## 1. Defining and Classifying Cancer



What is cancer? Is cancer one disease, or many? If many, how many
cancers are there? One might think that such questions are merely
empirical. However, it turns out that defining and classifying cancer
is contested not merely for empirical reasons. There are at least two
broader philosophical issues at stake. First, there is little
agreement on how to characterize disease, distinguish disease from
health, and thus classify diseases, generally (See the entry on
 concepts of health and disease).
 Second, cancers exhibit features that make generating classifications
difficult; they are heterogeneous, and variable in their etiology and
course of progression. In section 1.1, we will consider challenges
facing cancer classification given its intrinsic heterogeneity, and in
section 1.2, we will consider challenges facing cancer classification
given its variable natural history. 



*Prima facie*, what distinguishes diseases are typical
symptoms, disease course, and a common cause. However, cancer can be
present without clinically detectable symptoms, has variable disease
course, and is a multifactorial disease. It is difficult to pick out a
"causal bottleneck" uniquely necessary for cancer in
general, let alone each cancer type and subtype. While cancers are
typically classified by their cell-of-origin--or the tissue, or
organ, and cell-type where they originated (e.g., squamous cell
carcinoma of the skin or ovaries), it turns out that cancers can
contain mixes of different cells of origin, and cancers arising in the
same cells or tissues can have very different features, may evolve in
different ways, and have different vulnerability to treatment
(Marusyk, et al. 2012). How, then, are we to classify such poorly
behaving kinds? These puzzles led researchers to wonder whether there
are distinctive genetic or molecular "signatures" of each
cancer that can help us estimate risk of progression, or chances of
recurrence, as well as predict responses to treatment. Perhaps these
molecular signatures are the key to a natural taxonomy of cancer types
and subtypes?


### 1.1 Cancer's Intrinsic Heterogeneity



The hope that molecular signature will address these classification
problems in part inspired international efforts at sequencing cancer
genomes - the Sanger Institute's Cancer Genome Project,
and the U.S.'s Cancer Genome Atlas project (TCGA). Such projects
use high-throughput genome sequencing to identify both inherited and
somatically acquired mutations to genes affected in cancer cells, as
well as mutational processes and patterns of clonal evolution. The
TCGA aimed at generating a catalogue of mutations distinctive to of
each cancer type and subtype, resulting in thirty-three
"marker" papers, and some novel subclassifications. For
example, in 2015, a working group affiliated with TCGA published a
"Comprehensive Genomic Characterization of Head and Neck
Squamous Cell Carcinomas," which identified specific mutations
associated with human-papillomavirus-associated versus smoking-related
head and neck cancers. While such projects did result in a more
comprehensive understanding of cancer biology, they also raised many
questions. Cancer genomes have many mutations, so a central puzzle has
been determining which among the many changes to cancer genomes are
causally relevant to cancer progression, or which mutations are
"drivers" of cancer. Sorting the "signal" from
the noise has proven more difficult than anticipated.



There are very different rates, numbers and types of mutations across
and within different cancer types and subtypes - what is called
"intertumor heterogeneity." Which mutations play a more
significant causal role, viz. which outcome in different cancers, over
the course of cancer progression, is a difficult puzzle to solve. In
part as a result of this, the concepts of "driver
mutation" and "actionability" have come to be
deployed quite differently in different contexts, leading to problems
of slippage between criteria of application in research and clinical
contexts (Chin-Yee and Plutynski, forthcoming). Complicating this
further, a cancer may contain several subpopulations of cells (or,
multiple evolving lineages), with different numbers and types of
mutations within each lineage. Or, cancers have varying types and
degrees of "intratumor heterogeneity." So, samples of a
tumor taken when cancer is first diagnosed may be representative of
only one subpopulation of one stage of a dynamic process. Such samples
may not be representative of the genomic changes of greatest relevance
to late-stage disease. 



All this would be challenging enough as it is, but there is a yet
further challenge facing hierarchical subclassifications of cancer
based on molecular information. Cancers arising in the same tissue or
organ can be more genetically dissimilar than cancers arising in
different tissues or organs (Hoadley, et. al., 2014, 2018). Genomic
information, far from providing a way to sort all cancers into a
Linnean hierarchy, instead yields what philosophers have called
cross-cutting classifications (Khalidi, 1998).



Moreover, the effects of mutations are context-dependent, making their
causal relevance to any given cancer difficult to discern. Some
mutations may provide low or no advantage to the mutated cells, until
conditions change, such as the application of chemotherapies, which
can lead to proliferation of cells with specific mutations (Hsu et al.
2018). Even the order of acquisition of mutations can change the
biology and outcome of the disease. For example, depending on which of
two genes was first mutated, patients with myeloproliferative
neoplasms (MPN) have different risks of different outcomes (Ortmann et
al. NEJM 2015; Kent and Green 2017).



Several "extra-genomic" factors also play an important
role in cancer causation and thus, arguably, ought to play at least a
partial role in informing cancer taxonomy. For instance, it turns out
changes in microRNA in cancer cells--non-coding RNA, which
affects the expression of genes--can affect cancer initiation,
progression, and responses to drugs (Hrovatin, et al. 2018). Life
history (e.g. parity, or number of children, and age at first birth),
environmental exposures, and histories of infection, and immune
response, contribute differentially to cancer's age of onset,
rate of progression, histopathology, and response to treatment. For
instance, infectious agents (e.g., HPV- or HCV-) are associated with
cervical and liver cancers, and some proportion of esophageal cancers
(Cancer Genome Atlas, 2015; Morgan, 2022). Indeed, for several
decades, the primary cause of cancer was thought to be viral, and
inquiry into viral origins of cancer in part led to many insights into
cancer causation, whether originating in viruses or not (see, e.g.,
Morgan, 2022). Cancers of the prostate and breast are associated with
hormones, which are in turn affected by parity.
"Pregnancy-associated" breast cancers are cancers that
arise during or shortly after pregnancy (see, e.g., Schedin 2006). The
"same" cancer in the same tissue or organ can be
classified as either "somatic," or
"familial"--due in part to whether the patient
possesses inherited mutations that raise a risk of some cancers, such
as those with Li Fraumeni syndrome. Smoking associated lung cancers
and non-smoking associated lung cancers are distinct genetically,
epigenetically, and phenotypically (e.g., in gross pathology, course
of progression, as well as response to treatment).



Which of these several causal factors ought to figure centrally in
classification? Depending upon how fine-grained a characterization of
the causal basis of cancer one adopts, there could be hundreds or
thousands of cancer types and subtypes. It's far from clear
whether there is one obvious choice of causal basis or distinctive
properties that best reflects anything like a "natural"
division of type and subtypes. Indeed, the case of cancer raises the
larger question of whether one can or should expect a univocal
classification of diseases, more generally. Are diseases, particularly
multifactorial ones such as cancer, "natural" kinds? Or,
are they simply ways of carving up the world that we happen to find
useful? 



Several philosophers have weighed in on these matters (see, e.g.,
Ereshefsky & Reydon 2015; Lange 2007; Broadbent 2009 and 2014;
Williams 2011; Khalidi 2013; Sorenson 2011, Bechtel, 2019). The
questions these authors are addressing are both epistemic--what
warrants choice of criteria of classification of disease--and
metaphysical--given diseases like cancer lack essential
properties (or, properties belonging all and only to each type), is it
correct to think of diseases as "natural kinds"? Such
debates turn on more fundamental questions, such as whether there are
any general criteria for "naturalness" of scientific
categories, and if so, whether to take underlying causal or
constitutive bases, mechanisms, networks, or mere similarity or
"feature matching" as sufficient (Slater, 2014; Bechtel,
2019; Boniolo, et. al., 2021; Boniolo et al., 2023). 



Some philosophers have expressed skepticism about whether cancer, or
indeed, diseases, per se, are natural kinds. Lange (2007) for
instance, argues that, "a disease is a natural kind of
incapacity that features in interesting function-analytic explanations
of other unhealthful incapacities"(Lange, 2007, 266). Thus,
cancer is not a "natural kind," in his view, because the
"typical patterns of disruption of cell birth and death"
characteristic of cancer are multiply realized. Lange claims that
insofar as categories like "breast cancer" or "lung
cancer" seem to be increasingly replaced in modern medicine by
molecular subtyping, and molecular subtypes are not "natural
kinds of incapacity," they are not distinct diseases. Indeed,
many "disease kinds" lack unifying
"function-analytic" explanations. Thus, modern medicine
has, in a sense, led to the "end of diseases" as natural
kinds. In a similar vein, but for very different reasons, Sorenson
(2011) argues that diseases are "para-natural" kinds.
Insofar as diseases are "deficiencies," or
"departures from the norm," they lack the
"integrity" or "internal nature"
characteristic of natural kinds. Sorenson thus suggests that diseases
are "reflections" of natural kinds of process. They
"inherit the lawfulness and projectability of the natural kinds
that shape them." In other words, both Lange and Sorenson seem
similarly skeptical that diseases like cancer have the integrity or
explanatory role of natural kinds.



