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CE Update
Submitted 11.12.09 | Revisions Received 12.23.09, 1.7.10, and 3.9.10 | Accepted 3.22.10
Microarray-Based Gene Expression Profiling
for Molecular Classification of Breast Cancer
and Identification of New Targets for Therapy
Rupninder Sandhu, MBBS,1,3,4 Joel S. Parker, MS,2,4 Wendell D. Jones, PhD,5 Chad A. Livasy, MD,1,3,4
William B. Coleman, PhD1,3,4
(1Department of Pathology and Laboratory Medicine, 2Department of Genetics, 3Program in Translational Medicine, 4UNC Lineberger
Comprehensive Cancer Center, University of North Carolina, School of Medicine, Chapel Hill, NC 5Expression Analysis, Durham, NC )
DOI: 10.1309/LMLIK0VIE3CJK0WD
Abstract
Basal-like breast cancers represent approximately 15%–20% of all breast carcinomas,
are aggressive, have variable responses to
chemotherapy, and associated with poor
clinical outcome. The molecular mechanisms
governing the biological behavior of basal-like
breast cancers are not well understood. Hence,
it is difficult to predict which chemotherapeutics
are most likely to be effective and to determine
appropriate management strategies for individual patients. Transcriptomic analysis may
allow further stratification of basal-like breast
cancers enabling prediction of 1) cancer recurrence after surgery; 2) likelihood of metastatic
spread; 3) probable tissue sites for metastatic
spread; and 4) responses to specific therapies
and treatment modalities. Furthermore, careful
After reading this article, readers should be able to describe the basic
features of the various molecular subtypes of breast cancer, with special emphasis on traits characterizing basal-like breast cancer. Readers
should also understand the basic technical attributes of gene expression
microarrays, and be able to discuss the advantages and disadvantages
of using microarray-based gene expression profiling for making decisions
Breast cancer is a disease that is diverse in natural history,
response to treatment, and patient outcomes. It remains the
most common non-cutaneous female malignancy with an estimated 192,370 new cases in 2009 in the United States.1 Breast
cancer-associated mortality is second only to lung cancer in the
United States, with an estimated 40,170 deaths in 2009.1 Breast
cancer is also the most commonly diagnosed female malignancy
worldwide. According to the American Cancer Society, approximately 1.2 million women worldwide are diagnosed with
breast cancer each year. Breast cancer is not a single disease,
rather it represents a diverse spectrum of diseases including
several distinct biological entities and subtypes. These subtypes
are associated with specific morphological characteristics and
examination of microarray-based gene expression profiles may identify new molecular targets
(or pathways) for the development of targeted
therapeutics. Targeted therapies may prove to
be more efficacious in basal-like breast cancer
treatment than the cytotoxic chemotherapeutics
currently employed.
Keywords: AP breast, molecular diagnostics,
genetics
related to the clinical management of basal-like breast cancers. In particular, readers should understand the molecular assays already in clinical
use for breast cancer.
Molecular Diagnostics exam 81002 questions and corresponding
answer form are located after this CE Update on page 373.
different clinical outcomes.2-10 The molecular signatures of
these breast cancer subtypes reflect not only the distinct biological features of these malignant neoplasms, but also predict
their clinical behavior and responses to chemotherapy,11-14 with
certain subtypes having better outcomes than others. To some
extent, the observed variation in disease outcome among breast
cancer patients reflects the successful identification of therapeutic targets for some subtypes and the development of effective
targeted therapies.
The molecular mechanisms contributing to the biological/
clinical behavior of basal-like breast cancer are not well understood. These aggressive cancers constitute 15%–20% of all
breast carcinomas. They resist chemotherapy and are associated
Corresponding Author
Abbreviations
William B. Coleman, PhD
[email protected]
ER, estrogen receptor; PR, progesterone receptor; HER1 and HER2,
human epidermal growth receptors; OS, overall survival; DFS,
disease-free survival; PARP, Poly(ADP-ribosyl)ation polymerase;
SSP, single sample predictor
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Figure 1_Differential expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth receptor 2 (HER2), among
different subtypes of breast cancer. Breast cancer is classified into various subtypes based on differential immunohistochemical staining for
ER, PR, HER2, HER1 (not shown), and cytokeratins (not shown). Panels A-D, luminal A breast cancer; Panels E-H, luminal B breast cancer;
Panels I-L, HER2+ breast cancer; Panels M-P, basal-like breast cancer. Panels A, E, I, and M show H&E staining for each breast cancer subtype. Panels B, F, J, and N show immunostaining for ER and the results (ER+ or ER-) are indicated. Panels C, G, K, and O show immunostaining
for PR and the results (PR+ or PR-) are indicated. Panels D, H, L, and P show immunostaining for HER2 and the results (HER2+ indicative of
HER2 amplification or HER2-) are given.
