely related erties. Knudson [15] applied a deterministic model to his hypotheses for error correction or proofreading. The results analysis of the incidence of retinoblastoma over time (see of these experiments in conjunction with the theoretical Section 3). analysis demonstrated that proofreading by Escherichia coli Stochastic models evaluate the entire probability distri- DNA polymerase proceeds in two sequential kinetically dis- bution of random individual events. This kind of model is tinct steps, resulting in even higher accuracy (see Section 7) potentially more informative in that it considers rare events, [18]. not just average properties. Since cancers arise from single Application of a theory may lead to predictions of experi- cells, many theoreticians posit that rare events may determine mental results in advance, the most stringent test of a theory. the course of a malignancy. Typically one defines a variety of For example, Einstein’s theories predicted the extraordinary discrete states, for example, cell lineages with given numbers release of energy upon the annihilation of matter. Because of cancer-associated mutations, and the rates or probabilities of the complexity of biological systems, such an outcome of transition between the states. Often the different states of a is extremely difficult to achieve, and hence the dotted line in phenomenon of interest can be represented as a Markov chain. Fig. 1. Nonetheless, Knudson’s work on recessive oncogenes In a Markov chain, the system passes through the defined provides an example in oncology [15]. states in discrete steps with a given set of transition probabil- Experimental verification is the ultimate criterion for ities. The possibilities for where the system will go next, and establishing the value of any important theory. Theory can the chance it will “select” a particular option, depend only on then contribute substantially to evaluation and analysis of the where the system is at the moment (i.e. its present state) rather experimental data. At times, theory may lead to interpreta- than on how it got there (its history). This type of analysis can tions of experiment that are not evident from qualitative anal- in principle give the chance that the system is in a given state ysis. For example, Moolgavkar and Knudson [19] explained as a function of time or other key variables. However, utilizing the observed incidence curves of childhood cancers based on this approach often requires a detailed understanding of indi- a detailed model involving mutations and clonal expansion of vidual states and transitions, which is not always available. premalignant clones, to be discussed in Section 3. This was As the system complexity increases, the definition of all the far from obvious from inspection of these incidence curves. relevant states and the mathematical analysis of all the tran- At other times, a theoretical analysis can lead to rejection sitions between them can become daunting. The results will of conclusions which appeared to be intuitive. For example, be probabilities of discrete events, rather than average prop- multiple proposed biochemical mechanisms for preserving erties. For example, Knudson [15] modeled the probability the accuracy of DNA replication appeared intuitively to be in distribution for having bilateral or unilateral retinoblastoma 426 R.A. Beckman, L.A. Loeb / Seminars in Cancer Biology 15 (2005) 423–435 in the case of hereditary disease, and compared it to patient that most carcinogens are mutagens. Analyzing cancer death data, utilizing a Poisson process to derive a stochastic model. rates at all tumor sites in the United States, United Kingdom, Theoretical modeling frequently uses simplifying France, and Norway, he noted that cancer deaths increased assumptions. Simplifying assumptions eliminate complexi- approximately as the sixth power of age between the ages of ties which may be peripheral to the issue under consideration, 25 and 74. Thus, the logarithm of cancer deaths plotted versus allowing a focus on key features of a complex biological the logarithm of time yielded a straight line with slope six. system. For example, most models of genetic instability He postulated that seven genetic changes would therefore be assume that the rate of genetic change is constant at any required to produce a cancer cell: one pre-existing mutation location in the genome, even though there is evidence of and six subsequent mutations, the latter rate limiting, leading mutation “hot spots” which violate this assumption [20].In to a dependence on the sixth power of age. We note that other determining whether this simplifying assumption impacts interpretations of this data are possible. For example, Fisher the results when modeling genetic change in carcinogenesis, and Holloman [21] suggested that at least six cells must each one would need to know whether mutation “hot spots” exist acquire one mutation to form a sufficient cluster of genetically at key loci within cancer-associated genes. Simplifying altered cells to result in a tumor. This hypothesis results in assumptions should be disclosed in detail when a theoretical the same time dependence as that proferred by Nordling. model is presented. Where feasible, the truth of these Armitage and Doll [2] analyzed the data by organ spe- assumptions should be compared to what is known and cific tumor sites, utilizing incidence figures, which are more the impact of the simplification on the analysis should be pertinent than mortality figures. They noted that cancers in assessed. organs influenced by hormones, as well as lung cancer, which Limiting cases involve a particular type of simplifying is primarily caused by smoking, do not fit the sixth power assumption. Where a particular aspect of the problem is not incidence law as closely. They pointed out therefore that if determined from prior work, an extreme assumption may be the factors leading to various genetic changes varied over utilized and that case studied. Often if the conclusion is true in time, such that the risk of acquiring the next genetic change this type of extreme or “worst case” scenario, it can be shown was not constant, observed incidence would deviate from the to be true for any other reasonable scenario. For example, we sixth power law. The rate of increase of incidence at a time have examined the effect of mutations which reduce cellular point on a log–log plot reflects the rate of genetic change fitness on tumor evolution of mutator clones. Wherever there at some time years or even decades in the past, due to the was uncertainty regarding aspects of this problem, we made latency between initiation of carcinogenesis and appearance an extreme assumption which maximized the importance of of a clinical cancer. They also pointed out that fewer than the effect. Therefore, we argue that the true effect cannot be seven steps could still lead to a sixth power law if one or more than what we have estimated (Beckman RA, Loeb LA, more of those steps increased in greater than linear proportion Genetics, 2005, in press). with age. Fisher [6] considered a model in which premalig- Parameters are the variables which, based on the theoreti- nant clones of epithelial tumors expanded radially within an cal analysis, are expected to influence the outcome of interest. epithelial plane, such that the number of premalignant cells In some cases, values of the parameters are known from prior increased as the square of time. A mechanism involving three experiment, and therefore these values can be fixed. In other mutations, one pre-existent, and two rounds of radial clonal cases, the values of the parameters