On the Nature of Genetic Changes Requiredfor the Development of Esophageal Cancer

Pieter Uys and Paul D. van Helden*

MRC Centre for Molecular and Cellular Biology and Department of Medical Biochemistry, University of Stellenbosch,Tygerberg, South Africa

It is clear that genetic mutations are necessary for the development of cancer, but the exact number required is notclear, with estimates ranging from one critical hit (e.g., p53) to dozens or perhaps even hundreds of expressionchanges (by microarray analysis) or chromosomal aberrations. We have used a mathematical model to estimate the

critical number of mutations required for the development of esophageal cancer (EC) and to test for the likelihood ofan EC major susceptibility gene. Our results suggest that six or seven mutations are required for the development ofEC and that there is no evidence of a major susceptibility gene. This does not exclude the possibility that gene-environment interactions may not confer susceptibility or risk. The gradual accumulation of aberrant gene function

also can explain the progression of pathologic states seen in the esophagus, from early dysplasia through mild tosevere dysplasia and, finally, to cancer, as illustrated in our model. � 2003 Wiley-Liss, Inc.

Key words: genes; p53; mutations; dysplasia; esophageal; cancer

INTRODUCTION

It is generally believed that the development ofcancer is amulti-event process and not the result of asingle molecular event. Some of the lines of support-ing evidence are that the incidence of cancer gene-rally increases with advancing age, that the presenceof a so-called familial cancer gene in a person in-creases the likelihood of the development of thatcancer type but is in itself insufficient for develop-ment of that cancer, and that similar cancers candevelop in people without a familial gene (so-calledsporadic cases). Furthermore, in vitro studies haveshown that introduction of a single transforminggene to a nontransformed cell is insufficient fortumor development [reviewed in 1].The nature and number of the (mutagenic) events

that may be required for the development of cancerare not certain. Attempting to estimate the numberof events is complicated by the presence of suscept-ibility genes (e.g., BRCA1) or polymorphisms thatmay predispose particular individuals to cancer, forexample,N-acetyltransferase 2 susceptibility in blad-der cancer [2]. The use of screening techniques forexpression analysis and the use of fluorescent in situhybridization or other techniques to study chromo-somal rearrangements in cancer have shown a be-wildering number of changes [3–5]. Making sense ofthem is not necessarily particularly productive, in alllikelihood, because these events are end-stage anddonot represent causal events.Numerous mutations, especially at the gross level,

are common in cancer, as evidenced by the manychromosomal mutations seen in advanced tumors[5]. It is not at all clear, however, whether these

mutations are causal or merely represent tumor cellinstability and the final stage of the development ofthe cancer. Many attempts have been made toaddress this issue, including the use ofmathematicalmodels [6–9]. They have been used to predict thenumber ofmutations for the development of a giventype of cancer and also to assess whether there maybe a cancer gene for a particular cancer [6–8]. Thismethodology depends on the log/log plot of in-cidence compared with age. A linear relationshipallows one to estimate the number of mutations ac-cording to the slope of the line, where any deviationfrom linearity may be indicative of the presence of acancer gene.Esophageal cancer (EC) is prevalent in certain

regions of the world, but it is relatively rare else-where. For example, in certain areas within Iran,China, and South Africa or within certain ethnicgroups, there is a very high incidence of EC [10,11].In South Africa and elsewhere, there are numerousanecdotal reports of EC occurring in villages and infamilies, and there are documented cases in peopleyounger than 20 yr [11]. We therefore investigatedthis cancer to establish whether there is a likelihood

MOLECULAR CARCINOGENESIS 36:82–89 (2003)

� 2003 WILEY-LISS, INC.

*Correspondence to: MRC Centre for Molecular and CellularBiology and Department of Medical Biochemistry, University ofStellenbosch, PO Box 19063, Tygerberg, 7505, South Africa.

Received 21 June 2002; Revised 7 November 2002; Accepted 22November 2002

Abbreviation: EC, esophageal cancer.

DOI 10.1002/mc.10100

of the existence of a cancer gene for EC and todetermine howmanymutations are required for thedevelopment of this cancer. Furthermore, becausethere is a considerable difference in the incidencebetween males and females, we examined whetherthere were differences between the two genders.Finally, we considered the possible critical eventsin EC.

