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Cancer Cell

Review

Origins of Metastatic Traits

Sakari Vanharanta1 and Joan Massague1,2,*1Cancer Biology and Genetics Program, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA2Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA*Correspondence: [email protected]://dx.doi.org/10.1016/j.ccr.2013.09.007

How cancer cells acquire the competence to colonize distant organs remains a central question in cancerbiology. Tumors can release large numbers of cancer cells into the circulation, but only a small proportionof these cells survive on infiltrating distant organs and even fewer form clinically meaningful metastases.During the past decade, many predictive gene signatures and specific mediators of metastasis have beenidentified, yet how cancer cells acquire these traits has remained obscure. Recent experimental work andhigh-resolution sequencing of human tissues have started to reveal the molecular and tumor evolutionaryprinciples that underlie the emergence of metastatic traits.

The molecular and tumor evolutionary basis of metastasis is a

long-standing problem that particularly in the past few years

has started to move toward a resolution. Important elements of

the biologically complex metastatic cascade (Talmadge and

Fidler, 2010; Valastyan and Weinberg, 2011), its dynamic and

kinetic diversity across different cancer types and patient popu-

lations (Chiang and Massague, 2008), and the strong stochastic

nature at the cellular level (Kienast et al., 2010; Luzzi et al., 1998)

are coming to light at a brisk tempo. Work during the past

decade has demonstrated that the problem of metastasis can

be tackled experimentally at the molecular level. Progress in

experimental models and clinical research have led to the iden-

tification of genes that mediate various steps of the metastatic

cascade in different tumor types and target organs (Ell and

Kang, 2012; Guise, 2009; Nguyen et al., 2009a; Talmadge and

Fidler, 2010; Valastyan and Weinberg, 2011). Increased genome

sequencing output has facilitated the analysis of clonal relation-

ships between primary tumors and secondary lesions in clinical

samples (Campbell et al., 2010; Gerlinger et al., 2012; Yachida

et al., 2010). Taken together, these developments have revealed

a set of principles that illuminate fundamental questions on the

origins of metastatic traits. We resort to specific examples in or-

der to review here the nature and implications of these advances.

Early IdeasThe classical view of tumor progression, based on the clonal

evolutionary theory of cancer (Nowell, 1976), postulated that

the metastatic ability is conferred by random mutations in pri-

mary tumor cells that remain rare until clonally expanded and

selected at secondary organ sites (Fidler and Kripke, 1977).

This Darwinian view for metastatic progression was intuitively

appealing and supported by evidence from cancer cell trans-

plantation experiments in mice (Fidler and Kripke, 1977; Kripke

et al., 1978). Clinical observations were also consistent with

the view that metastasis is a rare achievement for cells in a

primary tumor, although one that follows predictable patterns

suggestive of specific mutations being responsible for its devel-

opment (Hess et al., 2006). Identifying these driver mutations,

however, remained an elusive goal.

Indications that the clonal selection model alone was not suf-

ficient to explain the development of metastatic traits emerged

410 Cancer Cell 24, October 14, 2013 ª2013 Elsevier Inc.

with the advent of genome-wide transcriptomic techniques

and their application to tumor samples. It became evident that

the likelihood of metastasis in many types of cancer could be

predicted from the overall gene expression profile of primary

tumors (van ’t Veer et al., 2002; van de Vijver et al., 2002), sug-

gesting that large segments of the cancer cell population con-

tained traits that predisposed these tumors to metastasis. This

was hard to reconcile with the idea of rare mutations as the

main cause of metastatic progression. It was proposed that

metastases may instead be an outcome of the same oncogenic

forces that drive the emergence of primary tumors (Bernards and

Weinberg, 2002). Experimental evidence for primary oncogenic

mutations as drivers of metastasis had precedent (Staller et al.,

2003). This view, however, had its problems too, as cancer so

clearly was an evolutionary phenomenon (Greaves and Maley,

2012). It was hard to imagine how metastasis could not be the

end result of strong selection imposed by different microenviron-

ments. A complementary view invoked the ‘‘seed-and-soil’’

hypothesis that was first enunciated by Paget in the 19th century

and, in today’s language, stated that cancer cells may seed

metastasis so long as they reach a compatible tissue microenvi-

ronment (Fidler, 2003). Compelling evidence has since accumu-

lated that partially supports each of these early ideas.

Metastasis StepsThe formation of clinically detectable metastasis is the end result

of a series of stochastic events that first allow cancer cells to

disperse and survive in distant sites and later to grow as second-

ary tumors (Figure 1). This process comprises steps of cancer

cell migration, local invasion, entry into the circulation, arrest at

secondary sites, extravasation, and colonization. The use of

‘‘colonization’’ as a single term belies the highly complex and

demanding process that awaits infiltrated cancer cells in distant

organs. Colonization can be parsed into steps of cancer cell sur-

vival upon entry into the tissue, formation of micrometastasis,

adoption of latency states that can last up to decades, reactiva-

tion of growth in the latent micrometastases, aggressive over-

taking of the host tissue, recirculation, and formation of tertiary

lesions in the same or different organs (Figure 1).

Viewing these steps as orderly cell biological phenomena akin

to developmental programs has been useful for mechanistic

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Initiating mutations Angiogenesis Stroma activation Invasion, EMT