are unknown or could rea- expansion (after the first and second mutation) was consis- sonably be expected to vary over a known range. In this case, tent with the sixth power law. It should be noted that each the parameters are adjustable. A common error is to use a genetic change must result in a phenotypic change leading to theoretical model with a large number of adjustable param- clonal expansion; thus alterations in tumor suppressor genes, eters to fit a relatively limited experimental dataset. The fact which require two rate limiting steps, would not fit into a that the model can fit the data does not guarantee the valid- sixth power model in this proposal. ity of the model in this case, since nearly any model could Cook et al. [3] examined more types of cancer and data be fit to the data by adjusting the parameters. In general, it from more countries. Utilizing this wider dataset, they found is important to verify that the number of fitted or predicted that the sixth power relationship held precisely in only a experimental data points exceeds the number of adjustable minority of cases, especially if the whole human lifespan was parameters in the model. The greater the excess of indepen- taken into account, where downward curvature in incidence dent experimental data points over adjustable parameters, the rates on a log–log plot was common. This latter phenomenon more valid the experimental confirmation of the theory. might be due to the delay between exposure to a risk factor and development of cancer. Thus, exposure of an elderly individual to a carcinogen might not result in a tumor prior to 3. How many genetic changes are required to that individual dying from another cause. Tumors from dif- produce a cancer cell? ferent organ sites had characteristic curvatures, either upward or downward. Again, time variation in exposure to causative Over half a century ago, Nordling [1] cited literature dating factors was viewed as the most likely explanation, and in back to the 1920s linking cancer to genetic change and noting some cases such as prostate cancer, the curves were best fit R.A. Beckman, L.A. Loeb / Seminars in Cancer Biology 15 (2005) 423–435 427 by assuming that the initiating event occurred later in life, ence the premalignant clonal expansion. The authors have so the data needed to be plotted as a function of time from demonstrated that as few as two mutations may be required, the initiating event. In addition, different cancers increased but they are careful to point out that the data are also compat- as different powers of time, ranging from 1 (melanoma) to ible with more mutational steps, especially in the absence of 11 (prostate cancer when time is measured from birth), with external promoters such as smoking or hormones. Doll [25] most cancers falling between 3.5 and 6.5. Pike [22] pointed points out that the incidence of lung cancer in non-smokers out that the incidence data could not prove a single unique fits very well to a curve increasing as the fourth power of time, mechanism for carcinogenesis. something which could be equally well modeled by the need Knudson [15] applied a deterministic model to the inci- for four new mutations or by a TSCE. Luebeck and Mool- dence of the childhood tumor retinoblastoma as a function of gavkar [26] performed a detailed analysis of the incidence of time from birth. He noted that the incidence of the hereditary colorectal cancer utilizing the U.S. Surveillance, Epidemiol- form was linear as a function of time from birth, whereas the ogy, and End Results (SEER) registry, and adjusting for birth incidence of the sporadic form increased with the square of cohort and calendar year effects that cause cancer incidence time. Noting that these two patterns could reflect the need to vary independently of mechanism. They studied a fam- for one and two genetic “hits”, respectively, he suggested ily of models with clonal expansion of premalignant clones that retinoblastoma originated from inactivation of both gene incorporated, concluding that a model with two rare events copies of a recessive gene which suppressed cancer. In hered- followed by clonal expansion and then a third rare event fits itary retinoblastoma, the individual has inherited the first the data best. A model with a fourth rare event required fits hit, inactivating one copy of the gene. The retinoblastoma the data nearly as well however. (Rb) gene was subsequently cloned [23] and shown to revert Thus, based on the epidemiologic data, it would appear tumorigenic properties of cell lines deficient in its function that anywhere from 2 to 7 rate limiting steps are required [24]. Today retinoblastoma is one of more than a dozen tumor prior to the clinical appearance of a tumor, and at least some of types in which tumor suppressor genes have been implicated these steps are likely to involve genetic change. In addition, it [16]. appears that the number of steps may depend on the organ site Knudson also applied a stochastic model to explain the of the primary tumor. Molecular and cell biological advances observed distribution of unilateral versus bilateral tumors in have clarified what some of these steps may be. Transforma- hereditary retinoblastoma. The appearance of a tumor (in this tion assays in vitro and tumorigenesis studies using rodent case due to the second mutation) is modeled using a Poisson xenograft models have defined six phenotypes which must process, which assumes that the probability of the second be acquired by a cell in order for it to become tumorigenic. mutation occurring is small and constant over small time These include self-sufficiency with respect to positive growth periods. He calculated that on average three tumors would signals, insensitivity to growth-inhibitory signals, evasion of appear, and from this fact and the relative incidence of hered- apoptosis, limitless replicative potential, sustained angiogen- itary and sporadic retinoblastoma, he showed that the two esis, and tissue invasion and metastasis [4]. More recently, “hits” in sporadic retinblastoma must each occur at a rate of transformation of cells in culture with genes carried by viral 10−7 per cell per year. It has subsequently been recognized vectors has defined six cellular control pathways which must that the two hits may not actually be inactivation of two copies be altered in human fibroblasts to create a tumorigenic phe- of the Rb gene. Rather the first hit may be inactivation of one notype in mouse xenografts and in vitro: p53, Rb, PP2A, copy of the Rb gene, leading to a heterozygous cell with one telomerase, Raf, and Ral-GEFs. Since p53 and Rb are tumor wild type and one mutated gene, and the second hit may be suppressor genes, up to eight mutations could be required. In a mutation which dramatically increases chromosomal insta- contrast, murine fibroblasts require only perturbation of two bility, leading to rapid loss of heterozygosity (LOH) at the Rb pathways, p53 and Raf. Thus, experimental carcinogenesis in gene locus (i.e. deletion of a chromosomal segment encom- mice may not be representative of human carcinogenesis. Tis- passing the second gene copy) [17]. sue specificity is observed for different cell types: embryonic Knudson’s work inspired more detailed and general two- kidney cells required perturbation of the PI3 kinase path- stage carcinogenesis models. Moolgavkar and Knudson [19] way but not the Raf pathway, and mammary epithelial cells developed a model in which after the first hit, there is expo- required perturbation of all the pathways considered, a total of nential clonal expansion of premalignant clones, followed by seven [5]. We note that while this discussion classifies tumors a second hit which need not necessarily be a mutation. This and their carcinogenic mechanism by organ site of origin, a type of model is termed a TSCE or “two-stage clonal expan- more fundamental classification of tumors is emerging based sion” model. The model is used to explain the incidence of on gene expression patterns. These molecular classifications all cancers, including childhood cancers, hormonally medi- can have prognostic value [27], and it is likely that these ated cancers, lung cancer, and sporadic and hereditary colon differing molecular subtypes of cancer will require different cancer. The model is also discussed in terms of the two-stage numbers of mutations in their pathogenesis. Since mutations paradigm for tumor initiation and promotion characteristic occur randomly throughout the genome, a cancer cell is likely of experimental carcinogenesis in rodents. Initiators would to harbor many random mutations in addition to those in the influence the mutation steps, whereas promoters would influ- critical sites in cancer-associated genes. 