Causal relationship epidemiology of cancer

causal relationship epidemiology of cancer

Bradford Hill's “Causation Criteria”. 4. Statistical Epidemiology looks for patterns of disease (time, place, . causal relationship to tumors. Distinguish between association and a causal relationship. the weight of evidence needed for determining causality versus taking public health action. . to deny that the association between cigarette smoking and lung cancer was causal. Seminars in Cancer Biology 14 () – Role and limitations of epidemiology in establishing a causal association. Eduardo L. Francoa,*.

This is important not only to understand cancer etiology, but also to design public health policies that target the right causal factors at the macro-level. Electronic supplementary material The online version of this article doi: In this paper we address the meaning of causality in the case of cancer. For many cancers, causes are still elusive and there is confusion in the literature between cause and mechanism.

In addition to the practical implications, there are also important conceptual philosophical aspects in defining what a cause is, with cancer being an interesting case. This is particularly pressing, in the light of the advancements of molecular biology and the use of biomarkers in cancer research. We first summarize the current views on carcinogenesis, and then explore the relevance of current philosophical interpretations of causality.

Is This Association Causal?

Our proposal is that the identification of causes of cancer rests on two components: For example, in the recent controversy on the carcinogenicity of red meat [ 2 ], the epidemiological literature consistently detected an increase in risk of colon cancer among red meat eaters difference-makingbut further confirmation of a causal relationship came from the mechanisms involved, such as the formation of carcinogenic nitroso-compounds in the intestine of red meat eaters.

Risk is just a measure of how much individual probability of cancer increases e. The molecular basis of cancer: This is necessary to understand causality, in the framework of cancer as an evolutionary Darwinian process. It is important to stress that cancer is not a single entity, and therefore pathways leading to cancer onset are diversified.

There are several implications for primary prevention derived from this definition represented in Additional file 1: Mutations can be neutral, detrimental or favorable for the expansion of a cell clone, depending both on the micro-environment, that exerts a selective pressure, and the previous history of mutations in the same cell.

Selectogens may include known risk factors for cancer, such as the metabolic syndrome, that are unlikely to have a mutational mechanism as their main mode of action, and may predominantly act by promoting the selection of cells already carrying somatic mutations.

It will also be critically important to understand how such non-mutagenic environmental exposures may interact with cellular processes that maintain the fidelity of DNA e. Is electrophilic or can be metabolically activated Parent compound or metabolite with an electrophilic structure e.

Induces oxidative stress Oxygen radicals, oxidative stress, oxidative damage to macromolecules e.

Causality and causal inference in epidemiology

Is immunosuppressive Decreased immunosurveillance, immune system dysfunction 8. Causes immortalization Inhibition of senescence, cell transformation Alters cell proliferation, cell death or nutrient supply Increased proliferation, decreased apoptosis, changes in growth factors, energetics and signaling pathways related to cellular replication or cell cycle Open in a separate window Macroenvironmental causes of cancer How are these concepts, at the level of the micro-environment, connected to external exposures in the macro-environment?

These preventable cancers are for the most part explained by external or internal—such as endogenous nitrosation exposures that are unlikely to act in isolation: This concept has been popularized by Rothman et al. The model gives an account of the multiple causes that in their combination lead to a particular effect. The above concepts allow us to bring together two domains that have been separated so far: Such exposures are likely to be a mixture of mutagens, such as aflatoxin B1, and selectogens, such as chronic inflammation caused by the hepatitis B virus; these two factors combine to increase the risk of e.

In other cases a single complex mixture, e. The future challenge will be to monitor this complex and changing ecology of cancer and other non-communicable diseasesand to relate these changes and interpret their effects with respect to the micro-environmental modifications. Equally, starting with the molecular modifications observed at the level of the micro-environment can reveal clues as to the ecology of cancer at the macro-environmental level.

An example comes from the recent observation that renal cell cancers in some regions in Europe have a somatic mutation spectrum that reflects exposure to an environmental carcinogen, aristolochic acid, previously considered as a risk factor for upper urothelial tract cancers [ 13 ].

New technologies can in principle allow us to monitor how the micro-environment can lead to selection of mutations and thus identify selectogens as additional targets for prevention. There are great expectations towards these omic technologies for the development and validation of a suite of new biomarkers to monitor the micro-environmental changes underlying cancer development. The fact that adult diseases such as cardiovascular diseases or cancer were influenced by previous exposure including in utero, e.

In sum, the most recent understanding of cancer etiology presents us with a complex scenario where disease here, cancer is the result of a process in which factors in the micro- and in the macro-environment interact. Such interactions are consistently found in the associations identified by studies in molecular epidemiology.

The challenge for molecular epidemiology is therefore to explain how biological mechanisms across the micro- and macro-environment contribute to causal reasoning. A philosophical understanding of cancer etiology Biomarkers: Biomarkers are key in causal analysis in cancer research and play a major role in our conceptualization of cancer causation.

An important question therefore concerns the kind of ontological status that we should give to biomarkers. But even within this continuum, such markers may represent a genuine event e. In fact, this is what molecular epidemiology routinely does. But, as Schulte noticed as early asthere are multiple ways of defining and measuring biomarkers, which raises the question of their ontological status.

The issue gets even more complex because molecular epidemiology is not interested in finding biomarkers per se, but in understanding the continuum of disease development from early exposures, via finding biomarkers. This conceptualization of biomarkers search—i. This calls for two remarks. On the one hand, biomarkers are not entities, things to which we can attribute some causal power, in the same sense as HPV virus has the power to initiate the onset of cervical cancer.

