SELDI-TOF MS Proteomics in Breast Cancer

Serum Proteomic Profiling in Breast Cancer

Serum has been widely used for study by SELDI proteomics as it is relatively minimally invasive, reproducible, and cheap to obtain. Based on the hypothesis that cancer-related protein/peptide signatures in sera can be used in the early detection of cancer, serum proteomic expression profiles have been studied as potential tools for early diagnosis. During tumorogenesis, differentially expressed serum proteins can originate from different sources. Direct or indirect communication of cells with the blood may lead to the release of tissue specific proteins upon cell damage or death. Serum profiles could also reflect specific cancer excreted proteins, host response, acute phase reactants, or elevated normal body protein levels in response to tumors. Since these profiles could represent altered phenotypic events during neoplastic transformation and progression, it is reasonable to expect characteristic proteomic profiles in the presence of cancer.

Serum Proteomic Profiling in Breast Cancer Prediction

Laronga et al. [16] utilized the optimized assay conditions described by Vlahou et al. [15]. The main peaks to discriminate between breast cancer patients and controls on cross validation were selected. Using this approach, they were able to differentiate between patients with cancer and controls with a sensitivity of 87% and a specificity of 93% on cross-validation. This is consistent with the findings of Vlahou on which many of the parameters of this study were based [17].

In a further pilot study, Li et al. [18] performed screening for potential biomarkers on the serum of 169 patients with breast cancer and healthy controls. Serum from participants was tested by chromatography using SELDI-TOF MS following standardized sample preparation [18]. A panel of three markers (BC1 4.3 kDa, BC2 8.1 kDa, BC3 8.9 kDa) [18] were selected based on their ability to distinguish between invasive disease and controls, or in situ disease. When applied to the same cohort, they achieved 93% sensitivity and 91% specificity in distinguishing between these groups. Nevertheless, as there were only four patients with DCIS, and this study could not test the ability of these markers in identifying the disease at an early in situ stage. In a separate study, the authors tested the same markers in a different patient population [17]. Serum samples obtained from 176 women prior to medical intervention were used. Similar to the original study [18], two peaks—BC2 (8.1 kDa) and BC3 (8.9 kDa)—were differentially overexpressed in breast cancer patients compared to healthy subjects, demonstrating the reproducibility of SELDI-TOF MS across different patient populations. Furthermore, the authors were able to identify these peaks as complement components C3adesArg and a C-terminal-truncated form of this component. This illustrates one of the concerns in such studies, where non-specific candidates qualify as discriminatory biomarkers. This study was unable to validate the third candidate peak previously detected, BC1 (4.3 kDa), which was identified as the truncated form of interalpha-trypsin inhibitor heavy chain H4 [17]. This peak was underexpressed in the breast cancer group within the first set [18] and overexpressed in the validation breast cancer population [17]. Clearly, reproducibility and robustness of the SELDI-TOF MS technique necessitates consistent collection, storage, handling, and sample preparation techniques, with similar protein array surfaces and analytical and bioinformatics settings.

In a second validation of this study, Mathelin and coworkers [19] also found that the combination of the two peaks BC1 (4.3 kDa) and BC3 (8.9 kDa) differentiated between breast cancer patients and healthy subjects. The BC2 marker recorded by Li et al. [18] was not recovered. This later found four peaks which could correspond to the BC1 and BC3 peaks identified in the previous work. The first two corresponding peaks, termed BC1a (4,286 Da) and BC1b (4,302 Da) could correspond to BC1 (4.3 kDa) from the previous work of Li et al., and were similarly underexpressed in the breast cancer patients. Likewise, the peaks named BC3a (8,919 Da) and BC3b (8,961 Da) appeared to correspond to the BC3 (8.9 kDa) peak from Li et al. and were overexpressed in the cancer group. Division of the four peaks into stringent (no error) and flexible (<10% error) enabled identification of 33% and 45% of patients with breast cancer, from patients with benign disease and controls, respectively. Interestingly, the proteomic expression patterns in patients with benign disease were similar to controls. In a trial to combine use of these peaks with an existing clinical tumor marker, Ca 15.3, the predictive power of the combination of these markers improved the ability for cancer detection. While this partially validated the earlier study of Li et al., the efficiency of these markers in indentifying patients with breast cancer was moderate, and in particular, the ability of the technique to detect early in situ disease remained unproven.

