REVIEWS Clinical management of breast cancer heterogeneity Dimitrios Zardavas, Alexandre Irrthum, Charles Swanton and Martine Piccart Abstract | Traditionally, intertumour heterogeneity in breast cancer has been documented in terms of different histological subtypes, treatment sensitivity profiles, and clinical outcomes among different patients. Results of high-throughput molecular profiling studies have subsequently revealed the true extent of this heterogeneity. Further complicating this scenario, the heterogeneous expression of the oestrogen receptor (ER), progesterone receptor (PR), and HER2 has been reported in different areas of the same tumour. Furthermore, discordance, in terms of ER, PR and HER2 expression, has also been reported between primary tumours and their matched metastatic lesions. High-throughput molecular profiling studies have confirmed that spatial and temporal intratumour heterogeneity of breast cancers exist at a level beyond common expectations. We describe the different levels of tumour heterogeneity, and discuss the strategies that can be adopted by clinicians to tackle treatment response and resistance issues associated with such heterogeneity, including a rationally selected combination of agents that target driver mutations, the targeting of deleterious passenger mutations, identifying and eradicating the ‘lethal’ clone, targeting the tumour microenvironment, or using adaptive treatments and immunotherapy. The identification of the most-appropriate strategies and their implementation in the clinic will prove highly challenging and necessitate the adoption of radically new practices for the optimal clinical management of breast malignancies. Zardavas, D. et al. Nat. Rev. Clin. Oncol. advance online publication 21 April 2015; doi:10.1038/nrclinonc.2015.73
Introduction
Breast International Group (BIG)-aisbl c/o Jules Bordet Institute (D.Z., A.I.), Jules Bordet Institute (M.P.), Boulevard de Waterloo 121, 1000 Brussels, Belgium. University College London Cancer Institute, Cancer Research UK Lung Cancer Centre of Excellence, Paul O’Gorman Building, Huntley Street, London WC1E 6DD, UK (C.S.). Correspondence to: D.Z. dimitrios.zardavas@ bigagainstbc.org
For many decades, cancer heterogeneity has been noted by pathologists, who have classified tumours that arise within the same organ into different histological subtypes. Tumour samples are often heterogeneous at the morpho logical level, and comprise different types of cells and a variable stromal composition between different regions. In the setting of breast cancer, such morphological hetero geneity formed the basis of the histological grading system, which provides prognostic information.1–3 The advent of new high-throughput technologies, such as gene- expression profiling and massively parallel sequencing, has enabled a detailed analysis of the molecular background of cancer.4–6 Numerous studies have been conducted across the full spectrum of cancers, and have revealed previously unknown genetic heterogeneity.7 These molecular dif ferences can occur either between different patients with the same tumour type (intertumour heterogeneity), or within the same patient (intratumour heterogeneity). Furthermore, intratumour heterogeneity itself can occur either between different geographical regions of a tumour (spatial intratumour heterogeneity), or as molecular evolu tion of a tumour over time (temporal intratumour hetero geneity; Figure 1).8–11 Of note, these two manifestations Competing interests M.P. is a board member for PharmaMar and receives consultant honoraria from Amgen, Astellas, AstraZeneca, Bayer, Eli Lilly, Invivis, MSD, Novartis, Pfizer, Roche-Genentech, Sanofi Aventis, Symphogen, Synthon, Verastem. The other authors declare no competing interests.
of intratumour heterogeneity are closely linked, as spatial heterogeneity can be subjected to changes over time, with further molecular evolution of the disease. In breast cancer, differences in the expression levels of the oestrogen receptor (ER), progesterone receptor (PR) and HER2—the triplet of established biomarkers used for clinical decision-making—have been reported among patients.12 However, different expression levels of ER, PR and HER2 have also been noted within the same patient with discordant levels noted between primary tumours and matched metastatic lesions (Table 1).13 This discord ance between matched primary tumours and metastatic lesions could reflect false-positive and/or false-negative results, actual intratumour heterogeneity, or changes in the biology of the disease during the process of metastasis.14–16 To further add to this complexity, apart from the epi thelial cellular compartment within breast cancers, stromal cells that are an important microenvironmental component of tumours, are highly variable and are hetero geneous between the different subtypes.17 Of note, it has been documented that the tumour-associated stroma undergoes extensive molecular evolution during cancer progression with regards to gene-expression profiles, with such results indicating that the tumour microenvironment participates and contributes to carcinogenesis in breast cancer.18 However, other results indicate that the tumour microenvironment is a genetically more-stable com ponent of the disease, offering promising therapeutic opportunities, as will be discussed later in this Review.19
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REVIEWS Key points ■■ Breast cancer is a heterogeneous group of diseases with different histological, prognostic and clinical aspects ■■ Heterogeneous expression of the oestrogen, progesterone, and HER2 receptors has been observed among different patients with breast cancer, as well as between matched samples from primary tumours and their metastases ■■ Powerful technologies, such as DNA microarrays and next-generation sequencing, are providing further insight into intertumour and intratumour heterogeneity ■■ Intratumour heterogeneity is documented at both spatial and temporal levels, with breast cancer cells behaving similarly to an evolving ecosystem, showing a molecular evolution in response to selective pressures ■■ Heterogeneity poses impediments to the successful clinical development of molecularly targeted agents ■■ Innovative approaches are urgently needed to overcome the hurdle of tumour heterogeneity and improve clinical outcomes for patients with breast cancer
a Spatial and temporal evolution
Adjuvant treatment and metastatic relapse
b
Neodjuvant treatment
c
Disease progression in the metastatic setting Intertumour heterogeneity Intratumour heterogeneity
Figure 1 | Schematic depiction of intertumour and intratumour heterogeneity in breast cancer. Different patients diagnosed with breast cancer have tumours with Nature Reviews | Clinical Oncology different molecular profiles, a phenomenon corresponding to intertumour heterogeneity. At the same time, within each individual patient intratumour heterogeneity is noticed, with the disease undergoing spatial and temporal evolution. The yellow background denotes primary disease; blue background denotes metastatic disease; and circles represent tumours. Different clinical scenarios can be identified. a | Patients diagnosed with primary breast cancer undergoing adjuvant treatment: for the ones that will later develop systemic relapse, heterogeneity can occur between the originating tumour and the metastases. b | Patients diagnosed with primary breast cancer undergoing neoadjuvant treatment: the primary tumour might alter its molecular profile during the treatment. c | Patients with metastatic breast cancer undergoing palliative treatment: the metastatic burden alters its molecular profile thus mediating treatment resistance and disease progression. Of note, intratumour heterogeneity can occur in every single tumour lesion during the life cycle of the disease.
Several studies have aimed to delineate the complex molecular background of breast cancer, which has advanced personalized treatment approaches and enabled the development of several agents that target specific molecular aberrations associated with breast cancer. 20 Nevertheless, tumour heterogeneity poses obvious impediments to the successful clinical develop ment of targeted agents.20–22 We provide an overview of the evidence, causes, and consequences of tumour heterogeneity in breast cancer. In addition, we propose therap eutic strategies that can help clinicians to circumvent the hurdles imposed by heterogeneity on disease management; finally, we elaborate on the new practices that will have to be adopted to facilitate the implementation of these new therapeutic strategies.