Long-term survival Stemness Immune evasion Cellular latency

Months to decades

Micrometastasis Macrometastasis

Continuous until tumor removal

Intravasation Circulation Extravasation Survival on arrival

Primary Reactivation Specialized cooption Recirculation Tertiary seeding

Dissemination Rx

Step

s Fu

nctio

ns

Before diagnosis After treatment Overt metastasis Figure 1. Metastasis Steps and BottlenecksThe sequence of metastasis steps starts with thedissemination of cancer cells from the primarytumor and ends in the formation of clinicallydetectable macrometastases. Whereas dissemi-nation is not severely rate limiting, only a smallfraction of disseminated cells form micrometa-stases, and only a small minority of micrometa-stases become macrometastases. A cancer cellmay reach distant organ capillaries within secondsof departing from a primary tumor and extravasateinto the parenchyma within hours, but it mayremain latent for decades. Progression of metas-tasis through each of these bottlenecks requiresseveral specialized functions including cancer cell-autonomous functions and the cooption of variouscomponents of the target tissue stroma. The prin-cipal functions involved in each step are listed ineach box. The thickness of the arrows reflects therelative rate of success of cancer cells at eachtransition. Residual tumor cells remaining aftertreatment (Rx) may rely on functions that had beenpreviously selected during the micrometastaticstate. Individual metastatic traits, e.g., the ex-pression of a metastasis gene, mediate particularfunctions that slightly increase the probabilitythat a cancer cell will perform a particular step ofmetastasis. All metastatic traits combined deter-mine the probability that cancer cells will achieveovert metastasis. For an individual cancer cellthis probability is very small, but for a large cancercell population this probability can be absolute.

Cancer Cell

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dissection of the metastatic process and has allowed the identi-

fication of a number of metastasis promoting and suppressing

genes and functions (Valastyan and Weinberg, 2011). However,

cancer is not a developmental program but an evolutionary pro-

cess during which only a minority of malignant cells succeed. In

this process, themediators of metastasis function as factors that

slightly increase, on a per cell basis, the probability of successful

completion of one or more steps of the metastatic cascade.

Metastasis BottlenecksBiologically, metastasis is a highly inefficient process. Aggres-

sive tumors are thought to release cancer cells into the circula-

tion by the thousands each day, as can be inferred from the

numbers of circulating tumor cells (CTCs) present in the blood

of cancer patients (Baccelli et al., 2013; Nagrath et al., 2007;

Stott et al., 2010) and experimental models systems (Yu et al.,

2012). Much research has revealed in exquisite detail the molec-

ular underpinnings of cell invasion, motility, and stromal interac-

tions that lead cancer cells to enter the circulation and reach

distant organs (Kessenbrock et al., 2010; Roussos et al., 2011).

However, leaving a tumor is the easy part. The odds that an

aggressive cancer cell in the circulation will form a metastatic

colony in some organ are vanishingly small.

Most cancer cells that leave a tumor die, andmuch of this attri-

tion happens as the circulating cancer cells infiltrate distant

organs (Kienast et al., 2010; Luzzi et al., 1998). Even cell popula-

tions that are experimentally enriched for metastasis-initiating

cells suffer extreme attrition in the organs that they invade

(Chambers et al., 2002). For example, cancer stem cells isolated

from patients with metastatic melanoma are capable of forming

a tumor when individually implanted in the skin of a mouse (Quin-

tana et al., 2008), but would probably not form a metastasis if

individually inoculated in the general circulation. The same is

true for colorectal cancer (CRC) cells when challenged to colo-

nize the liver parenchyma (Calon et al., 2012).

The main bottlenecks for metastasis formation therefore seem

to occur during the colonization of distant organs (Figure 1). The

metastatic compatibility of certain tissues as envisioned by the

seed-and-soil hypothesis is only relative. To infiltrating cancer

cells, the best soil is still a deadly soil, just a bit less deadly

than others. The stress of passing through endothelial barriers,

a lack of survival signals and a supportive stroma in the host tis-

sue, and an overexposure of solitary cancer cells to the perils of

innate immunity challenge cancer cells that infiltrate distant or-

gans (Chambers et al., 2002; Nguyen et al., 2009a; Vesely

et al., 2011). Why cancer cells so easily die at distant sites is

currently unknown, but this death en masse represents a major

barrier to metastatic cancer progression. Clinically, it is also

the most relevant barrier, because previous steps of cancer

cell emigration from primary tumors and distribution to distant

organs have already occurred for months at the time of cancer

diagnosis. Identifying the natural mechanisms that eliminate

disseminated cancer cells might facilitate the development of

new therapeutic strategies to prevent or combat metastasis.

Metastatic LatencyOvert metastasis may eventually arise from residual populations

of disseminated cancer cells that managed to survive in host

tissues. Clinically, ‘‘metastatic latency’’ refers to the period

elapsed between the diagnosis of a primary tumor and the emer-

gence of detectable metastatic lesions. At the cell biological

level, latency also refers to various states that disseminated

cancer cells may adopt during this period of indolence. These

states include growth arrest (‘‘dormancy’’) of solitary or micro-

metastatic cancer cells and unproductive micrometastatic

growth with cell proliferation counterbalanced by cell death or

Cancer Cell 24, October 14, 2013 ª2013 Elsevier Inc. 411

Met

asta

sis

Determinants of organ-specificdissemination

Circulationpatterns

RXEndothelial

barriersSurvivalniches

Reactivationsignals

Stromalpartners

Therapyresponse

Determinants of organ-specificcolonization

Pro

babi

lity

Time

Cancer A Cancer A + Rx Cancer B Cancer B + Rx

Lung metastasis Bone metastasis Liver metastasis Brain metastasis

Pro

babi

lity

Time

A

B

Figure 2. Determinants of Metastatic OrganTropism(A) Metastasis as a whole, and organ tropism inparticular, are determined by traits that operate intwo consecutive, but temporally separate stages:establishment of a disseminated tumor cell pop-ulation and development of overt metastasis.Depending on the tumor type, the separationbetween these two stages as determined by theirclinical manifestation may be weeks (e.g., in lungadenocarcinoma) or decades (e.g., in ER-positivebreast cancer). The organ tropism of cancer celldissemination and micrometastasis is determinedby circulation patterns from the primary tumor todifferent distant organs, the permissiveness ofcapillary walls to extravasation, and the presenceof locations (‘‘niches’’) in the invaded parenchymathat provide a supporting home for the survival andstemness of disseminated tumor cells. Capillarywalls can be permissive for extravasation (e.g., inthe fenestrated capillaries of the bone marrow),difficult (e.g., in the capillaries of the lungs) or verydifficult (e.g., the brain-blood barrier capillaries).The organ tropism of the overt colonization isdetermined by the existence of signals that re-activate latent metastatic cells, the ability of these

cells to co-opt specialized components of the parenchyma (e.g., osteoclasts in the bonemarrow, astrocytes in the brain), and the ability to evade therapy owing todrug access or intrinsic drug resistance properties.(B) Each type of cancer has a typical pattern of metastatic relapse, initially involving mainly one organ (e.g., prostate cancer metastasis to bone, sarcomametastasis to lung) or multiple organs (e.g., lung and breast carcinomas). Therapeutic treatments may suppress metastasis in all organs uniformly (as in‘‘Cancer A’’) or in some organs more than in others (as in ‘‘Cancer B’’). In the latter case, clinical management of the cancer alters the organ metastatic pattern ofthe disease.

