RESUME OF JOURNAL
BREAST CANCER
PROTEOMICS: A REVIEW FOR CLINICIANS
Introduction
Breast
cancer is one of the major health problems of the westernWorld; with the
highest incidence rates found in the developed countries. It is themost common
neoplasia among women, in whichmore than 1 million new cases occur every year,
and it is the first cause of death in women aged 40–59 years old. In the United
States, 207,090 new cases of the disease are estimated to occur in 2010 with an
expected mortality rate of 39,840 women (Jemal et al. 2010).
Breast
cancer is an acquired or inherited genetic disorder influenced by environmental,
behavioral, and reproductive factors. The most significant risk factors are
gender (being a woman) and age (growing older). Two distinct forms of the
disease are identified. Hereditary forms of cancers, which are often related to
mutations in two high-penetrance susceptibility genes referred to as BRCA-1 and
BRCA-2, account for 5–10% of cases. Women who are born with these mutations
have a 10–30-fold increased risk of developing breast cancer than the general
population as well as a cumulative lifetime risk of 60–80% (King et al. 2003).
Sporadic
forms account for 90–95% of cases and are consequences of a somatic mutation
over the lifetime without any hereditary predisposition; they appear to be
related to polymorphisms in low-penetrance genes that encode proteins involved
in DNA repair, cell signaling pathways, and estrogen metabolism (Mitrunen and
Hirvo-nen 2003; Weiss et al. 2005; Zhang et al. 2006).
The
control of breast cancer is based on early detection by mammography screening, which
is able to detect small lesions and enhance the chances of cure, reaching up to
90% of a 10-year disease-free survival. In the last few decades, the survival
rate improved due to advances in mammography and adjuvant therapy (Abramovitz
and Leyland-Jones 2006). Additionally, histopathologically identical tumors may
exhibit different biological behaviors in terms of severity, course, and
response to therapy, reflecting the disease heterogeneity and the
unpredictability of the individual’s immune response as well as the need for better
understanding of this disease (van’t Veer et al.2003).
At
the biological level, breast cancer is a complex disease caused by several
genetic and epigenetic alterations that ultimately lead to changes in cell
processes, including cell proliferation, apoptosis, and angiogenesis with subsequent
acquisition of a malignant phenotype (Reis-Filho and Lakhani 2003). The main
genetic abnormalities that are observed include increased proto-oncogene
expression, inactivation of tumor suppressor genes, chromosomal instability,
alterations in DNA repair genes, telomerase reactivation, and epigenetic
alterations, resulting in dys regulation of cell proliferation, clonal
selection, and tumor formation (Rodenhiser and Mann 2006).
One
of the major challenges in the study and treatment of breast cancer is the
resolution of the tumor heteroge- neity. Over the past decades, studies with
genomic and transcriptome approaches have identified key genes, e.g. BRCA-1 and
BRCA-2. Specific proteins, such as ER (estrogen receptor), PR (progesterone
receptor), and HER-2 have been validated as having prognostic and predictive value
of response to therapy. The cDNA-microarray technology has made it possible to
analyze millions of genes simultaneously through the mRNA expression, and to
classify breast cancers. The first level of classification separated ER-negative
from ER-positive tumors. Subsequent analyses have been carried out, and the
correlation with relevant clinical features, such as disease-free interval and
survival, has allowed the molecular classification with prognostic and
predictive value. Five breast cancer subtypes have been identified, of which the
luminal (A and B) type is ER-positive and accounts for 60% of the tumors; the
HER-2 overexpression type accounts for 15–20%; the ER and HER-2 negative basal-like
type accounts for 20% of the cases and has a poor prognosis; and the
normal-like type, which has no definitive clinical value (Perou et al. 2000;
Sorlie et al.2001).
ER-positive
tumors respond to hormone therapy (tamoxifen and/or aromatase inhibitors) and
tumors that are human epidermal growth factor receptor 2-positive (HER-2 positive)
are eligible for targeted therapy with trastuzumab, a monoclonal antibody,
whereas the basal-like type has a more aggressive phenotype and is less
responsive to the available treatment options (Perou et al. 2000; Sorlie et al.
2001; Reis-Filho and Tutt 2008).
