Cancer Research Annual Meeting 2010 Protein Translation and Cancer
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Cancer Research 66, 11825, December 15, 2006. doi: 10.1158/0008-5472.CAN-06-2337
© 2006 American Association for Cancer Research
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Cell, Tumor, and Stem Cell Biology

Proteomic Profiling of Primary Breast Cancer Predicts Axillary Lymph Node Metastasis

Taku Nakagawa1, Sharon K. Huang1, Steve R. Martinez1, Andy N. Tran1, David Elashoff2, Xing Ye2, Roderick R. Turner4, Armando E. Giuliano3 and Dave S.B. Hoon1

1 Department of Molecular Oncology, 2 Division of Biostatistics, and 3 Joyce Eisenberg Keefer Breast Center, John Wayne Cancer Institute; and 4 Department of Pathology, Saint John's Health Center, Santa Monica, California

Requests for reprints: Dave S.B. Hoon, Department of Molecular Oncology, John Wayne Cancer Institute, 2200 Santa Monica Boulevard, Santa Monica, CA 90404. Phone: 310-449-5267; Fax: 310-449-5282; E-mail: hoon{at}jwci.org.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
To determine if protein expression in primary breast cancers can predict axillary lymph node (ALN) metastasis, we assessed differences in protein expression between primary breast cancers with and without ALN metastasis using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Laser capture microdissection was performed on invasive breast cancer frozen sections from 65 patients undergoing resection with sentinel lymph node (SLN) or level I and II ALN dissection. Isolated proteins from these tumors were applied to immobilized metal affinity capture (IMAC-3) ProteinChip arrays and analyzed by SELDI-TOF-MS to generate unique protein profiles. Correlations between unique protein peaks and histologically confirmed ALN status and other known clinicopathologic factors were examined using ANOVA and multivariate logistic regression. Two metal-binding polypeptides at 4,871 and 8,596 Da were identified as significant risk factors for nodal metastasis (P = 0.034 and 0.015, respectively) in a multivariate analysis. Lymphovascular invasion (LVI) was the only clinicopathologic factor predictive of ALN metastasis (P = 0.0038). In a logistic regression model combining the 4,871 and 8,596 Da peaks with LVI, the area under the receiver operating characteristic curve was 0.87. Compared with patients with negative ALN, those with ≥2 positive ALN or non-SLN metastases were significantly more likely to have an increased peak at 4,871 Da (P = 0.016 and 0.0083, respectively). ProteinChip array analysis identified differential protein peaks in primary breast cancers that predict the presence and number of ALN metastases and non-SLN status. (Cancer Res 2006; 66(24): 11825-30)


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Breast cancer remains the most common malignancy affecting women in the United States (1). If primary breast cancer is diagnosed when tumors are small (<1 cm), with no metastasis to regional lymph nodes, 5-year survival exceeds 90%, emphasizing the importance of early detection (2). Conversely, for large (>5 cm) breast cancers with regional nodal metastasis, the 5-year survival can be as low as 40% (2). Several clinicopathologic factors have been recognized as having prognostic use in breast cancer, such as tumor size, histologic type, tumor grade, lymphovascular invasion (LVI), HER-2/neu overexpression, and hormone receptor status (3–6). The presence of axillary lymph node (ALN) metastasis, however, is the most important prognostic factor predicting breast cancer patient survival (7–9). At present, the best predictor of ALN metastasis is the presence or absence of metastasis in the sentinel lymph node (SLN). Level I and II ALN dissection is associated with upper extremity lymphedema, wound complications, or nerve injury in a significant proportion of patients. Although SLN biopsy is far less morbid than ALN dissection, it is not without risks or morbidity (10, 11). SLN biopsy has a low but measurable false-negative rate. Furthermore, it provides no information about the presence of additional non-SLN metastasis, which may occur in 40% to 70% of cases (12–14). Newer, more accurate, and less invasive means of predicting ALN metastasis would greatly improve breast cancer patient management and quality of life.

The prediction of ALN metastasis by evaluating primary breast cancer expression of tumor-related genes is potentially promising because the primary tumor is readily assessed after either resection or core needle biopsy. Gene expression at the protein level, unlike measurement of mRNA copy number, indicates a true functional state (15). The proteomic events of breast cancer transformation are complex and remain incompletely characterized. Currently, only estrogen receptor, progesterone receptor, and HER-2/neu have been widely accepted for routine clinical use, serving as prognostic and predictive factors for targeted therapies (16, 17).