A recent study by Van Winden and colleagues aimed to further validate the BC1, BC2, and BC3 peaks [20]. In this study, the SELDI spectra of 48 breast cancer patients were compared to 48 controls. Storage duration and/or batch effects but not participants’ age seemed to significantly affect the overall peak intensity but not the discriminatory pattern between the groups. Only one peak (BC1 4.3 kDa) was validated in this study and was underexpressed in patients with breast cancer, as previously predicted. Two ionic peaks possibly corresponding to the BC2 and the BC3 peaks were noted; however, their expression was inconsistent with the original study. This study demonstrates the difficulties in validating the results of SELDI, especially with the absence of subsequent protein sequencing platforms.

A further well-designed and internally validated study involving 310 subjects explored the ability of mass spectrometry for distinguishing between patients with early breast cancer (stage I) and healthy subjects [21]. Mammography was used to exclude cancer in the control group, and patients with a family history of breast cancer were excluded from the control group. Belluco and colleagues [21] used 109 controls and 109 patients with invasive ductal carcinoma (IDC), a blinded testing set of 46 controls and 46 patients with IDC, and finally an independent validation set of 46 patients (15 healthy controls and 31 with IDC) whose samples were collected and analyzed 14 months later. This study focused on the enrichment and selection of carrier proteins to which most of the lower molecular weight (LMW) biomarkers are linked. Specifically, an IMAC chip surface which has a high affinity for albumin was used to enable analysis of bound LMW proteins. In the training set, seven ion peaks were identified that discriminated between the groups with a sensitivity and specificity of 95% and 85%, respectively (Table 2). In the validation study, all the 17 T1a tumors were identified. This work demonstrates that SELDI profiling of serum can generate a robust classification of stage I breast cancer. When the SELDI-TOF results were used to further classify patients with equivocal mammography (34 patients), 94% of the patients with Breast Imaging Reporting and Data System score of 4 were correctly diagnosed. This potential clinical utility of mass spectrometry in improving the positive predictive value of mammography was clear in this work. This has led to the development of an ongoing prospective multicenter project between Italy and the USA involving 4,000 subjects to further consolidate these findings [21].

Serum Proteomic Biomarkers in BRCA1 Mutation Patients

BRCA1 mutations account for 7–10% of breast cancers and are associated with early development of breast cancer and poor outcome [22]. Up to 80% of patients with BRCA1 mutations ultimately develop breast cancer, and identification of cases that are less likely to develop cancer would enable more accurate targeting of risk reducing and therapeutic strategies. As such, BRCA1 mutation carrier testing is another promising application of SELDI-TOF MS. Studies by Becker [23] and Laronga et al. [16] have examined the role of SELDI proteomic profiling in patients with BRCA mutations.

Comparison of Proteomic Profiles Pre- and Post-surgery

Laronga and coworkers performed proteomic profiling on paired serum samples from 16 breast cancer patients pre- and post-surgery and 15 age-matched healthy samples [16] (Table 2). Profiling yielded three significant differentially expressed ion peaks between the pre- and post-surgery groups, and it was possible to differentiate 14 out of 16 of the post-treatment group from the pre-treatment samples, with a sensitivity and specificity of 75% and 87%, respectively.

Work was also performed to investigate whether samples from breast cancer patients post-surgery could be differentiated from healthy controls. This is of interest as if removing the tumor leads to a restoration of the normal healthy profiles it would be reasonable to link the characteristic peaks in the profile to the proteins shed or inhibited by the tumor. In contrast, however, the SELDI analysis clearly differentiated protein profiles between the controls and breast cancer patients post-surgery, with a final separation sensitivity of 93% and specificity of 73%. The post-treatment group partially retained protein profiles after surgery, which were absent in healthy individuals [14].

The Relationship Between Serum Proteomic Biomarker Profiles and Clinicopathological Variables

Studying the associations between potential proteomic biomarkers and clinicopathological factors may lend insight into the biological significance of proteomic profiles. For example, profiles strongly associated with estrogen receptor (ER) status might be expected to be due to proteins involved in hormonal signaling. It is also possible, however, that the absence of such a correlation with known clinicopathological variables might be due to the identification of new independent factors. However, in the study by Li et al. [17], BC2 and BC3 peaks did not correlate with tumor size, nodal involvement, tumor grade, or ER/progesterone receptor (PR) status. The authors hypothesized that these complement peaks are overexpressed in early breast cancer stages but underexpressed in advanced and metastatic disease. In addition, no correlation was found between the four peaks identified by Mathelin and colleagues and any clinical or histological parameters including age, nodal status, metastasis, vascular invasion, ER/PR, and Ca15.3, suggesting that the four biomarkers are independent to these factors [19]. Other groups have further confirmed these findings, demonstrating a lack of association between these proteomic markers and tumor characteristics; however, a reduction in the 8.1-kDa peak intensity noted in the post-menopausal subgroup warrants further evaluation [20].