In contrast, while Broadbent (2009, 2014), Williams (2011), and
Khalidi (2013) agree that diseases like cancer lack essential
properties, disease classifications are natural classifications,
insofar as they pick out distinctive "deviations from normal
functional ability," or homeostatic property clusters.
Broadbent's (2009, 2014) "contrastive" model of
disease classification takes diseases to be distinct in light of
distinctive causes. Where there is a difference in at least one of a
set of causes shared by "controls" and "cases"
(where cases have distinct "parts and processes deviating
(negatively) from normal functional ability"), we have a
distinct disease. Broadbent takes this account to explain why, for
instance, "cervical cancer is a disease, but HPV-itis is
not." Though, this case raises an interesting question as to
whether potential, rather than overt, dysfunction is more central to
disease classification. In public health contexts, it is often
important to control "asymptomatic," or potentially
dysfunctional states. Strains of HPV that do not currently cause
cancer could well evolve the capacity to do so. So, perhaps from a
public health perspective, "HPV-itis" is a perfectly
legitimate disease category, in service of deploying interventions
that limit the spread of HPV more generally. That is, from a
preventive medicine perspective, what matters is that a disease
category is a good one for predicting and intervening, even where
functional disruption does not always (or even most of the time)
eventuate. Which choice of classification we ought to make in
demarcating disease types might depend not only on which contrast we
are interested in explaining retrospectively, but also which kind of
intervention we wish to promote prospectively.



Khalidi (2013) argues that cancer might be a homeostatic property
cluster kind, on the grounds that cancer is driven by
"homeostatic" mechanisms--namely, mutations to
"caretaker" genes, or genes that play a
"gatekeeping" role in preventing or permitting the
emergence of a tumor. Acquisition of mutations to specific
"caretaker" genes, on this view, serve a function akin to
Boyd's (1999) "homeostatic mechanisms," in that they
permit the further acquisition of "hallmark" properties of
cancer cells. Khalidi is drawing upon a very common view of cancer,
articulated by two famous cancer researchers, Hanahan and Weinberg
(2000, 2011). They identify what they take to be the
"hallmarks" of cancer: "a small number of molecular,
biochemical, and cellular traits--acquired
capabilities--shared by most and perhaps all types of human
cancer." They identified these "six essential alterations
in cell physiology that collectively dictate malignant growth"
as: self-sufficiency in growth signals, insensitivity to antigrowth
signals, evasion of apoptosis (cell death), sustained angiogenesis
(the ability to develop a blood supply), tissue invasion and
metastasis (Hanahan & Weinberg 2000, 57). By way of example of a
caretaker, *TP53* is called a tumor suppressor, because it
carries the function of responding to signals associated with cell
damage by typically inducing apoptosis, or cell death. It is one of
the only genes mutated in a majority of cancers sequenced (Bailey, et
al. 2018). Thus, Weinberg (2013) characterizes it as "master
guardian and executioner," in that it prevents further mutations
by inducing cell death where there are signs of trouble. On
Khalidi's view, one ought to understand disruption of these very
capacities as "homeostatic mechanisms" for cancer.



At first blush, this seems a vivid case of a homeostatic property
cluster kind; on Khalidi's view, it is these distinctive
"caretaker" mutations that permit or promote cancer.
However, where we take a mechanism "for cancer" to begin
and end is in large part a pragmatic matter (Craver 2009); arguably,
one might carve up the "homeostatic mechanisms for" cancer
to yield many or few cancer subtypes (Plutynski, 2018). Hanahan and
Weinberg's initial picture has been complicated since 2001. In
2011, Hanahan and Weinberg updated their hallmarks to include four
more: "genome instability, which generates the genetic diversity
that expedites their acquisition, and inflammation, which fosters
multiple hallmark functions... reprogramming of energy
metabolism, and evading immune destruction." And, "In
addition to cancer cells, tumors exhibit another dimension of
complexity: they contain a repertoire of recruited, ostensibly normal
cells that contribute to the acquisition of hallmark traits by
creating the tumor microenvironment" (2011, 646). In other
words, hallmarks of cancer appear to no longer involve all and only
intrinsic properties of cancer cells, but also dynamic interactions
between cancer cells and the immune system and extra-tumor
environment. This may not be such a serious challenge to
Khalidi's view that caretaker mutation are mechanisms for
cancer. One could, arguably, just expand the mechanisms for
homeostasis to the tumor microenvironment. However, such a strategy is
complicated by the context-sensitivity of causes of cancer, discussed
above. Many cells on the surface of the skin as we age possess as many
mutations to "caretaker" genes as those in a breast tumor,
but most such skin cells are shed and never eventuate in disease
(Martincorena, et al. 2015). The "same" mechanisms may
well lead to harm in one context, and be entirely harmless in
another.



Several recent efforts at cancer classification use network,
"deep learning" methods or AI to mine data to identify
patterns of association or clusters of common features within and
across different cancer types. These methods have been used to address
the so-called "curse of dimensionality" problem: i.e.,
sort through enormous amounts of genomic data, and speed development
of tools for both diagnosis and treatment. Recent philosophical work
explores a variety of methodological and conceptual challenges facing
use these methods. While some are optimistic about the use of such
tools in classification of cancers and other diseases, others are more
skeptical (Boniolo & Nathan, 2016; Boniolo and Campagner, 2019a,
2019b; Bechtel, 2019; Shrager, et. al., 2019; Hey, et. al., 2019;
Chin-Yee and Upshur, 2019; Green et al., 2022; Plutynski, 2018;
Plutynski, 2022). Skepticism comes from several sources. First, some
raise methodological concerns about representativeness of samples,
sample quality and purity, batch effects, and the fact that methods of
both sequence analysis and data labeling and curation are constantly
being updated, such that data gathered even five years past may need
revisiting. Others warn that different ways of labeling the same
genetic information can lead to contradictory results in clinical
trials of novel cancer drugs, or mistaken inferences about likely
progression or treatment benefit (Hey, 2015, 2019; Hey, et. al., 2016,
2019). Some worry that genomic data is a static representation of gene
expression profile at a single point in time, but cancer is a dynamic
process, shaped in part by the "tumor microenvironment"
(TME) - the environment surrounding a tumor. If we are
interested in determining how cancers progress, or are likely to
respond to treatment, we arguably need to take multiple samples at
several points in time, and even broaden consideration of relevant
causal factors outside the (local) tumor environment, to the
"tumor organismal environment" (TOE) (Laplane, et. al.,
2018, 2019b). How best to integrate as wide as possible an array of
genomic, extra-genomic, and clinical information is not obvious, as
well as how best to take into consideration the dynamic features of
cancer progression.



The larger issues raised by this case is whether there is one way to
privilege choice of causal basis for classifying disease. Different
choice of temporal or spatial scale, or more or less fine-grained
characterization of causal processes, might yield multiple,
overlapping disease classifications. While some may be willing to bite
this bullet, and indeed, endorse pluralism and non-hierarchical
classifications (see, e.g., Khalidi 1998, 2013), others might be
concerned that this would yield too permissive an array of disease
types, or inconsistent classifications, thus complicating scientific
communication. More generally, this case raises some interesting
puzzles about the aims and methods of classification in the biomedical
sciences more generally. Given that genomic features of cancer are
unlikely to unproblematically yield a single natural taxonomy, we
might become skeptical of reductive pictures of disease classification
and explanation. It seems that classifications of disease like cancer
form a kind of 'hybrid'--they are intended to capture
natural regularities and their causes, but also to be of use for a
wide array of agents with different purposes. It's unclear
whether all these purposes can be served by one method or set of
classificatory criteria.