428 R.A. Beckman, L.A. Loeb / Seminars in Cancer Biology 15 (2005) 423–435

4. Is genetic instability essential for generating a ment of cancer-associated mutations without ever having had sufficient number of random mutations to produce a pure population with an intermediate number of mutations. key mutations in genes required for carcinogenesis? The latter phenomenon, termed “tunneling”, has been studied in detail using stochastic models, and the conditions for Loeb et al. [7–9] have suggested that mutator mutations, it occurring have been defined [35,36]. i.e. mutations which increase the rate of genetic change, may Thus, the occurrence of cancer without mutator mutations be required for cancer cells to be formed within a human life- must be at least considered possible. Indeed, Moolgavkar and time. The rate of mutation per nucleotide locus is estimated Knudson [19] have modeled cancer incidence rates success- − to be as low as 10 9 per cell generation in somatic cells [28], fully for a large variety of cancers without invoking mutator − and may be as low as 10 11 in the immortal stem cells which mutations. However, several caveats need to be borne in mind. likely give rise to tumors [29]. The number of stem cell gen- First, while the epidemiologic data could be consistent with erations in a human lifetime is unknown and varies among anywhere from 2–6 new mutations, the number of pheno- tissues, but estimates range from on the order of 102 for the types which must be acquired [4] and signaling pathways majority of reproductive stem cells which are usually not in which must be affected [5] suggests that the number of muta- cycle [30,31] to on the order of 104 for colonic stem cells [32]. tions might be six or more. The more mutations are required, The total number of cells in the human body is on the order the less of the slope of six on the log–log plots of cancer inci- of 1014 (based on a linear cell dimension of 10␮m, a den- dence can be due to clonal expansion. Second, the mutation sity of 1 g/cm3, and a human weight on the order of 100 kg), rates calculated by Moolgavkar and Knudson [19] and oth- although only a small fraction of these cells are stem cells. If ers are potentially consistent with wild type mutation rates in six independent, specific mutations are required for carcino- somatic cells [28], but as cancers are thought to arise from genesis, and in the absence of selection and clonal expansion stem cells, and these may have significantly lower mutation of premalignant clones, the probability of any one cell lin- rates [29], mutator mutations may actually be required even eage acquiring these independent mutations is the probability to generate the mutation rates calculated by Moolgavkar and of acquiring one mutation raised to the sixth power, where Knudson [19]. Third, the effect of clonal expansion is great- the probability of acquiring each mutation is approximately est if it occurs early, requiring a significant fitness advantage the mutation rate times the number of cell generations. If any of premalignant clones after only one or a small number of of these events required two mutations in a tumor suppres- mutations for a maximal effect. Fourth, the modeling of inci- sor gene, then the probability of producing a cancer would dence rates without a mutator mutation assumes that a cancer even be further reduced. The total probability of any stem cell cell once formed has a very high probability of becoming a in the body reaching this state would be approximately the clinical cancer [19]. This assumption may be false, due to probability for any one stem cell lineage multiplied by the host immune defenses, as well as the need for small cancer number of stem cells, a number which would be much less deposits to successfully induce angiogenesis. In that case, a than one for any of the parameter values mentioned above. higher rate of generation of cancer cells would be required to Thus, according to this argument, a mutator mutation would explain observed incidence rates. be required to explain the appearance of malignant cells. Given these considerations, we conclude that it has not However, as we have seen above, the number of genetic been conclusively demonstrated that carcinogenesis requires changes required to produce a cancer cell could be as few as a mutator mutation. Whether or not a mutator mutation is two, and that would significantly affect the calculation above. necessary depends on the wild type stem cell mutation rate, Moreover, it is likely that premalignant clones have some the number of mutations actually required for carcinogenesis, degree of survival advantage and some degree of expansion the degree and timing of selection for and clonal expansion of these clones occurs, enhancing the probability of mutation of premalignant clones, and the probability that a cell with for subsequent steps. Depending on the population size, for the genetic prerequisites for cancer develops into a clinical example, stochastic modeling has shown that in the presence cancer. of clonal expansion, two mutations can occur with two, one, or zero rate limiting steps, and can occur proportionately to either the first power or the square of time [33]. Selection of 5. Does genetic instability accelerate carcinogenesis? premalignant clones and subsequent expansion of them has been argued to be the basis of malignancies occurring without Based on epidemiological data, it can be inferred that there mutator mutations [6,14]. is a 20 year interval between initial exposure of an individ- Waves of mutation and clonal expansion can be sequential, ual to a carcinogen and the clinical detection of a tumor. leading to a succession of pure populations with increasing Whether or not mutator mutations are required for carcino- numbers of mutations [34]. Alternately after a given mutagenesis, they are likely to accelerate this process. Given a tion, there can be partial expansion of the clone followed by large variety of potential competing mechanisms of carcino- mutation of one of its members to the next state on the way to genesis, those which are more efficient are more likely to malignancy. In this case, mosaic populations will be present, be represented in clinical cancers. If we further assume that and a cell population may ultimately acquire a full comple- the majority of cancer cells created do not result in clinical R.A. Beckman, L.A. Loeb / Seminars in Cancer Biology 15 (2005) 423–435 429