METHODS

In this study we used the following mathematicalmodel [6–8], according to which a malignancyappears as a clinical entity only after the requisitenumber ofm sequential alterations has occurred in aprogenitor tumor cell: l(a)¼ kam�1. Here, a repre-sents age and l represents the age-specific incidencerate; k is the product of the probabilities of each ofmsomatic alterations in the DNA of the progenitortumor cell and is assumed to be constant. Thismodelassumes the following, to keep complexity to aminimum: (1) Amalignant tumor arises as the resultof the accumulation of genetic changes in a singlecell (the progenitor cell of the ultimate tumor). (2)The number of mutations in a specific tumor isindependent of the dose of exogenous carcinogens.(3) The proliferation rate of a particular tissueremains constant throughout life. (4) Only muta-tions that occur after birth are considered, andmutations present in a possible precursor tumor cellat birth are excluded (because a refers to age afterbirth).It should be noted that from the perspective of its

mathematical derivation, the model applies to asingle cohort of persons. For a to have a particularvalue, it signifies that all individuals in that cohortare that age. Therefore, an application of the modelrequires age-specific data pertaining to a singlecohort of people for all ages from birth to death.Such a set of data for humans does not exist andwould be entirely impractical to collect. Instead, it isnecessary to resort to data collected for a particularentire population during a selected year and sub-divided according to age group. Because these age-group subdivisions are not necessarily equal in size,the actual incidence within each group must bedivided by the population of that group to yield theincidence rate (typically incidence per 100000).Thus, instead of working with the original model,l(a)¼ kam�1, it is necessary to use rate(a)¼ kam�1.Thedata usually are available in 5-yr age spans, and themedian age in each division is used as a proxy for theage a of that division.The model is made amenable to investigation by

plotting log(rate) against log(age). This means thatinstead of studying rate(a)¼ kam�1, the followingequivalent functional form is used: log(rate(a))¼log(k)þ (m�1)log(a), or, more simply expressed,log(rate)¼ log(k)þ (m�1)log(age). This is a linear formwith the gradient equal to m�1. In principle, then,

the number of genetic alterationsm responsible for aparticular type of human cancer can be estimated bydetermining the slope of the straight line yieldingthe best fit to a plot of log(rate) against log(age).Care must be exercised, however, to exclude

certain points from the plot. In the case of EC, casesare seldom reported for people under the age of 20,even for a population having a relatively very highincidence rate, such as black males in South Africa.On average, in a given year fewer than six cases arediagnosed for that younger age group in a totalpopulation exceeding seven million in South Africa[11]. These rare cases probably are the outcomes ofmutations from teratogenic exposure or extremeexposure to environmental mutagens in early life.Assumption 4, stated above, precludes the use ofsuch data in the data-set model. The model must beapplied only to data that reflect the outcome ofsequences of mutations. A significant incidence ofEC cases appears only from about the age of 20 yr.The method to ascertain the appropriate cutoff age,which was used to exclude younger groups, is des-cribed in the footnote to Table 2.In addition, as has been observed by other re-

searchers, older age groups are subject to additionalfactors (such as degenerative diseases) that are notaddressed by the model. In particular, Assumption 3is invalid. As explained in the footnote to Table 2,however, data for the older age groups do notseriously perturb the gradient ascertained with onlythe ‘‘linear’’ section of the data. Data for incidencerates in South Africa were obtained from publishedinformation [11] derived from input from approxi-mately 80 different hospitals and pathology prac-tices [11]. Data fromNorwaywere obtained from theKreftregisteret [12], and from Ontario they wereobtained from Cancer Care Ontario [13].