### 1.2 Cancer's Variable Natural History



A major complication surrounding classification in the case of cancer
is that cancers have variable disease course (Foulds 1958; Cairns
1975; Lynch 2007; Bertolaso & Dupre 2018; Plutynski, 2018).
Not all cancers progress uniformly to metastasis and death; some
growths progress slowly or not at all. Some very common cancers (in
the prostate and thyroid) are either slow growing, or tend to remain
"indolent" (Esserman, et al. 2014; Siegel, et al. 2017).
There is some disagreement in the medical community about how to
characterize such cases; it's unclear whether they are best
viewed as very slow growing precancerous lesions, or perhaps ought not
be characterized as cancer, at all (Schwartz, 2014). Such cases also
raise a host of practical questions about the merits of screening, and
the risk of overdiagnosis--the diagnosis of disease that might
never have progressed in the lifetime of the patient (see, e.g.,
Walker & Rogers 2016; Hofman 2017; Esserman, et al. 2014; Welch
& Black 2010). They also raise interesting philosophical questions
about the nature of disease.



Some philosophers (Schwartz 2014), drawing upon the biostatistical
theory of disease as departure from age and sex-typical function
(Boorse 1977), argue that we ought to classify such early-stage
cancers as risk factors, rather than disease. Such growths are not
disease, on this view, because they are not atypical and do not yet
impair function--in fact as many as one half of men over 60 have
some lesions of the prostate (Welch & Black 2010).
Reclassification of such cancers as mere risk factors, Schwartz
argues, will prevent overdiagnosis and overtreatment. Other
philosophers (e.g., Reid 2017) argue that this approach fails to
acknowledge how the practice of diagnosis is itself a sort of
risk-benefit calculation, informed in part by our knowledge of the
prevalence of early-stage or indolent disease, and in part by more or
less precautionary values. It seems this debate is not only a
scientific one, but also a normative one; much like debates about PTSD
or obesity, there are normative dimensions to how we diagnose and
classify disease. Labeling can have both positive and negative
consequences for patients, clinicians, insurers, not to mention
developers and producers of screening, medical device, and
pharmaceuticals (Ereshefsky 2009). Overdiagnosis of early-stage
disease can lead to serious harms; treating a condition that may or
may not progress to invasive disease in the lifetime of a patient is
costly and harmful.



One hope driving the precision medicine research program is that the
identification of biomarkers of aggressive disease will resolve these
ambiguities with respect to early stage disease, as well as identify
targets of effective treatment (see, e.g., Collins, et al. 2005).
However, as we have seen, interpreting genomic data has been far from
straightforward, and classifying cancer types and subtypes by appeal
to genomic information alone can lead to a loss of important
information. While genomic and molecular data is no doubt useful, it
is not necessarily decisive. 



In sum, much like genes and species, diseases like cancer lack
essences, are heterogeneous, blur together at the edges, are caused in
many different ways, and have variable dynamics. Perhaps inconsistent
classifications are simply to be expected in such cases. That this is
so does not (necessarily) show that we ought to give up on realism
about disease kinds; indeed, perhaps we ought to endorse
"promiscuous realism" (Dupre 1995). As knowledge
has grown, and practical interests shift, classifications can multiply
and even cross-classify the same kinds. This is nothing to fear, so
long as the kinds we countenance do the predictive or explanatory work
intended for them, in the context intended. Others have a somewhat
less permissive view, however, though there remains a good deal of
dispute over how and which empirical constraints should govern
classification of "scientific kinds," whether or not these
categories count as examples of "natural" kinds (Slater
2014; Ereshefsky & Reydon 2015). (See the entry on
 scientific pluralism.)



Given the complex causal pathways and heterogeneity of cancer, perhaps
it is not surprising that, as Bertolaso (2016) argues, the history of
attempts to define cancer have floundered. Either cancer is defined so
vaguely as to include non-pathological states, or so narrowly as to
rule out cases that might otherwise be included. Among the many
definitions she canvases are: "abnormal proliferation,"
"unregulated growth," "a disease of cell
differentiation rather than multiplication," "the result
of destruction of tissue architecture," "a systems biology
disease," "blocked ontogeny," a disease of
"suppressed (sic) immune function," a
"metabolic" and "genomic" disease. Such a
variety of definitions is due to the fact that cancer has many causes,
and involves many different types of dysregulation, at a variety of
temporal and spatial scales. Suffice it to say, it is no small
challenge to identify defining features of cancer, let alone arriving
at a unified "theory" of disease etiology.



One might well ask, however, whether the lack of a unified theory of
cancer is a cause for concern. Is this a sign of immature science?
Kincaid has argued to the contrary (2008), "biomedical science
can make significant progress without precise definitions of disease,
without full-fledged theories of disease and of normal biological
functioning, and without disease entities being natural kinds"
(p. 368). Kincaid argues that "There is no clear definition of
what constitutes a cancerous cell in terms of necessary and sufficient
conditions. However, no one thinks that cancer research should stop
until we get this definition settled, or that creating linguistic
uniformity is important for understanding the science. The science
certainly makes progress nonetheless... Likewise, there is no
parsimonious theory of the disease and the normal functioning from
which it is a deviation. Instead, we have piecemeal causal
explanations" (p. 373). Kincaid is correct. Arguably, given the
context-dependence or local constraints on cancer causation, (and
perhaps also, diseases more generally) piecemeal reductive
explanations are often the best we can do (Schaffner, 2006).



The question of what function such a unified theory might serve is a
matter of some debate. A particular source of contention surrounds the
merits of the research program unified by a search for genetic
mechanisms associated with cancer cell behavior (Strauss, et. al.,
2021). This research program has received many different names: the
"oncogene paradigm," (Bishop 1995; Morange 1997)
"vision," (Morange 1997), "theory," or
"paradigm," (Sonnenschein & Soto 2008; Soto and
Sonnenschein, 2004, 2005), "model" (Temin 1974; Vogelstein
& Kinzler 1993), "theory-method package" (Fujimura
1987, 1992), and "framework" (Blasimme, et al. 2013). Some
contend that focus on genetic mechanisms is a "failed"
paradigm, or a "degenerating" (as opposed to
"progressive") research program. Resolving such debates
seems to require first that we have some measure of progress -
hardly a straightforward matter, given the variety of goals of a
scientific research program, and their transformations, over time.
While some see the central goal of research as the generation of true
theories, others focus on the generation of novel experimental
traditions, the generation of fruitful lines of inquiry, the
generation of local (as opposed to general) explanations of particular
patterns or processes, or useful ways of classifying data. A more
modest way of framing the question surrounding the
"oncogene" paradigm's failure or success might be to
focus on how commitment to this "explanatory framework"
has been useful in some ways, less so in others. 



For instance, Blasimme, et al. (2013) characterize the search for
oncogenes as an "explanatory framework": "causal
patterns, schemata (in the sense of Darden 2002), intuitions,
hypotheses, evidential standards and various bits of evidence and data
coming from different experimental settings and instrumental
devices," which "establish selective and local criteria of
causal relevance that drive the search for, characterization and use
of biological mechanisms." Rather than consisting of sets of
laws or general principles, such frameworks help generate strategies
in the search for causes, and narrow the field of inquiry. Blassime,
et al. argue that "explanatory frameworks allow for changes of
scientific perspective on the causal relevance of mechanisms without
necessarily fully replacing previous explanatory frameworks" (p.
375). Explanatory frameworks, in other words, can
"coexist" or be "gradually displaced," rather
than stand in mutually exclusive relations with one another. As new
questions arose, and new information was discovered, this explanatory
framework became elaborated. Depending upon how permissive or
restrictive our characterization of this framework, it need not be
viewed as at odds with, for instance, attention to other causal
factors in cancer, as discussed below.


## 2. Explaining Cancer: Theories, Models and Mechanisms



What does it mean to explain cancer? The question is ambiguous, and
fraught. For, by "cancer" one could have several targets
of explanation in mind: the general process of transformation of a
cell into a "cancer cell," a very general characterization
of the process of carcinogenesis as a whole, the process of invasion
and metastasis in solid tumors in particular, the emergence and roles
of intratumor heterogeneity, differential rates of incidence of
different cancer types, patterns of cancer incidence or mortality in
different environments, socioeconomic groups, races, ages, or sexes.
Each of these explanatory targets arguably calls for quite different
*kinds* of explanation, and not merely different explanatory
information. The fact that these explanations are different in kind
may (in part) explain why there appears to be such a variety of views
amongst philosophers concerning the aim and character of explanation
in cancer research, drawing upon a long history of debate among
philosophers of biology concerning norms of explanation (See the
entries on
 scientific explanation
 and
 philosophy of systems and synthetic biology).
 Perhaps not surprisingly, also, there is also a history of dispute
amongst cancer scientists about competing research agendas and norms
of explanation.


