Keywords

6.1 Introduction

Complexity pervades breast tumours at every level – from (epi)genomic, transcriptomic and proteomic landscapes, through to cellular composition and clinical behaviour. This lack of compositional uniformity is referred to as heterogeneity. In breast cancer, this has been historically categorised as intertumoural heterogeneity (diversity between separate tumours) and intratumoural heterogeneity (diversity within a tumour). However, the distinction is becoming increasingly blurred as we understand more about the pathobiology of breast cancer progression. While a single cell acquires the somatic mutations sufficient to launch oncogenic transformation, the cells that eventually comprise clinically detectable deposits arise from clonal selection and expansion as a consequence of a range of different selection pressures, and this has important implications for diagnosis, treatment and drug resistance.

Molecular confirmation of breast cancer heterogeneity has been driven by unparalleled expansion of next generation sequencing technologies over the last decade, with advances in tumour profiling also evolving the traditional taxonomy. Categorising breast tumours into diagnostic and prognostic groups has always been the basis for clinical management, but the fully personalised model we are striving for will feature an unprecedented level of precision, matching each patient with the best possible treatments according to specific molecular alterations underpinning their disease. Navigating and rationalising the exponentially growing wealth of new molecular information remains a major challenge to clinical translation.

This chapter will consider the traditional histopathologic classification of breast cancer, broadly examine the molecular basis of genetic, cellular and microenvironment heterogeneity and the ways in which new knowledge is being integrated to complement the existing taxonomy. Finally, we examine the impact of breast cancer heterogeneity on clinical management and translation of emerging research.

6.2 Current Histopathologic Classification of Breast Cancer

6.2.1 Histological Subtypes

Breast carcinoma encompasses a large group of tumours with different morphological, phenotypic and molecular characteristics, and the current classification includes a spectrum of in situ (pre-invasive) to invasive disease. This chapter focuses on invasive disease, where tumour cells breach the basement membrane and invade surrounding tissue, although there is increasing recognition of heterogeneity within in situ carcinoma [1]. The World Health Organisation (WHO) maintains a diagnostic framework that provides practical information to guide tumour diagnosis and patient management (Table 6.1) [2, 3]. Invasive cancers are initially stratified according to cellular and architectural growth patterns, into histological ‘special types’ with distinct morphology (25–30% of cases, including 5–15% lobular carcinomas; for examples see Fig. 6.1a, b). As a diagnosis of exclusion, tumours without discriminating morphological features are classified as invasive carcinoma of no special type (IC-NST; 40–75% of cases) [3]. Whilst this distinction appears straightforward, many tumours comprise mixed histology (Fig. 6.1c) – thresholds are used to definitively categorise individual cases, though these are somewhat arbitrary. For example, a pure ‘special type’ diagnosis is applied if >90% of the tumour area comprises the special morphology, but in ‘mixed’ cases, separate areas within the same tumour exhibit both ‘non-special’ and ‘special’ morphology (10–49% [2]).

Table 6.1 WHO classification of breast carcinoma (2012)
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Fig. 6.1
figure 1

(a) Breast cancer heterogeneity exemplified by histological subtypes. Haematoxylin and eosin-stained breast cancer tissues visualised by light microscopy. (b) Morphological variation within one histological type, invasive lobular carcinoma (ILC). FNA, fine needle aspiration cytology. (c) Mixed histological subtypes: (i) Invasive carcinoma (no special type) mixed with invasive mucinous carcinoma; (ii) metaplastic carcinoma exhibiting marked variability in both epithelial and stromal compartments; (iii) intratumoural heterogeneity for HER2 shown by silver in situ hybridisation (SISH) – a nest of cells exhibits ERBB2 gene amplification (circled), while others remain diploid (arrows)

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In some instances, tumours with distinct morphological features share underlying genetic mutations [4]. Secretory carcinomas are associated with a t(12;15)(p13;q25) translocation and the resulting ETV6-NTRK3 fusion gene [5]; and, like their counterpart in the salivary gland, adenoid cystic breast carcinomas consistently harbour the t(6;9)(q22–23;p23–24) translocation, leading to MYB-NFIB gene fusion and over-expression of the MYB oncogene [6]. Genotype-phenotype correlation is epitomised by E-cadherin, which is genomically ‘lost’ or dysregulated in lobular carcinomas, and tends to occur concomitantly with specific mutations in PTEN, TBX3 and FOXA1 [7, 8]. However, even subtypes with shared morphological features and mutations exhibit substantial inter-tumoural diversity. For example, within lobular carcinoma, the largest group of special types, genomic and transcriptomic analysis highlighted the existence of distinct prognostic subtypes [8]. Thus overall, histological subtyping alone provides imperfect prognostic information – its value comes from integration with other histopathologic information, namely grade, stage and biomarker status.

