Causality in cancer research: a journey through models in molecular epidemiology and their philosophical interpretation
Vineis et al. Emerg Themes Epidemiol
Causality in cancer research: a journey through models in molecular epidemiology and their philosophical interpretation
Paolo Vineis 0 3
Phyllis Illari 2
Federica Russo 1
0 MRC/PHE Centre for Environment and Health School of Public Health, Imperial College London, St Mary's Campus - Norfolk Place , London W2 1PG , UK
1 Department of Philosophy, University of Amsterdam , Amster- dam , The Netherlands
2 Department of Science and Technology Studies, University College , London, London , UK
3 MRC/PHE Centre for Environment and Health School of Public Health, Impe- rial College London, St Mary's Campus - Norfolk Place , London W2 1PG , UK
In the last decades, Systems Biology (including cancer research) has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making different traditions of thought compatible: (a) causality in epidemiology and in philosophical theorizing-notably, the “sufficient-component-cause framework” and the “mark transmission” approach; (b) new acquisitions about disease pathogenesis, e.g. the “branched model” in cancer, and the role of biomarkers in this process; (c) the burgeoning of omics research, with a large number of “signals” and of associations that need to be interpreted. In the paper we summarize first the current views on carcinogenesis, and then explore the relevance of current philosophical interpretations of “cancer causes”. We try to offer a unifying framework to incorporate biomarkers and omic data into causal models, referring to a position called “evidential pluralism”. According to this view, causal reasoning is based on both “evidence of difference-making” (e.g. associations) and on “evidence of underlying biological mechanisms”. We conceptualize the way scientists detect and trace signals in terms of information transmission, which is a generalization of the mark transmission theory developed by philosopher Wesley Salmon. Our approach is capable of helping us conceptualize how heterogeneous factors such as micro and macro-biological and psycho-social-are causally linked. 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.
Systems biology; Evidential pluralism; Information transmission; Difference-making; Mechanism
What we mean by “cause of a disease” has an obvious
practical significance, for example for the development of
drugs and preventive interventions (e.g. vaccination
programmes). We believe that—building on current models
of cancer causality, and in particular the model offered by
“molecular epidemiology” —there is the need to
reconcile the conceptual interpretation of causality and its
biological foundation. 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. Mechanisms do not
need to be fully known for hazard identification (which
can come from epidemiology alone, as was the case of
smoking and cancer), but knowledge of mechanisms
supports causal reasoning in both hazard identification and
risk assessment (this is the idea of “evidential pluralism”
that we also discuss later).
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
We first summarize the current views on
carcinogenesis, and then explore the relevance of current
philosophical interpretations of causality. We argue that using
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mechanisms to support causality claims in observational
epidemiology is not just a matter of adding more
finegrained associations, but to understand “why” there are
such associations. Our proposal is that the identification
of causes of cancer rests on two components: (1)
“difference-making”, and (2) “mechanism”. For example, in the
recent controversy on the carcinogenicity of red meat ,
the epidemiological literature consistently detected an
increase in risk of colon cancer among red meat eaters
(difference-making), but 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.g.
in the exposed compared to the unexposed),
conditionally on red meat consumption, but—with notable
exceptions—a sound conclusion for a causal relationship also
requires the identification of a plausible mechanism .
The molecular basis of cancer: the
We start with the mechanisms that underlie cancer onset,
i.e. the sequence of molecular events that lead from a
normal cell to a cancer cell. 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 have been
several important developments in the molecular
interpretation of carcinogenesis in recent decades, including
(a) a wide set of mutagenic events which encompasses
single base substitutions as well as larger structural
genetic alterations; (b) an understanding of the crucial
role of epigenetic changes (defined as functional changes
in DNA that do not involve a change in the nucleotide
sequence); (c) an acknowledgement of the importance
of selection of mutated or epimutated cells; and (d) the
unifying concept of “branched evolution”, i.e. evolution
occurs in a branched manner in several tumor types,
leading to intratumor diversity, with the selective
advantage of any genotype depending on the environment .
There are several implications for primary prevention
derived from this definition (represented in Additional
file 1: Figure S1).
