table A and B

Molecular classification of breast cancer: What the

pathologist needs to know

Emad A Rakha and Andrew R Green

Academic Pathology, Division of Cancer and Stem Cells, School of Medicine, University of

Nottingham and Nottingham University Hospital NHS Trust, Nottingham, UK

Correspondence

Emad Rakha

Department of Histopathology, Division of Cancer and Stem Cells, School of Medicine,

The University of Nottingham and Nottingham University Hospitals NHS Trust,

Nottingham City Hospital, Nottingham, NG5 1PB, UK. Email: [email protected]

Key words: breast cancer, molecular taxonomy, gene expression profiling, luminal, HER2,

basal-like, next generation sequencing, molecular prognostic assays.

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ABSTRACT

Breast cancer (BC) is a heterogeneous disease featuring distinct histological, molecular

and clinical phenotypes. Although traditional classification systems utilising

clinicopathological and few molecular markers are well-established and validated they

remain insufficient to reflect the diverse biological and clinical heterogeneity of BC.

Advancements in high-throughput molecular techniques and bioinformatics have

contributed to the improved understanding of BC biology, refinement of molecular

taxonomies and the development of novel prognostic and predictive molecular assays.

Application of such technologies is already underway, and is expected to change the way

we manage BC. Despite the enormous amount of work that has been carried out to develop

and refine BC molecular prognostic and predictive assays, molecular testing is still in

evolution. Pathologists should be aware of the new technology and be ready for the

challenge. In this review, we provide an update on the application of molecular techniques

with regard to BC diagnosis, prognosis and outcome prediction. Current contribution of the

emerging technology to our understanding of BC is also highlighted.

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INTRODUCTION

Historically breast cancer (BC) was classified based on clinicopathological features mainly

tumour stage, and grade. Other morphological features such as histological type,

proliferation status and lymphovascular invasion are also recognised as important

morphological prognostic variables that reflect tumour biology (1, 2). Over time,

knowledge about BC biology has significantly increased and led to the understanding that

BC represents a heterogeneous group of tumours and that tumour behaviour and response

to therapy is determined by the underlying biological features. The expression of oestrogen

receptor (ER), progesterone receptor (PgR) and the human epidermal growth factor

receptor 2 (HER2) that were originally identified as predictive of response to systemic

therapy are now recognised to be the main determinants of BC biology and can be used to

refine BC molecular and prognostic taxonomy. More recently, molecular data arising from

a variety of high throughput techniques have been used to refine BC stratification and

develop prognostic and predictive classification with the aim of individualised therapy.

Although molecular taxonomy of BC based on gene expression profiling, proteomics,

DNA copy number alteration and chromosomal changes, mutation status, methylation and

microRNAs has been expanding for many years and has increased our knowledge of BC

biology, its clinical application remains limited. The introduction of next generation

sequencing (NGS) or massively parallel sequencing (3) appears to have opened new

avenues for decoding BC molecular complexity, refine molecular classification and

identify new therapeutic targets. These molecular techniques hold promise for improving

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diagnosis, prediction of outcome and behaviour, and in aiding selection of therapies for

individual patients (4). However, its clinical utility is still under investigation (5).

Pathologists are currently using conventional and novel molecular techniques on routine

practice to help diagnosis of morphologically challenging entities, to assess the expression

of hormone receptors and HER2 status on every BC and help oncologists to refine the

prognostic stratification of BC and complement the morphological variables with

molecular biomarkers. Although immunohistochemistry (IHC) remains the most

commonly used conventional molecular technique, other techniques are increasingly used

in routine practice including in situ hybridisation (ISH), RT-PCR, and in some centres NGS

and expression microarrays. In the research setting, several other molecular techniques are

u s e d i n c l u d i n g c o m p a r a t i v e g e n o m i c h y b r i d i s a t i o n ( C G H ) , e x p a n d e d

immunohistochemistry with tissue microarrays and proteomics. In this review, the main

applications of molecular techniques on BC are highlighted with emphasis on the practical

applications which can be generally divided into three main categories; diagnosis,

molecular prognostic and predictive taxonomies.

Using molecular biomarkers in the diagnosis of breast lesions

In addition to prognosis and treatment response prediction, molecular biomarkers are

frequently used in the diagnosis of challenging breast lesions; to differentiate between

benign and malignant entities, in situ and invasive tumours, subtyping of certain lesions

and determination of the tissue of origin of less differentiated malignant tumours. The most

frequent technique utilising in this aspect is IHC often using a panel of biomarkers (6, 7).