6.2.2 Prognostic and Predictive Subgroups

Histopathologic assessment routinely involves quantification of prognostic factors, which predict the natural history of disease irrespective of therapy, and predictive factors, which indicate the likely response to a specific treatment. The disease stage, histological grade and tumour expression of receptors for oestrogen, progesterone and human epidermal growth factor (ER, PR and HER2) are the cornerstones of current prognostic and predictive algorithms. The American Joint Committee on Cancer TNM (tumour/node/metastasis) staging system stratifies broadly based on the burden of the disease by measuring the tumour size, the number of lymph nodes involved, and the extent of distant metastatic disease; stage IV is the most advanced disease, while stage I is the least advanced.

Histological grade is a powerful prognostic indicator [9, 10] and correlates with morphology and molecular features [11]. It is calculated from the degree of nuclear pleomorphism, ‘tubule’ formation (resemblance to normal ducto-lobular gland structure) and the number of mitoses per ten high power microscope fields [12]. Grading reflects a collective morphological assessment of the biological characteristics of a tumour and therefore encompasses intra-tumoural heterogeneity. It is highly reproducible, and remains a component of widely used prognostic algorithms (e.g. Nottingham and Kalmar Prognostic Indices [13,14,15]), as well as predictive algorithms used to guide the prescription of chemotherapy [16, 17]. Pathologists have been describing heterogeneity for decades, but given that clinical behaviour is still diverse within these three broad categories, so there is much scope for grading to be complemented by molecular information.

Breast cancers are routinely analysed for ER, PR and HER2 using IHC-based assessment of protein expression levels and frequency. This information is both prognostic and predictive, reflecting critical growth factor signalling dependencies that can be targeted for therapeutic benefit. PR is induced by oestrogen signalling (thus is a surrogate for ER activity), and adds value to the power of ER for predicting response to therapy [18, 19]. ER/PR-positive tumours tend to be lower grade and associated with better outcomes than ER/PR-negative cases, and are candidates for endocrine therapy (e.g. tamoxifen, fulvestrant, aromatase inhibitors). The gene encoding HER2 (ERBB2) is amplified and/or over-expressed in 15–20% of invasive breast cancers, and correlates with poor prognosis but is also a marker of sensitivity to HER2-targeted therapy (standardly trastuzumab and pertuzumab with chemotherapy) [20,21,22,23,24]. Tumours that are negative for ER/PR and HER2 are currently classified as triple negative breast cancers (TNBC), where there are intensive research efforts ongoing to substratify molecularly distinct subgroups that could be suitable for new therapeutic approaches targeting anti-tumour host immunity, DNA repair and/or specific signalling pathways [25] (see Sect. 6.4.2).

Importantly, the expression of ER/PR and HER2 is not always uniform, implying that not all tumour cells are dependent on their growth factor ligands. ER/PR-positivity is currently defined by a diagnostic threshold of only 1% [26]. Testing to define HER2 status is based on either protein over-expression as demonstrated by IHC and/or testing for gene amplification. Criteria for gene amplification are based on ERBB2 copy number or ERBB2:CEP17 ratio, determined using in situ hybridisation (ISH). If the results are equivocal, orthogonal testing is recommended [27]. Whilst conservative cut-offs ensure patients are eligible for treatments that may confer marginal benefit, heterogeneity undoubtedly impacts the clinical response. For example, HER2 heterogeneity is related to low levels of gene amplification, which is more common in ER/PR-positive tumours and associated with shorter disease-free survival [28,29,30] (Fig. 6.1c).

6.3 Molecular Basis for Heterogeneity in Breast Cancer

The molecular basis for heterogeneity can be divided into tumour cell-intrinsic factors, such as genomic alterations, and their impact on pre-existing differentiation programs in the cell-of-origin; as well as extrinsic factors in the tumour microenvironment. However this division is purely for academic purposes – in reality the tumour and nontumour components are admixed and constantly engaged in feedback signalling [31, 32].

6.3.1 Genetic Heterogeneity

Tumourigenesis occurs by inappropriate expansion of genetically altered clones (groups of isogenic tumour cells derived from a common ancestor; Fig. 6.2, inset) via branching evolution, where the acquisition of a new genetic alteration in a multipotent cell capable of self-renewal represents an evolutionary branching point, and the initiation of a new ‘subclone’ (Fig. 6.2). Mutations that confer a selective advantage to a clone in its particular microenvironment are referred to as ‘drivers’, while those that do not immediately confer a selective advantage are ‘passengers’. They can be distinguished using algorithms that calculate the rates of non-synonymous vs synonymous mutation in each gene, non-random clustering of mutations and/or gene amplification combined with over-expression, which imply positive evolutionary selection [33,34,35].