The current gold standard for the separation and analysis of multiple proteins is two-dimensional PAGE (18–21). This technique is labor intensive, requires large quantities of starting material, and is impractical for assessing large numbers of patient samples in a clinical setting. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is an analytic technology that can be used to rapidly search for and identify multiple differentially expressed proteins in a large sample set. Unique profiles are generated by affinity-capturing proteins of interest within a complex mixture of proteins. Captured proteins are then analyzed by SELDI-TOF-MS, generating a spectral map depicting approximations of the molecular weight and relative concentration of each protein (Ciphergen Biosystems, Inc., Fremont, CA). Laser capture microdissection (LCM) allows for the precise collection of homogeneous cell populations. The combination of SELDI-TOF-MS technology with LCM of enriched cell populations provides an opportunity for generating specific protein profiles of malignant breast cancer cells (22, 23).

We hypothesized that proteomic alteration in primary breast cancer tissues could predict SLN metastasis, non-SLN metastasis, ALN metastasis, and overall number of metastatic ALN.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Patient selection. Female breast cancer patients treated at the Joyce Eisenberg Keefer Breast Center at St. John's Health Center (Santa Monica, CA) between September 2003 and February 2005 were enrolled. The study protocol was approved by the Saint John's Health Center and John Wayne Cancer Institute human subjects Institutional Review Board. All patients provided informed consent to participate in the study.

Sixty-five patients with invasive breast cancer undergoing resection and SLN biopsy and/or ALN dissection were enrolled. Patients with carcinoma in situ were excluded. Patients were staged according to the American Joint Committee on Cancer (AJCC) 6th edition staging manual (2). No subjects had a history of prior malignancy, immunodeficiency, autoimmune disorders, hepatitis, or HIV infection. All investigators, other than the surgeon and surgical pathologist, were blinded as to the ALN status of the patients in this study.

Specimens and LCM. Breast cancers were resected and embedded in OCT medium (Sakura Finetek, Inc., Torrance, CA). A cryostat (CryoJane, Instrumedics, Inc., St. Louis, MO) was used to cut 6-µm sections at –25°C. Frozen sections were stored at –80°C until LCM. Sections were mounted onto glass slides, fixed in 75% alcohol for 30 seconds, and stained with HistoGene staining solution (Arcturus Bioscience, Inc., Mountain View, CA) and then dehydrated in ethanol and cleared in xylene. After 5 minutes of air drying, microdissection was done with a PixCell IIe LCM system (Arcturus Bioscience). All tissue slides were reviewed by a surgical pathologist (R.R.T.), whose expertise is in breast cancer pathology. Areas of microdissection of tissue were identified by a surgical pathologist (R.R.T.). Captured cells were dissected from the slides using a 7.5-µm laser beam diameter for 2.0 milliseconds, 60 mW power, and at least 2,000 targeted shots for each sample. To avoid proteolysis, total time for LCM was kept to <30 minutes for each slide. To each LCM cap, 8.0 µL of lysis buffer were added (8 mol/L urea and 1% CHAPS in PBS) followed by incubation for 15 minutes at room temperature in a humidity chamber. Cell lysates were then transferred to 0.5 mL tubes and stored at –80°C until analysis.

SELDI ProteinChip array. Immobilized metal affinity capture (IMAC-3) SELDI ProteinChip arrays were used for protein capture (Ciphergen Biosystems). IMAC-3 arrays were activated with 0.1 mol/L CuSO4 for 5 minutes followed by a distilled water rinse using a bioprocessor (Ciphergen Biosystems). Arrays were incubated with 50 µL binding buffer [0.1 mol/L sodium phosphate, 0.5 mol/L NaCl (pH 7)] for 5 minutes followed by the application of 3.5 µL of cell lysate to each spot, which was allowed to bind at room temperature for 45 minutes. Spots were washed twice with binding buffer for 5 minutes and then rinsed with distilled water. All samples were run in duplicate.