Fig. 2. Effect of possible acceleration of carcinogenesis by a mutator mutation. If mutator mutations accelerate carcinogenesis, more malignant clones will be formed by this mechanism. The mechanism which produces the most malignant clones is by chance more likely to produce the clone that evades host defenses and establishes a blood supply, leading to a clinically detectable cancer. cancers due to host immune defenses and failure to stimu- vulnerability to apoptosis, mitigating the effect of NCS [40]. late angiogenesis when the tumor deposit exceeds a critical Fourth, the model assumed a relatively high level of vulner- size, highly efficient mechanisms of carcinogenesis may be ability of the cell to mutation, basing its maximum assumed needed to explain observed rates of cancer (Fig. 2). vulnerability on studies of random mutations in proteins [41], In considering the relative efficiency of carcinogenesis in and thus not allowing for redundancy of cellular pathways. the presence and absence of mutator mutations, several fac- Both dominant and recessive RF mutations were considered. tors which will potentially work against mutator clones must The model identified key parameters which determine the be considered. The first of these is the increased acquisition influence of NCS and defined the conditions under which of potentially deleterious mutations reducing the fitness of it might have a substantial effect. Despite the fact that the mutator clones. This could lead to preferential extinction of model examined limiting, maximal cases for NCS, the anal- mutator clones, a process termed negative clonal selection ysis indicated that NCS did not limit the growth of mutator (NCS). clones significantly in most cases. Beckman and Loeb (Genetics, 2005, in press) utilized a A second factor limiting mutator clones is that cells must deterministic model to evaluate a limiting case maximizing first acquire a mutator mutation to become a mutator clone, the impact of negative clonal selection due to accumulation of adding at least one extra step to the process of carcinogene- “reduced fitness” (RF) mutations. Several factors make this sis. Beckman and Loeb (manuscripts in preparation) utilize model a limiting case. First, NCS was considered in isolation a deterministic model to define the conditions under which, for its impact on survival of mutator clones, neglecting clonal despite this additional mutation step, mutator pathways are expansion due to cancer-associated mutations that would favored over non-mutator pathways to carcinogenesis, both increase fitness. In fact, oncogenic mutations that increase in the presence and absence of NCS. Mutator pathways are fitness could be very significant and oppose the effects of favored to a greater extent if more mutation steps are required NCS. Second, it was assumed that any clone with reduced for carcinogenesis, and the mutator mutation occurs early. fitness will become extinct with absolute certainty. In fact, Nowak et al. [17] utilize a stochastic model to consider stochastic modeling demonstrates that clones with reduced a similar question in the case of chromosomal instability fitness can still survive in small populations such as might be mutations in colorectal cancer carcinogenesis, and also con- expected in intestinal crypts, favoring mutator mutations and clude that genetic instability can be favored in early stages of their spread by random drift [37,38]. While organization of carcinogenesis. Komarova and Wodarz [42] have examined tissues into small cellular compartments may serve to limit chromosomal instability with both stochastic and determinis- the spread of premalignant cells with a survival advantage tic models, considering both the extra time it takes to acquire a [39], ironically it also favors genetic instability. Third, the chromosomal instability mutation, and the possible deleteri- model assumed a constant level of vulnerability to mutations ous consequences of chromosomal instability. They conclude reducing fitness. In fact, it is likely that during carcinogen- that chromosomal instability may not accelerate carcinogenesis cell lineages will acquire mutations which reduce their esis, but if it occurs due to environmental factors, the degree 430 R.A. Beckman, L.A. Loeb / Seminars in Cancer Biology 15 (2005) 423–435 of it is tuned to optimally balance accelerated carcinogenesis mentally observed fidelity of non-enzymatic polymerization with deleterious consequences on fitness of the clone. [46]. The high fidelity of DNA polymerization is due to two factors: first, the improved selection of the complementary 6. Is the baseline level of genetic instability in wild nucleoside triphosphate mediated in part by solvent exclusion type stem cells, from which cancers originate, [47] and conformational changes at each nucleotide addition “optimal” for maximizing the rate of tumor step [48], and second, by editing or “proofreading” of errors evolution? immediately after they are inserted by the enzyme [18]. While mutations at the polymerization site are likely to In so far as carcinogenesis can be viewed as an evolu- have a greater effect on fidelity of polymerization with puri- tionary process, parallels are drawn between the evolution of fied DNA polymerases, mutations that dimish proofreading tumor clones and species evolution [14]. Deterministic mod- are more likely to result in enhanced mutagenesis in cells. els have found an optimal mutation rate for the evolution of The latter mutations might not diminish the rate of chain viral subspecies that allows for the maximum rate of vari- elongation (although elongation of the nascent DNA chain ation without creating too many deleterious mutants. This past an error is slower relative to elongation past a correct mutation rate is approximately equal to the reciprocal of the insertion [18]) and therefore might not result in a prolifera- genome length [43,44]. tive disadvantage. DNA polymerase proofreading has been This approach can be adapted to derive an optimal muta- analyzed extensively both theoretically and experimentally. tion rate for carcinogenesis, and define key parameters which For E. coli DNA polymerase I, the first DNA polymerase affect this optimum (Beckman and Loeb, manuscript in to be studied in depth, an enzymatic activity which could preparation). Komarova and Wodarz [42] calculate an opti- proofread in principle, the 3 → 5 exonuclease activity, was mum rate for chromosome loss should a chromosomal insta- discovered experimentally [49], leading to a variety of subtly bility mutation occur. Their calculated optimum rate of chro- different theoretical treatments of proofreading [18,50–55]. mosome loss is robust over a wide range of parameter values Based on a deterministic analysis of branching reaction path- and agrees with experiment. ways, Beckman, Loeb and colleagues [18,56–58] designed In general, the optimal genetic instability rate for tumor a series of experiments to distinguish among these models. evolution depends on the type of instability and may be dif- These experiments utilized the addition of two byproducts ferent from that for species evolution. and one substrate of the DNA polymerization reaction which could cause mutagenesis by affecting proofreading (“probes of proofreading”), and determined their effects singly and in 7. What kinds of genetic instability may contribute to combination. The results were consistent with only one of the carcinogenesis? theoretical models, and demonstrated that E. coli DNA polymerase I proofreads a given new base twice before moving Genetic instability may affect the enzymes which replicate on to the next base [18]. The two sequential editing steps give DNA (DNA polymerases), enzymes which repair DNA (mis- a high fidelity at a relatively low energy cost [59]. The two match repair enzymes, nucleotide-excision repair enzymes), editing steps may correspond to distinct sites on polymerases proteins which affect chromosomal stability (chromatin which have been seen in crystallographic studies [60,61]. structure and condensation proteins, kinetochore proteins, To date, evidence for carcinogenesis mediated by the pro- spindle proteins), and proteins which control apoptosis and duction of mutator mutants in mammalian DNA polymerases cell cycle regulation in response to DNA damage (p53 and is limited. DNA polymerase-␦ is the major DNA polymerase pRb). Mutations in each of these pathways have been related in mammalian cells; it is involved in multiple DNA synthe- to the pathogenesis of cancer, either in humans or in ani- sizing processes. A mutator mutation in the proofreading mals. The association of these forms of genetic instability activity of DNA polymerase-␦ increases the incidence of with cancer can be viewed as experimental support for the lymphomas and epithelial tumors in mice [62]. It is pos- mutator hypothesis. sible that similar relevant mutations will be discovered in humans. The theoretical work on proofreading suggests that 7.1. DNA polymerases and genetic instability some proofreading activities which would not be apparent in more conventional assays could be discovered by assaying The first line of defense against genetic instability is for the mutagenic effect of an added proofreading probe, the the remarkable fidelity of DNA polymerases and associ- pyrophosphate anion [18]. ated multienzyme complexes that replicate DNA. Purified DNA polymerase-␤ is responsible for resynthesis of DNA replicative DNA polymerase can copy DNA with a fidelity after removal of altered bases by glycosylases including bases as high as one misincorporation per 10 million nucleotides altered by oxygen reactive species [63]. Based on scattered polymerized. This is five orders of magnitude higher than reports from multiple laboratories, it has been estimated would be expected theoretically on the basis of Watson–Crick that 30% of human tumors examined to date express DNA base pairing alone [45], or on the basis of the experi- polymerase-␤ variant proteins [64]. One of these variants, R.A. Beckman, L.A. Loeb / Seminars in Cancer Biology 15 (2005) 423–435 431

K289M, which that was identified in human adenocarcinoma mismatch repair [80]. It has been assumed that DNA poly- of the colon, confers a 16-fold increase in mutation frequency merase slippage and a lack of mismatch repair contribute to in copying repetitive sequences in DNA [65]. the occurrence of MIN [80]. Recently, five new error-prone DNA polymerases have been identified in human cells [66]. These error-prone DNA 7.3. Chromosomal instability polymerases can copy past altered non-repaired lesions in DNA. In addition, they frequently incorporate non- Aneuploidy (alterations in chromosome number), gross complementary nucleotides when copying normal DNA tem- chromosomal translocations, and molecular loss of heterozy- plates. DNA polymerase ␩ (Pol ␩) copies past UV-dimers in gosity (LOH) without grossly visible karyotypic changes are DNA templates; deletion of Pol ␩ in patients with a variant all common features of tumors. The majority of colon can- form of xeroderma pigmentosum, XP-V, results in a 1000- cers exhibit aneuploidy and other chromosomal alterations, fold increase in incidence of skin cancers after UV-exposure comprising the phenotype of chromosomal instability (CIN). [67,68]. Presumably, in the absence of pol ␩, another error- They display an extraordinary rate of alteration in chromo- prone polymerase copies past the thymidine dimers resulting some number determined by fluorescence in situ hybridiza- in enhanced mutagenesis. It has been postulated that non- tion (FISH) at approximately 10−2 per chromosome per cell inherited skin cancers are also associated with mutation or generation [81]. Cell fusion studies indicate that the CIN diminished production of Pol ␩ [69]. These bypass DNA genotype is genetically dominant. Generally, a given colorec- polymerases may exhibit specificity as to the types of lesions tal cell line will exhibit MIN or CIN but not both. that can be accommodated within the active site. Pol ␬ is Greater than 100 genes leading to chromosomal instabil- able to copy benzyopyrene adducts in DNA and even incor- ity have been identified in yeast, including those affecting porate benzypyrene adducted dGTP; however, it is ineffective microtubules, kinetochores, chromosome condensation, sis- in bypassing thymidine dimers [70]. In contrast, Pol ␩ fails ter chromatid cohesion, and cell cycle checkpoints for mitosis to copy benzypyrene adducts in DNA. The expression lev- [82]. Thus far, the molecular basis of CIN has not been els of bypass DNA polymerases have been quantitated in identified in the majority of human cancers. Some tumors several human cancers. Pol ␬ is upregulated in lung cancers with colon cancer exhibit CIN on the basis of a heterozy- [71], and over-expression of at least one of these error-prone gous mutation in the mitotic spindle checkpoint gene hBUB1 DNA polymerases was reported in 45% of the 68 human [83,84]. tumors samples studied [72]. It has not been established The Adenomatous Polyposis Coli (APC) gene is a reces- whether or not these polymerases are mutated in cancer sive cancer-associated gene, mutation of which is believed cells. to be one of the earliest steps in the pathogenesis of colorectal cancer. One copy of the gene is already mutated in 7.2. DNA repair enzymes and genetic instability familial adenomatous polyposis (FAP), a disorder resulting in the growth of hundreds to thousands of colorectal polyps The second line of defense is the removal of damaged by the teenage years. Invariably, one or more of these polyps nucleotides in DNA that have mutagenic potential. The progresses to carcinoma. Given that this is a recessive tumor nucleotide-excision repair pathway removes and replaces suppressor gene, it can be inactivated either through mutation DNA nucleotides damaged by a variety of exogenous agents, of both copies, or through mutation in one copy followed by including ultraviolet light [73]. Patients with the autosomal deletion of chromosomal segments encompassing the second recessive disorder xeroderma pigmentosum are sensitive to copy (LOH). The latter mechanism would be accelerated by ultraviolet light, and develop cutaneous tumors in exposed a CIN mutation. Utilizing stochastic modeling techniques to areas at a rate of up to 1000 times that of the general popula- simulate small cell populations within colonic crypts, Nowak tion [74]. These patients have defective nucleotide-excision et al. [17] determine under what conditions the CIN mutation repair [75]. Mutations in other DNA synthesizing enzymes would likely be the initial event in colorectal carcinogenesis, are also associated with cancer. Amongst the multiple DNA or might be the second event following mutation of the first helicases in human cells are members of the recQ family. copy of APC, as a function of various key parameters. The Mutations in the genes that encode these helicases are found results indicate that only a small number of dominant CIN in Bloom’s and Werner’s syndromes, rare inherited diseases genes need to exist for CIN to predominate as an initial step with a high incidence of cancer [76,77]. under a variety of conditions. Similar results obtain upon Mismatch repair genes correct single base substitutions in consideration of a large cell population using a deterministic DNA [78]. A subset (13%) of individuals with sporadic colon model. cancer and the vast majority of those with hereditary non- Komarova et al. [85] utilize a stochastic model to eval- polyposis colorectal cancer (HNPCC, an inherited syndrome uate the rate of formation of dysplastic crypts by CIN and with increased colon cancer incidence) exhibit microsatellite MIN mechanisms in sporadic colon cancer, HNPCC, and instability (MIN), i.e. instability in the length of repetitive FAP, obtaining broad qualitative agreement with the rela- DNA sequences termed microsatellites [10,11,79]. In addi- tive importance of CIN and MIN and the number of polyps tion to colon cancer, many tumor cell lines exhibit deficits in generated in these conditions. 432 R.A. Beckman, L.A. Loeb / Seminars in Cancer Biology 15 (2005) 423–435