Fig. 6.2
figure 2

The branching phylogenetic tree analogy of clonal progression, where major branch-points represent the acquisition of each new driver, and distance from the ground is proportional to divergence from the original founding clone. Coloured bunches of leaves represent major subclones with the same driver combinations, with individual leaves as minor subclones harbouring additional genomic alterations and phenotypic differences (see Fig. 6.3). Evidence suggests that clonal evolution occurs intermittently, with essential fitness advantages acquired at the earliest stages of tumourigenesis in short mutational bursts (often copy-number alterations), followed by stable clonal expansions that form the bulk of the tumour. Selective clonal ‘sweeps’ may also occur in response to new extrinsic selection pressure (e.g. systemic agents that prune particular subclones). Circulating tumour cell seeds are shed into the blood and lymphatics – those with the requisite capabilities may colonise suitable soil in distant organs, where the branching evolution process continues. (inset) Classic clonal expansion, where genetic diversity arises through iterative rounds of somatic mutation (coloured dots) and cell division (arrows), and heritable alterations are passed to daughter cells. Curved arrow = self-renewal of a cancer stem cell at the top of the clonal hierarchy

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Historically, heterogeneity has been considered a byproduct of classical Darwinian evolution, where de novo mutations conferring a fitness advantage result in rapid expansion and positive selection of the new clone at the expense of others, resulting in its mutation profile dominating a whole region of the tumour (a so-called ‘clonal sweep’) [36]. This model implies that tumour cells sustain mutations at a fairly constant tempo, but recent studies suggest that tumourigenesis can be driven by just a few major expansion events followed by long periods of relative evolutionary stasis, challenging gradualistic clonal expansion dogma [37,38,39,40,41,42]. By sequencing multiple single cells from a tumour, several groups have now found evidence for so-called ‘big-bang’ dynamics in breast cancer, where critical copy-number alterations are thought to occur as early ‘bursts’, superimposed with cumulative point mutations that contribute to the genetic diversity observed in tumour biopsies [41, 43]. These detailed studies have given weight to the more progressive ‘punctuated’ model of evolution – a hybrid of the big-bang and classic clonal expansion paradigms where evolutionary tempos are sporadic.

Large international consortia (The Cancer Genome Atlas (TCGA [44]) and the International Cancer Genome Consortium (ICGC [45, 46])) have made significant inroads characterising the genomic diversity of breast cancer using next-generation sequencing of RNA and DNA from human clinical samples (Table 6.2). An initial survey of 100 tumours identified at least 40 different genes harbouring driver mutations, but these were present in over 70 different combinations [46], with each occurring in less than 10% of tumours [47, 48]. Due to the low overall frequencies of driver mutations, larger cohorts have been required to confirm recurrent alterations. More recently, a landmark whole genome sequencing study using the largest cohort to date (n = 560), identified 93 protein-coding genes as probable drivers, including five with no previously described link to breast cancer (MED23, FOXP1, MLLT4, XBP1, ZFP36L1) [49]. Whilst almost all the tumours harboured at least one driver, no two tumours out of 560 shared the same combination. Some of the more frequent changes identified include ERRB2, CCND1, AKT1 (amplified and over-expressed), PIK3CA, GATA3 (amplified and overexpressed and/or activating mutation), TP53, PTEN and CDH1 (copy-number loss or inactivating mutation leading to functional insufficiency) [44, 46, 48, 50, 51]. It is thought that breast cancer driver mutations occurring with a frequency of greater than 2% are now known, but it is expected that additional, low frequency drivers are still likely to be found in minor subgroups (e.g. male breast cancer, histological special types) [52]. The particular combination of driver alterations, together with thousands of passenger mutations and structural rearrangements make each breast cancer unique [49, 53, 54]. In general, high levels of genomic heterogeneity tend to be associated with worse clinical outcomes [55].

Table 6.2 Landmark next generation sequencing studies in breast cancer
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Focusing on driver mutations has helped to understand the hallmark processes underpinning breast cancer development and define possible drug targets, but there is also increasing interest in passenger mutations – not only in terms of their influence on progression in the context of exposure to extrinsic selection pressures, but as a genomic record of the mutational processes that occurred throughout the development of each tumour. Mutational process signatures are dynamic, varying spatially and temporally depending on both exogenous and endogenous factors (e.g. carcinogen exposure, age-related change or DNA repair defects) [53]. Complex mathematical analysis has identified 21 substitution signatures with different clinicopathologic associations and underlying aetiologies. Like individual mutations, signatures are clonal and coexist at variable frequencies within cancer deposits of each patient. Some are common to different cancers (e.g. age-related), while others are tumour type-specific (e.g. C·G ➔ T·A transitions are a feature of signature 7, associated with UV-induced DNA damage in cutaneous cancers) [56, 57].