SELDI-TOF-MS analysis. After the spots on the IMAC-3 arrays were air dried, 0.5 µL of a saturated solution of sinapinic acid in 0.5% (v/v) trifluoroacetic acid and 50% (v/v) acetonitrile was applied to each bait surface, allowed to air dry, and reapplied. Mass-to-charge (m/z) spectra of proteins with affinity to the chelated metal surface were generated in a Protein Biology System IIc TOF-MS (PBS-IIc, Ciphergen Biosystems). Laser intensity was set at 220 and detector sensitivity at 9 to acquire an optimal mass of 3 to 50 kDa and a maximum mass of 150 kDa. External calibration was done using hirudin BHVK (7,034 Da; Ciphergen Biosystems), bovine cytochrome c (12,230 Da; Ciphergen Biosystems), equine myoglobin (16,951 Da; Ciphergen Biosystems), and bovine carbonic anhydrase (29,023 Da; Ciphergen Biosystems) as standards. All array binding and SELDI-TOF-MS were done on the same day. To confirm the consistency of our assay, all samples were run in duplicate. Because estimates of the coefficient of variation (CV) based on samples of two observations could be inaccurate, we adopted the root-mean-square (RMS) method, which combines individual CVs calculated for each pair into an overall measurement (24).

Statistical analysis of SELDI-TOF mass spectra. All of the duplicated spectra were compiled, and the protein peak intensities were normalized to the total ion current m/z values from 3,000 to 150,000 Da using Ciphergen ProteinChip software 3.2.0. For each sample, 7,930 m/z values were exported. The m/z values <3,000 Da, corresponding to the signal from the sinapinic acid matrix, were omitted, yielding 5,258 points/sample for statistical analysis.

Next, peak clustering was done with Biomarker Wizard software (Ciphergen Biosystems) with 5/2 S/N filter span and 10% minimum spectrum detection. Peak intensities from duplicate samples were then averaged. Ultimately, a final set of 40 identified peak clusters was used for analysis.

To identify biomarkers predicting ALN metastasis, we compared primary tumors with ALN metastasis (n = 24) with ALN metastasis-free tumors (n = 41). Of 24 patients having primary tumors with ALN metastasis, 19 had SLN biopsies and 5 had level I and II ALN dissections. Values of the mass spectrum data were converted to a Z-score based on the mean and SD of each marker. Logistic regression was used to compare each peak between the two ALN groups to determine the set of differentially expressed peaks. A P value of <0.05 was considered significant. For each peak, we also constructed receiver operating characteristic (ROC) curves to evaluate the predictive power of each biomarker. The area under the curve (AUC) was computed via numerical integration. {chi}2 and Fisher's exact tests were used to compare categorical clinical variables between ALN metastasis-negative and metastasis-positive groups.

Next, multivariate logistic regression was used to construct a classification model to discriminate between ALN metastasis-negative and metastasis-positive groups. Forward stepwise model selection was used to build the logistic regression model, which included the significant univariate peaks and known clinicopathologic prognostic factors. Based on the logistic regression model, a ROC curve was constructed using the predicted probabilities of nodal metastasis from the model.

We used 8-fold cross-validation to validate the logistic regression model. Briefly, the data set was divided into eight subsets. Each time, one of the eight subsets was used as the test set and the other seven subsets were combined to form a training set. A logistic regression model was constructed based on the training set while keeping the predictors from the final multivariate model. The predicted probability of ALN metastasis was calculated using the resulting logistic regression equation, and a cutoff probability of 50% was used to predict the presence or absence of ALN metastasis for each patient in the test set. The validation was repeated eight times, and the average error rate across all eight trials was computed.

ANOVA was done to compare the final peaks in the logistic model with the number of ALN metastasis (node negative, 1 metastatic node, ≥2 metastatic nodes) and overall nodal status (node negative, SLN positive, non-SLN positive). The least-significant-difference method was used for the pair-wise comparisons once the overall F test was significant at the 0.05 level.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Patient characteristics. The clinicopathologic characteristics of ALN-positive and ALN-negative breast cancer patients are listed in Table 1 . Of the 65 tumors assayed, 25 were AJCC stage I, 29 were AJCC stage II, and 11 were AJCC stage III. ALN metastases were present in 24 (37%) patients, whereas 41 (63%) patients were node negative. Neither group showed a relationship between the ALN metastasis with age, histologic grade (well/moderate/poor), histologic type, hormone receptor status, HER-2/neu expression, tumor size (≤2 cm versus >2 cm), DNA ploidy, S phase, Ki-67, or p53 values. LVI was the only clinicopathologic factor to correlate with ALN metastasis (P = 0.0002).


