Evolutionary Determinants of Cancer

Abstract

‘Nothing in biology makes sense except in the light of evolution’

Th. Dobzhansky, 1973

Our understanding of cancer is being transformed by exploring clonal diversity, drug resistance and causation within an evolutionary framework. The therapeutic resilience of advanced cancer is a consequence of its character as complex, dynamic and adaptive ecosystem engendering robustness, underpinned by genetic diversity and epigenetic plasticity. The risk of mutation-driven escape by self-renewing cells is intrinsic to multicellularity but is countered by multiple restraints facilitating increasing complexity and longevity of species. But our own has disrupted this historical narrative by rapidly escalating intrinsic risk. Evolutionary principles illuminate these challenges and provide new avenues to explore for more effective control.

Introduction

We’ve come a long way since the ancient Greeks asserted that cancer was a manifestation of black bile and constitutional melancholy. And there is much to celebrate from preventative vaccines to high resolution imaging diagnostics and genome-guided personalised medicines, and, in some cases, cures of otherwise lethal malignancies. But the reality is that cancer still exerts a massive burden on society worldwide with no immediate prospects for effective control (1). Major cancer research funding bodies, governments and international agencies explicitly recognise the magnitude of the problem and have set priorities for prevention, early diagnosis and improved treatment (2). But do we have an adequate grasp of the basic biology?

Is there a fully coherent explanation of why the lifetime risk of cancer is now so extraordinarily high? Do we really understand the covert process by which the progeny of one mutated cell may, or (more often) may not, after anything between one and fifty years, execute a lethal hijack of our tissues? Why do we still express surprise, as well as disappointment, when a heralded new drug prompts tumour regression followed, almost inevitably, by recurrence and resistance?

Dobzhansky’s well-known remark is a truism not just for mainstream biology but, arguably, for much of medicine. In this review, I advance the argument that nothing makes sense in cancer except in the light of evolution. Cancer is, in its essence, a Darwinian dilemma. This should inform our attempts to control it.

Intra-clonal diversity

Cancer has a multi-layered, Russian doll-like diversity that impacts on all areas of clinical practice (Fig 1). Arguably the feature that provides the greatest challenge to therapeutic control is the dynamic genetic diversity, coupled with epigenetic plasticity, within each individual cancer (3, 4).

Figure 1.

Cancers’ Russian doll diversity. The marked differences in age-dependent incidence rates of different cancer types (or sub-types), particularly between children and adults, is likely to reflect developmentally constrained windows of risk from stem cell proliferation.

The concept that cancers are driven by genetic abnormalities, diversify and evolve over time can be traced back to Boveri (5) and, later, in mid-20^th century, to astute pathologists interested in the natural history of cancer (6). However, the origins of our current evolutionary perspective of intra-clonal genetic complexity in cancer lie in the 1970s with Peter Nowell’s synthesis on clonal evolution (7) and John Cairns’ view of intrinsic mutability of DNA in stem cells, paired with natural selection, as a liability (8).

In more recent years, the advent of cancer genomics (9), multi-regional sequencing (10, 11), genetic screening of single cells (12-14) or single cell-derived clones (15) and single cell sequencing (16-19) has revealed the striking intra-clonal genetic diversity in cancer. The result has been a vivid new portrait of genetic architectures and inferred clonal phylogenies or evolutionary trees in cancer. These are reminiscent, in broad view, of Charles Darwin’s iconic ‘I think’ tree drawing of 1837 and parallel current phylogenomics research on species, with similar bioinformatic and computational challenges (20). Some strikingly consistent features of cancer clones have emerged that illuminate the underlying process, including branching or variegating genetic architectures, parallel or convergent evolution and spatial segregation of sub-clones in primary sites and metastases (Fig 2). This contrasts with the earlier view of cancer clone evolution as linear clonal succession. A few cancers might still appear to develop in this way but deeper genetic integration is likely to reveal more complex and branching clonal structures. The variegated, genetic architecture of cancer is of some consequence for therapy. Actionable ‘driver’ mutations may prove to be sub-optimal targets for therapy if they are confined to sub-clonal branches of the cancer’s evolutionary tree (21).

Figure 2.

1. Revealed by single cell genetic analysis or inferred bioinformatically from in depth sequencing (9-13). The number of genetically distinct sub-clones identified depends upon the depth of genome sequencing, the number of cells interrogated and the number and nature of mutations screened. Single cell whole genome sequencing suggests that every cell is unique (16) and therefore a tumour has, in one sense, as many sub-clones as there are cells.
2. Variegation of genetics and clonal phylogeny architecture inferred from single cell genetics or multi-regional sequencing using maximum parsimony, other phylogenetic methods or probabilistic algorithms(192). Note that at present cancer biologists use no single or uniform platform to infer and depict phylogenetic trees. There is much to learn here from phylogenomics of species (20).
3. Dramatic changes in clonal structure are reflected in clonal sweeps as in metastases or drug resistant recurrence. These abrupt adaptive changes are prompted by stringent selection.
4. Reiterated or independent mutations in same gene in sub-clonal branches (11, 13): a result of convergent evolution and strong selective pressure favouring those mutations.
5. Revealed by comparative clonal phylogenetics of cancers from multiple patients with the same subtype of disease (11-13). Branching clonal architectures, reiterated mutations (or convergent evolution) in different side branches and overall uniqueness of each clonal architecture are all features of a complex adaptive or evolving biological system.
6. Trunkal mutations are defined as those shared by all cells in all extant sub-clones. When more than one is present, it is likely that they were acquired sequentially and some early ‘branching’ may be lost.
7. Xenotransplantation studies using immune-deficient mice suggest that sub-clones have variable competitive ability to generate leukaemias or cancers in vivo but in most cases this capacity resides in more than one or several sub-clones (13, 112).
8. Comparative genomics of matched relapse/recurrence plus diagnostic samples made it possible to detect the drug resistant mutations in the primary material (i.e. at low frequencies prior to selection) (80, 81) as well to identify minor sub-clones that have spawned metastases or relapses (22, 23, 193).
9. Many cancers appear to show some topographical segregation of genetically distinct sub-clones revealed in tissue sections or micro-dissected regions of primary tumours (11, 194) and in metastatic lesions (22, 23) though admixtures are also common (10).