Laronga et al. attempted to use SELDI-TOF to distinguish sentinel lymph node (SLN)-positive from SLN-negative patients in 98 women [14]. Out of the node-positive group, 22/27 samples were correctly classified compared to 55/71 correctly classified samples in the node-negative group. This produced a classification tree with a sensitivity of 81% and a specificity of 77%. The main differentiating factors were three low mass peaks on the IMAC-Cu and three high mass peaks on the SAX surfaces. If axillary node status could be accurately determined using proteomic profiling approaches, node-negative patients could be spared unnecessary axillary surgery with its attendant complications.

Nipple Aspirate Fluid Proteomic Profiling

Intraductal sampling and diagnostic techniques include nipple aspiration, ductal lavage, and duct endoscopy. These are minimally invasive and allow direct access to the ductal system where most breast malignancies arise. This could be particularly useful in young women where mammographic sensitivities are reduced. Breast duct fluid is a rich source of protein from the immediate vicinity of tumors and their local microenvironment and may represent a superior source of cancer biomarkers due to this close proximity. Similarly, in early stage in situ (DCIS) disease where the basement membrane remains intact, ductal fluid may contain tumor markers that are excluded from the systemic circulation. The main disadvantage of nipple aspirate fluid (NAF), however, lies in the low cellular yield making cytological analysis difficult. Recent technological advances have enabled rapid protein biomarker identification from small volume NAF using SELDI-TOF MS.

Nipple Aspirate Fluid Proteomics of the Ipsilateral Affected Breast in Patients with Breast Cancer Compared to Controls

Noble et al. [24] compared NAF proteomes from women with unilateral breast cancer and healthy female controls. NAF was collected from both breasts of 21 patients newly diagnosed with unilateral breast cancer and a further 44 samples from healthy subjects with a family history of breast cancer.

Comparing proteomic profiles of NAF from the ipsilateral affected breast and NAF from healthy controls revealed six peaks on the CM10 surface and three on the IMAC that were significantly different between the two groups (Table 2). Five breast cancer biomarker peaks were identified that were more frequently expressed in women with breast cancer compared to women without [25]. The most promising peak that best distinguished between the two groups was a 15.9-kDa protein. This protein was identified on an H4 array with the aid of the virtual tryptic digest database as the β chain of hemoglobin. Two other peaks at 8 and 31.8 kDa were identified as the doubly charged and diameric forms of the same protein, respectively. Clearly, validating this biomarker was critical in this instance.

To validate this pilot study and to establish a breast cancer predictive model using NAF protein profiling, Sauter et al. [26] analyzed NAF from 114 women (Table 2). None of the previously identified protein peaks were found to be associated with breast cancer in this study. Seven new candidates were detected, of which three were associated with breast cancer (Table 2). These remained significant discriminators even after excluding women with pathological nipple discharge and duct papillomas. Furthermore, two peaks at 5.2 and 33.4 kDa differentiated between DCIS and benign disease, and the peak at 33.4 kDa differentiated DCIS from IBC. Importantly, these peaks were consistent in differentiating DCIS from the benign group, which may provide an early prediction of the development of neoplastic disease. Overexpression of the peaks at 5.2, 13.88, and 33.4 kDa was also shown to be associated with both DCIS and IBC but not with atypical ductal hyperplasia (ADH) or benign disease. The authors also attempted to build a predictive module using the newly discovered peaks in association with other clinical variables. A model including age, parity, and the peak at 11,880 Da using the H4 chip was found best with a sensitivity and specificity of 40% and 94%, respectively.