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  		Table 1. Patient characteristics

 
Protein profiling of primary breast cancers with and without nodal metastasis. To identify biomarkers predictive of ALN metastasis, we compared primary breast cancers with (n = 24) and without (n = 41) ALN metastasis. Using the ProteinChip and Biomarker Wizard softwares, a set of 40 identified peak clusters was analyzed. In a univariate analysis, 3 of 40 (7.5%) peaks were significantly associated with ALN metastasis [4,871 Da: odds ratio (OR), 2.0; 95% confidence interval (95% CI), 1.1–3.5; P = 0.022; 8,596 Da: OR, 0.52; 95% CI, 0.28–0.98; P = 0.042; and 17,230 Da: OR, 0.53; 95% CI, 0.29–0.97; P = 0.040, respectively]. The AUC for these peaks was 0.70, 0.66, and 0.64, respectively. The 4,871-Da peak was overexpressed in breast cancers with ALN metastasis, whereas the 8,596-Da and 17,230-Da peaks were underexpressed.

Clinical variables and peaks that were significant in the univariate analysis were included in the multivariate logistic regression model. A stepwise procedure was used for covariate selection to determine which factors were associated with ALN metastasis in breast cancer patients. Histologic type was excluded from the model, as the pathologic diagnosis was infiltrating ductal carcinoma in 60 of 65 (92%) patients. In 24 patients, the S phase was indeterminate due to overlapping diploid and aneuploid cell populations. To maximize the power of our multivariate model, S phase was excluded. Histopathologically representative examples of primary and ALN-negative and ALN-positive tissue sections are shown in Fig. 1 .


Figure 1
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  		Figure 1. Representative H&E-stained paraffin-embedded primary tumor and paired lymph nodes. A, C, and E, primary tumors. B, D, and F, respective paired ALN. B and D, histopathology (+) ALN metastases. E, histopathology (–) ALN. Magnification, x200.

 
Accordingly, LVI (OR, 7.4; 95% CI, 1.9–29; P = 0.0038) and protein peaks at 4,871 Da (OR, 2.2; 95% CI, 1.1–4.7; P = 0.034) and 8,596 Da (OR, 0.31; 95% CI, 0.12–0.80; P = 0.015) were significantly correlated with ALN metastasis (Table 2 ). Of the two protein peaks, the 4,871-Da peak was overexpressed in breast cancers with ALN metastasis (Fig. 2A ), whereas the 8,596-Da peak was overexpressed in breast cancers without ALN metastasis (Fig. 2B). The AUC was calculated from the multivariate logistic regression model using a cutoff probability of ALN metastasis of 50.0% and a sensitivity and specificity of 63.6% and 92.1%, respectively. The calculated AUC, combining LVI with the 4,871-Da and 8,596-Da protein peaks, was 0.87 (Fig. 3 ). The 8-fold cross-validation error rate based on logistic regression models was 26%.


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  		Table 2. Multivariate logistic regression for lymph node metastasis (n = 60)

 

Figure 2
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  		Figure 2. Scatter plots for ALN metastasis-negative and metastasis-positive groups using multivariate logistic regression. {circ}, values of individual patients. A, expression level of the 4,871-Da protein peak appeared in significantly higher levels in the ALN-positive group (OR, 2.2; 95% CI, 1.1–4.7; P = 0.034). B, the 8,596-Da protein peak appeared in significantly higher levels in the ALN-negative group (OR, 0.31; 95% CI, 0.12–0.80; P = 0.015).

 

Figure 3
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  		Figure 3. The ROC curve analysis of combined protein biomarkers (4,871 and 8,596 Da) in addition to LVI. The combination of the two protein peaks and LVI predicted ALN metastasis with a sensitivity of 63.6% and specificity of 92.1%. The calculated AUC was 0.87.

 
Prediction of the number of ALN metastasis and SLN status. To assess whether the identified biomarkers could predict the number of ALN metastasis and SLN status, an ANOVA comparing the number of positive nodes (node negative versus 1 metastatic node versus ≥2 metastatic nodes) was done. The expression level of the 4,871-Da peak was significantly associated with increasing number of metastatic ALN (overall test, P = 0.035); the P values for the pair-wise comparison of node negative versus 1 metastatic node, 1 metastatic node versus ≥2 metastatic nodes, and node negative versus ≥2 metastatic nodes were 0.14, 0.44, and 0.016, respectively (Fig. 4A ). We further classified the number of positive nodes according to AJCC 6th edition staging criteria: N0, metastasis negative; N1, 1 to 3 positive nodes; and N2, ≥4 positive nodes. Using this categorization, the P values for the pair-wise comparison of node negative versus 1 to 3 metastatic nodes, 1 to 3 metastatic nodes versus ≥4 metastatic nodes, and node negative versus ≥4 metastatic nodes were 0.13, 0.38, and 0.015, respectively. No significant differences were noted for the 8,596-Da peak (data not shown).