### 2.1 The Mechanistic Picture of Cancer



For most of the last twenty or so years of the 20th Century
and the first decades of the 21st, the central concern of
the majority of cancer researchers has been identification of
mechanisms at the cell and molecular level that regulate cell birth
and death. Two characteristic features of cancer cells are their
uncontrolled growth, and failure to undergo cell death. This is often
associated with changes to the "cellular machinery," or
mutations and associated disruption of causal pathways that affect
regulation of cell division or cell death (Hanahan & Weinberg
2000). The "somatic mutation theory" (sometimes, SMT) or "multistage"
theory of cancer, is roughly the view that changes in cells that lead
to these disruptions in regulation are due to a series of mutations,
chromosomal alterations, and epigenetic changes to cells acquired
during somatic cell division over the course of a lifetime. Onset of
cancer, on this view, may be accelerated either by inheritance of
"oncogenes" or "tumor suppressors," or by
damage to DNA, for instance, due to smoking or UV radiation.



This picture of cancer was pieced together over decades from various
sources of evidence--from Virchow's observations of
chromosomal abnormalities in cancer cells, to epidemiological patterns
of incidence in cancer, to the discovery of the "src"
gene--one of the first mutations found to be associated with
cancer. This vision of cancer as the product of oncogenes replaced a
"regulatory" view of cancer, according to which cancer was
a "breakdown in normal constraints on growth" (for a
history, see, e.g., Morange 1993, 1997, 2003). The "oncogene
paradigm" arose at the same time that some of the first
bioengineering technologies, which enabled early cancer researchers to
use the techniques of molecular biology to study genes associated with
cancer progression (Fujimura 1987). While the SMT is often associated
with a reductionist research program (see the entry on
 reductionism in biology),
 in the sense that many cancer scientists within this program seek to
decompose the system of interest and understand the parts in operation
in relative isolation, not all early advocates of the multistage
theory were cell and molecular biologists. Two of the first advocates
of the theory--Armitage and Doll--were epidemiologists. They
found that curves of average lifetime incidence of lung cancer in
smokers appeared to have the same shape as that of nonsmokers, simply
shifted to earlier ages. This suggested that cancer could arise from a
rate-limited series of events acquired over the life course. Since it
was known that mutations could be acquired during somatic cell
division (where "somatic" mutuations are acquired over the
course of a lifetime, and "heritable" mutations refer to
mutations in germ cells), and chromosomes of cancer cells were
disrupted, the obvious implication seemed to be that cancer cells were
in part a product of somatic mutations. Since the 1980s, hundreds of
genes have been identified, mutations to which are associated with the
hallmarks of cancer. Carcinogenesis, on this view, results from damage
to DNA, or errors in the process of cell division, as well as either
endogenous factors (various hormonal or other factors that promote
cell growth), or exogenous factors (infection, and inflammation),
which over time can lead to dysregulated cell growth.



In one paradigmatic example, Vogelstein and his group collected tissue
samples (premalignant polyps) from patients with a familial or
inherited form of bowel cancer (FAP, or familial adenomatous
polyposis). They discovered that mutations to genes associated with
various cancers accumulated in series of steps, what has been called
the "Vogelstein cascade." The study of familial or
strongly inherited forms of cancer (cancers that appear at relatively
young ages, and occur in families) has enabled the identification of
several genes, mutations to which (whether inherited or acquired
during somatic cell division) play a role in cancer: APC, RB, and p53
(associated with familial adenomatous polyposis, retinoblastoma, and
Li Fraumeni syndrome, respectively).



This picture of explaining cancer might be expected to resonate quite
well with the picture of scientific explanation advocated by what has
been called the "new mechanists." According to this view,
one of the central aims of biological scientists is the identification
of mechanisms. The biological sciences do seem to be unique in their
focus on mechanisms, rather than laws or general principles, of the
sort (typically imagined) to be central to the physical sciences (See
the entry on
 mechanisms in science).
 A mechanistic explanation identifies parts and processes that
typically yield specific outcomes of interest (Machamer, et al. 2000).
If we wish to understand regulation of apoptosis (cell death), for
instance, we might identify particular genes and proteins that play an
important role in cell senescence, and determine how their activity
and organization yield (or fail to yield) a given outcome. This
mechanistic picture of the aims and character of explanation in the
biological sciences seems prima facie to characterize current cancer
research quite well, or at least the past 25 years of this
research.



However, several philosophers have argued that this picture fails to
capture the diverse explanatory strategies carried out by cancer
scientists (Green, et. al., 2018; see also next section). Indeed, even
advocates of the mechanistic perspective have argued that it needs to
be amended to accommodate the complex causal processes involved in
cancer's etiology. For instance, according to Bechtel (2018), a
linear picture of mechanisms for cancer requires
"expanding" so as to accommodate the complex organization
of mechanisms. He characterizes the mechanisms associated with
apoptosis, for instance, as made up of two types:
"primary" and "control" mechanisms. Failure to
respond to signals promoting apoptosis in cancer may be a result of
disruption in either one (or both) such mechanisms. Attention to the
complex negative feedback processes of control and constraint
affecting primary mechanisms is essential to explaining the emergence
of cancer. As evidence of the significance of such control mechanisms,
he points out how cancer researchers identify regulatory networks or
feedback loops, associated with specific functions. Bechtel's
distinction between "primary" and "control"
mechanism raises some interesting conceptual and methodological
questions about the role of "organization" in functional
explanation, and how organization may be key to biological
"autonomy." Cancer may be a case study drawing attention
to the relationships between structural organization and functional
behavior in biological systems, tracking insights going back to
Aristotle, and continuing in the work of Kant, Bernard and Piaget
(see, e.g., Mossio & Bich, 2017; Mossio, et. al., 2009;
Montevil & Mossio 2015).



The kinds of pathways Bechtel identifies in cancer might either be
represented as "purposive" in some sense (in that
"controller" versus "primary" mechanism are
re-oriented in cancer in service of promoting unlimited growth of
cells), or as breakdowns in purposive organization (in that these same
mechanisms typically promote survival of the organism as a whole).
Relatedly, the same mechanisms might well serve as
"controller" in one context, and "primary" in
another. That is, this distinction itself may be relatively fluid, or
context dependent. In recent work by Bich and Bechtel (2022), and
others (see, e.g., Bich, et. al., 2016), this manner in which
organization and 'self-organization' of biological systems
is a product or co-regulation of multiple mechanisms, with larger
implications for a variety of diferent biological systems, is
developed further.


### 2.2 Complicating the Mechanistic Picture: Tissue Organization, Stem Cells and Systems Theory



Critics of the somatic mutation theory and the search for mechanisms
of cancer at the cell and molecular level have raised a variety of
objections, some methodological, some conceptual, and some empirical.
Indeed, some critics combine all three. In the late 1990s, and early
2000s, Soto and Sonnenschein proposed an alternative to the
"somatic mutation theory" (SMT), the Tissue Organization
Field Theory (or, TOFT) (Sonnenschein & Soto 2000). They object
not only to reductionist methodology of advocates of SMT, but also to
the empirical claim that mutations are in fact causes of cancer. They
argue instead that cancer is a product of "alterations in the
communication among cells and tissues that affect tissue
architecture" (2013, p. 90). On this view, mutations do not
"lead the way" in cancer, but instead follow upon
disruptions to tissue organization. It is these alterations of
patterns of interaction between cells that leads to "downward
causation effects" on cells and cellular components, inducing
aneuploidy and mutations (Soto & Sonnenschein 2006; Sonnenschein
& Soto 2008). Let us address their arguments, turning first to
methodological concerns about warranted inference from cell culture or
model organism work to general claims about cancer etiology.



Given that the development of cancer is an immensely complex causal
process that cannot be observed directly, many scientific claims about
cancer's causes are based on indirect inference, using model
organisms or cells in culture. For instance, tumors are grown in nude
mice using xenotransplantation, or tumors are observed to grow in mice
in which "driver genes" are "knocked out."
Soto and Sonnenschein have argued that these artificial experimental
circumstances render judgments about the causal efficacy of mutations
in cancer suspect. They are raising methodological questions about the
scope and limitations of model organism work. Soto and Sonnenschein
further argue that there are other equally adequate explanations of
cancer's emergence, and these explanations can accommodate
phenomena that the SMT cannot. For instance, they claim that cancer
cells' normalization when placed in healthy tissue cannot be
explained by the SMT. Tissue organization, on their view, exerts
"downward causation" on cancer cells, preventing or
promoting their development into a tumor. Their claims about
"downward causation" have led philosophers to weigh in on
the debate (Malaterre 2011; Bertolaso 2016; Green 2021). Malaterre
(2011) argues that advocates of TOFT need not commit to downward
causation (interpreted as an appeal to non-reductive physicalism, or
emergent properties). Instead, he argues, dynamic relationships
between variables that supervene over molecular and cellular
activities can do the same work, via a "higher level," or
less granular explanation of cancer etiology.