Heterogeneity in breast cancer Intertumour heterogeneity For several decades, widespread histological variation of breast cancer has been a well-described phenomenon, as reflected in the WHO classification of this disease into 17 categories, on the basis of various microscopic features.23 However, the clinical relevance of this histo logical classification is limited, because the vast major ity of cases (>70%) are classified as ‘invasive ductal carcinoma not otherwise specified’ (IDC NOS), and these cases demonstrate considerable divergence in treatment sensitivities and clinical behaviour.24 Of note, specific histological subtypes, such as medullary or meta plastic breast cancers, have been associated with specific molecular alterations and distinct outcomes, with the former subtype associated with favourable and the latter with worse clinical outcomes; however, these subtypes represent a small proportion of cases.25–27 Microarray technology has enabled the analysis of the expression levels of thousands of genes simultane ously, contributing to a further understanding of the intertumour heterogeneity of breast cancer. The seminal classification studies led to a refined molecular character ization and classification of breast cancer, identifying four different so-called intrinsic subtypes: luminal A, luminal B, HER2-enriched and basal-like breast cancer. The luminal A subtype is usually positive for expression of the ER and/or PR, with a low pathological grade and low proliferation rates; luminal B cancers are ER‑positive and/or PR‑positive tumours, have a higher grade and higher proliferation index, with lower responsiveness to endocrine treatment and worse clinical outcome than luminal A subtypes; HER2-enriched tumours are characterized by the amplification of the HER2 (ERBB2) gene (or other genes in the same amplicon) and are mostly associated with a high pathological grade; lastly, basal-like breast cancers express markers of basal-cellular origin, such as basal cytokeratins, are associated with aggressive behaviour and poor prognosis, and typi cally do not express hormone receptors or HER2 (triple negative breast cancer, TNBC, phenotype).28–32 Of note, the initial study that led to the classification of the breast cancer intrinsic subtypes assessed a low number of primary breast tumours and only measured
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REVIEWS Table 1 | Selected studies assessing intratumour heterogeneity Study
Marker
Tumour type
Matched cases (n)
Discordance rate (%)
Impact on clinical outcome
Regitnig et al. (2004)167
HER2
All
31
16.1
NA
Amir et al. (2012)168
ER/PR/ HER2
All
121
ER: 16 PR: 40 HER2: 10
None
Niikura et al. (2012)169
HER2
HER2positive
182
24
Yes: OS; HR 0.47; P = 0.003
Li et al. (1994)170
ER/PR
All
83
ER: 29 PR: 44
Yes: OS; 27.6 ± 7.4 months versus 50.6 ± 7.6 months; P = 0.04
Curtit et al. (2013)14
ER/PR/ HER2
All
269
ER: 17 PR: 29 HER2: 4
NA
Liu et al. (2012)171
ER/PR/ HER2
All
46
ER: 30.4 PR: 54.3 HER2: 10.9
NA
Abbreviations: ER, oestrogen receptor; HER2, human epidermal growth factor 2; NA, not applicable; OS, overall survival; PR, progesterone receptor.
the expression levels of a subset of the human genes.28 Subsequent studies focusing on the heterogeneous group of basal-like and/or triple-negative breast cancer (TNBC) revealed additional subtypes, such as claudinlow tumours that showed a gene-expression profile similar to that of mammary stem cells, with upregu lated immune response genes, mesenchymal features, and positivity for markers of epithelial-to-mesenchymal transition;33,34 and the ‘molecular apocrine’ tumours, showing androgen receptor (AR) positivity and activa tion of the AR‑signalling pathway.35 In another study, cluster analysis of pooled gene-expression profiles of 587 TNBC cases from 21 different datasets identified six tumour subtypes, including two basal-like, an immuno modulatory, a mesenchymal, a mesenchymal stem-like, and a luminal androgen receptor subtype.36 Interestingly, this study presented some preclinical evidence indicating that these six disease subtypes have distinct sensitivity profiles to anticancer agents; however, these findings need to be validated in the clinic. Further refinement of the molecular taxonomy and the intertumour heterogeneity of breast cancer was achieved through the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study,37 an integrated analysis of copy-number and gene-expression profiling data from approximately 2,000 patients with primary breast cancer that was clinically annotated. Splitting many of the intrinsic subtypes, METABRIC identified 10 different molecular disease subtypes, defined as integra tive clusters, which showed distinct clinical outcomes.37 New insights into the intertumour heterogeneity of breast cancer were obtained with the implementation of next-generation sequencing (NGS) in the analysis of primary tumour collections (Table 2).5 These studies revealed that numerous different types of molecular aberrations—namely point mutations, small insertions and deletions, amplifications, segmental duplications,
translocations, and inversions—can affect the genomic landscape of breast tumours. One key finding of these studies is that very few cancer-related genes are mutated at high frequency, with the exception of somatic muta tions in GATA3, TP53 and PIK3CA that are seen at >10% incidence across all subtypes of breast cancer.38–40 High intertumour heterogeneity was further highlighted from a study by Stephens et al.,41 which identified 73 differ ent combinations of mutations in cancer-related genes among 100 breast tumours sequenced, and a study by Banerji et al., 42 in which whole-exome sequencing (WES) data for 103 primary breast tumours of differ ent subtypes were reported. Interestingly, the latter study detected recurrent mutations in the CBFB transcription factor gene and deletions of its partner RUNX1 (in six out of 103 cases), whereas a recurrent MAGI3–AKT3 gene fusion was found to be enriched in TNBC (in five out of 72 cases).42 In 2012, The Cancer Genome Atlas (TCGA) Network reported the results of molecular profiling of hundreds of primary breast cancers across different platforms by analysing genomic DNA copynumber arrays, DNA methylation, exome sequencing, mRNA arrays, microRNA sequencing, and reverse-phase protein arrays.43 This integrated approach demonstrated the existence of four main breast cancer classes, each of which showed significant molecular heterogeneity. In parallel with this vast genetic heterogeneity, this study highlighted specific protein-expression patterns and signalling pathways within each subtype that could provide the rationale for exploring new therapeutic targets, an important requirement, especially for women with TNBC.
Intratumour heterogeneity Spatial variation Spatial heterogeneity refers to heterogeneity seen between different regions of a primary tumour, between a primary tumour and a metastatic lesion, or between metastatic lesions, and is commonly observed at the histopathological level. In a proof-of-principle study, high-resolution microarray-based comparative genomic hybridization (CGH), TP53 sequencing, and fluores cence in situ hybridization (FISH) were performed among morphologically different areas of six cases of primary metaplastic breast cancer to assess whether this morphological diversity was underpinned by genetic heterogeneity.44 Of note, in most cases, all morpho logically distinct components from each tumour were clonal, displaying remarkably similar genetic profiles. However, in two cases, different genetic aberrations were found within the same morphological component of the tumour.44 A similar study provided additional evidence of intratumour genetic heterogeneity in breast cancer, demonstrating that morphologically distinct regions of a tumour can be associated with distinct genetic aberra tions.45 Focal apocrine differentiation can be associated with a subset of lesions in TNBC; using high-resolution microarray-based CGH, the regions of apocrine differen tiation were found to harbour genomic gains and losses on chromosome arms 9p and 9q, respectively, whereas
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REVIEWS Table 2 | Next-generation sequencing study for primary breast tumours Study
Subtype
Methods applied (number of samples assessed)
Findings
Banerji et al. (2012)42
All
WGS and WES (17) WGS (5) WES (86)
Known recurrent somatic mutations Identification of new alterations Recurrent fusion gene in TNBC
Ellis et al. (2012)166
Luminal
Targeted DNA sequencing (77) WGS (46) WES (31)
Distinct phenotypes associated with specific patterns of somatic mutations
Shah et al. (2012)161
TNBC
RNA sequencing (80) Targeted DNA sequencing (65)
Widely differing mutational spectra within TNBC
Stephens et al. (2012)41
All
WES (100)
Infrequently mutated genes account for almost half of driver genetic events in breast cancer
TCGA (2012)37
All
mRNA microarrays (547) DNA methylation (802) SNP arrays (773) miRNA sequencing (697) Whole-exome sequencing (507) RPPA (403)
Comprehensive catalogue of likely genomic drivers of the most common breast cancer subtypes Diverse genetic and epigenetic alterations converge phenotypically into four main breast cancer classes
Abbreviations: RPPA, reverse phase protein array; SNP, single nucleotide polymorphism; TCGA, The Cancer Genome Atlas; TNBC, triple-negative breast cancer; WES, whole-exome sequencing; WGS, whole-genome sequencing.