Breast cancer genomes are characterised by 12 substitution signatures, with six consistently detected in at least 20% of cases [49, 53, 57, 58]. Amongst these, signatures 1 and 5 (which are similar and often classified together as 1B) are associated with age, while signatures 2 and 13 are associated with APOBEC cytidine deaminases, which are normally involved in antiviral immunity and RNA editing, but can also act on long stretches of single-stranded DNA thought to arise during abnormal DNA replication [49]. Signatures 3 and 8 are associated with BRCA1/BRCA2 deficiency, defective homologous recombination repair, and short (<10 kb) deletions/tandem duplications [49]. Of the rarer signatures (<20% cases), 6, 20, and 26 are associated with mismatch repair deficiency, while 17, 18, and 30 are of unknown aetiology. The potential implications and clinical applications of mutational signature composition are currently under investigation [58]. For example, they may be useful for characterising cancers with unknown primary origin at diagnosis [59]. Also, signatures 3 and 13 are associated with increased lymphocytic infiltrate and better clinical outcomes, raising the possibility that free DNA and/or mutant peptides associated with this pattern are more immunogenic compared with other mutational processes [60] (see Sect. 6.3.3). Finally, there are possibilities for developing signature-based predictive models, such the ‘HRDetect’ algorithm that quantifies somatic BRCA1/2 deficiency, a candidate biomarker of response to polyADP-ribose polymerase (PARP) inhibitors [61, 62].

Sequencing the genomes and transcriptomes of single breast tumour cells is now offering additional insights into heterogeneity. For example, single-cell genome analysis has been applied to understand the dynamics of clonal selection across cohorts of patient-derived tumour xenografts [63]. Also, in an elegant and clinically relevant application of RNA-sequencing, Lee and colleagues compared breast cancer cell subpopulations exhibiting resistance to the microtubule poison paclitaxel in vitro [64]. Residual cells that persisted after treatment expressed variants involved a variety of cellular processes logically connected to drug resistance, including microtubule stabilization and stress. But critically, individual cells expressed different combinations of variant transcripts, suggesting that transcriptional heterogeneity can ultimately generate equivalent phenotypes. The expression profiles of individual cells were not apparent in a pooled analysis of the bulk population, or even as few as five cells. Thus single-cell sequencing has the potential to illuminate aspects of plasticity and clonal evolution that would not be apparent from analysis of tissue homogenates.

6.3.2 Cellular Heterogeneity

Gene expression studies comparing breast tumours with normal breast tissue identified groups of tumours exhibiting transcriptomic similarity to particular mammary epithelial compartments. For example, ‘luminal-like’ tumours are most similar to the specialised luminal epithelia that line ducts and lobules of the breast (Sect. 6.4.1), while the expression profile of ‘basal-like’ tumours resembles luminal progenitor cells (Sect. 6.4.2) [65]. Functional evidence supporting the idea that global tumour gene expression profiles could reflect the cell type of origin came from transgenic mouse experiments, where oncogenic mutations were introduced into specific compartments of the mouse mammary gland, resulting in formation of tumours that phenocopied metaplastic or BRCA1-mutant breast cancer [66, 67]. Thus, heterogeneity reflects the consequences of superimposing the mutational landscape over pre-programmed phenotypic determinants. The cells comprising a tumour exhibit restricted versions of the normal mammary epithelial lineage hierarchy, depending on which cell type sustained the founding oncogenic hits, and how the unique spectrum of alterations impacted lineage differentiation programming in its daughters (for example, de-differentiation and phenotypic plasticity). Diversification is also achieved through phenotypic drift (stochastic heterogeneity [31, 68]; Fig. 6.3a). The significance of this is highlighted by the association between stem-like phenotypes and poor outcomes in breast and other cancers [69,70,71], though it is worth considering that primitive, stem-like cells may be associated with metastasis and treatment resistance simply because they have more potential for generating clonal complexity (i.e. better substrates for natural selection) [31], not necessarily because they possess equivalent normal stem cell functions like efficient drug efflux and slow cell cycling.

Fig. 6.3
figure 3

Heterogeneity represents the collective consequences of superimposing the mutational landscape over pre-existing phenotypic programs and interaction with the microenvironment. (a) Breast tumour cells exhibit restricted versions of the normal mammary epithelial lineage hierarchy, depending on which cell type sustained the founding oncogenic hits, and how the unique spectrum of alterations impacted lineage differentiation programming in its daughters. The differentiation states (different colours) of stem-like (black nuclei), committed progenitor and daughter cells contribute to phenotypic diversity (deterministic heterogeneity). Phenotypic flux due to cell-specific biochemical processes (patterning in daughter cells) also contributes to phenotypic diversity (stochastic heterogeneity). Daughter cells may acquire stem-cell activity through genetic alteration (dashed arrow) or de-differentiation (blue arrow), acquiring stem cell activity and initiating new clones. (b) Stromal elements influence tumour cell phenotypes and clonal selection, and are in turn altered by their interactions with tumour cells, contributing to intratumoural heterogeneity. The figure shows various stromal cell types: cancer-associated adipocytes and fibroblasts (CAAs/CAFs), tumour-infiltrating lymphocytes (TILs), tumour-associated macrophages (TAMs) and circulating tumour cells (CTCs) liberated into surrounding blood vessels. Ongoing tumour cell proliferation fuels pro-tumourigenic cycles of local hypoxia and neoangiogenesis, resulting in chaotic microvascular networks that cannot adequately deliver systemic therapy

