Plasma Biomarker Profiles Differ Depending on Breast Cancer Subtype but RANTES Is Consistently Increased

Breast cancer is responsible for more than 40,000 deaths per year in the United States. Early detection appears to be the best method to increase survival rates (1, 2), and considerable effort has been directed toward improving mammography and other imaging tools to improve detection of small tumors (3, 4). Even so, early detection of breast cancer remains problematic. In the case of mammography, the area under the curve (AUC) for receiver operating characteristic (ROC) values has been estimated to range from 0.76 to 0.82 (5, 6). The limitations of existing screening methods suggest a need for alternative tests to assist in the early detection of breast cancer. This need has emphasized the potential value of circulating biomarkers for early detection. However, established breast cancer biomarkers have not proven useful for this purpose (7).

One reason for the lack of good markers for early detection may be the phenotypic diversity of breast cancer, which would be expected to complicate biomarker identification. Five major subtypes of breast cancer have been identified on the basis of gene expression or protein profiles in tumor tissue (8–12). These subtype expression patterns closely align with traditional histologic classifiers related to the overexpression of estrogen receptor (ER⁺) and/or the HER2 receptor (HER2⁺; refs. 10, 13). Results from the current study suggest that patterns of circulating biomarkers also show breast cancer subtype dependence.

All samples were obtained and analyzed in accordance with the human subjects Institutional Review Boards at Duke University Medical Center, Durham, NC and the Pacific Northwest National Laboratory. All blood samples were collected after a positive mammogram or breast examination that led to referral for image-guided biopsy. All samples were collected at the time of biopsy, such that the benign controls and cancer patients were not defined until later, after pathology. It is unknown whether the samples were collected before or after the biopsy, but all samples were collected during the same visit. Therefore, there was concurrent collection of both benign and malignant cases in the same clinical venue using identical methods, with both the patient and the phlebotomist ignorant of the presence or absence of breast cancer. This sample collection design eliminates the possibility of some potentially confounding factors, including systematic differences in sample acquisition, handling, and storage, and patient-specific confounders, such as presurgery fasting and elevated stress levels that could result from knowing that cancer is present. Benign controls were patients with a positive screen but without breast cancer, including ductal carcinoma in situ, as determined by pathology evaluation of the biopsy.

The human plasma samples (both benign and invasive) were collected at Duke University Medical Center from 2000 to 2005. One hundred twenty-five samples were selected such that the different groups were age matched and each group contained equal numbers of women younger than 50 and older than 50 years. The cancer groups were also selected on the basis of receptor status and analyzed in 2 sets. The first group contained 20 ER⁺/HER2⁻ tumors, 19 ER⁻/HER2⁺, and 20 benign controls. The second group contained 22 ER⁺/HER2⁺ tumors, 24 ER⁻/HER2⁻ tumors, and 21 benign controls. The patients for both tumor sets were between the ages of 19 and 78 years, with a median age of 56 years. After collection, samples were aliquoted and stored at −80°C until analyzed.

The protocols and reagents used in the ELISA microarray have been previously described in detail (14). The sandwich ELISA microarray platform, when employing the same set of reagents and assays as used in this study, has been shown to lack any assay cross-reactivity and to be able to quantitatively measure purified antigens spiked into human serum (15). All reagents have been previously described in detail including commercial sources and concentrations used (15). Capture antibodies were printed on aminosilanated glass slides (Erie Scientific) that were stamped by the manufacturer with a hydrophobic barrier to create 16 identical wells (1 chip per well) on each slide. A sandwich ELISA for green fluorescent protein, which was spiked into the samples and standards at 100 pg/mL, was used for quality evaluation and data normalization across chips, as previously described (16). Each reagent was printed 4 times per chip, once in each of 4 identical quadrants. Successful printing of the capture antibodies was confirmed using the Red Reflect option on a ScanArray ExpressHT microarray laser scanner (PerkinElmer). Standard curves for the ELISAs were generated as described previously (14). Fifteen microliters of each diluted sample or standard mixture was placed on each of 3 replicate chips. Thus, each sample was analyzed on 3 chips, and each chip contained quadruplicates spots for each assay, for a total of 12 replicates per sample for each ELISA analysis. Sample positioning was blocked by study group, and each of the 3 sample replicates were analyzed on a different slide. The data from a single dilution of the plasma were used for each of the 23 ELISAs (Supplementary Table S1), with the selected dilution being the one that produced the most signal intensities for the samples that were within the optimal range of the standard curve.