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IHC plays a useful role in diagnosing spindle cell lesions, identification of myoepithelial

cells, differentiate between ductal and lobular phenotype and between hyperplastic

epithelial proliferative process from neoplastic clonal epithelial proliferation and in the

classification of papillary lesions. Cytokeratins can be used to detect small nodal

metastases or subtle invasive carcinomas such as invasive lobular carcinomas. IHC also is

helpful in recognising metastases to the breast and mammary carcinomas metastasising to

extramammary tissues. Different antibodies are useful for different tumours: PAX8 and

WT1 for ovarian carcinoma; TTF1 for thyroid and pulmonary adenocarcinoma; melan-A,

HMB45 and S100 for melanoma; and lymphoid markers for lymphoma. Specific genetic

translocations are also helpful for diagnosis of certain breast lesions (see below) and for

exclusion of specific soft tissue tumours when identified on a biopsy as a component of

other mammary-specific lesions; for instance pure stromal component of a malignant

phyllodes tumour to be differentiated from other soft tissue sarcomas that may have

different management strategies (8).

Companion diagnostics in breast cancer

The ability to predict an individual’s response to a specific therapy is the main aim in

modern precision medicine. A molecular diagnostic tool in the field of cancer therapy was

first used in the 1970s to predict response of BC to the selective oestrogen receptor

modulator, tamoxifen based on the expression of ER (9). Currently, several targeted cancer

therapies are utilised in standard oncological care and this field is expanding. As a result,

the concept of “companion diagnostics” has emerged which can be defined as a diagnostic

test used as a companion to a therapeutic drug to determine its applicability to a specific

patient. Currently, the US Food and Drug Administration (FDA)-approved companion

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diagnostics are utilised in BC tests for the presence of HER2 protein overexpression or

gene amplification. Despite not considered companion diagnostics by the FDA, ER and

PgR testing is mandatory for effective hormone therapy decision making and can be

considered as companion diagnostics in BC. Although prognostic multigene assays are not

companion diagnostics per se, as they are not linked to a particular drug, they can result in

changes in clinical decisions and treatment course based on their outcome predictions

(Table 1).

HORMONE RECEPTOR TESTING: Hormone receptor status is determined by the tumour

cells’ expression of nuclear receptors for oestrogen (ER) and progesterone (PgR).

Biochemical ligand-binding assays were initially used to detect ER and PgR, but they

required fresh tissue and were technically challenging and therefore IHC assays have

become routine. Different scoring methods are in use for determining the level of

expression but the most widely used systems are the Allred scoring and the histochemical

score (H-score) methods which both assess the proportion and intensity of staining that are

summed to give an overall score. However, the currently agreed cut-off of positivity of ER

and PgR for management purpose relies on proportion scoring and is 1% (10). Patients

with BC showing any nuclear expression of hormone receptor in invasive tumour cells

above the cut-off are likely to respond to hormone therapy and are therefore potential

candidates for this therapy. However, for a diagnostic purpose, i.e. determination of a

mammary origin of a metastatic carcinoma, a more stringent definition of positivity is

often used based on the pathologist’s discretion. Although current guidelines indicate that

IHC is used for determination of hormone receptor status (10) in BC, ER and PgR are

component genes of some multigene assays including Oncotype DX. Information

6

regarding hormone receptor status using these assays can be used as an additional quality

measures for assessment methods. Discrepancy of results should trigger a reflex test.

HER2 TESTING: HER2 is overexpressed in 12% to 20% of BC most often because of

HER2 gene amplification. Because of its predictive value, guideline recommendations for

its assessment (11) and their updated versions (12, 13) have been published to provide

guidance on HER2 testing in BC. Key aspects of these guidelines include a

recommendation that all BC be tested for HER2 using IHC and subsequently with ISH in

borderline positive IHC cases using a validated test. It should be recognised that both IHC

and ISH represent an attempt to convert a continuous biological variable into a dichotomous

category and borderline or equivocal cases exist and a reflex test is recommended to reduce

the proportion of these cases. The use of the updated definition of positivity of HER2 has

reduced the proportion of these borderline cases (12, 13).

Ki67 PROLIFERATION INDEX: The Ki67 proliferation index has been investigated as a

BC prognostic and predictive factor in various settings (14). Ki67 is assessed in routine

practice using IHC however, its analytic validity remains a matter of debate and formal

inter-and intra-laboratory standardisation hampers its use in routine practice for

management decision (15). Ki67 can be used in routine practice to i) determine the

proliferation status in poorly fixed specimens, or 2) stratify grade 2 tumours into two

prognostically distinct classes (16) akin to the molecular grade index (17). Ki67 is also used

a component of some prognostic tools (18) however; the published 2016 American Society

of Clinical Oncology (ASCO) clinical practice guideline on BC (15) recommends that Ki67

labelling index determined by IHC should not be used to guide choice on adjuvant

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chemotherapy with intermediate quality of evidence base and moderate strength of

recommendation.