Figure 4
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  		Figure 4. The scatter plots of the 4,871-Da protein peak for all tissue samples. {circ}, values of individual patients. A, ANOVA for group comparison of number of positive node (node negative versus 1 metastatic node versus ≥2 metastatic nodes) was done for expression level of the 4,871-Da peak. The P values for pair-wise comparison of node negative versus 1 metastatic node, 1 metastatic node versus ≥2 metastatic nodes, and node negative versus ≥2 metastatic nodes were 0.14, 0.44, and 0.016, respectively. B, ANOVA for group comparison of SLN status (node negative versus SLN positive versus non-SLN positive) was done for expression level of the 4,871-Da peak. The P values for pair-wise comparison of node negative versus SLN positive, SLN positive versus non-SLN positive, and node negative versus non-SLN positive were 0.10, 0.33, and 0.0083, respectively.

 
Similarly, an ANOVA comparing the SLN status (node negative versus SLN positive versus non-SLN positive) was also done. The expression level of the 4,871-Da peak was significantly associated with SLN and non-SLN metastases (overall test, P = 0.017); the P values for the pair-wise comparison of node negative versus SLN positive, SLN positive versus non-SLN positive, and node negative versus non-SLN positive were 0.10, 0.33, and 0.0083, respectively (Fig. 4B). No significant differences were noted for the 8,596-Da peak (data not shown).

Reproducibility. We selected five m/z peaks, which had higher mean intensity among our results, to calculate the RMS overall percentage CV. The mean mass, SD, and RMS overall percentage CV for detected protein peaks are shown in Table 3 . In this study, the RMS overall percentage CVs for intensity were 10% to 15%. The RMS overall percentage CVs for m/z were 0.050% to 0.17%. These data show that the reproducibility (CV) of protein detection using SELDI-TOF-MS is acceptable.


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  		Table 3. SELDI-TOF assay reproducibility of duplicate sample and CV

 

   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
SELDI-TOF-MS can generate protein profiles that can accurately distinguish neoplastic from nonneoplastic tissue for several cancer types (25–29). In particular, several studies of SELDI-TOF-MS analysis in breast cancer have been reported, with most illustrating the potential of proteomic profiling to discriminate between malignant and nonmalignant states with reasonable sensitivity and specificity (19, 24, 27, 30–32). Several of these studies were done, not with tissue, but with body fluids, such as serum, plasma, nipple aspirate fluid, or ductal lavage fluid (19, 24, 27, 30, 31, 33–35). Of these, serum is the most commonly used. The reproducibility and reliability of serum-based assays are questionable (36), however, because it is difficult to accurately identify low intensity peaks in serum among the numerous background peaks of housekeeping proteins (29). Assaying tissue allows for the direct study of a more homogeneous array of proteins, making the chance of discovering reliable biomarkers in tissue higher than in body fluids.

Kuerer et al. (37) suggested that the protein expression patterns of nipple aspirate fluid by SELDI-TOF-MS may be associated with the presence and absolute number of ALN metastases in women with breast cancer. However, in their study, no information was provided about the specific candidate markers used for predicting ALN metastasis.

In our study, we used a SELDI-TOF-MS technique in combination with tissue microdissection to identify difference in the protein profile of primary breast cancers with and without ALN metastasis. Three protein peaks were found to be differentially expressed between patients with and without ALN metastasis (P < 0.05). In a subsequent multivariate analysis, two candidate protein peaks associated with ALN metastasis were identified. These two peaks were more informative for predicting ALN metastasis than any other clinicopathologic factor, with the exception of LVI. When combined with LVI, the overexpression of a protein peak at 4,871 Da and the underexpression of a protein peak at 8,596 Da were highly predictive of ALN metastasis.

Overexpression of the 4,871-Da peak could distinguish those patients with ≥2 positive ALN from those with metastasis-free ALN. The same peak could identify AJCC N0 from AJCC N2 patients with ≥4 metastatic nodes. N2 patients have significantly worse outcomes than node-negative or N1 patients (7, 38) and may benefit from adjuvant axillary radiation in addition to a complete ALN dissection.

