Epidemiological models and prevention of cancer

Annals of Oncology 2: 559-563, 1991. C 1991 Kluwer Academic Publishers. Printed in the Netherlands.

Commentary Epidemiological models and prevention of cancer P. Vineis & F. Faggiano Unit of Cancer Epidemiology, Department of Biomedical Science and Human Oncology, Turin, Italy

Observation and experiment

As Dr. Calum Muir pointed out in an editorial [1], all of the known causes of human cancer have hitherto been discovered by epidemiologists. In addition, effective therapies for cancer are still available only for a limited number of patients [2], while elimination of known risk factors could help to avert a large proportion of malignant tumors. However, epidemiology has received relatively little attention in comparison with other branches of medical research. This is particularly obvious when one considers the availability of positions at the University (at least in Italy), and the criteria for allocating funds for cancer research: for example, the American National Cancer Institute devotes about 5% of its funds to epidemiology, while other agencies (with the noteworthy exception of the International Agency for Reseach on Cancer) very often spend less than 1% [1]. Why is epidemiology less popular than, say, experimental approaches to cancer? There seem to be at least two reasons. First, epidemiology is an observational discipline, and the strength of its causal reasoning is considered to be lower than that of disciplines based on voluntary modification of nature by the researcher. Secondly, cause-effect relationships in cancer epidemiology are stochastic rather than deterministic, and for practical reasons this is not appealing, since one cannot predict the individual outcome exactly, but only estimate a probability that disease will occur. However, neither argument is completely convincing, as we will attempt to demonstrate. One of the best presentations of the methods of medical research is Claude Bernard's Introduction a I'etude de la medecine experimentale, in which the author states that one cannot sharply distinguish between observation and experiment - that both are essential for an understanding of biology. For example, it is inaccurate to claim that the observer is passive while the experimental scientist is active, implying that the former is easily misled in his reasoning by fortuitous events while the latter derives his knowledge from a conscious manipulation of nature. There are many well-known examples of 'passive experiments': one is the bombing of Hiroshima and Nagasaki, which, from the point of view of the researcher, was of an experi-

mental nature, although not by his decision. As for 'active observation', medical research abounds with examples. In fact, the distinctive characteristic of epidemiology, in contrast to much of clinical research, is that it strongly emphasizes the need for a sound planning of observation to render it as similar as possible to an experiment. The emphasis placed by Claude Bernard on a priori hypothesis and the importance of ideas in guiding the biological observation is reflected by the emphasis placed by all epidemiologists on the importance of the study design and on a logical consideration of bias or confounding [3,4]. Claude Bernard uses the example of astronomy, in which no experiment is possible, even though important relevant scientific information has been acquired after careful observation guided by astute reasoning. Bernard's entire book is based on the concept that no science would be possible without a priori hypotheses, from which observations or experiments follow according to the complexity of the subject and the practical opportunities. Stochastic vs. deterministic

It is certainly true that for practical purposes it is better to have deterministic rather than stochastic knowledge of phenomena: it would be better to be able to predict exactly what will happen to a given individual after exposure to a chemical rather than simply estimating his probability of getting cancer. However, there are at least four possible counter-arguments: (a) a stochastic outcome is to be expected when the chain of events leading to cancer is complex, as has been clearly demonstrated (see below); (b) a probabilistic approach has been adopted in this century's physics, and the stochastic nature of cause-effect relationships cannot be cited to discriminate between 'certain' and 'uncertain' knowledge; (c) the distinction between stochastic and deterministic relationships is a question of level of the observation: measurements are made (in the form of 'mean' or 'median' values) to predict what will happen to a population (of humans or of molecules) but not to the individual subject or the individual molecule; (d) in fact, those who advocate a deterministic explanation of cancer onset make reference to its

560

mechanism rather than the actual frame of causation. As far as points b and c are concerned, it is certainly of interest to report the words of the founders of modern probabilistic physics, Boltzmann and Maxwell. Both of them stated independently, and at about the same time, that a probabilistic approach to the study of gases was unavoidable, without losing any of its scientific character. '... the smallest portion of matter which we can subject to experiment consists of millions of molecules, not one of which ever becomes individually sensible to us. We cannot, therefore, ascertain the actual motion of any one of these molecules, so that we are obliged to abandon the strictly historical method, and to adopt the statistical method of dealing with large groups of molecules' [5]. What is interesting in this statement is that Maxwell draws a clear parallel with the statistical study of human populations as initiated by Quetelet. Therefore, strange as it may seem, contemporary physics and epidemiology have some common roots in nineteenth century social theory. It should not be assumed that the difference between 'mechanism' and 'frame of causation' applies only to such a complex phenomenon as cancer. A more familiar example, such as why a death from a car accident occurred, involves the same distinction. In practice, the closer one looks at the event, the less stochastic will be one's reasoning: for example, if the explanation of the accident is sought in the 'cardiac tamponade following the collision between two vehicles', this interpretation is clearly deterministic; but when one examines retrospectively what brought about the collision (drunken driving, wet road, technical failures...), it inevitably falls into the stochastic domain: not all drunken drivers have accidents, driving on wet roads is not always followed by crashes, and so on. In order to identify a deterministic relationship one has to go so close to the final outcome, that cause and effect can practically not be distinguished from one another.

