The breast cancer genome - a key for better
Hans Kristian Moen Vollan1,2,3,4and Carlos Caldas3,4,5*
Molecular classification has added important knowledge to breast cancer biology, but has yet to be implemented
as a clinical standard. Full sequencing of breast cancer genomes could potentially refine classification and give a
more complete picture of the mutational profile of cancer and thus aid therapy decisions. Future treatment
guidelines must be based on the knowledge derived from histopathological sub-classification of tumors, but with
added information from genomic signatures when properly clinically validated. The objective of this article is to
give some background on molecular classification, the potential of next generation sequencing, and to outline
how this information could be implemented in the clinic.
Molecular classification of breast cancer
The diversity of breast cancer has been acknowledged
for decades, but recent technological advances in mole-
cular biology have given detailed knowledge on how
extensive this heterogeneity really is. Traditional classifi-
cation based on morphology has given limited clinical
value; mostly because the majority of breast carcinomas
are classified as invasive ductal carcinomas, which show
a highly variable response to therapy and outcome .
The first molecular sub-classification with a major
impact on breast cancer research was proposed by
Perou and colleagues where the tumors were subdivided
according to their pattern of gene expression [2,3]. Five
groups were identified and named Luminal A, Luminal
B, Basal-like, Normal-like and the HER-2-enriched sub-
groups. These intrinsic subgroups have been shown to
be different in terms of biology, survival and recurrence
rate [3,4]. The molecular subgroups have been extended
to also include a sixth subgroup which has been named
the claudin- low group, based on its low expression
level of tight junction genes (the claudin genes) . Dif-
ferent methods for the assignment of individual tumors
to its molecular subgroup is proposed; each based on
the expression levels of different sets of genes [4,6,7].
The agreement between methods on how to classify
individual tumors are not optimal and how to establish
more robust single sample predictors is actively debated
Aneuploidy is the presence of an abnormal number of
parts of or whole chromosomes and is one feature that
clearly separates cancer cells from normal cells. This
was proposed as being important in cancer nearly a cen-
tury ago by Theodor Boveri . With array-based com-
parative genomic hybridization (aCGH) a genome wide
profile of the copy number alterations in the tumor can
be obtained. These patterns are related to the molecular
subtypes with distinct differences in the number of
alterations between the subtypes [13-16]. These copy
number alterations (CNAs) alter the dosage of genes
and highly influence the level of expression [17,18]. This
frequently affects the activity in oncogenes and tumor
suppressor genes and in this way CNAs are important
for the carcinogenic process. CNAs in tumors are a
result of deregulated cell cycle control and of DNA
maintenance and repair . Different patterns of copy
number alterations have been identified with distinct
differences; simplex profiles are characterized by few
alterations and complex genomic profiles have extensive
changes . Complex genomic rearrangements are
areas with high-level amplifications and have prognostic
value in breast cancer even when they do not harbor
known oncogenes, suggesting that the phenotype of
defect DNA-repair may be associated with more aggres-
sive disease [20,21].
Alterations in the expression pattern are caused by
changes at the genomic level and a robust classification
* Correspondence: email@example.com
3Breast Cancer Functional Genomics, Cancer Research UK Cambridge
Research Institute, Cambridge, UK
Full list of author information is available at the end of the article
Vollan and Caldas BMC Cancer 2011, 11:501
© 2011 Vollan and Caldas; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
of breast cancer for clinical use should probably take
these more into account. Changes at the genomic level
include point mutations, changes in copy number and
epigenetic events. These are characteristics that enable
and drive carcinogenesis together with tumor-promoted
The era of sequencing of cancer genomes
We are now in the exciting era of full sequencing of
cancer genomes. Paired-end sequencing is based on
massive parallel sequencing of short stretches of nucleo-
tides at each end of fragmented DNA . The basis of
paired-end sequencing technology is shown in Figure 1.
Next generation sequencing gives additional information
to cancer genomics at many levels, including point
mutations, insertions, deletions, copy number and trans-
locations depending on the level of the coverage .
The copy number alterations in breast cancer are well
characterized by aCGH, but sequencing has given
important insight into how alterations are structured
given that information on translocations/rearrangements
is added .