GENETIC TESTS AND DIAGNOSIS: Some diagnostic microarray-based gene expression

tests were developed for identification of cancer tissue of origin. These include the

Pathwork Tissue of Origin Test that was developed using a 2000-gene classification model

for identification of tumour tissue of origin with an overall accuracy in identifying the

primary site of poorly differentiated tumours up to 90% (19) and the THEROS Cancer

TYPE ID® which is a RT-PCR-based test using 92 genes and FFPE samples (20).

Importantly, some special type mammary carcinomas show specific translocations which

characterise these tumours and can be used as diagnostic adjunct. Secretory carcinoma of

the breast is characterised by a balanced translocation of genetic material between

chromosomes 12 and 15 (t(12;15)) creating a new gene in which the 5' region of ETV6 is

fused to the 3' region of NTRK3 producing ETV6-NTRK3 fusion gene (57).

Mucoepidermoid carcinoma which is a rare type of metaplastic BC is characterised by a

translocation between chromosome 11 and 19 (t(11;19)(q21;p13)) creating a novel fusion

product between mucoepidermoid carcinoma translocated 1 (MECT1) and Mastermind-

like gene family (MAML2); MECT1-MAML2 fusion gene (58). Adenoid cystic

carcinomas as well as cylindroma show a specific translocation t(6;9)(q22-23;p23-24)

creating MYB-NFIB fusion gene (59). In a study of breast adenoid cystic carcinoma

mixed with a high grade triple negative BC components, the MYB-NFIB fusion gene was

detected in both tumour subtypes and it was postulated that the progression from adenoid

cystic carcinoma to high-grade triple-negative BC of no special type may involve the

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selection of neoplastic clones and/or the acquisition of additional genetic alterations with

enrichment of mutations affecting certain genes such as FGFR1 (21).

Prognostic and predictive taxonomies

Molecular classification of breast cancer

BC has been classified based on the expression of biomarkers using a variety of

techniques, concepts and applications. Based on the expression of individual biomarkers,

BC can be classified into ER positive and ER negative, HER2 positive and HER2 negative.

Although this appears as a simplified molecular classification system, it remains as the

most important and informative molecular BC taxonomy to date for clinical management

in routine practice (15). These two markers with or without addition of other biomarkers;

namely PgR and Ki67 can be used in combination to provide further important prognostic

information (22, 23). For instance the response of ER positive HER2 negative tumours to

hormone therapy is different to ER positive HER2 positive tumours. Despite the predictive

and prognostic value of hormone receptors and HER2, complex molecular classifications

based on multiple markers utilising high-throughput techniques have attracted attention as

a novel method for molecular taxonomy. Molecular classification of BC was initially

investigated using loss of heterozygosity analysis (LOH), karyotyping and CGH, which

identified key genomic alterations including losses, gains and amplifications of genomic

DNA (24-27). This provided the early framework for a molecular classification system that

stratified BC into distinct classes. Global gene expression profiling (GEP) studies of BC

using unsupervised clustering techniques have provided a more established molecular

classification system and identified distinct clusters or intrinsic subtypes based on the

quantitative expression of several genes (transcriptome profiles) (28, 29). Subsequent

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class discovery studies have also reported an association between molecular intrinsic

subtypes and patient outcome and that these classes are associated with distinct biological

pathways making them potential candidates for targeted therapy.

In the pioneer GEP study by Perou and colleagues in 2000 (28) using the expression of a

subset (n=496) of differentially expressed genes termed the 'intrinsic' gene set, it was

demonstrated that BC at the transcriptome level is not a single disease. Despite the fact that

each individual tumour features a unique GEP related to its specific biological features and

genetic abnormalities, tumours clustered together to produce distinct reproducible classes

based on transcriptomic profiles with common overlapping features. In Perou’s study (28)

two main clusters were identified and appeared to be related to ER expression. The ER+

cluster was enriched with ER, ER-related genes and other genes characteristic of the luminal

epithelial cells and this class was termed as Luminal to indicate its molecular similarity to

them. The other major class contained ER-negative tumours and showed three distinct

subclasses termed HER2-positive, basal-like and normal breast-like. The HER2 subgroup

was characterised by overexpression of HER2 and other genes pertaining to the HER2

amplicon. The basal-like class was largely characterised by the lack of expression of ER and

HER2 and by positive expression of genes characteristic of basal-like cells of the breast and

by high proliferative activity. The normal breast-like class displayed a triple-negative

phenotype but did not cluster with the basal-like centroid and was characterised by

expression profiles similar to those found in normal breast tissue.