with poor clinical outcomes, contributing disproportionately
to breast cancer-related mortality.15 In this article, we discuss
the possible use of microarray-based gene expression profiling
to further examine basal-like breast cancers and to identify
gene expression signatures that predict their clinical behaviors
and responses to treatment. Transcriptomic analysis of basallike breast cancer may enable further subclassification within
this molecular subtype, creating biological subgroups reflecting
differences in 1) cancer recurrence after surgery; 2) likelihood
of metastatic spread; 3) probable tissue sites for metastatic
spread; and 4) responses to specific therapies and treatment
modalities. Furthermore, careful examination of microarraybased gene expression profiles may identify new molecular
targets (or pathways) for development of targeted therapeutics
directed against biological subgroups of basal-like breast cancer. Targeted therapies may prove to be more efficacious in
basal-like breast cancer treatment than the cytotoxic chemotherapeutics currently employed. Application of personalized
and targeted therapies will improve long-term outcomes for
patients with basal-like breast cancer and will lessen the burden
associated with over-treatment of these patients.
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Breast Cancer: A Heterogeneous Entity
Breast cancer is a diverse disease with a number of morphologic subtypes. Invasive ductal carcinoma is the most common
morphological subtype, representing 80% of the invasive breast
cancers. Invasive lobular carcinoma is the next most common
subtype, representing approximately 10% of invasive breast cancers. The less common subtypes of the invasive breast cancers
include mucinous, cribriform, micropapillary, papillary, tubular,
medullary, metaplastic, and inflammatory carcinomas. Representative examples of invasive ductal carcinomas are shown in
Figure 1.
Routine subclassification of invasive ductal carcinomas is
accomplished by immunostaining tumor tissues for estrogen
receptor (ER), progesterone receptor (PR), human epidermal
growth receptors (HER1 and HER2), and various cytokeratins.
The differential expression of ER, PR, and HER2 in different
subtypes of breast cancer based upon immunohistochemical
staining is shown in Figure 1. The differential expression of
these protein biomarkers is used as an immunohistochemical
surrogate for gene expression analysis to determine molecular
subtype. Approximately 70%–75% of invasive breast cancers
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express the ER positive cancers (ER+). Collectively, the ER+
malignant neoplasms are classified as luminal cancers. These
cancers are further subclassified into luminal A and luminal
B subtypes based on their HER2 status and proliferation rate.
The majority of ER+ tumors also express PR. The presence
of normal PR levels suggests an intact ER signal transduction
pathway in the breast cancer cells, and PR expression typically
follows the ER expression pattern. The ER- breast cancers are
subclassified as HER2+ and basal-like based on the HER2 overexpression/gene amplification, basal cytokeratin expression, and
EGFR (HER1) expression. An immunohistochemical staining
proxy based on 5 biomarkers classifies breast cancers into the
major subtypes (shown schematically in Figure 2): 1) ER+ are
subclassified into luminal A (ER+, PR+, HER2-) and luminal
B (ER+, PR+, HER2+); 2) ER negative cancers (ER-) are subclassified into triple-negative breast cancer (ER-, PR-, HER2-)
and human epidermal growth factor receptor 2-positive (ER-,
PR-, HER2+); and 3) unclassified cancers (negative for all 5
markers).2-4,16 Basal-like breast cancers are distinguished from
other triple-negative breast cancers (ER-, PR-, HER2-) by
expression of cytokeratin 5/6 and/or EGFR. There is no international consensus on the criteria used to define cancers as basal-like in formalin-fixed, paraffin-embedded tissues. Therefore,
the term basal-like is not yet routinely used in clinical practice.
Rather, the basal-like breast cancers are contained in the triplenegative classification.
Breast cancers, like most epithelial cancers, are associated
with better treatment and survival outcomes when diagnosed
at an early stage. However, outcomes of early stage breast cancers differ depending upon the molecular subtype (Figure 3).
In general, the ER+ breast cancer subtypes (luminal A and luminal B) exhibit a good prognosis and excellent long-term survival
(approximately 80%–85% 5-year survival), while the ER- subtypes (HER2-positive and basal-like) are difficult to treat and are
associated with poor prognosis (approximately 50%–60% 5-year
survival). The ability of patients with ER+ breast cancers to
survive their disease reflects the availability of effective targeted
therapy in the form of anti-estrogen treatment (eg, tamoxifen).