• Cancers occur in stages that correspond to increasing
complexity of molecular changes (“intratumor
diversity”), with two metastases or two areas in the same
localized tumour having a different set of mutations.
• 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.
The latter concept is called “historical contingency”
 and corresponds to the influence that previous
mutations have on the effects of subsequent
mutations on protein structure and function, and also on
the evolution of entire gene regulatory networks .
• In the onset of cancer in individuals, both mutagens
and “selectogens” play a role, i.e. the individual
cancer reflects the history of exposures that both induce
mutations and facilitate the selection of existing
mutations. 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
Smith et al.  have identified ten “hallmarks of
carcinogens”, in the context of the IARC Monographs (Table 1);
these correspond to the main mechanisms identified so
far in the pathways to cancer, and at least four of these
are not based on mutagenesis, e.g. chronic inflammation.
1. Is electrophilic or can be metabolically activated
2. Is genotoxic
3. Alters DNA repair or causes genomic instability
4. Induces epigenetic alterations
5. Induces oxidative stress Oxygen radicals, oxidative stress, oxidative damage to macromolecules (e.g. DNA, lipids)
6. Induces chronic inflammation
7. Is immunosuppressive
8. Modulates receptor-mediated effects
9. Causes immortalization
It is likely that in the “branched evolution” paradigm, risk
factors acting via these mechanisms play the role of
selectogens. 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.g. DNA repair and replication), thus affecting
the “endogenous” mutations seen in different types of
Macroenvironmental causes of cancer
How are these concepts, at the level of the
micro-environment, connected to external exposures in the
macroenvironment? Based on epidemiological evidence, we
know that some 40–50% of cancers would be
preventable if current knowledge about risk factors were to be
translated into preventive interventions [7–9]. There is
broad consensus on these estimates in the
epidemiological community, though the concept of “attributable risk”
is still debated and is methodologically weak (for
limitations see ).
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: even a “necessary” cause of cancer, human
papilloma virus (HPV), is itself not sufficient to cause cervical
cancer in an individual. Though all cervical cancers need
exposure to HPV, being exposed to HPV needs other
additional components in the causal constellation that
led to an individual’s cancer. On a population scale, HPV
is probably able to explain 100% of cervical cancer cases
(in principle cervical cancer can be eradicated by
vaccination), but each individual case is not entirely explained
by HPV alone: for example, exposure to the virus
happens in a socio-economic context that is also part of the
etiology of cancer (including other sexually-transmitted
infections and behaviours that interact with the virus).
The model of causation that applies to single
individuals is called the “sufficient-component-cause framework”,
and it considers sets of actions, events, or states of nature
that together lead to the outcome under consideration.
This concept has been popularized by Rothman et al.
 through the metaphor of “pies”: the constellation
of exposures that has led to cancer in an individual or
a group of individuals is represented as a pie where the
slices are different components and the totality of them
is causally sufficient. The model gives an account of the
multiple causes that in their combination lead to a
particular effect. The model usefully captures multi-causality
and the interaction between component causes (in other
words their “organization”).
The above concepts allow us to bring together two
domains that have been separated so far: the “ecology of
cancer” at a population level (the macro-environment)
and the mechanisms of carcinogenesis (the
micro-environment) at the individual level. Additional file 2: Figure
S2 shows the “ecology” of some common cancers in
different countries, though the picture is rapidly changing
because of globalization : the Figure suggests that in
each area there are some forms of cancer that prevail due
to the local predominant exposures. 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.g. hepatocellular carcinoma in Asia
and sub-Saharan Africa. In other cases a single complex
mixture, e.g. tobacco smoke, can comprise a combination
of mutagens and selectogens.
The future challenge will be to monitor this complex
and changing ecology of cancer (and other
non-communicable diseases), and 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
macroenvironmental 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 .
The attempt to connect the external (macro) with the
internal (micro) environment has been explored within
“exposome” research . While the
macro-environment represents the “external exposome”, the
microenvironment can be explored as a part of the “internal
exposome” using the new high-throughput technologies
of epigenomics, transcriptomics, miRNA, proteomics
and metabolomics. The connection between the
external environment and internal biological changes has
been the goal of molecular epidemiology for decades, as
expressed for example in Schulte and Perera’s  book.