start of exposure, duration, years since cessation) and age at cancer onset. Several reviews have been written on the topic [6, 7]. The most popular of the models developed by epidemiologists is the 'multistage' model, according to which the numbers of stages required for cancer onset can be inferred by the relationship between age and the incidence of cancer. On this basis, it has been estimated that there are around 5 or 6 such stages. In addition, the model makes certain predictions about the stage of action of a single carcinogenic exposure, using, information concerning time variables. Recently the multistage model has received some disconfirmation and an alternative model (Moolgavkar's two-stage model: [8]) has received more attention. The Moolgavkar model has three attractive features: it is simpler and more compelling than the multistage model; it fits better with some epidemiologic data (particularly in the case of retinoblastoma); it adapts to experimental data on rodent skin painting (e.g., Hennings et al. 1983: [9]). The latter data suggest that skin carcinomas occur as a consequence of a first mutation (giving rise to sporadic benign papillomas), followed by clonal selection and by a second mutation which transforms papillomas into malignant tumors. This sequence is exactly what was predicted by the epidemiological model. Refutation of the multistage model has come from data on ex-smokers and lung cancer [10, 11]: whilst the multistage model predicted that the excess risk among ex-smokers would either increase or stabilize, thorough analysis of large data sets indicates that the excess risk actually decreases. In the case of bladder cancer the decrease after discontinuation is even more dramatic than for lung cancer. Another limitation of the multistage model is the number of assumptions which must be met; the Moolgavkar model is more parsimonious. However, one gets the impression that there is no clear choice among alternative mathematical models of carcinogenesis. The choice is usually based on statistical grounds (goodness-of-fit), but there is room for uncertainty and subjectivity. Therefore, greater use of Causes and mechanisms: hypotheses on cancer biological data should be made in the interpretation of mathematical models, for example, the aforementioned Despite the practical usefulness of a distinction be- animal experiments suggesting the need for two mutatween frame of causation' and 'mechanism', more tions in skin carcinogenesis. cooperation between epidemiology and the laboratory The mathematical analysis of epidemiologic data is warranted in order to clarify in detail how environ- (i.e., analysis of time variables) may give general indicamental exposures can induce cancer. The elucidation of tions about an early or late stage of action, but probmechanisms, while not necessarily helpful in prevent- ably nothing more. Even the possibility of inferring the ing exposure to suspected carcinogens, is important for number of stages from mathematical relationships the credibility of the associations found by epidemi- between age and risk or between dose and risk is quesologic research. Particularly in the last few years, many tionable. A comparison between the results of mathenew suspected carcinogenic exposures have been put matical modelling of time variables and biological forward by epidemiologists, and the disentanglement of knowledge is needed. The following are some of the genuine causal associations from fortuitous or con- biological markers which might be considered: founded observations requires the development of an - genotoxicity: a genotoxic effect is more likely to appropriate biological interpretation. be irreversible, although repair mechanisms may Epidemiology has contributed to the knowledge of intervene; in contrast, a non-genotoxic effect is the mechanisms of carcinogenesis mainly by studying more likely to reverse quickly.after discontinuathe relationship between temporal variables (age at tion of exposure;

561

- formation of DNA adducts (as indirect indicator of interaction with DNA); - metabolic activation/inactivation: polymorphism for metabolic pathways may explain much of the inter-individual variability in response to carcinogens, and might for example explain the shape of a dose-response relationship (as an alternative to making inferences about the number of stages). The following are some of the most promising fields of investigation on cancer mechanisms, from an epidemiologic point of view: 1. Retinoblastoma: although there is no known chemical exposure which is relevant, this is the best example of correspondence between molecular carcinogenesis and mathematical modelling. 2. Lung cancer and smoking: there is a considerable amount of literature, with some conflicting interpretations. Overall, it is reasonable to conclude that smoking acts at both an early and a late stage. In addition, there are interesting and important data on genetically-based metabolic polymorphism as an effect modifier [12]. 3. Bladder cancer and smoking: there is a large amount of literature and, more importantly, some interesting contrasts to lung cancer: (a) The decrease in risk after discontinuation of exposure is very rapid; (b) the relative risk increases in a lessthan-linear way with the dose: this pattern is clearly different from that shown for lung cancer, and is in agreement with data on hemoglobin adducts [13], suggesting a possible role for metabolic activation/inactivation; (c) much is known about metabolic polymorphisms involved in bladder carcinogenesis (see, for example, 14), and about other biochemical aspects (adducts with hemoglobin and with DNA in exfoliated bladder cells and bladder biopsies; genotoxicity of aromatic and heterocyclic amines). 4. Non-Hodgkin's lymphomas and immunodeficiency: this is a good example of presumably nongenotoxic late-stage action (quick onset of cancer after start of exposure) [15]. B-cell neoplasms seem to be induced by B-cell transformation and proliferation, caused, for example, by EBV, followed by T-cell impairment (caused by immunosuppressive drugs).