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6.3.3 Microenvironment Heterogeneity

Non-tumour elements contributing to breast cancer heterogeneity include soluble and extracellular matrix proteins, fibroblasts, endothelia, adipocytes, macrophages and other leukocytes [72] (Fig. 6.3b). A large effort has been directed at investigating clinical implications of the breast cancer microenvironment [73,74,75]. One area in which there have been key recent developments is in understanding how vascular perfusion dynamics impacts tumour progression and treatment efficacy. Ongoing proliferation in solid tumours fuels cycles of hypoxia and neoangiogenesis, and this in turn creates a chaotic, dysfunctional microvascular bed, with (paradoxically) areas of sluggish blood flow in an otherwise hypervascular environment [76,77,78]. It is thought that inefficient perfusion directly reduce the delivery of systemic therapeutics, but that drug efficacy is also reduced indirectly in poorly oxygenated tissues. This is because radiotherapy and some chemotherapeutics act by damaging tumour DNA, but breaks are more readily repairable in hypoxic conditions, allowing cells to escape fatal chromosome aberrations, and instead, erroneously repair DNA to increase genetic diversity [79]. Hypoxia is also associated with mesenchymal/stem-cell phenotypes, inflammation, fibrosis, poor drug uptake and immune suppression [80]. This knowledge has driven attempts to improve efficacy and reduce the likelihood of relapse using combination therapies that increase oxygenation by ‘normalising’ the vascular bed. Ironically, this strategy uses agents that target vascular endothelial growth factor and its receptor (VEGF/VEGFR), originally intended to starve tumours of nutrients and oxygen (e.g. bevacizumab).

Tumour-infiltrating lymphocytes (TILs) have also been intensively studied in the last 5 years, with evidence rapidly accumulating to support a role in clinical management. A fundamental function of host immunity is to detect and eradicate abnormalities arising from neoplastic transformation (immune-surveillance). Considering that breast tumours are diagnosed once they are detectable by mammography and/or palpation, they are already successfully evading elimination at diagnosis, but chemotherapy and radiotherapy can produce neoantigens that effectively kick-start the immune response, and new therapies that reactivate anticancer immune responses are currently being assessed in clinical trials (e.g. immune-checkpoint inhibitors, personalised cancer vaccines and adoptive T-cell therapy [81, 82]). TILs are most frequent in HER2+ and TN disease, where the overall degree of infiltrate is associated with better outcome, even amongst TNBC patients with residual disease following neoadjuvant chemotherapy, an otherwise poor prognostic group [83].

The breast cancer immune microenvironment is a complex mixture of different functional subsets – mostly T-cells, with smaller proportions of B-cells, dendritic cells, neutrophils, macrophages and natural killer (NK)-cells; with different effects on tumour progression. For example, NK and CD4+ Th1-cells are generally associated with favourable outcomes, whilst myeloid-derived suppressor cells (MDSC) and gamma-delta regulatory T-cells (γδ-Treg) suppress anti-tumour immunity and are associated with poor response to chemotherapy [84,85,86,87]. Ultimately, the particular constitution of lymphocytic infiltrate (ratios of different TIL subsets and effectors/modifiers they produce) reaches equilibrium with the tumour compartment, and shapes the microenvironment along a spectrum from an immuno-stimulatory, anti-tumour milieu, to a pro-tumourigenic environment geared toward wound-healing. Despite this complexity, TILs are routinely enumerated en masse by examination of haematoxylin and eosin (H&E)-stained tissue sections. Special IHC stains are used occasionally, but at this stage this is purely to help discriminate intratumoural TILs from tumour cells, rather than identify different functional subpopulations [88]. Apart from the fact that the full clinical implications of functional TILs heterogeneity are still being elucidated, there are challenges with standardisation in the diagnostic laboratory, as TILs reside mostly in the stromal compartment, which varies with tumour architecture, and enumeration on two-dimensional tissue sections is difficult because they are heterogeneously distributed in three dimensions. But even without universal standardisation, and somewhat crude histopathologic assessment of overall infiltrate density, there is already strong evidence from multiple prospective clinical trials supporting the prognostic and predictive significance of TILs in HER2+ and TN disease [81]. Thus, improvements in the precision of TIL-based biomarker development and companion therapies will likely play favourably into personalised clinical management models.