Currently,
the proponents of this classification have suggested that the normal-like
subtype might be basically an artifact of sample representation, that is,
contamination of the mammary tissue by normal cells (Parker et al. 2009; Peppercorn
et al. 2008). More recently, other three ER-negative subtypes have been
described, the molecular apocrine tumor, the interferon, and lastly the
claudin-low, which express breast epithelial stem cell markers (CD44? /CD24); a
subsequent definition of their clinical signifi- cance is still needed (Weigelt
et al. 2010). Two gene sig- natures, the Mammaprint and the Oncotype X, which
come from these studies, are being tested in prospective phase III trials
entitled MINDACT and TAILORx, respectively. Despite its undeniable
contribution, the ‘gene signature’ is not a definitive classification method, but
rather a developing work model that needs to be refined, considering that more
subtypes have been described (Reis-Filho and Lakhani 2008).
A
means of complementing the genetic information on breast cancer is the study of
the protein content of the genome, the so-called proteome (Wilkins et al.
1996). Whereas the human genome has approximately 35,000 genes and
theoretically the ability to encode up to 35,000 corresponding proteins, the
occurrence of alternative RNA splicing and posttranslational modifications
(PTM), such as phosphorylations, acetylations, and glycosylations, or protein
cleavages may increase the expression of proteins to 500,000–1,000,000. The
proteins reflect more accurately the intrinsic genetic mechanisms of the cell
and their impact on the microenvironment, since they are the effectors and
characterize more accessible therapeutic tar- gets than the nucleic acids
(Andersson et al. 2007).
Studies
on the proteome in breast cancer have used tissue samples as well as biological
fluids including serum, plasma, saliva, nipple aspirate, and cerebrospinal fluid
in search for the detection of diagnostic, predictive, and/or prognostic
biomarkers (Hondermarck et al. 2001; Bertucci et al. 2006; Gast et al. 2009). Some
proteome studies have identified proteins of potential clinical significance. In
this overview, we present features of proteomic technology and its main
implications, focusing on the protein profile in tumor tissues/cells through
MALDI/SELDI, as well as on the current proteomic challenges in the breast
cancer study.
Purpose
Breast
cancer is one of the major health problems of the Western world. Although the
survival rate has improved with progress in screening and adjuvant systemic
therapies, one-third of the patients with initial breast tumor have recurrence
of the disease 10 years after the diagnosis, demonstrating the presence of
micrometastasis. The underlying molecular mechanism of the disease needs to be
better understood. Allied to genomics, proteomics tech-nologies promise to be
valuable for identifying new markers that improve screening, early diagnosis,
prognosis and prediction of therapeutic response or toxicity, as well as the
identification of new therapeutic targets. In this review, we present features
of proteomic technology and its main implications, focusing on the protein
profile in tumor tissues/cells through MALDI/SELDI, as well as on the current
proteomic challenges in the breast cancer study.
Methods
We
performed a research of protein profiling studies using mass spectrometry in
breast cancer to identify potential biomarkers.
Results
Table 1 Protein profile
of tissues studies performed in breast cancer by MALDI/SELDI-TOF MS
|
|||||||
Author
|
Matrix
|
Mass spectrometry
|
Samples (n)
|
Platform
|
ID proteins
|
Expression
|
Function
|
BC BB HC
|
validation
|
± In
|
|||||
Traub et al. (2005)
|
Tissue
|
SELDI(SAX;WCX;LYSIS)
|
20 -
-
|
n.p.
|
No
|
Cancer
|
-
|
Umar et al. (2005)
|
Tissue
|
MALDI;LCM;IMS
|
05 -
03*
|
n.p.
|
No
|
Cancer
|
-
|
Sanders et al.
(2008)
|
Tissue
|
MALDI;LCM;IMS
|
60 -
83
|
MALDI
|
Ubiquitin
|
Cancer
|
-
|
Many
protein peaks have been reported to bear significant diagnostic, prognostic or
predictive value; however, the candidate biomarkers have not been validated for
use in clinical patient care.
Discussion & Conclusions
In
the past decades, several MALDI/SELDI studies aimed at investigating breast
cancer diagnostic, and prognostic markers have been performed in distinct
biological samples and have detected various peaks of differentially expressed
proteins; however, only a few of these peaks have been structurally identified,
denoting thus that the reproducibility of results is a challenge.
