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).