Interestingly, the peak at 4,871 Da was also significantly overexpressed in patients with non-SLN metastasis. Patients with a non-SLN metastasis must have a minimum of two ALN metastases: ≥1 in the SLN and ≥1 in the non-SLN. Therefore, the ability of the 4,871-Da peak to identify patients with ≥2 total ALN is concordant with its ability to distinguish those with non-SLN metastasis. Because 40% to 70% of patients will have non-SLN metastasis, the identification of these patients before or after SLN biopsy would dramatically influence decisions about extended radiation therapy portals or further axillary surgery after SLN biopsy alone. Similarly, the ability to identify patients unlikely to have non-SLN metastases could perhaps spare patients extended axillary treatment after SLN biopsy. Finally, the 4,871-Da peak may be detected in the serum of breast cancer patients, where it may serve as a biomarker with use in making decisions about operative therapy, adjuvant treatment, and aggressiveness of clinical follow-up.

By searching the protein database (39), we may obtain some insight into the identity of the protein at 4,871 Da. The peak may represent thymosin ß-10, a member of a family of highly conserved small acid peptides that control the growth and differentiation of many cell types (40, 41). Thymosin ß-10 can act as a major actin-sequestering factor (42) and is suggested to play a role in invasion and metastasis (40, 41). Thymosin ß-10 is highly expressed in fetal tissues and down-regulated in normal adult tissues (43); however, its overexpression is seen in many tumors, including breast cancer. Already a potential biomarker (40, 41), thymosin ß-10 will require further studies to determine its significance to breast cancer.

Recently, Ricolleau et al. (44) identified the protein at 8,560 Da as ubiquitin, a 76-amino acid residue protein associated with a good prognosis in node-negative breast cancer. Similarly, we showed that the 8,596-Da peak was underexpressed in breast cancers with ALN metastasis, indicating a good prognosis.

The peaks in our study may be useful as prognostic and predictive biomarkers, but we must be cautious until the identities of these proteins become identified and validated. We are currently isolating and characterizing the peaks detected in our study to determine their protein identities. We have shown that SELDI-TOF-MS analysis of ProteinChip arrays can distinguish differential protein peaks in primary breast cancers that predict the presence and number of ALN metastases and non-SLN status. Further study is needed to identify these proteomic biomarkers.


   Acknowledgments
 
Grant support: California Breast Cancer Research Program of the University of California grant 9DB-0098, Fashion Footwear Association of New York, The Avon Foundation (New York, NY), and The Leslie and Susan Gonda (Goldschmied) Foundation (Los Angeles, CA).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

We thank Vu D. Vu for specimen procurement support and Sandy L. Nguyen for editorial assistance.

