Cancer heterogeneity: implications for
R Fisher1,2, L Pusztai3and C Swanton*,1,4
1University College London Cancer Institute, London, UK;2Department of Medicine, Royal Marsden Hospital, London UK;3Yale
University Cancer Center, New Haven, CT, USA and4Translational Cancer Therapeutics Laboratory, Cancer Research UK London
Research Institute, 44 Lincoln’s Inn Fields, London WC2A 3LY, UK
Developments in genomic techniques have provided insight into the remarkable genetic complexity of malignant tumours. There is
increasing evidence that solid tumours may comprise of subpopulations of cells with distinct genomic alterations within the same
tumour, a phenomenon termed intra-tumour heterogeneity. Intra-tumour heterogeneity is likely to have implications for cancer
therapeutics and biomarker discovery, particularly in the era of targeted treatment, and evidence for a relationship between intra-
tumoural heterogeneity and clinical outcome is emerging. Our understanding of the processes that exacerbate intra-tumoural
heterogeneity, both iatrogenic and tumour specific, is likely to increase with the development and more widespread implementation of
advanced sequencing technologies, and adaptation of clinical trial design to include comprehensive tissue collection protocols. The
current evidence for intra-tumour heterogeneity and its relevance to cancer therapeutics will be presented in this mini-review.
Tumour heterogeneity refers to the existence of subpopulations of
cells, with distinct genotypes and phenotypes that may harbour
divergent biological behaviours, within a primary tumour and its
metastases, or between tumours of the same histopathological
subtype (intra- and inter-tumour, respectively). With the advent of
deep sequencing techniques, the extent and prevalence of intra-
and inter-tumour heterogeneity is increasingly acknowledged.
There are features of intra-tumour heterogeneity that form part
of routine pathologic assessment, but its determination does not
yet form part of the clinical decision-making process. This mini-
review aims to summarise the evidence supporting the extent,
causes and consequences of intra-tumour heterogeneity, and will
suggest how this knowledge may be integrated into future clinical
practice and research efforts to optimise patient care and clinical
outcomes. Inter-tumour heterogeneity is well described in breast
cancer and other solid tumours, but is beyond the scope of this
article (for a comprehensive review, see Russnes et al, 2011).
THE SPECTRUM OF CANCER HETEROGENEITY
Clinico-pathological heterogeneity and its molecular basis.
Morphological variation between different regions of a tumour
has long been familiar to histopathologists. For this reason, it is
routine for pathologists to examine multiple sections from the
same tumour, but to report only the highest grade. Nuclear
pleomorphism is another example of intra-tumour heterogeneity,
which is accounted for in breast cancer grading. It is also readily
apparent to those clinicians treating cancer that there is marked
variation in tumour behaviour between patients with the same
tumour type, and between different tumour sites in the same
patient; the latter is usually manifested as differential or mixed
responses to therapy (Figure 1). Intuitively, common clinico-
pathological observations such as these could be attributable to
intra-tumour heterogeneity, but studies are only now beginning to
formally evaluate this relationship. It is also likely that other
factors, such as pharmacodynamics, contribute to the non-
uniformity of drug response.
Initial proof that multiple subclones of differing genetic status
existed within the same tumour was provided by G-banding
karyotyping and fluorescent in situ hybridisation (FISH) studies,
demonstrating discrete patterns of chromosomal rearrangements
and copy number alterations of representative genomic loci
(reviewed in Navin and Hicks, 2010). Completion of the human
genome project was a prelude to understanding the true genetic
complexity underlying cancer, providing the opportunity to
systemically sequence somatic coding and non-coding aberrations
on a large scale.
*Correspondence: Professor C Swanton;
Received 30 August 2012; revised 19 November 2012; accepted 23 November 2012;
published online 8 January 2013
& 2013 Cancer Research UK. All rights reserved 0007– 0920/13
Keywords: intra-tumour; heterogeneity; next-generation sequencing; therapeutics
British Journal of Cancer (2013) 108, 479–485 | doi: 10.1038/bjc.2012.581
Although other forms of intra-tumoural heterogeneity exist,
such as phenotypic heterogeneity, this review will focus on the
impact of genetic intra-tumour heterogeneity on treatment
stratification and therapeutic outcome.
Clonal evolution as a model of tumour progression and
heterogeneity. A clonal evolutionary model of cancer develop-
ment was first proposed by Nowell (1976) and elaborates upon
Darwinian models of natural selection – that is, genetically
unstable cells accumulate genetic alterations, and that selective
pressures favour the growth and survival of variant subpopulations
with a biological fitness advantage. This model, in partnership with
cancer genomic instability mechanisms exacerbating structural and
numerical chromosomal instability (McGranahan et al, 2012),
might contribute to the presence of molecular heterogeneity within
tumours, which increases the pool of genetic variants to be tested
by selection and thus increase the chance that a subclone will have
a growth and/or survival advantage. The concept of ‘driver’ and
‘passenger’ mutations is an important component of this model:
driver somatic mutations are those that increase the fitness of the
cell, allowing this cell lineage to populate the tumour, whereas
passenger mutations are neutral or deleterious mutations that
persist because they are genetically linked to driver mutations (for
a detailed review, see Sprouffske et al, 2012). While driver
mutations are central to our current understanding of the genetic
basis of human cancer, it is our view that both types of mutation
have direct implications for cancer therapeutics, because the
heterogeneous events may determine resistance outgrowth and
thus patient demise, discussed later in this review.