these alterations were not present in non-apocrine areas of the same carcinoma.45 A larger study also interrogated spatial genetic heterogeneity among 20 cases of primary IDC, irrespective of morphological heterogeneity, through a method called Sector-Ploidy-Profiling (SPP) that incorporates tumour macrodissection, flow-sorting of genomic subpopulations by DNA content, and CGH analysis.46 This study classified primary breast tumours into two groups based on the levels of genetic hetero geneity observed: monogenomic tumours, showing highly similar profiles in different geographical areas of the tumour; and polygenomic tumours, with different subclonal populations of cancer cells populating one or more distinct areas of the tumour.46 In comparison with microarray or CGH analysis, NGS offers the compelling advantage of providing an assess ment of spatial intratumour heterogeneity, with no need for tumour microdissection and separate analyses of geographically distinct cancer-cell populations, owing to the estimation of allelic frequencies of mutational events detected within the bulk tumour.47 Therefore, a number of NGS studies of collections of primary breast cancers have provided additional evidence of the spatial intratumour heterogeneity observed in this disease. Nik-Zainal and colleagues performed whole-genome sequencing (WGS) across 21 primary breast tumours of different subtypes, and reconstructed the phylogenetic tree of the disease using newly developed bioinformatic algorithms.48,49 This study showed that several discrete subclones were present in each breast-cancer sample, harbouring different somatic mutations, copy-number aberrations, and chromosomal rearrangements.49 Temporal evolution Beyond spatial heterogeneity in tumour samples, accumulating evidence has documented the evolution of tumour cells during the disease history of cancer, supporting the theory that cancer behaves as an evolv ing ecosystem under the selective pressures applied by
both the host immune response and anticancer thera peutics.50 The change of genetic aberrations occurring throughout the lifespan of the disease is called temporal heterogeneity and has been observed in three differ ent scenarios: the transition of in situ breast carcinoma to invasive cancer, the evolution of primary breast tumours over time, and the progression of primary breast tumour to metastatic disease. Beyond the natural evolution of the disease, temporal heterogeneity can be observed as a consequence of anticancer treatments. For example, Balko and colleagues51 analysed the genome of 74 patients with residual TNBC after neoadjuvant treat ment and discovered that the post-neoadjuvant therapy residual cancer had a different genomic landscape com pared with matched pretreatment specimens. Integrated NGS and digital RNA profiling revealed a molecular evolution of the residual disease, with the enrichment in MCL1 amplification, PTEN deletions and/or mutations, JAK2 amplifications, as well as CDK6/CCND1–3 ampli fication in the post-neoadjuvant-chemotherapy setting.51 These results indicate that the genomic landscape of breast cancer might undergo a substantial change under the selective pressure of anticancer treatment, with direct implications for the development of molecularly targeted agents in the post-neoadjuvant and/or metastatic setting. Ongoing studies, such as the AURORA initiative of the Breast International Group,52 are systematically assess ing this phenomenon in the metastatic setting by using molecular characterization of metastatic disease and matched post-treatment-progression samples. In terms of disease evolution over time, ductal carci noma in situ (DCIS) is considered a precursor of IDC, as evidenced by pathological, epidemiological, and molecu lar studies.53,54 Recent studies reported high concord ance in terms of mutational status of PIK3CA between matched cases of DCIS and IDC.55,56 In an attempt to delineate the dynamics of genomic alterations in the transition from DCIS to IDC, 13 cases of synchronous DCIS and IDC were analysed. Specifically, multicolour
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REVIEWS FISH probe panels comprising markers for five onco genes and three tumour-suppressor genes were used to visualize copy-number changes at the single-cell level in these two disease entities.57 This analysis indicated that DCIS had a lower degree of chromosomal instability compared with IDC; additionally, in six of the cases, a switch of genomic imbalances between DCIS and IDC occurred—that is, a shift in the abundance of a major clone between the DCIS and the IDC component.57 Similarly, Hernandez et al.58 performed microarraybased CGH and Sequenom MassARRAY (a DNA analy sis platform that efficiently and precisely measures the amount of genetic variability) in matched frozen samples of DCIS and IDC from 13 patients with breast cancer, to compare their mutational profiles. Genomic similarities between the matched lesions were detected; however, cir cumstantial evidence suggested that the transition from DCIS to IDC is mediated by the selection of nonmodal clones harbouring specific genetic aberrations.52 The most-extensively studied form of temporal intra tumour heterogeneity in breast cancer relates to the differences between a primary breast tumour and its associated metastatic lesions. In one of the first studies that addressed this issue, the investigators utilized CGH and FISH to analyse 29 primary breast tumours and their paired asynchronous metastases.59 Interestingly, in 31% of the cases analysed, the genetic composition of the matched tumour differed almost completely, supporting the notion that the biological properties of primary breast cancer poorly reflect those of its metastatic counterpart.53 Further NGS analyses of paired primary breast tumours and associated metastatic lesions have been reported, substantiating the heterogeneity underpinning meta static progression.55–59 A study reporting WGS data for a lobular breast cancer and a subsequent metastasis, which occurred 9 years later,60 showed marked allelic variation; out of 32 somatic nonsynonymous mutations detected in the metastasis, only five were prevalent in the DNA of the primary tumour, whereas another six were present at lower frequencies (1–13%), two were undetermined and 19 were not detected.60 Another study reporting the sequencing results of a primary basal-like inflamma tory breast tumour and a subsequent brain metastasis that occurred 8 months after completion of neoadjuvant chemotherapy showed a wide range of mutant allele fre quencies in the primary tumour, indicating considerable intratumour heterogeneity.61 With regard to temporal tumour evolution, the metastasis was found to contain two de novo mutations and a large deletion that was not present in the primary tumour, with an additional 20 shared mutations being substantially enriched in the disseminated lesion.61 The studies we have discussed suggest that the extent of temporal intratumour heterogeneity detected might be proportional to the time interval between the diagnosis of the primary tumour and the occurrence of metastatic relapse. In a study on an autopsy series of 10 patients with metastatic breast cancer, WES and copy-number analysis were used to reconstruct the trajectories of breast cancer progression.62 The degree of the genetic heterogeneity
between primary and metastatic disease, assessed both at the mutation and copy-number level, was proportional to the time elapsed between the two events.62 Overall, more systematic extensive molecular profiling data need to be generated in order to delineate the potentially dif ferent mutational profiles between primary breast cancer and the subsequent metastatic recurrent disease. Such data will have a clear impact on the use of targeted therapeutics for patients with metastatic disease.
Heterogeneity at the single-cell level
The aforementioned data, both in terms of spatial and temporal heterogeneity, originate from studies that analys ed the bulk of the tumour cells, thus limiting the ability to detect diversity of molecular aberrations between cells of the same tumour. Even the powerful NGS tools that revolutionized the field of cancer genom ics have mostly been used to capture the genomic diver sity of populations of cancer cells, limiting the perception of the level of intratumour heterogeneity. Nevertheless, the potential to analyse single cells can further advance our knowledge in this area. A different NGS approach, called single-nucleus sequencing, which combines flow-based cell sorting, whole-genome amplification, and NGS, was used to determine genomic copy-number profiles of single tumour cells. 63 Specifically, 100 single cells from a heterogeneous, in terms of ploidy, TNBC were profiled, along with an additional 100 individual cells derived from a homogeneous primary breast tumour and a paired liver metastasis.63 This study provided a measure of the extended genomic heterogeneity seen in these tumours; its findings supported a punctuated model of clonal evolution, according to which one or more sequential clonal expansion steps took place, with few gradual intermediates.63 In a subsequent study, the same research group devel oped a single-tumour-cell sequencing method, called nuq-seq, which enabled assessment of genome-wide mutations at base-pair resolution in nuclei undergoing DNA replication.64 This novel approach was combined with targeted duplex single-molecule sequencing in tumour cells derived from a patient with an ER‑positive and a triple-negative breast tumour, providing further evidence for the vast genomic (spatial intratumour and intertumour) heterogeneity associated with breast cancer.65 Notably, this study showed that although a large number of mutations are common to most of the individual cells in a given tumour, a greater number of subclonal and de novo mutations are unique to individual cells. Furthermore, the triple-negative breast tumour was found to have increased mutation rates, compared with the ER‑positive tumour, with the latter showing a muta tion rate comparable to those reported for normal cells, and the former showing a 13.3-fold increase relative to normal cells, based on mathematical modelling data.65 Finally, two separate ‘mutational clocks’ were found to operate in breast cancer: a large-scale clock with struc tural DNA changes occurring early in tumour develop ment, during punctuated bursts of genomic evolution;
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REVIEWS Table 3 | Main characteristics of the clonal evolution and cancer stem cell model Characteristic
Clonal evolution
Cancer stem cell
Cellular hierarchy
No
Yes
Cell of origin
Any cell
Any cell
Tumorigenic cell
Any cell
Cancer stem cells
Source of heterogeneity
Any tumour cell, through genomic instability combined with selective pressures from various sources (that is, therapies applied, microenvironment, hormonal)
Cancer stem cells, through aberrant differentiation
Cell type driving tumour progression
‘Fittest’ clone formed as a result of the genomic instability and selective pressures applied
Cancer stem cells
Drivers of treatment resistance
‘Fittest’ cells that have genetic and/or epigenetic aberrations mediating either primary resistance or acquiring new mutations, thus mediating acquired and/or secondary resistance
Cancer stem cells with primary treatment resistance capabilities
Type of heterogeneity
Phenotypic, genetic and epigenetic
Phenotypic
Therapeutic challenges
and a small-scale clock characterized by the gradual accumulation of point mutations, which contributed to the extensive subclonal diversity.64 These results not only represented an important technical milestone to further our insights into the genomic heterogeneity at the individual tumour-cell level, but also set the stage to detect rare cancer subclones ‘hidden’ within tumours— new potential therapeutic targets that could improve clinical outcomes.66
Models of cancer evolution
Indeed, evidence of the tumour microenvironment directing gene expression had been reported more than three decades ago.74 Overall, these three theories should not be perceived as mutually exclusive, and preclini cal evidence has been generated that support all three models. The priority should be the clinical translation of these theories and their respective supporting evidence, with a more-holistic view and approach to interrogation of cancer having the highest chances of leading to new treatment options.
