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Keywords:

  • automated quantitative analysis;
  • HER-2;
  • epidermal growth factor receptor;
  • HER-3;
  • HER-4;
  • immunohistochemistry;
  • survival

Abstract

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Conflict of Interest Disclosures
  7. References

BACKGROUND:

Assessment of outcome using a single prognostic or predictive marker is the current basis of targeted therapy, but is inherently limited by its simplicity. Multiplexing has provided better classification, but has only been done quantitatively using RNA or DNA. Automated quantitative analysis is a new technology that allows quantitative in situ assessment of protein expression. The authors hypothesized that multiplexed quantitative measurement of ErbB receptor family proteins may allow better prediction of outcome.

METHODS:

The authors quantitatively assessed the expression of 6 proteins in 4 subcellular compartments in 676 patients using breast carcinoma tissue microarrays. Then using Cox proportional hazards modeling and unsupervised hierarchical clustering, they assessed the prognostic value of the expression singly and multiplexed.

RESULTS:

Epidermal growth factor receptor (EGFR), HER-2, and HER-3 expression were associated with decreased survival. Multivariate analysis showed high HER-2 and HER-3 expression maintained independence as prognostic markers. Hierarchical clustering of expression data defined a small class enriched for HER-2 expression with 40% 10-year survival, compared with 55% using conventional methods. Clustering also revealed a similarly poor-prognostic subgroup coexpressing EGFR and HER-3 (but low for estrogen receptor, progesterone receptor, and HER-2) with a 42% 10-year survival.

CONCLUSIONS:

This work shows that the combined analysis of protein expression improved prognostic classification, and that multiplexed models were superior to any single-marker–based method for prediction of 10-year survival. These methods illustrate a protein-based, multiplexed approach that could more accurately identify patients for targeted therapies. Cancer 2009. © 2009 American Cancer Society.

HER-2 (also known as ErbB2 or neu) belongs to the ErbB family of 4 type I tyrosine kinase receptors, including epidermal growth factor receptor (EGFR), HER-3, and HER-4, that homo- and heterodimerize to activate distinct programs of proliferation, survival, migration, and angiogenesis.1 In breast cancer, this family also demonstrates cross-talk with the hormone receptors for estrogen (ER) and progesterone (PR), as well as other pathways.2

ErbB2 amplification is an important molecular alteration in breast cancer, and we hypothesized that interactions of HER-2 with other ErbB family members might improve our ability to classify HER-2+ breast cancer for the purposes of prognosis. Although members of the HER family have been measured for prognostic value,3-12 they have never previously been rigorously multiplexed, because nearly all previous studies have been scored by traditional pathologist-based methods.

To measure tumor-specific content, most quantitative protein measurement techniques such as mass spectrometry require microdissection and are optimized with frozen specimens. Automated quantitative analysis (AQUA) measures protein expression levels in situ in formalin-fixed, paraffin-embedded tumor samples and allows discrimination of subcellular compartments.13, 14 The AQUA method has been validated in breast cancer and shown to be comparable to protein levels measured by enzyme-linked immunosorbent assay (ELISA),15, 16 with coefficients of variation <5%. To test the hypothesis that quantitative multiplexed analysis will improve prognostic value, we have assessed the expression of 6 targets (ER, PR, EGFR, HER-2, HER-3, HER-4) in 4 subcellular compartments, using AQUA in an archival tissue microarray (TMA) collection of invasive breast carcinoma.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Conflict of Interest Disclosures
  7. References

Cell Lines

A TMA containing cores from formalin-fixed, paraffin-embedded cell pellets was used as a control for staining and AQUA analysis. JEG-3, SKOV3, and CHO cells were obtained from the Maihle laboratory at Yale University. A431, HL60, MDA-MB-453, MDA-MB-231, MDA-MB-468, SW-480, SK-BR-3, MCF-7, BT-549, T-47D, MDA-MB-435S, and BT-474 cell lines were purchased from the American Type Culture Collection (Manassas, Va). BAF3 cells were obtained from a laboratory in the Department of Genetics at Yale University, and culture conditions and cell line TMA construction have been published in detail elsewhere.15, 16 Our laboratory protocol for processing cell lines is also available on the Web (http://www.tissuearray.org).