6.4 Molecular Classification of Breast Cancer

Molecular profiling has shifted the ways that breast cancer development and heterogeneity are considered. Transcriptomic studies began more than 15 years ago, with the segregation of 38 invasive breast carcinomas by unsupervised hierarchical clustering of gene expression profiles [89, 90]. These ‘intrinsic subtypes’ have since been extensively confirmed in the field as robust biological entities with distinct mutation profiles and clinical outcomes [70, 91,92,93] (Table 6.3). In some instances, underlying expression profiles provide a molecular explanation for well-known clinical or morphological features. For example, the clinical behaviour of ER-positive tumours depends largely on histologic grade, and in the intrinsic subtype taxonomy they segregate into luminal-A and -B groups, distinguished by expression of proliferation gene networks [89, 90, 93,94,95].

Table 6.3 Molecular breast cancer subtypes
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Subsequent technological advances have increased the breadth and resolution of the transcriptomic taxonomy (more tumours, more extensive coverage and more accurate RNA quantification), providing a deeper understanding of the underlying biology and potential clinical implications. For example, a subgroup of ER-negative IC-NST that frequently exhibits medullary and metaplastic features is enriched with a ‘claudin-low’ gene cluster [71, 96]. Another is the ‘molecular apocrine’ (mApo) group, which is largely triple-negative, yet paradoxically expresses ER-responsive, luminal genes due to expression of the androgen receptor (AR; Table 6.3) [97, 98]. In smaller cohorts with less statistical power, mApo tumours would be classed as basal-like or HER2-enriched [94, 97, 99]. The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) took expression profile analysis to a different level by integrating expression with copy-number data to stratify 2000 breast tumours on the basis of cancer driver profiles [50]. The ten ‘integrative subgroups’ overlap variably with intrinsic subtypes and are associated with distinct survival trends.

6.4.1 Heterogeneity in Luminal/ER+ Breast Cancer

The largest subgroup of breast cancers is defined by expression of ER in at least 1% of tumour cells, a conservative cut-off that qualifies around 70% of breast cancer patients for endocrine therapy; though ER+ disease still exhibits marked clinical variability, particularly with respect to late recurrences [50, 100]. While endocrine therapy significantly increases relapse-free survival overall, almost a quarter of patients still relapse within 10 years of diagnosis, with some evidence that mutations in the gene itself (ESR1), or ER signalling regulators (e.g. SMRT) could contribute [101,102,103]. In terms of predictive molecular features, luminal-B tumours tend to respond less well to endocrine therapy but better to chemotherapy than luminal-A tumours, with the exception of ‘atypical’ luminal-A cases, which harbour more TP53 mutations and copy-number aberrations [101, 104]. Deep molecular analyses have also identified unique differences between luminal-A ILC and IC-NST, with ILCs featuring loss of CDH1, AKT signalling activation and FOXA1 mutations, while IC-NST showed intact cellular adhesion and GATA3 mutations [8]. Analysing ILCs as a separate group stratified them into reactive-like, immune-related and proliferative subgroups [8]. It is also worth highlighting that there are two different ligand-activated oestrogen receptors, encoded by separate genes: ERα (ESR1) and ERβ (ESR2) [105]. ERα is the dominant isoform and the best predictor of response to hormone therapy, but a possible role for ERβ in regulating the immune microenvironment is also now emerging [106], and may provide further insights into luminal breast cancer heterogeneity in the future.

6.4.2 Heterogeneity in Triple-Negative Breast Cancer

TNBC is arguably the most heterogeneous of the major breast cancer subgroups. It is more frequent in individuals with inherited mutations in BRCA1 and other DNA repair genes and carries a poor prognosis overall, although some patients have complete, durable responses to treatment. TNBCs are usually highly proliferative and of higher histological grade, though there are low-grade variants with a more protracted natural history (namely, adenoid cystic and secretory carcinomas) [70, 90, 91, 107,108,109,110,111,112,113,114,115]. A defining characteristic of TNBC is its paradoxically impressive initial response to neoadjuvant chemotherapy, yet poor 5-year survival rate. The discrepancy is due to a chemotherapy-resistant subgroup found to have residual disease at the time of breast surgery, which is associated with brain and liver metastasis [116]. TNBC patients who do not relapse within 3 years have a prognosis comparable to ER+ disease [109].

Despite high overall mutation loads, the most common variants are in TP53 (around 80% of cases, with more frequent nonsense and frameshift mutations than ER/HER2+ disease) and PI3K in around 10% of cases [117]. On the other hand, TNBCs harbour characteristic chromosomal alterations, categorised as ‘simple’, ‘amplifier/firestorm’ or ‘complex/sawtooth’ depending on the complexity of causal rearrangements [118]. Integrating CNA with expression data in basal-like TNBC revealed that alterations tend to converge on two major oncogenic signalling pathways: EGFR-ras-MEK and PI3K-mTOR. Common alterations include loss of PTEN and INPP4B and amplification of CDK1, MYC and AKT3. Interestingly, this profile confers more similarity to serous ovarian cancer than other breast cancers, providing a genomic link between two malignancies associated with germline BRCA1 mutation [117, 119]. mTOR/MEK inhibitors are promising as combination agents with neoadjuvant chemotherapy, however they are associated with significant toxicity and dosing schedules are still being optimised [120].