Subsequent GEP studies indicated that the luminal class, which comprises the majority of

BC is heterogeneous with respect to the expression of other genes and outcome (30). The

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Luminal cluster was further stratified into subclasses with at least two distinct subclasses

reported in many studies; luminal A and luminal B subtypes. Most studies indicated that

luminal B tumours are associated with a worse prognosis than tumours of the luminal A

class however, the molecular definition was variable and not reproducible. In general it was

characterised by ER expression but with higher proliferation rates and/or HER2 expression

and low or absent PR expression (31). Other luminal subclasses have been described

including luminal C (32) and luminal N (33) but the classification into luminal A and B

remains the most validated sub-classification despite the limitations described above.

Similar to the luminal class, some studies have classified basal-like tumours into several

subgroups. In a previous study of 587 triple negative BC, Lehman and colleagues (34)

reported six subtypes displaying unique GEP. These include basal-like I, basal-like II, an

immunomodulatory, a mesenchymal, a mesenchymal stem-like, and a luminal androgen

receptor (AR) subtype. Other authors have reported four subtypes of triple negative BC

(luminal AR, mesenchymal, basal-like immune-suppressed, and basal-like immune-

activated) (35) and we have split them into those that have high or low p53 expression (33).

In addition to their clinical relevance, the molecular intrinsic subtypes showed distinct

pattern of genomic alterations, emphasising the divergent biological characteristics of

tumours from these classes. For instance, the luminal A class has the greatest number and

diversity of significantly mutated genes, with PIK3CA at 45% being the most frequent.

Luminal B cancers showed mutations affecting both TP53 and PIK3CA. A high proportion

of tumours in the HER2 class show a high frequency of TP53 and PIK3CA mutations.

H E R 2 - l u m i n a l - l i k e t u m o u r s h a d h i g h e r e x p r e s s i o n o f g e n e s s u c h

as GATA3, BCL2 and ESR1 and higher frequency of GATA3 mutations. TP53 mutation is

most frequent in the basal-like cancers, with most of the significantly mutated genes in

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luminal tumours being absent. Whilst TP53 mutations are present in the basal-like and

HER2 tumours, the type of mutation in this gene differs between subtypes (36).

In recent years several consortia were launched, with researchers from around the world

collaborating to map the genomes of BC and other cancer. These consortia and other

research groups started using the ‘multi-omic’ approaches involving different technology

platforms: genomic DNA copy number arrays, DNA methylation arrays, exome

sequencing, mRNA arrays, microRNA sequencing and reverse-phase protein arrays to

develop a more global and integrated ‘picture’ of BC. An example of this genomics-driven

classification of BC based on an integrative analysis of GEP and genome-wide copy

number alterations (CNAs) was reported by the Molecular Taxonomy of Breast Cancer

International Consortium (METABRIC) (37). This study of 2,000 BC reported that the

number of molecular subtypes is likely to be 10, which are called “integrative clusters” and

that these subtypes showed distinct clinical behaviour. The Cancer Genome Atlas (TCGA)

network (38) have analysed 466 BC using five platforms; genomic DNA copy number

arrays, DNA methylation, exome sequencing, mRNA arrays, microRNA sequencing and

reverse phase protein arrays. The integrated information across platforms demonstrated the

existence of four main BC classes, identified two novel protein expression defined

subgroups related to stroma / microenvironment’s elements and provided key insights into

previously-defined gene expression subtypes. They have also identified specific signalling

pathways dominant in each molecular subtype and hypothesised that much of the clinically

observable heterogeneity and plasticity occurs within, and not across, these major

molecular subtypes of BC (38).

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To overcome the problems of fresh tissue, the availability of microarray-based technology,

cost and assay reproducibility, other techniques such as RT-PCR and IHC coupled with

tissue microarrays using a smaller set of genes have been introduced to replicate this

molecular taxonomy and to identify intrinsic subtypes in routine practice. Two main

approaches have been identified. The first approach was based on identifying a minimum

gene sets from microarray-based studies and used the minimum set of genes that can

reliably identify the GEP defined classes. One successful example is the PAM50

(Prediction of Microarray using 50 classifier genes plus 5 reference genes) classifier (39)

that categorises BC into four intrinsic subtypes; luminal A, luminal B, HER2-enriched, and

basal-like. The other surrogate approach to identify intrinsic BC subtypes includes using

tissue microarrays and IHC utilising a large panel of biologically relevant biomarkers and