Callesen
et al. (2008), in a systematic review of 20 MALDI/SELDI studies, compared
discriminatory peaks of candidates to diagnostic markers. They reported the
occurrence of substantial heterogeneity in the studies with regards to
experimental design, biological variation, preanalytical conditions,
collection, and computational data analysis method. But they still found common
features among the studies and demonstrated that 45% of the peaks previously
related to breast cancer in these studies were also observed in a recent
experimental study performed by the same authors. Indeed, studies testing the
effects of different variables including storage tubes, clotting time,
incubation temperature, storage temperature, and handling have proven the
importance of uniform handling to exclude systemic preanalytical inconsistency
and false discovery (Zeidan et al. 2009).
Nonstandardized protocols in different
validation studies have generated conflicting results, including clear variations
in the discriminatory power and direction of several putative biomarkers. The
frequently identified proteins consist of normal cell proteins and high-abundant
serum proteins involved in blood coagulation and acute inflammatory response
(Table 1). As the candidate proteins are among
the least abundant, they might be below the detection threshold of the methods used.
Perhaps, because of that, the specific proteins secreted by the tumor have not
yet been detected (Gilabert et al. 2010).
Recently,
MS-based ‘off-gel’ quantitative proteomics methods have been employed and have
provided a means of increasing the number of proteins identified. The multiplex analysis
of up to eight samples can be achieved using iTRAQ technology (Aggarwal et al.
2006).
iTRAQ
has some advantages: it provides an opportunity to incorporate internal control
samples for normalizing different patient sets from distinct experiments and
combination of the peptide signals, increasing the chance of generating quality
MS/Ms for a more definitive protein identification (Sutton et al. 2010). A
preliminary study using iTRAQ-2D-LC–MS/MS has compared three low-grade breast
cancer tissue samples with different metastatic potentials (primary tumor
without metastasis, lymph node metastasis, and distant metastasis). It was
possible to identify 605 non-redundant proteins, demonstrating the ability of
the method in defining the differential protein spectrum in relation to the
disease progression, confirmed by qRT-PCR (Bouchal et al. 2009).
In
another pilot MALDI MS/MS study, in which two lysis buffers (RIPA and urea) were
used to maximize the protein extraction; normal and tumor biopsy samples of
three patients were analyzed. After tryptic digestion, the resulting peptides
were tagged with iTRAQ and separated by IEF and RP nano-HPLC. Four-hundred one
proteins were identified, of which 63 (13%) were plasma proteins, 58 (12%) were
extracellular proteins, and 360 (75%) were cellular proteins; with remarkable
differences in protein expression between normal and tumor tissues and between adenoma
and invasive cancer (Sutton et al. 2010). Although a variety of proteomics
approaches are being used in order to explain the underlying mechanisms of breast
cancer, there is still a long way to go. Currently, preliminary results with
iTRAC have revealed the strength of quantitative methods in identifying
proteins that change significantly throughout the disease course (Bouchal et al.
2009; Sutton et al. 2010).
Indeed,
validation studies of biomarker candidates have been performed only for a few
proteins detected by mass spectrometry. Thus, the proteins C3adesArg, C3adesArgD8,
ITIH4 fragments, alpha-1 haptoglobulin and the fibrinogen fragment (m/z 2660)
identified in serum and plasma; and the protein S100-A9 detected in samples of tumour
tissue have been evaluated. Nevertheless, some of these studies found
contradictory results (except for m/z 2660 and S100-A9), thus warranting future
clarification of the actual values of these markers (Gast et al. 2009).
In
addition, some proteins candidates for breast cancer markers have been
identified for other cancer types, such as the C3adesArg for colorectal cancer
and the apolipoprotein A-I for ovarian cancer, demonstrating the lack of specificity
(Habermann et al. 2006; Zhang et al. 2004). The few validation studies
performed are all retrospective and, to date, no breast cancer biomarker
protein has been validated sufficiently to be included in prospective clinical
trials. Finally, the question whether a protein biomarker identified in tissue
or fluids (plasma, serum) can be valuable rests primarily on the ability to
address the complexity associated with breast cancer and the human proteome.
For this, in order to correlate multiple sources of data, bioinformatics and
systems biology techniques can help reduce this complexity significantly (Zhang
and Chen 2010).