Instead, biomarkers are clues, indicators, markers to detect in order to reconstruct the missing link.

causal relationship epidemiology of cancer

On the other hand, and related to the previous point, we need to say in which sense, if any, these continuous links, or processes, between exposure and disease are causal. This is all the more important because we seek to link heterogeneous levels as the macro- and the micro-environment. In sum, our approach aims to address two main questions: We discuss these two issues in reverse order: Information transmission and the link between macro- and micro-environment Finding a coherent conceptualization of the link between the macro- and the micro-environment is important for the following reason.

By and large, traditional epidemiology has done this successfully for a long time: But with the advent of molecular epidemiology, these associations also relate factors at very different levels the micro and macro environments.

This rests on a change of the scale of measurement: In fact, measurements now taking place at the same level allow the researcher merely to establish another association or series of associations difference-making relationsalbeit at a much lower level now. For instance, we might establish a robust correlation between the level of a certain chemical in the air and the biomarker of early clinical changes of a targeted disease lung cancer. It only estimates a more precise measure connecting levels of hazards and levels of omic changes.

To be sure, this search finding appropriate biomarkers obviously relies upon studying associations, e. On the other hand, we need to place this reconstructed link into a plausible network of relations i. It is important to note that linking, here, cannot be seen by the naked eye, and not even using sophisticated experimental set-ups. Instead, the scientist reconstructs the linking by putting together the pieces of the evidential puzzle, just as a crossword puzzle [ 20 ]. Biological theory needs to be complemented with the results of omic analyses, which in turn need sophisticated and complex statistical analyses.

It is in this sense that cancer etiology needs a plurality of evidence from which to make causal inferences. All this requires considerable empirical evidence and much interpretation of the evidence using the appropriate concepts.

Without any one of these exposures, the disease would not have occurred. Thus, each component fulfills the counterfactual question: In most circumstances not just one, but several exposures must be present for an individual to get the health outcome.

A single cause does not produce the disease, but the combination of many causes produces the disease. This is what we mean when we talk about a multifactorial disease.

When each exposure is necessary for the person to develop the health indicator, but no one exposure in and of itself is the sole cause, we say that the exposures are insufficient. They are each necessary but insufficient to produce disease on their own. Together, the set of exposures that, alone, are necessary but insufficient become a sufficient set of exposures that cause the health indicator in at least one person. Sufficient sets of exposures are those that produce the health indicators of interest.

Different individuals will have different sets of individual components that combine to produce a sufficient cause i. If one were to apply the sufficient-component cause model to tuberculosis TBone possible cause might be represented by the pie chart below.

This sufficient cause may have applied to many of the people who developed TB in the United Kingdom during the 19th and 20th century. The line graph below shows the annual mortality from TB perpopulation from to During this time span the introduction of "the hygienic idea" and the subsequent development of public health initiatives led to gradual improvements in living conditions, including less crowding, better ventilation, and better nutrition.

The decreased prevalence of these components is likely to have been responsible for the steady decline in TB mortality seen during this period.

causal relationship epidemiology of cancer

It is well known that the wars had a widespread impact on the population and that nutrition suffered and people were sometimes seeking shelter in bomb shelters that were poorly ventilated and crowded.

The sufficient-component model to the left offers a coherent explanation for the cause of TB mortality in a large proportion of the population during this period, and it also explains the steady decline punctuated with the temporary increases seen during war time.

There may, however, be many sufficient causes of TB which may differ in their components, although some components might be shared among different sufficient causes. Consider, for example, the two sufficient causes below. Among the three sufficient causes of TB illustrated above, there are both similarities and differences in the composition of the components. They also differ in the number of components. For example, an individual with AIDS and poor nutrition would be severely immunocompromised, so the only component needed to complete the causation of TB would be exposure to the TB bacillus.

The figure below outlines many of the key factors and events in the transition of a normal cell to a cancerous cell. A variety of environmental factors chemical carcinogens, radiation, viruses, etc. There are DNA repair mechanisms, but these are not always successful in repairing damage; in addition, some people have inherited defects in DNA repair mechanisms.


If repair is unsuccessful, a mutation may result, and if the mutation occurs in a proto-oncogene or an anti-oncogene, regulation of cell replication may be lost. If a third mutation were to occur, damaging apoptosis, then the final component cause is in place, and the cell will be cancerous. The image below illustrates a sufficient cause that reflects these events. Note also that there may be a period of time before the cancer is detectable or produces symptoms.

Necessary Components Note that in the three sufficient causes of TB above, exposure to TB is present in all three because it must be present for a TB infection to occur. On the other hand, TB exposure by itself will not result in infection unless other components are also present. In other words exposure to the TB bacillus is a necessary, but not solely sufficient component. However, many, if not most, sufficient causes do not have a necessary component.

Features of the Sufficient-Component Cause Model Aschengrau and Seage point out some of the key features of the sufficient-component cause model: A cause is not a single component, but a minimal set of conditions or events that inevitably produces the outcome. Each component in a sufficient cause is called a component cause, and epidemiologists tend to refer to the components as "causes" because the outcome will not occur by that pathway if any one of the components is missing or prevented within a given sufficient cause model.

Consequently, it is not necessary to identify all of the component causes in order to prevent the disease outcome.

There may be a number of sufficient causes for a given disease or outcome. A component cause that must be present in every sufficient cause of a given outcome is referred to as a necessary cause. The completion of a sufficient cause is synonymous with the biologic occurrence of the outcome, e. The components of a sufficient cause do not need to act simultaneously; they can act at different times.

For example, a mutation in a proto-oncogene in a prostate cell may promote cell replication at one point in time, and it may be some time later when another mutation diminishes the function of an anti-oncogene in the same cell.