Clonal evolution as a basis for tumour progression has been
clearly demonstrated in recent studies of haematological cancers,
and in brain, breast, and pancreatic tumours (Sidransky et al, 1992;
Shah et al, 2009; Campbell et al, 2010; Yachida et al, 2010; Ding
et al, 2012; Schuh et al, 2012; Walter et al, 2012). Sequencing
analysis of paired primary tumour and relapse genomes from eight
patients with acute myeloid leukaemia (AML) revealed a founding
clone was present in the primary tumour, which had evolved and/
or expanded in the relapsed tumour (Ding et al, 2012). In patients
with transformed myelodysplastic syndromes, matched samples
from the antecedent myelodysplastic stage and the secondary AML
stage were also clonal; transformation to AML was associated with
acquisition of many new mutations in a founding clone to form a
daughter subclone (Walter et al, 2012). Another model for tumour
progression, the cancer stem cell (CSC) hypothesis, may provide a
complementary and not necessarily mutually exclusive explanation
for intra-tumour heterogeneity. For a detailed discussion on the
relationship between the CSC hypothesis and intra-tumour
heterogeneity, readers are directed to reviews by Navin and
Hicks (2010) and Sprouffske et al (2012).
Tumour heterogeneity: a dynamic state. Spatial and temporal
heterogeneity may permit the tumour as a whole to adapt to a
fluctuating tumour microenvironment. There are intriguing
clinical examples of variation in temporal patterns of tumour
genomic architectures within multiple myeloma patients, and in
some patients a distinct pattern of complex clonal competition was
observed (Keats et al, 2012). In one cytogenetically high-risk
patient, serial samples were available for the entire disease course,
enabling longitudional copy number analysis. This study demon-
strated two major clonal progenitors and subsequent divergence.
At different time points, the suppression and reappearance of
clones appeared to correlate with timing of drug therapy but the
plasma cell leukaemic clone 2.2, which determined patient death,
was hardly detectable at the outset, raising clear problems for
predictive or prognostic biomarker strategies at diagnosis. A
similar finding was recently reported in patients with chronic
lymphocytic leukaemia (Schuh et al, 2012). Our further inter-
pretation of the experimental data is that two subclones can exist in
Figure 1. Serial computed tomography (CT) scans from a patient treated with the mammalian target of rapamycin (mTOR) inhibitor everolimus for
metastatic renal cell carcinoma demonstrate a reduction in the size of a metastasis at the medial right lung base (A) but an increase in the size of a
metastatic lesion in the left retroperitoneum (B). A differential response such as this may reflect the presence of tumour heterogeneity, or altered
pharmacodynamics at separate disease sites.
BRITISH JOURNAL OF CANCER
a ‘dynamic equilibrium’, constantly competing for clonal dom-
inance in the context of systemic therapy. A third study using
whole genome sequencing in a patient whose disease had
transformed from multiple myeloma to plasma cell leukaemia
indicated the presence of subclones at diagnosis, which waxed and
waned in dominance as the cancer progressed (Egan et al, 2012).
Clonal cooperativity has also been reported; Inda et al (2010)
showed in vitro and in vivo that in glioblastoma, the mixture of
epidermal growth factor receptor (EGFR)-mutant and EGFR-wild
type glioma cells enhanced tumour growth. This occurred through
a paracrine mechanism, in which EGFR-mutant cells express
cytokines such as IL-6 and leukaemia inhibitor factor that activate
EGFR-wild type, driving their proliferation.
In summary, it is argued that heterogeneous tumours should be
viewed as complex ecosystems or societies, in which even a minor
tumour subpopulation may influence growth of the entire tumour
and thereby actively maintain tumour heterogeneity (Heppner,
1984; Marusyk and Polyak, 2010; Bonavia et al, 2011). In this
model, subclones occupy various niches within the tumour
microenvironment and the survival advantage of the tumour
‘society’ exceeds those of the individual subpopulation; relation-
ships between subclones may be competitive, commensal, or
mutualistic for this purpose.
CURRENT EVIDENCE FOR INTRA-TUMOUR
Next-generation sequencing (NGS) technologies are adding new
evidence for genetic diversity both within and between common
tumours. NGS methodologies comprehensively and systematically
determine both nucleotide sequence and copy number of genomic
loci, and have the advantage of being able to simultaneously
sequence heterogeneous mixtures of genomes in a given sample,
which might include tumour, stromal, and immune cells.
Both NGS and comparative genomic hybridisation studies in
breast cancer suggest marked genetic intra-tumour heterogeneity
(Torres et al, 2007; Shah et al, 2009; Navin et al, 2010). In
particular, Navin et al (2010) demonstrated the existence of
subclones that were derived from a common clonal progenitor,
which varied between samples taken from different regions of the
same primary tumour as well as subclones with distinct patterns of
DNA copy number variation within the same sector of tumour. In
these polygenomic tumours, subpopulation of cells could be
anatomically separate or intermixed. There was no correlation
between different tumour grades or immunohistochemical staining
patterns and genomic heterogeneity.
Intratumour heterogeneity in glioblastoma heterogeneity in
glioblastoma is also apparent (Snuderl et al, 2011; Nickel et al,
2012; Szerlip et al, 2012). One study identified intermingled
population of cells with mutually exclusive amplifications of
different receptor tyrosine kinases (RTKs) such as PDGFRA,
MET, and EGFR, with subpopulation sharing similar alterations in
CDKN2A and TP53 genes, consistent with a single common
ancestral precursor (Snuderl et al, 2011). A second study confirmed
intra-tumour heterogeneity of amplification of RTKs in glioblastoma
and demonstrated that such heterogeneity results in functionally
distinct and reduced sensitivity to targeted therapeutics (Szerlip et al,
2012). Spatial and temporal heterogeneity was characterised by
targeted NGS in seven primary and recurrent tumour samples from
one patient with glioblastoma; in this study, the variant allelic
frequency of somatic mutations in EGFR, PI3KCA, PTEN, and
TP53 vs wild type varied between focal regions of the same tumour,
and between the time points of diagnosis, first recurrence and
second recurrence (Nickel et al, 2012).