Image from Charles Darwin’s Notebook B, 1837.

The clonal phylogenies we capture are mostly based on single time point snapshots. They allow us to infer a historical pattern of clonal evolution but we may miss early clonal events, the dynamics and underestimate complexity. To some extent, this is rescued by multi-regional sampling coupled with the application of molecular clocks and spatio-temporal modelling (10), by serial sampling from different stages of disease (22, 23) or by ‘real time’ screening of tumour-derived DNA fragments in plasma (24-26). Phylogenetic trees based upon diagnostic sample snapshots may also fail to capture the order of early clonal events or ancestral clones. The order of driver mutations may be lost in subsequent selective sweeps or in regional samples with sub-clonal dominance (11, 27). Ancestral clones may become extinct or persist at very low frequencies. These early stages of clonal evolution can however be recovered, at least in some cancers. In childhood acute lymphoblastic leukaemia (ALL), the early or initiating genetic events and covert pre-leukaemic clones are discernible in monozygotic twins (28, 29) who share single pre-malignant clones spawned and shared in utero (30). In acute myeloid leukaemia (AML), pre-leukaemic cells persist at low frequency but can be purified (31).

Charles Darwin imagined that evolution proceeds at a slow, steady pace. Others have argued that evolution is best characterised by long periods of stasis interrupted by explosive change – or punctuated equilibrium (32). There is increasing evidence for the latter in cancer (33), propelled by the onset of genetic instability (34), chromothripsis (35) or high impact single mutations (36, 37).

At one point, the prevailing view or assumption was that cancer clone evolution was a matter of linear, sequential clonal succession but it doesn’t often look as simple as that. After initiation, multiple sub-clones often co-exist signalling parallel evolution with no selective sweep or clear fitness advantage. This can take the form of a starburst ‘big bang’ (10) or branching clonal architectures (11, 13).

Nowell made no reference to Darwinian selection in ecosystems in his seminal 1976 review but this is how we now see cancer clone development: diversification and selection in the context of tissue ecosystem pressures (38-42). The recurrent or ‘driver’ mutations we identify via genomic sequencing have been distilled from a cacophony of random mutational noise and only qualify as ‘drivers’ if they impart an ecosystem dependent fitness advantage, i.e. they are adaptive. In this sense, cancer cells evolve by scaling higher or different fitness peaks as the ecosystem context changes. We sometimes designate ‘drivers’ in a fashion that implies they have intrinsic or fixed fitness attributes. In reality their functional impact is highly contingent upon both genotypic context, especially via epistasis (see Box 1), as for most mutational innovations in evolution (43), and contemporary selection pressures within the local cancer ecosystem (44) (see Box 1). Cellular fitness is expressed, assessed and selected principally via the phenotypic features registered as ‘hallmarks’ of cancer (45), all of which ultimately impact on fitness via survival and/or reproduction of cells.

Box 1. The ‘driving’ test for mutations in evolution and cancer.

Fitness test 1

Does it alter protein function (↑ or ↓)? ^a

✓ → proceed to 2

Fitness test 2

Does it alter cellular phenotype in a stable fashion endowing potential fitness advantage?

Context

Genotypic network regulating phenotypes: mutational impact minimised or re-equilibriated via negative feedback, redundancy and buffering capacity.

Epistatic (non-additive) interactions with other inherited and acquired gene variants in the network will determine potential fitness ^b.

Cell type: some mutations in cancer will only alter phenotype in particular cells ^c.

✓ → proceed to 3.

Fitness test 3

Does the mutation induced phenotypic change endow a sustained fitness advantage (via survival and reproduction)?

Context

The prevailing ecosystem or microenvironmental selective pressures and restraints. Is there a serendipitous match?

✓ → proceed to 4.

Fitness test 4

Is the mutation encoding fitness advantage stably propagated?

Context

Is the mutation present in a cell that can propagate it extensively or indefinitely =

• - any bacterial cell

• - germ cells in sexually reproducing organisms

• - self-renewing cells in cancer (see text).

✓ Passed. → now repeat for each mutation

1. Only a few mutations in a gene can lead to a fitter protein (191). Loss of function is a more accessible change.

2. The role of epistasis and genotype networks in influencing fitness in cancer cells is seen with mutations such as those in BRCA1, MYC and RAS. These potentially powerful drivers only impact on fitness if the apoptotic or senescence response they elicit from the signalling network is complemented by other mutations that block this default option (137, 183). The impact that epistasis and genotypic networks have on fitness precludes any straightforward measurement of the fitness advantage of individual ‘driver’ mutations.

3. E.g. BCR-ABL1 only in haemopoietic stem cells, transcription factor mutations and fusion genes, in particular lineages.