RESULTS

Cancer incidence data were accessed from pub-lished data and analyzed as described above. Data formales and females were analyzed separately. Anexample of the type of data used is shown in Table 1.This table shows incidence rate by age for blackmalesin the South African population. Figure 1 is the plotof log(rate) against log(age) derived from these data.This plot is typical for EC. It clearly shows thedeviation from linearity for the younger age groupsdue to the extremely low incidence rate in youngpersons. Nonlinearity also occurred at the upper endof the plot. These points corresponded to the elderlysector of the population and, as mentioned earlierregarding the applicabilityof themodel,Assumption3 was violated, because increased incidence of deathdue to degenerative disease would result in a changein the relative disease distribution away from cancer.A problem with such data is that the environ-

mental exposure experienced, for instance, 70–74 yrago by the group aged 70–74 yr is highly unlikely to

MUTATIONS IN ESOPHAGEAL CANCER 83

be similar to that experienced by the current groupaged 0–4 yr. This objection, however, relates toAssumption 2. Essentially, only the linear section ofthe plot should be used to estimate m, as explainedearlier. This procedure was followed in our study ofdata concerning males and females from variousregions of the world or different population groups.The data sets need to involve large populations suchthat incidence rates are big enough over a range ofage groups to provide significant statistical correla-tions. Log/log plots for some of these data sets areshown in Figures 2–4. Note that within the linearsection of each of these graphs, deviations from alinear trend are negligible in all populations tested,as shown by the high values for R2. The statisticalanalyses were conducted with the standard SPSSpackage.The number of mutations required for the devel-

opment of EC can be obtained from the slope (m�1)of these graphs and is summarized in Table 2. Theresults suggest that six to sevenmutations are neces-sary for the development of EC in males or femalesand, furthermore, that this is a constant, despitesignificant differences in incidence and lifetime riskin different populations and regions of the world.(Confidence limits for this estimate are shown in thefootnote to Table 2 and in Table 5.) The high R2

values (see Table 5) show that there is no significantdeviation fromlinearityover the age range examinedin any population, in contrast to lymphosarcomas,for example [8]. This suggests that there is no ECgene.The graphs show approximately the same slope

irrespective of sexorpopulationbut are shifted to theright or left as befits high-incidence or low-incidencecommunities and sex differences. The intercept is anagemeasurement and is effectively ameasure of risk,with males at higher risk than females and people inhigh-risk areas at higher risk than others (earlier ageof onset).

DISCUSSION

Our chief objective in this study was to determinethe number of mutations required (the value of m)for development of EC in many different popula-tions and to ascertainwhether different values appli-ed to these different populations and whether thereis an EC gene. Any such differences would demandexplanation, if not a revisiting of the hypotheses ofthe model itself and the assumptions made in itsuse with actual data. Values of m for EC have beenreported previously as 7 [6], 8 [7], and 9 [8]. Theresults presented here suggest that the value form isprobably 6–7 and that it is a constant for differentgeographic regions and populations. The range re-ported earlier varies from approximately 5.9 to 7.5[6], which is in close agreement with our range (5.6–7.1). Our results suggested that a stepwise mutationmodel accounts for EC. The occurrence of EC in very

Table

1.

Inci

dence

of

EC

Am

ong

Bla

ckM

ale

sin

South

Afr

ica—

1988*

Age

gro

up

(yr)

0–

45

–9

10

–14

15

–19

20

–24

25

–29

30

–34

35

–39

40

–44

45

–49

50

–54

55

–59

60

–64

65

–59

70

–74

75

–79

80

–84

85þ

Media

nage

(yr)

27

12

17

22

27

32

37

42

47

52

57

62

67

72

77

82

87

Inci

dence

01

11

22

10

37

65

128

195

217

224

272

159

139

47

23

19

Inci

dence

/100

000

00.0

60

0.0

67

0.0

72

1.7

39

0.8

24

3.6

91

8.3

09

20.8

96

38.1

01

53.4

85

68.9

56

107.9

7100.7

7195.7

5129.3

598.0

77

87.6

91

log(a

ge)

0.3

0.8

51.0

81.2

31.3

41.4

31.5

11.5

71.6

23

1.6

72

1.7

16

1.7

56

1.7

92

1.8

26

1.8

57

1.8

86

1.9

138

1.9

395

log(r

ate

)�

�1.2

2�

1.1

7�

1.1

40.2

4�

0.0

80.5

70.9

21.3

21.5

81

1.7

28

1.8

39

2.0

33

2.0

03

2.2

92

2.1

12

1.9

916

1.9

43

*The

ext

rem

era

rity

of

EC

am

ong

the

younger

age

gro

ups

isem

phasi

zed

by

the

exa

mple

that

the

inci

dence

per

100

000

of

0.0

72

inth

eage

gro

up

15

–19

repre

sents

one

case

ina

gro

up

of

1381

588

indiv

iduals

.Rate

equals

inci

dence

per

100

000.