Standard curves from the ELISA microarray analyses were generated using the Protein Microarray Analysis Tool (ProMAT), a custom software program that we developed specifically for this use (17). This software is freely available at the Pacific Northwest National Laboratory Web site (18). Mann–Whitney nonparametric tests (P ≤0.05) were used to compare the protein concentrations in the human plasma samples across groups (19). The Mann–Whitney rank sum and Spearman correlation P values were calculated using SigmaPlot (SYSTAT). Cancer/no cancer evaluations were quantified and visualized using ROC curves (20). These curves were generated using R (21). The empirical “stepped” ROC curves, and the AUC values that were derived from these curves, were estimated with the ROCR library (22). Smooth ROC curves and approximate 90% confidence bounds were estimated using Monte Carlo simulation. For these analyses, the data were log transformed to produce normally distributed values. The success of the log transformation was evaluated with Kolomogorov–Smirnov and Shapiro–Wilk normality tests (23). Twenty samples were simulated with draws from an estimated normal distribution representing each of the cancer/no cancer classes, and an empirical ROC curve was constructed and stored. This was repeated 500 times. The 5%, 50%, and 95% quantiles of the point-wise distribution of the 500 ROC curves were calculated and then smoothed with a moving average to estimate the mid ROC curve with approximate 90% CI bounds. Smooth ROC curves complementing their empirical ROC counterparts were accepted for assays with admissible normality tests (P < 0.10) and empirical ROC curves contained within the ROC confidence envelope. A linear combination of assay scores was used to estimate a composite assay score from multiple assays. A composite multivariate assay score was calculated using a linear discriminant algorithm. The ability of the multivariate assay test to discriminate between cancer and no cancer was evaluated using the previously described ROC method on the composite multivariate assay scores.

We compared plasma protein profiles between women with breast cancer and age-matched controls with benign disease. This initial sample set was composed of individual plasma samples from 20 women with ER⁺/HER2⁻, 18 with ER⁻/HER2⁺ breast cancer, and 18 with benign breast disease. The individual biomarkers that were significantly increased in plasma from the ER⁺/HER2⁻ cases compared with the benign controls were amphiregulin (AmR; P = 0.015), heparin-binding epidermal growth factor (HB-EGF; P = 0.03), RANTES (P = 0.002), and TGFα (P = 0.01; Fig. 1; see Supplementary Table S1 for assay abbreviations). For ER⁻/HER2⁺ group, EGF (P = 0.02), HB-EGF (P = 0.001), RANTES (P = 0.002), and TGFα (P = 0.001) were significantly increased compared with subjects with benign breast disease. Only 1 analyte, matrix metalloproteinase (MMP9), was significantly decreased (P = 0.009), and this decrease was only observed in patients with ER⁺/HER2⁻ tumors.

ROC curves were generated for the biomarkers that were statistically different (see above) between the single receptor-positive cases and the benign controls. The RANTES ROC curves were similar (Fig. 2) for ER⁻/HER2⁺ and ER⁺/HER2⁻ groups, each having an AUC value of 0.82 (Supplementary Table S2). AmR concentrations, which were significantly altered for the ER⁺/HER2⁻ group but not the ER⁻/HER2⁺, produced AUC values of 0.73 and 0.53, respectively (Figs. 1 and 2). Differences were also observed for EGF (AUC values of 0.67 and 0.75, respectively), HB-EGF (0.73 and 0.85), TGFα (0.84 and 0.77), and MMP9 (0.79 and 0.63). We evaluated the effect of combining multiple assays using linear discriminant analysis (Fig. 3). On the basis of the AUC values, the multi-assay tests (Fig. 3) generally had higher sensitivity and specificity than the single analyte tests (Fig. 2). Of the paired biomarker analyses, MMP9 and TGFα yielded the best AUC value of 0.96 for the ER⁺/HER2⁻ versus benign control comparison (Supplementary Table S2).