Together, these studies have produced critical information on somatic events underpinning TNBC development, however the tumour microenvironment receives little weight in genome-focused approaches. The percentage of ‘contaminating’ non-tumour cell types is considered a limitation in genomics, but given that immunogenicity is a strong determinant of clinical outcome in TNBC, with TILs at the forefront of personalised therapy for this patient group, there is a valid argument for stratifying TNBCs in a way that encompasses the complexity in tissue homogenates, rather than filtering it out. Two landmark studies attempted to classify TNBC on the basis of unsupervised clustering of gene expression data from large tumour cohorts [121, 122]. They identified four to six major clusters associated with distinct functional gene networks, genomic alterations and clinical outcomes. Independent analyses incorporating laser capture-micro-dissected tumour samples, patient-derived xenografts and cell lines (which lack human non-tumour elements) subsequently showed that tumour cellularity is a critical determinant of clustering [123, 124], and the field is not yet united on a robust classifier that mathematically accounts for this. But overall, the data suggest that basal-like TNBCs (70–80% of cases) can be further segregated according to the degree of active immune infiltrate and tumour-specific immunity, while non-basal TNBCs (20–30%) can be defined by whether they engage ER-independent hormone signalling or exhibit mesenchymal and stem-like features (previously identified as ‘claudin-low’ as an extension of the intrinsic subtype classifier) [71, 121, 124]. Interest is also gathering around epigenetic dysregulation, and whether differentially methylated regions of the TNBC genome could underpin some of this biological variability [125].

6.5 Clinical Implications of Breast Cancer Heterogeneity

6.5.1 Molecular Diagnostic Tools

As molecular subtypes have emerged, the clinical corollary has been to develop risk stratification signatures. However, many offer no significant benefit beyond current practice standards. In order to be useful, risk stratification signatures must add value to existing histopathological data, accurately classify individual cases (so-called ‘single sample predictor’) and be readily implemented in the diagnostic laboratory. From a clinical perspective, the most important contribution of molecular subtyping has been the recognition of the luminal A/B subdivision in ER-positive disease, which has informed the development of MammaPrint® [126,127,128,129,130], Oncotype DX® [131,132,133], EndoPredict® [134] and Prosigna® [94, 131,132,133]. These tests quantitatively assign the risk of recurrence in ER-positive, node-negative patients, and have implications for sparing a proportion of low-stage patients from receiving chemotherapy. The ‘Nottingham Prognostic Index Plus’ is another good example of early transitioning to precision medicine – it attempts to blend traditional parameters with a broader IHC panel to better define molecular subtypes and improve stratification [135]. The topic of molecular signatures in breast cancer prognostication has been recently reviewed [136].

Precision medicine is predicated on employing genomics and gene expression profiling to personally tailor a treatment regimen. Theoretically, this is optimal as it allows targeting of appropriate pathways while minimising treatment with agents that may have limited or no benefit. Yet, identifying a targetable mutation does not guarantee that the matching treatment will be effective (for example, one study reported that only 36% of somatic variants were actually expressed [51]). With the exception of ERRB2, no single genomic alteration is a clinically useful predictor of therapeutic efficacy [137]. This was exemplified recently in the PALOMA-1 trial where CDK4/6 amplification was not a reliable predictor of response to a CDK4/6 inhibitor [138]. Predictive tools that incorporate multiple alterations or that complement existing algorithms such as the Nottingham Prognostic Index, are the ideal way to move forward.

It is clear that molecular profiling has a role in breast cancer management, but incorporating the technology and knowledge into routine clinical practice is a major challenge. In the public health setting, there are considerable logistic and economic barriers related to standardization, accreditation and reliable service delivery. The requirements will include infrastructure that is cost-effective and can adapt to evolving technologies; dynamic, curated databases of clinically actionable variants/signatures; major changes to pathology and oncology training programs, and possibly an entirely new precision oncology specialization stream that incorporates genetic counselling, as there are important ethical issues around synthesizing and communicating complex diagnostic results to the clinician and patient in a meaningful way. Finally, how should mutations which are of as-yet-unknown clinical significance be dealt with? Sequencing of close to 1000 breast cancers has identified 128 genes with putative targetable alterations [139]. For the vast majority, genotype-drug efficacy relationships are still being elucidated in preclinical and clinical studies, and the cost-effectiveness and overall benefit of matching treatments are unknown [140].