7.4. Cell cycle checkpoints and genetic instability apparent, they contain 108 to 109 cells. Thus, if each cancer cell contains thousands of random mutations, there could be Cell cycle checkpoints prevent progression through the as many as 1012 random mutations within the tumor cell pop- cell cycle, allowing time for DNA repair and accelerating ulation, Thus, within a tumor there would be multiple cells apoptosis in the presence of unrepaired DNA damage. Thus, that contained mutations in genes or regulatory sequences abrogation of these checkpoints promotes genetic instability. that rendered those cells resistant to specific chemotherapies. The two most extensively studied checkpoint genes, p53 and In the presence of chemotherapy, the mutant cells would have pRb, are among the most frequently mutated in human can- a growth advantage. Accordingly, treatment with chemother- cers [16,86]. Other checkpoint genes include the ATM gene, apy selects resistant mutants, and genetic instability exac- which is mutated in ataxia telangiectasia, another rare inher- erbates this problem. It is advisable to treat with non-cross ited disorder with increased susceptibility to cancer [87]. resistant therapeutic combinations to reduce the probability There is also evidence that BRCA1 and BRCA2, inherited of mutation to resistance to all of the applied therapies [90]. breast and ovarian cancer susceptibility genes, are involved These combinations can either be applied together (at reduced in cell cycle checkpoints [88]. dose to keep the overall toxicity tolerable) or sequentially at high dose, as has been argued based on growth kinetic argu- ments [91]. 8. What is the role of genetic instability in relation to therapy? 9. Could the clinical appearance of cancer be Genetic instability may play a role in the mechanism of prevented by decreasing the rate of genetic change? action of chemotherapy. As has been discussed above, genetic instability increases the rate of acquiring mutations which Cancer typically occurs late in life and evolves over may be essential for carcinogenesis, but also accelerates the decades. In that regard, even a modest reduction in the rate of acquisition of deleterious mutations which reduces the fit- carcinogenesis could delay the onset of cancer by decades, ness of clones potentially leading to their extinction. Beyond perhaps even placing the projected time of cancer develop- a critical value, further increases in genetic instability are ment outside the human lifespan. Given the role of mutation likely to exceed the optimum and lead to extinction of clones in carcinogenesis, enhanced genetic stability could slow car- due to NCS. The majority of chemotherapeutic agents inter- cinogenesis in a meaningful way, leading to “prevention by fere with aspects of DNA metabolism and are mutagens. If the delay”. cancer consists of genetically unstable cells, further increased Even a two-fold reduction in the rate of accumulation of mutagenesis due to chemotherapy could lead to accumulation mutations in cancer cells might have profound effects on the of deleterious mutations and extinction of malignant clones. age at which patients succumb to certain adult cancers. Pre- Thus, chemotherapy may create conditions in which evolu- vention by delay may be particularly applicable to cancers tion selects for genetically stable cells, as has been elucidated associated with prolonged chronic inflammation due to either in detail by Komarova and Wodarz [42] utilizing a determin- bacterial infection (gastric cancer) [92], viral infection (hep- istic model. atitis B or C) [93], immunological insufficiencies (ulcerative A common side effect of chemotherapy is the induction of colitis) [94], or unknown etiology (chronic pancreatitis)[95]. second malignancies. Given that cytotoxic chemotherapy is In the case of primary hepatoma resulting from hepatitis virus mutagenic, it is not surprising it would be carcinogenic. Some B, infection occurs in early infancy and in some individu- authors have suggested that in addition to directly causing als persists chronically. It takes 40 years from the start of mutations, chemotherapy may select for genetic instability in infection to tumor detection and hepatitis increases the risk normal tissues. This is because cells with intact DNA repair of subsequent hepatoma by more than 200-fold. A reduc- mechanisms will spend significant time out of cycle repair- tion in the rate of mutation accumulation by only two-fold ing the DNA damage due to chemotherapy. In normal tissues, could delay the clinical appearance of the tumor from age 50 the mutation burden may be low enough that genetic instabil- to age 90. A similar analysis pertains to hepatoma associated ity mutants may be selected without exceeding the optimum with persistent infection with hepatitis C virus. Machida et al. mutation rate [42]. comment on the induction of a mutator phenotype after infec- Genetic instability may also play a role in resistance to tion with hepatitis C virus (HCV) both in vitro and in vivo cancer therapies. The probability that a tumor will contain [96]. They observe a 5- to 10-fold elevation in the mutation no cells with pre-existing resistance to a given therapy goes frequency at different loci, including immunoglobulin heavy down exponentially both with the size of the tumor and chain, BCL-6, p53, and ␤-catenin. It has been postulated that with the underlying mutation rate [89]. A mutator pheno- the persistence of viral hepatitis results in an inflammatory type would not only generate the mutations that are required reaction with generation of oxygen reactive species which for tumor progression but would in parallel generate an even could lead to DNA damage and mutagenesis. Thus, drugs larger number of mutations that would render cells resistant that scavenge oxygen free radicals might have a place in the to chemotherapeutic agents. By the time tumors are clinically prevention of primary hepatoma. R.A. Beckman, L.A. Loeb / Seminars in Cancer Biology 15 (2005) 423–435 433