The clinical utility of combinations of different candidate biomarkers might be greater if the individual biomarkers were independent indicators of the presence of cancer. We therefore compared biomarker concentrations across groups using the Spearman correlation test for those biomarkers that showed significant differences between study groups. We also determined whether the concentrations of these biomarkers correlated with age. These tests showed that plasma concentrations of the 4 EGFR ligands, AmR, EGF, HB-EGF, and TGFα, were generally significantly correlated, with the sole exception of AmR and TGFα (Supplementary Table S3). In addition, RANTES significantly correlated with EGF (P ≤ 0.001), HB-EGF (P = 0.022), and TGFα (P ≤ 0.001). Age did not correlate with the levels of the different biomarkers. Overall, subsets of the biomarkers are correlated but not all of the biomarkers are. Thus, these results suggest that multiple biological processes may be regulating concentrations of these circulating proteins.

To better determine the effects of breast cancer subtypes on biomarker profiles, we analyzed a second set of plasma samples that were obtained from 22 ER⁻/HER2⁻ cases, 24 ER⁺/HER2⁺ cases, and 21 age-matched benign controls. Plasma from ER⁻/HER2⁻ patients had significantly increased concentrations of VEGF (P = 0.006) and RANTES (P = 0.009) relative to the benign controls (Fig. 4). In plasma samples from patients with ER⁺/HER2⁺ tumors, platelet-derived growth factor (PDGF; P = 0.001) and RANTES (P = 0.002) were significantly increased relative to plasma samples from benign controls (Fig. 4). In contrast to the first set of data, the EGFR ligand HB-EGF was decreased (P = 0.044) in the plasma from subjects with ER⁺/HER2⁺ tumors compared with the benign disease. In addition, TNFα (P = 0.006) and MMP1 (P = 0.048) were significantly decreased in plasma samples from ER⁺/HER2⁺ (Fig. 4). None of the proteins we assayed were significantly decreased in the ER⁻/HER2⁻ patients. Single-assay ROC curves for PDGF, RANTES, or VEGF produced AUC values of 0.67, 0.76, and 0.76, respectively, for ER⁻/HER2⁻ and 0.81, 0.80 and 0.67, respectively, for ER⁺/HER2⁺ (Fig. 5). When results from PDGF, RANTES, and VEGF assays were combined, the AUC values for each of the breast cancer subtypes improved to 0.82 (Fig. 6). The highest AUC value was 0.88 and was observed for the ER⁺/HER2⁺ plasma set using RANTES, PDGF, VEGF, and TNFα (Supplementary Table S4).

We determined whether these biomarkers in this second sample set correlated with each other or age (Supplementary Table S5). Plasma concentrations of RANTES were significantly correlated with PDGF and VEGF (P ≤ 0.001); however, PDGF and VEGF levels were not significantly correlated with each other. Of the 3 proteins that decreased significantly in this sample set, only HB-EGF and MMP1 levels were correlated (P < 0.001). Age did not correlate with any of the ELISA data except for a negative correlation with MMP1 (P < 0.001).

We used a custom ELISA microarray platform to evaluate 23 candidate biomarkers in plasma samples from women with newly diagnosed breast cancer. Data from these analyses were compared with data from plasma samples from women with benign breast disease who were also undergoing image-guided biopsy over the same time period and using the same collection and processing methods as the breast cancer cases. We found that ten analytes (AmR, EGF, HB-EGF, MMP1, MMP9, PDGF, RANTES, TGFα, TNFα, and VEGF) were significantly altered in at least 1 of the 4 breast cancer subtypes, which were defined by ER and HER2 tumor expression levels. With the exception of RANTES, significant changes in biomarker expression were subtype dependent. This observation is consistent with gene and protein expression studies that show unique expression patterns in tumors based on cancer subtypes (8–13). All 10 proteins that we found to be altered in the circulation have previously been reported to have altered expression in breast cancer tissue at the mRNA or protein level (12, 24–28), suggesting that altered protein secretion by the breast tumors may be responsible for the changes we observe in the circulating proteins. However, when levels of a biomarker are decreased, it is unlikely that this effect is due to tumor secretion of the biomarker. Rather, indirect effects of the tumor on biomarker levels are likely to be important. For example, tumor-dependent secretion of proteolytic enzymes could be a factor.