Spatial genomic heterogeneity has been recently documented in
renal cell carcinoma (RCC) raising the potential for tumour
sampling bias to confound biomarker interpretation (Gerlinger
et al, 2012). Exome sequencing of multiple tumour samples from
primary and metastatic lesions in two patients with clear cell RCC
revealed extensive intra-tumour heterogeneity, which was demon-
strated in genetic and transcriptomic analyses. Distinct loss of
function somatic events in multiple tumour suppressor genes
occurred in spatially separated regions of the same tumour,
demonstrating convergent evolution and potentially predictable
routes to tumour progression. Approximately 30–35% of muta-
tions were shared between regions taken from multiple sites of the
primary and metastases. Supporting evidence for intratumour
heterogeneity in clear cell RCC, single cell exome sequencing of a
clear cell RCC confirmed a complex genomic landscape, with a
small number of genes mutated in a large proportion of cells and a
greater number of genes mutated at low frequency (Xu et al, 2012).
Importantly, some of the earlier studies based on Sanger
sequencing or low depth NGS may underestimate the degree of
genomic heterogeneity due to limitations of sequencing depth
that preclude the identification of rarer tumour subpopulations,
and the study by Xu et al suggests that single cell approaches
may be required to assay rare subclones. Furthermore, aberrations
mediated through post-translational and epigenetic modifications as
well as stochastic and unpredictable behavioural diversity of
subclones with similar genotypic identities (Kreso et al, 2012) are
likely to complicate the picture of intratumour heterogeneity further.
THE GENOMIC RELATIONSHIP BETWEEN PRIMARY AND
Emerging data from deep sequencing analyses suggest that
understanding clonal heterogeneity and the evolution of tumour
subclonal architecture from the primary to the metastatic tumour
sites and during therapy may provide important insight into the
metastatic process and the emergence of drug resistance during
There is now compelling evidence to support a branched
evolutionary pattern of tumour growth across many haematological
(Anderson et al, 2011) and solid tumours (Shah et al, 2009;
Campbell et al, 2010; Yachida et al, 2010; Gerlinger et al, 2012; Wu
et al, 2012). Comparison of the somatic mutational status of
primary and metastatic tumour sites indicates that these may vary
substantially, and phylogenetic reconstruction of tumour evolution
in some of these studies suggests that a minor subpopulation. This
was elegantly demonstrated in a recent ‘bicompartmental’ mouse
model of medulloblastoma; complex genetic events were shared
between metastatic tumour sites and a restricted population of their
matched primaries, highly supportive of a common progenitor cell
initiating metastatic outgrowth (Wu et al, 2012). The observation
that some genetic aberrations were found solely in either primary or
metastatic tumours lead to the proposal that further genetic
divergence and evolution occurred independently at both sites after
tumour dissemination. Similarly, the identification of somatic
mutations in clear cell RCC that are shared by the primary tumour
or metastatic sites only or are specific to one tumour region (private
mutations) provides further evidence for ongoing independent
subclonal evolution within distinct and spatially separated tumour
regions (Gerlinger et al, 2012).
IMPLICATIONS FOR TARGETED THERAPEUTICS
The issue of cancer heterogeneity, including the relationships
between subpopulation within and between tumour lesions, may
have profound implications for drug therapy in cancer. Targeted
therapy, which attempts to exploit a tumour’s dependence on a
BRITISH JOURNAL OF CANCER
critical proliferation or survival pathway, has significantly
improved patient outcomes in a range of solid tumour types, but
in the majority of advanced disease cases, it is also apparent that
targeted therapeutics do not help all molecularly selected patients
and even when clinical benefit is observed, it is often of limited
duration (Gore and Larkin, 2011; Diaz et al, 2012). Tumour
heterogeneity may partly explain these clinical phenomena.
Considering an ‘actionable mutation’ within a model of clonal
dominance, occurring in early cancer cell progenitors and present
in the trunk of the tumour, may provide a more tractable approach
for therapeutic targeting and for the identification of robust
predictive biomarkers which are less susceptible to tumour
sampling bias (Yap et al, 2012); such actionable ‘trunk’ mutations
would be ubiquitous, clonally dominant driver events present in all
tumour cells (Figure 2). However, if a tumour contains multiple
branched events, depicting intra-tumour heterogeneity, then even
the targeting of a driver event may not significantly influence
treatment outcome due to a low-frequency subpopulation
harbouring a resistance event in the tumour branches, leading to
subclonal selection and the acquisition of drug resistance, as
observed for the low-frequency gatekeeper mutation in EGFR in
non-small cell lung cancer (NSCLC) (Su et al, 2012). Furthermore,
we hypothesise that the role of somatic mutations as driver or
passenger events is dynamic, subject to environmental and
treatment selection pressures where passengers may become
drivers and vice versa (Yap et al, 2012). Conceivably, clinical
strategies may have to adapt to the possibility that branched, low
frequency somatic events determine patient outcome and ther-
apeutic failure. Indeed, by extension, heterogeneous somatic events
may render tumour dependence of early truncal drivers redundant.