then applying unsupervised clustering techniques to identify molecular classes with and

without comparison with GEP defined molecular subtypes. In a previous study we have

applied 25 IHC biomarkers to 1,076 unselected BC series (40) and identified seven

molecular classes primarily based on ER and HER2 expression. For a practical use in

clinical routine practice, the number of biomarkers was reduced for to 10 biomarkers

which produced comparable classification power (33). As the performance of

clinicopathological factors varies among the molecular classes, the concept of refining the

traditional Nottingham Prognostic Index (NPI) that utilises grade, size and lymph node

status applied equally to BC cases regardless of the molecular features. NPI Plus (NPI+)

was based on classifying BC into seven distinct molecular classes using the 10 biomarkers

followed by incorporation of clinicopathological variables to identify distinct prognostic

groups with each of the classes (33). Using the NPI+ formulae, through incorporating

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molecular features and clinicopathological parameters, an improved patients’ outcome

stratification was achieved superior to the traditional NPI (33).

Although the identification of the intrinsic subtype-based molecular classification of BC

has attracted attention, and improved our understanding of BC biology and increased hope

in refinement of BC therapy prediction, their application in routine practice has been less

successful. Targeted therapy of BC still relies of ER and HER2 regardless of the molecular

class of the tumour; for instance HER2 positive BC patients are candidates for HER2

targeted therapy regardless of the intrinsic class whether HER2-enriched or luminal.

Despite the limited clinical applicability, GEP has opened new avenues for refinement of

BC molecular prognostication as it has led to the introduction of the molecular multigene

assays that aim to identify subgroups of BC associated with outcome or specific response

to therapy (41). This approach is based on identification of a set of genes (gene signature)

that can be used collectively to identify tumours with specific biological or clinical

features. The term “genomic signatures” was used to refer to the expression of a set of

genes in a biologic sample using microarray technology while “metagene” refers to a

single aggregate measure of the expression of a group of genes that usually show

coordinated expression in a set of samples and defined by mathematical combination of the

genes of interest. Most of these multigene assays were used in BC to stratify prognostically

clinically relevant groups into low and high risk subgroups to guide further treatment.

Although these molecular classification systems have provided fascinating new insights

into BC biology and they may have provided more prognostic and better predictive power

than conventional variables and complement them, we still have a long way to go in terms

of delivering truly personalised medicine and further work is needed.

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The first multigene prognostic assay was developed by van't Veer et al. (42) who used a

class prediction approach utilising a 70-gene set associated with the likelihood of metastasis

within 5 years. This 70-gene signature was validated in a subsequent study (42) and was

later commercially marketed as the MammaPrint assay (Agilent, Amsterdam, the

Netherlands). Other gene signatures have been developed based on prediction of outcome or

response to specific therapy and used as prognostic and predictive signatures in the clinical

context used in their development and validation. The most commonly available multigene

assays include PAM50 risk of recurrence score (Prosigna kit) (43), Oncotype DX assay (44),

Breast Cancer Index (BCI) (45), EndoPredict (46) and MammaPrint score [4]. Other studies

have attempted to generate multigene predictors based on a hypothesis derived from in vivo

or in vitro experiments or on genes characteristic of a biological process and then applied to

BC samples (47, 48). Examples include genes associated with host immune responses,

wound healing and other stromal gene signatures that carry prognostic value independent of

ER status and proliferation and may represent candidate predictive markers for targeted

therapies (49) (Table 2).

Despite the minimal overlap between various gene signatures, most of them show clinically

significant risk stratification particularly in the clinically indeterminate group of ER-

positive, HER2-negative and lymph node-negative or with low nodal burden disease (15,

50). These multigene prognostic assays are used to stratify BC into distinct prognostic

groups; high risk and low risk groups with intermediate risk group in some tests. Patients in

the low risk group can avoid chemotherapy while patients in the high risk group are

considered as a candidate for chemotherapy. Current evidence indicates that these

multigene prognostic assays have limited clinical application in ER negative, HER2 positive

15

and advanced stage tumours as patients with these tumours are typically offered

chemotherapy(15).