Stephens et al. described multiple rearrangement
architectures after sequencing 9 breast cancer cell lines
and 15 tumors . Intrachromosomal rearrangements
were found to be far more frequent than between chro-
mosomes and the most common event was tandem
duplications, but with a high degree of variation among
tumors. They hypothesized that these extensive altera-
tions are a consequence of a DNA repair defect that
leads to a ‘mutator phenotype’ similar to what causes
microsatellite instability in other cancers. Breakpoints
tended to fall into areas with microhomology and non-
template sequences. Fusion genes are hybrid genes
formed from two separate genes (for example, by trans-
locations), which can lead to functional proteins with
oncogenic properties. These are important in leukemias
and lymphomas, but the role of fusion genes in breast
cancer is unclear . Stephens et al. found enrichment
for alterations within genes and 29 of these were pre-
dicted to generate in-frame gene fusions. Transcripts
were found for 21 of these, but none of these were
recurrent among cancers . Sequencing of the cell
line MCF-7 has revealed that breakpoints that are evenly
dispersed over the genome tend to be in areas of low
copy repeats while the more clustered breakpoints occur
close to high-level amplified genes, pointing to different
mechanisms for genomic instability . Important
point mutations are present already at an early stage, as
has been shown in a comparative deep sequencing study
of the genomes, and transcriptomes of a primary lobular
tumor and its distant metastasis 9.5 years later .
The sequencing technology is now capable of sequen-
cing genomes of single cells. As there are heterogeneity
among cells of the tumor and infiltration of normal cells
and inflammatory cells, picking the right cell to
sequence may be challenging. Navin et al. sequenced
100 single cells from a polygenic tumor that revealed
four distinct groups of genomes; the diploids and the
pseudo-diploids (representing normal cells and immune
cells), one hypo-diploid and two aneuploid groups .
Their analysis suggests that these represent three clonal
expansions in the primary tumor as they share many
common aberrations. A total of 52 cells from a second
tumor and 48 cells from a paired liver metastasis were
sequenced and the results indicated that a clonal expan-
sion from a single aneuploid cell had formed the pri-
mary tumor and that one of these had metastasized to
the liver forming the metastasis.
Deep sequencing of cancer genomes is a costly process
and the amount of biological material needed has been a
challenge, but technology is moving fast and both cost
and tissue demands are continuously decreased. Interna-
tional consortia have formed to do large-scale analysis of
cancer genomes at all different levels of large sets of
tumors that will provide essential future information on
the landscape of cancer genomes .
Implementation strategies in the clinic
Molecular classification has had limited implementation
in standard clinical treatment guidelines [30,31]. There
are two molecular signatures that are approved for clini-
cal use in breast cancer; one microarray-based for fresh
frozen tumor material (Mammaprint®, Agendia, Irvine,
CA, USA) and one PCR-based for paraffin embedded
tumor material (OncotypeDX®, Genomic Health, Inc.,
Redwood City, CA, USA) [32,33]. The evolving knowl-
edge from molecular classification provides information
about disrupted pathways in great detail as well as glo-
bal changes in expression of genes and genomic altera-
tions. At the same time it is important to acknowledge
that existing data for treatment guidelines are based on
traditional histopathology and some single molecular
markers. To build treatment algorithms that integrate
all existing knowledge is currently the challenge.
We believe that the baseline will still be traditional
histopathology combined with clinical staging, but with
a second layer of molecular classification with subtype
specific prognostic and predictive tests (Figure 2). The
heterogeneity of breast cancer makes it likely that differ-
ent tests should be considered in the different clinical
settings. Prognostic tests like MammaPrint or Oncotype
DX must be validated for such subgroups of patients
and their use must be limited to groups where their
prognostic power is validated. Such validation in clini-
cally relevant groups of patients is crucial. Many prog-
nostic signatures are published but inadequate
validation makes clinical use futile .