However, among the ER+ breast cancers, the luminal B neoplasms are associated with a significantly worse prognosis than
luminal A subtype4 (Figure 3). This difference in outcome is
partly due to variations in response of ER+ subtypes (luminal A
and luminal B) to anti-estrogenic treatment.17 Targeted therapy
of HER2 overexpressing breast cancers, (luminal B or HER2positive [ER-] neoplasms) with trastuzumab (herceptin), either
concurrent or sequential with adjuvant chemotherapy has
improved survival for these breast cancer subtypes.18
Basal-like breast cancers are characterized by autonomy of
growth that is independent of expression of hormone receptors.
Since these cancers lack the appropriate targets for the drugs like
tamoxifen (targeting ER) and trastuzumab (targeting HER2),
patients with these cancers do not derive benefit from these
drugs. Basal-like breast cancers are associated with overall poor
prognosis and shorter long-term survival. The poor clinical outcomes associated with basal-like breast cancer reflect the fact that
these cancers show variable response to chemotherapy or recur
following therapy. Lack of identification of “druggable” targets
in basal-like breast cancers and poor prognosis makes the identification of molecular signatures and therapeutic targets in these
cancers to be of utmost significance. No widely available targeted
therapies for this breast cancer subtype have been developed to
date, although phase II studies of Poly(ADP-ribosyl)ation polymerase (PARP) inhibitors have shown promising results.19
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Figure 2_Schematic illustrating various breast cancer subtypes. The
blue and pink rectangles group the subtypes based on the expression
of ER/PR, positive in the blue (Luminal A and Luminal B) and negative (HER2+ and basal-like) in the pink. The central grey rectangle
(with black outline) indicates the presence of HER2 amplification in
Luminal B and HER2+ subtypes.
Figure 3_Survival plot of 294 breast cancer patients. A Kaplan-Meier
survival plot of overall survival corresponding to 294 breast cancers from the publicly available UNC database is shown grouped by
molecular subtype. The P-value was calculated using the Log-rank
test. Details of these 294 samples along with clinical annotation can
be found at https://genome.unc.edu/pubsup/breastGEO/.
Basal-Like Breast Cancer
Discovery of Basal–Like Breast Cancers
The basal breast cancer subtype was first described in studies based on immunohistochemistry.20-23 These cancers are
designated basal-like because they exhibit some cellular characteristics associated with the basal myoepithelial cell layer, such
as expression of cytokeratins 5/6, 14, or 17, vimentin, and laminin, but these tumors are clearly not derived from myoepithelial
cells.24-26 The basal-like breast cancer subtype was rediscovered
following the application of microarray-based gene expression
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profiling to breast cancer classification.2-7,9 That the basal-like
breast cancers were identified independently by 2 different
methodologies indicates strongly that these cancers represent
a distinct biological entity. Basal-like breast cancers are best
classified through gene expression profiling.2-7,9 However, in
routine clinical practice, immunohistochemistry has become the
surrogate for the gene expression analysis for diagnosis of basallike breast cancers (Figure 1). Correctly classifying these cancers
significantly impacts clinical decisions and research efforts. In
the clinic, there is a need to correctly identify breast cancer
subtypes for prognostication purposes in relation to individual
patients and for decision-making related to appropriate treatment course. On the other hand, in the research environment,
correct breast cancer subclassification is essential to ensure investigations expand our understanding of the biological basis
for the behavior and characteristics of these cancers.
Association With Risk Factors
The development of basal-like breast cancer is associated
with distinct risk factors, including early-onset menarche,
younger age at first full-term pregnancy, high parity combined with lack of breast feeding, and abdominal adiposity
(based upon waist-hip ratio).27 These breast cancers are overrepresented among patients of certain age and ethnic groups,
and are frequently associated with certain genetic mutations.
Specifically, basal-like breast cancer is overrepresented among
premenopausal, African-American women.11 However, these
cancers can and do affect women of every age and ethnic
group.27 The differences in distribution of basal-like breast
cancer by age and race can be partially attributed to variations
in the distribution of the risk factors described and to other
risk factors (eg, waist-hip ratio, use of lactation suppressants,
and overexpression of leptin receptor).27 In addition, basal-like
breast cancer occurs more frequently among hereditary breast
cancer patients harboring germ-line BRCA1 mutation.28 Foulkes and colleagues showed that 17/72 triple-negative breast
cancers harbored a BRCA1 mutation, and 88% (15/17) of
these expressed the basal-like phenotype.29 Likewise, Sorlie and
colleagues observed that 100% (18/18) of breast cancers from
patients carrying BRCA1 mutations clustered within the basallike subgroup.4 However, the other molecular subtypes of breast
cancer can be associated with BRCA1 mutations as well.