In some cases, the match between mutational change, phenotypic consequences and selective pressure is clear (Fitness test 3 in Box 1): p53 mutations are highly recurrent in cancer (~50% of cases), suggesting they provide a fitness advantage in the face of common selective pressures. The contextual or adaptive significance can be seen in the ubiquitous presence of p53 mutations in UVB (and genotoxic stress) exposed skin (46), in response to genotoxic therapy (47) or oxidative damage (44, 48). As a remarkable ecological parallel, naked mole rats living under hypoxic subterranean conditions have a constitutive variant at Arg174 of p53 that is the same as some acquired p53 mutations in cancer cells (49). The selective logic is in evasion of the p53-dependent apoptosis pathway.

Matching of mutational adaptations in emergent sub-clones to particular selective pressures is also clearly evident with drug resistance mutations which are present in drug targets or response pathways (50, 51). And, similarly, when cancers cells are predated by foreign (allogeneic) immune cells, genomic deletion of mismatched HLA alleles selects for immunological invisibility (52, 53).

Evolutionary change operates not by radical innovation from a blank canvas but by tweaking what already exists. The emergence of mutational complexity, clonal trajectories, or architectures and clinical subtypes are therefore constrained by the cell type of origin (54) and by the early or initiating mutations that re-set genotypic signalling networks and cellular phenotypes (10, 55). One interesting consequence of this is that some tumours may be ‘born to be bad’ (10).

Ecosystem selective pressures in cancer are diverse and imperfectly understood. They include exogenous exposures, multiple endogenous restraints and, arguably the most potent, selection pressure of all, cancer therapeutics. The nature and timing of these pressures will shape clonal architectures and dynamics and influence clinical response. Within tissue microenvironments, cancer sub-clones indulge in reciprocal dialogues with each other and with stromal, endothelial and inflammatory cells, modulating each other in the struggle to maximise fitness. This can complicate analysis of fitness features as sub-clones can cooperate as recipients of ‘public goods’ provided by a subset of cancer cells or via paracrine loops (56-58). Production of a ‘public goods’ signal comes at a cost so the assumption is that there is likely to be some alternative or overall fitness benefit to the producer.

Cancer clone evolutionary progression and metastases involve multiple, reiterated or distinctive selection hurdles (59) that can only be negotiated via randomly generated variants. Genetic and chromosomal instability in cancer cells increases the odds of a beneficial or ‘rescue’ mutation arising (60, 61) and can be considered an adaptive response to genotoxic or other stresses, as in bacteria (62). But the probability of success is still limited. Genetically unstable clones can be less fit and reside closer to the edge of collapse or error catastrophe as in hypermutable bacteria and viruses (58). Hence, most initiated cancer clones or tumours never maximise fitness as malignant derivatives. Which is fortunate since it is very likely that as we age, we all accumulate mutant clones and covert, incipient cancers (63-65).

Cancer and ecosystems broadly are sufficiently similar that concepts and computational, mathematical models derived from ecology, such as evolutionary game theory(66), are now being profitably harnessed to investigate cellular interactions and clonal dynamics in cancer (67, 68) and to explore novel evolutionary approaches to therapy (see below).

Cancer as a complex, adaptive system

As in evolution in ecosystems, it’s not all mutation-driven natural selection in cancer clones. Early phases of clonal expansion in particular may well involve neutral drift and fortuitous advantage (69), or disadvantage (44). Epigenetic plasticity may, in some contexts, be critical (70) (see further below). A somewhat broader way to encapsulate the evolutionary complexity of cancer and its resilience is as a complex, adaptive system (71, 72).

The idea of complex systems derives from chaos theory and mathematics and has wide applications including engineering, meteorology and economics (73). Evolution of complex systems is common in nature, examples being the single cell, neural networks and the brain, the immune system, whole ecosystems and cancer. The essence of biological complex systems is their non-linear dynamics and, especially, their adaptability or resilience, maintaining functionality, in the context of stress or challenge. They execute this via a few seminal features including highly networked signalling with built in redundancy, negative feedback, buffering and modularity (72). And, additionally, can re-equilibrate into alternative steady states via phenotypic plasticity. For complex systems with built in genetic diversity – species in ecosystems, the immune system (74) and cancer, there is the adaptive tactic of differential survival and reproduction, or clonal selection, to maximise fitness in relation to the prevailing challenges. Complex adaptive systems generally evolve in the direction of increased robustness which equips them to withstand multiple destructive challenges whether they be climate change, nutrient loss, predators or infections (72). Or, in cancer, to withstand or bypass, the inherent evolutionary restraints on cells becoming cheating or selfish replicators and, as a bonus, evade cancer therapeutics (75).

We see how robustness of a complex system underpins resilience in cancer by considering the multiple routes adopted by cells for therapeutic escape or drug resistance (76) (Table 1). The ‘classical’ or mutational routes to resilience are the inevitable consequence of a numbers game with intrinsic mutability of cellular DNA, as with antibiotic resistance in bacteria. And, as with evolutionary innovations in general, mutations in cancer arise by a stochastic process that may leave mechanistic footprints (77) but is entirely independent of, or blind to, functional consequences or utility (78). A vivid demonstration of this principle comes from the finding of mutations resistant to modern antibiotics in bacteria frozen in permafrost for 30,000 years (79). And in cancers, we now have the anticipated evidence that emergent drug mutations in disease recurrence or relapse can be backtracked to a time preceding drug exposure, i.e. before they were positively selected (80, 81).

Table 1.