Received 6/26/06. Revised 10/10/06. Accepted 10/16/06.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Jemal A, Siegel R, Ward E, et al. Cancer statistics, 2006. CA Cancer J Clin 2006;56:106–30.[Abstract/Free Full Text]
  2. Greene FL, Page DL, Fleming ID, et al. Cancer staging manual. 6th ed. New York: Springer; 2002.
  3. Silverstein MJ, Skinner KA, Lomis TJ. Predicting axillary nodal positivity in 2282 patients with breast carcinoma. World J Surg 2001;25:767–72.[CrossRef][Medline]
  4. Olivotto IA, Jackson JS, Mates D, et al. Prediction of axillary lymph node involvement of women with invasive breast carcinoma: a multivariate analysis. Cancer 1998;83:948–55.[CrossRef][Medline]
  5. Gann PH, Colilla SA, Gapstur SM, Winchester DJ, Winchester DP. Factors associated with axillary lymph node metastasis from breast carcinoma: descriptive and predictive analyses. Cancer 1999;86:1511–9.[CrossRef][Medline]
  6. Giuliano AE, Barth AM, Spivack B, Beitsch PD, Evans SW. Incidence and predictors of axillary metastasis in T1 carcinoma of the breast. J Am Coll Surg 1996;183:185–9.[Medline]
  7. Fisher B, Bauer M, Wickerham DL, et al. Relation of number of positive axillary nodes to the prognosis of patients with primary breast cancer. An NSABP update. Cancer 1983;52:1551–7.[CrossRef][Medline]
  8. Carter CL, Allen C, Henson DE. Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer 1989;63:181–7.[CrossRef][Medline]
  9. Fisher ER, Anderson S, Tan-Chiu E, Fisher B, Eaton L, Wolmark N. Fifteen-year prognostic discriminants for invasive breast carcinoma: National Surgical Adjuvant Breast and Bowel Project Protocol-06. Cancer 2001;91:1679–87.[CrossRef][Medline]
  10. Wilke LG, McCall LM, Posther KE, et al. Surgical complications associated with sentinel lymph node biopsy: results from a prospective international cooperative group trial. Ann Surg Oncol 2006;13:491–500.[CrossRef][Medline]
  11. Giuliano AE, Jones RC, Brennan M, Statman R. Sentinel lymphadenectomy in breast cancer. J Clin Oncol 1997;15:2345–50.[Abstract/Free Full Text]
  12. Changsri C, Prakash S, Sandweiss L, Bose S. Prediction of additional axillary metastasis of breast cancer following sentinel lymph node surgery. Breast J 2004;10:392–7.[CrossRef][Medline]
  13. Chu KU, Turner RR, Hansen NM, Brennan MB, Giuliano AE. Sentinel node metastasis in patients with breast carcinoma accurately predicts immunohistochemically detectable nonsentinel node metastasis. Ann Surg Oncol 1999;6:756–61.[CrossRef][Medline]
  14. Veronesi U, Paganelli G, Viale G, et al. A randomized comparison of sentinel-node biopsy with routine axillary dissection in breast cancer. N Engl J Med 2003;349:546–53.[Abstract/Free Full Text]
  15. Cazares LH, Adam BL, Ward MD, et al. Normal, benign, preneoplastic, and malignant prostate cells have distinct protein expression profiles resolved by surface enhanced laser desorption/ionization mass spectrometry. Clin Cancer Res 2002;8:2541–52.[Abstract/Free Full Text]
  16. Duffy MJ. Predictive markers in breast and other cancers: a review. Clin Chem 2005;51:494–503.[Abstract/Free Full Text]
  17. Bast RC, Jr., Ravdin P, Hayes DF, et al. 2000 update of recommendations for the use of tumor markers in breast and colorectal cancer: clinical practice guidelines of the American Society of Clinical Oncology. J Clin Oncol 2001;19:1865–78.[Abstract/Free Full Text]
  18. Dwek MV, Alaiya AA. Proteome analysis enables separate clustering of normal breast, benign breast, and breast cancer tissues. Br J Cancer 2003;89:305–7.[CrossRef][Medline]
  19. Li J, Zhang Z, Rosenzweig J, Wang YY, Chan DW. Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. Clin Chem 2002;48:1296–304.[Abstract/Free Full Text]
  20. Srinivas PR, Verma M, Zhao Y, Srivastava S. Proteomics for cancer biomarker discovery. Clin Chem 2002;48:1160–9.[Abstract/Free Full Text]
  21. Wulfkuhle JD, McLean KC, Paweletz CP, et al. New approaches to proteomic analysis of breast cancer. Proteomics 2001;1:1205–15.[CrossRef][Medline]
  22. Li C, Hong Y, Tan YX, et al. Accurate qualitative and quantitative proteomic analysis of clinical hepatocellular carcinoma using laser capture microdissection coupled with isotope-coded affinity tag and two-dimensional liquid chromatography mass spectrometry. Mol Cell Proteomics 2004;3:399–409.[Abstract/Free Full Text]
  23. Fuller AP, Palmer-Toy D, Erlander MG, Sgroi DC. Laser capture microdissection and advanced molecular analysis of human breast cancer. J Mammary Gland Biol Neoplasia 2003;8:335–45.[CrossRef][Medline]