Molecular classification of special breast tumour types

Most of the molecular profiling data of BC relate to ductal NST carcinomas which comprise

approximately 75% of BC and their diagnosis is one of exclusion, when a tumour does not

fit into a defined special subtype. Comprehensive molecular analysis of ILC (51) which is

the most common special BC type revealed that besides E-cadherin loss which is the best

known ILC genetic hallmark there are specific mutations targeting PTEN, TBX3, and

FOXA1 as ILC enriched features. PTEN loss associated with increased AKT

phosphorylation appeared to be highest in ILC among all breast cancer subtypes. Global

gene expression profiling revealed the existence of 3 subtypes of ILC; reactive-like,

immune-related and proliferative classes (51). These subtypes showed many significant

genomic features at the mRNA and protein/phosphoprotein level with 1,277 genes

differentially expressed between ILC subtypes. However, no difference between these ILC

subtypes was identified in terms somatic mutations or DNA copy-number alterations. As

expected the proliferative subtype was associated with the worst outcome whilst the

reactive-like was associated with the best outcome (51). At the DNA ILC cases were

significantly enriched for CDH1 mutations and mutations affecting TBX3 and FOXA1.

GATA3 mutations appeared to be the second most discriminant event between ILC and

ductal NST carcinoma after CDH1 mutations. In addition homozygous losses of the PTEN

locus (10q23) and PTEN mutations were more frequent in ILC (51).

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Analysis of pure mucinous BC subtype indicated that they show a relatively low level of

genetic instability and they tend to be homogeneously and preferentially clustered together,

separately from ductal NST carcinomas. They less frequently harbour gains of 1q and 16p

and losses of 16q and 22q than grade- and ER-matched ductal NST, and no pure mucinous

carcinoma displayed concurrent 1q gain and 16q loss, a hallmark genetic feature of low-

grade ductal NST (52). Pure invasive micropapillary carcinoma that has a characteristic

morphological appearance with a so-called inside-out growth pattern shows specific copy

number aberrations (53), high cyclin D1 expression, high proliferation rates, and MYC

(8q24) amplification (54) compared to ER-matched and grade-matched ductal NST.

Special subtypes that belong to the basal-like subgroup include carcinomas with medullary

features, as well as metaplastic carcinomas and salivary–gland-like tumours such as adenoid

cystic carcinoma. Adenoid cystic carcinoma forms an interesting paradox as it sits within

the basal-like group, which is generally regarded as of poor prognosis, yet its clinical

behaviour is generally indolent. This underscores the astonishing heterogeneity that can

occur even within individual intrinsic subtypes. In a previous study of acinic cell

carcinomas (ACCs) of the breast using massively parallel sequencing (55), our group

identified that the most frequently mutated gene is TP53 with a complex patterns of gains

and losses similar to those of common forms of triple negative BC. Additional somatic

mutations affecting breast cancer-related genes found in ACCs included PIK3CA, mTOR,

CTNNB1, BRCA1, ERBB4, ERBB3, INPP4B, and FGFR2. Using NGS approach, our group

also demonstrated that microglandular adenosis/atypical microglandular adenosis,

particularly those associated with triple negative BC harboured at least one somatic non-

17

synonymous mutation with identical TP53 mutations and similar patterns of gene CNAs in

microglandular adenosis and in the associated triple negative BC. Clonal shifts in the

progression from microglandular adenosis to atypical microglandular adenosis and/or to

triple negative BC were also observed. On the other hand pure microglandular adenosis

lacked clonal non-synonymous somatic mutations and displayed limited copy number

alterations (56). Importantly, these findings, in conjunction with others, underscore the

significance for microglandular adenosis in clinical diagnosis. In another study of

infiltrating epitheliosis using the same techniques (57), we demonstrated high prevalence of

somatic mutations affecting PI3K pathway genes, suggesting that these lesions may be

neoplastic rather than hyperplastic. The landscape of somatic genetic alterations found in

infiltrating epitheliosis is similar to that of radial scars/complex sclerosing lesions,

suggesting that they may represent one end of this spectrum of lesions.

There is also a strong evidence to indicate that the considerable molecular heterogeneity of

BC is already present at the pre-invasive level with genomic, transcriptomic and phenotypic

similarities found between ductal carcinoma in situ (DCIS) and coexisting invasive

carcinoma (58). Similar to invasive BC, frequent genetic and genomic events have been

reported in DCIS with several studies provided detailed descriptions of DCIS genomic,

transcriptomic and proteomic profiling however, to date there are relatively little molecular

data that can be used to predict the risk of progression to invasive tumour or risk of

recurrence.

Next generation sequencing (NGS)

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The introduction of NGS or massively parallel sequencing (MPS) has revolutionised BC

genetics and genomics and is expected to assist in utilising for personalised treatment of BC

patients. Common approaches to NGS include whole-genome sequencing (sequences the

complete genome of a sample), whole-exome sequencing, targeted exome sequencing

(target-enrichment methods to capture genes of interest), and hotspot (sequences selected

regions/regions with recurrent mutations of selected genes of interest) sequencing. NGS has

been used to characterise genomic alterations such as copy number changes, insertions/

deletions and mutations, facilitate sequencing at a greater depth (at the base-pair level)

allowing the identification of subclonal mutations and help distinguishing the “driver”

mutations that contribute to cancer development from the “passengers” mutations that do

not appear to play a significant role in disease progression. In addition to providing

information about the genomic landscape of BC, MPS has confirmed both inter-tumour and

intra-tumour heterogeneity and showed that each BC is largely unique.