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
microenvironmental changes underlying cancer development.
It is becoming increasingly clear that
non-communicable diseases are influenced by events that took place
throughout an individual’s life-course, in both the
macroand micro-environments. The concept of “branched
evolution” stimulates fresh thought on the relevance
of timing of exposures in relation to subsequent cancer
risk. For example, given that certain “driver” mutations
may only exert their carcinogenic effects in the context
of favorable selective conditions at the level of the
microenvironment, one can postulate that past exposures
may leave genetic or epigenetic alterations that are only
expressed far later in time, contingent on subsequent
exposures. The fact that adult diseases such as
cardiovascular diseases or cancer were influenced by previous
exposure including in utero, e.g. nutrient deficiency in
later generations due to the Dutch famine during the
World War II , suggests that the whole lifecourse has
an impact on adult disease. This poses particular
challenges to the identification of risk factors that may exert a
type of “hit-and-run” effect.
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
microand macro-environment contribute to causal reasoning.
A philosophical understanding of cancer etiology
Biomarkers: the link between the macro‑ and the
In order to causally link the micro- and
macro-environments, omic technologies provide a key set of
instruments in cancer research: these allow us to connect
exposure and disease by finding the “right” biomarkers.
Biomarkers are key in causal analysis in cancer research
and play a major role in our conceptualization of cancer
causation. This is well expressed in the diagram that
connects exposure markers, early effect markers and
susceptibility markers in the classical “molecular epidemiology”
paradigm, as described in Schulte and Perera’s  book
and further elaborated recently .
In 1998, the National Institute of Health
Biomarkers Definitions Working Group defined a biomarker as
“a characteristic that is objectively measured and
evaluated as an indicator of normal biological processes,
pathogenic processes, or pharmacologic responses to a
therapeutic intervention.” Biomarkers are largely
constructed by cross-checking data that are generated by
some machines (e.g. mass-spectrometry) and
subsequently analyzed using other machines (computers and
their algorithms). An important question therefore
concerns the kind of ontological status that we should give to
biomarkers. Strictly speaking, they don’t seem to be just
‘objects out there’. Schulte and Perera  describe
biomarkers in terms of ‘events’ in the continuum from
exposure to disease. But even within this continuum, such
markers may represent a genuine event (e.g. direct
exposure to a pollutant), may be correlated with such an event
(the classical example of yellow fingers in heavy smokers),
or even be a predictor of the event without being causally
associated to it (like the association between two X
chromosomes and the propensity to wear skirts). The fact that
biomarkers are hardly corresponding to “causal”
molecular entities does not imply that they cannot be measured.
In fact, this is what molecular epidemiology routinely
does. But, as Schulte noticed as early as 1993, there 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.
Similarly, in other contributions, the technologies used to
detect biomarkers (some of which are called omic
technologies) are said to provide the ‘missing link’ between
exposure and disease or, given the previous discussion,
between the macro- and the micro-environment [17–19].
This conceptualization of biomarkers search—i.e. as the
continuum linking exposure and disease—emphasizes
processes rather than things or objects. 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. 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: first, how to understand
causal production from the macro- to the
micro-environment, and second, why it is important to have a coherent
conceptualization of such causal links. We discuss these
two issues in reverse order: spelling out the second
question will provide further motivation for our approach.
Information transmission and the link between macro‑
Finding a coherent conceptualization of the link between
the macro- and the micro-environment is important for
the following reason. The macro-environment consists
of biological agents, pollutants and chemicals we are
exposed to, but also of social interactions and
“psychosocial factors”. The micro-environment, instead, is made
of biochemical and molecular processes measured at
different “omic levels”. How to make the causal link between
the macro- and the micro-environment plausible, beyond
a “coarse-grained” difference-making relation between
By and large, traditional epidemiology has done this
successfully for a long time: establishing robust
associations between classes of exposures and classes of
diseases. 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:
environmental exposure has traditionally been assessed by
measuring the levels of individual chemicals in, say, air or water.