Morphological Features
Morphologically, basal-like breast cancers are characterized by the presence of central necrotic zones, pushing borders,
and conspicuous lymphocytic infiltrate.30-34 The presence of
metaplastic elements3,30-32 and medullary/atypical medullary
features31,32,35 are more prevalent in basal-like breast carcinomas
than in other types of breast cancer. Recent studies have shown
that more than 90% of metaplastic breast carcinomas,3 as well
as the majority of medullary carcinomas,35,36 exhibit a basal-like
phenotype. Basal-like breast cancers are aggressive, with high
rates of cellular proliferation, high histological grade, and extremely poor clinical outcomes.3,4 These factors combine to account for the disproportionate contribution of basal-like breast
cancer to breast cancer mortality. It has been suggested the high
level of cellular proliferation observed in these neoplasms might
account for the over-representation of basal-like breast cancers
among the so-called interval breast cancers (the cancers arising
between annual mammograms).
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Clinical Behavior of Basal-Like Breast Cancers
Currently, there is no consensus on the immunohistochemical criteria for the diagnostic classification of basal-like breast
cancers. Studies have shown the profile constructed using ER-/
PR-, HER2-, CK5/6+, and/or EGFR+ is 100% specific but only
55% to 76% sensitive.37 Breast cancers that are ER-/PR-/HER2are broadly classified as triple-negative neoplasms (Figure 1).
The triple-negative breast cancers include most (or all) basal-like
breast cancers.38 Interpreting the percentage of positive cells and
intensity of immunohistochemical staining is subjective. Variability in immunostaining techniques and procedures is a concern as
well. Hence, standardization and/or automation of immunostaining procedures and interpretation to remove technical and subjective variation will benefit this analysis in the clinical laboratory.
The low sensitivity associated with the classification of basal-like
breast cancers using immunohistochemical staining may indicate
that these cancers are much more heterogeneous than previously
thought. Gene expression profiling-based molecular classification
of breast cancers predicts the general clinical behavior of breast
cancers corresponding to the different molecular subtypes. Microarray studies show the basal-like breast cancers express a common
gene expression signature, and these cancers are associated with an
extremely bad prognosis.3 Among the patient cohort examined in
the initial study of this type, 100% of the patients with basal-like
subtype succumbed to their disease within 4 years of diagnosis.3
Basal-like breast cancers respond to preoperative (neoadjuvant)
chemotherapy.39,40 However, despite the observation of pathologic complete response in many patients, these individuals exhibit poor long-term survival. The poor survival outcomes among
these patients, despite response to chemotherapy, may be related
to a higher likelihood of relapse in individuals where pathologic
complete response was not achieved.40
The malignant neoplasms constituting the basal-like breast
cancer subtype are not biologically homogeneous. For example,
in 1 study unsupervised hierarchical clustering within 43 cytokeratin-14 positive (basal-like phenotype) tumors revealed 4 clusters, and 1 of these displayed a worse prognosis than the other
3, strongly suggesting intra-subtype heterogeneity.41 Variable
prognosis within the basal-like subtype has also been reported
by other groups.42,43 Rakha and colleagues divided the basal-like
breast cancers into those with a dominant basal pattern (>50%
of cells are positive for cytokeratin 5/6 and 14) and the remaining basal cancers (<50% of cells are positive for cytokeratin 5/6
and 14).42 These subsets of basal-like breast cancers demonstrated differences in grade, presence of pushing margins, local
spread, and long-term outcomes (disease-free survival [DFS]
and overall survival [OS]).42 Likewise, Laakso and colleagues
distinguished basal (uniformly positive for cytokeratins 5 and
14) and basoluminal (heterogeneously positive for cytokeratin
expression) subtypes of basal-like breast cancers and showed
that these subsets of basal-like cancers differ with respect to
tumor size, proliferation index, expression of other markers
(like vimentin), and recurrence-free survival.43 These observations underscore the necessity to further define biological subsets of basal-like breast cancer (particularly in terms of clinical
behavior). The pattern of metastatic spread among basal-like
breast cancers has been suggested to be different compared
to other breast cancer subtypes. The basal-like breast cancers
have a tendency to disseminate through hematogenous routes,
involving the brain (resulting in a higher rate of cerebral metastasis) and lung, and are less likely to spread to the lymph nodes,
liver, or bones.33,44-46 Prognosis of cancer is linked to various
clinical parameters, including tumor size, tumor grade, lymph
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node status, and the presence of distant metastasis. However,
among basal-like breast cancers, prognosis has been shown to
be less dependent on tumor size, tumor grade, and lymph node
status reflecting the deviant nature of these cancers. Expression
of basal markers like cytokeratins 5/6 was associated with poor
outcome and proved to be a prognostic factor independent of
the usual clinical parameters.47 These observations highlight the
requirement to examine further the transcriptome of basal-like
breast cancers in order to uncover the molecular basis for the
biological behavior of subsets of these cancers.47
Using Microarray-Based Gene Expression
Patterns to Understand the Etiology of
Basal-Like Breast Cancers
The phenotypic diversity among basal-like breast cancers is
a manifestation of differences in the transcriptional programs of
the constituent neoplastic cells. Microarray-based gene expression
analysis can be used to analyze, display, and systemically classify
the molecular heterogeneity observed among basal-like breast
cancers. The role of genetic changes altering the expression of
oncogenes and tumor suppressor genes as causative events in development and progression of cancer has been firmly established.