Heterogeneity of secondary somatic mutations and drug
resistance. Drug resistance is arguably the most critical problem
faced by oncologists, and as a result almost all patients with
metastatic solid tumours (with some notable exceptions such as
seminoma) die of their disease. There are many examples of drug
resistance conferred by the emergence of subclones harbouring
specific somatic gene mutations. Imatinib-resistant mutations in
the BCR-ABL fusion gene have been identified in patients with
chronic myeloid leukaemia; some of these mutations have been
shown to precede systemic treatment, and additionally, to co-exist
with subclones carrying different imatinib-resistant mutations in
treatment naı ¨ve patients (Shah et al, 2002). Intra-tumour
heterogeneity of drug resistance mechanisms occurs in gastro-
intestinal stromal tumours (GIST) treated with imatinib or
sunitinib; 9 of 11 patients with oncogenic KIT mutations developed
secondary drug-resistant mutations, 6 of whom had two or more
different mutations in separate metastases, and 3 of whom had 2
secondary KIT mutations in the same metastasis (Liegl et al, 2008).
The bewildering complexity witnessed by multiple distinct mutations
occurring in separate or identical metastases begins to illuminate the
vast somatic mutational reservoir present in these tumours.
A similar theme exists in NSCLCs with a mutation in the EGFR
– secondary mutations confer insensitivity to the EGFR tyrosine
kinase inhibitor (EGFR-TKI) gefitinib, and have been identified in
patients with clinical resistance to gefitinib, but also in untreated
patients (Inukai et al, 2006). Su et al (2012) analysed NSCLC
samples for one such mutation in EGFR, the T790M mutation,
from treatment-naı ¨ve patients, and from patients before and
during treatment with EGFR-TKIs. The presence of low-frequency
T790M mutations before treatment predicted for shorter progres-
sion-free survival. In anaplastic lymphoma kinase (ALK)-
rearranged lung cancers, B25% of patients with acquired
resistance to the ALK inhibitor crizotinib exhibited a secondary
mutation in the ALK tyrosine kinase domain, but these differed
substantially between patients, and in a subset of patients, more
than one resistance mechanism appeared to occur simultaneously
(Katayama et al, 2012).
A unique example of intra-tumour heterogeneity and transpla-
cental transfer of melanoma, intratumour heterogeneity and its
contribution to therapeutic failure was recently described in a
tragic case of transplacental transfer of melanoma (Sekulic et al,
2012). Genomic analysis of melanoma tumours from the mother
(pre-vemurafenib treatment and at relapse) and the baby (before
vemurafenib treatment) revealed two distinct but related clones in
the mother, only one of which was present in the infant’s tumours.
The shared clone appeared to be sensitive to vemurafenib
treatment, while the unique clone progressed and lead to relapse
in the mother.
Taken together, these findings suggest that biomarker efforts
may have to rise to the challenge of identifying low frequency
events, that may conceivably be spatially separated, in tumours
before therapy to predict outcome. Such observations suggest that
suitable combinatorial approaches specific to each individual
patient’s tumour subclonal genetic heterogeneity composition
might have to be considered to limit the acquisition of drug
resistance; a single drug may not be adequate to treat a genetically
heterogeneous tumour, since a pre-treatment cancer cell popula-
tion harbouring resistance mutations, even if present at a low
frequency, can contribute to therapeutic failure and poor outcome.
It is also plausible that there is a therapeutic window of
opportunity in which to treat a driver mutation, before clonal
expansion and divergence occurs (Gerlinger and Swanton, 2010).
In a study from Ding et al (2012), evidence that the loss of
founding clones and gain of mutations in the genomes of eight
patients with relapsed AML was influenced by interval cytotoxic
chemotherapy raised the possibility that cytotoxic treatment may
contribute to relapse by inducing subclonal evolution.
KDM5C (splice site)
SETD2 (splice site)
Renal cell carcinoma
Figure 2. Trunk-branch model of tumour heterogeneity using single
patient examples of renal cell carcinoma (Gerlinger et al, 2012).
Ubiquitous driver events are common to all tumour sites and represent
the trunk, while the heterogeneous passenger mutations, found at one
site of disease but not another, are found in the branches of this model.
It seems likely that ‘actionable mutations’, that is, those that could be
therapeutically targeted and which could serve as useful predictive
biomarkers, may prove more tractable when confined to the trunk.
However, it is also plausible that heterogeneous minority
subpopulation in the branches contribute to treatment resistance and
failure, and targeting heterogeneous events in the branches may prove
beneficial if paracrine signalling occurs from these subclones
stimulating growth of dominant clones. It is also worth noting that in
some glioblastoma and breast tumours, genetic events may be present
in both the trunk and the branches (Shah et al, 2009).
BRITISH JOURNAL OF CANCER
It is important to note that cancer drug resistance occurs
for reasons other than tumour heterogeneity, with the tumour
microenvironment playing a key role. For example, innate
resistance to BRAF inhibition in BRAF-mutant tumour cell lines
was shown to be mediated by secretion of hepatocyte growth factor
and its receptor, MET, by stromal cells, and reversal of resistance
(Straussman et al, 2012). Drug resistance can also be driven by
non-mutational and epigenetic mechanisms that may be reversible
(Lackner et al, 2012).
The impact of tumour heterogeneity on biomarker validation
for targeted therapy. Clinically useful and economically viable
targeted therapy relies upon biomarkers that are predictive of
response to treatment. Tumour heterogeneity and tumour
sampling bias may have had a negative impact on the discovery
and validation of predictive biomarkers. For example, in metastatic
RCC, despite identification of biologically relevant signalling path-
ways, there are limited genomics tools that facilitate prediction of
response or resistance to targeted therapy. It should be noted that
this may also reflect a lack of systematic tissue collection and
translational research in the drug development trials for this disease.