Patients

The Yale breast cancer cohort consists of 676 samples of invasive breast carcinoma collected serially from the Yale University Department of Pathology archives from 1961 to 1983 (Table 1). Slides were reviewed for tumor volume, and all samples were included that could be adequately sampled for the study. This cohort contains approximately half lymph-node–positive specimens and half lymph-node–negative specimens. Patient outcome was collected from the medical records and the Connecticut Tumor Registry. TNM classification was applied retrospectively according to guidelines from the AJCC Cancer Staging Manual, Sixth Edition.17 For the 630 patients with outcome data, the mean follow-up time is 12.5 years, and the mean age at diagnosis is 58.1 years. The median follow-up time is 8.8 years, and the median age at diagnosis is 58.0 years. A total of 334 patients were censored at 10 years, and 228 were uncensored at 10 years. Of the 334 censored patients, their median follow-up was 18.9 years, with the minimum at 4.2 months. Complete treatment information was not available for the entire cohort; however, most patients were treated with postsurgical local irradiation. None of the lymph-node–negative patients was given adjuvant systemic therapy.

Table 1. Population Characteristics for Yale Breast Carcinoma Tissue Microarray
VariableNo.%
  1. IDC indicates invasive ductal carcinoma; ILC, invasive lobular carcinoma.

Age at diagnosis, y630100.00
 ≤5044370.32
 >5018128.73
 Unknown60.95
Nuclear grade  
 110917.30
 230648.57
 316626.35
 Unknown497.78
Tumor type  
 Ductal51982.38
 Lobular467.30
 IDC-ILC365.71
 IDC-low risk294.60
Nodal stage  
 pN031650.16
 pN115424.44
 pN29515.08
 pN36410.16
 Unknown10.16
Laterality  
 Left30348.10
 Right29747.14
 Bilateral193.02
 Unknown111.75
Histologic grade  
 1121.90
 217427.62
 312920.48
 Unknown31550.00
Tumor stage  
 T126542.06
 T222736.03
 T3538.41
 T4386.03
 Unknown477.46
Distant metastasis  
 M059293.97
 M1294.60
 Unknown91.43

Specimen Characteristics

Formalin-fixed paraffin-embedded tumor blocks from each patient were used in the construction of the tissue microarrays, with 1 0.6-mm core transferred to a recipient paraffin block. Slides cut from 2 independent constructions were used in this study for each target. A sequential hematoxylin and eosin–stained slide was histologically assessed by a pathologist to ensure adequate tumor sampling. TMA construction was performed with a tissue-arraying instrument (Beecher Instruments, Silver Springs, Md) using a method that was described previously.18 All precut sections were coated in paraffin and stored at room temperature in a nitrogen chamber before staining to prevent loss of antigenicity.19

Assay Methods

Slides were stained by a modified indirect immunofluorescence method as described previously.13 Primary antibodies used to define the tumor compartment of each histospot included mouse monoclonal cytokeratin AE1/AE3 (M3515, Dako Corporation, Carpinteria, Calif) or wide-spectrum screening rabbit anticow cytokeratin antibody (Dako Z0622), each at 1:100. Estrogen receptor (Dako clone 1D5) and progesterone receptor (Dako clone PgR636) were each used at 1:50 and incubated for 1 hour at room temperature. Other target antibodies were incubated overnight at 4°C and included EGFR used neat (Dako pharmDx kit clone 2-18C9), HER-2 at 1:8000 (Dako A0485), HER-3 at 1:200 (clone RTJ1, Vector Laboratories, Burlingame, Calif), and HER-4 at 1:400 (sc-283, Santa Cruz Biotechnology, Santa Cruz, Calif). Secondary labeling of targets was performed by signal amplification using horseradish-peroxidase-labeled secondary reagents (species-specific Dako Envision) followed by Cy-5 tyramide incubation. 4′,6-Diamidino-2-phenylindole (DAPI) in an antifading mounting medium was used to stain the nuclear compartment (Prolong Gold, Invitrogen, Eugene, Ore).

Positive and negative controls were included in a specialized “boutique” array stained simultaneously containing 40 cases from a previously described breast carcinoma tissue microarray,16 as well as 15 formalin-fixed, paraffin-embedded cancer cell lines exhibiting variable levels of expression for each marker analyzed. In addition, a breast cancer test slide was stained with each experiment without primary antibody.