Evolutionary origins of selection for drug resistance in cancer

		Mechanism	Origins
A.	Genetic	• Selection of pre-existing, stochastic mutations in drug response pathway	The ‘classical’ escape route: the inevitable consequence of mutation rates and clone size.
		• Segregation of drug targets in sub-clones	For targeted therapy. The consequence of genetic variegation.
B.	Epigenetic	• Quiescent stem cells	An ancient survival trait – ‘hunkering down’ to protect against stress. Evident in bacteria and normal stem cells.
		• Signal block bypass	For targeted therapy. A consequence of signal network complexity, redundancy and multiplicity of microenvironmental signals. An early evolutionary innovation – as seen in bacteria and yeast.

The epigenetic routes to therapeutic escape in cancer have a similar evolutionary antiquity. Dormancy is a common tactic (82). Bacteria, as well as normal stem cells, adopt a dormant state – or ‘hunker down’ when under stress (83). It’s a very old trick but it works. How it works in normal (84) and malignant stem cells (82) isn’t entirely clear but it can involve cell cycle quiescence, sanctuary niches and active drug efflux. Signal block bypass, for some targeted therapies (85, 86), is a consequence of highly networked signalling for cellular metabolism and cycling with built-in redundancy as a failsafe. Blocked signal pathways can be rescued by alternative microenvironmental signals (87). Complex genetic and signalling networks were a very early evolutionary innovation in bacteria (88) and are very evident in yeast (89). Signalling network complexity and plasticity was a feature of totipotent or stem cells of early multicellular organisms, as in their current descendents (90-92), and has been elaborated over evolutionary time along with increasing diversity of form and longevity. It is therefore unsurprising that cancer cells, and perhaps especially cancer stem cells, have a robustness that allows them to exploit both genetic diversity or epigenetic plasticity when therapeutically targeted.

Cancer stem cells as ‘units of evolutionary selection’

Stem cells are integral to the developmental assembly and sustainability of multicellular life (91). Their fate options when dividing are to either produce replicas of themselves – to ‘self-renew’, or alternatively, to spawn differentiating progeny. This epigenetic plasticity is regulated by environmental cues within tissue niches and is orchestrated by key intracellular signals. Genes encoding the latter are recurrently mutated in cancer, effectively trapping cells in a self-renewing mode (93-95). Normal cells can transiently switch to symmetrical self-renewal in response to regenerative demand but in cancer, the balance of stem cell fate decisions shifts to self-renewal, at the expense of differentiation, in a stable fashion. Experimental evidence with both xenotransplants of human cancers and lineage tracing in murine models confirms that sub-populations of self-renewing cells sustain tumour growth (96-98) and establish metastases (99). Depending upon the mutations involved and the stage of disease, the normal hierarchical or differentiation-linked lineage structures will be maintained or collapse.

There is an argument that clonal evolution and cancer stem cell generated cellular hierarchies are alternative models of cancer clone diversity and progression (100). An alternative view is that the two are inextricably linked (42, 98). The notion here is that self-renewing stem cells – irrespective of their variable phenotypes and frequencies, and the presence or absence of hierarchical lineage structures, are the focus of evolutionary selection in cancer, generating clonal architectures (Fig 1), metastases and drug resistant recurrence, i.e. they are the predominant ‘units of selection’ and cellular drivers of cancer (101).

The definition of units of selection is a contentious issue amongst evolutionary biologists with semantic and philosophical overtones (102, 103). An evolutionary perspective on hierarchical levels of selection may be helpful. Evolution by natural selection has been in business for some several billion years on planet Earth and will happen whenever a few simple conditions are met (Fig 3). And, over very protracted periods of evolutionary time, the nature of the individual entities or ‘units’ that are selectable has shifted into increasingly higher orders of hierarchical complexity - from molecules to cells to multicellular organisms to societies or groups of individuals (104, 105) (Fig 3). At each of these major evolutionary transitions, the capacity of the smaller entities to selfishly replicate (and be available for selection) is rendered subservient to the fitness of the higher order entities, though multi-level selection is still possible. However, at each transition point the criteria for qualifying as effective units of selection are the same (Fig 3).

Figure 3.

From an evolutionary perspective, clonal cheating or cancer is atavism; a return to a former lifestyle in cells with a 600 m year memory of unicellular selfishness. Cancer cell behaviour is more than a superficial parallel to atavism if protists or early stem cells in the first multicellular animals had the inherent potential to express many or most of the essentially hallmark (or ‘fitness’) features of cancer. Cancer mutations, from this evolutionary perspective, do not so much innovate novel phenotypes but rather decouple ancient and normally transient phenotypes from appropriate networked control (108). The physicist Paul Davies independently arrived at a similar conclusion though he coupled it with the view that evolutionary atavism was incompatible with clonal selection models of cancer (195). It isn’t. : at each of these levels, individual entities may aggregate into conglomerates to optimise fitness (e.g. molecular complexes, colonial bacteria and protists, social insects) in which case selection may act at the group level.

The multi-cellular condition required conflict resolution and management of risky tradeoffs as the ‘lower order’ entity - stem cells in multicellular organisms, contribute to fitness of the individual via replicative ability. There are opportunities for cheating, particularly when this is fuelled by mutational diversity, as in other social groupings in biology (106, 107). Cancer is an outcome of mutation-fuelled cheating and, from an evolutionary and phenotypic perspective, is atavistic in nature (108-110) (see Fig 3).