Base-line genomic instability and intra-tumour heterogeneity
may itself be a biomarker of poor clinical outcome (Birkbak et al,
2011; McGranahan et al, 2012). In Barrett’s oesophagus for
example, clonal diversity appears to predict for increased risk of
progression to invasive adenocarcinoma (Maley et al, 2006) and
many studies have revealed the association of CIN with poor
clinical outcome (reviewed in McGranahan et al, 2012). There is
accumulating evidence to support intra-tumour heterogeneity, to
which CIN is likely a major contributor, as a mechanism of drug
resistance (Gerlinger and Swanton, 2010); CIN ovarian cancers
may be more resistant to taxane chemotherapy (Swanton et al,
2009) and CIN colorectal cancer cell lines appear to be intrinsically
multidrug resistant (Lee et al, 2011). Emerging NGS data may
support these concepts; whole genome or exome sequencing of
pre- and post-treatment biopsy samples of breast cancer patients
treated with neoadjuvant aromatase inhibitors (AIs) in the
ACOSOG Z1031 trial suggested that tumours which were more
molecularly heterogeneous at the outset had a worse outcome; the
background mutation rate among AI-resistant tumours was nearly
double that of AI-sensitive tumours (Ellis et al, 2012).
It seems apparent that predicting drug response with clinically
qualified biomarkers, is going to be challenging in practice due to
the polygenic nature of drug resistance and the contributions of
intra-tumour heterogeneity to this process, where low frequency,
regionally separated subclones may influence therapeutic outcome.
Conceivably, baseline estimates of intra-tumour heterogeneity may
prove a more tractable approach to assess the propensity of a
tumour to adapt to drug exposure.
Using intra-tumour heterogeneity to therapeutic advantage.
The prospect of treating multiple genetic subpopulation within a
tumour is discouraging, but it is possible that intra-tumour
heterogeneity could be exploited for therapeutic advantage.
Gatenby et al argue that an adaptive and dynamic model of
cancer therapy is more likely to provide durable tumour control,
and propose an experimental ‘evolutionary double blind therapy’
in which two therapies are given sequentially, each driving tumour
cells to specific adaptations which render them vulnerable to the
second therapy (Cunningham et al, 2011). This strategy has yet to
be applied to clinical cancer therapy, but provides a good example
of how improved knowledge of intra-tumour heterogeneity may be
highly relevant to advances in the field of medical oncology. There
is recent evidence of improved prognosis in breast cancer patients
with tumours defined as having extreme CIN compared with those
with intermediate CIN, suggesting that there may be an ideal level
of genomic instability for tumorigenesis and that an excessive level
may be deleterious to cancer cell viability, that may be influenced
by cytotoxic therapy (Birkbak et al, 2011; Roylance et al, 2011). It is
intriguing to speculate that such a paradoxical relationship between
this pattern of genomic instability, contributing to intra-tumour
heterogeneity, and clinical outcome might be therapeutically
exploitable. Finally, identifying drivers or suppressors of genome
instability in solid tumours, whose activation or inactivation is
required to initiate intra-tumour heterogeneity and diversity, may
provide a tractable route to ultimately attempt to limit tumour
A systematic approach to the study of molecular heterogeneity in
cancer is required, which is somewhat hindered by current trial
design and funding mechanisms. Single biopsy specimens of
primary tumours performed for diagnostic purposes, which are
frequently archival, may not fully represent a genetically diverse
malignancy with multiple metastatic sites, and sequencing
techniques may not be sufficiently sensitive to detect low frequency
events in tumour subclones. Often clinical practice is guided by the
molecular analysis of primary tumours and we assume that all
primary tumour characteristics are carried over to metastases later
in the disease course. While this would hold true for trunk driver
events, potentially important molecular changes will be missed if
repeat biopsies are not performed during the evolution of the
disease. Anticancer treatments may cause selection pressures and
influence the complex mutational landscape within a tumour,
analogous to the pruning of some tumour branches and the
selection of other heterogeneous branched events through therapy.
Future clinical trials and biomarker studies might consider
longitudinal analyses of tumour evolution through the disease
course. We argue that much will be learned through the acquisition
of multiple tumour samples from different regions of the primary
and metastatic tumour sites and at sequential time points in the
disease course, including before, during therapy and at disease
progression, to study the changing nature of tumours over time.
The requirement for multiple tumour biopsies is ethically and
clinically challenging and new approaches, such as plasma
circulating free tumour DNA sequencing technologies are primed
to analyse tumour evolution over time (Dennis and Chiu 2011).
Cancer heterogeneity has potentially far-reaching consequences for
cancer therapeutics, but translational research examining this issue
is in its early stages. This is likely to change rapidly, with the
ongoing development of advanced genomic technologies and
increased access to these, and adaptation of clinical trial design to
include comprehensive longitudinal tissue collection protocols.
There is accumulating evidence for substantial genetic diversity
both within and between many common solid tumours, but less is
known about how such diversity is generated or its impact upon
clinical outcomes such as response or resistance to anticancer
therapies and the natural history of the disease. Conceivably,
tumour heterogeneity may impede the identification of predictive
biomarkers, and the quest for personalised, or even curative
treatment, and is an area of cancer research worthy of intensive
and collaborative effort.
Anderson K, Lutz C, van Delft FW, Bateman CM, Guo Y, Colman SM,
Kempski H, Moorman AV, Titley I, Swansbury J, Kearney L, Enver T,
BRITISH JOURNAL OF CANCER
Greaves M (2011) Genetic variegation of clonal architecture and
propagating cells in leukaemia. Nature 469: 356–361.
Birkbak NJ, Eklund AC, Li Q, McClelland SE, Endesfelder D, Tan P, Tan IB,
Richardson AL, Szallasi Z, Swanton C (2011) Paradoxical relationship
between chromosomal instability and survival outcome in cancer. Cancer
Res 71: 3447–3452.