Do such cases genuinely count as downward causation in, for instance,
Kim's (2000) sense? After all, strictly speaking, the idea that
causation is synchronic across the whole and part of the same entity
seems contradictory. Soto and Sonnenschein propose that the kind of
causation they are invoking is not synchronic, but "diachronic
emergence": "cellular and tissue events occurring before
the expression of a particular set of genes takes place may act
downwardly modifying the expression of these genes at a later
time" (Soto, et al. 2008, 271). This, however, seems to be not
as radical a metaphysical thesis as talk of downward causation may
initially suggest. This seems instead a form of constraint. Green
(2021) takes "downward causation" in such contexts to
refer to : "the role of solid-state tissue properties in tumor
progression ... understood as constraining relations between
tissue-scale and micro-scale variables" (p. 1). While this
suggestion is "a 'weaker' form of downwards
causation than the one Kim (2000) and others oppose," and so
departs from standard use of the expression, the advantage of this
weaker notion that it better captures what scientists have in mind
when arguing for the "necessity of higher-scale features for the
realization and understanding of biological phenomena."
Similarly, Bertolaso (2016) argues that cancer is an instance of
synchronic reflexive emergence--"a different mode of
causation. In such a mode, effects are not expressed in terms of a
progression of events, but of maintenance of states at different
levels of biological organization" (Bertolaso 2016, p. 95).



It seems that each of these authors is pointing to an important role
for structural organization of tissues in the maintenance of
homeostasis or conversely in carcinogenesis. It seems such examples do
not point to "downward causation," in Kim's sense,
but instead suggest that we simply cannot predict or explain cancer
solely in terms of properties of "micro-" level parts but
need to expand our understanding of the causes of cancer to include
maintenance of tissue integrity. That is, the case of cancer seems to
require a richer, more integrative and interdisciplinary approach to
investigating causation at multiple levels.



It seems that each of these authors is pointing to an important role
for structural organization in the maintenance of homeostasis as
playing an important role in carcinogenesis. Indeed, evidence provided
by Bissell's lab (Weaver, et al. 1997, 2002) seems to indicate
that structural features of the extracellular matrix somehow either
prevent the emergence of disease, or can accelerate it (on
Bissell's lab, see Plutynski 2018b). It seems such examples do
not point to "downward causation," but instead suggest
that we need to expand our understanding of the causes of cancer to
include maintenance of tissue integrity. That is, the case of cancer
seems to require a richer, more integrative and interdisciplinary
approach to investigating cancer.



Soto, Sonnenschein, and others have argued that the SMT and TOFT are
mutually incompatible and cancer research needs a paradigm shift
(Bizzarri and Cuccina, 2016). Others argue that they can be fruitfully
integrated (Bertolaso, 2011; Marcum 2005; Malaterre, 2007; Bedessem
and Ruphy, 2015; Baxendale, 2019). Today, SMT and TOFT represent two
extremes along a continuum of views about the central causal role of
genetic mutations in cancer. Most researchers grant that mutations are
one among many other causal factors in cancer.



For instance, the tumor microenvironment (TME) is now considered
another key factor (see Maman and Witz, 2018 for a historical review).
However, there are some open questions about what falls within the
TME's spatial and temporal boundaries, and what causal relevance
spatial organization and temporal order have for cancer (Rondeau et
al. 2019; Laplane et al., 2018, 2019b). Some interesting ambiguous
cases are provided by nutrition (Sholl 2022) and by the microbiota
(Sholl et al. 2022). Increasingly, there are indications that changes
in the microbiome (both of cancer, and of the organism as a whole) may
play an important role in cancer initiation and progression,
suggesting that data about the microbiome may be relevant to
diagnosis, prognosis, and choice of therapies (Sholl et al. 2022).
Moreover, given that microbes can be transmitted vertically (during
cell division) as well as horizontally (between neighboring cells),
and are themselves subject to evolution, Sepich-Poore et al. (2022)
suggest reconsidering clonal evolution in cancer as a multispecies
process. Experimental study of the role of microbes in cancer is
difficult, however, raising several questions about pragmatic and
epistemic trade-offs. The complexity and heterogeneity on both sides
of the microbiome-cancer relationship makes generating robust causal
hypotheses difficult (Parke and Plutynski, 2023).



Another major research program that in some ways challenges a
mechanistic picture of cancer is the "cancer stem cell"
theory (CSC). Fagan (2016) characterizes the notion of a cancer stem
cell, as well as the current state of the field, as follows:



> 
> Cancer stem cells (CSCs) are thought to be a small subpopulation of
> self-renewing stem cells within a tumor or blood-borne cancer, which
> are responsible for maintaining and growing the malignancy. This idea
> has significant clinical implications. If the CSC model is correct,
> the current clinical strategy of seeking to eradicate *all*
> cancer cells should be revised, to specifically target CSC.
> 



The cancer stem cell theory in part is descended from the view that
cancers are in several ways akin to ordinary tissue (though
distorted), containing a mixture of proliferating stem cells and their
more differentiated progeny. This view played an important role in
cancer research in the 1960s and 1970s and is sometimes called the
"developmental" theory of cancer, in light of the fact
that it takes cancer to be a disease of "development," or
cellular differentiation in particular (Pierce & Speers 1988,
Morange 2015, Laplane 2014). This model of cancer etiology was revived
in the 90s, when Lapidot, Bonnet, and Dick identified "leukemic
stem cells" (Lapidot, et al. 1994, Bonnet and Dick 1997), and
was expanded upon in the early 2000s with the identification of an
analogous cell population in breast cancer, suggesting that CSCs are a
general feature of all cancers (Al-Hajj, et al. 2003). The notion that
there are subpopulations of cells within a tumor that play a
distinctive role--either in initiating a cancer, or in seeding
new cancers, has been influential in current research, suggesting new
avenues for both treatment and prevention.



Historically the cancer stem cell theory (CSC) was framed in
opposition to the somatic mutation theory and the clonal evolution
model, according to which cancer cells diversify and evolve through
the acquisition of mutations (e.g. Reya, et al. 2001; Wicha, et al.
2006; Shipitsin, et al. 2007). But soon it appeared that rather than
see the two theories as mutually exclusive, we might see the two as
complementary, in the process of integration, or overlapping models.
Fagan (2016) argues that on a "model-based" approach,
it's not clear that these two theories are inconsistent. Laplane
(2018) has pointed out how the CSC can serve to supplement the
evolutionary perspective. The presence and number of cancer stem cells
can affect the rate and nature of evolutionary change in a population
of cancer cells. Essentially, if all cells in a tumor are descended
from one or a few cancer stem cells, this restricts population size,
and thus suggests that to a large extent cancer progression is driven
by drift, rather than selection. Lyne et al. (2021) develop this
argument with a mathematical model, and question the ubiquitous
assumption that linear evolution, in which each new clone arises from
the latest one and tend to almost outcompete it, is the output of
natural selection. Such inference can only be warranted if the number
of contributing stem cells is high enough to prevent clonal evolution
by drift.



There are several open questions about the causal role of stem cells
in both preventing and promoting cancer. Cairns (1975) originally
proposed the hypothesis that the hierarchical organization of tissues,
with the tissue stem cells producing all the other cells, may prevent
cancer, by allowing only a few relatively protected cells to serve as
progenitors in somatic cell division, thus reducing the risk of
accumulated mutations (Cairns 1975). More recently, a similar idea led
to the claim that the risk of cancer is mostly a matter of bad luck,
proportional to the number of stem cell divisions in each tissue
(Tomasetti and Vogelstein 2015; Tomasetti et al. 2017). This has led
to a vivid debate on the explanatory relevance of the variation in the
number of stem cell divisions by tissues in the variation of the
lifetime risk of cancer in these tissues. However, the lack of
conceptual clarity and argumentative consistency of the initial papers
and its critics has hindered the debate (Plutynski 2021b). 