Epidemiology and Public Health: the example of methylene chloride

Epidemiology is the basic discipline for public health decisions. Not only all the known causes of human cancer have been identified by epidemiology, but concrete prospects of reducing cancer mortality can only come from the application of epidemiological knowledge and experience to the field of public health. Such a transfer of knowledge and technology to public health, however, is not straightforward, and requires the integration of

Table 1. Five-year incidence of myocardial infarction in the UK Heart Disease Prevention Project. Entry characteristic Risk factors alone Ischaemia Ischaemia + risk factors All men

% of men

%of Ml cases

MI incidence rate %

15 16 2

32 41 12

7 11 22

100

100

4

Fromref [17).

different disciplines including experimental oncology. We will support this statement with the example of a widely used solvent, methylene chloride. Methylene chloride has been studied in experimental animals, inducing lung adenomas and carcinomas in rats and mice, with a clear dose-dependent response. Also an excess of liver cancers was found. From these experiments, both the FDA (Food and Drug Administration) and the EPA (Environmental Protection Agency) have estimated its carcinogenic potency in order to predict the human risk. According to the FDA, the unit risk is 4.4 cancers x 10~4 per mg/kg body weight per day of a continuous lifetime exposure; according to the EPA the same estimate is 1.4 x 10~2 per ppm per day of exposure. Although the two measures differ considerably (the second is in fact an upperbound risk), both can be used for a comparison with human data. Tollefson and colleagues [16] compared the outcome of a large epidemiological study on workers exposed to methylene chloride with the two estimates derived from experimental studies. Their epidemiological study, based on accurate measurements of individual methylene chloride exposures, is of excellent quality. In fact, the human evidence is essentially negative, in contrast to the animal evidence (an excess of pancreatic cancer in humans is difficult to interpret due to small numbers). The problem is to understand whether the negative result is still compatible with an effect of methylene chloride as predicted by animal studies. Tollefson and others compute that the expected excess number of lung and liver cancers was 0.22 deaths predicted according to the FDA estimate, and 7.2 according to the EPA estimate. This means that in the entire cohort a maximum of 7.2 additional deaths, due to lung or liver cancer, were expected on the basis of a very conservative extrapolation (i.e., probably an overestimation) from animal data. In other words, if the EPA estimate is applied, one would expect to have about 24 deaths from lung or liver cancer whereas 17.1 were observed, while with the FDA estimate the number expected would be 17.3. In both cases the epidemiological study had insufficient power to detect a statistically significant excess risk. The conclusion of the authors is that the epidemiological findings do not contradict the animal evidence for the carcinogenicity of methylene chloride. Even when one adopts an extremely conservative upper-

562

bound approach, the difference between observed deaths and expected estimated deaths is too small to achieve statistical significance. This is, therefore, an example of how a good epidemiological study, with accurate measurement of levels of exposure to a single chemical, is unlikely to be able to falsify a hypothesis generated from animal data. In general, practical reasons such as the size and informativeness of the studies render the confirmation and, even worse, falsification of epidemiological hypotheses a difficult task. This is particularly true when a very specific exposure is considered, i.e., when a decision with respect to public health could be affected by the epidemiological investigation. Individuals and population: the Prevention Paradox