Bonavia R, Inda MM, Cavenee WK, Furnari FB (2011) Heterogeneity
maintenance in glioblastoma: a social network. Cancer Res 71: 4055–4060.
Campbell PJ, Yachida S, Mudie LJ, Stephens PJ, Pleasance ED, Stebbings LA,
Morsberger LA, Latimer C, McLaren S, Lin ML, McBride DJ, Varela I,
Nik-Zainal SA, Leroy C, Jia M, Menzies A, Butler AP, Teague JW,
Griffin CA, Burton J, Swerdlow H, Quail MA, Stratton MR,
Iacobuzio-Donahue C, Futreal PA (2010) The patterns and dynamics of
genomic instability in metastatic pancreatic cancer. Nature 467:
Cunningham JJ, Gatenby RA, Brown JS (2011) Evolutionary dynamics in
cancer therapy. Mol Pharm 8: 2094–2100.
Dennis Lo Y, Chiu RW (2011) Plasma nucleic acid analysis by massively
parallel sequencing: pathological insights and diagnostic implications. J
Pathol 225: 318–323.
Diaz Jr LA, Williams RT, Wu J, Kinde I, Hecht JR, Berlin J, Allen B, Bozic I,
Reiter JG, Nowak MA, Kinzler KW, Oliner KS, Vogelstein B (2012) The
molecular evolution of acquired resistance to targeted EGFR blockade in
colorectal cancers. Nature 486: 537–540.
Ding L, Ley TJ, Larson DE, Miller CA, Koboldt DC, Welch JS, Ritchey JK,
Young MA, Lamprecht T, McLellan MD, McMichael JF, Wallis JW, Lu C,
Shen D, Harris CC, Dooling DJ, Fulton RS, Fulton LL, Chen K,
Schmidt H, Kalicki-Veizer J, Magrini VJ, Cook L, McGrath SD,
Vickery TL, Wendl MC, Heath S, Watson MA, Link DC, Tomasson MH,
Shannon WD, Payton JE, Kulkarni S, Westervelt P, Walter MJ,
Graubert TA, Mardis ER, Wilson RK, DiPersio JF (2012) Clonal evolution
in relapsed acute myeloid leukaemia revealed by whole-genome
sequencing. Nature 481: 506–510.
Egan JB, Shi CX, Tembe W, Christoforides A, Kurdoglu A, Sinari S, Middha S,
Asmann Y, Schmidt J, Braggio E, Keats JJ, Fonseca R, Bergsagel PL,
Craig DW, Carpten JD, Stewart AK (2012) Whole genome sequencing of
multiple myeloma from diagnosis to plasma cell leukemia reveals genomic
initiating events, evolution and clonal tides. Blood 120(5): 1060–1066.
Ellis MJ, Ding L, Shen D, Luo J, Suman VJ, Wallis JW, Van Tine BA, Hoog J,
Goiffon RJ, Goldstein TC, Ng S, Lin L, Crowder R, Snider J, Ballman K,
Weber J, Chen K, Koboldt DC, Kandoth C, Schierding WS, McMichael JF,
Miller CA, Lu C, Harris CC, McLellan MD, Wendl MC, DeSchryver K,
Allred DC, Esserman L, Unzeitig G, Margenthaler J, Babiera GV,
Marcom PK, Guenther JM, Leitch M, Hunt K, Olson J, Tao Y, Maher CA,
Fulton LL, Fulton RS, Harrison M, Oberkfell B, Du F, Demeter R,
Vickery TL, Elhammali A, Piwnica-Worms H, McDonald S, Watson M,
Dooling DJ, Ota D, Chang LW, Bose R, Ley TJ, Piwnica-Worms D,
Stuart JM, Wilson RK, Mardis ER (2012) Whole-genome analysis informs
breast cancer response to aromatase inhibition. Nature 486: 353–360.
Gerlinger M, Rowan A, Horswell S, Larkin J, Endesfelder D, Gronroos E,
Matthews PM, Stewart N, Mcdonald A, Butler N, Jones A, Raine D, Santos
K, Varela C, Nohadani I, Eklund M, Spencer A, Dene B, Clark G,
Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal PA,
Swanton C (2012) Intratumor heterogeneity and branched evolution
revealed by multiregion sequencing. N Engl J Med 366: 883–893.
Gerlinger M, Swanton C (2010) How Darwinian models inform therapeutic
failure initiated by clonal heterogeneity in cancer medicine. Br J Cancer
Gore ME, Larkin JM (2011) Challenges and opportunities for converting renal
cell carcinoma into a chronic disease with targeted therapies. Br J Cancer
Heppner GH (1984) Tumor heterogeneity. Cancer Res 44: 2259–2265.
Inda MM, Bonavia R, Mukasa A, Narita Y, Sah DW, Vandenberg S,
Brennan C, Johns TG, Bachoo R, Hadwiger P, Tan P, Depinho RA,
Cavenee W, Furnari F (2010) Tumor heterogeneity is an active process
maintained by a mutant EGFR-induced cytokine circuit in glioblastoma.
Genes Dev 24: 1731–1745.
Inukai M, Toyooka S, Ito S, Asano H, Ichihara S, Soh J, Suehisa H,
Ouchida M, Aoe K, Aoe M, Kiura K, Shimizu N, Date H (2006) Presence
of epidermal growth factor receptor gene T790M mutation as a minor
clone in non-small cell lung cancer. Cancer Res 66: 7854–7858.
