Journal of Cancer Therapy, 2012, 3, 797-809
http://dx.doi.org/10.4236/jct.2012.325101 Published Online October 2012 (http://www.SciRP.org/journal/jct) 797
Novel Methods in the Study of the Breast Cancer Genome:
Towards a Better Understanding of the Disease of Breast
Cancer
Jian Li1,2*, Xue Lin1*, Nils Brünner3, Huanming Yang2#, Lars Bolund1,2#
1Department of Biomedicine, University of Aarhus, Aarhus, Denmark; 2BGI, Shenzhen, China; 3Department of Veterinary Pathobi-
ology, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark.
Received August 15th, 2012; revised September 17th, 2012; accepted September 29th, 2012
ABSTRACT
Rapidly developing sequencing technologies and bioinformatic approaches have provided us with an unprecedented
instrument allowing for an unbiased and exhaustive characterization of the cancer genome in genetic, epigenetic and
transcriptomic dimensions. This review introduces recent exciting findings and new methodologies in genomic breast
cancer research. With this d evelopment, cancer genome research will illuminate new delicate interactio ns between mo-
lecular networks and thereby u nrave l th e u nderlying b iological mechanisms for cancer initiation and progression. It also
holds promise for providing a molecular clock for the estimation of the temporal processes of tumorigenesis. These
methods in combination with single cell sequencing will make it possible to construct a family tree elucidating the evo-
lutionary lineage relationships between cell populations at single-cell resolution. The anticipated rapid progress in ge-
nomic breast cancer research should lead to an enhanced understanding of breast cancer biology and guide us towards
novel ways to ultimately prevent and cure breast cancer.
Keywords: Breast Cancer Genome; Massively Parallel Sequencing; Pathway-Oriented Analysis; Mitochondrial
Genome; Temporal Order of Aberrations; Single Cell Sequencing; Microbiome
1. Introduction
Breast cancer is the second most commonly diagnosed
cancer and seriously threatens women health [1]. As a
complex disease, both genetics and environmental causes
are implicated in the tumorigenesis of breast cancer. The
catalogue of inherited or somatic mutations accumulated
in a cancer genome encompasses substitutio ns of nucleo-
tides, insertions and deletions, translocations and other
chromosomal rearrangements as well as copy number
changes [2]. Many efforts have been spent in the last
decade to identify the spectrum of genes associated with
breast cancer [3]. Genes, such as BRCA1 and BRCA2,
with high penetrance mutations are involved in approxi-
mately 70% of breast cancers in high-risk families. How-
ever, they only account for a minority of all breast cancer
cases [4]. In general, <10% of breast cancer cases are
thought to be hereditar y in a Mendelian fashion and usu-
ally a somatic “second hit” in the homologous normal
allele is required for disease development.
Thus, to identify low penetrance susceptibility gene
variants (inherited or somatically acquired) has become
an area of interest in breast cancer research. Genome-
wide association studies (GWAS) are commonly used for
the search for correlations between disease incidence and
genetics. GWAS routinely encompasses tens of thousands
of patient samples and scans the full length of the ge-
nomes [5]. GWAS have identified 25 genetic loci associ-
ated with breast cancer risk [5]. Still, to date, GWAS can
only account for 9% - 10% of breast cancers [5]. Even
when considering all types of genetic studies, some 70%
of breast cancer cases remain unexplained [5,6]. It has
become obvious that genetic factors only account for part
of the phenotypic variance [7]. Breast cancer develop-
ment represents a multiple-step process and the risk in-
creases with age. Environmental degenerative factors no
doubt play an important role in breast cancer tumori-
genesis. Epigenetic changes, including somatically ac-
quired (and sometimes germ line transmitted) chemical
modifications of DNA (without DNA sequence changes)
as well as DNA binding small RNAs and proteins (e.g.
histones), bridge the gap between genetics and the envi-
ronment significantly improving our understanding of the
*These two authors (Jian Li and Xue Lin) equally contributed to this
work.
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Novel Methods in the Study of the Breast Cancer Genome: Towards a Better Understanding of the
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798
disease of breast cancer [8,9].
The emergence of massively parallel sequencing tech-
nology provides researchers with an unprecedented pow-
erful tool for breast cancer research. Currently, there are
five commonly applied massively parallel sequencing
technologies: 454 Life Sciences (Roche) applies a py-
rosequencing approach [10], Illumina/Solexa uses the
principle of sequencing by synthesis (SBS) with reversi-
ble dye terminators [11], Applied Biosystems SOLiD
[12] and Complete Genomics [13] perform sequencing
by ligation strategies, and Ion Torrent [14] utilizes an ion-
sensitive SBS principle for sequencing. Although these
sequencing platforms are technically quite diverse, they
share many common features: Similar process of library
preparation, ampl ification of lib raries prior to sequencing,
and similar process of sequencing by an automated series
of enzyme-driven biochemical and fluorescent imaging
based data acquisition steps [15]. This allows ultra-deep
investigations of breast cancer genomes and their epige-
netic modifications in a fast and cost-effective way, with-
out the requirement of abundant amounts of material
[2,16]. Here we briefly describe current genomic ap-
proaches applied in breast cancer research.