  24. Sauter ER, Shan S, Hewett JE, Speckman P, Du Bois GC. Proteomic analysis of nipple aspirate fluid using SELDI-TOF-MS. Int J Cancer 2005;114:791–6.[CrossRef][Medline]
  25. Petricoin EF, Ardekani AM, Hitt BA, et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002;359:572–7.[CrossRef][Medline]
  26. Malik G, Ward MD, Gupta SK, et al. Serum levels of an isoform of apolipoprotein A-II as a potential marker for prostate cancer. Clin Cancer Res 2005;11:1073–85.[Abstract/Free Full Text]
  27. Li J, Orlandi R, White CN, et al. Independent validation of candidate breast cancer serum biomarkers identified by mass spectrometry. Clin Chem 2005;51:2229–35.[Abstract/Free Full Text]
  28. Rosty C, Christa L, Kuzdzal S, et al. Identification of hepatocarcinoma-intestine-pancreas/pancreatitis-associated protein I as a biomarker for pancreatic ductal adenocarcinoma by protein biochip technology. Cancer Res 2002;62:1868–75.[Abstract/Free Full Text]
  29. Soltys SG, Le QT, Shi G, Tibshirani R, Giaccia AJ, Koong AC. The use of plasma surface-enhanced laser desorption/ionization time-of-flight mass spectrometry proteomic patterns for detection of head and neck squamous cell cancers. Clin Cancer Res 2004;10:4806–12.[Abstract/Free Full Text]
  30. Mendrinos S, Nolen JD, Styblo T, et al. Cytologic findings and protein expression profiles associated with ductal carcinoma of the breast in ductal lavage specimens using surface-enhanced laser desorption and ionization-time of flight mass spectrometry. Cancer 2005;105:178–83.[CrossRef][Medline]
  31. Coombes KR, Fritsche HA, Jr., Clarke C, et al. Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization. Clin Chem 2003;49:1615–23.[Abstract/Free Full Text]
  32. Pusztai L, Gregory BW, Baggerly KA, et al. Pharmacoproteomic analysis of prechemotherapy and postchemotherapy plasma samples from patients receiving neoadjuvant or adjuvant chemotherapy for breast carcinoma. Cancer 2004;100:1814–22.[CrossRef][Medline]
  33. Li J, Zhao J, Yu X, et al. Identification of biomarkers for breast cancer in nipple aspiration and ductal lavage fluid. Clin Cancer Res 2005;11:8312–20.[Abstract/Free Full Text]
  34. Hu Y, Zhang S, Yu J, Liu J, Zheng S. SELDI-TOF-MS: the proteomics and bioinformatics approaches in the diagnosis of breast cancer. Breast 2005;14:250–5.[CrossRef][Medline]
  35. Pawlik TM, Fritsche H, Coombes KR, et al. Significant differences in nipple aspirate fluid protein expression between healthy women and those with breast cancer demonstrated by time-of-flight mass spectrometry. Breast Cancer Res Treat 2005;89:149–57.[CrossRef][Medline]
  36. Coombes KR, Tsavachidis S, Morris JS, Baggerly KA, Hung MC, Kuerer HM. Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform. Proteomics 2005;5:4107–17.[CrossRef][Medline]
  37. Kuerer HM, Coombes KR, Chen JN, et al. Association between ductal fluid proteomic expression profiles and the presence of lymph node metastases in women with breast cancer. Surgery 2004;136:1061–9.[CrossRef][Medline]
  38. Jatoi I, Hilsenbeck SG, Clark GM, Osborne CK. Significance of axillary lymph node metastasis in primary breast cancer. J Clin Oncol 1999;17:2334–40.[Abstract/Free Full Text]
  39. Gromov PS, Ostergaard M, Gromova I, Celis JE. Human proteomic databases: a powerful resource for functional genomics in health and disease. Prog Biophys Mol Biol 2002;80:3–22.[CrossRef][Medline]
  40. Verghese-Nikolakaki S, Apostolikas N, Livaniou E, Ithakissios DS, Evangelatos GP. Preliminary findings on the expression of thymosin ß-10 in human breast cancer. Br J Cancer 1996;74:1441–4.[Medline]
  41. Santelli G, Califano D, Chiappetta G, et al. Thymosin ß-10 gene overexpression is a general event in human carcinogenesis. Am J Pathol 1999;155:799–804.[Abstract/Free Full Text]
  42. Lee SH, Zhang W, Choi JJ, et al. Overexpression of the thymosin ß-10 gene in human ovarian cancer cells disrupts F-actin stress fiber and leads to apoptosis. Oncogene 2001;20:6700–6.[CrossRef][Medline]
  43. Hall AK. Thymosin ß-10 accelerates apoptosis. Cell Mol Biol Res 1995;41:167–80.[Medline]
  44. Ricolleau G, Charbonnel C, Lode L, et al. Surface-enhanced laser desorption/ionization time of flight mass spectrometry protein profiling identifies ubiquitin and ferritin light chain as prognostic biomarkers in node-negative breast cancer tumors. Proteomics 2006;6:1963–75.[CrossRef][Medline]




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