AQUA of Tissue Microarrays

A complete and detailed discussion of the AQUA method has been published previously.13, 20 Briefly, monochromatic images of each histospot were acquired on an Olympus AX-51 epifluorescence microscope (Olympus, Melville, NY) using a motor-driven stage and automated custom software, and high-resolution (1024 × 1024 pixel; 0.5 μm) digital images were analyzed using AQUA. A binary image (tumor mask) was created from the cytokeratin image of each histospot, representing areas of tumor epithelium. Histospots were excluded if the tumor mask represented <5% of the total histospot area. DAPI images were used to define the nuclear compartment within each histospot, and the membrane compartment was defined by perimembranous coalescence of cytokeratin immunoreactivity with specific exclusion of the nuclear compartment.

Application of the rapid exponential subtraction algorithm was used to improve subcellular localization; it is an image processing methodology which accounts for compartment overlap because of the thickness of tissue sectioning on glass slides by subtracting out-of-focus from in-focus image data according to a specialized algorithm. Target protein expression was quantified by calculating Cy5 fluorescent signal intensity on a scale of 0-255 within each image pixel. The Cy-5 wavelength is used for target labeling because it is outside the range of tissue autofluorescence. An AQUA score was generated by dividing the sum of target signals within the tumor mask by compartment area. After validation of images to ensure adequate tumor sampling and to exclude any normal epithelium, the AQUA scores were normalized to a 100-point scale and averaged from 2 tumor samples. Although AQUA scores were calculated for each biomarker in 4 subcellular compartments, we restricted survival analysis to the dominant subcellular localization (nuclear: ER, PR; non-nuclear: HER-3; membranous: EGFR, HER-2). In this cohort, HER-4 expression was observed in all 3 compartments, so the total AQUA score in the tumor mask was considered for analysis.

A recent analysis of AQUA for HER-2 measurement showed a strong correlation between AQUA scores, quantitative ELISA protein measurements, and HER-2/neu gene amplification for a standard set of breast cancer cell line controls.15 We repeated both cell line and breast tumor samples used in this study as a reference for HER-2 positivity.

Statistical Methods

The statistical calculations were performed using JMP Version 5.0 (SAS, Cary, NC). Disease specific survival (DSS) was chosen as the endpoint in the present study. Kaplan-Meier plots were used to illustrate the survival in groups of HER-2+ patients classified by the methods studied, and the log-rank test to test for equality of survival curves. Hazard ratios were estimated using Cox regression. All P values corresponded to 2-sided tests, and values <.05 were considered significant.

Unsupervised hierarchical average-linkage clustering was performed using Cluster and Treeview (Eisen Laboratory, Stanford University, Palo Alto, Calif). Tumors in the Yale cohort that had a value for at least 5 of 6 biomarkers (n = 550) were included in the clustering. AQUA scores were converted to z scores before clustering to normalize between markers.21 For cluster assignment, the distance from dendogram root node was chosen to maximize number of clusters as well as to ensure that each cluster contained at least 5% of the population. No formal statistical test was used to select the number of clusters other than the limitation imposed by the number of subjects in each cluster.

RESULTS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Conflict of Interest Disclosures
  7. References

In the AQUA method, cellular compartments and targets are labeled in situ using antibodies conjugated to fluorochrome dyes. Figure 1 shows representative images from immunofluorescent labeling of 6 target biomarkers (ER, PR, EGFR, HER-2, HER-3, HER-4) in the breast cancer samples studied, and each panel shows an enlarged view of the pixel area scored as tumor (tumor mask, Lower Left) and subcellular compartments (Upper Right), as well as Cy5 image for each target (Lower Right). Expression was predominantly nuclear for ER and PR, and membranous for EGFR and HER-2. HER-3 expression was both membranous and cytoplasmic, notably excluded from the nuclear compartment (non-nuclear). HER-4, however, showed 3 distinct patterns of expression: non-nuclear (Fig. 1F), membranous (Fig. 1G), and nuclear (Fig. 1H). Nuclear localization of HER-4 has been described previously, where it is thought to be involved in the transcription of target genes involved in mammary differentiation.22