Although array-based approaches remain broadly ap-
plied for RNA analyses at present, transcriptome se-
quencing is becoming increasingly important, as sequenc-
ing has a greater dynamic range and provides the possi-
bility to discover new transcripts, sequence variants and
splicing events [17,18]. RNA sequencing allows deep
mapping of short RNA fragments (17 - 22 nucleotides),
thus exponentially increasing our knowledge of the biol-
ogy, diversity and abundance of small RNA populations
[19,20].
Despite the fact that a number of whole breast cancer
genomes have already been sequenced [21-23], the
analyses of particularly informative sectors of the cancer
genome, e.g. sequencing the DNA sequence based on cap-
turing the exomes an d DNA sequences coding for known
micro-RNAs, are likely to be carried out commonly [24].
Exome sequencing applies affinity-enrichment tech-
niques to enrich exome sequences from the genome be-
fore sequencing, thereby allowing a deep characterization
of the target sequence for a decreased cost [25]. Mas-
sively parallel sequencing also can efficiently sequence
small genome fragments that have been randomly col-
lected from the tumor genome to reveal copy number
changes (low coverage sequencing) [16]. The relative
number of sequenced short DNA fragments in equal-
sized bins distributed along the genome, can be regarded
as an estimate of the relative copy number at different
genomic locations [16].
Massively parallel sequencing has also dramatically
increased our ability to survey genome-wide epigenetic
markers. Chromatin immunoprecipitation followed by
sequencing (ChIP-Seq) uses antibodies to pull down tar-
get DNA to globallysurvey the DNA binding pattern of
a protein of interest [26]. This method is also applied
for measurement of histone modifications. DNA methyl-
lation as an important epigenetic mechanism has been
extensively studied. To date, there are three main ap-
proaches that are compatible with massively parallel se-
quencing for genome-wide mapping of DNA methylation
information: 1) endonuclease digestion-based methods
such as modified methylation specific digital karyo-
typing (MMSDK) [27]; 2) affinity enrichment-based
methods such as methylated DNA binding domain se-
quencing (MBD-Seq) [28] and methylated DNA im-
munoprecipitation sequencing (MeDIP-Seq) [29]; and 3)
bisulfite conversion-based methods such as MethylC-
Seq (methylome) [30,31] and reduced representation
bisulfite sequencing (RRBS) [32]. Exhaustive compari-
sons of these DNA methylation assays have been re-
cently carried out by several groups, and these studies are
invaluable when selecting methods for DNA methyl-
lation analysis [33-36]. With the cost of sequencing the
whole human genome dropping towards 1000 US dollars
(http://www.genome.gov/12513210) [37] in the near fu-
ture, a revolutionary era of personalized medical care for
breast cancer patients will soon become a reality. For
example, the elucidation of a number of intrinsic breast
cancer subtypes [38] has added significantly to our un-
derstanding of breast cancer heterogeneity and also pro-
vides tools that can be used to select the right treatment
for the right patient at the right time. The important ad-
vances in cancer genome analysis brought about by the
application of massively parallel sequencing have al-
ready been discussed in detail in many other reviews
[2,16,39,40]. In the present review, we will introduce and
highlight some new research directions, which we expect
will lead to an increased understanding of the breast can-
cer disease.
2. Breast Cancer Genome Research
2.1. Pathway-Oriented Analysis Based
on Integration of Multiple “Omic”
Dimensions
One important insight obtained from the large-scale mu-
tational analyses carried out in pioneering large-scale
sequencing studies of breast and colon cancer was the
importance of taking a pathway-oriented strategy [41-43].
A pathway-oriented model of tumorigenesis is also sup-
ported by the observation that although different genes
may be mutated in the same type of tumors, these genes
often belong to a more limited number of pathways and
biological processes [41]. For example, breast and colo-

Novel Methods in the Study of the Breast Cancer Genome: Towards a Better Understanding of the
Disease of Breast Cancer 799
rectal cancers both have frequent mutations in PIK3CA
pathway genes, but these mutations are not always in the
same genes [41]. The cancer genome can be dysregulated
through multiple mechanisms including mutations in
coding and non-coding sequences, alterations in DNA
copy number and organization, and aberrations in modi-
fications of DNA and DNA related proteins [16]. The
abnormalities may simultaneou sly occur in a key gene in
an independent or synergistic manner, leading to dys-
function of this gene, thereby fueling tumorigenesis. Al-
ternatively, these abnormalities can target different genes
that are connected within a pathway and, thereby,
through dysfunction of the pathway, ultimately facilitate
cancer development. A classic example of this is the tu-
mor suppressor gene, TP53, which can be inactivated
in three ways: through homozygous deletions in the
17p13.1 region; through hypermethylation of TP53 pro-
moter to epigenetically silen ce the expression ; or through
mutations that cripple the function of TP53. More inter-
estingly, these multiple mechanisms can collaborate to
cause dysfunction of this gene; for instance, one allele
may be inactivated by mutation whilst the other allele
may subsequently be silenced by DNA methylation of its
promoter region. Alternatively, one mutated allele in
combination with a subsequent copy loss of the second
allele or epigenetic silen cing of the other allele can even-
tually completely inactivate the gene function. Allele-
specific gene expression regulated by epigenetic mecha-
nisms was previously regarded as mainly constrained to
genomic imprinting. A recent study of the DNA methy-
lome of human peripheral blood mononuclear cells
demonstrated that the regulation mechanisms of alle-
lic-specific gene expression by allelic-specific DNA me-
thylation may exists a more comprehensive biological
phenomenon [31], which underscores the relevance of an
integrative analysis involving multiple dimensions of
biological information. This can be even more important
in cancer genome research, because genomic abnormali-
ties observed in cancers can be associated with abroad
range of biological characteristics. Thus, breast cancer is
undoubtedly a complex disease, both in its biological
mechanisms and in its final biological endpoints.