Currently, the mechanisms underlying tumour hetero geneity in breast cancer are widely debated, as a clear understanding of the cell-of-origin, as well as the tumour progression path of this disease, remains lacking. 67 Two theories were initially proposed to explain these phenomena observed in cancer overall (Table 3). The first theory, known as the cancer stem cell (CSC) theory, suggests a hierarchical organization within tumours, with a minority of dedifferentiated CSCs at the apex of this hierarchy that are able to differentiate aberrantly into heterogeneous groups of cancer cells.68 Of note, preclinical evidence challenged this theory, indicating that a subpopulation of basal-like human mammary epi thelial cells can spontaneously dedifferentiates into stemlike cells.69 A subsequent study reinforced these findings further, suggesting that breast cancer stem-like cells arise de novo from non-stem-like cells.70 The second theory (clonal evolution model), formalized in a seminal article by Peter Nowell in 1976,71 suggests that once a cancer has been established from a single cell of origin, genetic variability ensues due to genetic instability and selective pressures, leading ultimately to the generation of moreaggressive subclones. A third theory is now recognized: the tumour microenvironment has been proposed to be a major determinant of tumour molecular heterogeneity, as exemplified by the fitness advantage seen when speci fic molecular characteristics, such as the ability of some cancer-a ssociated fibroblasts in breast tumours to produce IL‑6, are detected within this compartment.72,73
Tumour heterogeneity is a cause of concern for several reasons. Intertumour heterogeneity implies that every breast cancer can be different in every patient, preclud ing ‘one-size-fits-all’ therapeutic approaches. Breastcancer researchers and clinicians have largely embraced this concept, with considerable means invested in the development and application of targeted, personalized therapies. However, it remains to be seen whether the large-scale implementation of this concept is practically and economically viable. Intratumour heterogeneity poses even more funda mental challenges.75 The coexistence of multiple sub clones with different sets of molecular aberrations and different drug sensitivities implies that therapeutic strat egies targeted at predominant aberrations might not be effective against the whole tumour. This scenario is further complicated by the fact that this differential sen sitivity is itself the substrate of tumour evolution, result ing in the emergence of drug-resistant clones; various studies of non-small-cell lung cancer demonstrated that tumours developed resistance to either EGFR76–78 or ALK inhibition.79,80 The existence of extensive intertumour and intra tumour heterogeneity in breast tumours creates chal lenges for clinicians, and for patients with this disease. Having discussed the causes and consequences of such heterogeneity, we now discuss five different strategies to circumvent this phenomenon.
Development of combination targeted therapies At the theoretical level, the case for combination thera pies to tackle tumour heterogeneity is compelling. More than three decades ago, Goldie and Coldman81,82 sup ported the strategy of combined anticancer treatment, either concomitantly or sequentially, based on math ematical modelling of tumour growth. They proposed that increasing mutation rates would result in a height ened chance for the emergence of resistant phenotypes, thus providing a conceptual link between intratumour heterogeneity and treatment resistance.81 Subsequent large-scale genotyping studies have suggested that each breast tumour harbours genetically distinct cancer-cell subpopulations.41–43,48,49 Within each breast cancer, dif ferent mutations could be the molecular drivers that fuel cancer progression, in particular, in tumours with high mutation rates. This phenomenon, known as clonal interference has been identified in evolving microbial populations, and for cancer cells it could imply that
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REVIEWS different driver events should be targeted to achieve tumour eradication.83,84 An example of how clonal inter ference necessitates ad hoc anticancer combination therapies is highlighted by cases of glioblastoma multi forme (GBM), a notoriously difficult-to-treat primary brain tumour,85 with independent focal amplification of EGFR and PDGFRA.86 Notably, experiments in cell lines derived from tumour samples, with the co-amplification of EGFR and PDGFRA either in one or different regions (spatial segregation) of the cancer, indicated that dual EGFR–PDGFRA inhibition is needed for tumour eradi cation, thus supporting the concept of combination therapies to tackle tumour heterogeneity.86 Further evidence supporting the need for the clini cal development of combination therapies derive from the plasticity of cancer cells, which under the selective pressures of anticancer treatment are able to rewire their internal signalling and gene-regulatory network. This approach consists of developing combinations of targeted agents to block the primary drivers of breast cancer progression, together with an upfront blockade of the molecular mediators of treatment resistance. For example, recent preclinical findings have reported that a well-orchestrated reprogramming of the kinome activi ties in TNBC cells upon MEK–ERK pathway inhibition mediates drug resistance.87 An effective drug response in these cells could be restored by the addition of a multitargeted tyrosine kinase inhibitor against the upregu lated kinases, suggesting that once the adaptive response mechanisms of cancer cells have been identified, their blockade can translate into improved antitumour activ ity.87 This improvement could be achieved by an upfront blockade of an anticipated adaptive response; however, the heterogeneity per se could pose a hurdle to this approach, as prediction of the adaptive mechanism in any given patient would be a difficult task. An effi cient approach could be a sensitive monitoring of the evolving molecular landscapes of the disease, through plasma-based analyses.88 Nonetheless, a potential barrier to the efficient clinical development of rationally chosen combinations of targeted agents is their toxic effects. In the setting of breast cancer, the dual blockade of PI3K and MEK kinases is a therapeutic strategy that shows great promise based on the strong preclinical evi dence;89–91 however, the clinical development of such combinations has proven extremely challenging, owing to the toxicity profile of the combination used.92 A study has challenged this approach, indicating that seemingly counterintuitive combinational treatments can lead to improved antitumour efficacy.93 In fact, a joint computational and experimental approach, which used RNA-interference to model tumour heterogeneity within a well-characterized lymphoma cell line and considered drug efficacy as well as drug adverse effects to select effective drug combinations, identified non- intuitive therapeutic options, other than the ones derived from the characterization of the predominant clone.94 Confirming previous results,93 the treatment combi nations that were predicted to be efficient for hetero geneous tumours did not contain the single-best agent
for targeting the predominant subclonal population.94 These findings need further validation in vivo; however, they indicate that optimal drug combinations should be selected taking into account tumour heterogeneity. Of note, this approach relies on the accurate detection of the several oncogenic molecular aberrations present in a given (heterogeneous) cancer, and their quantification. The latter processes is confounded by issues of altered ploidy and variable purity of the tumour samples analy sed. Nonetheless, the development of the sophisticated algorithm ABSOLUTE, which can be applied to various cancer types, represents a promising step forward as it enables rare subclonal alterations to be detected at the single-cell level. 95 In the original article describing the ABSOLUTE algorithm,95 the authors analysed 214 matched ovarian carcinoma tumour and normal samples, and were able to quantify subclonal aberrations and to demonstrate their prevalence within the tumour-cell populations. The output of the algorithm is not a list of aberrations per se, but an assessment of aberrations in the context of the purity and ploidy of the tumour sample. To infer the purity and ploidy of the tumour sample, the input data can be microarray data (for example, Affymetrix SNP 6.0), or massively parallel WGS or WES data. The next step is to compute, based on the inferred purity and ploidy, the copy number per cancer cell of the aberrations (for example, point mutations detected by WES). This information, in turn, enables detection of subclonal aberrations (when the copy number is
Introduction
Breast International Group (BIG)-aisbl c/o Jules Bordet Institute (D.Z., A.I.), Jules Bordet Institute (M.P.), Boulevard de Waterloo 121, 1000 Brussels, Belgium. University College London Cancer Institute, Cancer Research UK Lung Cancer Centre of Excellence, Paul O’Gorman Building, Huntley Street, London WC1E 6DD, UK (C.S.). Correspondence to: D.Z. dimitrios.zardavas@ bigagainstbc.org