Cancer Cell

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loss of stem cell fitness (‘‘stemness’’). These various states could

coexist in the population of disseminated cancer cells residing in

a patient. The biology of metastatic latency and reactivation is

largely unknown, at least partially due to the lack of suitable

experimental systems that would model this aspect of metas-

tasis. However, recent work in mouse models of breast cancer

provides insights into the kind of differentiation signals and stro-

mal interactions that may be involved (Gao et al., 2012; Lu et al.,

2011).

The clinical course of metastatic tumor progression can vary

dramatically between tumor types and between patients.

Some locally invasive cancers, such as glioblastomas, form

distant metastases only rarely (Beauchesne, 2012; Lun et al.,

2011), whereas other tumors of the brain, such as medulloblas-

toma, frequently metastasize (Wu et al., 2012). Lung and pancre-

atic cancers are frequently associated with metastasis at the

time of diagnosis (Feld et al., 1984; Werner et al., 2013) whereas

breast and prostate cancers are not (Lim et al., 2012; Popiolek

et al., 2013). Some tumors metastasize so avidly that cancer in

these patients is diagnosed as metastases of unknown primary

source (Pavlidis and Pentheroudakis, 2012).

Assessing the true temporal patterns of primary tumor, latent

metastasis, and overt metastasis only based on clinical observa-

tion is challenging. For example, pancreatic adenocarcinoma

was thought to metastasize early because in many cases metas-

tasis is already present at the time of disease diagnosis. How-

ever, mathematical modeling of exome sequencing data from

matched primary and metastatic lesions suggests that this

may be due to late diagnosis, not early metastasis, and the

development of metastatic pancreatic cancer may in fact take

decades (Yachida et al., 2010). Nevertheless, the fact that

some tumor types, such as estrogen-positive breast cancer

(Lim et al., 2012) and prostate cancer (Popiolek et al., 2013),


are associated with long latency periods between primary tumor

resection and the development of metastasis indicates that

cancer cells in these tumors are disseminated long before they

acquire the capabilities of metastatic colonization.

Metastasis Organ TropismSolid tumors display dramatic variation in the pattern of metas-

tasis. Some mainly relapse in one particular organ (e.g., prostate

cancers in bone, ocular melanomas in liver, sarcomas in lungs)

whereas others relapse in multiple organs (e.g., triple-negative

breast cancers, skin melanomas, lung cancers, renal carci-

nomas). Blood circulation patterns can direct cancer cells to a

particular organ, as in the case of the mesenteric circulation di-

recting CRC cells to the liver. However, most solid tumors

release cells into the general circulation reaching many organs.

The fenestrated endothelium of bonemarrow and liver capillaries

is more permissive than are the contiguous capillary walls in

other organs, particularly in the brain. The capacity of circulating

cancer cells to pass through endothelial walls may, therefore, in-

fluence the organ tropism of tumors. Furthermore, because the

vast majority of cancer cells infiltrating a distant organ die, the

capacity of circulating cancer cells to resist decimation in spe-

cific organ microenvironments is another determinant of organ-

specific metastasis (Nguyen et al., 2009a) (Figure 2A).

Mediators of extravasation have been identified (Bos et al.,

2009; Clark et al., 2000; Gupta et al., 2007; Padua et al., 2008;

Weis et al., 2004; Wolf et al., 2012). Expression of these genes

in cancer cells increases their accumulation as disseminated

seeds in susceptible organs, thereby augmenting the probability

of eventual relapse in those organs. The same applies to media-

tors of cancer cell survival in distant organs (Chen et al., 2011;

Valastyan et al., 2009; Zhang et al., 2009). The expression of

these mediators in broad segments of the cancer cell population

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in primary tumors is reflected in metastasis signatures that

predict relapse to various organs in clinical tumor cohorts

(Bos et al., 2009; Cheung et al., 2013; Minn et al., 2005; Nguyen

et al., 2009b; Padua et al., 2008; Tavazoie et al., 2008; Valastyan

et al., 2009; Vanharanta et al., 2013; Zhang et al., 2009). These

findings have provided important insights into the biology ofmet-

astatic dissemination and the proteins and microRNAs medi-

ating it.

Although circulation patterns, extravasation barriers, and sur-

vival on arrival are three key determinants of the capacity of

particular tumors to seed specific organs, a different set of con-

ditions determine the capacity of micrometastatic seeds to

develop into macrometastases (Figure 2A). Overt colonization

critically depends on the capacity of disseminated cancer cells

to benefit from specific stromal components in a particular

organ. For example, cancer cells exploit osteoclasts in the

bone marrow for osteolytic metastasis (Ell and Kang, 2012;

Guise, 2009) and astrocytes in the brain parenchyma for cerebral

metastasis (Kim et al., 2011; Xing et al., 2013). These traits may

come into play only when disseminated cells emerge from the

latent state, months to decades after having seeded that organ.

This implies that in addition to the tumor reinitiation phenotypes

inherited from the cell-of-origin or acquired during earlier steps

of tumor progression, some of the overt colonization traits may

be selected only after micrometastatic cells initiate aggressive

outgrowth in the target organ parenchyma.