A deeper understanding of breast cancer therefore re-
quires broad investigations of the breast cancer genome
in different dimensions followed by integrative analysis
of the findings using a pathway-oriented strategy. Addi-
tionally, pathway analyses can facilitate the selection of
genes for further functional analyses [44]. Recent breast
cancer genome studies have focused their efforts on inte-
grative analysis for large-scale sequencing data. As ex-
amples of this, many studies have involved combina-
tional analysis of sequencing data from the genome,
exome and/or transcriptome to evaluate the impact of
mutations or genome rearrangements on gene expression
[45,46]. Integrating DNA copy number, RNA transcrip-
tome, and CpG island methylation profiles, Sun et al.
systematically examined the genomic features underlying
the estrogen receptor positive (ER+) and estrogen recep-
tor negative (ER-) breast cancer phenotypes [47]. These
studies demonstrate the transition of the strategy of breast
cancer research from focusing on a handful gene sets to
multi-dimensional investigations including the whole ge-
nome using pathway-orie n ted m od els.
2.2. Mitochondrial Genome Analysis
The human mitochondrial genome is a 16.6 kb dou-
ble-stranded circular DNA molecule presenting a copy
number that varies widely according to the cell type [48].
Because of a lack of histone protection, limited repair
capacity and proximity to superoxide radicals, mito-
chondrial DNA (mtDNA) has a higher susceptibility to
damage, compared with the nuclear genome [49]. Addi-
tionally, the absence of introns in the mitochondrial ge-
nome leads to more frequent coding sequence mutations,
which affect mitochondrial function. Numerous somatic
mutations of mtDNA have been observed in breast can-
cer [50,51]. The dysfunction of the mitochondria has
long been suspected to contribute to the development and
progression of cancer [52]. At present, a primary goal is
to assess the fun ctional role of the various mitochondrial
mutations in the initiation and progression of breast can-
cer, with a specific focus on identifying mutations asso-
ciated with acquired adaption for rapid proliferation un-
der hypoxic conditions, as well as mutations related to
drug metabolism. Thus, mtDNA mutations may have
potential value as cancer biomarkers, for example to pre-
dict the metabolism of different chemotherapeutic drugs,
i.e. to predict sensitivity/resistance to treatment. More-
over, the majority of mtDNA mutations have been ob-
served to be homoplasmic in early preneoplastic and
cancerous lesions, i.e. the mutated mtDNA predominates
and is readily detectable in tumor biopsy material with
amounts reported to be 19 - 22 times more abundant than,
for example, mutated TP53 DNA [53]. The success in
identifying mtDNA mutations in material obtained from
fine-needle aspirates und erscores the potential possibility
of using this methodology in clinical practice [51,54].
Distinguishing the spectrum of mutations related to can-
cer from age related mutations is necessary, since mito-
chondrial mutations have also been reported to occur as a
function of the agi ng process [55].
The heterogeneity of the mitochondrial genome must
be considered during analysis. To address this issue, ultra
high-depth sequencing and additional association analy-
ses are required. Still, research into the mitochondrial

Novel Methods in the Study of the Breast Cancer Genome: Towards a Better Understanding of the
Disease of Breast Cancer
800
genome is relatively neglected, since previous studies
using massively parallel sequencing have mainly focused
on the characterization of the cancer nuclear genome.
Thanks to the abundant copy numbers of mitochondria,
obtaining mitochondrial sequences is a common bonus
derived from whole cancer nuclear genome sequencing.
Taking advantage of th is mitochondrial genome infor ma-
tion will hopefully provid e us with a better understanding
of the associations between the breast cancer genome and
the diverse range of breast cancer phenotypes. According
to our experience, even using low-coverage genomic
sequencing (typically one gigabase per sample), we can
obtain 100% coverage and more than 300× depth of mi-
tochondrial genome sequence. Alternatively, implemen-
tation of custom-designed enrichment assays that spe-
cifically capture mtDNA from total isolated DNA can be
used to achieve in depth target sequencing.