NGS have indicated that the mutation frequencies found in BC are lower than some other

cancer such as lung squamous cell carcinoma or bladder urothelial carcinomas but are

similar to those of ovarian and renal clear cell carcinomas. Somatic driver point mutations

and/or copy number changes were identified in at least 40 cancer genes with a maximum of

6 mutated cancer genes in an individual BC though 28 cases of the 100 BC showed a single

driver (59). Seven of those 40 cancer genes (TP53, PIK3CA, MYC, ERBB2, FGFR1,

CCND1 and GATA3) were mutated in >10% of cases and these contributed 58% of driver

mutations (59). Overall, BC was found to have a mean of 56.9 (range 5–374) somatic

mutations per cancer. NGS has also demonstrated spatial and temporal intratumour

heterogeneity of BC at a level beyond common expectations. Various degrees of intratumour

19

genetic heterogeneity have been demonstrated in BC even in the absence of overt

histological phenotypic heterogeneity. Triple negative and basal-like tumours tend to have

greater intra-tumoural heterogeneity than non-basal-like tumours. Mutations in common

driver genes such as TP53, PIK3CA, and PTEN are usually found in high clonal frequencies

and several somatic mutations are present in only a fraction of cancer cells. NGS also

showed that the constellations of somatic mutations found between a primary BC and its

metastases (temporal heterogeneity) and between distinct areas within the primary tumour

(spatial heterogeneity) are not identical providing further evidence to indicate that BC

evolve over the course of the disease. This clonal genetic heterogeneity may explain

resistance of some BC to selective environmental pressures and therapy.

An increasing number of molecularly targeted drugs are available in the clinic as approved

drugs or in the context of clinical trials (http://www.clinicaltrials.gov) and these include

HER4, EGFR, VEGF, VEGFR, FGFR, KIT, BRAF, mTOR, PDGFR, MEK, TIE2, FLT3,

SRC, RET, PD1, PDL1 and others (36). These drugs target specific molecular abnormalities,

including mutated protein kinases and amplified or rearranged genes. BCs that carry any of

these abnormalities particularly if they harbour the sensitising genomic abnormality is

expected to respond to the corresponding targeted therapies. For example, the HER2 gene-

amplified BC benefit from HER2-targeted therapies.

Future perspectives: As a natural extension of the increasing application of the high-

throughput sequencing technology, the list of cancer driver genes is growing, and a

considerable number of these are potentially targetable. This may also help to understand

the mechanisms underlying treatment failure. Furthermore, the identification of targets holds

20

great potential for monitoring clonal evolution in response to treatment and, hence, the early

detection of treatment failure. Application of such technologies is already underway is

expected to result in further refinement of BC prognostication and prediction of response to

specific therapies.

In conclusion: Molecular testing has become increasingly important in the prevention,

diagnosis, and treatment of BC. Despite the enormous amount of work that has been carried

out to develop and refine BC molecular classification, it is still in evolution. With the

increasing use of more sophisticated high-throughput techniques such as NGS, large

amounts of data will continue to emerge, which could potentially lead to identification of

novel therapeutic targets and allow more precise classification systems that can predict

outcome and response to therapy. 

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Table 1: Summary of the molecular assays commonly used in breast cancer diagnosis prognosis and therapy prediction in clinical practice

ER=oestrogene receptor, PgR= Pprogesterone receptor, IHC= immunohistochemistry, RT-PCR, reverse transcription-polymerase chain reaction, FISH, florescence in situ hybridisation.

Assay Analyte Method Applications

Diagnostic assays

Diagnostic biomarker panels

Proteins IHC IHC biomarkers can be used for diagnosis of histologically challenging breast lesions, differentiate between mammary and non- mammary tumours, and between metaplastic spindle cell carcinomas from other benign spindle cell lesions. IHC can also be used to differentiate ADH/low grade DCIS from hyperplasia, in situ from invasive carcinomas and ductal from lobular tumours.