84 UYS AND VAN HELDEN

young people may be explained by a very rare ECgene, but it ismore likely due to teratogenic exposureplus extreme exposure to carcinogens in early life.One might explain a difference in incidence be-

tween males and females in terms of exposure toetiological agents, owing to behavioral differences inmany societies. Provided, however, that the data areaccurate and there is no sex-bias in health-careprovision or major lifestyle change differences inone sex over the past approximately 40–60 yr, thesedifferences should not affect m�1 or m estimates.This is the case for data concerning whites in SouthAfrica and for data fromOntario and Norway. As thedata show, however, there are substantial differencesin the estimates form�1 betweenmales and femalesin the black population in SouthAfrica, although the95% confidence intervals intersect. In South Africa,the incidence and caseload of EC are high in theblack population, but apparent sex differencesprobably reflect a dramatic lifestyle change amongblackmales and females in South Africa over the past70 yr. Among other factors, such changes include

Figure 1. Change in incidence rate with age for cancer of the esophagus, shown on a double log scale, for blackmales in South Africa in 1988.

Table 2. Estimation of the Number of Mutations (m)Required for Development of EC*

Population Males (m�1) Females (m�1)

White (South Africa) 5.01 4.98Canada (Ontario) 5.81 6.08Black (South Africa) 5.78 4.58Norway 5.88 5.95

*The model used to estimate the number of mutations neededfor cancer to develop is log(rate)¼ log(k)þ (m�1) � log(age) or,showing the variable dependency, log(rate(age))¼ log(k)þ(m�1) � log(age). In this model, we want to determine the sen-sitivity of the estimated value of m according to the influence of avariable number of cases on the values of rate(age) for any givenvalue of age.To this end, by implicit differentiation of the above equation, wecan calculate dm/d(rate) from 1/rate(age)¼ log(age) � dm/d(rate).Thus, dm/d(rate)¼ 1/(log(age) � rate(age)).This may be expressed using the notation of differentials asfollows:Dm�D(rate)/(log(age) � rate(age)).Thus, Dm is proportional to D(rate), with proportionality factorp¼1/(log(age) � rate(age)).In the case of esophageal cancer, the incidence among personsbelow the age of about 20 yr is extremely low (on the order ofone case per million population (see Table 1). This has the effectof inflating the p for younger age groups compared with that forolder age groups, where the rate is much higher (typically onecase per two thousand population). Thus, for younger agegroups p is inflated by a factor of about 500 compared with p forolder age groups. A single sporadic case contributing to D(rate)for a young age group thus has a vastly greater effect on Dmthan would be the case for a sporadic case in an older age group.A further aggravating factor is the presence in p of the reciprocalof the term log(age). This further inflates p for younger agegroups compared with older age groups. In essence, for youngerage groups, errors (‘‘noise,’’ sporadic cases) in data for rate(age)produce an unacceptably large undue effect in the estimationof m.We conclude that to estimate the regression line, data from theyounger age groups must be excluded where those data clearlycomprise sporadic cases, arising from very early events. Thecutoff age group below which data should be excluded is thatage group that divides the incidence data into two distinctgroups. For the younger age groups the value of p is orders ofmagnitude smaller than that for the older age groups. The valuesof p that lead to choices of appropriate cutoff ages are illustratedin Table 3 for two of the data sets.

Table 3. Establishment of Cutoff Ages for Estimatesof m*

Median age inyr of group p for black males p for white males

17 0.5232 0.10 0.5937 0.04 0.1442 0.08

*This table shows the dramatic change in the order of magnitudeof p found in the age groups selected for the lower-age cutoffgroups compared with the next-older age groups, as illustratedfor black males and white males. For the younger age groups,the effect is even more pronounced, owing to the extreme rarityof esophageal cancer at early ages, where the rate is on the orderof one or two per million population members and where,indeed, some years may elapse with no cases at all, as shown inTable 4.