In cancer, selection is for fitness of somatic cell variants to survive and reproduce. But can it be any cancer cell? Any dividing cell within a cancer clone can acquire, say, a drug resistant mutation, since this is an entirely stochastic process. But in practice, the most effective units of selection in cancer will be cell populations that have sufficient genetic diversity (- to provide a phenotypic substrate for selection) and with extensive self-renewal (-to propagate any selected trait). The genetics of cancer stem cell populations have not been adequately explored but there is evidence from xenotransplants that these cells are genetically diverse in individual patients (13, 15, 111, 112).

A complication in this argument comes from the fact that self-renewal or ‘stem-ness’ is an epigenetic state, not a fixed entity (113). This is true of normal as well as cancer stem cells (114). Normal stem cells are prime targets for the initiation of malignant transformation (115) but downstream progenitors, prior to terminal differentiation, can acquire self-renewal capacity by mutational changes (116) or micro-environmental pressures, as in zones of hypoxia (117) or with metastatic spread and epithelial-mesenchymal phenotypic transition (118). Such progenitor conversion to a stem cell state could be either an initiation event that might or might not lead to overt malignancy or a secondary selective event (31). The frequency of self-renewing cells or cells that can regenerate cancers in xenotransplant assays varies over several orders of magnitude, from one in a million up to almost 100%, between different cancers but also within a cancer with progression of disease (119-121). This huge variation, coupled with phenotypic plasticity, and concerns over the efficiency of in vivo xenotransplantation cell assays for stem cells, confounds examination of stem cell function in cancer (122, 123). Additionally, genetically homogeneous cancer stem cells show functional variability in propagating activity and drug sensitivity (70). But these biological complexities do not contradict the notion that cells with self-renewal are the principal units of evolutionary selection. Mutations that confer drug resistance can only impact with any clinical consequence if they are present in cells with self-renewal potential. Metastatic spread will be minimal unless it is seeded by cells with inherent or inducible self-renewing capacity.

The pivotal role of self-renewing cells as the prime selectable entity in clonal evolution is strongly endorsed by the two sets of findings. Firstly, that the quantitative burden of stem cells in several cancer types assessed to date is associated with disease progression or clinical failure (121, 124-128). And, secondly, that self-renewal appears to be a tractable target for effective therapeutic control (129-131).

Evolutionary risks and restraints

There is a sense in which a baseline or intrinsic cancer risk can be seen as a trade-off or legacy of how life has evolved; with the necessity for both mutability of DNA and stem cell proliferation (108). It is unsurprising that cancer is found, or can be induced, in most classes or clades of multicellular animals including those near the base of the metazoan phylogenetic tree, in Cnidaria (132, 133).

But risk to reproductive success does not go unchecked in evolution. The transition to multi-cellularity, some 600 m years ago required acquisition of controls to maintain the cellular integrity of tissues and restrain clonal cheating and cancerous proclivities (134). It is revealing then that most cancer suppressor genes arose at this time (135) though regulation of regenerative proliferation by stem cells may have been their primary function rather than cancer prohibition (92). Similarly, p53, as an orchestrator of stress responses, emerged in the common ancestors of unicellular protists and metazoan animals (136). DNA repair capacity, as might be expected, emerged much earlier with bacterial cells (135). Restraints on cellular escape are multiple including intracellular negative signalling feedback controls triggering default pathways of apoptosis or senescence in cells with compulsive oncogene-driven proliferation (137). Microenvironmental controls include space or architectural constraints (138) and metabolic limitations (e.g. oxygen diffusion, pH). Since long lived, essential and numerically limited stem cells are the most ‘at risk’ cell population, they should have been afforded special protection via evolutionary adaptation. This we see reflected in their residencies in epithelia, distal to external exposures and in relatively hypoxic niches that may minimise DNA damage (117). Perhaps counter-intuitively, stem cells reduce risk by proliferative quiescence. Most of the proliferative expansion required in steady state tissue turnover is executed by transitory progenitor or precursor cells where mutations will have less impact (139). Risk of clonal escape by mutant stem cells is also constrained by proliferative dynamics within the confines of niche architectures. Stem cells ‘take turns’ at exiting quiescence to proliferate. As a consequence mutant and potentially malignant stem cells may be flushed out by normal stem cells before they can establish clonal dominance (44). Drug efflux pumps are very active in most normal and cancer stem cells, not to frustrate therapy (which they do) but as a longstanding adaptation to protect the limited pool of essential normal stem cells, and other critical tissues sites (the placenta and blood-brain barrier), from xenotoxic damage (140).

As multi-cellular animals became more complex, larger, resilient and longer lived, then risk of clonal escape will have escalated. The beneficial, adaptive traits of wound healing and angiogenic capacities, an invasive trophoblastic placenta and mutagenic recombinases in lymphoid cells all come at a potential trade-off price in cancerous currency. But then tight regulation and additional restraints will have accrued – as an adaptive response to increased risk (141). Overall, risks and restraints will have been balanced at a level that minimises life-threatening malignancy during reproductively active lifespans – the period during which natural selection has an opportunity to operate.

Long lived and very large animals are interesting and challenging in this respect. Subterranean mole rats can live for ~30 years and appear to be cancer resistant. The bowfin whale lives for 200 or more years and has 1000 times more cells than humans but appears to suffer very little cancer. How is it possible if cancer risk has a simple relationship to stem cell division and errors in DNA? The conundrum is referred to as Peto’s Paradox (142). And the solution may be close to hand from recent comparative genomics which suggests that long lived species have adopted, we assume via natural selection, a variety of mechanisms to restrain increased cancer risk, operating via DNA repair efficacy, cell cycle regulation, growth factor receptor signalling and contact inhibition of cells (143, 144).