Both the determinants of metastatic seeding as well as those

of overt colonization of different organs underlie the patterns of

metastatic organ tropism of each type of cancer (Figure 2A),

that is, the probability that a particular cancer will relapse in

specific organ sites. It is possible that in the absence of an

effective treatment, and if given enough time, metastasis would

emerge in all organs in every case. Indeed, advances in disease

management are prolonging the life of patients with metastases,

but also changing the patterns of metastasis in certain types of

cancer (Figure 2B). For example, a current rise in the incidence

of brain metastasis of HER2+ breast cancer is attributable to

the fact that therapeutic agents targeting the HER2 oncoprotein

are effective in controlling extracranial metastases but less

effective against brain metastases (Sledge, 2010). Thus, differ-

ences in sensitivity to therapy of disseminated cancer cells in

different organs constitute one more determinant of metastasis

patterns.

Clonal Evolution and Tumor HeterogeneityGenetic Analysis of Human Cancer

Cancers arise through cycles of mutation and clonal selection

(Stratton et al., 2009). A classical genetic model of human cancer

progression is provided by the analysis of CRC development.

These tumors arise through progression of small adenomas

into full-blown carcinoma with associated mutations that drive

tumor progression (Fearon and Vogelstein, 1990). Inactivation

of the APC tumor suppressor is the gatekeeper event, followed

by mutations in KRAS, TP53, SMAD4, PIK3CA, TGFBR2, and

others (Jones et al., 2008). Based on these observations, it

was envisioned that yet other genetic alterations would make

carcinomas metastatic (Fearon and Vogelstein, 1990). When

technology enabled exome sequencing of human tumor sam-

ples, a comparison of primary tumors and matched metastases

found no mutations in CRC that were specifically and recurrently

associated with metastasis (Jones et al., 2008).

Two important conclusions emerged from these studies. First,

the intervening period between tumor initiation, i.e., the time

when normal cells turn into premalignant precursors of cancer,

and the emergence of invasive carcinoma far exceeds the addi-

tional time that it takes for the development of metastasis (Jones

et al., 2008). Second, mathematical modeling of tumor evolution

based on the genetic data suggested that the selective advan-

tage provided by individual driver mutations is on average rather

low, only �0.4% (Bozic et al., 2010).

These lessons are complemented with insights from other

cancers. Instead of recurrent metastasis-specific mutations,

alterations are found in genes that are commonly mutated in

primary tumors. For example, comparative genomic sequencing

of several tumor specimens of the same patient has revealed

metastasis-associated mutations in renal, pancreatic, and

lobular breast cancers (Gerlinger et al., 2012; Shah et al., 2009;

Yachida et al., 2010). Metastatic clones in pancreatic cancer

may harbor amplifications of the oncogenes KRASG12V, MYC,

and CCNE1 (Campbell et al., 2010). Mutant alleles of the tumor

suppressors TP53, SETD2, and KDM5C were found in renal car-

cinoma metastases (Gerlinger et al., 2012). In a patient with ER-

positive breast cancer from whommetastatic cells were isolated

9 years after primary tumor resection, the most compelling

metastasis driver mutation was in ERBB2 (Shah et al., 2009),

which harbors similar activating mutations in primary breast can-

cers as well (Bose et al., 2013; Cancer Genome Atlas Network,

2012). In basal breast cancer, a similar approach revealed differ-

ences in mutant allele frequencies between primary and meta-

static tumors, but the affected genes were the same with few

exceptions (Ding et al., 2010). Single cell analysis of a triple nega-

tive breast cancer resulted in compatible observations (Navin

et al., 2011).

Although no metastasis-specific, recurrent, driver mutations

have been demonstrated thus far, metastatic cell clones clearly

do not represent the whole primary tumor population, but only

parts of it (Campbell et al., 2010; Gerlinger et al., 2012; Yachida

et al., 2010). In a pancreatic cancer, lung metastases and perito-

neal metastasis were shown to descend from different clones in

the primary tumor (Campbell et al., 2010; Yachida et al., 2010),

demonstrating in tissue samples what has long been clear in

experimental models of metastasis: different metastatic clones

differ in their ability to colonize specific organs (Nguyen et al.,

2009a). In prostate cancer, on the other hand, metastatic tumors

in different target organs seem to arise from a single metastatic

clone (Liu et al., 2009).

The longer the latency period between the emergence of a pri-

mary tumor and that of distant metastases, the more likely it is

that metastatic clones will carry specific genetic alterations.

The pressure to adapt to, alter, and eventually overtake the

host stroma combined with the genetic drift of genomically un-

stable cancer cells all but ensure that some genetic alterations

will be enriched for in metastatic clones. The number of metasta-

tic tumor specimens that have been analyzed in comparative

genomic studies remains low, and it is not clear whether any of

the metastasis-associated driver mutations identified have

higher mutation frequencies in metastatic lesions more generally

(Figure 3). An emerging conclusion suggests, however, that the


Rel

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Driver mutations -1

1

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Figure 3. Genetic Alterations in Metastatic CancerA schematic shows the possible classes of metastasis-associated cancerdriver mutations as a continuum ranging from primary tumor-specific tometastasis-specific genetic lesions. Systematic cancer genome resequencingefforts are beginning to shed light on the mutational complement of metastaticcancers by comparing the genetic alterations in primary tumors and corre-sponding metastatic lesions in a procedure termed comparative lesionsequencing (Jones et al., 2008). Ideally, several regions of individual tumorsare sampled in order to account for clonal heterogeneity. Mutations that wouldhave a higher selective advantage at the metastatic site would also have ahigher mutation frequency in metastatic lesions. Conversely, some mutationsthat are advantageous at the primary site might make cancer cells less fit formetastatic progression, in which case the mutation frequency would be higherin primary tumors. At extremes, a type of mutation would be either fullymetastasis- or primary tumor-specific, respectively. Relative difference inmutational frequency between primary andmetastatic cancer (Df) is defined as[f(T) � f(M)]/[f(T) + f(M)], where f(T) and f(M) refer to the mutational frequency ofa gene in primary tumors and corresponding metastasis, respectively. a,Mutations with higher frequency in primary tumor; b, mutations with equalfrequency in both primary and metastatic tumors; g, mutations with higherfrequency in metastatic tumors; d, metastasis-specific mutations.