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Figure 1. Immunofluorescent immunohistochemistry is shown for automated quantitative analysis. In each panel, representative pseudo-colored images are shown of cytokeratin (Upper Left), tumor mask (Lower Left), nuclear (blue), and non-nuclear or membrane compartments (green) (Upper Right), and target expression (red) after rapid exponential subtraction algorithm application (Lower Right). Panels show (A) estrogen receptor, nuclear expression; (B) progesterone receptor, nuclear expression; (C) epidermal growth factor receptor, membranous expression; (D) HER-2, membranous expression; (E) HER-3, non-nuclear expression; (F) HER-4, non-nuclear expression; (G) HER-4, membranous expression; and (H) HER-4, nuclear expression.

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The YTMA49 cohort is composed of 676 breast cancer cases from the Yale Pathology archives with extensive annotation including long-term follow-up, as described previously (Table 1).16 After standardization by internal controls, AQUA measurements from 2 tumor samples were averaged. Expression data for at least 5 of 6 biomarkers and survival information were available from 550 patients, whereas 126 patients were excluded because of insufficient data.

HER-2 and HER-3 Are Independent Biomarkers of Breast Cancer Survival

As demonstrated in Tables 2 and 3, Cox proportional hazards regression was used to assess the association of each marker with 10-year DSS univariately and in multivariate models. As previously described, high AQUA HER-2 (P = .001) and low AQUA ER (P = .010) and AQUA PR (P = .002) scores were significantly associated with decreased survival.23 In contrast, using ordinal (0-3+) immunohistochemistry (IHC) scores to stratify survival in the same way, low ER (P = .007) and PR (P = .010) scores are associated with decreased survival, but HER-2 expression is not (P = .11). High AQUA EGFR scores trended toward association with decreased survival, but were only of borderline significance (P = .065). In addition, AQUA HER-3 scores were inversely associated with survival (P = .003).

Table 2. Association Between Continuous AQUA Scores and Breast Cancer-specific Survival by Cox Univariate Analysis With 10-year Follow-up
Univariate Cox Proportional Hazard Model
VariablesRisk Ratio95% Confidence Intervals*Effect (Chi-square)P
  • AQUA indicates automated quantitative analysis; ER, estrogen receptor; PR, progesterone receptor; EGFR, epidermal growth factor receptor.

  • *

    All confidence intervals are reported with lower and upper limits.

AQUA ER0.9890.980-0.9976.686.010
AQUA PR0.9860.975-0.9959.178.002
AQUA EGFR1.0090.999-1.0183.408.065
AQUA HER-21.0171.008-1.02610.959.001
AQUA HER-31.0161.006-1.0269.076.003
AQUA HER-41.0000.985-1.0130.002.969
Age at diagnosis0.9980.988-1.0090.087.768
Tumor stage  41.307.000
 T2 vs T12.0801.519-2.865  
 T3 vs T21.5931.055-2.347  
 T4 vs T30.8440.471-1.469  
Nodal stage  60.145.000
 pN1 vs pN02.2621.623-3.146  
 pN2 vs pN11.2730.872-1.843  
 pN3 vs pN21.3140.855-2.007  
Distant metastasis  28.735.000
 M1 vs M04.6462.837-7.185  
Nuclear grade  14.683.001
 2 vs 11.2140.834-1.813  
 3 vs 21.6261.211-2.175  
Table 3. Association Between Multivariate Cox Survival Models and Breast Cancer-specific Survival With 10-year Follow-up
VariablesRisk Ratio95% Confidence Interval*Effect (Chi-square)P
  • AQUA indicates automated quantitative analysis; pT, pathological tumor; pN, pathological nodal; ER, estrogen receptor; PR, progesterone receptor; EGFR, epidermal growth factor receptor.

  • A. Models of each biomarker, including tumor and nodal stage.

  • B. All ErbB biomarkers, including tumor and nodal stage.

  • C. Cluster groups from multiplexed AQUA scores with TNM staging variables.

  • *

    All confidence intervals are reported with upper and lower limits.