Which begs an obvious question. Has evolution passed us by?

The evolutionary dimensions of cancer cause

Ageing, non-human primates (in captivity) have low cancer rates. The lifetime risk of cancer for humans in Westernised or developed societies is now at around 1 in 2. To some extent, this grim statistic is the flip side of our success of avoiding predators, both microbial and human. But the retort that cancer is the inevitable price to pay for longevity and ageing (145) is to short change the argument - though clearly there is a strong association between ageing and cancer risk.

Figure 4 illustrates a simplified view of a causal network for cancer clone initiation and evolution. The roulette wheel of stochastic, potentially cancerous mutations instigating and driving clonal evolution in stem cells is the proximate causal mechanism in each and every cancer. But this game, underpinned by evolutionary legacies, does not have fixed odds. Mutation rates and stem cell pool size and cycling frequencies are not intrinsic physico-chemical properties like radioactive decay. They are variables subject to extrinsic as well as mutational modulation. Data from genome-wide association studies (GWAS) indicates that risk of every cancer type evaluated to date is modulated by inherited gene variants (146). And epidemiological data on incidence rates of all common cancers suggests that exposures or lifestyle associated factors can influence risk substantially, 2-100 fold (1). Chance pervades all of these parameters, so for each and every individual patient with cancer it certainly is ‘bad luck’.

Figure 4.

Lifestyle exposures can (a) impact directly on mutation rate, e.g. genotoxic ionising radiation or genotoxic chemicals such as benzo(a)pyrene in cigarette smoke, integrated viruses, or chronic inflammation and oxidative stress. Alternatively, (b) they can indirectly increase mutation probability via replicative stress on stem cells (e.g. via toxic damage stimulated regeneration, or persistent or cyclical proliferative stimulation, e.g. hormonal stimulation or microbial infection. Excess calories (diet/exercise balance) can feed extra proliferative cycles via IGF1 levels.

Inherited genetic variants can impact directly on mutation rate (c) via, for example, diminished DNA repair or can influence cancer risk ‘downstream’ by epistatic interaction with somatic mutations (d) (see text and Fig 2). Inherited variants can also impact on intrinsic risk (e,f) via their effect on exposures/lifestyle, for example via skin pigmentation (and UVB impact), nicotine addiction (and cigarette carcinogen impact) or efficacy of immune response to infections (146).

Epidemiologists have explicitly recognised the multi-factorial nature of causation in cancer (147) but as pragmatic scientists we often focus on selected components of causal networks which we may then label as ‘the cause’. This feeds into public expectations of singular and simple explanations and distorts our understanding of causal complexity.

Darwinian medicine adopts a different perspective on causation, through the lens of evolution. It addresses the question of how our historical past has shaped our vulnerability to diseases, such as cancer (148) both as a species, and with respect to variation between individuals. Why do we carry inherited allelic variants increasing risk of cancer? Why should common lifestyle, behavioural factors or exposures increase intrinsic risk. And what can we do about it?

Fast track to a Darwinian bypass?

Humans are unique in our fast track and exotic social evolution, the product of a sapient brain, which has bequeathed many benefits but also unintended consequences for cancer risk. First, we live for decades after ceasing reproduction. The abrupt female menopause, uncoupled to general senescence, is uniquely human (149) and andropausal decline in men means that only a few ageing men will have offspring. Hunter-gatherers, despite their low average lifespans, did and still do survive post-reproductively into their seventies or beyond (150) but it’s only in relatively recent human history that this has been the norm. Natural selection only works effectively up to and during reproductively active life, as JBS Haldane and Peter Medawar both argued many decades ago. Unlike mole rats and whales that reproduce for most of their protracted lifespans and have had millions of years to adapt, we have engineered longer post-reproductive lives in a rapid way that reduces opportunity or time for adaptive evolutionary processes to operate. A caveat is that some adaptive selection for post-reproductive longevity may have occurred via the ‘grandmother’ effect which effectively transfers fitness benefits to offspring. But that aside, the extra time available is then more spins of the roulette wheel of stem cell division, mutations and increased risk of cancer clone initiation or promotion. But the epidemiological incidence data tells us that it cannot just be extra time for mutational accidents to accumulate.

The evolutionary ‘mismatch’

‘Evolution has no eyes to the future’

George Williams, 1966

Context is everything in evolutionary adaptations. A winning fitness trait can become a loser if circumstances, or selective pressures, change. As they do. The vast majority of once winning or successful species on the planet no longer exist.

We have reshaped our ecology and lifestyles in a way that has resulted in a mismatch with our inherent genetics, the latter profiles reflecting adaptations to earlier and very different environmental circumstances (108, 148, 151). And the mismatch ratchets up intrinsic or baseline risk by directly or indirectly increasing mutation rates and/or stem cell turnover (Fig 4).

Needless to say, nothing in our evolutionary history as a species could have adequately adapted our lungs or livers to the chronic barrage of carcinogenic chemicals in cigarette tar. But, that major cancer cause to one side, what were at one time beneficial adaptations have now become liabilities, increasing risk of cancer.