Cancer Cell

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mechanism of action of the metastasis-associated gene prod-

ucts is not likely to be metastasis-specific.

Lessons from Mouse Models

Recent work combining analysis of human tissues and mouse

models of CRC revealed a strong impact of nongenetic events

on the probability of liver metastasis (Calon et al., 2012). CRC

cells that frequently become insensitive to the tumor suppressive

action of TGF-b via genetic inactivation of the TGF-b signaling

pathway can afford to overexpress this cytokine, which

enhances metastasis-formation in the liver by inducing the

secretion of the prosurvival cytokine interleukin-11 from stromal

fibroblasts (Calon et al., 2012). Thus, a genetic event in cancer

cells enables the selection of a trait—high expression of TGF-

b—that strongly favors metastasis by profitably engaging the

stroma. What drives the high expression of TGF-b in TGFBR2

mutant CRC cells may not be another mutation but rather a

diverse set of epigenomic regulators of TGF-b production, any

of which underlies this highly valuable trait.

Several studies have identified pathways that promote or

suppress metastasis in genetically engineered mouse models

of cancer. Tumor initiation byPten loss in the prostate can trigger

activation of the TGF-b tumor-suppressor pathway through

SMAD4 expression (Ding et al., 2011). In this context, yet uniden-

tified signals can induce the expression of COUP-TF2, which can

inhibit SMAD4 and consequently allow the tumors to metasta-

size to lymph nodes and the lungs (Qin et al., 2013). Sim-

ultaneous inactivation of Pten, Tp53, and Smad4 in the mouse

prostate gives rise to aggressive tumors with some bone meta-

static activity, whereas tumors with Pten and Tp53 mutations


alone are more indolent (Ding et al., 2012b). Smad4 inactivation

thus seems to be a critical event in the development of advanced

prostate cancer that eventually may also form metastasis. Simi-

larly, Nkx2.1 loss in KrasG12D/Tp53�/�-driven lung adenocarci-

nomas allows tumor progression and metastasis (Winslow

et al., 2011). Activation of Notch signaling upon Aes deletion in

mouse intestinal tumors promotes local invasion, extravasation,

and metastasis (Sonoshita et al., 2011). These mouse studies

point at important pathways as mediators of tumor progression,

including metastasis, but they do not provide evidence for

metastasis-restricted genetic alterations. A possible exception

is provided by the results of a genetic screen that used trans-

poson-based insertional mutagenesis in medulloblastoma:

certain insertions promoted leptomeningeal metastasis on both

Ptch mutant and Tp53 mutant backgrounds (Wu et al., 2012).

Thus, a minority clone in a primary tumor was prompted by a

particular genetic alteration to form metastasis, providing evi-

dence that a clonal mutation can provide metastatic advantage.

In somemouse models, cancer cell dissemination is observed

before primary tumors become overtly invasive (Husemann

et al., 2008; Rhim et al., 2012). Even nontransformed stem cells

placed in the circulation can infiltrate the lungs and survive for

extended periods of time (Podsypanina et al., 2008). These ob-

servations raise the possibility that early-disseminated cancer

cells may evolve independently of, and in parallel with, cancer

cells in the primary tumor (Klein, 2009). However, this model is

not independently validated by genetic data from human tumor

samples. It also remains unknown whether the progeny of the

cells that disseminate early form clinically manifest metastases

later on. It could be that cancer cells that disseminate later after

having become more aggressive in the primary tumor stand a

better chance of forming metastases.

Amplifying Oncogenic SignalingIf not through genetic alterations, how do clones with high met-

astatic propensities emerge under selective pressure? Accumu-

lating evidence from integrative analysis of metastatic xenograft

models and large clinical gene expression data sets suggests

that the selected traits augment the robustness of signaling

pathways that already are active in primary tumors, increasing

the odds that cancer cells will thrive in distant organs. In this

model, metastatic fitness is a function of the signaling amplitude

of certain oncogenic pathways.

In breast cancer cells of the triple-negative (ER–, PR–, HER2–)

subtype, several prometastatic genes have been identified that

amplify the output of cell survival and stemness pathways that

are already selected for in the primary tumor. For example,

high expression of the vascular cell adhesion molecule-1

(VCAM-1) in breast cancer cells hypersensitizes the PI3K-Akt

cell survival pathway to activation by limiting external signals.