A. Individual Models; Each AQUA biomarker+(pT, pN)
AQUA ER0.9870.978-0.9968.132.004
AQUA PR0.9850.974-0.9958.543.003
AQUA EGFR1.0111.001-1.0214.387.036
AQUA HER-21.0171.007-1.02610.540.001
AQUA HER-31.0121.001-1.0224.520.034
AQUA HER-41.0060.988-1.0210.478.489
B. All ErbB Biomarkers+(pT, pN)
AQUA EGFR1.0040.989-1.0180.366.545
AQUA HER-21.0171.006-1.0269.101.003
AQUA HER-31.0131.001-1.0254.257.039
AQUA HER-41.0030.984-1.0190.095.758
C. MultiplexAQUA+TNM
Cluster group (all vs ErbB-low VI)  18.144.003
 ERI0.6290.429-0.892  
 PRII0.7540.554-1.009  
 HER-2/HER-3III1.741.166-2.504  
 EGFR/HER-3IV1.3830.996-1.88  
 HER-3/HER-4V0.9930.605-1.533  
Tumor stage  26.686>.001
 T2 vs T12.0721.474-2.931  
 T3 vs T21.1990.754-1.857  
 T4 vs T31.0250.548-1.868  
Nodal stage  24.07>.001
 pN1 vs pN01.7431.199-2.529  
 pN2 vs pN11.2640.819-1.926  
 pN3 vs pN21.2490.763-2.036  
Distant metastasis  12.276>.001
 M1 vs M03.4471.801-6.102  

The prognostic significance of HER-3 was further explored using the X-tile software program19 to define optimal population cut points in a training set of half the patients in the cohort with both HER-3 expression data and outcome information (n = 260) and validated by Kaplan Meier analysis in the remaining half (n = 261). In the validation set half of the cohort, grouping by HER-3 expression with a cut point of AQUA >25, we find high levels of HER-3 associated with 53% 10-year survival, compared with 69% in the low HER-3 group (log-rank P = .0096).

Next, we constructed a multivariate Cox model including pathological tumor (pT) and nodal (pN) stage with AQUA scores from each of the biomarkers tested and observed that 5 of the 6 biomarkers (ER, PR, EGFR, HER-2, HER-3) were independently correlated with patient outcome when assayed using AQUA. Whereas AQUA ER and AQUA PR were associated with more favorable prognosis, AQUA EGFR, AQUA HER-2, and AQUA HER-3 were associated with decreased survival (Table 3A). When we included AQUA expression scores from all ErbB receptors in a multivariate model including pT and pN , both AQUA HER-2 and AQUA HER-3 remained independent prognostic factors (Table 3B).

ErbB Family Coexpression Is Associated With Prognosis

We used unsupervised average linkage hierarchical clustering to examine the relative coexpression of the AQUA targets measured (Fig. 2). Before clustering, data were normalized for variance between experiments by z score transformation (AQUAz). Six distinct clusters were observed, labeled Cluster I-VI in Figure 2 and colored red (high) to green (low) by the distance from the mean for each target. Clusters I and II were notable for high expression of ER and PR and separated by higher levels of HER-3 and HER-4 in Cluster II. Cluster III was enriched for HER-2 and HER-3 expression and had low levels of hormone receptor expression, whereas high HER-3 and EGFR expression was found in Cluster IV. HER-3 and HER-4 expression was enriched in Cluster V. The largest cluster (VI) included some cases with high expression of EGFR or HER-2, but it had relatively low levels of all targets. Of note, high expression of both EGFR and HER-2 was rarely observed.

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Figure 2. Unsupervised hierarchical clustering of protein expression data measured in the Yale archival cohort is shown. Tumor samples had scores for at least 5 of 6 biomarkers (n = 550), and data from 1 subcellular compartment were included for each marker: estrogen receptor (ER; nuclear), progesterone receptor (PR; nuclear), epidermal growth factor receptor (EGFR; membranous), HER-2 (membranous), HER-3 (non-nuclear), and HER-4 (non-nuclear). Automated quantitative analysis (AQUA) scores were converted to z scores21 (AQUAz) before clustering to normalize between markers, as described in Materials and Methods. Values for protein expression are shown as a heat map, and each data point is represented by a bar colored according to its value's distance from the target mean of the cohort in units of standard deviation. Black indicates a protein expression level equal to the mean; green indicates a protein expression level below the mean; red indicates a protein expression level above the mean; and gray indicates missing values. The branch lengths and pattern of the dendrogram demonstrate the relatedness of the tumors on the vertical axis and the antibody staining on the horizontal axis. Note that ER scores lower than the mean are not necessarily negative, but need to be displayed and assessed in this manner for clustering analysis. Further analysis of the ER scores in this cohort may be found in previous work.13 To the right of the colored heat map is a second black and white heat map that shows a binary indication of the standard biomarkers as assessed by a pathologist for each case. White is negative, black is positive, and gray is unknown or missing data. IHC indicates immunohistochemistry.