Katayama R, Shaw AT, Khan TM, Mino-Kenudson M, Solomon BJ,
Halmos B, Jessop NA, Wain JC, Yeo AT, Benes C, Drew L, Saeh JC,
Crosby K, Sequist LV, Iafrate AJ, Engelman JA (2012) Mechanisms of
acquired crizotinib resistance in ALK-rearranged lung cancers. Sci Transl
Med 4: 120ra17.
Keats JJ, Chesi M, Egan JB, Garbitt VM, Palmer SE, Braggio E, Van Wier S,
Blackburn PR, Baker AS, Dispenzieri A, Kumar S, Rajkumar SV, Carpten
JD, Barrett M, Fonseca R, Stewart AK, Bergsagel PL (2012) Clonal
competition with alternating dominance in multiple myeloma. Blood
Kreso A, O’Brien CA, Van Galen P, Gan O, Notta F, Brown AMK, Ng K, Ma J,
Wienholds E, Dunant C, Pollett A, Gallinger S, McPherson J, Mullighan
CG, Shibata D, Dick JE. Variable clonal repopulation dynamics influence
chemotherapy response in colorectal cancer. Science; e-pub ahead of print,
13 December 2012.
Lackner MR, Wilson TR, Settleman J (2012) Mechanisms of acquired
resistance to targeted cancer therapies. Future Oncol 8: 999–1014.
Lee AJ, Endesfelder D, Rowan AJ, Walther A, Birkbak NJ, Futreal PA,
Downward J, Szallasi Z, Tomlinson IP, Howell M, Kschischo M,
Swanton C (2011) Chromosomal instability confers intrinsic multidrug
resistance. Cancer Res 71: 1858–1870.
Liegl B, Kepten I, Le C, Zhu M, Demetri GD, Heinrich MC, Fletcher CDM,
Corless CL, Fletcher JA (2008) Heterogeneity of kinase inhibitor resistance
mechanisms in GIST. J Pathol 216: 64–74.
Maley CC, Galipeau PC, Finley JC, Wongsurawat VJ, Li X, Sanchez CA,
Paulson TG, Blount PL, Risques RA, Rabinovitch PS, Reid BJ (2006)
Genetic clonal diversity predicts progression to esophageal
adenocarcinoma. Nat Genet 38: 468–473.
Marusyk A, Polyak K (2010) Tumor heterogeneity: causes and consequences.
Biochim Biophys Acta 1805: 105–117.
McGranahan N, Burrell RA, Endesfelder D, Novelli MR, Swanton C (2012)
Cancer chromosomal instability: therapeutic and diagnostic challenges.
EMBO Rep 13: 528–538.
Navin N, Krasnitz A, Rodgers L, Cook K, Meth J, Kendall J, Riggs M,
Eberling Y, Troge J, Grubor V, Levy D, Lundin P, Maner S, Zetterberg A,
Hicks J, Wigler M (2010) Inferring tumor progression from genomic
heterogeneity. Genome Res 20: 68–80.
Navin NE, Hicks J (2010) Tracing the tumor lineage. Mol Oncol 4: 267–283.
Nickel GC, Barnholtz-Sloan J, Gould MP, McMahon S, Cohen A, Adams MD,
Guda K, Cohen M, Sloan AE, LaFramboise T (2012) Characterizing
mutational heterogeneity in a glioblastoma patient with double recurrence.
PLoS One 7: e35262.
Nowell PC (1976) The clonal evolution of tumor cell populations. Science 194:
Roylance R, Endesfelder D, Gorman P, Burrell RA, Sander J, Tomlinson I,
Hanby AM, Speirs V, Richardson AL, Birkbak NJ, Eklund AC,
Downward J, Kschischo M, Szallasi Z, Swanton C (2011) Relationship of
extreme chromosomal instability with long-term survival in a
retrospective analysis of primary breast cancer. Cancer Epidemiol
Biomarkers Prev 20: 2183–2194.
Russnes HG, Navin N, Hicks J, Borresen-Dale AL (2011) Insight into the
heterogeneity of breast cancer through next-generation sequencing. J Clin
Invest 121: 3810–3818.
Schuh A, Becq J, Humphray S, Alexa A, Burns A, Clifford R, Feller SM,
Grocock R, Henderson S, Khrebtukova I, Kingsbury Z, Luo S, McBride D,
Murray L, Menju T, Timbs A, Ross M, Taylor J, Bentley D (2012)
Monitoring chronic lymphocytic leukemia progression by whole genome
sequencing reveals heterogeneous clonal evolution patterns. Blood
Sekulic A, Hingorani P, Lenkiewicz E, Holley T, Barrett M, Zizmann V,
Trent J (2012) Clonal evolution underlying transplacental transfer and
vemurafenib resistance in melanoma. Pigment Cell Melanoma Res 25: 886.
Shah NP, Nicoll JM, Nagar B, Gorre ME, Paquette RL, Kuriyan J, Sawyers CL
(2002) Multiple BCR-ABL kinase domain mutations confer polyclonal
resistance to the tyrosine kinase inhibitor imatinib (STI571) in chronic
phase and blast crisis chronic myeloid leukemia. Cancer Cell 2: 117–125.
Shah SP, Morin RD, Khattra J, Prentice L, Pugh T, Burleigh A, Delaney A,
Gelmon K, Guliany R, Senz J, Steidl C, Holt RA, Jones S, Sun M, Leung G,
Moore R, Severson T, Taylor GA, Teschendorff AE, Tse K, Turashvili G,
Varhol R, Warren RL, Watson P, Zhao Y, Caldas C, Huntsman D,
Hirst M, Marra MA, Aparicio S (2009) Mutational evolution in a lobular
breast tumour profiled at single nucleotide resolution. Nature 461: 809–813.