Thus newer finer-grained measurements initially try to
restore some kind of “scale homogeneity”: measure the
level of a pollutant or of a chemical externally and then
measure changes at genomic, transcriptomic, proteomic,
or metabolomic levels internally. Although ‘scale
homogeneity’ is restored through making all measurements
chemico-biological measurements, the problem is not
In fact, measurements now taking place at the same
level allow the researcher merely to establish another
association or series of associations (difference-making
relations), albeit 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). But
this doesn’t establish a causal link yet. It only estimates
a more precise measure connecting levels of hazards and
levels of omic changes. On the one hand, to establish a
causal link we still need to find the right “intermediate”
biomarkers, the ones that are linked to exposure and to
disease. To be sure, this search (finding appropriate
biomarkers) obviously relies upon studying associations, e.g.
via omics analyses. On the other hand, we need to place
this reconstructed link into a plausible network of
relations (i.e. the mechanisms of carcinogenesis described in
the first part of the paper), and this is precisely the kind
of ‘biological thinking’ mentioned earlier. It is important
to note that linking, here, cannot be seen by the naked
eye, and not even using sophisticated experimental
setups. Instead, the scientist reconstructs the linking by
putting together the pieces of the evidential puzzle, just as
a crossword puzzle . 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. One such concept is information transmission, as
we argue later.
A second, more important, reason why the problem is
not solved is that although homogeneity in the scale of
measurement is restored by using biological
measurements, this makes the results harder to interpret, because
the interpretation still has to identify causes at the macro
level, i.e. the level of environmental exposure causing
disease. We need this causal knowledge to design
appropriate public health interventions. To sum up: we measure
everything at the micro-level (level of pollutant, and level
of metabolite) but ultimately what we want to know is
how and to what extent environmental pollutants or
psycho-social factors cause diseases. The problem molecular
epidemiology faces is: how can we understand
macrofactors causing micro-factors, or vice versa? What we
have to establish is a continuous linking, not just
(finergrained) correlations at a different level of measurement.
Continuous linking can be conceptualized as information
transmission, as we explain next.
Productive causality as information transmission
We mentioned earlier that causal claims about exposure
and cancer involve statements about risks, i.e.
differencemaking: whether certain exposures are good predictors of
disease, at different stages of disease development, or at
different stages of life, etc. Simultaneously, we also look
for evidence about how exposure leads to developing
disease. Typically, ‘how’ exposure leads to disease has been
understood in terms of the mechanisms that produce
disease, mainly with the study of biomarkers. Mechanisms
provide us with information about how causes produce
effects. This position is called, in philosophy, evidential
pluralism, to emphasize the need for multifold (or
multilayered) evidence in order to establish causal claims .
A prestigious example of evidential pluralism is the joint
use of epidemiological evidence (difference-making)
and mechanistic evidence (productive causality) in the
Monographs of the International Agency for Research on
The difference-making component of evidential
pluralisms is, in a sense, less controversial than the productive
component, as even theorizers of agnostic data-driven
approaches will agree that the search for robust
statistical associations lies at the very heart of data-intensive
science. What remains controversial is what
biomarkers are marks of within a mechanistic understanding of
cancer etiology. This is problematic because, as discussed
before, we want to establish links between macro- and
micro-factors. On the one hand, causal relations are not
reduced to bio-chemical relations and, on the other hand,
they are not mere (finer-grained) statistical associations
If the causal link connects factors at different scales and
of different types, then the notion of productive causality
(i.e. how causes and effects are linked) needs
reconceptualization. But the type of linking sought may be different
depending on the scientific context or the purpose of the
There are several candidates for characterizing links;
we mention the two most prominent here. First:
Wesley Salmon’s “mark transmission theory” [22–25]. In
Salmon’s view, the central question is how to distinguish
between causal processes and non-causal (or pseudo)
processes. Simply put, causal process transmit marks,
while pseudo-processes don’t. Think about what
happens when introducing a mark in a process: if the
process is causal, the mark persists at a later stage. A stock
example is denting a car, and observing that the dent is
transmitted along with the movement of the car, while
its shadow will not further transmit the mark. This
shows that the moving car is a causal process, while a
moving shadow is not. However, not every process can
be marked, and Salmon formulated the approach in
counterfactual terms: a casual process is one that could
be marked and that could transmit the mark. Causal
processes, in this approach, are those transmitting
physics quantities, such as energy or momentum (think of
billiard balls colliding). However, this approach is
tailored to physics and does not provide the conceptual
tools to understand the macro–micro linking
mentioned above. Second: the ‘complex-systems’ approach
. According to this approach, to establish causal
relations one needs to identify mechanisms, in the sense
of complex systems that link causes and effects. Such
approach, however, emphasizes the organization of
different components of a mechanism, rather than the
continuum linking exposure to disease. For instance, a
mechanistic explanation sheds light on the way a gene
normally is methylated, and how it is hypomethylated
when exposed to tobacco smoking. We can shed light
on these mechanistic aspects by identifying the relevant
molecular entities and activities involved, and their
organization. But this is not very illuminating about the
continuous link between exposure and disease, that is
the process initiated with exposure and that eventually
leads to disease development, via several intermediate
The link is instead better conceptualized using the
notion of “information transmission”. Note that
information transmission does not coincide with transfer of
biological information between macro- and
micro-factors. Instead, information transmission refers to how the
scientist reconstructs the linking between macro- and
micro-factors, putting together all the available pieces of
the evidential puzzle. In other words, information
transmission is at the level of epistemology, not of ontology.