Chromosomal translocation and fusion genes

Tumour DNA

FISH or RT-PCR Specific translocations are reported to be specific to certain breast lesions such as secretory and mucoepidermoid carcinoma of the breast

Predictive and prognostic assays

ER and PgR expression Protein IHC Prediction of hormone therapy response, prognostic and diagnostic markers. Also provide some information on response to chemotherapy; ER- tumours respond better than ER+ tumours

HER2 status Protein IHC Protein overexpression and/or gene amplification predict response to antiHER2 targeted therapy. It also provides prognostic information and can be used to help in diagnosis (ie Paget’s disease)

Tumour DNA (HER2 gene copy number)

ISH; FISH, CISH, DDISH

Germ line testing Non tumour DNA

DNA sequencing

BRCA-germ line positivity predicts good response to PARP inhibitors and other synthetic lethality approach and to some extent chemotherapy. Identify genetic predisposition and indication for counsel patients and relatives in addition to guiding screening

Ki67 labelling index Protein IHC It used in some centres to assess proliferation status as a prognostic variable. Also used as a component of some prognostic tools such as PREDICT. May be useful in poorly fixed tumours and in grade 2 cancers

Multigene assays Tumour RNA

RT-PCR, Expression microarrays and nCounter technology platform

Prognostic in ER+, HER2- breast cancer and mainly used in patients with lymph node negative ER+ HER2- tumours treated with hormone therapy to determine the risk and benefits of using chemotherapy. Currently not recommended in metastatic, locally advanced or advanced stage tumours and have limited prognostic value in ER- and HER2 positive tumours

Protein IHC

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Table 2: Summary of prognostic multigene assays that are currently available for early stage invasive breast cancer

Test Name Component gene(s)

Intended Clinical Utility Biological Material/ Technology

Classificati on

MammaPrint/ Amsterdam Signature (42)

70 genes (first prognostic gene signature to be identified)

Node negative ER+ or ER- Estimates the recurrence risk. Test may extend to node positive patients

Fresh or FFPE, Microarray

2 categories: low risk and high risk

Oncotype DX (recurrence score) (44)

21 genes (16 cancer-related and 5 controls)

ER+, HER2-, node- negative BC. Predicts the likelihood of chemotherapy benefit as well as recurrence in hormone therapy treated patients. Test may extend to node- positive patients

FFPE, RT- PCR

3 categories: low risk (RS<18), intermediat e risk (RS 18–30), or high risk (RS>31)

EndoPredict (60)

11 genes (8 cancer-related and 3 controls)

Predicts distant and late recurrences in ER+ / HER2- node negative and positive patients treated with endocrine therapy alone

FFPE, RT- PCR

Two categories. Can be combined with tumour size and nodal status to produce clinical score (EPclin)

Prosigna (PAM50) Kit (risk of recurrence assay; ROR) (43)

50 genes (used in the PAM50 molecular classification assay) and 5 control genes

ER+ node negative and positive treated with hormone therapy. Evaluates distant recurrence-free survival at 10 years

FFPE, RT- PCR and by the nCounter Dx analysis system

3 categories: low, intermediat e and high risk

Breast Cancer Index (BCI) (17)

A combination of MGI (5 genes) and the 2 gene ratio (H/I) (HOXB13:IL17B R)

Risk of distant recurrence in ER+ node negative BC. Risk of late distant metastasis and benefit from extended (>5 years) endocrine therapy

FFPE, RT- PCR

Two categories

The Rotterdam Signature (61)

76 genes (60 genes for ER+ and 16 genes for ER-patients)

Node negative patients. Predict recurrence in ER+ treated with tamoxifen

Fresh tissue, Microarray

Two categories

BluePrint® Molecular Signature

80-gene profile Classifies tumours into intrinsic subtypes to suggest the potential effect of adjuvant therapy

FFPE, Microarray

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FFPE, Formalin-fixed paraffin-embedded, ER, oestrogen receptor; PgR, progesterone receptor; RT-PCR, reverse transcription-polymerase chain reaction, FISH, florescence in situ hybridisation.

IHC and in situ hybridisation-based assays

IHC4 score 4 genes (ER, PgR, Ki67 and HER2

ER+ patients treated with hormone therapy

FFPE and IHC

Insight Dx Mammostrat Plus

9 genes (As above plus ER, PgR, KI67 and HER2)

As above, plus hormone receptor/HER2 status

FFPE, additional genes evaluated by IHC and FISH

2 categories

Assays based on signatures characteristic of a biological process

Wound-response signature(62)

442 genes Node negative and positive

Fresh, Microarray

2 categories

Immune signatures 14 genes related to immune function

Predictive for relapse in trastuzumab-treated HER2+ patients

2 categories

Invasiveness Gene Signature (IGS)

186 genes Predict 10-year distant metastasis free survival in node negative patients

Fresh, Microarray

2 categories

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