In addition to the difficulties described in the methylene chloride example, which supports the need for a cooperation between epidemiology and the laboratory not only for the ascertainment of mechanisms but also for public health decisions, a more general 'paradox' is inherent in the prevention of chronic diseases. In a recent key epidemiology paper [17], Geoffrey Rose describes one of the main problems with which practical epidemiologists and public health researchers struggle every day: the Prevention Paradox, the reasoning of which is as follows: for many well-known risk factors for chronic diseases, a certain preventive goal can be achieved through two radically different strategies, one at the level of high-risk individuals and the other at the population level. For example, Table 1 shows the five-year incidence of myocardial infarction in relation to the presence of different individual characteristics. It is evident that the relative importance of the presence of both ischaemia and the risk factors is much greater than that of either characteristic alone: the individuals with both characteristics, in fact, have a five-year incidence rate of 22%, vs. 7% for individuals with risk factors only and 11% for those with ischaemia only. However, if one considers the prevalence of the characteristics considered, those with both ischaemia and risk factors comprise only 2% of the population, while those with ischaemia alone comprise 16%: this means that taking measures at the occurrence of both characteristics will prevent as few as 12% of the MI cases, vs. 32% obtained by managing risk factors and 41% by managing ischaemia alone. The meaning of the example is clear: if one restricts preventive practices to very selected populations at high risk, one will achieve great relative success (incidence rate declining from 22% to less than 4%), but minor absolute success (only 12% of all MI prevented). The reason is that the absolute number of cases prevented in a population depends not only on the strength of the association between the risk factor and the disease, but also on the prevalence of the risk factor in the population, and the two are very often inversely proportional. As an

extreme situation, one might encounter a single individual with almost 100% probability of getting lung cancer, being the only person in the population who is a heavy smoker exposed to high levels of asbestos and heavy metals, and with a fast' debrisoquine metabolic phenotype: however, the absolute number of cases prevented (N = 1) will be a very small proportion of all the cases occurring in the population. More often, however, epidemiologists encounter the opposite situation: a large proportion of subjects exposed to a small increase of the risk. The conclusion drawn by Rose is that it is better to act at the population level rather than at the high-risk individual level. This deduction is based on two different lines of reasoning: one is the computation mentioned above, and the other is the consideration that individuals are reluctant to modify their habits, so that it is difficult to persuade them to avoid exposure to agents which might cause a disease in the course of the next two decades; on the contrary, it is relatively easier to act on the entire population by, say, increasing taxes for alcohol or cigarettes or putting iodine in drinking water. The Prevention Paradox which ensues is thus formulated: A preventive measure which brings much benefit to the population offers little to the individual participants (or, in other words, grateful patients are few in preventive medicine, where success is marked by a non-event). References 1. Muir CS. Epidemiology, basic science, and the prevention of cancer implications for the future. Cancer Res 1990; 50: 6441-8. 2. Tomatis L (ed). Cancer Causes, Occurrence and Control. IARC Scie. Publ. No. 100. International Agency for Research on Cancer, Lyon, 1990. 3. Rothman KJ. Modem epidemiology. Little, Brown and Co. Boston and Toronto, 1986. 4. Vineis P. Causal inference in epidemiology. Theoret Med (in press), 1991. 5. Porter TM. The rise of statistical thinking, 1820-1900. Princeton University Press, 1986. 6. Hayes RB, Vineis P. Time dependency in human cancer. Tumori 1989; 75: 189-95. 7. Day NE, Brown CC. Multistage models and primary prevention of cancer. JNC1 1980; 64:977-89. 8. Moolgavkar SH. Model for human carcinogenesis: action of environmental agents. Environ Health Perspect 1983; 50: 285-91. 9. Hennings H, Shores R, Wenk ML, Spangler EF, Tarone R, Yuspa SH. Malignant conversion of mouse skin tumors is increased by tumor initiators and unaffected by tumor promoters. Nature 1983; 304:67-9. 10. Freedman DA, Navidi WC. Ex-smokers and the multistage model for lung cancer. Epidemiology 1990; 1: 21-9. 11. Moolgavkar SH, Dewanji A, Luebeck G. Cigarette smoking and lung cancer reanalysis of the British doctors' data. JNCI 1989; 81:415-20. 12. Caporaso N, Tucker MA, Hoover RN et al. Lung cancer and debrisoquine metabolic phenotype. JNCI 1990; 82: 1264-72. 13. Bartsch H, Caporaso N, Coda M et al. Carcinogen hemoglobin adducts, urinary mutagenicity, and metabolic phenotype in active and passive cigarette smokers. JNCI 1990; 82: 1826-31.

563 14. Schulte PA. The role of genetic factors in bladder cancer. Cancer Det Prev 1988; 11: 379-88. 15. Kinlen L. Immunosuppressive therapy and cancer. Cancer Surveys 1982; 1:565-83. 16. Tollefson L, Lorentzen RJ, Brown RN, Springer JA. Comparison of the cancer risk of methylene chloride predicted from animal bioassay data with the epidemiologic evidence. Risk Analysis 1990; 10:429-35. 17. Rose G. Sick individuals and sick populations. Int J Epidemiol 1985; 14: 32-8.

Received 2 April 1991; accepted 3 April 1991. Correspondence to: Paolo Vineis, M.D. Cattedra di Epidemiologia dei Tumori Departimento di Scienze Biomediche e Oncologia Umana Via Santena 7 I - 10126 Torino, Italy