Discussion of the role and nature of cancer stem cells, however, is
complicated by the fact that there is a great deal of dispute around
what counts as a cancer stem cell (for questions about what counts as
a stem cell, outside of cancer, see Fagan's work, especially
Fagan 2013 and Fagan 2021). Laplane (2016), Fagan (2016), and Germain
(2014) draw attention to the fact that the concept of cancer stem cell
is multiply ambiguous, in at least the following ways:


* First, when we speak of cancer stem cells, we may be referring to
their capacities, or to their historical role or genealogy, i.e., to
the fact that they were the cells from which other cancer cells
originate. That is, some take cancer stem cells to be defined in terms
of their distinctive capacities and some in terms of their
relationship to other cells--in particular, to their
ancestor-descendent relationships in a population of cells in a tumor.
The "cancer stem cell model" is sometimes simply taken to
refer to any model of a tumor that treats the population of cells as
having a hierarchical relationship, where one or a few cells propagate
the tumor, whether or not those cells have distinctive properties that
cause them to stand in that relationship.
* Second, there are several different kinds of historical role that
CSCs might play: They may be all and only those cells that initiate a
cancer under natural conditions, they may be those cells which
propagate a cancer in situ, or they may be those cells that are
capable of propagating a cancer in an experimental animal.
Xenotransplantation experiments used to assess CSCs are "highly
artificial" contexts, that may or may not warrant the
conclusions researchers claim to establish. Xenotransplantation, for
instance, has been used to articulate and test hypotheses about
"intrinsic tumorigenicity" of CSCs. The problem with using
these tools, however, is that the matter of when and whether a cell is
"tumorigenic" varies across different backgrounds. For
example, CSCs may develop tumors in some mouse models and not others,
depending on their immune deficiency. Which experimental context is
relevant to understanding how and why cancer is caused by CSCs thus
depends upon which causes we wish to isolate, and how.
* Third, some take the concept of CSC to be restricted to normal
stem cells, which some believe are the most likely precursors to
cancer. Others hold that cells that originate a tumor have stem-like
properties but may or may not derive from normal stem cells.



What's clear is that some populations of cells in a tumor appear
to have "stemness" properties--the ability to renew
oneself. What is less clear is whether these capacities are context
sensitive, and how and by which cells they may be acquired. The
plasticity of many types of cancer cells suggests that they may
acquire a stemness phenotype, and even shift back to non-stem
phenotype. Given this, it appears that the CSC is just one of a
continuum of general views, some of which take only specific types of
cells to be precursors to cancer and others which grant that many
different types of cells have the potential to develop such
properties. That is, the real question at issue is whether the cells
that initiate a tumor are in some way distinctive or require
distinctive precursors, and how properties of stemness are acquired.
Whether stemness is intrinsic or acquirable, niche-dependent or
independent, moreover, will drastically change the therapeutic
strategy. The CSC model (the idea that CSC are at the origin of cancer
development, resistance to treatment, and relapse) might be true and
yet CSC-targeting strategy might fail. Indeed, what kind of property
"stemness" is may vary across cancers, and even over time
within a cancer (Laplane and Solary 2019a; Laplane 2016).



In sum, there appear to remain several open questions about the nature
of cancer stem cells, and more generally, how to best intervene upon
cancer to successfully eradicate the disease. According to the model
of the disease inherited from the latter third of the 20th Century,
somatic mutations, acquired over a lifetime, to the acquisition of
cancer's "hallmarks." So, the best strategy for
intervention may be targeting specific pathways associated with these
hallmarks. In contrast, according to the TOFT, changes in tissue
organization yield carcinogenesis. Thus, the best strategy for
intervention might be finding ways to intervene in the tissue
microenvironment, so as to create an environment less hospitable to
the growth of cancer cells. According to the cancer stem cell theory,
cancer arises in cells that have distinctive properties: continuous
renewal, and indefinite growth, permitting the emergence of new
disease. On this view, eradicating these stem cells seems essential to
cancer treatment. Philosophical work on cancer causation and
explanation can better illuminate the competing presuppositions of
different approaches, clarify their commitments, and offer insights
into fruitful integration of new data. It is already clear that
philosophical discussions of ambiguities in how we define and measure
stemness is essential to understanding how best to prevent recurrence
and treat the disease (Clevers 2016).


## 3. Cancer as a Byproduct: Evolution, Development, and Aging



Dobzhansky (1973) wrote that "nothing in biology makes sense
except in light of evolution." Can evolutionary thinking shed
light on cancer? After all, cancer is ordinarily understood to be a
case of failure of otherwise functional controls on cell birth and
death. At first pass, this view seems fundamentally at odds with
taking an evolutionary perspective on cancer. For, how can something
that is an exemplary case of "dysfunction" count as an
evolutionary process or product of adaptive evolution? Moreover, how
ought we to square this picture of cancer with the fact that cancer
cells seem to co-opt developmental processes--i.e., the process
of "de-differentiation" typical of early embryonic cells?
Is cancer a byproduct of development? Finally, cancer seems to
increase in incidence as we age. Is cancer simply a byproduct of
aging? Relatedly, if--as some argue--aging itself is
selected for (a contested question!), is cancer in some sense
"adaptive"? All these perspectives on cancer require us to
perhaps rethink the nature of function and dysfunction, or at very
least, what such terms mean in the context of discussion of cancer.
Some argue that there is a straightforward sense to be made of
scientists' talk of cancer cells performing
"functions," insofar as they are involved in activities
that contribute to the self-maintenance and continued presence of the
systems of interest, namely cancer cells (Goldwasser, 2023,
forthcoming). This sense of "function" is a
non-evolutionary, but part-function account. On the other hand, some
argue that cancer is a product of evolutionary trade-offs in fitness
at different temporal and spatial scales over the course of our life
and evolutionary history. On this view, the best perspective to take
on cancer's "functions" may be a
"multilevel" evolutionary perspective--i.e., thinking
of evolution by natural selection as operating at different temporal
and spatial scales, both sequentially, and in some cases,
simultaneously, sometimes yielding unfortunate "cross-level
byproducts" of selection at one temporal and spatial scale, for
entities or processes at other temporal or spatial scales (cf. Okasha
2006). Can such apparently contradictory accounts of cancer's
"functional" status be reconciled?



First, it is relatively uncontroversial that understanding a
species' evolutionary history, and the selective trade-offs they
face over the course of their life history, can inform our
understanding of how and why they are more or less vulnerable to
disease. For instance, some species are more vulnerable to cancer than
others. Why? Comparative biology--the comparison of different
species--may help us identify mechanisms associated with disease
vulnerability, onset and progression, when they arose, where and why
they are shared, as well as how they have diverged (Aktipis, et al.
2015; Aktipis, 2020). Comparing and contrasting how development,
immunity, and other mechanisms of suppression of cancer across species
can help cancer researchers identify targets of opportunity for either
treatment or prevention of cancer. In addition, we can look to unique
features of our own evolutionary history in order to explain patterns
of disease incidence, suggesting selective "mismatches"
with our ancestral environment, a kind of "byproduct"
explanations of our vulnerability to diseases. This enterprise is
broadly characterized as "evolutionary medicine" (Crespi
& Summers 2005; Gluckman, et al. 2009; Stearns & Koella 2007;
Sun, et al. 2014).



While the broad principles behind evolutionary medicine are relatively
uncontroversial--we all are evolved organisms after
all--philosophers of biology have been skeptical of particular
claims about how our evolutionary history has shaped our vulnerability
to disease, either because evidence in support of evolutionary
"mismatch" with our ancestral environment is both scant
and disputed, or because many arguments in evolutionary medicine make
"adaptationist" assumptions, i.e., assumptions that a
given trait is adaptive, or selectively advantageous, founded on at
best "just so" stories (Valles 2012; see also Murphy
2006). Others offer more nuanced accounts of the variety of senses in
which evolutionary 'mismatch' explanations go forward, and
are supported by a variety of evidence (Bourrat and Griffiths
forthcoming).