The clearest case is with the evolution of skin pigmentation and risk of cancer (148). Dark, or highly melanised skin evolved some ~1.2 million years ago via purifying selection of a very active melanocortin receptor 1 allele (152). The selective pressure was solar UVB damage to the skin with consequent skin cancer (152), folate loss (153) or other lethal pathologies. The reverse, de-pigmentation, was an evolutionary adaptation acquired in migrants from Africa to higher latitudes in Europe some 40,000-60,000 years ago. The benefit is thought to reflect a reduced need for melanin to protect against solar UVB damage and an increased need for UVB-induced vitamin D synthesis (though other adaptive benefits including resistance to frostbite may have been involved) (152, 153). White skinned individuals are orders of magnitude more at risk of the three major types of skin cancer. But, critically, overwhelmingly in the context of our mismatched behaviour in relation to sun and UVB exposure. We were, historically, latitude adapted but are now maladapted in this geographic context.

Another potent example of evolutionary mismatch is with breast cancer (108, 151). Non-seasonal, monthly oestrus was a relatively early human innovation and the fecundity benefit, underpinned by gene variants, would have been subject to positive selection. This will have carried a trade-off risk with more proliferative stress to breast (and ovary) epithelial cells. But this in turn was diminished by early pregnancy, protracted breastfeeding and frugal diets. Those brakes on spins of the roulette wheel of proliferating stem cells have been taken off in modern, affluent societies. And contrary-wise, the risk further escalated by dietary/exercise changes that fuel higher IGF1 and oestrogen levels, earlier menarche, delayed menopause and more cell cycles.

Plainly our register of calorie intake versus usage with physical activity, or our metabolic rate, is out of kilter with our ancestral phenotypes and with the relationship seen across mammalian species between size, longevity and metabolic rates (154). And, experimentally, it’s clear that dietary calorie restriction, or genetic manipulation of IGF1 levels in mice, reduces cancer risk substantially (155). Humans with genetically determined low growth hormone receptor levels have very low cancer rates (156). Diet and calorie expenditure is difficult to study epidemiologically but it is possible that its overall impact on cancer risk in developed societies is second only to that of smoking. We do not need to adopt Palaeolithic diets to fix this.

Other aspects of social evolution may have resulted in mismatches with our ancestral past that escalate intrinsic risk. Infections are causal factors in some 3-35% of cancers in different societies and are particularly prevalent in less developed societies (1). Higher density and unhygienic living that arose after the agricultural revolution, starting some 12,000 years ago, combined with proximity to domestic animals, would have greatly increased the opportunities for microbial transmission. Endemic, early and persistent or chronic infection is a feature of most cancers associated with specific infections(157). Paradoxically, the opposite trend may have increased risk of a few cancers in more developed or affluent societies. The greatly improved hygiene and markedly reduced opportunities for infectious spread may have resulted in increased risk of some cancers in the young via immune dysfunction. The immune system has evolved to both recognise and require some infectious exposure early in life to balance or shape its subsequent regulation and repertoire (74). Absent or delayed infection in the very young, a feature of modern societies, may result in later dysregulated responses to infections that can trigger childhood acute lymphoblastic leukaemia in susceptible individuals (158).

There is a striking parallel, or validation, of the evolutionary mismatch view of cancer causation. When we artificially and rapidly manipulate the lifestyle and bodies of domesticated animals, as with perpetual egg laying battery hens (159) and breeding of large, heavy dogs (160), the same thing happens – cancer rates shoot up, in tissues where you would expect them to: in ovaries and long bones respectively. Darwin would surely have recognised that clue.

What is the evolutionary rationale for inherited risk variants?

Any modelling of the impact of exposures or lifestyle factors in increasing cancer risk via evolutionary mismatches and the stem cell mutational lottery needs to accommodate the variation in risk within human populations that has a constitutive or genetic basis. Family studies, sibling and twin risk estimates and GWAS (146) all testify to the fact that inherited susceptibility impacts on cancer risk in humans and in some cancers, this appears to make a substantial contribution to variation in overall risk – in prostate and breast cancer for example with high monozygotic twin concordance rates (161). A few highly penetrant but relatively rare mutant genes can contribute to this risk, BRCA1 and 2 and other genes involved in DNA repair being the prime examples. But most of the 200 or more SNP or allelic variants so far described that increase cancer risk are common in Caucasian populations (5-80% frequencies). The increased risk attributable to each allelic variant is modest with odds ratio in the range of 1.01 to 2. Most of the variants at present do not have clearly attributable biological functions but many SNP variants are in putative or defined regulatory, non-coding regions, in some cases regulating the same genes that harbour somatic, mutations in cancer (146, 162). A plausible rationale for this is that many of these inherited variants are integrated into the genotype networks in cells and in this way, epistatically, they can increase the impact that acquired mutations will have (- via the filtering effect of genotypic networks – see Box 1).

But what could be the evolutionary rationale, if any, for the high frequencies of an allele that increases cancer risk? It is possible that at least some of the inherited variants increasing cancer risk were positively selected in our ancestors for some benefit they endowed despite now, in a different or mismatched context, their deleterious trade-off (163, 164). Many human genes show signatures of historical positive selection, especially those involved in the immune response, skin pigmentation, signal transduction, olfaction and fertility(165). Several of the SNP variants identified in GWAS for skin cancer are in the same gene region that underwent adaptive, allelic variation in relation to depigmentation of Caucasian skin (166). The ‘top hit’ in testicular cancer GWAS (O.R. = 3.07), a SNP in the p53 binding domain of the c-KIT enhancer, has a signature of prior positive, evolutionary selection (167). Whether that variant was of adaptive benefit for skin pigmentation or fertility remains unclear. But it’s now a liability.

One testable proposition is that some of the many genes increasing susceptibility to prostate and breast cancer have been under historical positive selection because of the fertility benefits they once provided – via oestrogenic or androgenic signalling pathways (164). If so, this places some individuals at greater risk of evolutionary mismatches.