MUTATIONS IN ESOPHAGEAL CANCER 85

rapid urbanization and increased access to tobaccoand alcohol. These changes could have a differentimpact on various age cohorts in the two sexes.Essentially, for tumorigenesis to proceed, a series

of mutations is predicted to be required. Theuniversality of the estimate for m suggests that thisphenomenon probably involves sequences of eventsthat are similar from one population group toanother. It has been suggested that the first step intumorigenesis is a mutation that frees a cell fromnormal growth restriction andaccelerates its growth.Examples of such events might be p53 and p16 genemutations [1,10]. Subsequent mutations accelerategrowth (or relax growth constraints) still further,which leads to ever-increasing cell proliferation [1].This would predict the existence of numerousdifferent histopathological stages during the devel-opment of EC. Such pathologic features, in fact, areknown and may include low-grade, mild, or severedysplasia and adenocarcinoma. This process issimilar to the adaptive mutagenesis seen in unicel-lular organisms [14,15]. Unfortunately, the process,

which is advantageous in unicellular organisms, isadvantageous to a subpopulation of cells in themulticellular organism but ultimately deleterious tothe organism as a whole.The question remaining unanswered by this work

iswhether themanymutations requiredmust alwaysfollow a certain sequence and be initiated by onemutational event (a single target gene). Accumulat-ing evidence suggests that this is not the case. Forexample, while mutations in p16 and p53, genescontrolling the cell cycle, have been detected in EC[10], they are not found in all cases (range, 18–77%)[10,16]. Furthermore, p16 and p53mutations do notnecessarily occur together in any given tumor [10and unpublished observations]. Alternatively, theydo occur together but are not detected or reported.Onemaytherefore speculate that alterations inmanyother critical genes must be necessary for develop-ment of EC.It is tempting to propose that a loss (or gain) of

function of a master gene (such as p53 or p16) is anecessary requirement in all cases. This may be so,

Table 4. Incidence per 100 000 Among Various Population Groups

Age group

25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74

White females (South Africa) 0.76 0.905 1.8 3.2 4.9 6.95 5.8Ontarian females 1 5 4 11 20 27Black females (South Africa) 1.995 3.735 6.4 13.1 14.8 19.6 40.4 36.4Norwegian females 0.1 0.4 2.5 1.3 3.5 6.1 8.5White males (South Africa) 0.49 1.955 3.415 5.125 8.92 11.62 14.15 32.15Ontarian males 5 7 19 32 43 66Black males (South Africa) 0.585 2.75 6.2 16.05 27.3 41.75 50.5 76.5 77Norwegian males 1.3 3.0 5.4 9.3 13.7 24.3 31.7Median age 27 32 37 42 47 52 57 62 67 72

Figure 2. Age-specific EC incidence plotted against age (double log scale) for black females in South Africa (BF),white females in South Africa (WF), and females in Ontario (ONTF). The formulas describing the plots are given onthe figure, from which m�1 values can be derived.

86 UYS AND VAN HELDEN

but it can be argued that the genes may vary fromcase to case, thus offering an explanation for thefailure to detect p53 or p16 (or any other given gene)mutations in all EC cases examined. The alternativeexplanations are that mutations do occur in all casesbut are not detected for technical reasons or thatregulatory mechanisms change expression levels(thus altering function)withoutmutations in codingregions. Changes in expression levels are frequentlyreported for numerous cell-cycle regulatory proteinsin EC [17–23], which may be regulated by methyla-tion changes [24] or polymorphisms [25], amongother mechanisms [18,26].Thus, aberrant function of a key set of genes is the

likely cause of EC. The genes so far identified withcommon aberrations (expression, mutation, poly-morphisms, allelic imbalances) are p53 [1,10,16,19–23], p16 or INK4A [1,10,18,27], p27 [2], p21 [22,25],Ki67 [19,20], pRb [1,18,26,27], and FHIT [1,17,24]. In