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We calculated 5- and 10-year DSS rates in the cluster groups. Despite differential ErbB family expression, the high hormone-receptor groups had comparable survival rates (ER-I, 79.8% 5-year and 58.3% 10-year vs PR-II, 77.7% 5-year and 60.4% 10-year). The HER-2/HER-3 Cluster III had the lowest DSS rate (5-year, 44.9%; 10-year, 39.4%). Only 2 of 3 of HER-2–positive patients by conventional IHC (HercepTest 3+) are included in this group, with the majority of remaining HER-2+ patients (20%, 12 of 60) found in Cluster VI. Low survival rates were also observed in the HER-3/EGFR Cluster IV (5-year DSS, 56.2%; 10-year, 42.0%). This group may define the so-called “triple negative” class9, 24, 25 of breast cancer, because these cases are low for ER, PR, and HER-2. The HER-3/HER-4 Cluster V is associated with relatively good outcome (5-year DSS, 70.0%; 10-year, 51.7%). Survival in the low HER family Cluster VI was similar to that in the hormone-receptor–positive Groups I and II (5-year DSS, 74.4%; 10-year, 61.7%). The association of clustering subgroups with outcome was independent of TNM staging parameters when assessed by a multivariate Cox proportional hazards model (P = .003, Table 3C).

Multiplexing AQUA Scores Improves Classification of HER-2+ Breast Cancer

To compare the multiplexed AQUA method to conventional methods, we have done a Kaplan-Meier analysis (Fig. 3). Traditional IHC on the TMA failed to reach significance in this cohort (IHC 3+, Fig. 3A). The addition of quantitative analysis and the use of a previously determined optimized AQUA cut point score16 resulted in an improvement in the prognostic value that achieves statistical significance (Fig. 3B). However, selection of a class of HER-2+ patients defined by hierarchical clustering (Cluster III, Fig. 3C) defines a smaller subset with substantially worse outcome. The median time from diagnosis to death from breast cancer was 98 months in the group defined by IHC, 55 months in the group defined by AQUA, and only 43 months in the group defined by clustering. In contrast, low HER-2 patients had a median survival of almost 200 months by all 3 classification methods.

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Figure 3. HER-2 status and patient outcome are shown by conventional immunohistochemistry (IHC), automated quantitative analysis (AQUA) HER-2, or multiplexed AQUA. (A) The Kaplan-Meier survival curve analysis used 10-year disease specific survival as the clinical endpoint of interest. Patients were grouped by immunohistochemical score using the HercepTest antibody and scoring guidelines. Green indicates HER-2 equivocal or negative patients by IHC score of 0, 1+, or 2+ (5-year survival, 72.1%; median survival, 198 months). Red indicates HER-2–positive patients by IHC score of 3+ (5-year survival, 56.1%; median survival, 98 months). The inset shows the frequency distribution of IHC scores. (B) Kaplan-Meier survival curve analysis of Yale patients grouped by HER-2 AQUA score is shown. The optimal cut point of 18 was chosen based on a previously published analysis.16 Green indicates HER-2–negative patients by AQUA (5-year survival, 73.5%, median survival, 198 months). Red indicates HER-2–positive patients by AQUA (5-year survival, 48.6%; median survival, 55 months). The inset shows the frequency distribution histogram of average HER-2 AQUA scores. The AQUA HER-2+ group includes 71.67% of IHC 3+ tumors. (C) Kaplan-Meier survival curve analysis of Yale patients is shown, grouped by AQUA multiplexed analysis with clustering as shown in Figure 1. Survival curves are colored according to the corresponding cluster on the heat map y axis. The HER-2 enriched cluster (III) is depicted in red (5-year survival, 44.9%; median survival, 43 months). This group includes 66.7% of the IHC 3+ tumors. The epidermal growth factor receptor (EGFR)/HER-3 cluster is depicted in orange (5-year survival, 44.9%; median survival, 43 months). For all other cluster groups, 5-year survival was 73.2%, and median survival was 199 months.