A tumour biopsy is a static representation of a small fraction of a larger mass taken at a single point in time. Given that diagnostic tests used today are still relatively low-resolution with conservative thresholds, tumour under-sampling is not currently a major consideration in clinical practice. However given that precision medicine aims to select rational combinations of targeted agents according to an individual tumour’s profile, and yet drug resistant, metastatic clones may represent a minor component of the tumour, the risk of under-sampling will be magnified in a precision oncology context [141, 142]. The potential consequences of basing clinical management on small tumour biopsies has been exemplified by spatial and temporal heterogeneity in amplification and over-expression of HER2 during disease progression [30]. In the case of HER2+ breast cancer, trastuzumab therapy does not preclude later development of metastatic disease; in fact distant recurrence is common and can be HER2-negative [70, 143].

6.5.2 Heterogeneity in Metastatic Breast Cancer

Historically, metastasis was viewed as a complication of end-stage disease with the assumption that distant cancer deposits were virtually the same as the primary tumour from which they arose (linear progression) [144]. However, we have known for some time that the expression of clinically relevant biomarkers can differ between primary and metastatic tumours from the same patient (e.g. Fig. 6.4; [145,146,147,148,149,150,151,152]), and American Society of Clinical Oncology (ASCO) practice guidelines now recommend direct biopsy of accessible metastases for repeat HER2/ER/PR testing [153]. More recently, sequencing studies have provided compelling evidence for parallel evolution in regional metastases [154,155,156,157,158]. By applying whole genome sequencing to a large cohort of breast and matching metastatic tumours, Yates and colleagues found that at the time of initial diagnosis, primary tumour genomes are suitable proxies for subclinical metastatic deposits (very encouraging in terms of guiding adjuvant therapy for early breast cancer), but resistant deposits undergo clonal expansion and further diversification, acquiring additional driver alterations before becoming clinically detectable [158] (Fig. 6.2). These findings suggest that early detection of therapeutic resistance will be crucial for optimising therapy, and that re-biopsy will be critical for the success of molecular-targeted therapies in the metastatic setting.

Fig. 6.4
figure 4

Metastatic breast cancer heterogeneity exemplified by variable expression of progesterone receptor (PR). Approximately 1% of tumour cells in the primary breast tumour are positive for PR, whereas the liver metastasis is negative, and the dural lesion is virually 100% positive. IHC, immunohistochemistry

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Single-cell genomics can be used to track genomic and transcriptional changes in individual cells, and track clonal evolution over time. There has also been great enthusiasm around applying this technology to circulating tumour cells (CTCs), shed from solid tumours into the circulation or lymphatics and detectable as a source of genetic material [159,160,161]. CTCs predict survival, disease-related mortality, response to treatment and early disease recurrence [162,163,164], and it has been argued that the ‘liquid biopsy’ could represent the entire tumour genome, and provide a means for sensitively monitoring response to treatment over time [165]. However, there are major logistic barriers to realising this goal, not least of which are the low and variable concentrations of CTCs in peripheral blood (1–10 CTC per 10 mL [166]). On the other hand, multiple proof-of-principle studies have underscored the utility of circulating tumour (ct)DNA as an early indicator of therapeutic resistance and the presence of residual disease [165, 167, 168], and suggested value as a collective representation of the tumour genome [169], helping to overcome issues related to tissue sampling bias [170]. As ctDNA profiling is incorporated into more clinical trials, significant developments are expected in the forseeable future [171].

6.6 Concluding Remarks

New knowledge about the extent of heterogeneity in breast cancer and how this relates to clonal evolution is changing the way we think about research, diagnosis and treatment. From a clinical standpoint, the most dire complication of breast cancer heterogeneity is therapeutic resistance. To tackle this, the field has taken a three-pronged approach: (1) reduce the likelihood of relapse in the first place by using more effective agents (and likewise, use agents more effectively) for early breast cancer; (2) detect therapeutic resistance early so that treatment regimens can be optimised; and (3) expand the arsenal of second- and subsequent- line agents so that metastatic disease can be stabilised for as long as possible (though a future goal will be to treat metastatic disease with curative intent).

Achieving these expectations will require continued investment in research and development to identify new therapeutic targets; including druggable alterations, synthetic lethal vulnerabilities, and innovative strategies that combine therapies for maximal efficacy and/or to simultaneously target minor subclones that cause resistance. In parallel, we will need to implement high-resolution companion molecular diagnostic assays that are scalable, adaptive and cost-effective in the public health setting. Implicit in this will be algorithms that accurately predict risk profiles and rank suitable therapeutic regimens according to each tumour’s molecular profile, potentially highlighting clinical trial suitability. Clinical training programs will need to evolve to meet precision oncology demands, as oncologists of the future will be expected to synthesise complex diagnostic information, assess optimal therapeutic strategies and deliver complicated diagnostic results and recommendations to their patients. Finally, we need public investment and innovation in clinical trials, including measures to drastically increase access to patients outside major metropolitan centres. Apart from improving equity, conducting trials on a broader scale will be necessary to determine dosing regimens, timing and drug interactions, and to achieve adequate recruitment as the numbers of new targets and agents coming online increases.