An evolutionary fix?

Waiting for natural, evolutionary processes to adapt to increased risk for cancer is not an option for us. Neither is manipulation of constitutive genotype (excepting embryo selection for high risk). The key question becomes: how can we best thwart the evolutionary resilience of cancer clones? In many ways, the challenge is similar to that we face with infectious parasites, bacteria and viruses: it’s a race against evolutionary progression and acquisition of drug resistance (168). Fig 5 illustrates a pragmatic, framework prescription highlighting the contribution that might come from evolutionary considerations (169).

Figure 5.

Modified from reference (169). %: my very approximate estimates of the proportion of cancer deaths, worldwide, that might be avoidable via these three routes.

• a
  , see (75).
• b
  , see references (85, 196).
• c
  , see (183).
• d
  , see references (13, 66, 197).
• e
  , see (198).
• f
  , see references (197, 199).
• g
  , see (200).
• h
  , see (201).
• i
  , see references (40, 110)

The most effective way to thwart evolution – of microbial infections or cancer cells, is to stop it happening in the first place, i.e. by prevention (see Fig 5 legend). If Tomasetti and Vogelstein were correct, this would be a lost cause for most types of cancers which they ascribe to chance mutation alone (170). The range of incidence rates for common cancers (1) suggests however that a high fraction are potentially preventable (108).

Effective prevention of cancer is difficult for a variety of logistical, social and economic reasons. Even in the best case scenario, we will be left with a significant cancer burden. One challenge then is for systematic surveillance and early intervention when clone diversity, robustness and probabilities of drug resistance and metastases are less than maximised. Where early intervention is invasive (e.g. Barrett’s oesophagus, prostate and breast cancer), there is an urgent need for prognostic markers, particularly since only a minority of detected tumours are likely to progress (63, 141). The question then becomes: can we predict evolution in cancer clones before it happens?

The stochastic or random nature of mutations, the impact of drift, the diversity and idiosyncratic patterns of clonal architecture in cancer (Fig 2) might suggest that evolution is not predictable in advance. Both complex system theory and evolutionary theory argues otherwise. Emergent fitness landscapes are heavily constrained by starting conditions, epistatic gene networks and the prevalence of particular ecological selection pressures. As a consequence, options are limited and evolutionary trajectories are frequently convergent in terms of phenotypic innovation and to the recurrency of underlying genotypic changes (171).

In cancer clonal evolution, the key convergent phenotypes are metastases and drug resistance. Experimental modelling of evolution suggest that provided there is some understanding of the fitness landscapes for these features, it should be possible to predict the likelihood or probability of future occurrence (172, 173). This has been tested with respect to predicting antibiotic drug resistance (174).

Pragmatically, developing predictive tests for progression of disease and drug resistance might best be achieved via surrogates for fitness. Genetic diversity and size of the stem cell pool is likely to be a key variable reflecting the substrate available for selection. Progression of disease, or poor clinical outcome after treatment, have been found to be significantly associated with higher levels of stem cell activity (see above), as has genetic diversity within whole tumour populations (175, 176). Mathematical modelling and ecological theory suggests that harshness or diversity of the ecosystem of cancer, outwith tumour cells themselves, should also be associated with progression of disease (177). This is endorsed by computational imaging of cellular diversity in tumour sections (178), expression signatures of stromal fibroblasts (179) and whole tumour imaging (180). These data encourage the notion that evolutionary prognostication may assist patient management in the future.

Evolutionary therapeutics?

Even with optimised prevention, early diagnosis and intervention, there will remain cancers that develop in a covert fashion and present late in their evolutionary trajectories with metastatic lesions or drug-resistant sub-clones on board. We see this currently with pancreatic cancer, glioblastoma, lung cancer and many ovarian cancers for which the challenge of sustained control is considerable. It is well recognised that drug combinations will be required to thwart resistance (181), but the design of therapeutic strategies should be informed by a sounder grasp of the underlying biology (110) and explored by appropriate in vitro and in vivo modelling. There is a largely untapped opportunity here to exploit ideas derived from computational analysis of signal networks, teasing out inherent fragilities (75, 181-183), evolutionary game theory (184) and ecology (185) (Fig 5). Some of the tactics available may not bring about elimination of all cancer cells but rather slow down the evolutionary process, as with aspirin in gastro-intestinal tumours (186, 187) or redirect and sustain stem cell dormancy, as with ABL kinase inhibitors in chronic myeloid leukaemia (188). Serial monitoring is key to control of evolving populations (189) and in cancer this is now possible in patients via tumour-derived DNA in plasma (24-26) and in model systems by high resolution cell lineage tracking (190).

Since the resilience and robustness of cancer clones resides to a large extent in its branching or variegated evolutionary character, a better understanding of how this process is driven might provide therapeutic routes to reduce adaptiveness or fitness. Cancer is replete with evolutionary legacies. It might well yield to an evolutionary fix.

Significance.

Lifetime risk of cancer now approximates to 50% in Western societies. And, despite many advances, the outcome for patients with disseminated disease remains poor, with drug resistance the norm. An evolutionary perspective may provide a clearer understanding of how cancer clones develop robustness and why, for us as a species, risk is now off the scale. And, perhaps, what we might best do to achieve more effective control.

Abbreviations list

SNV

single nucleotide variant

CNA

copy number alteration

UVB

ultraviolet light B

IGF1

insulin-like growth factor-1

SNP

single nucleotide polymorphisms

GWAS

genome wide association studies