For many decades, cancer heterogeneity has been noted by pathologists, who have classified tumours that arise within the same organ into different histological subtypes. Tumour samples are often heterogeneous at the morpho logical level, and comprise different types of cells and a variable stromal composition between different regions. In the setting of breast cancer, such morphological hetero geneity formed the basis of the histological grading system, which provides prognostic information.1–3 The advent of new high-throughput technologies, such as gene- expression profiling and massively parallel sequencing, has enabled a detailed analysis of the molecular background of cancer.4–6 Numerous studies have been conducted across the full spectrum of cancers, and have revealed previously unknown genetic heterogeneity.7 These molecular dif ferences can occur either between different patients with the same tumour type (intertumour heterogeneity), or within the same patient (intratumour heterogeneity). Furthermore, intratumour heterogeneity itself can occur either between different geographical regions of a tumour (spatial intratumour heterogeneity), or as molecular evolu tion of a tumour over time (temporal intratumour hetero geneity; Figure 1).8–11 Of note, these two manifestations Competing interests M.P. is a board member for PharmaMar and receives consultant honoraria from Amgen, Astellas, AstraZeneca, Bayer, Eli Lilly, Invivis, MSD, Novartis, Pfizer, Roche-Genentech, Sanofi Aventis, Symphogen, Synthon, Verastem. The other authors declare no competing interests.
of intratumour heterogeneity are closely linked, as spatial heterogeneity can be subjected to changes over time, with further molecular evolution of the disease. In breast cancer, differences in the expression levels of the oestrogen receptor (ER), progesterone receptor (PR) and HER2—the triplet of established biomarkers used for clinical decision-making—have been reported among patients.12 However, different expression levels of ER, PR and HER2 have also been noted within the same patient with discordant levels noted between primary tumours and matched metastatic lesions (Table 1).13 This discord ance between matched primary tumours and metastatic lesions could reflect false-positive and/or false-negative results, actual intratumour heterogeneity, or changes in the biology of the disease during the process of metastasis.14–16 To further add to this complexity, apart from the epi thelial cellular compartment within breast cancers, stromal cells that are an important microenvironmental component of tumours, are highly variable and are hetero geneous between the different subtypes.17 Of note, it has been documented that the tumour-associated stroma undergoes extensive molecular evolution during cancer progression with regards to gene-expression profiles, with such results indicating that the tumour microenvironment participates and contributes to carcinogenesis in breast cancer.18 However, other results indicate that the tumour microenvironment is a genetically more-stable com ponent of the disease, offering promising therapeutic opportunities, as will be discussed later in this Review.19
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REVIEWS Key points ■■ Breast cancer is a heterogeneous group of diseases with different histological, prognostic and clinical aspects ■■ Heterogeneous expression of the oestrogen, progesterone, and HER2 receptors has been observed among different patients with breast cancer, as well as between matched samples from primary tumours and their metastases ■■ Powerful technologies, such as DNA microarrays and next-generation sequencing, are providing further insight into intertumour and intratumour heterogeneity ■■ Intratumour heterogeneity is documented at both spatial and temporal levels, with breast cancer cells behaving similarly to an evolving ecosystem, showing a molecular evolution in response to selective pressures ■■ Heterogeneity poses impediments to the successful clinical development of molecularly targeted agents ■■ Innovative approaches are urgently needed to overcome the hurdle of tumour heterogeneity and improve clinical outcomes for patients with breast cancer
a Spatial and temporal evolution
Adjuvant treatment and metastatic relapse
b
Neodjuvant treatment
c
Disease progression in the metastatic setting Intertumour heterogeneity Intratumour heterogeneity
Figure 1 | Schematic depiction of intertumour and intratumour heterogeneity in breast cancer. Different patients diagnosed with breast cancer have tumours with Nature Reviews | Clinical Oncology different molecular profiles, a phenomenon corresponding to intertumour heterogeneity. At the same time, within each individual patient intratumour heterogeneity is noticed, with the disease undergoing spatial and temporal evolution. The yellow background denotes primary disease; blue background denotes metastatic disease; and circles represent tumours. Different clinical scenarios can be identified. a | Patients diagnosed with primary breast cancer undergoing adjuvant treatment: for the ones that will later develop systemic relapse, heterogeneity can occur between the originating tumour and the metastases. b | Patients diagnosed with primary breast cancer undergoing neoadjuvant treatment: the primary tumour might alter its molecular profile during the treatment. c | Patients with metastatic breast cancer undergoing palliative treatment: the metastatic burden alters its molecular profile thus mediating treatment resistance and disease progression. Of note, intratumour heterogeneity can occur in every single tumour lesion during the life cycle of the disease.
Several studies have aimed to delineate the complex molecular background of breast cancer, which has advanced personalized treatment approaches and enabled the development of several agents that target specific molecular aberrations associated with breast cancer. 20 Nevertheless, tumour heterogeneity poses obvious impediments to the successful clinical develop ment of targeted agents.20–22 We provide an overview of the evidence, causes, and consequences of tumour heterogeneity in breast cancer. In addition, we propose therap eutic strategies that can help clinicians to circumvent the hurdles imposed by heterogeneity on disease management; finally, we elaborate on the new practices that will have to be adopted to facilitate the implementation of these new therapeutic strategies.
Heterogeneity in breast cancer Intertumour heterogeneity For several decades, widespread histological variation of breast cancer has been a well-described phenomenon, as reflected in the WHO classification of this disease into 17 categories, on the basis of various microscopic features.23 However, the clinical relevance of this histo logical classification is limited, because the vast major ity of cases (>70%) are classified as ‘invasive ductal carcinoma not otherwise specified’ (IDC NOS), and these cases demonstrate considerable divergence in treatment sensitivities and clinical behaviour.24 Of note, specific histological subtypes, such as medullary or meta plastic breast cancers, have been associated with specific molecular alterations and distinct outcomes, with the former subtype associated with favourable and the latter with worse clinical outcomes; however, these subtypes represent a small proportion of cases.25–27 Microarray technology has enabled the analysis of the expression levels of thousands of genes simultane ously, contributing to a further understanding of the intertumour heterogeneity of breast cancer. The seminal classification studies led to a refined molecular character ization and classification of breast cancer, identifying four different so-called intrinsic subtypes: luminal A, luminal B, HER2-enriched and basal-like breast cancer. The luminal A subtype is usually positive for expression of the ER and/or PR, with a low pathological grade and low proliferation rates; luminal B cancers are ER‑positive and/or PR‑positive tumours, have a higher grade and higher proliferation index, with lower responsiveness to endocrine treatment and worse clinical outcome than luminal A subtypes; HER2-enriched tumours are characterized by the amplification of the HER2 (ERBB2) gene (or other genes in the same amplicon) and are mostly associated with a high pathological grade; lastly, basal-like breast cancers express markers of basal-cellular origin, such as basal cytokeratins, are associated with aggressive behaviour and poor prognosis, and typi cally do not express hormone receptors or HER2 (triple negative breast cancer, TNBC, phenotype).28–32 Of note, the initial study that led to the classification of the breast cancer intrinsic subtypes assessed a low number of primary breast tumours and only measured
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REVIEWS Table 1 | Selected studies assessing intratumour heterogeneity Study
Marker
Tumour type
Matched cases (n)
Discordance rate (%)
Impact on clinical outcome
Regitnig et al. (2004)167
HER2
All
31
16.1
NA
Amir et al. (2012)168
ER/PR/ HER2
All
121
ER: 16 PR: 40 HER2: 10
None
Niikura et al. (2012)169
HER2
HER2positive
182
24
Yes: OS; HR 0.47; P = 0.003
Li et al. (1994)170
ER/PR
All
83
ER: 29 PR: 44
Yes: OS; 27.6 ± 7.4 months versus 50.6 ± 7.6 months; P = 0.04
Curtit et al. (2013)14
ER/PR/ HER2
All
269
ER: 17 PR: 29 HER2: 4
NA
Liu et al. (2012)171
ER/PR/ HER2
All
46
ER: 30.4 PR: 54.3 HER2: 10.9
NA
Abbreviations: ER, oestrogen receptor; HER2, human epidermal growth factor 2; NA, not applicable; OS, overall survival; PR, progesterone receptor.