In a previous article  we suggest that we need to
explore the prospects of the notion of information that
comes from the way scientists themselves explain the role
of biomarkers; in this context, the idea of ‘picking up
signals’ recurs, for instance:
From these two parallel analyses [statistical
analyses], we obtained lists of putative markers of (i) the
disease outcome, and (ii) exposure. These were
compared in a second step in order to identify
possible intersecting signals, therefore defining potential
intermediate biomarkers .
What is the signal that we have to pick up? In what
sense will this give us the sought production-relation
between exposure and disease? Our suggestion is to
conceptualize the detection and tracing of signals in terms
of information transmission, as sketched above. This, we
submit, is a generalization of Salmon’s mark transmission
The key difference with Salmon processes consists in
the marking aspect. Salmon’s approach rests on the
introduction of the mark. However, in most cancer research
we look for existing marks from exposure to disease that
transmit along the process, without introducing them
ourselves. Cancer research is largely an observational
rather than an experimental science.
This understanding of causal production as
information transmission takes full advantage of a
conceptualization in terms of mark transmission in processes,
without being tied to the quantities of physics, say energy
or momentum, being transmitted. It also takes full
advantage of a conceptualization in terms of mechanisms,
because knowledge of relevant molecular or
biochemical mechanisms will indicate where to look for signals,
for instance choosing appropriate omics levels for the
analyses of biological specimens. In this sense we say that
mechanisms are information channels: “biochemical or
molecular spaces” where we look for the flow of
information that we try to intercept using biomarkers .
Ultimately, we want to understand the whole
phenomenon of carcinogenesis: all the relevant omics levels
involved, how they interact, and build reliable models
of the dynamic evolution of whole systems under many
different exposure conditions. The concept giving the
dynamic evolution is information transmission. The flow
is in the link, and the link, as suggested, is best thought
of as informational. More precisely, it is given by the
scientists’ reconstruction of the information transmission
through the different types of analyses, i.e. by putting
together all the pieces of the “evidential puzzle”.
The question remains: what exactly does information
mean? In Genome Wide Association Studies (GWAS),
there is at least some possibility of a clear (univocal)
definition of information, as genes are more clearly
defined than in most omic measurements, and
substantive informational concepts make sense when applied
to genes. Instead, in Exposome Wide Association
Studies (EWAS) information is still not well-defined .
(Often omic “signals” are only “features”, i.e. they need to
be decoded after discovery). However, the diversity and
richness of informational concepts (many of which
currently being developed and discussed), is not a weakness
of an informational approach, but a virtue. This is
captured, for instance, by philosophical accounts, especially
those developing qualitative notions of information.
One such account is semantic information, namely what
the observer (here, the scientist) can process, looking at
the data, omic analysis, biological theory, etc. It is in this
sense that information transmission cannot be reduced
to biological information, but it is certainly part of it.
One advantage of information transmission is that it
is capable of offering a structure for thinking about how
heterogeneous factors such as micro and
macro-biological and social—are linked; this is a pressing issue in the
light of results of omic studies and also for the design of
public health policies.