2.3. The Temporal Order of Genome Changes in
the Evolution of the Breast Cancer Genome
Molecular characterization of human cancers usually
gives a catalogue of genomic and epigenetic abnormali-
ties reflecting years of somatic changes until the sam-
pling time point [56]. Efforts to elucidate the temporal
order of aberrations are performed by examination of a
series of samples such as paired matched primary tumors
and metastases or sporadic samples ordered according to
different clinicopathological stages. These studies have
revealed mutations associated with tumor progression
and metastasis [57,58]. The differences between the pri-
mary tumor and its metastasis actually present a molecu-
lar profile of the late stages of tumorigenesis, and the
molecular characterization of progression between spo-
radic samples intrinsically contains bias from different
genetic backgr oun ds.
Copy neutral loss of heterozygosity (CN-LOH), as a
frequently observed event in tumors, offers a unique op-
portunity for illustrating the longitudinal evolution of
somatic events, beginning early in tumorigenesis in a
single cancer [56,59]. CN-LOH, also referred to as uni-
parental disomy ( UPD), is a loss of on e copy (allele) of a
heterozygous chromosomal region followed by a dupli-
cation of the other allele, yielding a homozygous chro-
mosomal region without a copy number change (Figures
1(a) and 1(b)). The process of CN-LOH can reveal im-
portant information contained in the evolutionary history
of somatic aberrations: If a mutation precedes a regional
UPD duplication, its copy number is doubled, i.e. homo-
zygous, and mutations following such a duplication event
appear in haploid copy number, i.e. heterozygous [56,59].
Based on this principle, simple mutations preceding a
chromosomal duplication event show discretely higher
copy numbers compared to those occurring after duplica-
tions and the ratio of heterozygous to homozygous muta-
tions in CN-LOH regions directly reflects the temporal
order of the duplication in tumorigenesis (Figures 1(a)
and 1(b)) [56]. In practical analysis, mutants can be dis-
cretely classified as homozygous mutations (high allele
frequency) and heterozygous mutations (low allele fre-
quency). The difference in allele frequency, i.e. shifts
between homozygous and heterozygous mutations, can
reveal the temporal order of genetic events that occurred
in different regions in a single cancer genome. Individual
somatic homozygous mutations accompanied by abun-
dant heterozygous mutations in a CN-LOH region, im-
plies that the homozygous mutations are early events in
tumorigenesis, since a long period after a duplication
event would allow this region to accumulate numerous
new heterozygous mutations. On the other hand, a major-
ity of homozygous mutations with a concurrent minority
of heterozygous mutations implies that a new duplicatio n
event has occurred in the recent past, in which the previ-
ous heterozygous mutations have been lost and only one
allele’s information is retained and doubled. Thus new
heterozygous mutations are quite limited due to the short
period of accumulation after the duplication event.
This principle is also valid for trisomic regions. A
trisomic region can be obtained through two different
patterns: It can be the result of a simple duplication in
which one allelic chromosomal region is doubled. In this
case, the trisomic region harbors both heterozygous and
disomic homozygous mutations (Figure 1(c)). Alterna-
tively, a CN-LOH event could be followed by a secon-
dary duplication to generate a trisomic region. In this
scenario, the trisomic region harbors three types of muta-
tions; heterozygous, disomic homozygous and trisomic
homozygous mutations (Figure 1(d)).
Taken together, combining the information of the alle-
lic frequency of mutations and the corresponding chro-
mosomal copy number allows the measurement of the
relative order of progressive events determining a can-
cer’s individuality [56,59]. Durinck et al. recently ap-
plied this principle to delineate the temporal order in
cancer evolution of skin and ovarian cancers [56]. In that
study, based on investigations of the allele frequency of
the mutations and the corresponding copy number profile,
the mutation of TP53 was revealed as an initial event
prior to the substantial numbers somatic mutations in
tumor development of both types of cancers. Notably, the
method introduced by Durinck et al. can sharply deline-
ate the wide spread genomic instability for any type of
cancer, setting the stage for determining the genetic
events in the progression of breast cancer too. Delinea-
tion of the temporal order in cancer evolution will offer
important information for future characterization of the
succession of molecular changes and identification of

Novel Methods in the Study of the Breast Cancer Genome: Towards a Better Understanding of the
Disease of Breast Cancer
801
(a) and (b) show the principle of determining the temporal order of point mutations and copy-neutral loss of heterozygosity (CN-LOH) events [56]. In
(a), homologous chromosomes (one chromosome is in red and its homolog is in green) accumulate different mutations in their alleles. These mutations
are heterozygous (yellow line). A CN-LOH event (highlighted by blue rectangle) occurs at an early stage. Thus, the number of heterozygous mutations
(yellow li nes) is limited d ue to a r elativel y short time all owed for mutation s to accu mulate. Du ring th e CN-LOH ev ent, th e loss of one allelic chromo-
somal region is compensated by duplication of its homolog. The previously heterozygous mutations on the homolog become homozygous (dark blue
lines) by this duplication event and, thus can be classified as early. The heterozygous mutations not located in CN-LOH regions remain intact. The new
mutations arising after the CN-LOH event are heterozygous (yellow lines). Since this CN-LOH occurred early, a long period allowed for accu mul ati on