Caloric restriction reduces tumor incidence in laboratory [9] Loeb LA. Cancer cells exhibit a mutator phenotype. Adv Cancer animals [97], and also reduces the mutation rate, possibly Res 1998;72:25–56. due to reduced production of reactive oxygen species [98]. [10] Ionov Y, Peinado MA, Malkhoysan S, Shibata S, Perucho M. Ubiquitous somatic mutations in simple repeated sequences reveal Reactive oxygen species could also potentially be scavenged a new mechanism for colonic carcinogenesis. Nature 1993;363: by dietary intake of antioxidants. Thus, reduction of reac- 558–61. tive oxygen species may provide a simple pathway to cancer [11] Fishel R, Lescoe MK, Rao MRS, Copeland NG, Jenkins NA, Garber prevention by delay. J, et al. The human nutator gene homolog MSH2 and its association In contrast, compensation for the effects of altered DNA with hereditary non-polyposis colon cancer. Cell 1993;75:1027–38. [12] Lengauer C, Kinzler KW, Vogelstein B. Genetic instabilities in repair mechanisms or altered DNA polymerases in promot- human cancers. Nature 1998;396:643–9. ing genetic instability would be challenging with current [13] Paulovich AG, Toczyski DP, Hartwell LH. When checkpoints fail. technologies, as it might require comprehensive gene ther- Cell 1997;88:315–21. apy. Theoretical modeling also suggests that intracellular [14] Tomlinson IPM, Novelli MR, Bodmer WF. The mutation rate and expression of pyrophosphatase, an enzyme capable of low- cancer. Proc Natl Acad Sci USA 1996;93:14800–3. [15] Knudson AG. Mutation and cancer: statistical study of retinoblas- ering the concentration of the pyrophosphate anion, might toma. Proc Natl Acad Sci USA 1971;68:820–3. enhance proofreading by DNA polymerases [18], leading to [16] Knudson AG. Antioncogenes and human cancer. Proc Natl Acad Sci a decreased mutation rate, an idea that could be tested in cell USA 1993;90:10914–21. culture. [17] Nowak MA, Komarova NL, Sengupta A, Jallepalli PV, Shih IM, Vogelstein B, et al. The role of chromosomal instability in tumor initiation. Proc Natl Acad Sci USA 2002;99:16226–31. [18] Beckman RA, Loeb LA. Multistage proofreading in DNA replication. 10. Conclusion Q Rev Biophys 1993;26:225–331. [19] Moolgavkar SH, Knudson AG. Mutation and cancer: a model for Genetic instability has a likely role in cancer pathogen- human carcinogenesis. J Natl Cancer Inst 1981;66:1037–52. esis as well as a possible role in sensitivity and resistance [20] Schaaper RM, Dunn RL. Spontaneous mutation in the Escherichia to therapy. The field has witnessed synergistic contributions coli lacI gene. Genetics 1991;129:317–26. [21] Fisher JC, Hollomon JH. A hypothesis for the origin of cancer foci. of theory and experiment supporting the role of genetic Cancer 1951;4:916–8. instability at multiple levels including epidemiology, genet- [22] Pike MC. A method of analysis of a certain class of experiments in ics, molecular biology, and clinical oncology. This trend is carcinogenesis. Biometrics 1966;22:142–61. likely to continue in the future as the remaining questions are [23] Friend SH, Bernards R, Rogelj S, Weinberg RA, Rapaport JM, Albert addressed. DM, et al. A human DNA segment with properties of the gene that predisposes to retinoblastoma and osteosarcoma. Nature (London) 1986;323:643–6. [24] Huang HJS, Yee JK, Shew JY, Chen PL, Bookstein R, Friedman T, Acknowledgements et al. Suppression of the neoplastic phenotype by replacement of the RB gene in human cancer cells. Science 1988;242:1563–6. We thank Ms. Jessica Hsu for assistance in preparing fig- [25] Doll R. The age distribution of cancer: implications for models of carcinogenesis. J R Soc Med [Ser A] 1971;134:133–66. ures and Ms. Mary Whiting for bibliographic assistance. This [26] Luebeck EG, Moolgavkar SH. Multistage carcinogenesis and work was supported in part by grants to LAL from the U.S. the incidence of colorectal cancer. Proc Natl Acad Sci USA National Cancer Institute CA 78885 and National Institutes 2002;99:15095–100. of Health CA 102029. [27] Lossos IS, Czerwinski DK, Alizadeh AA, Wechser MA, Tibshirani R, Botstein D, et al. Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. N Engl J Med 2004;350:1828–37. References [28] Albertini RJ, Nicklas JA, O’neill JP, Robison SH. In vivo somatic mutations in humans: measurement and analysis. Annu Rev Genet [1] Nordling CO. A new theory on the cancer-inducing mechanism. Br 1990;24:305–36. J Cancer 1953;7:68–72. [29] Cervantes RB, Stringer JR, Shao JA, Tischfield JA, Stambrook PJ. [2] Armitage P, Doll R. The age distribution of cancer and a multi-stage Embryonic stem cells and somatic cells differ in mutation frequency theory of carcinogenesis. Br J Cancer 1954;8:1–12. and type. Proc Natl Acad Sci USA 2002;99:3586–90. [3] Cook PJ, Doll R, Fellingham SA. A mathematical model for the age [30] Baca OG, Tacheeni OS, Akporiaye ET, Deblasie R, Crissman distribution of cancer in man. Int J Cancer 1969;4:93–112. HA. Cell cycle distribution patterns and generation times of L929 [4] Hanahan D, Weinberg RA. The hallmarks of cancer. Cell fibrobalst cells persistently infected with Coxiella burnetti. Infect 2000;100:57–70. Immun 1985;47:366–9. [5] Rangarajan A, Hong SJ, Gifford A, Weinberg RA. Species and cell [31] Baker FL, Sanger LJ, Rodgers JW, Jabboury K, Mangini OR. Cell type-specific requirements for cellular transformation. Cancer Cell proliferation kinetics of normal and tumor tissue in vitro: quiescent 2004;6:171–83. reproductive cells and the cycling reproductive fraction. Cell Prolif [6] Fisher JC. Multiple-mutation theory of carcinogenesis. Nature 1995;28:1–15. 1958;181:651–2. [32] Tomlinson I, Sasieni P, Bodmer W. How many mutations in a cancer? [7] Loeb LA, Springgate CF, Battula N. Errors in DNA replication as a Am J Pathol 2002;100:755–8. basis of malignant changes. Cancer Res 1974;34:2311–21. [33] Nowak MA, Michor F, Komarova NL, Iwasa Y. Evolutionary dynam- [8] Loeb LA. Mutator phenotype may be required for multistage car- ics of tumor suppressor gene inactivation. Proc Natl Acad Sci USA cinogenesis. Cancer Res 1991;51:3075–9. 2004;101:10635–8. 434 R.A. Beckman, L.A. Loeb / Seminars in Cancer Biology 15 (2005) 423–435

[34] Nowell PC. The clonal evolution of tumor cell populations. Science [59] Freter RP, Savageau MA. Proofreading systems of multiple stages 1976;194:23–8. for improved accuracy of biological discrimination. J Theoret Biol [35] Iwasa Y, Michor F, Nowak MA. Stochastic tunnels in evolutionary 1980;85:99–123. dynamics. Genetics 2004;166:1571–9. [60] Ollis DL, Brick P, Hamlin R, Yuong NG, Steitz TA. Structure of [36] Komarova NL, Sengupta A, Nowak MA. Mutation-selection net- large fragment of Escherichia coli DNA polymerase I complexed works of cancer intitiation: tumor suppressor genes and chromosomal with dTMP. Nature (London) 1985;28:762–6. instability. J Theoret Biol 2003;223:433–50. [61] Johnson SJ, Taylor JS, Beese LS. Processive DNA synthesis [37] Michor F, Frank SA, May RM, Iwasa Y, Nowak MA. Somatic selec- observed in a polymerase crystal suggests a mechanism for the tion for and against cancer. J Theoret Biol 2003;225:377–82. prevention of frameshift mutations. Proc Natl Acad Sci USA [38] Michor F, Iwasa Y, Komarova NL, Nowak MA. Local regu- 2003;100:3895–900. lation of homeostasis favors chromosomal instability. Curr Biol [62] Goldsby RE, Lawrence NA, Hays LE, Olmsted EA, Chen X, Singh 2003;13:581–4. M, et al. Defective DNA polymerase-␦ proofreading causes cancer [39] Cairns J. Mutation selection and the natural history of cancer. Nature susceptibility in mice. Nat Med 2001;7:638–9. 1975;255:197–200. [63] Sobol RW, Horton JK, Kuhn R, Gu H, Singhal RK, Prasad R, et [40] Komarova NL, Wodarz D. Evolutionary dynamics of mutator phe- al. Requirement of mammalian DNA polymerase-␤ in base-excision notypes in cancer: implications for chemotherapy. Cancer Res repair. Nature 2001;379:183–6. 2003;63:6335–42. [64] Starcevic D, Dalal S, Sweasy JB. Is there a link between DNA [41] Guo HH, Choe J, Loeb LA. Protein tolerance to random amino acid polymerase beta and cancer? Cell Cycle 2004;3:998–1001. substitution. Proc Natl Acad Sci USA 2004;101:9205–10. [65] Lang T, Maitra M, Starcevic D, Li SX, Sweasy JB. A DNA poly- [42] Komarova NL, Wodarz D. The optimal rate of chromosome loss merase beta mutant fom colon cancer cells induces mutations. Proc for the inactivation of tumor suppresssor genes in cancer. Proc Natl Natl Acad Sci USA 2004;101:6074–9. Acad Sci USA 2004;101:7017–21. [66] Friedberg EC, Wagner R, Radman M. Specialized DNA poly- [43] Eigen M. Viral quasispecies. Sci Am 1993;269:42–9. merases, cellular survival, and the genesis of mutations. Science [44] Nowak MA, May RM. Virus dynamics, mathematical principles of 2002;296:1627–30. immunology and virology. New York: Oxford University Press; 2000. [67] Matsuda T, Bebenek K, Masutani C, Hanaoka F, Kunkel TA. Low [45] Mildvan AS. Mechanisms of enzyme action. Annu Rev Biochem fidelity DNA synthesis by human DNA polymerase-eta. Nature 1974;43:357–99. 