the expression levels of a subset of the human genes.28 Subsequent studies focusing on the heterogeneous group of basal-like and/or triple-negative breast cancer (TNBC) revealed additional subtypes, such as claudinlow tumours that showed a gene-expression profile similar to that of mammary stem cells, with upregu lated immune response genes, mesenchymal features, and positivity for markers of epithelial-to-mesenchymal transition;33,34 and the ‘molecular apocrine’ tumours, showing androgen receptor (AR) positivity and activa tion of the AR‑signalling pathway.35 In another study, cluster analysis of pooled gene-expression profiles of 587 TNBC cases from 21 different datasets identified six tumour subtypes, including two basal-like, an immuno modulatory, a mesenchymal, a mesenchymal stem-like, and a luminal androgen receptor subtype.36 Interestingly, this study presented some preclinical evidence indicating that these six disease subtypes have distinct sensitivity profiles to anticancer agents; however, these findings need to be validated in the clinic. Further refinement of the molecular taxonomy and the intertumour heterogeneity of breast cancer was achieved through the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study,37 an integrated analysis of copy-number and gene-expression profiling data from approximately 2,000 patients with primary breast cancer that was clinically annotated. Splitting many of the intrinsic subtypes, METABRIC identified 10 different molecular disease subtypes, defined as integra tive clusters, which showed distinct clinical outcomes.37 New insights into the intertumour heterogeneity of breast cancer were obtained with the implementation of next-generation sequencing (NGS) in the analysis of primary tumour collections (Table 2).5 These studies revealed that numerous different types of molecular aberrations—namely point mutations, small insertions and deletions, amplifications, segmental duplications,
translocations, and inversions—can affect the genomic landscape of breast tumours. One key finding of these studies is that very few cancer-related genes are mutated at high frequency, with the exception of somatic muta tions in GATA3, TP53 and PIK3CA that are seen at >10% incidence across all subtypes of breast cancer.38–40 High intertumour heterogeneity was further highlighted from a study by Stephens et al.,41 which identified 73 differ ent combinations of mutations in cancer-related genes among 100 breast tumours sequenced, and a study by Banerji et al., 42 in which whole-exome sequencing (WES) data for 103 primary breast tumours of differ ent subtypes were reported. Interestingly, the latter study detected recurrent mutations in the CBFB transcription factor gene and deletions of its partner RUNX1 (in six out of 103 cases), whereas a recurrent MAGI3–AKT3 gene fusion was found to be enriched in TNBC (in five out of 72 cases).42 In 2012, The Cancer Genome Atlas (TCGA) Network reported the results of molecular profiling of hundreds of primary breast cancers across different platforms by analysing genomic DNA copynumber arrays, DNA methylation, exome sequencing, mRNA arrays, microRNA sequencing, and reverse-phase protein arrays.43 This integrated approach demonstrated the existence of four main breast cancer classes, each of which showed significant molecular heterogeneity. In parallel with this vast genetic heterogeneity, this study highlighted specific protein-expression patterns and signalling pathways within each subtype that could provide the rationale for exploring new therapeutic targets, an important requirement, especially for women with TNBC.
Intratumour heterogeneity Spatial variation Spatial heterogeneity refers to heterogeneity seen between different regions of a primary tumour, between a primary tumour and a metastatic lesion, or between metastatic lesions, and is commonly observed at the histopathological level. In a proof-of-principle study, high-resolution microarray-based comparative genomic hybridization (CGH), TP53 sequencing, and fluores cence in situ hybridization (FISH) were performed among morphologically different areas of six cases of primary metaplastic breast cancer to assess whether this morphological diversity was underpinned by genetic heterogeneity.44 Of note, in most cases, all morpho logically distinct components from each tumour were clonal, displaying remarkably similar genetic profiles. However, in two cases, different genetic aberrations were found within the same morphological component of the tumour.44 A similar study provided additional evidence of intratumour genetic heterogeneity in breast cancer, demonstrating that morphologically distinct regions of a tumour can be associated with distinct genetic aberra tions.45 Focal apocrine differentiation can be associated with a subset of lesions in TNBC; using high-resolution microarray-based CGH, the regions of apocrine differen tiation were found to harbour genomic gains and losses on chromosome arms 9p and 9q, respectively, whereas
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REVIEWS Table 2 | Next-generation sequencing study for primary breast tumours Study
Subtype
Methods applied (number of samples assessed)
Findings
Banerji et al. (2012)42
All
WGS and WES (17) WGS (5) WES (86)
Known recurrent somatic mutations Identification of new alterations Recurrent fusion gene in TNBC
Ellis et al. (2012)166
Luminal
Targeted DNA sequencing (77) WGS (46) WES (31)
Distinct phenotypes associated with specific patterns of somatic mutations
Shah et al. (2012)161
TNBC
RNA sequencing (80) Targeted DNA sequencing (65)
Widely differing mutational spectra within TNBC
Stephens et al. (2012)41
All
WES (100)
Infrequently mutated genes account for almost half of driver genetic events in breast cancer
TCGA (2012)37
All
mRNA microarrays (547) DNA methylation (802) SNP arrays (773) miRNA sequencing (697) Whole-exome sequencing (507) RPPA (403)
Comprehensive catalogue of likely genomic drivers of the most common breast cancer subtypes Diverse genetic and epigenetic alterations converge phenotypically into four main breast cancer classes
Abbreviations: RPPA, reverse phase protein array; SNP, single nucleotide polymorphism; TCGA, The Cancer Genome Atlas; TNBC, triple-negative breast cancer; WES, whole-exome sequencing; WGS, whole-genome sequencing.
