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Molecular Oncology, Markers, Clinical Correlates |
1 Genetic Pathology Evaluation Centre of the Department of Pathology, and Prostate Research Centre of Vancouver General Hospital, British Columbia Cancer Agency and University of British Columbia; 2 Laboratory for Oncogenomic Research, Department of Pediatrics, British Columbia Research Institute for Children’s and Women’s Health, University of British Columbia; 3 Department of Pathology, and 4 Cancer Control Research Program, British Columbia Cancer Agency, Vancouver, Canada; and 5 Department of Pathology, Stanford University Medical Center, Stanford, California
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ABSTRACT |
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 Top ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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0.05) and 4 markers showed a trend toward significance (P 0.2). Unsupervised hierarchical clustering analysis was done by using these 21 immunomarkers, and this resulted in identification of three cluster groups with significant differences in clinical outcome. 
2 analysis showed that expression of 11 markers significantly correlated with membership in one of the three cluster groups. Unsupervised hierarchical clustering analysis with this set of 11 markers reproduced the same three prognostically significant cluster groups identified by using the larger set of markers. These cluster groups were of prognostic significance independent of lymph node metastasis, tumor size, and tumor grade in multivariate analysis (P = 0.0001). The cluster groups were as powerful a prognostic indicator as lymph node status. This work demonstrates that hierarchical clustering of immunostaining data by using multiple markers can group breast cancers into classes with clinical relevance and is superior to the use of individual prognostic markers. |
INTRODUCTION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Gene expression profiling has allowed stratification of breast tumors into clinically relevant groups through the use of expression levels of thousands of genes rather than single prognostic indicators. Novel prognostically relevant subsets of breast cancer that are not identifiable on routine light microscopy have been identified by this approach (2, 3, 4, 5, 6, 7) . The objective of this study was to apply analytical techniques, developed for gene expression profiling, to determine whether multiple immunohistochemical prognostic markers could be used to identify prognostically relevant groups of breast cancer patients, and to determine the optimal panel of immunomarkers necessary to define those groups.
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MATERIALS AND METHODS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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20 mm, and 149 had tumors >20 mm. Adjuvant therapy varied substantially during the period 1974 through 1995, and no information on individual adjuvant treatment was available. Ethical approval was obtained from the institutional ethical review board to perform this study.
Tissue Microarrays and Immunostaining.
Tissue microarrays (TMAs) were designed as described previously (8
, 9)
by using two 0.6-mm tissue cores per case, taken from formalin-fixed, paraffin-embedded archival tumor blocks. All of the immunostains were done with standardized protocols.6
The panel of antibodies consisted of those available in the Genetic Pathology Evaluation Centre that showed case-to-case variation within the series of breast carcinomas (i.e., none of the tumors showed identical or near-identical staining patterns). The antibody choice was empirical, based on availability, suitability for paraffin-embedded archival material, and biological and clinical relevance to breast cancer, and was also partially driven by an attempt to reproduce the classification based on recent investigations in gene expression profiles in breast cancer (Table 1
; Refs. 2, 3, 4, 5, 6, 7
). Once new antibodies were selected, each antibody was titrated with four to five different dilutions (with at least a 2-fold difference between each dilution) on the whole-mount tissue sections, according to the manufacturer’s recommendation (Table 1)
. If there were no well-established positive-control tissues, we used our in-house multitumor array to find model positive-control tissue. If signal-to-background ratio was not acceptable for the dilution tested, the pretreatment/incubation time/concentration were readjusted. Immunostains were scored semiquantitatively by two pathologists considering either cytoplasmic or membranous staining intensity, or percentage of positive nuclei (Table 1)
. Any discrepancies were resolved with a multihead microscope. The higher score was considered as a final score in case of a difference between duplicate tissue cores. Scoring was done without knowledge of patient outcomes.
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Univariate analyses were performed by using Kaplan-Meier estimates and log-rank tests, with raw score data obtained for each individual biomarker (according to the empirical score system described in Table 1
). The binarization of score data for the purpose of correlational and multivariate analysis (i.e., determining "positive" versus "negative" staining results) was done with historically established cutoffs for 27 of 31 markers. For four biomarkers (IGFBP2, IGFBP5, TIP1, and HSP27) binary cutoff points were found by testing multiple combinations of ordinal score groups (based on a separate test of equality of all factor levels for each stratum), e.g., 0 versus 1, 2, 3 or 0, 1, 2 versus 3, but not 0, 2 versus 1, 3, and so forth) to measure the clinical effect of expression levels of these proteins to disease-specific survival. Clustering analysis was based on the complete dataset and not on the binary results.