VCAM-1 can be engaged by a4b1 integrins on tumor-associated

macrophages, leading to activation of ezrin, a PI3K and Akt

adaptor protein (Chen et al., 2011) (Figure 4A). Similarly, hyper-

activity of the tyrosine kinase Src in breast cancer cells sensitizes

the PI3K-Akt pathway to activation by CXCL12 (via its receptor

CXCR4) and IGF1 in the bone marrow stroma (Zhang et al.,

2009). Also, expression of the extracellular matrix protein tenas-

cin C (TNC) in breast cancer cells augments the output of the

Notch and Wnt pathways in support of metastasis-initiating

A

VCAM-1

RTK PI3K Survival

SRC

GPCR PI3K Survival

Periostin Tenascin C

Jagged NOTCH Stemness

Wnt TCF Stemness

B

DNA demethylation

Histone H3K27 demethylation

VHL HIF2 VEGFA Tumorigenesis

CXCR4

CYTIP

Chemotaxis

Survival

Met

asta

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Pathway activity

Met

asta

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Pathway activity

Oncogenic potential Metastatic potential

Oncogenic potential Metastatic potential

Figure 4. Amplification and Expansion of Oncogenic Pathways asMetastatic Traits(A) Metastatic traits acquired by a quantitative gain in pathway output.Amplification of the signaling capacity of cell survival and proliferation path-ways provides survival and stemness advantages to disseminated cancercells. In this case, the level of metastatic fitness is proportional to therobustness of the signaling pathway. In primary tumors with a relatively highabundance of growth and survival signals, the signaling capacity of oncogenicpathways such as PI3K, Notch, andWnt, is sufficient to support tumor growth.However, for disseminated cancer cells reaching distant tissue microenvi-ronment where pathway-activating signals are scarce, the signaling capacityof these pathways is not sufficient for survival. The traits selected under suchpressure include the expression of components that amplify the signalingcapacity of the pathway in response to limiting levels of pathway activators inthe host microenvironment. In triple-negative breast cancer examples includeVCAM-1 and SRC as amplifiers of the PI3K pathway in cancer cells reachingthe lungs or the bone marrow, respectively, whereas Tenascin C and Periostinact as amplifiers of theWNT andNOTCHpathways in cancer cells reaching thelungs.(B) Metastatic traits acquired by a qualitative expansion of pathway output.Tumor-initiating pathways may additionally provide prometastatic traits bygaining access to subsets of target genes that enhance the homing ofdisseminated cancer cells to sites of survival. In this case, the level of meta-static fitness is not linearly proportional to the signaling strength of the pathwaybut depends of the pathway activating an additional set of effectors. Oneexample is provided by the expansion of target genes that the HIF pathwayactivates in renal cell carcinoma as a result of epigenetic modifications thatopen the gene promoters to access by activated HIF.

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cell fitness in the lungs (Oskarsson et al., 2011). The main inter-

action partner of TNC in the extracellular matrix of adult stem

cells, periostin (POSTN), binds and concentrates Wnt to the

same end (Malanchi et al., 2012) (Figure 4A).

An interpretation of these observations is that metastatic cells

need to optimize the output of their vital intracellular stemness

and survival pathways when venturing away from the primary

tumor and infiltrating alone distant sites. The activity level of

these pathways provided by the early oncogenic events may

have allowed the tumor to progress locally, but in metastatic

sites with limiting levels of trophic cues signal amplification by

factors such as VCAM-1, Src, TNC, and POSTN might be

required for cell survival. This same purpose may be served,

on a lesion-specific basis, by the noted amplifications of

KRASG12V, MYC, and CCNE1 in pancreatic cancer metastasis

(Campbell et al., 2010), or the expression of certain mediators

of cancer invasion in melanoma (Scott et al., 2011). Conversely,

some of the genes associated with lung metastasis in breast

cancer patients and in experimental systems were found to

also be associated with primary tumor growth (Minn et al.,

2007; Minn et al., 2005). Thus, the signal optimization that is ul-

timately required for metastasis can increase the fitness of can-

cer cells and get selected for already in primary tumors.

Expanding the Output of Oncogenic PathwaysThe involvement of oncogenic pathways in conferring metastatic

traits is not limited to quantitative gains in the robustness with

which these pathways transmit stemness and survival signals.

The target gene spectrum of a transcriptional program can

also undergo qualitative modulation through alterations in chro-

matin accessibility of its target loci (John et al., 2011). In the

context of metastatic progression, such modulation could

expand the output of an already activated oncogenic pathway

by allowing it to express, in addition to the basic tumorigenic

functions, metastatic phenotypes as well (Figure 4B).

One example has been identified in renal cell carcinoma, a

tumor type that is initiated by loss of the von Hippel-Lindau

tumor suppressor (VHL) and consequent activation of hypoxia-

inducible transcription factors (HIFs) (Kaelin, 2008). During

tumor progression, histone H3 lysine 27 (H3K27) demethylation

and DNA demethylation can allow the VHL-HIF pathway to

access new target genes, such as the chemokine receptor

CXCR4 and cytohesin 1-interacting protein (CYTIP), which

mediate metastatic invasion and outgrowth in the lungs and

bones (Vanharanta et al., 2013). Alterations in the distribution

of the repressive H3K27me3 chromatin mark are also associ-

ated with metastatic progression in breast cancer, where high

expression of the noncoding RNA HOTAIR can redistribute

H3K27me3 across the genome in order to repress metastasis

suppressor genes (Gupta et al., 2010). Moreover, the genome-

wide binding patterns of the estrogen receptor evolve during

breast cancer progression, with possible functional conse-

quences (Ross-Innes et al., 2012).

These examples illustrate how the genetic activation of an

oncogenic pathway during tumor initiation does not automati-

cally lead to the manifestation of this pathway’s full malignant

potential. Rather, a pathway that initially drives primary tumor

formation may evolve through quantitative and/or qualitative

amplification of its output, ultimately providing the cell with a

metastatic advantage.

Sources of Influence: Germline, Plasticity, Stroma, andTherapyThe intrinsic metastatic traits of cancer cells may be further en-

riched or diminished by various sources of influence. Germline

variants play a role in cancer predisposition (Pharoah et al.,

2004). It is therefore also likely that human germline variants

affecting tumor progression exist, as has been demonstrated

by genetic mapping analysis in mice (Park et al., 2005). There

is evidence for a heritable component in human cancer prog-

nosis (Hemminki et al., 2011), but identifying specific poor prog-

nosis variants has proven difficult. Certain gene polymorphisms

seem to correlate with poor clinical outcome, one example being

the FGFR4G388R variant (Spinola et al., 2005). However, the diffi-

culty of conducting genome-wide association studies on cancer


Cancer Cell

Review

progression have, at least for now, prevented a thorough assess-

ment of the germline component of metastatic propensity.

The ability to adopt phenotypic changes, including changes

into a more stem-like phenotype, can help cancer cells through

metastasis progression bottlenecks. Cytokine signals induce

the reactivation of developmental epithelial-to-mesenchymal

transition (EMT) programs in cancer cells (Yang and Weinberg,

2008). EMT has been proposed to provide cancer cells with

several prometastatic traits, including stemness, motility, and

resistance to therapy, all at once (Mani et al., 2008; Yang et al.,

2004). Although the role of EMT in developmental processes is

well established (Thiery et al., 2009), its significance for cancer

progression is still being defined. For example, EMT-inducing

transcriptional regulators can both promote (Yang et al., 2004)

and inhibit metastatic colonization (Ocana et al., 2012; Tsai

et al., 2012). Also, metastatic lesions in patients show epithelial,

not mesenchymal features. It has been suggested that EMT pro-

vides a transient benefit in cancer cell dissemination but must be

reverted by mesenchymal-to-epithelial transition (MET) at the

metastatic site (Korpal et al., 2011; Ocana et al., 2012). Although

several molecular players in EMT andMET can impart metastatic

traits in experimental systems, more work is required for an un-

derstanding of the specific changes that these mediators trigger

as well as their specific contributions to metastasis.