these alterations were not present in non-apocrine areas of the same carcinoma.45 A larger study also interrogated spatial genetic heterogeneity among 20 cases of primary IDC, irrespective of morphological heterogeneity, through a method called Sector-Ploidy-Profiling (SPP) that incorporates tumour macrodissection, flow-sorting of genomic subpopulations by DNA content, and CGH analysis.46 This study classified primary breast tumours into two groups based on the levels of genetic hetero geneity observed: monogenomic tumours, showing highly similar profiles in different geographical areas of the tumour; and polygenomic tumours, with different subclonal populations of cancer cells populating one or more distinct areas of the tumour.46 In comparison with microarray or CGH analysis, NGS offers the compelling advantage of providing an assess ment of spatial intratumour heterogeneity, with no need for tumour microdissection and separate analyses of geographically distinct cancer-cell populations, owing to the estimation of allelic frequencies of mutational events detected within the bulk tumour.47 Therefore, a number of NGS studies of collections of primary breast cancers have provided additional evidence of the spatial intratumour heterogeneity observed in this disease. Nik-Zainal and colleagues performed whole-genome sequencing (WGS) across 21 primary breast tumours of different subtypes, and reconstructed the phylogenetic tree of the disease using newly developed bioinformatic algorithms.48,49 This study showed that several discrete subclones were present in each breast-cancer sample, harbouring different somatic mutations, copy-number aberrations, and chromosomal rearrangements.49 Temporal evolution Beyond spatial heterogeneity in tumour samples, accumulating evidence has documented the evolution of tumour cells during the disease history of cancer, supporting the theory that cancer behaves as an evolv ing ecosystem under the selective pressures applied by
both the host immune response and anticancer thera peutics.50 The change of genetic aberrations occurring throughout the lifespan of the disease is called temporal heterogeneity and has been observed in three differ ent scenarios: the transition of in situ breast carcinoma to invasive cancer, the evolution of primary breast tumours over time, and the progression of primary breast tumour to metastatic disease. Beyond the natural evolution of the disease, temporal heterogeneity can be observed as a consequence of anticancer treatments. For example, Balko and colleagues51 analysed the genome of 74 patients with residual TNBC after neoadjuvant treat ment and discovered that the post-neoadjuvant therapy residual cancer had a different genomic landscape com pared with matched pretreatment specimens. Integrated NGS and digital RNA profiling revealed a molecular evolution of the residual disease, with the enrichment in MCL1 amplification, PTEN deletions and/or mutations, JAK2 amplifications, as well as CDK6/CCND1–3 ampli fication in the post-neoadjuvant-chemotherapy setting.51 These results indicate that the genomic landscape of breast cancer might undergo a substantial change under the selective pressure of anticancer treatment, with direct implications for the development of molecularly targeted agents in the post-neoadjuvant and/or metastatic setting. Ongoing studies, such as the AURORA initiative of the Breast International Group,52 are systematically assess ing this phenomenon in the metastatic setting by using molecular characterization of metastatic disease and matched post-treatment-progression samples. In terms of disease evolution over time, ductal carci noma in situ (DCIS) is considered a precursor of IDC, as evidenced by pathological, epidemiological, and molecu lar studies.53,54 Recent studies reported high concord ance in terms of mutational status of PIK3CA between matched cases of DCIS and IDC.55,56 In an attempt to delineate the dynamics of genomic alterations in the transition from DCIS to IDC, 13 cases of synchronous DCIS and IDC were analysed. Specifically, multicolour
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REVIEWS FISH probe panels comprising markers for five onco genes and three tumour-suppressor genes were used to visualize copy-number changes at the single-cell level in these two disease entities.57 This analysis indicated that DCIS had a lower degree of chromosomal instability compared with IDC; additionally, in six of the cases, a switch of genomic imbalances between DCIS and IDC occurred—that is, a shift in the abundance of a major clone between the DCIS and the IDC component.57 Similarly, Hernandez et al.58 performed microarraybased CGH and Sequenom MassARRAY (a DNA analy sis platform that efficiently and precisely measures the amount of genetic variability) in matched frozen samples of DCIS and IDC from 13 patients with breast cancer, to compare their mutational profiles. Genomic similarities between the matched lesions were detected; however, cir cumstantial evidence suggested that the transition from DCIS to IDC is mediated by the selection of nonmodal clones harbouring specific genetic aberrations.52 The most-extensively studied form of temporal intra tumour heterogeneity in breast cancer relates to the differences between a primary breast tumour and its associated metastatic lesions. In one of the first studies that addressed this issue, the investigators utilized CGH and FISH to analyse 29 primary breast tumours and their paired asynchronous metastases.59 Interestingly, in 31% of the cases analysed, the genetic composition of the matched tumour differed almost completely, supporting the notion that the biological properties of primary breast cancer poorly reflect those of its metastatic counterpart.53 Further NGS analyses of paired primary breast tumours and associated metastatic lesions have been reported, substantiating the heterogeneity underpinning meta static progression.55–59 A study reporting WGS data for a lobular breast cancer and a subsequent metastasis, which occurred 9 years later,60 showed marked allelic variation; out of 32 somatic nonsynonymous mutations detected in the metastasis, only five were prevalent in the DNA of the primary tumour, whereas another six were present at lower frequencies (1–13%), two were undetermined and 19 were not detected.60 Another study reporting the sequencing results of a primary basal-like inflamma tory breast tumour and a subsequent brain metastasis that occurred 8 months after completion of neoadjuvant chemotherapy showed a wide range of mutant allele fre quencies in the primary tumour, indicating considerable intratumour heterogeneity.61 With regard to temporal tumour evolution, the metastasis was found to contain two de novo mutations and a large deletion that was not present in the primary tumour, with an additional 20 shared mutations being substantially enriched in the disseminated lesion.61 The studies we have discussed suggest that the extent of temporal intratumour heterogeneity detected might be proportional to the time interval between the diagnosis of the primary tumour and the occurrence of metastatic relapse. In a study on an autopsy series of 10 patients with metastatic breast cancer, WES and copy-number analysis were used to reconstruct the trajectories of breast cancer progression.62 The degree of the genetic heterogeneity
between primary and metastatic disease, assessed both at the mutation and copy-number level, was proportional to the time elapsed between the two events.62 Overall, more systematic extensive molecular profiling data need to be generated in order to delineate the potentially dif ferent mutational profiles between primary breast cancer and the subsequent metastatic recurrent disease. Such data will have a clear impact on the use of targeted therapeutics for patients with metastatic disease.
Heterogeneity at the single-cell level
The aforementioned data, both in terms of spatial and temporal heterogeneity, originate from studies that analys ed the bulk of the tumour cells, thus limiting the ability to detect diversity of molecular aberrations between cells of the same tumour. Even the powerful NGS tools that revolutionized the field of cancer genom ics have mostly been used to capture the genomic diver sity of populations of cancer cells, limiting the perception of the level of intratumour heterogeneity. Nevertheless, the potential to analyse single cells can further advance our knowledge in this area. A different NGS approach, called single-nucleus sequencing, which combines flow-based cell sorting, whole-genome amplification, and NGS, was used to determine genomic copy-number profiles of single tumour cells. 63 Specifically, 100 single cells from a heterogeneous, in terms of ploidy, TNBC were profiled, along with an additional 100 individual cells derived from a homogeneous primary breast tumour and a paired liver metastasis.63 This study provided a measure of the extended genomic heterogeneity seen in these tumours; its findings supported a punctuated model of clonal evolution, according to which one or more sequential clonal expansion steps took place, with few gradual intermediates.63 In a subsequent study, the same research group devel oped a single-tumour-cell sequencing method, called nuq-seq, which enabled assessment of genome-wide mutations at base-pair resolution in nuclei undergoing DNA replication.64 This novel approach was combined with targeted duplex single-molecule sequencing in tumour cells derived from a patient with an ER‑positive and a triple-negative breast tumour, providing further evidence for the vast genomic (spatial intratumour and intertumour) heterogeneity associated with breast cancer.65 Notably, this study showed that although a large number of mutations are common to most of the individual cells in a given tumour, a greater number of subclonal and de novo mutations are unique to individual cells. Furthermore, the triple-negative breast tumour was found to have increased mutation rates, compared with the ER‑positive tumour, with the latter showing a muta tion rate comparable to those reported for normal cells, and the former showing a 13.3-fold increase relative to normal cells, based on mathematical modelling data.65 Finally, two separate ‘mutational clocks’ were found to operate in breast cancer: a large-scale clock with struc tural DNA changes occurring early in tumour develop ment, during punctuated bursts of genomic evolution;
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REVIEWS Table 3 | Main characteristics of the clonal evolution and cancer stem cell model Characteristic
Clonal evolution
Cancer stem cell
Cellular hierarchy
No
Yes
Cell of origin
Any cell
Any cell
Tumorigenic cell
Any cell
Cancer stem cells
Source of heterogeneity
Any tumour cell, through genomic instability combined with selective pressures from various sources (that is, therapies applied, microenvironment, hormonal)
Cancer stem cells, through aberrant differentiation
Cell type driving tumour progression
‘Fittest’ clone formed as a result of the genomic instability and selective pressures applied
Cancer stem cells
Drivers of treatment resistance
‘Fittest’ cells that have genetic and/or epigenetic aberrations mediating either primary resistance or acquiring new mutations, thus mediating acquired and/or secondary resistance
Cancer stem cells with primary treatment resistance capabilities
Type of heterogeneity
Phenotypic, genetic and epigenetic
Phenotypic
Therapeutic challenges
and a small-scale clock characterized by the gradual accumulation of point mutations, which contributed to the extensive subclonal diversity.64 These results not only represented an important technical milestone to further our insights into the genomic heterogeneity at the individual tumour-cell level, but also set the stage to detect rare cancer subclones ‘hidden’ within tumours— new potential therapeutic targets that could improve clinical outcomes.66
Models of cancer evolution
Indeed, evidence of the tumour microenvironment directing gene expression had been reported more than three decades ago.74 Overall, these three theories should not be perceived as mutually exclusive, and preclini cal evidence has been generated that support all three models. The priority should be the clinical translation of these theories and their respective supporting evidence, with a more-holistic view and approach to interrogation of cancer having the highest chances of leading to new treatment options.