Multivariate analyses were performed with a proportional hazard model (i.e., Cox regression) and a backward stepwise method to remove variables from the model. A significant difference was declared if the P value from a two-sided test was less than 0.05 and a near-significant result was declared if the P value was less than 0.2.
We used unsupervised hierarchical clustering analysis to organize TMA score data (i.e., the results of immunostaining) into meaningful structures, applying the same approach that has previously been adopted for cDNA microarrays (10
, 11)
and has also been applied to TMA data (9)
. Clustering analysis organizes cases according to the similarity or dissimilarity of immunostaining profiles, placing the cases with similar immunoprofiles together as neighboring rows in the clustergram. The relationship between cases and immunomarkers is depicted graphically as a dendrogram in which branch length is determined by the correlation between immunostaining results. All raw score data for each biomarker (as shown in Table 1
) were used for clustering analysis. Only cases with immunostaining data for 80% or more of the markers under consideration were entered into clustering analysis. 2 tests were used to determine which markers contributed to the formation of cluster groups. The agreement in classification of cases based on different sets of immunomarkers was assessed with the kappa statistic. A kappa value of 0.41 to 0.6 indicates moderate agreement, 0.61 to 0.8 substantial agreement, and more than 0.8 near-perfect agreement (12)
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Software for TMA data management was used [Deconvoluter version 1.04, EXCEL for Windows; Liu et al. (11) ]. Unsupervised hierarchical clustering analysis with average and complete linkage algorithms was done with GeneCluster, and graphical representation of the results of clustering was done with TreeView.7 SPSS for Windows version 11.0 (Chicago, IL) was used for statistical analysis of the data.
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RESULTS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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. Missing immunostain data were usually caused by loss of a core from the section, less commonly by exhaustion of tumor material in the core. The list of 21 markers that were prognostically significant (P 0.05) or approached significance (P 0.2) in univariate analyses is shown in Table 2
. The remaining 10 markers were not prognostically significant (P > 0.2). Examples of representative immunostaining results for two cases, with four immunomarkers per case, are shown as Fig. 1
. Results of immunostaining with 11 immunomarkers per case, for all cases, can be viewed online at http://gpec.bliss.ubc.ca (under "view/Clustering Breast Cancer").8
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. The ten markers, which did not show significance or trend toward significance in univariate analysis were then excluded from the clustering model. Clear separation of cases into three distinct groups with large linkage distances was apparent when clustering was done with the 21 markers listed in Table 2
(Fig. 2B)
. To determine the stability of the cluster groups and to possibly reduce the number of markers needed to define cluster groups, we clustered the data by using, in turn, 20, 19 (Fig. 2C)
, 18, and 17 markers by stepwise exclusion of CD68, HSP27, p63, and CK5/6 [markers that showed a trend toward significance (P 0.2) in univariate analysis]. Prognostically significant cluster groups were identified by clustering that was based on 20, 19, and 18 markers. The most significant survival differences between cluster groups were seen with the set of 19 markers (Table 2)
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2 tests, where each marker was measured across the defined three cluster groups formed by the set of 19 markers. We found that Her3, Cox2, NSE, Relaxin, CD10, YB1, IGFBP2, and IGFBP5 showed no significant differences in staining results among the three cluster groups, whereas the remaining 11 markers (ER, PR, Her2, p53, Ki67, CA IX, TIP1, stromal CD117, PTEN, p63, and CK5/6) showed statistically significant difference in distribution between the three cluster groups (Table 3)
. Hierarchical clustering was performed with the set of these 11 markers (Fig. 2D)
. The overall concordance between assignment of individual cases to one of the three cluster groups formed when 19 versus 11 markers were used, respectively, was near perfect (kappa = 0.89). There were highly significant differences in disease-specific survival and overall survival among the 3 cluster groups defined with 11 markers (Fig. 3A and B)
. Cluster groups 2 and 3 were characterized by predominance of ER- and PR-negative breast cancers, whereas cluster group 1 consisted of tumors that were uniformly ER positive and PR positive in a majority of cases (Table 3)
. Accordingly, comparison was made between survival differences based on cluster group designation and ER/PR status (Fig. 3C and D)
. The differences in survival of patients in different cluster groups was greater than was seen with ER and/or PR alone. Also, there was a highly significant positive correlation between cluster groups and tumor grade (P < 0.001) but only a trend to correlation with tumor size (P = 0.07). No correlation was observed with lymph node status or patient age.