A variety of genetic lesions have been associated with the development of basal-like breast cancers. These include gene deletions,
point mutations (TP53 gene mutation),3,48,49 as well as gene
amplifications. Other anomalies associated with the development
of basal-like breast cancers involve chromosomal aberrations
(X chromosome isodisomy, 1q and 8q gains, X losses),48 loss of
heterozygosity (on 4p and 5q),48 and aneuploidy. Basal-like breast
cancers also occur frequently in hereditary breast cancer patients
with germ-line BRCA1 mutations.28
Epigenetic events constitute another common molecular
alteration that has been implicated in cancer initiation and
progression. These changes involve 1) alteration of the DNA
methylation status of the promoter regions of transcribed genes,
and 2) alterations in the maintenance of chromatin structure.
Cancer-associated epimutations can inactivate tumor suppressor
genes (in absence of any genetic alteration) or activate protooncogenes. Promoter hypermethylation of genes including p16,
retinoic acid receptor β, BRCA1, and E-cadherin have been
identified to play a role in breast cancer initiation and progression.50 More specifically, promoter hypermethylation of CST6,
E-cadherin, ER, CEACAM6, LCN2, and SCNN1A genes are
frequently observed in basal-like breast cancers and differentiates them from other breast cancer subtypes.51,52
Single initiating mutations and a series of mutations causing transformation of normal cells into cancer cells, as well as
epigenetic alterations leading to cancer development, have been
identified. After these initial events occur, neoplastic cells undergo
a series of changes due to the interactions between the altered cells
and external signals.53 As a consequence, the incipient cancer cells
arising from similar initiating events may diverge, resulting in neoplasms that are quite dissimilar from 1 another. These differences
may be molecular (reflecting gene expression patterns), but otherwise they are not easily discernible (at the level of cellular morphology) and may represent the mechanisms accounting for biological
subsets within the basal-like breast cancer subclass. Hence,
identification of mutations and epimutations within basal-like
breast cancers will be important to understanding the molecular
etiology of these cancers as well as to identifying potential targets
to treat these cancers. Obviously, differences in the transcriptome
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of individual basal-like breast cancers can be uncovered through
analysis of gene expression patterns that today are most often
accomplished through DNA microarray analysis, a technology
measuring the expression of thousands of genes simultaneously.
The microarray enables the study of complex phenotypes at the
molecular level, allowing association of gene expression with
tumors having differing phenotypes. Given that the phenotype is
determined by gene expression, the clinical behavior of subgroups
of basal-like breast cancer will be associated with specific gene
expression patterns, potentially leading to identification of
therapeutic targets or biomarkers for progression.