RANTES has been previously reported to be increased in the plasma of women with breast cancer (29, 30). Our results not only verify these prior studies, but we extend these results by showing that circulating levels of RANTES are increased in at least 4 subtypes of breast cancer. Notably, single-assay analysis of RANTES produced AUC values that ranged from 0.76 to 0.82, and RANTES consistently improved accuracy when included in subtype-specific panels of biomarkers. Plasma levels of RANTES correlated with levels of 3 EGFR ligands, EGF, HB-EGF, and TGFα, in the first sample set (Supplementary Table S3). We have observed that RANTES secretion is increased upon EGFR activation in cultured human mammary epithelial cells (31), suggesting that the correlation of RANTES and the EGFR ligand levels in the plasma may also reflect EGFR regulation of RANTES secretion in humans. RANTES is an inflammatory cytokine and circulating levels of this protein are altered in a variety of diseases besides breast cancer, suggesting that RANTES alone will not be a specific marker for early detection of breast cancer. Even so, our data suggest that RANTES may be useful in discerning between mammographic true and false positives and that RANTES may provide useful data when included in a panel of biomarkers. The combination of biomarkers may potentially prove more specific for breast cancer than any individual marker.

Previous studies have shown that overexpression of individual HER receptors or their ligands in breast tumors promotes survival and resistance to chemotherapy (32–36). AmR, EGF, and TGFα also have important roles in normal mouse mammary gland development, and each of these EGFR ligands apparently has a different role in this process (37). In the current study, these EGFR ligands were commonly increased in plasma from women with breast cancer. In addition, we observed subtype-dependent differences in the EGFR ligands, suggesting that, as in mammary development, expression of the individual ligands may reflect differential tumor characteristics. Furthermore, it has been shown that normal serum levels of EGFR ligands are sufficient to stimulate EGFR activity in cultured cells (38), further supporting the concept that an increase in circulating EGFR ligands may directly affect tumor biology. That is, a further increase in circulating levels of EGFR ligands in breast cancer patients could further activate the EGFR and thereby facilitate mammary cell growth and tumor development. Thus, these results suggest that circulating levels of EGFR ligands may be an important etiologic factor in the development of breast cancer.

MMP9 is synthesized as a “pro” form that is proteolytically cleaved to produce a smaller, but catalytically active, protease (reviewed in ref. 39). The ELISA analysis we use only detects the activated (i.e., proteolytically truncated) form of MMP9, which was decreased in ER⁺/HER2⁻ breast cancer. A variety of proteases can activate MMP9 in artificial systems employing purified proteins, but it is unclear what protease(s) activates MMP9 in vivo (39). Our data are consistent with a previous report that circulating levels of truncated MMP9 are decreased in breast cancer patients (40). Even so, this prior study also found that the proMMP9 form is increased in the same subjects. Given the opposite effects of breast cancer on the precursor and processed forms of MMP9 (40), we speculate that the activity of other protease(s) that process MMP9 may be more important than MMP9 secretion in altering the circulating levels of MMP9 in breast cancer subjects. This concept is supported by evidence that cancer cells secrete a variety of proteases (41).

In conclusion, identifying useful circulating markers for the detection of breast cancer has remained problematic. Our results suggest that the discovery of biomarker panels for the early detection of breast cancer may be complicated by the heterogeneity of this disease. However, results from 2 independent sample sets confirm that plasma levels of RANTES are commonly increased in breast cancer. Furthermore, the use of nonspecific biomarkers such as RANTES in combination with breast cancer or breast cancer subtype–specific biomarkers may produce a superior assay that may prove useful as an adjuvant to current screening methods. We also identify 2 panels of biomarkers that are able to discriminate between benign breast disease and the most common receptor statuses found in breast cancer, which are ER⁺ (about 60%–67% of all breast cancers) and HER2⁺ (about 20%; ref. 42). These biomarker panels are very promising, but the data are novel and need to be replicated in an independent sample set. Intriguingly, these panels contain several EGFR ligands. Given the central role of the EGFR in regulating mammary cell growth and migration, these data suggest that circulating levels of these ligands could be a factor in breast cancer development and/or progression.

No potential conflicts of interest were disclosed.

This work was funded by National Cancer Institute Early Detection Research Network grants CA117378 and CA084955.

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