In all tumors, a variety of non-neoplastic stromal cells interact

with the cancer cells. This can result in both pro- and antitumori-

genic effects through often complex molecular mechanisms

(Hanahan and Coussens, 2012). Several specific examples

ranging from systemic to juxtacrine signaling loops that modu-

late primary tumor progression and metastasis have been

described, most of which represent reactive interactions be-

tween stroma and cancer cells (Acharyya et al., 2012; Calon

et al., 2012; Chen et al., 2011; DeNardo et al., 2009;

Gocheva et al., 2010; Granot et al., 2011; Karnoub et al., 2007;

Labelle et al., 2011; Lin et al., 2001; Lu et al., 2011; Magnon

et al., 2013; McAllister et al., 2008; Nieman et al., 2011; Pen-

cheva et al., 2012; Png et al., 2012; Qian et al., 2011; Schwitalla

et al., 2013; Sethi et al., 2011). Such tumor-shaping cues are not

exclusively produced by stromal cells, but other environmental

conditions, such as hypoxia, can also induce metastasis-pro-

moting changes in cancers (Chaturvedi et al., 2013; Eisinger-Ma-

thason et al., 2013; Erler et al., 2006; Gilkes et al., 2013). Tumor

stromal signals also play a role in attracting circulating metasta-

tic cells in a tumor self-seeding process that favors the clonal

expansion of these cells (Comen et al., 2011; Kim et al., 2009).

In addition to the reactive and often reversible interactions with

the components of the microenvironment, the stroma contrib-

utes to tumor progression through more subtle ways by altering

local selective pressures (Gillies et al., 2012). This can lead to the

selection of cells with enhanced metastatic potential. For

example, many of the stromal cells in tumors originate from the

bone marrow, potentially making the tumor stroma more

‘‘bone marrow-like’’ through secretion of soluble factors such

as CXCL12 and IGF1. Cancer clones evolving under the influ-

ence of such factors can then become enriched for qualities

that allow them to survive in and take advantage of the bone

marrow already before reaching the bone. This phenomenon of

‘‘metastasis seed preselection’’ provides one possible explana-

tion for the emergence of metastatic traits already at the primary


tumor site (Zhang et al., 2013) and it could be further amplified by

tumor seeding with CTCs released from metastatic lesions (Kim

et al., 2009).

Metastatic cancers are also exposed to, and evolve under,

therapy-induced pressures (Diaz et al., 2012). Increasing evi-

dence suggests that there are molecular links between metasta-

tic cancer progression and drug resistance. For example in

breast cancer, the transmembrane protein metadherin (MTDH)

provides cancer cells with protection against chemotherapy as

well as enhanced capability to metastasize to the lung (Hu

et al., 2009). Moreover, chemotherapy can modify the tumor

microenvironment and consequently enhance the expression

of factors such as the chemokine CXCL1 that can foster therapy

resistance and promote metastasis via stromal interactions

(Acharyya et al., 2012). The traits conferring resistance to thera-

pies may therefore also drive metastatic tumor progression.

Deterministic versus Stochastic Emergence ofMetastasis ClonesEven the most predictive markers of metastatic tumor progres-

sion only indicate a higher probability of metastasis at the organ-

ismal level. At the cellular level, the degree of randomness is

even greater, as the tissues of patients who ultimately develop

one lethal metastatic lesion may carry thousands of dissemi-

nated micrometastatic cells that never took off. Therefore,

despite the identification of molecular mediators that confer a

metastatic phenotype and constitute the deterministic compo-

nent of the metastatic process, metastases also display strong

stochastic features (Talmadge and Fidler, 1982). Both syngeneic

mouse models as well as human xenograft systems have

demonstrated that at least in experimental systems stable met-

astatic cancer cell clones do exist (Talmadge and Fidler, 2010),

and analysis of clinical cancer data sets have shown that tumors

that are prone to metastasis share gene expression traits with

such clones (Bos et al., 2009; Minn et al., 2005; Nguyen et al.,

2009b; Pencheva et al., 2012; Vanharanta et al., 2013). Also,

certain well-characterized prometastatic functions are specif-

ically required for colonization of particular organ sites, such as

the bone marrow (Ell and Kang, 2012). All this suggests that spe-

cific heritablemolecular alterations, both genetic and epigenetic,

determine the capacity of cancer cells to form metastasis.

However, a stochastic nature of metastatic cancer progres-

sion is evident at several levels. First, tumor evolution feeds

from random heritable alterations that increase diversity upon

which selection can act. Of the possible evolutionary paths avail-

able for a given cancer cell clone, only some can result in a met-

astatic phenotype. As determined by the combined effects of

their tissue of origin and acquired alterations, many cancer

clones may end up in fitness peaks that are not compatible

with metastases. A phenotype that is beneficial during early

phases of tumorigenesis may thus prevent the same clone

from becoming metastatic later on (Figure 5A). Second, any can-

cer phenotype can be reached through multiple evolutionary

routes, which is reflected by the diverse mutational comple-

ments of individual cancers. As a consequence, no two cancer

clones are identical (Figure 5B). Third, as demonstrated in exper-

imental transplantation models, even within clones endowed

with high metastatic fitness, few cells actually form ametastasis.