Clinical Utility of Microarray-Based Gene
Expression Profiles
Neoplasms representing different subsets of basal-like
breast cancers may have the same clinical stage based on traditional classification criteria and may be histologically and
morphologically identical, yet their biological (clinical) behavior
may be remarkably different. Survival rates associated with basal-like breast cancers are dismal. Numerous studies have shown
that patients with basal-like breast cancer exhibit significantly
shorter OS and DFS and have high rates of tumor recurrence,
highlighting the aggressive course of these cancers. A retrospective study of 49 basal-like and 49 grade and age-matched non
basal-like tumors54 showed that patients with basal-like breast
cancers were associated with significantly shorter OS and
DFS and a higher recurrence rate. Another study based upon
a cohort of 930 breast cancer patients showed that expression
of basal cytokeratins (indicative of basal-like phenotype) was
associated with poor progression-free survival and poor overall
survival.37 Analysis of 496 primary breast cancers from the
Carolina Breast Cancer Study showed that progression-free survival differed by breast cancer subtype and survival time is significantly shortened in basal-like and HER2+ subtypes.11 These
studies are consistent with similar observations of poor prognosis in basal-like cancers made by numerous other groups before
and after these aforementioned studies.3,4,9,14,47,55,56 In the absence of molecular targets (like ER or HER2), options for basallike breast cancer therapy are limited to aggressive cytotoxic
chemotherapy. However, cytotoxic chemotherapy (whether
neoadjuvant or adjuvant) has proven largely ineffective in the
treatment of basal-like breast cancer based on OS among these
patients. The general failure of chemotherapy in the treatment
of basal-like breast cancer may be related to the lack of targeted
approaches and/or our current inability to stratify patients according to their likelihood of response to specific drugs or treatment modalities. Transcriptomic analysis may be instrumental
in identifying possible targets for treatment of basal-like breast
cancers. Data generated from microarray-based gene expression analysis will enable the generation of patterns associated
with different biological subsets of cancerous cells (for instance,
from different basal-like breast cancers). Subsequently, these
gene expression patterns can be clustered based upon intrinsic
differences in gene expression (unsupervised cluster analysis) to
uncover gene-gene relationships or be analyzed in the context
of a particular endpoint (supervised analysis) (Figure 4). The
supervised analysis is frequently used to identify gene sets that
differentiate neoplasms associated with a pre-identified clinical
feature or outcome (Figure 4). Hence, supervised analysis will
help identify gene expression patterns among basal-like breast
cancers displaying response (or lack of response) to a specific
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drug or some other biological characteristic (such as metastasis
to brain).
Identifying Basal-like Breast Cancer
Basal-like breast cancers represent a significant fraction of
the triple-negative breast cancers (ER-, PR-, HER2-). The immunohistochemical expression pattern of ER-, PR-, HER2-, cytokeratin 5/6+, and/or EGFR+ represents the signature of basal-like
breast cancers. However, evaluation of breast cancers using immunohistochemistry has low specificity. Microarray analysis of
basal-like breast cancers show high levels of expression of the proliferative gene cluster (including Ki-67, proliferating cell nuclear
antigen, and topoisomerase II) and low expression of the ER
cluster of genes (GATA binding protein 3) and the HER2+ cluster of genes (growth receptor factor bound protein 7).53,57 Hence,
microarray profiling represents a more powerful technology that
is better able to subclassify breast neoplasms but lacks practical
utility due to batch effects and required instrumentation, in addition to currently being a more expensive method than immunohistochemistry. Nevertheless, microarray-based gene expression
signatures can be mined to identify gene expression patterns
specific for basal-like breast cancer that can be assayed using other
(non-array based) methods. These expression patterns distinct to
basal-like breast cancers will reflect the complex tumor biology
associated with abnormal activation of multiple, non-linear pathways seen specifically in these cancers. Once the gene set associated with these patterns is identified, it can then be validated and
henceforth used to identify basal-like breast cancers in the undiagnosed tumors. Analysis of expression of a limited number of genes
is advantageous over whole genome analysis (given similar levels
of sensitivity/specificity) by being less expensive, more practical,
and yet still biologically based (Figure 5). The application of nonarray gene expression assays (such as real-time PCR methods) will
abrogate the need for surrogate methods like immunohistochemistry for establishing breast cancer molecular subtypes.
The evidence that the basal-like breast cancer subtype is heterogeneous is expanding, and there is a need to characterize the
biological subsets of these breast cancers. The massively parallel
nature of microarray based gene expression analysis should provide
the depth of information necessary to further stratify these breast
cancers. However, the established gene expression signatures used
to classify basal-like breast cancers6,7,58 will not enable greater
stratification. Thus, the microarray data need to be carefully
mined in order to identify the gene expression patterns associated
with different subsets of basal-like breast cancer. It may be useful
to establish biological subgroups of basal-like breast cancer (based
upon known clinical behavior) prior to a supervised analysis to
identify the desired gene expression signatures. Establishment of
Figure 4_Cluster analysis of gene expression data for 294 breast cancers. (A) Unsupervised cluster analysis of gene expression data from 294
breast cancers from the publicly available UNC database. Details of these 294 samples along with clinical annotation can be found at https://
genome.unc.edu/pubsup/breastGEO/. Cluster analysis was performed using the intrinsic gene list (Hu and colleagues)55 at https://genome.unc.
edu/pubsup/breastTumor/data/306-genes-clid-build-161-vs-326-probes.xls. Gene expression data were generated using Agilent microarrays.