Systems biology is driven by technology (the
development of omics) and by statistical modelling and
bioinformatics. It is high time to bring biological thinking back.
To address the new challenges of epidemiology, the
concept of the “exposome” has been proposed, initially by
Wild et al. , and then expanded by others, particularly
Rappaport and Smith  who functionalized the
exposome in terms of chemical signals detectable in
biospecimens. This is consistent (and in fact is an extension) of
previous work on molecular epidemiology by e.g. Schulte
and Perera . The canonical exposome concept refers to
the totality of exposures from a variety of sources
including chemical agents, biological agents, radiation, and
psychosocial components from conception onward, over a
complete lifetime . We offered a unifying framework
to incorporate omic data into causal models, using the
position called “evidential pluralism”: causal reasoning
is based on both “difference-making” and the
underlying biological mechanisms. In particular, we
conceptualize the way scientists detect and trace signals in terms
of information transmission, which is a generalization
of Salmon’s mark transmission theory. One advantage
of information transmission is that it is capable of
helping us conceptualize how heterogeneous factors such as
micro and macro-biological and psycho-social—are
causally linked. What we want to make clear is that—though
it is often thought that going down the molecular level
means to add details to a macro-level causal relations—
this is in fact not the case. A good example in this respect
is epigenetics, which shows that the way in which the
macro is causally linked to the micro is not simply a
matter of adding details to the same mechanism, but a
matter of transmission of information from outside the body
downstream to DNA and then the informational chain in
the cell. This is important not only to understand cancer
etiology, but also for the design of public health policies.
In fact, public health interventions cannot target
biomarkers, but the right causal factors at the macro-level,
such as environmental hazards and socio- economic and
Additional file 1: Figure S1. Branched evolution (3).
Additional file 2: Figure S2. The macro-environment: ecology of cancer
in a historical perspective. Examples are purely illustrative.
This work has been partially supported by the Exposomics EC FP7 Grant
[Grant Agreement No: 308610] and the Lifepath EU H2020 grant [Proposal
Number: SEP-210176796] to Paolo Vineis, and partially supported by the Arts
and Humanities Research Council via grant AH/M005917/1 to Phyllis Illari and
Federica Russo. The authors thank Christopher Wild for comments on the first
part, Martyn Smith and Duncan Thomas for thoughtful suggestions.
1. Schulte PA , Perera FP . Molecular epidemiology: principles and practice . Cambridge: Academic Press ; 1993 . p. 588 . ISBN 0-12-632346-1.
2. Vineis P , Stewart BW . How do we judge what causes cancer? The meat controversy . Int J Cancer . 2016 ; 138 ( 10 ): 2309 - 11 .
3. Clarke B , Gillies D , Illari P. Mechanisms and the evidence hierarchy . Topoi . 2014 ; 33 : 339 - 60 .
4. Gerlinger M , McGranahan N , Dewhurst SM , et al. Cancer: evolution within a lifetime . Annu Rev Genet . 2014 ; 48 : 215 - 36 .
5. New AM , Lehner B. Systems biology: network evolution hinges on history . Nature . 2015 ; 523 ( 7560 ): 297 - 8 .
6. Smith MT , Guyton KZ , Gibbons CF , et al. Key characteristics of carcinogens as a basis for organizing data on mechanisms of carcinogenesis . Environ Health Perspect . 2015 ; 124 ( 6 ): 713 .
7. Vineis P , Wild CP . Global cancer patterns: causes and prevention . Lancet . 2014 ; 383 ( 9916 ): 549 - 57 .
8. Parkin DM , Boyd L , Walker LC . The fraction of cancer attributable to lifestyle and environmental factors in the UK in 2010 . Br J Cancer . 2011 ; 105 (Suppl 2): S77 - 81 .
9. Whiteman DC , Webb PM , Green AC , Neale RE , Fritschi L , Bain CJ , Parkin DM , Wilson LF , Olsen CM , Nagle CM , Pandeya N , Jordan SJ , Antonsson A , Kendall BJ , Hughes MC , Ibiebele TI , Miura K , Peters S , Carey RN. Cancers in Australia in 2010 attributable to modifiable factors: summary and conclusions . Aust N Z J Public Health . 2015 ; 39 ( 5 ): 477 - 84 .