of more new heterozygous mutations prior to the sampling time point (the left pane in (a)). By contrast, a majority of homozygous mutations with a
concurrent minority of heterozygous mutations implies that a duplication event has occurred in the recent past, in which the previous heterozygous
mutations have become homozygous and new heterozygous mutations are limited due to the short period of accumulation after the duplication event
(the left pane in (b)). By using sequencing or microarray technologies, heterozygous mutations (newly accumulated, indicated by open circles) and
homozygous mutations (generated by the duplication, indicated by blue solid circles) in CN-LOH region (indicated by a horizontal thick solid black
line) can be identified by their allele frequency. Mutations located in non-CN-LOH regions are shown by open stars (the middle panes in (a) and (b). A
statistical model is applied to determine the temporal order of CN-LOH by calculating the densities for the allele frequency of heterozygous and ho-
mozygous mutations. The ratio of h eterozygous to homozygous mutations in CN-LOH regio ns directly r eflects the temporal order of the duplications in
tumorigenesis (the right panes in (a) and (b). This principle can also be applied to determine the temporal order for trisomic regions. A trisomic region
can be acquired by two distinct types of events: It can be the result of a simple duplication (highlighted by a red rectangle) in which one allelic chro-
mosomal region is doubled. In this case, the trisomic region harbors both heterozygous and disomic homozygous mutations (c).Alternatively, a
CN-LOH event (highlighted by a red rectangle) is followed by a secondary duplication (highlighted by a second red rectangle) to generate a trisomic
region. In this scenario, the trisomic region harbors three types of mutations; heterozygous: disomic homozygous and trisomic homozygous mutations
(d).
Figure 1. Conceptual framework defining the temporal orde r of genetic events in ca ncer genome evolution based on the rela-
tionship between point mutations and chromosome aberrations.

Novel Methods in the Study of the Breast Cancer Genome: Towards a Better Understanding of the
Disease of Breast Cancer
802
driver mutations in early breast cancer tumorigenesis,
thereby supporting the development of novel cancer de-
tection assays and the establishment of new innovative
targeted treatment modalities [56].
2.4. Single-Cell Sequencing
Breast cancer is a complex disease in part because the
progression of breast cancer is a dynamic evolutionary
process in the temporal dimension, and in part because
breast cancer neoplasms contain highly heterogeneous
cell populations in the spatial dimension. Tumor hetero-
geneity is an unavoidable fact in cancer research, because
it is related to many of the important features of tumori-
genesis including tumor progression, metastasis and
therapeutic resistance [60,61]. Breast cancer is a typically
heterogeneous cancer type, composed of diverse malig-
nant epithelial su bpopulations mixed with non-malignan t
tissues, such as infiltrating stromal cells, and cells from
the immune system, such as infiltrating lymphocytes [62].
In some scenarios, normal cell populations may contrib-
ute to more than 50% of the total extracted DNA or RNA
[63]. To address tumor heterogeneity, one solution is to
select samples enriched for tumor content (at least 80%)
and perform in depth sequencing to obtain sufficient se-
quenced data for characterization of dominant cancerous
populations. This strategy is not optimal for studies aimed
at reconstructing the evolutionary history and revealing
the hierarchical structures in cell populations, since the
subtle, important information from special rare subpopu-
lations of cells may be masked, or even lost, in the data
obtained from mixed bulk populations. Recently devel-
oped sequencing approaches for single cells at transcrip-
tomic [64], genomic (DNA copy number profile) [65]
and exomic [66,67] levels provide a new strategy for
improved characterization of tumorigenesis. These ap-
proaches also offer promising tools for the early detec-
tion of compromised genes involved in cancer initiation,
deciphering intratumour heterogeneity, monitoring the
most malignant cells and capturing circulating tumor
cells, thus guiding clinical therapy [63] (Figure 2).
Isolation of individual cellsis a prerequisite for sin-
gle-cell genomic and transcriptomic analyses. Several
attempts have been made to stratify cell subpopulations
using regional macrodissection, fluorescence-activated
cell sorting (FACS), laser capture microdissection (LCM)
and other forms of micromanipulation (Figure 2). Mac-
rodissection can retain the anatomical in formation, there-
by providing a possibility to clarify the relationship be-
tween cells in special proximity where they share the
same microenvironment. Additionally, this method is
easily performed without the requirement of special
equipment. However, one drawback of this is that it only
can provide a gross stratification rather than single-cell
resolution. FACS can collect cells according to the
fluorescent characteristics of each cell, but selected cell
populations based on limited number of labeled features
may remain heterogeneous according to other cellular or
molecular properties. In addition, anatomical information
would be lost in the procedure of making the suspension
of cells from dissociated tissue. LCM enables users to
individually collect target cells, thereby providing an
ideal and well characterized biological material for sub-
sequent analysis. But LCM is labor-intensive and time-
consuming. Micromanipulation can also capture single
cells from cultured cell, dissociated tissue or biopsy ma-
terial according to a given feature, but with the same
shortcomings as LCM.