Gene and sample clusters were generated from loess normalized and gene centered data with Pearson correlation and average linkage. (B)
Supervised cluster analysis of gene expression data for the same 294 breast cancers and intrinsic genes, ordered by single sample predictor
(SSP) subtype assignment. The color bar indicates subtype assignment by the SSP classifier with Luminal A in blue, Luminal B in light blue,
HER2+ in pink, basal-like in red, and normal-like in green. In the heatmap, red indicates relatively higher expression and green indicates
relatively lower expression.
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do not efficiently distinguish between
biological subgroups of basal-like breast
cancers. Hence, it will be necessary to
identify molecular biomarkers for this
disease to improve our ability to predict
the overall severity of the disease. The
ability to identify the neoplasms that
will progress and spread rapidly versus
those that will take a less aggressive
course will improve disease management by ensuring that patients receive
appropriate treatment (aggressive vs
more conservative therapy). Hence,
gene expression biomarkers with good
predictive power will enhance and
improve the stratification of patients
compared to the current stratification
of patients using traditional prognostic
factors. Aggressive chemotherapy is not
administered to all basal-like breast cancer patients with the expectation that
everyone needs it or that every patient
will benefit from it, but it is given to
all because of our current inability to
stratify patients.57
Identifying Optimal
Chemotherapy Regimens
Basal-like breast cancers respond
differentially to standard adjuvant chemotherapy regimens. Defining the gene
expression profiles specific to the various heterogeneous entities within the
basal-like breast cancer class will enable
Figure 5_Cluster analysis of gene expression data from 202 FFPE breast cancers. Heatmap of
clinicians to optimize chemotherapy
202 FFPE samples assayed for 50 genes by qRT-PCR. Sample processing and gene expression
regimens for different subsets of basaldata generation was described by Parker and colleagues.64 Cluster analysis was performed on
like breast cancer. The optimal chemohousekeeper normalized, gene centered, and sample standardized expression estimates using
therapy combination and schedule for
Pearson correlation and average linkage clustering. The color bar indicates subtype assignan individual patient will be a function
ment: Luminal A in blue, Luminal B in light blue, HER2+ in pink, basal-like in red, and normalof presence/absence of molecular targets
like in green. In the heatmap, red indicates relatively higher expression and green indicates
relatively lower expression.
for specific chemotherapeutic agents.
It is very likely that subsets of basal-like
breast cancers will be identified that
are resistant to anthracyclines but may
gene expression signatures corresponding to biological subgroups
respond to other classes of chemotherapeutic agents (like taxanes
of basal-like breast cancers will enable patient stratification and
or platinating agents) or vice versa. It is vital to establish not only
will potentially provide insight into etiology. In addition, estabthe drug combination for chemotherapy, but also the treatment
lishment of gene expression signatures associated with biological
schedule. Dose-dense or accelerated chemotherapy may be indisubsets of basal-like breast cancers will enable identification of
cated for patients with specific gene expression patterns, whereas
molecular targets for therapy corresponding to clinical subsets,
patients with a different but distinct gene expression patterns
advancing personalized therapy for these cancers.
may fare equally well or better with conventional chemotherapy
dosing schedules, all with the same chemotherapeutic drugs. As
our knowledge related to the biology of basal-like breast cancers
Prognostic Parameters
evolves, potential targets and drugs will be identified. The PARP
Gene expression analyses can be employed to predict imporinhibitors, for example, show promising results in ongoing phase
tant prognostic parameters both in terms of treatment and natuI and phase II trials.19 A paired correlative analysis of gene expresral history of disease. Identifying the gene expression patterns
sion profiling on 50 patients with triple-negative breast cancer
associated with shorter time to progression, lymph node invasion, in this study revealed upregulation of PARP1 gene expression
hematogenous spread, and metastatic tendencies to different
compared to non-triple negative breast cancer controls.19 This
viscera among different subsets of stage-identical basal-like breast
underscores the significance of identifying patients as potential
cancers will be monumental in predicting the clinical course of
responders and non-responders based on the gene expression prodisease. It is widely accepted that conventional prognostic factors
filing. Knowing the likelihood of success for a specific drug and
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dosing-combination schedule for a particular patient subgroup
at the outset of treatment will make first-line treatments more
efficacious, will spare patients unnecessary treatment (and related
morbidity), and will save precious time, thereby significantly increasing the chances of a successful outcome. It merits repeating
that the application of personalized therapies will eliminate the
unnecessary exposure and side effects associated with treatment in
patients determined to be likely non-responders to a specific drug
or regimen. Elimination of overtreatment will improve the quality of life for the subset of patients destined to be non-responders
to certain chemotherapeutics and/or regimens but undergo chemotherapy and endure its side effects because of our current lack
of stratification capability. In addition, stratification in terms of
responders and non-responders to a specific treatment regimen
will relieve a huge economic burden incurred by inappropriate
use of drugs deemed to be ineffective in some subsets.