addition, there is evidence for human papilloma-virus as a gene-modifying agent in EC [28], whichmayexert its effect bybinding to suchproteins asp53or pRb [1].The stepwise (cumulative) model for carcinogen-

esis could explain several observations: first, thepathologic features seen in many tissues, from low-grade through mild to severe dysplasia as well asadenomas and carcinomas [1,14–26]; second, thefrequently relatively low proportion of aberrantproteins expressed in dysplasias [16–28]; and, third,heterogeneity in tumors. Figure 5 illustrates this. Inthis model, gradually accumulating mutations, ex-pression changes, or other alterations cause patho-logic transformations of varying severity. A similargrade of abnormal cell function and morphologicchange may be initially caused by quite differentmolecular events but ultimately largely (or entirely)common cumulative events at the tumor stage. It is

Figure 3. Age-specific EC incidence plotted against age (double log scale) for black males in South Africa (BM),white males in South Africa (WM), and males in Ontario (ONTM). The formulas describing the plots are given on thefigure, from which m�1 values can be derived.

Figure 4. Age-specific EC incidence plotted against age (double log scale) for males (M) and females (F) inNorway. The formulas describing the plots are given on the figure, from which m�1 values can be derived.

MUTATIONS IN ESOPHAGEAL CANCER 87

possible that sequences involving different events,some of which may be common key events, mayresult in carcinomas. Some sequences may involvesix steps and others seven steps. Our results gave aweighted average of these possible values. Withinthis paradigm, sequences of five steps may rarelyoccur and sometimes even sequences entailing eightsteps. These would be exceptional occurrences. Inthis respect, persons with predisposing polymor-phisms might require fewer additional events, thusimparting heterogeneity to the population andresulting in non-integer solutions for the calcula-tion, as was found (Tables 2 and 5).It can be argued that the most profitable route to

reducing EC incidence therefore is to identify the‘‘master genes’’ or the key events predisposing to thedevelopment of EC. This may well be the p16 or p53mutations that have been observed [1]. A carefulstudy of the nature of these mutations as well asthose in other important genes should allow forthe identification of putative mutagens. Given the

risk factors identified for EC, these factors probablyinclude properties of smoke, nitrosamines, or fungaltoxins.The nature of mutations may not be similar in all

regions, because the database of p53 mutations (forexample) in EC shows a very wide spectrum ofchanges. The shift to the left or right of the log-logplots in different populations is a functionof the age-related incidence of EC and shows that the inducingenvironmental conditions are critically important.These factors include exposure tomutagens aswell asthe general health of the population, which is afunction of nutrition. Deficiencies in crucial dietaryprotective factors, such as minerals and vitamins[29,30], probably are important. Thus, as argued bythose engaged in health-promotion efforts, reduc-tion in EC incidence might be achieved through riskreduction (avoidance) and improved nutrition. Thisis particularly important for those with early lesions,such as dysplasia, which is probably a necessary butnot an inevitable step en route to cancer.

Table 5. Estimates of m�1 and Confidence Limits (CIs)

R2 m�1 95% CI lower limit 95% CI upper limit

White male (South Africa) 0.96 5.0 3.9 6.2Black male (South Africa) 0.97 5.0 3.9 6.0Ontarian male 0.98 5.7 4.6 6.7Norwegian male 0.99 5.9 5.5 6.3White female (South Africa) 0.97 5.0 4.2 5.8Black female (South Africa) 0.97 4.6 3.8 5.3Ontarian female 0.88 6.1 1.8 10.4Norwegian female 0.80 6.0 3 9

Figure 5. Model for aberrant gene expression or function. In this model single aberrant gene function leads toearly lesions (ED), such as dysplasia. As changes accumulate, the severity of pathologic transformation increasesfrom mild dysplasia (MD), until a stage of severe dysplasia (SD) or cancer is attained. This model, which of necessityis simplified, can explain the molecular heterogeneity seen in various pathologic conditions and the concept thatdysplasias are lesions that can progress easily to cancers.

88 UYS AND VAN HELDEN

ACKNOWLEDGMENTS

We thank the Cancer Association of South Africa(Esophageal Cancer Consortium) for assistance withfunding for research.

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