2000;404:1011–3. [46] Lohrmann R, Orgel LE. Efficient catalysis of polycytidylic [68] Cleaver JE, Kraemer KH. Xeroderma pigmentosum. In: Scriver CR, acid-directed oligoguanylate formation by Pb2+. J Mol Biol Beudet AL, Sktm WS, Valle D, editors. Metabolic basis of inherited 1980;142:555–67. disease. New York: McGraw-Hill; 1989. p. 2949–71. [47] Petruska J, Sowers LC, Goodman MF. Comparison of nucleotide [69] McCulloch SD, Kokoska RJ, Masutani C, Iwai S, Hanaoka F, Kunkel interactions in water, proteins, and vacuum: model for DNA poly- TA. Preferential cis-syn thymine dimer bypass by DNA polymerase merase fidelity. Proc Natl Acad Sci USA 1986;83:1559–62. ␩ occurs with biased fidelity. Nature 2004;428:97–100. [48] Sloan DL, Loeb LA, Mildvan AS, Feldman RJ. Comparison of the [70] Bi X, Slater DM, Ohmori H, Vaziri C. DNA polymerase kappa geometry of nucleoside triphosphates on DNA polymerase and pyru- is specifically required for recovery from the benzo[a]pyrene-di- vate kinase as determined by NMR. J Biol Chem 1975;250:8913–20. hydrodiol epoxide (BPDE)-induced S-phase checkpoint. J Biol Chem [49] Brutlag D, Kornberg A. Enzymatic synthesis of deoxyribonucleic 2005;280:22343–55. acid. XXXVI. A proofreading function for the 3 → 5 exonucle- [71] Bavoux C, Leopoldino AM, Bergoglio V, O-Wang J, Ogi T, Bieth ase activity in deoxyribonucleic acid polymerases. J Biol Chem A, et al. Up-regulation of the error prone DNA polymerase {kappa} 1972;247:241–8. promotes pleiotropic genetic alterations and tumorigenesis. Cancer [50] Hopfield JJ. Kinetic proofreading: a new mechanism for reducing Res 2005;65:325–30. errors in biosynthetic processes requiring high specificity. Proc Natl [72] Albertella MR, Lau A, O’Connor MJ. The overexpression of spe- Acad Sci USA 1974;71:4135–9. cialized DNA polymerases in cancer. DNA Repair 2005;4:583–93. [51] Ninio J. Kinetic amplification of enzyme discrimination. Biochimie [73] Wood RD. DNA repair in eukaryotes. Annu Rev Biochem 1975;57:587–95. 1996;65:135–67. [52] Bernardi F, Sagh M, Dorrizzi M, Ninio J. A new approach to DNA [74] Kraemer KH, Lee MM, Scotto J. DNA repair protects from against polymerase kinetics. J Mol Biol 1979;129:93–112. cutaneous and internal neoplasia: evidence from xeroderma pigmen- [53] Clayton LK, Goodman MF, Branscomb EW, Galas DJ. Error induc- tosum. Carcinogenesis 1984;5:511–4. tion and correction by mutant and wild type T4 DNA poly- [75] Cleaver JE. Defective repair replication of DNA in xeroderma pig- merases: kinetic error discrimination mechanisms. J Biol Chem mentosum. Nature 1968;218:652–6. 1979;254:1902–12. [76] Ellis NA, Lennon DJ, Proytcheva N, Alhadeff B, Henderson EE, [54] Fersht AR, Knill-Jones JW. Contribution of 3 leads to 5 exonuclease German J. Somatic intragenic recombination within the mutated activity of DNA polymerase III holoenzyme from Escherichia coli locus BLM can correct the high sister-chromatid exchange phe- to specificity. J Mol Biol 1983;165:669–82. notype of Bloom syndrome cells. Am J Hum Genet 1995;57: [55] Kuchta RD, Benkovic P, Benkovic SJ. Kinetic mechanisms whereby 1019–27. DNA polymerase I (Klenow) replicates DNA with high fidelity. Bio- [77] Shen JC, Loeb LA. The Werner syndrome gene: the molecular basis chemistry 1988;27:6716–25. of RecQ helicase-deficiency diseases. Trends Genet 2000;16:213–20. [56] Loeb LA, Dube DK, Beckman RA, Koplitz M, Gopinathan KP. [78] Kunkel TA. DNA mismatch repair. The intricacies of eukaryotic On the fidelity of DNA replication: nucleoside monophosphate spell-checking. Curr Biol 1995;5:1091–4. generation during polymerization. J Biol Chem 1981;256:3978– [79] Peinado MA, Malkhosyan S, Velazquez A, Perucho M. Isolation and 87. characterization of allelic losses and gains in colorectal tumors by [57] Kunkel TA, Schaaper RM, Beckman RA, Loeb LA. On the fidelity of arbitrarily primed polymerase chain reaction. Proc Natl Acad Sci DNA replication. XII. Effect of the next nucleotide on proofreading. USA 1992;89:10065–9. J Biol Chem 1981;256:9883–9. [80] Umar A, Kunkel TA. DNA-replication fidelity, mismatch repair and [58] Kunkel TA, Beckman RA, Loeb LA. On the fidelity of DNA syn- genome instability in cancer cells. Eur J Biochem 1996;238:297–307. thesis: pyrophosphate-induced misincorporation allows detection of [81] Lengauer C, Kinzler KW, Vogelstein B. Genetic instability in col- two proofreading mechanisms. J Biol Chem 1986;261:13610–6. orectal cancers. Nature 1997;386:623–7. R.A. Beckman, L.A. Loeb / Seminars in Cancer Biology 15 (2005) 423–435 435

[82] Kolodner RD, Putnam CD, Myung K. Maintenance of genome sta- [92] Baik SC, Youn HS, Chung MH, Lee WK, Cho MJ, Ko GH, et al. bility in Saccharomyces cerevisiae. Science 2002;297:552–7. Increased oxidative DNA damage in Heliocobacter pylori-infected [83] Cahill DP, Lengauer C, Yu J, Riggins GJ, Willson JKV, Markowitz human gastric mucosa. Cancer Res 1996;56:1279–82. SD, et al. Mutations of mitotic checkpoint genes in human cancers. [93] Machida K, Cheng KTH, Sung VMH, Lee KJ, Levine AM, Lai Nature 1998;392:300–3. MMC. Hepatitis C virus infection activates the immunologic (type II) [84] Bardelli A, Cahill DP, Lederer G, Speicher MR, Kinzler KW, Vogel- isoform of nitric oxide synthase and thereby enhances DNA damage stein B, et al. Carcinogen-speciﬁc induction of genetic instability. and mutations of cellular genes. J Virol 2004;78:8835–43. Proc Natl Acad Sci USA 2001;98:5770–5. [94] Rabinovitch PS, Gollahon KA, Jackson AL, Palanca BJ, Shen [85] Komarova NL, Lengauer C, Vogelstein B, Nowak MA. Dynamics of WT. Flow cytometric assay of DNA damage by alkaline unwind- genetic instability in sporadic and familial colorectal cancer. Cancer ing coupled with DNA repair endonucleases. Cytometry 1996; Biol Ther 2002;1:685–92. (Suppl 8):38. [86] Levine AJ. p53, the cellular gatekeeper for growth and division. Cell [95] Brentnall TA, Chen R, Lee JG, Kimmey MB, Bronner MP, Haggitt 1997;88:323–31. RC, et al. Microsatellite instability and K-ras mutations associ- [87] Rotman G, Shiloh Y. ATM: from gene to function. Hum Mol Genet ated with pancreatic adenocarcinoma and pancreatitis. Cancer Res 1998;7:1555–63. 1995;55:4264–7. [88] Zhang H, Tombline G, Weber BL. BRCA1, BRCA2, and DNA dam- [96] Machida K, Cheng KTN, Sung VMH, Shimodaira S, Lindsay KT, age response:collision or collusion? Cell 1998;92:433–6. Levine AM, et al. Hepatitis C virus induces a mutator phenotype: [89] Goldie JH, Coldman AJ. A mathematical model for relating the drug enhanced mutations of immunoglobulin and protooncogenes. Proc sensitivity of tumors to their spontaneous mutation rate. Cancer Treat Natl Acad Sci USA 2004;101:4262–7. Rep 1979;63:1727–33. [97] Michels KB, Ekbom A. Caloric restriction and incidence of breast [90] Goldie JH, Coldman AJ. Genetic instability in the development of cancer. JAMA 2004;291:1226–30. drug resistance. Semin Oncol 1985;12:222–30. [98] Aidoo A, Mittelstaedt RA, Bishop ME, Lyn-Cook LE, Chen YJ, [91] Norton L, Simon R. Tumor size, sensitivity to therapy, and design Duffy P, et al. Effect of caloric restriction on Hprt lymphocyte muta- of treatment schedules. Cancer Treat Reps 1977;61:1307–17. tion in aging rats. Mutat Res 2003;527:57–66.