Sidransky D, Mikkelsen T, Schwechheimer K, Rosenblum ML, Cavanee W,
Vogelstein B (1992) Clonal expansion of p53 mutant cells is associated
with brain tumour progression. Nature 355: 846–847.
BRITISH JOURNAL OF CANCER
Snuderl M, Fazlollahi L, Le LP, Nitta M, Zhelyazkova BH, Davidson CJ, Download full-text
Akhavanfard S, Cahill DP, Aldape KD, Betensky RA, Louis DN, Iafrate AJ
(2011) Mosaic amplification of multiple receptor tyrosine kinase genes in
glioblastoma. Cancer Cell 20: 810–817.
Sprouffske K, Merlo LM, Gerrish PJ, Maley CC, Sniegowski PD (2012) Cancer
in light of experimental evolution. Curr Biol 22: R762–R771.
Straussman R, Morikawa T, Shee K, Barzily-Rokni M, Qian ZR, Du J, Davis A,
Mongare MM, Gould J, Frederick DT, Cooper ZA, Chapman PB, Solit DB,
Ribas A, Lo RS, Flaherty KT, Ogino S, Wargo JA, Golub TR (2012)
Tumour micro-environment elicits innate resistance to RAF inhibitors
through HGF secretion. Nature 487(7408): 500–504.
Su KY, Chen HY, Li KC, Kuo ML, Yang JC, Chan WK, Ho BC, Chang GC,
Shih JY, Yu SL, Yang PC (2012) Pretreatment epidermal growth factor
receptor (EGFR) T790M mutation predicts shorter EGFR tyrosine kinase
inhibitor response duration in patients with non-small-cell lung cancer. J
Clin Oncol 30(4): 433–440.
Swanton C, Nicke B, Schuett M, Eklund AC, Ng C, Li Q, Hardcastle T, Lee A,
Roy R, East P, Kschischo M, Endesfelder D, Wylie P, Kim SN, Chen JG,
Howell M, Ried T, Habermann JK, Auer G, Brenton JD, Szallasi Z,
Downward J (2009) Chromosomal instability determines taxane response.
Proc Natl Acad Sci USA 106: 8671–8676.
Szerlip NJ, Pedraza A, Chakravarty D, Azim M, McGuire J, Fang Y, Ozawa T,
Holland EC, Huse JT, Jhanwar S, Leversha MA, Mikkelsen T,
Brennan CW (2012) Intratumoral heterogeneity of receptor tyrosine
kinases EGFR and PDGFRA amplification in glioblastoma defines
subpopulations with distinct growth factor response. Proc Natl Acad Sci
USA 109: 3041–3046.
Torres L, Ribeiro FR, Pandis N, Andersen JA, Heim S, Teixeira MR (2007)
Intratumor genomic heterogeneity in breast cancer with clonal divergence
between primary carcinomas and lymph node metastases. Breast Cancer
Res Treat 102: 143–155.
Walter MJ, Shen D, Ding L, Shao J, Koboldt DC, Chen K, Larson DE,
McLellan MD, Dooling D, Abbott R, Fulton R, Magrini V, Schmidt H,
Kalicki-Veizer J, O’Laughlin M, Fan X, Grillot M, Witowski S, Heath S,
Frater JL, Eades W, Tomasson M, Westervelt P, DiPersio JF, Link DC,
Mardis ER, Ley TJ, Wilson RK, Graubert TA (2012) Clonal architecture of
secondary acute myeloid leukemia. N Engl J Med 366: 1090–1098.
Wu X, Northcott PA, Dubuc A, Dupuy AJ, Shih DJH, Witt H, Croul S,
Bouffet E, Fults DW, Eberhart CG, Garzia L, Van Meter T, Zagzag D,
Jabado N, Schwartzentruber J, Majewski J, Scheetz TE, Pfister SM,
Korshunov A, Li X-N, Scherer SW, Cho Y-J, Akagi K, Mac D, onald TJ,
Koster J, McCabe MG, Sarver AL, Collins VP, Weiss WA, Largaespada
DA, Collier LS, Taylor MD (2012) Clonal selection drives genetic
divergence of metastatic medulloblastoma. Nature 482: 529–533.
Xu X, Hou Y, Yin X, Bao L, Tang A, Song L, Li F, Tsang S, Wu K, Wu H,
He W, Zeng L, Xing M, Wu R, Jiang H, Liu X, Cao D, Guo G, Hu X,
Gui Y, Li Z, Xie W, Sun X, Shi M, Cai Z, Wang B, Zhong M, Li J, Lu Z,
Gu N, Zhang X, Goodman L, Bolund L, Wang J, Yang H, Kristiansen K,
Dean M, Li Y (2012) Single-cell exome sequencing reveals single-nucleotide
mutation characteristics of a kidney tumor. Cell 148: 886–895.
Yachida S, Jones S, Bozic I, Antal T, Leary R, Fu B, Kamiyama M, Hruban RH,
Eshleman JR, Nowak MA, Velculescu VE, Kinzler KW, Vogelstein B,
Iacobuzio-Donahue CA (2010) Distant metastasis occurs late during the
genetic evolution of pancreatic cancer. Nature 467: 1114–1117.
Yap TA, Gerlinger M, Futreal PA, Pusztai L, Swanton C (2012) Intratumor
heterogeneity: seeing the wood for the trees. Sci Transl Med 4: 127ps10.
This work is licensed under the Creative Commons
Attribution-NonCommercial-Share Alike 3.0 Unported
License. To view a copy of this license, visit http://creativecommons.
BRITISH JOURNAL OF CANCER