The amount of material isolated from individual cells
using the above approaches is usually very small. Thus,
an amplification step of the DNA or mRNA (through
amplification of cDNA) extracted from captured single
cells is necessary. Whole genome DNA amplification
approaches, such as PCR-based amplification [68,69]
and isothermal multiple displacement amplification [70],
provide tools for relatively unbiased increasing of DNA
material from single cells. The method of isothermal
multiple displacement in particular has been demon-
strated to ensure a highly efficient and good quality rep-
resentative amplification of the template genome [70].
RNA is prone to degradation, thus stabilization of RNA
is necessary for single-cell transcriptomic analysis. To
maximize the sensitivity o f sub seq u ent sequen cing analy-
ses, elimination of genomic DNA contamination is also
recommended. The methods for increasing the amounts
of RNA include linear in vitro transcription (IVT)-based
and exponential PCR-based methods [71]. With im-
provement in methodologies, ~10 pg of total RNA and
~0.1 pg of mRNA, in a typical mammalian cell, can be
converted to up to 3-kb fragments of cDNA, followed by
uniform amplification that can increase the yield around
ten million-fold to match the requirement for down-
stream analyses with a high reproducibility [64,71,72].
The techniques and methods applied in single-cell tran-
scriptome analyses have recently been highlighted and
discussed by Tang et al. [71].
In early studies, single-cell genomic and transcrip-
tomic analyses mainly utilized microarray-based tech-
nologies such as array-CGH and gene expression micro-
arrays. Massively parallel sequencing not only ensures
deeper measurement of DNA copy number and tran-
scriptomic profiles, but also directly provides sequence
information. Recently, some attempts utilizing the appli-
cation of massively parallel sequencing platforms for sin-
gle-cell analysis, have been reported [64-67]. Navin and
his colleagues applied single-nucleus sequencin g (S NS) t o

Novel Methods in the Study of the Breast Cancer Genome: Towards a Better Understanding of the
Disease of Breast Cancer 803
Multiple approaches can be used to obtain single cells for different analyses [64-67]. Samples can be obtained as fol-
lows: Surgical removal of primary breast cancer (purple) (a); Fine needle biopsy of axillary lymph node (b); Fine nee-
dle biopsy of primary breast tumor (c); Capturing of circulating tumor cells from blood (d); Surgical dissection of tho-
racic vertebral metastasis (red) (e); Macrodissection collects primary breast cancer (purple) and distant metastasis (red)
(f); Isolation of cancer single cells can be performed by micromanipulation; g), laser capture microdissection (h); and
fluorescence-activated cell sorting (FACS) (i); Polygenetic analysis at the single-cell level is applied to uncover the
evolutionary relationship between cancer cell populations (j).
Figure 2. Schematic indicating the framework of single cell sequencing analysis for breast cancer.
investigate tumor population structure and evolution in
two human breast cancer cases through the investigation
of copy number profiles [65]. SNS was demonstrated to
be a reproducible method the sequencing result from a
single-cell showed a high correlation (R2 > 0.85) with
that from a million cells [65]. Tang et al first reported the
transcriptome analysis of single cell mouse blastomeres
in combination with massively parallel sequencing tech-
nology [71]. In their study, numerous known transcripts
and splicing isoform expression patterns were identified
at single cell resolution . Notably, thousands of pr eviously
unknown exon exon junctions were found in the tran-
scriptome, indicating the potential value of this applica-
tion in transcriptomic analysis for cancer single cells [64].
Recently, single-cell exome sequencing method was in-
troduced [66,67]. Hou and his colleagues carried out
whole-exome single-cell sequencing of a JAK2-negative
myeloproliferative neoplasm [67] and Xu and his col-
leagues carried out single-cell exome sequencing of a
clear cell renal cell carcinoma (ccRCC) [66]. These two
studies opened the way for detailed analyses of a variety
of tumor types and other complex diseases, thereby sup-

Novel Methods in the Study of the Breast Cancer Genome: Towards a Better Understanding of the
Disease of Breast Cancer
804
porting the development of more effective therapies,
which are targeted to the relevant cells [66,67].
Phylogenetic analysis is a commonly used bioinfor-
matic tool in research of the evolutionary relationship
between cancer cell subpopulations [57,58,65]. Cancer
progression can be regarded as a micro-evolutionary
process: A cancer begins with an initiating aberratio n in a
normal cell that confers a selective growth advantage.
Subsequently, successive clonal expansions occur fueled
by the acquisition of additional aberrations, correspond-
ing to progression stages. At the same time, there is a
massive loss of clones with lower fitness. In the late
phases of tumorigenesis, founder cells within the cancer
give rise to seeding clones that can colonize distant or-
gans and hence initiate a disease stage characterized by
metastatic lesions [73]. In phylogen etic analysis, if single
cells have similar DNA sequences they likely originate
from a common ancestor and locate in a lineage branch
with short evolutionary distances in a phylogenetic tree.