Developing Resistance to Chemotherapeutics
Changes in the biological properties of a given neoplasm
over time resulting in resistance to chemotherapy are not well
understood. Identifying gene expression patterns associated with
neoplasms that respond to drug treatment (responders) and
responders that have become chemo-resistant (following treatment) will benefit our understanding of the biology of the process
but may also lead to the development of targeted chemotherapy
to treat the resistant phenotype. For example, if the resistant
phenotype is associated with methylation-dependent silencing of
gene(s) whose expression is vital for the chemotherapeutic drugs
to be effective, demethylating agents could be employed to sensitize the cancer cells, resulting in the re-establishment of chemosensitivity. Comparing the gene-expression profile in cancers
before and after the development of chemotherapy resistance
will define the molecular determinants of drug resistance.
Microarray-Based Assays in Clinical Use in Breast
Cancers
Several molecular assays currently employed in the clinical
assessment of breast cancer are derived from microarray-based
gene expression profiling. One example of a microarray-based
assay is MammaPrint (Agendia, Amsterdam, The Netherlands)
or the 70-gene prognosis profile that was approved by the Food
and Drug Administration (FDA) in February 2007. This assay
is offered as a prognostic test for breast cancer patients that
are ER+ or ER-, lymph node-negative (stage I-II), and under
age 61. This test differentiates between patients at low risk or
at high risk for metastasis on the basis of a score yielded by the
assay.59,60 High-risk patients are recommended for more aggressive chemotherapy compared to patients with a low score.
Another good example is the Oncotype Dx Recurrence Score
(Genomic Health, Redwood City, CA), which is based upon a
gene expression signature identified through supervised analysis
of microarray data.61 This is a 21-gene prognostic and predictor
assay based on a continuous variable algorithm used to predict
the likelihood of relapse among patients with ER+, lymph
node-negative, early stage breast cancer.62,63 The score reflects
the potential for recurrence in early stage breast cancer, stratifying the patients that need to be aggressively treated versus those
where conservative treatment will suffice.
A number of studies have been conducted or are in progress
based on the microarray-based assays in all subtypes of breast cancer, including basal-like breast cancer. For example, Rouzier and
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colleagues conducted a study on 82 breast cancers to study the
response to preoperative chemotherapy.39 Basal-like and HER2+
subtypes showed higher rates of pathological complete response
when treated with doxorubicin-containing and paclitaxel-containing regimens compared to the luminal tumors.39 Gene expression
analysis showed that none of the genes associated with pathological complete response in basal-like cancers were associated with
pathological complete response in HER2+ tumors. These studies
suggest that gene expression profiling can be clinically predictive
for a variety of outcomes, such as disease progression, response to
particular therapy, and likelihood of relapse.
Summary in Perspective
The basal-like breast cancer subtype represents 15%–20%
of invasive breast cancers, is associated with poor prognosis, and
contributes disproportionately to breast cancer mortality. At
present, the genes responsible for the aggressive behavior of this
breast cancer subtype are largely unknown, and no widely available targeted therapies exist for this form of breast cancer. The
generally poor prognosis associated with basal-like breast cancer
partially reflects the current inability to stratify these neoplasms
relative to their clinical behavior. The detailed characterization
of the transcriptome of basal-like breast cancers and careful
mining of the gene expression profiles corresponding to subsets
of neoplasms within the basal-like breast cancer subtype holds
promise for 1) identifying new targets for therapy; 2) determining which therapies will be most effective; and 3) optimizing
diagnostic and predictive testing. New investigations are needed
to examine the relationship between gene expression patterns
among basal-like breast cancers and their response to specific
therapies. Studies are needed to differentiate the gene expression signature of the subset of basal-like cancers metastasizing to
the brain vs the subset undergoing lymphatic or visceral spread.
Identification of the more clinically aggressive subset tending
to metastasize to the brain at the outset will enable clinicians
to treat these patients aggressively to alter the clinical course of
the disease, thus improving the outcome for individual patients.
Additional studies are also needed to stratify the gene expression signatures predicting sensitivity or resistance to specific
chemotherapeutic regimens in order to optimize and personalize
chemotherapy. Finally, it is desirable to examine gene expression patterns among basal-like breast cancers before and after
development of resistance to specific drugs to identify molecular
targets as a basis for second line treatments. LM
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