In a multicenter study involving 95 patients, Li and colleagues [13] attempted to profile breast fluid proteomes obtained from both ductal lavage and NAF. Using an optimized immobilized metal affinity surface protocol, Li et al. [13] identified three potential biomarkers which differentiated cancer fluids from controls. Three differentially expressed peaks (3.375, 3.447, and 3.490 kDa) were identified as human neutrophil peptides (HNP)1−3 (Table 2). This was also verified using SELDI-TOF MS immunocapture assay on breast cancer samples which originally revealed higher HNP1–3 peak intensities. Moreover, Li et al. [13] used enzyme-linked immunosorbent assay to quantify the three biomarkers providing further validation of their findings using SELDI. HNP was shown not to be the result of blood contamination and had been previously detected in tissue samples from lung, oral, colorectal, and renal cancers [27–30]. Their high level in cancers might be explained by tumor cell invasion and release of neutrophils and eosinophils, or by direct secretion of these peptides.

NAF Proteomics of the Contralateral Unaffected Breast in Patients with Breast Cancer Compared to Ipsilateral Affected Breast Cancer

While previous studies applying different profiling techniques have shown significant proteomic differences between NAF from tumor-bearing and normal breasts in breast cancer patients [31, 32], Noble et al. [24] found no significant differences between these paired profiles using SELDI. Comparative profiling between NAF from the unaffected breast in patients with breast cancer and NAF from controls was also performed [24], and interestingly, no differences were noted. While further validation studies are required, the absence of discriminatory peaks between the NAF from the ipsilateral affected breast and the contralateral unaffected breast is striking. These results led the researchers to hypothesize that in breast cancer a field change occurs across both breasts, and that NAF proteomics may have more value in breast cancer risk assessment, rather than in diagnosis or screening. While comparison between the two sides from the same individual is conceivably an attractive approach providing an internal control for hormonal and environmental effects, it would not be as informative if this hypothesized field change takes place. Yet again, this is a suitable setting for paired SELDI and other proteomic profiling studies using one cohort to address these conflicting results.

Cytosolic Protein Profiling in Breast Cancer

Low abundance biomarker proteins related to tumor cells or their microenvironment might be diluted, altered, or fragmented once shed in the bloodstream. This may have an adverse effect on the results obtained by MS profiling. Cytosolic extracts represent an alternative source of tissue, possibly with higher concentrations of the relevant proteins. Moreover, profiling the cytoplasmic proteome has the advantage of helping detect novel intracellular pathways and interactions involved in tumorogenesis. Alterations in mitochondrial proteins have also been linked to cancer development and progression and could be detected by this method. Overall, studying the protein from the native tumor may lead to important insights into tumor differentiation, invasiveness, and responsiveness to adjuvant therapies. Conversely, however, this type of study is less likely to help with early detection of carcinogenesis, since by definition, samples are taken from the tumor.

Node status remains the strongest prognostic marker in breast cancer and helps determine the need for adjuvant therapies. Nevertheless, a significant proportion of node-negative patients develop disease recurrence and earlier identification and treatment of this high risk group may be advantageous.

Ricolleau et al. [33] performed cytosolic protein profiling on 60 samples from patients with node-negative sporadic breast cancer. Thirty of these patients had developed a distant relapse and 30 remained disease-free. Samples were analyzed using IMAC-Cu and SAX chips. Seventy-three peaks were generated from both arms, and two of these peaks at 8.5 and 19.8 kDa had prognostic potential. This was further confirmed by repeated random sampling, and the 8.5-kDa peak was found to be expressed at lower levels in patients with metastatic disease while the 19.8-kDa peak was found at higher levels in these patients. These peaks were further identified as ubiquitin and ferritin light chain (FLC), respectively, and the results validated by western blotting and immunohistochemistry. Combining these new molecular biomarkers of disease relapse with a pre-existing model for prognostication (The St. Gallen risk profile age <35, size >20 mm, negative hormone receptors, grade 3) led to improved discrimination between the groups of patients who suffered a systemic relapse and those who did not. This promising line of work, therefore, supports the notion that proteomic profiling may one day help improve our ability to classify patients based on molecular parameters and helps determine treatment options.

A further study was conducted by Brozkova and coworkers [14]. Focusing on linking proteomic patterns to tumor clinicopathological characteristics, this group examined non-metastatic breast cancer tissue lysates from 105 patients on IMAC30 ProteinChip Arrays. One hundred thirty peaks were detected, which could cluster the tumors into five distinct groups differing on tumor type, nuclear grade, presence of hormonal receptors, mucin 1 and cytokeratin 5/6, or cytokeratin 14 expression. This study also showed clustering of biomarker patterns into five further smaller subgroups associated with tumor type, hormonal receptor status, and nuclear grade. Hormone receptor expressing luminal group, HER2/neu positive and the basal high proliferation gene expression subtypes were generated by hierarchical clustering. Strikingly, this molecular tumor classification was identical to those generated by complementary DNA genomic expression profiling [34]. In addition, this study successfully identified heat shock protein (HSP)27 and Annexin V within the tumor subgroup biomarker classifiers. Both proteins were mainly overexpressed in the luminal subcohort. Overall, this study illustrates the potential utility of SELDI-TOF MS proteomic approaches in identifying clinically relevant tumor subgroups and expanding our understanding of breast cancer tumor behavior, which is a key step towards individualization of patient therapy [14, 35].