The lineage branch will be split, when a ‘speciation’
(founder cell) event occurs, in which a single ancestral
lineage gives rise to two or more daughter lineages (ex-
tended clones). Consequently, through phylogenetic
analysis of data generated by sequencing of multiple
samples ordered by the progressive stages of cancer, such
as the normal epithelium, carcinoma in situ, infiltrating
carcinoma, lymph node metastasis, and distant metastasis,
it would be possible to construct the evolutionary rela-
tionships between single cells, identify the founders re-
sponsible for initiating next stage and determine their
molecular features as well as estimate time intervals be-
tween the successive stages.
Single-cell sequencing and related bioinformatic
analyses open a new avenue for breast cancer research.
These methods may have great importance for future
breast cancer genome studies-especially with a continu-
ous reduction in sequencing costs and the emergence of
more powerful sequencing technologies. Limited to cur-
rent conditions, there are some drawbacks in the present
methodologies, such as the relatively low coverage in
single-cell sequencing and sequence information not be-
ing fully exploited [65]. Compared with genomic infor-
mation, transcriptomes from single cells present with
more variability due to the influences from epigenetic
events, the circadian clock, the cell cycle, microenvi-
ronmental niches as well as “transcriptional noise” [71].
The evidence of stochastic characteristics in gene ex-
pression among single cells underscores the importance
and necessity of applying multiple single-cell transcrip-
tomic analyses, and also highlights the challenge in un-
derstanding and interpreting the gene expression results
from individual cells [71]. Epigenetic abnormalities may
also contribute to breast cancer progression, but DNA
methylation analysis for single cells has not yet been
developed, mainly due to lack of proper amplification
methods. At present, no DNA amplification method is
able to properly retain the DNA methylation information
in newly amplified DNA copies. If a technical break-
through can occur in single cell epigenetic analysis, the
evolutionary models currently being constructed on the
basis of single-cell genomic data will be improved by
addition of epigenetic information. Simultaneous analy-
sis of DNA, DNA modification and mRNA from the
same individual cells will be an ideal strategy for the
comprehensive and precise interpretation of the func-
tional alterations occurring in single cancer cells.
Accomplishing the above goal will depend on ad-
vances in sequencing technology. Nanopore DNA se-
quencing is one of a number of promising single mole-
cule sequencing approaches that can directly sequence
DNA or RNA molecules using tiny amounts of material
without the requirement of an amplification and labeling
step [74,75]. DNA methylation information would be
available in the direct readout b y precisely distingu ishing
unmethylated cytosines from methylated cytosines in the
DNA sequence [76]. Therefore, we believe single-cell
sequencing in combination with novel sequencing tech-
nologies will bring a revolutionary change in breast can-
cer research.
2.5. The Microbiome
Beside the aforementioned progress, microbiome and
metagenomic studies will be other promising fields in
cancer research. Microbes inhabiting the human body,
including eukaryotes, archea, bacteria and viruses, are
collectively known as microbiome. Bacteria alone are
estimated to outnumber human cells by an order of mag-
nitude and the gene set of a microbiome is approximately
150 times larger than the human gene complement
[77,78]. Increasing evidences implicate the microbiome
as crucially important for metabolism, immune defense,
and the development of diverse disorders including can-
cers. In recent years, microbiome research has been
boosted through such large-scale sequence-based human
microbiome projects as Metagenomics of the Human
Intestinal Tract (MetaHIT) and the Human Microbiome
Project (HMP). A variety of microbial communities have
been characterized by massively parallel sequencing,
sequence analysis and functional studies [77-79]. Follow-
ing the establishment of microbiome catalogs and refer-
ences as well as the development of laboratory and bio-
informatic approaches-especially, investigations of the
correlation with host phenotype-the microbiome will
become an important aspect in cancer research. In the
context of breast cancer research, the next effort will be
to establish cause and effect relationship between the
microbiome and breast cancer susceptibility.

Novel Methods in the Study of the Breast Cancer Genome: Towards a Better Understanding of the
Disease of Breast Cancer 805
3. Challenges and Progress
Rapid development of improved methods for studying
the breast cancer genome poses many future challenges.
Some challenges will arise from analysis of numerous
short reads the amounts of which are several magnitudes
higher than those traditionally obtained by Sanger se-
quencing. Thus, the first challenge is to meet growing
computational requirements such as sufficient storage,
data transfer and assembly. Secondly, there is an urgent
need for fast, accurate and user-friendly bioinformatic
approaches for data mining to realize the fu ll potential of
these improved sequencing technologies. Numerous re-
cently published bioinformatic tools offer a wide variety
of options for broad “omics” analysis, but also result in
questions on which method provides the best results.
Thus, exhaustive comparisons between algorithms, in-
corporating miscellaneous analytic methods into an inte-
grative pipeline, evaluating the statistical power, sensi-
tivity and specificity of software developed for the same
analyses, will be required for standardization of bioin-
formatics inanalyses of the breast cancer genome.