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DISCUSSION

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Conflict of Interest Disclosures
  7. References

In this study, we measured the protein expression of the ErbB family (EGFR, HER-2, HER-3, HER-4) and the hormone receptors ER and PR using the AQUA method in a large retrospective cohort of breast cancer patients and assessed target coexpression and association with breast cancer survival using proportional hazards modeling and hierarchical clustering. The implications of aberrant ErbB expression have been explored by many previous investigations, and overall, HER-2 has been consistently associated with a shorter time to progression and decreased survival time, whereas correlative findings of the other ErbB receptors have varied widely.3-12 This is the first report of a quantitative protein detection method linking multiplexed ErbB expression to long-term patient outcome.

Most clinicians currently rely on clinicopathological parameters such as tumor size and nodal status, as well as ER, PR, and HER-2 tissue biomarkers to assess an individual's prognosis after surgery for primary breast cancer. We find that among the biomarkers measured, AQUA ER, AQUA PR, AQUA EGFR, AQUA HER-2, and AQUA HER-3 were significantly associated with long-term survival and independent of parameters of tumor size and nodal metastasis. We were unable to reproduce previous observations that HER-4 is a favorable prognostic biomarker.7, 8 In addition, the poor prognostic association of AQUA HER-2 and AQUA HER-3 were independent of other ErbB expression patterns. Clustering of the protein expression data revealed groups of breast cancer patients that coexpress sets of ErbB family members. Multiplexing of AQUA scores by hierarchical clustering classification was superior to conventional IHC or univariate AQUA classification of HER-2+ breast cancer for prognosis, and this effect is independent of current clinical staging variables.

This sort of clustering analysis has potential for use in classification of breast cancers in a manner similar to that done by cDNA array type studies.24 Although only 6 markers are used in this study, we included the 3 standard markers that are used in standard management of breast cancer (ER, PR, and HER-2), which allowed us to identify the triple negative subset of breast cancers. Examination of Figure 2 shows that the triple negative class self-assorts into Cluster IV. It is interesting to note that this study suggests that there are probably 2 biological classes within that group: the subset that is triple negative but expresses high levels of EGFR, and a second subset that is triple negative with high levels of HER-3. This observation confirms previous work reporting a high correlation between triple negative cases and EGFR overexpression.9, 25 Further studies are needed to assess the significance of the HER-3+ subdivision, but it could have implications for new ErbB-targeted therapies such as pertuzumab and cannertinib.26

Limitations of this study include its retrospective nature and the incompleteness of the treatment data. However, the collection and investigation of archival cohorts such as this allow valuable insights into the relationship of breast cancer outcome with the molecular features of primary tumors. These correlative studies suggest the investigation of multiplexed assessment of biomarkers as a method to predict response to therapy. In this study, assay conditions were carefully controlled using a specialized control cell and tissue microarray, which should ensure reproducibility in future studies now underway in cohorts treated with ErbB-targeted therapies. The results reported here show the power of quantitative protein-based multiplexed analysis. By collection of continuous scores proportional to protein expression of ErbB family members, we are able to define subsets of our cohort that show grouping that is analogous to cDNA-based classifications and is more specific and informative for prediction of outcome.

Conflict of Interest Disclosures

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Conflict of Interest Disclosures
  7. References

This work was supported by an Avon-NCI Progress for Patients grant and National Cancer Institute grants R33 CA 106,709 and R33 CA 110,511 to David L. Rimm and by a Medical Scientist Training Program grant to Jennifer M. Giltnane.

Robert L. Camp is a stockholder in, scientific founder of, and consultant to HistoRx, a private corporation to which Yale University has given exclusive rights to produce and distribute the software and technologies embedded in AQUA. Yale University retains patent rights for the AQUA technology.

David L. Rimm is a stockholder in, scientific founder of, and consultant to HistoRx.

References

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Conflict of Interest Disclosures
  7. References