Vollan and Caldas BMC Cancer 2011, 11:501
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Figure 1 The basis of translocation mapping from paired-end sequencing. (a) Paired end sequencing is based on sequencing a short
sequence of nucleotides of each end of fragmented and amplified genomic DNA. Reads without the desired length are filtered out. All reads
are aligned to a reference genome. The average number of reads per genomic locus is called the coverage of the genome of the sequenced
sample. A high coverage (20× to 40×) is needed for detection of point mutations while a much lower coverage is required for other analysis
such as copy number and mapping of translocations. The number of reads that map to a locus can be regarded as a function of the number of
copies of that locus. As reads can be binned across windows the coverage does not need to be high for such analyses. (b) When a part of a
chromosome is fused to a part of another chromosome the read from this region will have a sequence in one end that maps to one
chromosome and the other end maps to another. When this pattern is consistent over several reads the translocations can be precisely mapped.
Intrachromosomal rearrangements are mapped the same way. (c) A circos plot of a breast cancer genome. The chromosomes are arranged as a
circle from chromosome 1 to the sex chromosomes X and Y. The outer part of the circle shows the chromosomes with cytoband information.
The blue line represents the copy number at the given loci. The lines in the middle represent translocations. The inter-chromosomal
translocations are in purple and the intra-chromosomal translocations are shown in green. Part (c) is modified from Russnes et al. .
Vollan and Caldas BMC Cancer 2011, 11:501
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At present, patient genotype information is not
included in treatment of breast cancer. We indicate in
Figure 2 that genotype testing in the future should be
included parallel to assessment of the tumor. Germline
variation in genes involved in drug metabolism may
guide the choice of drugs as well as dosage monitoring,
as the influence of CYP2D6 variants on Tamoxifen
metabolism . Germline mutations leading to defi-
cient proteins (like BRCA1/2) increase the risk of breast
cancer, but can also be exploited in therapy. Cells with
deficient BRCA have impaired homologous recombina-
tion (HR) and are dependent on alternative DNA repair
mechanisms. Inhibition of poly ADP ribose polymerase
(PARP) leads to the accumulation of multiple DNA
double strand breaks and without efficient repair
mechanisms the cell dies [36,37]. Such a synthetic leth-
ality approach is a promising therapeutic strategy.
The highly individualized information provided from
deep sequencing has the potential to find individualized
biomarkers for treatment and disease monitoring
[38,39]. Deep sequencing of single cells will give detailed
information about the clonal landscape in tumors .
It is likely that clonal diversity affects the response to
chemotherapy . Targeted therapy approaches have a
great potential in oncology, but resistance to the agents
is a clinical problem. In colorectal cancer, it has been
shown that treatment with Cetuximab, an inhibitor of
EGFR, is ineffective in the presence of an activating
mutation of k-ras, a downstream protein in the EGFR
signaling pathway . This mechanism of drug resis-
tance is likely to be present for other agents as well.
Deep sequencing of cancer genomes makes it possible
to have full mutational information on the important
pathways, and methods to characterize the gene sets of
mutations are being developed [42,43]. For several of
the important carcinogenic pathways several inhibitors
exist and more will come. The prospect is, therefore, for
better prognostication, prediction and targeted therapy
as the main result of full characterization of cancer
Results from next generation sequencing have the
potential for revolutionizing the understanding of malig-
nant disease. The challenge remains in the integration
of new results with existing knowledge based on histo-
pathological stratification of breast cancer.
aCGH: array comparative genomic hybridization; CNA: copy number
alterations; HR: homologous recombination; PARP: poly ADP ribose
polymerase; PCR: polymerase chain reaction.
1Department of Genetics, Institute for Cancer Research and Department of
Breast and Endocrine Surgery, Division of Surgery and Cancer, Oslo
University Hospital Radiumhospitalet, 0310 Oslo, Norway.2Institute of Clinical
Medicine, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway.3Breast
Cancer Functional Genomics, Cancer Research UK Cambridge Research
Institute, Cambridge, UK.4Department of Oncology, University of Cambridge,
Li Ka-Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.5Cambridge
Breast Unit, Addenbrooke’s Hospital and Cambridge National Institute for
Health Research Biomedical Research Centre, Cambridge University Hospitals
NHS Foundation Trust, Hills Road, Cambridge CB2 0QQ, UK.
HKMV and CC wrote the paper. Both authors have read and approved the
CC is a Section Editor for BMC Cancer. The authors declare that they have no
Received: 4 May 2011 Accepted: 30 November 2011
Published: 30 November 2011
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