In addition to bioinformatic methods, as the corner-
stone for cancer genome research, more representative
human reference genomes ar e greatly required. With grow-
ing number of published reference genomes and an in-
creasing knowledge of the variations in the normal hu-
man genome, the previous single consensus representation
of the genome is not sufficient, especially in regions with
complex allelic diversity. This challenge is being ad-
dressed by an effort to create assemblies that better repre-
sent the diversity (http://www.ncbi.nlm.nih.gov/projects/
genome/assembly/grc/). At the same time, functional an-
notation projects will provide the necessary information
for elucidating dysfunctions of protein-coding genes
(GENCODE proj ect ( http://www.sanger.ac.uk/gencode/))
[80] and defining functional elements (http://encodepro-
ject.org/ENCODE/) [81]. Another important resource for
cancer genome research is well-annotated databases.
Advances in understanding the cancer genome depend on
the access to comprehensive catalogues of variations in
the human genome in normal populations. These normal
variations are well collected, curated and updated by
many different databases, according to different variation
patterns, for example, SNPs in dbSNP [82] and the Hap-
Map database [83], copy number variations in DGV [84],
dbVar (http://www.ncbi.nlm. nih.gov/dbvar/) and com-
prehensive human genome variations in The 1000 Ge-
nomes Project [85].
A more difficult challenge is the defining of normal
epigenetic references, because epigenetic information is
reversible and presents in highly tissue-specific and de-
velopmentally associated patterns. Recently, the Epige-
nomics Mapping Consortium has been working to pro-
duce a public resource of epigenomic maps (DNA me-
thylation, histone modifications and related chromatin
features) for stem cells and primary ex vivo human fetal
and adult tissues represen tative of normal human biology,
thereby offering the normal counterpart for cancer re-
search [86]. These databases, either presenting the reper-
toire of oncogen ic variations [3] or collections of normal
variations (see above), which are well-curated and pe-
riodly updated, have provided profound value for cancer
genome research by providing comprehensive references
and aiding in identifying novel aberrations for individual
studies. The relationship between large-scale sequencing
projects for the construction of reference databases and
the many milestone events of cancer genome sequencing
has been well described in a recent review [87].
Large-scale sequencing of cancer genomes, including
breast cancer, is rapidly providing an astronomical amount
of data, which will offer many new candidates that will
be assumed to play pivotal roles for a given cancer phe-
notype. Careful functional studies of mutated genes are
required for ultimate proof of the relationship between
cancer gene status and clinical behavior [41]. How to
validate these candidate genes will become a crucial
challenge for researchers using routine assays such as
cell lines or animal models. High-throughput RNA inter-
ference screens in combination with the adaptation of
existing model systems, will be a promising tools for
refining the potential candidates provided by large-scale
sequencing by further functional studies [16].
The many applications and analyses using massively
parallel sequencing platforms have not yet been fully
optimized, standardized and systematically evaluated for
samples routinely processed in cancer pathology in cli-
nical practice. This poses a gap between bench and bed-
side. To address this important matter, comprehensive
coordinated international collaboration is necessary for
the standardization of laboratory endeavors and bioin-
formatic analyses [24].
4. Conclusion
The completion of the draft of the human genome sig-
naled the ushering in of the genomic era [88]. Thereafter,
revolutionary breakthroughs in sequencing technology, a
spectacular blossoming of bioinformatics and an acceler-
ating accumulation of sequencing data, bring unprece-
dented opportunities as well as challenges to cancer re-
search. Recently, the International Cancer Genome Con-
sortium (ICGC) was launched to coordinate the large-
scale sequencing of the genomes, epigenomes, and tran-
scriptomes for 50 different cancer types and/or subtypes
[24]. The goal of the project is to define catalogues of
cancer genomic abnormalities and translate the findings
of these genomic analyses into clinical utility [24]. This

Novel Methods in the Study of the Breast Cancer Genome: Towards a Better Understanding of the
Disease of Breast Cancer
806
project not only has a profound influence on present can-
cer research, but more importantly, it heralds the start of
the era of personalized medicine [24]. Consequently, we
can anticipate that sequencing and genomic analysis will
play an important role in clinical practice. In the not too
distant future, sequencing may become a population
screening approach for the early detection of breast can-
cer, and sequencing of the breast cancer genome of indi-
vidual patients may be routinely ap plied to confer guide-
lines for personalized breast cancer patient management.
5. Acknowledgements
We thank for the support from the project “Molecular
Tools for Optimal Personalized Treatment of Breast Can-
cer” under the auspices of Sino-Danish Breast Cancer
Research Centre, financed by the Danish National Re-
search Foundation (Grundforskningsfonden), and the Na-
tional Natural Science Foundation of China (30890032,
31161130357). We are also grateful to the Chinese 863
Program (2012AA02A201, 2012AA02A502), Guang-
dong Innovative Research Team Program (2009010016)
and A Race Against Breast Cancer.
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