Of course, there are better and worse such arguments; the best
arguments consider not only the widest array of evidence, but also
trade offs in fitness, as well as the role of constraints arising out
of development and life history. Selective trade-offs are long known
to play a causal role in disease. In the case of cancer, some have
speculated that traits adaptive early in life may yield fitness costs
later in life. A vivid example may be early rapid growth, which can
both increase reproductive fitness, but also apparently increase risk
of prostate cancer in men and breast cancer in women (Giles, et. al.,
2003; Ahlgren, et. al., 2004; Summers, et al., 2008; Alvarado, 2013;
Boddy, et. al., 2015). In contrast, "mismatch" hypotheses
suggest that traits that may have been adaptive in the past leave us
vulnerable to disease in our current environment. For instance, many
advocates of evolutionary medicine have argued that the high
correlation between nulliparity and cancer risk might be explained by
our evolutionary history. The argument relies on the hypothesis that
women in the evolutionary past were pregnant for much of their lives,
starting at an earlier age; breast development and differentiation was
thus adapted to a lifetime of frequent pregnancy and nursing. Delaying
or avoiding pregnancy thus changes the developmental processes typical
for our species, which could increase breast cancer risk. Of course,
such hypotheses are contentious; there is always the potential for
confounding causes (in this case, of increased cancer risk due to a
variety of risk factors at work in modern society) (Greaves 2001).
Nonetheless, in a 1970 case-control study conducted at eight different
locations around the globe, a WHO group led by McMahon et al.,
estimated that "breast cancer risk for women having their first
birth under the age of 20 years is about half that for nulliparous
women," and that "women having their first child when aged
under 18 years have only about one-third the breast cancer risk of
those whose first birth is delayed until the age of 35 years or
more." Evolutionary "byproduct" explanations appeal
to our evolutionary history to explain this observation. Both the
above arguments may be making speculative assumptions about the
adaptive advantage of size or early age of sexual maturity in our
evolutionary past. Sex differences in various aspects of cancer risk,
mortality, and progression thus provide an interesting case study for
developing more sophisticated means of both defining and
operationalizing sex differences in scientific research, and testing
hypotheses about role of sex in disease (Mauvais-Jarvis, et. al.,
2020, DiMarco, et. al., 2022)



Another type of "byproduct" explanation of
cancer--though one that appeals to a much more distant event in
our evolutionary history--is that cancer is a product (or perhaps
byproduct, or re-enactment) of the emergence of multicellularity. At
one point in the very distant past, single celled organisms formed
collectives that cooperate; these collectives eventually became
multicellular organisms. Any collective of cells, especially
collectives whose survival and reproductive success depends on
functional organization, are potentially vulnerable to breakdown in
cooperative organization. On this view, then, cancer is a product of
breakdown in mechanisms that permitted the emergence of
multicellularity--the mechanisms that protect us from
"revolt from within" are not error-free, and over time
will fail. Such discussions raise interesting questions about the
nature of individuality (For a vivid discussion, see, e.g., Okasha
2006; Godfrey-Smith 2009; Bouchard & Huneman 2013; Clarke 2011,
2013; Love & Brigandt 2017; Bueno, et al. 2019; Pradeu 2012, 2013,
2016, 2019). Pradeu (2019, chapter 4) has distinguished two ways in
which the immune system can contribute to cancer seen as a breakdown
of individuality: either through a breakdown of its normal functioning
(e.g. the immune system should eliminate cancer cells but fails to do
so) or "the immune system acts normally and immune-mediated
decohesion is due to an abnormal context" (e.g. the tumor
produces an abnormal context that stimulates an immune response such
as healing when there is no wound to heal).



According to some (Greaves & Maley 2012; Merlo, et al. 2006) the
evolutionary picture of cancer dovetails nicely with the somatic
mutation theory; somatic cells divide and acquire mutations during our
lifetimes; some of these mutations involve failures in regulatory
pathways that ordinarily "enforce" functional
organization, and thus cooperation. In this way, an evolutionary
perspective--understanding how evolution of multicellularity
required the emergence of cooperative organization--is essential
to understanding cancer. Moreover, on this view, cancer itself may be
viewed as an evolutionary process--the emergence of adaptive
features of cancer cells, where these "adaptations" enable
short term "fitness," or relative success at survival and
reproduction. Several scientists have developed theoretical models of
this process, linking it to empirical data, e.g., on the emergence of
chemotherapy resistance (Frank 2007; Wodarz & Komarova 2015). On
this view, cancer is both a process and byproduct of multi-level
selection, where selection may be understood as operating at several
levels of biological organization simultaneously, or sequentially
(Damuth & Heisler 1988; Lean & Plutynski 2016). That is,
cancer cells may bear adaptations, and they are also evolutionary
byproducts of selective processes at other levels of organization. It
appears that cancer cells co-opt or hijack otherwise adaptive features
of organisms. Signaling pathways that are ordinarily in service of
adaptive functions in early stages of development or wound healing are
reactivated in (some) cancer cells, in service of the transition to
metastasis.



This is a classic example of a cross-level selective byproduct (Okasha
2005, 2006). Traits advantageous at one level in the organization of
multicellular organisms may be coopted by component parts. Some of the
capacities that invasive cancer cells acquire (the capacity to invade
and metastasize) are in fact due to a change in phenotype from
epithelial to mesenchymal type cells, and losing adhesive properties
enables such cells to invade the lymph and blood system. Lean and
Plutynski (2016) have argued that cancer may in some ways parallel the
patterns of emergence of multicellularity as characterized by Damuth
and Heisler (1988), shifting over the course of emergence of disease
from a simple selective process between individual cells to several
kinds of multilevel selective process (MLS1 to MLS2). Critics contest
that the process of metastasis only weakly mimics MLS2, however
(Germain & Laplane 2017).



Whether or in what sense cancer cells are "selfish" or
"cheat" is a topic of debate (Aktipis 2020; Okasha
forthcoming; Pradeu et al. 2023). Critics argue that the analogy is
misleading, given that non-transmissible cancers are evolutionary
dead-ends (Gardner, 2015; Shpak and Lu, 2016). Okasha (2021) argues
that cancer cells could be genuine case of cheaters if the atavistic
theory is true (which is by itself a matter of debate), i.e., if
cancer cells revert to a unicellular mode of life that have been
repressed during the transition to multicellularity. To be sure, there
are both analogies and disanalogies between evolution in whole
organisms, and the evolving population of cancer cells in a tumor
(Germain 2012). However, thinking of cancer as a dynamic,
evolutionary process, has great potential for applications in cancer
treatment, and perhaps also, in prevention. For instance, some have
suggested that we might give young girls drugs or nutritional
supplements that remodel the breast in ways akin to pregnancy, as a
way to prevent the emergence of breast cancer (Katz, et
al. 2015). Others suggest that modeling the evolution of multi-drug
resistance may help prevent one of the major causes of cancer
mortality. Drugs can be more or less effective in different patients
and lose their effectiveness over time. An evolutionary perspective on
cancer may shed light on how drug resistance comes about, in patients
with more or less intratumor heterogeneity (Greaves 2001, 2007; Frank
& Nowak 2004; Merlo, et al. 2006; Greaves & Maley, 2012).



A third type of byproduct explanation for cancer is the view that
cancer is a disease of aging--that is, cancer incidence by and
large increases as we age, perhaps in part as a byproduct of
breakdowns in mechanisms associated with immune response and tissue
integrity. This claim raises some broader questions about how we
assess function and dysfunction, or how we assess function at
different temporal and spatial scales of analysis, as well as how we
understand the role of the immune system and the integrity of the
organism as an individual. Indeed, all evolutionary and byproduct
explanations of cancer raise similar philosophical questions about
hypothesis testing, as well as definitions of "function"
and "individuality," both at the individual and group
level, and over the course of one's life history. On the
evolutionary view of cancer, for instance, cancer cells are in some
sense highly adaptive (Hausman 2012; Germain 2012; Godfrey-Smith
2009). One relatively controversial view of aging, first proposed by
August Weisman, is that aging has the evolutionary
advantage--effectively clearing the way for the young. If indeed
cancer is a necessary byproduct of aging, and aging is selected for,
then on one view, cancer may be thought of as adaptive. Such a view
raises a variety of questions about evidence and adaptationist
explanations.(see the entry on
 "adaptationism").
 


## 4. The Science-Value Interface and Aims of Cancer Research



There are several ways in which biomedical and public health research
on cancer intersects with debates about the proper role of values in
science. First, epidemiological and toxicological research is used in
support of regulatory policy and toxic tort law. Decisions about when
and what is "carcinogenic" have significant public health
import, and thus raise a variety of philosophical questions about
evidence, values, risk, precaution, and communication of scientific
results (Mayo & Hollander 1994; Cranor, 1993, 2006, 2011, 2017;
Valles, 2018; Elliott & Resnick, 2014; John, 2018).