Currently, the mechanisms underlying tumour hetero geneity in breast cancer are widely debated, as a clear understanding of the cell-of-origin, as well as the tumour progression path of this disease, remains lacking. 67 Two theories were initially proposed to explain these phenomena observed in cancer overall (Table 3). The first theory, known as the cancer stem cell (CSC) theory, suggests a hierarchical organization within tumours, with a minority of dedifferentiated CSCs at the apex of this hierarchy that are able to differentiate aberrantly into heterogeneous groups of cancer cells.68 Of note, preclinical evidence challenged this theory, indicating that a subpopulation of basal-like human mammary epi thelial cells can spontaneously dedifferentiates into stemlike cells.69 A subsequent study reinforced these findings further, suggesting that breast cancer stem-like cells arise de novo from non-stem-like cells.70 The second theory (clonal evolution model), formalized in a seminal article by Peter Nowell in 1976,71 suggests that once a cancer has been established from a single cell of origin, genetic variability ensues due to genetic instability and selective pressures, leading ultimately to the generation of moreaggressive subclones. A third theory is now recognized: the tumour microenvironment has been proposed to be a major determinant of tumour molecular heterogeneity, as exemplified by the fitness advantage seen when speci fic molecular characteristics, such as the ability of some cancer-a ssociated fibroblasts in breast tumours to produce IL‑6, are detected within this compartment.72,73
Tumour heterogeneity is a cause of concern for several reasons. Intertumour heterogeneity implies that every breast cancer can be different in every patient, preclud ing ‘one-size-fits-all’ therapeutic approaches. Breastcancer researchers and clinicians have largely embraced this concept, with considerable means invested in the development and application of targeted, personalized therapies. However, it remains to be seen whether the large-scale implementation of this concept is practically and economically viable. Intratumour heterogeneity poses even more funda mental challenges.75 The coexistence of multiple sub clones with different sets of molecular aberrations and different drug sensitivities implies that therapeutic strat egies targeted at predominant aberrations might not be effective against the whole tumour. This scenario is further complicated by the fact that this differential sen sitivity is itself the substrate of tumour evolution, result ing in the emergence of drug-resistant clones; various studies of non-small-cell lung cancer demonstrated that tumours developed resistance to either EGFR76–78 or ALK inhibition.79,80 The existence of extensive intertumour and intra tumour heterogeneity in breast tumours creates chal lenges for clinicians, and for patients with this disease. Having discussed the causes and consequences of such heterogeneity, we now discuss five different strategies to circumvent this phenomenon.
Development of combination targeted therapies At the theoretical level, the case for combination thera pies to tackle tumour heterogeneity is compelling. More than three decades ago, Goldie and Coldman81,82 sup ported the strategy of combined anticancer treatment, either concomitantly or sequentially, based on math ematical modelling of tumour growth. They proposed that increasing mutation rates would result in a height ened chance for the emergence of resistant phenotypes, thus providing a conceptual link between intratumour heterogeneity and treatment resistance.81 Subsequent large-scale genotyping studies have suggested that each breast tumour harbours genetically distinct cancer-cell subpopulations.41–43,48,49 Within each breast cancer, dif ferent mutations could be the molecular drivers that fuel cancer progression, in particular, in tumours with high mutation rates. This phenomenon, known as clonal interference has been identified in evolving microbial populations, and for cancer cells it could imply that
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REVIEWS different driver events should be targeted to achieve tumour eradication.83,84 An example of how clonal inter ference necessitates ad hoc anticancer combination therapies is highlighted by cases of glioblastoma multi forme (GBM), a notoriously difficult-to-treat primary brain tumour,85 with independent focal amplification of EGFR and PDGFRA.86 Notably, experiments in cell lines derived from tumour samples, with the co-amplification of EGFR and PDGFRA either in one or different regions (spatial segregation) of the cancer, indicated that dual EGFR–PDGFRA inhibition is needed for tumour eradi cation, thus supporting the concept of combination therapies to tackle tumour heterogeneity.86 Further evidence supporting the need for the clini cal development of combination therapies derive from the plasticity of cancer cells, which under the selective pressures of anticancer treatment are able to rewire their internal signalling and gene-regulatory network. This approach consists of developing combinations of targeted agents to block the primary drivers of breast cancer progression, together with an upfront blockade of the molecular mediators of treatment resistance. For example, recent preclinical findings have reported that a well-orchestrated reprogramming of the kinome activi ties in TNBC cells upon MEK–ERK pathway inhibition mediates drug resistance.87 An effective drug response in these cells could be restored by the addition of a multitargeted tyrosine kinase inhibitor against the upregu lated kinases, suggesting that once the adaptive response mechanisms of cancer cells have been identified, their blockade can translate into improved antitumour activ ity.87 This improvement could be achieved by an upfront blockade of an anticipated adaptive response; however, the heterogeneity per se could pose a hurdle to this approach, as prediction of the adaptive mechanism in any given patient would be a difficult task. An effi cient approach could be a sensitive monitoring of the evolving molecular landscapes of the disease, through plasma-based analyses.88 Nonetheless, a potential barrier to the efficient clinical development of rationally chosen combinations of targeted agents is their toxic effects. In the setting of breast cancer, the dual blockade of PI3K and MEK kinases is a therapeutic strategy that shows great promise based on the strong preclinical evi dence;89–91 however, the clinical development of such combinations has proven extremely challenging, owing to the toxicity profile of the combination used.92 A study has challenged this approach, indicating that seemingly counterintuitive combinational treatments can lead to improved antitumour efficacy.93 In fact, a joint computational and experimental approach, which used RNA-interference to model tumour heterogeneity within a well-characterized lymphoma cell line and considered drug efficacy as well as drug adverse effects to select effective drug combinations, identified non- intuitive therapeutic options, other than the ones derived from the characterization of the predominant clone.94 Confirming previous results,93 the treatment combi nations that were predicted to be efficient for hetero geneous tumours did not contain the single-best agent
for targeting the predominant subclonal population.94 These findings need further validation in vivo; however, they indicate that optimal drug combinations should be selected taking into account tumour heterogeneity. Of note, this approach relies on the accurate detection of the several oncogenic molecular aberrations present in a given (heterogeneous) cancer, and their quantification. The latter processes is confounded by issues of altered ploidy and variable purity of the tumour samples analy sed. Nonetheless, the development of the sophisticated algorithm ABSOLUTE, which can be applied to various cancer types, represents a promising step forward as it enables rare subclonal alterations to be detected at the single-cell level. 95 In the original article describing the ABSOLUTE algorithm,95 the authors analysed 214 matched ovarian carcinoma tumour and normal samples, and were able to quantify subclonal aberrations and to demonstrate their prevalence within the tumour-cell populations. The output of the algorithm is not a list of aberrations per se, but an assessment of aberrations in the context of the purity and ploidy of the tumour sample. To infer the purity and ploidy of the tumour sample, the input data can be microarray data (for example, Affymetrix SNP 6.0), or massively parallel WGS or WES data. The next step is to compute, based on the inferred purity and ploidy, the copy number per cancer cell of the aberrations (for example, point mutations detected by WES). This information, in turn, enables detection of subclonal aberrations (when the copy number is