The fate of phenotypically identical metastatic cells will therefore

Met

asta

tic fi

tnes

s

Cell of origin + somatic alterations M

etas

tatic

fitn

ess

Cell of origin +somatic alterations

BA

C

Obs

erve

d m

etas

tatic

co

mpe

tenc

e

Cells at a metastatic fitness peak α

Figure 5. Levels of Stochasticity in Metastatic Cancer ProgressionThe stochastic nature of the evolution of metastatic cancer clones is manifestat several levels of cancer progression.(A) For every cancer clone, depending on their cell of origin and previoussomatic alterations, only a defined set of fitness peaks are achievable, and onlysome of these peaks can reach high metastatic fitness (red peaks). Startingfrom a normal cell, cancer cell clones move within fitness landscapes byacquiring random sets of driver alterations. This leads each clone to one of thepossible fitness peaks (red arrows). The ratio of possible metastatic andnonmetastatic evolutionary paths for a given cancer clone may vary greatlydepending on the cell of origin or initial mutational events. Some tumors mayhave none whereas some may have several ways to become metastatic. Thisis reflected in the fact that the incidence of metastatic progression variesbetween tissue and tumor types.(B) The (epi)genetic changes that allow different cancer clones to climb up thesame metastatic fitness peak are never identical. Thus, even tumors that arisefrom identical normal cells through identical tumor-initiating mutations, andthat ultimately display clinically similar metastatic phenotypes, have acquiredthose metastatic traits through a different evolutionary path (dotted red lines).(C) Of the cellular progeny of a cancer clone that, in principle, have the qualitiesthat are required for metastatic colonization, only a minuscule fraction (a) willever form meaningful metastasis. Thus, even for cells that have reached ametastatic fitness peak, the attrition rates remain extremely high. This meansthat the fate of phenotypically identical metastatic cells is in most cases notidentical.

Cancer Cell

Review

never be identical (Figure 5C). The existing genetic evidence

would be compatible with a stochastic model whereby highly

aggressive cancer cell clones that emerge from primary onco-

genic events all have the potential to form metastasis at a very

low frequency (Jones et al., 2008).

Closing the circle, some of the observed randomness in the

metastatic process might in fact be the consequence of factors

yet to be discovered. For example, the formation of metastasis

may be possible only at certain defined microenvironmental

niches, such as in the hematopoietic stem cell niche in the

bone marrow (Ding et al., 2012a; Shiozawa et al., 2011). The

need of cancer cells to fall in a suitable niche in order to thrive

could provide a partial explanation to the stochastic nature of

metastasis-formation (Ghajar et al., 2013). At a more general

level, systemic inflammatory reactions triggered by primary

tumors may modulate the stroma at distant organs to unevenly

increase metastatic colonization of otherwise resistant tissues

(Kaplan et al., 2005).

The development of metastatic cancer clones is thus the end

result of an intimate interplay between selection and stochastic

events. As we learn more, an increasing fraction of the random-

ness in the process may turn out to be nonrandom. However,

only by appreciating that metastasis results from a combination

of nonrandom determinants and random chance we may arrive

at a comprehensive understanding of the origins of metastatic

traits.

Conclusions and PerspectiveProgression from an early neoplastic lesion to a metastatic can-

cer is a long evolutionary process that often takes years, if not

decades. Rather than involving mutations that individually pro-

vide strong competence to perform multiple metastasis steps,

metastasis involves multiple alterations, each providing a slight

selective advantage in at least one of the many steps of metas-

tasis. Multiple such alterations must accumulate for a metastatic

clone to emerge. The metastatic cell is a rare end result of exten-

sive genomic and epigenomic tinkering. Identifying the most

demanding steps in metastasis and therapeutically targeting

the metastatic traits that mediate these steps are aims of future

research.

Even with the most aggressive metastatic clones, the success

rate of metastatic colonization remains low. The vast majority of

disseminated cancer cells in any target tissue die before they can

form metastatic colonies. The molecular mechanisms of this

widespread death are for the most part unknown. Identifying

what kills disseminated cancer cells en mass may provide clues

for therapeutic intervention against the survivors.

Metastatic cancer cell clones are genetically distinct and do

not necessarily represent the majority of a primary tumor. Clonal

heterogeneity within primary tumors is a source for the selection

of metastatic cancer cells. Sampling of multiple locations within

a tumors and its metastasis is needed in order to establish the

clonal evolution of metastatic processes.

High-throughput genetic analysis of primary human cancers

and their metastatic counterparts have identified metastasis-

associated mutations but not metastasis-specific mutations.

Many, though not all, likely are passenger events without causal

role in metastasis progression. Hence, from a genetic perspec-

tive, metastasis is an extension of primary tumor progression,

not a distinct step with characteristic mutational determinants.

The lack of singular mutational events that would selectively

confer metastatic potential may reflect the fact that the pheno-

types of metastatic cells are complex and not achievable

through a single alteration.

Cancers evolve under strong microenvironmental and ther-

apy-induced pressures. Both stromal cells and therapeutic

agents can limit cancer growth but they can also skew the clonal

distribution of a tumor and thus provide opportunities for the

expansion andmetastatic dissemination of selected cancer sub-

clones. Therapy-resistance and metastasis are thereby molecu-

larly linked.

Mutational activation of oncogenic pathways does not auto-

matically lead to the manifestation of their full tumorigenic and

metastatic potential. In metastatic cancer cells, signaling path-

ways have been tuned through selective pressures to become

as prometastatic as possible, resorting to many of the principles

enunciated above. Unlike developmental processes with layers

of regulators providing robustness to the system, the emergence

of highly metastatic cancer clones often seems to be


Cancer Cell

Review

accompanied by the loss of such safety nets in order to achieve

maximal output of oncogenic pathways. This may lead to vulner-

abilities that could be useful for therapeutic intervention.

ACKNOWLEDGMENTS

We thank members of the Massague lab for insightful discussion. Work onmetastasis in our laboratory was supported by National Institutes of Healthgrants R37-CA34610, P01-CA94060, P01-CA129243, and U54-163167;Department of Defense Innovator award W81XWH-12-0074; and the Alanand Sandra Gerry Metastasis Research Initiative. J.M. is an investigator ofthe Howard Hughes Medical Institute.

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