Evidence for claims about carcinogenicity is often indirect, and
underdetermination is rife. In establishing claims about
carcinogenicity, there are a variety of choices regarding methodology,
source and type of evidence, and standards of significance (Elliott,
2011; Broadbent, 2013; Valles, 2018, 2021). Even optimally done
epidemiological studies, or toxicological studies at best lend high
probability to claims about health risk, in part because such studies
often "black box" the intermediate causes. In such
contexts, there is "inductive risk", or the risk of error
either in over- or under-estimation of actual risk of cancer, and
thus, room for values to play a role. Several philosophers of science
have weighed in on this matter of when and whether values ought to
play a role in such contexts, whether precautionary judgments are
appropriate, as well as whether "opening" the black box is
always ideal (see, e.g., Mayo 1988; Douglas 2000, 2009; Brown 2013;
Steel 2007, 2010, 2015; Elliott 2011; Shrader-Frechette 1993, 1994,
2002, 2004; Mitchell 2009; Russo & Williamson, 2007; Broadbent,
2011; DiMarco, 2021). As one might imagine, there is also a
substantive history of debate among epidemiologists, public health
scientists, scholars of the law, around when we have good reason to
claim that X or Y is carcinogenic, or what counts as good evidence,
going back to the dispute between Doll and Hill and their detractors
regarding the causal link between smoking and lung cancer (Hill,
1965). For a compelling history of this literature, see, e.g., Proctor
(1996), or more recently, Oreskes and Conway (2011), or Shostack
(2013). Problems of underdetermination are no less rife in the context
of assessments of "effectiveness" of cancer screening and
prevention (see, e.g., Solomon 2015; Stegenga 2018; Plutynski 2017)
(For a discussion, see the entry on
 philosophy of medicine.)



Second, much of basic cancer research--e.g., research on the cell
and molecular bases of the disease--is supported by federal
funds, which are allocated in the hopes that such research will
(eventually) yield better health outcomes. Yet, the relationship
between "bench and bedside" is indirect, and shaped by
economic, social, and political factors, some more pernicious than
others (Prasad, 2020; Tabery, 2023). So, it's unclear whether
and how "basic" cancer science ought to be evaluated in
light of whether or how it leads to better health outcomes. However,
this is hard to avoid, particularly when so many lives depend upon a
research program's promised outcomes.



This whole matter is complicated by the fact that the study of cancer
is itself big business. Cancer pharmaceuticals, medical devices, and
cancer research are major drivers of the economy. The average annual
price for a new cancer medicine is rising rapidly and now approaches
$150,000 (Booth, et. al., 2022); and some estimate cost of cancer care
is globally to be in the region of $458 billion by 2030 (Callahan
& Darzi, 2015). These high costs brings into focus several ethical
concerns regarding the economics of cancer prevention and care.
Critics have raised questions about the quality of research and
regulatory standards, economic incentives misaligned with promoting
overall quality of life, misleading representation of public health
and clinical information, and rising costs and downstream impact on
underserved populations, and equity in access to care (World Health
Organization, 2022). Several have expressed concerns regarding whether
the vast funds invested in cancer research--at least during the
past 25 years or so--have shifted mortality rates sufficiently to
warrant the expense. According to one line of thinking, the centrality
of cancer in biomedical research is a product of several historical,
economic, institutional, and social forces in combination, some of
which are self-perpetuating, primarily driving by commercial interests
(Proctor 1996; Fujimura 1996; Clarke & Fujimura 2014; World Health
Organization, 2022). Ever since Nixon's 1971 call for a
"war" on cancer, advertised implications for cancer
treatment or prevention have been used to bolster much of basic
research into genetics, genomics, and cell and molecular biology,
fostering investment in biotechnology. Some have argued that this has
led to an unduly excessive, or disproportionate fear of cancer as a
disease, an excess of anxiety, and perhaps also unnecessary or
unwarranted use of medical screening and testing (Aronowitz 2007,
2009, 2015; Welch & Black, 2010).



On the other hand, cancer research has led to important innovations in
science and medicine with impacts much wider than cancer itself. In
the U.S., excessive regulations on research by, e.g., the FDA on the
design and conduct of clinical trials for approval of drugs, some
argue, may in part be slowing research. Some argue that we ought to
lift restrictions on such tests for novel and more
"precise" (or targeted) drugs that might benefit very few.
Such matters are of course intertwined with larger debates around when
and whether we have sufficient evidence to claim that, e.g., this or
that mode of intervention is effective (Ashcroft 2002; Cartwright
2011; Howick 2011; Stegenga 2015; Teira 2011; Teira, et al. 2015;
Gonzalez-Moreno, et al. 2015; Deaton & Cartwright
2018).



One area where such questions are particularly fraught is
"precision" medicine. In service of this end, several
tools and technologies have been developed. One is simply sequencing
of individual cancers, with the idea that a more fine-grained analysis
of the molecular and genomic features of each cancer promotes more
effective prediction and control of cancer risk, enables detection of
cancers at much earlier stages, and promotes less debilitating, more
targeted treatments. But it comes with the risk of overdiagnosis and
overtreatment (Vogt, et. al., 2019). With sensitive technologies and
more fine-tuned risk information, the practice of medicine has been
shifting from diagnosis (and treatment) of disease, to constant
surveillance and intervention on disease risk, as well as higher rates
of overdiagnosis and overtreatment (diagnosis and treatment for a
proto-disease state that may never have progressed in the lifetime of
a patient) (Welch, et al. 2011; Esserman, et al. 2014). Other tools of
precision medicine include models such as organoids and
patient-derived xenografts (PDXs). Organoids are 3D cultures developed
from tumor samples of individual patients, in service of developing
patient-specific drug screening (Huang et al. 2015; Ooft et al. 2019;
Sachs et al. 2018). PDXs are immunodeficient mice engrafted with human
tumors that are used as surrogate models. Use of these technologies
has both immense potential benefit and carries some risks. On the one
hand, attention to the particular genetic variation unique to a given
patient variation will presumably allow for more targeted, effective
prognoses and therapies (Boniolo, 2017). On the other hand, use of
these tools involves both setting aside the assumption that sample
size is an important component in clinical trials, and assuming or
hoping that the organoid or mouse model is representational of the
target (Lillie et al. 2011; Green et al. 2019, 2021, 2022). Some
question whether the methods of testing new drugs in oncology suffer
from systematic problems, leading to less successful outcomes than
hoped or advertised. In particular, some have argued that surrogate
endpoints used in many clinical trials fail to predict which drugs
improve overall survival, accelerated approval is granted for drugs
that do not meet the criterion of serving an "unmet" need,
approval is granted for drugs that are tested against poor
comparators, in unrepresentative populations, or approved for minimal
improvements in surrogate outcomes, enthusiasts hype drugs before
demonstrated to be effective, and that there is a revolving door
between the FDA and the very same pharmaceutical companies that seek
approval for new drugs. Moreover, it seems pharmaceutical prices are
not determined by clinical benefit, nor are they justified by research
and development costs (Prasad, 2020; Prasad & Gale 2016; Prasad,
et al. 2016; Beneduce & Bertolasao, 2022). Such skepticism should
raise genuine concerns--especially given the hopes of patients
and families hanging upon the promises of such treatments, and the
overall costs of both cancer care, and precision medicine research.
However, there are several solutions that have been proposed (Prasad,
2020), such as eliminating consulting payments from the pharmaceutical
industry to researchers, conducting trials to study outcomes that
matter to patients (such as overall mortality, as opposed to surrogate
outcomes), comparing tested drugs to the current standard of care (as
opposed to poor comparators), testing drugs in populations that are
average type of people with cancer (not exceptionally healthy), or by
and large promoting more affordable drugs (globally).



In sum, current practices of cancer research, screening and treatment
raise a number of both methodological and ethical questions: Is
investment in precision medicine likely to yield the benefits
promised? When is medical intervention on disease risk (rather than
disease itself) unduly excessive? What exactly does it mean to speak
of effective medical intervention? Is overdiagnosis and overtreatment
a serious harm, or is it simply an inevitable byproduct of an
otherwise effective strategy--treating disease risk? How ought
clinicians to communicate about risk and benefit of novel targeted
interventions to patients, especially where there are gray areas of
benefit and harm? Are the (frequently) excessive costs of, and
inequitable access to, cancer care, matters of justice?



Cancer research--and especially the hope and hype surrounding
precision medicine--provides a focused lens through which to
consider problems central to critical examination of the concepts and
methods of the biomedical sciences. That is, cancer and the scientific
study of cancer illustrate challenges facing disease classification,
fuzzy borders between disease versus health, the problems with genetic
essentialism, the ever-present reference class problem and ever
promised solutions, as well as specific ways in which
underdetermination of evidence in biomedicine shapes matters of
justice in public health. Moreover, it provides a case study in how
matters of evidence, disease status, and questions of values and
justice are deeply intertwined.