10. Shield KD , Parkin DM , Whiteman DC , Rehm J , Viallon V , Micallef CM , Vineis P , Rushton L , Bray F , Soerjomataram I. Population attributable and preventable fractions: cancer risk factor surveillance, and cancer policy projection . Curr Epidemiol Rep . 2016 ; 3 ( 2 ): 201 - 11 .
11. Rothman KJ , Greenland S , Lash TL. Modern epidemiology . 3rd ed. Philadelphia: Lippincott, Williams & Wilkins; 2008 .
12. Vineis P , Stringhini S , Porta M. The environmental roots of non-communicable diseases (NCDs) and the epigenetic impacts of globalization . Environ Res . 2014 ; 133 : 424 - 30 .
13. Scelo G , Riazalhosseini Y , Greger L , et al. Variation in genomic landscape of clear cell renal cell carcinoma across Europe . Nat Commun . 2014 ; 5 : 5135 .
14. Wild CP , Scalbert A , Herceg Z. Measuring the exposome: a powerful basis for evaluating environmental exposures and cancer risk . Environ Mol Mutagen . 2013 ; 54 ( 7 ): 480 - 99 .
15. Heijmans BT , Tobi EW , Stein AD , et al. Persistent epigenetic differences associated with prenatal exposure to famine in humans . Proc Natl Acad Sci USA . 2008 ; 105 ( 44 ): 17046 - 9 .
16. Gallo V , Egger M , McCormack V , Farmer PB , Ioannidis JP , Kirsch-Volders M , Matullo G , Phillips DH , Schoket B , Stromberg U , Vermeulen R , Wild C , Porta M , Vineis P , STROBE Statement. STrengthening the reporting of observational studies in epidemiology-molecular epidemiology (STROBE-ME): an extension of the STROBE statement . PLoS Med . 2011 ; 8 ( 10 ): e1001117 .
17. Vineis P , Chadeau-Hyam M. Integrating biomarkers into molecular epidemiological studies . Curr Opin Oncol . 2011 ; 23 ( 1 ): 100 - 5 .
18. Vineis P , Khan AE , Vlaanderen J , et al. The impact of new research technologies on our understanding of environmental causes of disease: the concept of clinical vulnerability . Environ Health . 2009 ; 8 : 54 .
19. Thomas DC . High-volume “-omics” technologies and the future of molecular epidemiology . Epidemiology . 2006 ; 17 ( 5 ): 490 - 1 .
20. Haack S. Defending science-within reason: between scientism and cynicism . New York : Prometheus Books ; 2007 .
21. Pearce N , Blair A , Vineis P , et al. IARC monographs: 40 years of evaluating carcinogenic hazards to humans . Environ Health Perspect . 2015 ; 123 ( 6 ): 507 - 14 .
22. Salmon WC . Scientific explanation and the causal structure of the world . Princeton: Princeton University Press ; 1984 .
23. Salmon WC . Causality and explanation: a reply to two critiques . Philos Sci . 1997 ; 64 ( 3 ): 461 - 77 .
24. Dowe P. Wesley Salmon's process theory of causality and the conserved quantity theory . Br J Philos Sci . 1992 ; 59 ( 2 ): 195 - 216 .
25. Dowe P. Causality and explanation: review of Salmon . Br J Philos Sci . 2000 ; 51 : 165 - 74 .
26. Illari P , Williamson J. What is a mechanism? Thinking about mechanisms across the sciences . Eur J Philos Sci . 2012 ; 2 ( 1 ): 119 - 35 .
27. Illari P , Russo F. Information channels and biomarkers of disease . Topoi . 2016 ; 35 ( 1 ): 175 - 90 .
28. Chadeau-Hyam M , Athersuch TJ , Keun HC , et al. Meeting-in-the-middle using metabolic profiling: a strategy for the identification of intermediate biomarkers in cohort studies . Biomarkers . 2011 ; 16 ( 1 ): 83 - 8 .
29. Rappaport SM , Smith MT. Epidemiology . Environment and disease risks. Science . 2010 ; 330 ( 6003 ): 460 - 1 .