Salivary Proteomic Profiling in Breast Cancer

The level of many salivary constituents is altered in disease states compared to healthy controls. For example, there are differences in the levels of kallikreins, CA125, epidermal growth factor, and cErb2 in the saliva of healthy compared to diseased subjects [36–39]. Additionally, the collection of samples is safe, simple, inexpensive, easily repeatable, and most importantly non-invasive. Researchers have previously studied the possibility that saliva may be used to diagnose systemic diseases [40–42], and Streckfus et al. [43] used SELDI to detect putative breast cancer markers in saliva. Pooled reference samples and individual saliva from three healthy controls and three patients with ductal carcinoma in situ were analyzed and four differentially expressed protein peaks detected (Table 2). While this is a small exploratory study, it demonstrates the sensitivity and threshold of SELDI-TOF MS in detecting significant differentially expressed peaks in saliva between different patient groups. In particular, this feasibility study proved that SELDI can be used to detect alterations of salivary proteins in cancer. While the results of this pilot study are encouraging, larger and blinded validation studies will be required to confirm the value of the differentially expressed protein peaks detected. In a more recent study using LC–MS/MS, Streckfus et al. have provided further support for the notion that salivary protein profiles can provide useful information to classify patient populations [44].

Shortfalls in SELDI-TOF MS Profiling

With the aid of recent advances in proteomic technologies and mining techniques, the limited number of proteins detected using serum samples can be augmented. SELDI as a high-throughput solid phase extraction technology is a useful means of sample protein extraction, fractionation, and detection. Accordingly, SELDI was perceived as a suitable platform to enhance the power of profiling studies through increasing the analysis of both samples and proteomic signals. Nevertheless, a low molecular mass detection preference, added to its selective surfaces and its semi-quantitative abilities, are all factors that could limit the detection of biomarkers. Other technical aspects including its low resolution, the slow progression to validation, identification, and clinical utility, have all contributed to the negative views of SELDI-based approaches.

Variability and the lack of sequencing abilities currently limit the use of SELDI. In addition, discrepant reports failing to validate earlier SELDI results have cast doubts on SELDI as a technique [45]. It is known that biased non-biological variability can result from any minor non-standardized step(s) in any proteomic profiling work, starting from sample collection and ending by data processing and analysis [46–48]. Studies testing the effects of different variables including storage tubes, clotting time, incubation temperature, storage temperature, and handling proved the importance of uniform handling to exclude systemic pre-analytical inconsistency and false discovery [49, 50]. Non-standardized protocols in different validation studies have generated conflicting results including clear variations in the discriminatory power and direction of several putative biomarkers. These factors have all raised concerns on the performance of SELDI [51]. In a real world setting, however, unifying patient cohorts, collection protocols, storage conditions, handling, and analysis settings is a challenging task. Nevertheless, adequately powered upstream studies analyzing large cohorts would attenuate this variability effect and select for potential clinically valid biomarker patterns. Moving from detection to identification of biomarkers is a well-known limitation of this MS-based tool. Nonetheless, potent cancer biomarker signatures can be transiently used in multicenter validation providing a proof supporting or dismissing their relivance. Identification of these markers can then be established using other conventional downstream methods.

The identification of acute phase proteins or proteins resulting from exoprotease activities as candidate biomarkers have also raised concerns about the reliability of this type of profiling [52]. We believe these findings do not imply underperformance per se, given that any signal including proteolytic products and acute response players can reflect a milestone in tumorogenesis. As long as candidate biomarkers are validated and do not fall within the false-positive bracket, their role in cancer evolution and diagnosis remains viable.

Transparent and standardized biomarker discovery programs are crucial for any unbiassed studies. Candidate biomarker validation, verification, and authentication should be the basis for any future biomarker discovery agendas. Adopting this strategy with strict standardized protocols would reduce false positives and recover some of the missing confidence in this exciting field.