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Multivariate Analysis of Cluster Groups and Clinicopathologic Variables.
Multivariate analysis showed independence of cluster groups defined by 19 (as well as by a reduced set of 11) markers from nodal status and tumor size for both disease-specific survival and overall survival. The relative risk of death from breast cancer for patients in cluster group 1 versus groups 2 and 3, combined, was comparable with the relative risk associated with positive lymph node status (Table 4)
. Another multivariate model included cluster groups of 11 markers, ER, and Her2, and the clinicopathologic variables of lymph node status, tumor size, tumor grade, and patient age. After six cycles of iteration, when the least significant variables were eliminated, only cluster groups and lymph node status (P = 0.001) remained of independent prognostic significance with respect to disease-specific survival. When overall survival was considered, cluster groups (P = 0.002), lymph node status (P = 0.039), and tumor size (P = 0.008) remained of independent significance. None of the immunomarkers, used in isolation, achieved the prognostic significance of cluster groups in terms of disease-specific or overall survival.
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DISCUSSION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The potential for combinations of prognostic markers to be superior to any single marker has been observed previously (19 , 20) . Clinical oncologists have been using Tumor-Node-Metastasis (TNM) classification for decades (21) , which is a form of supervised clustering analysis combining tumor size, lymph node status, and distant metastasis. Unsupervised hierarchical clustering analysis has been used to classify tumors based on mRNA expression levels of thousands of genes. Prognostically relevant cluster groups have been identified for breast cancer, lymphoma, and lung cancer with this approach (2 , 3 , 6 , 16) . There have been attempts to classify breast cancers based on hierarchical clustering analysis of immunomarker data, but the prognostic significance of the cluster groups identified is unclear because of limited available outcome data or none (22 , 23) .
We applied unsupervised (i.e., without consideration of other histologic and clinical variables) hierarchical clustering analysis to immunostaining data and identified prognostically significant cluster groups. We were able to identify a minimal set of 11 biomarkers necessary to define these cluster groups. ER and PR status of tumors were the major determinants of cluster group designation in hierarchical clustering analysis; however, the prognostic value of these two markers in either univariate or multivariate analyses was much less significant than for the cluster groups formed by multiple immunomarkers. Although this establishes that consideration of multiple markers is superior to single markers, the basis for this superiority is important to consider. A small proportion of ER- and/or PR-positive tumors clustered along with ER-/PR-negative cases, which suggests that expression of multiple negative prognostic markers associated with tumor aggressiveness in these tumors overrides the effect of positive prognostic factors such as ER. All of the patients were treated at the British Columbia Cancer Agency according to provincial treatment guidelines; however, management varied, based on the year of diagnosis, and detailed treatment information is not available. This is a limitation of this study.
Sorlie et al. (2 , 24) , van ’t Veer et al. (3) , and van de Vijver et al. (25) independently identified subtypes of invasive breast cancer based on independent sets of gene expression data. Sorlie et al. (2 , 24) were able to consistently identify basal-like, Her2-overexpressing, luminal type and normal-breast-like tumor subgroups with, respectively, worst, poor, intermediate, and good prognosis with hierarchical clustering analysis. Nevertheless, it was not possible to classify a significant proportion (6–36%) of breast cancers into these categories (2) , which raised questions about further validation of this newly proposed classification system and its application to practice. Although cDNA microarray technology is widely used in cancer research, it is still far from clinical implementation because of the cost of the assay, the necessity of validation of initial findings, and the lack of standardization of protocols. Immunohistochemistry is routinely available in clinical laboratories and has proved to be a reliable and reproducible ancillary method in anatomic pathology. Application of TMAs has dramatically reduced the cost and time required for the testing of multiple biomarkers on large series of cases (26 , 27) . TMAs have been validated as useful tools for the study of prognostic markers in breast cancer (28) ; the concordance between TMAs and whole-section immunostains had been consistently high (22 , 28 , 29) , and the prognostic value of markers when assessed with TMAs has been as good as, or superior to, that when whole sections were used (30) .
Clinical application of clustering analysis of immunomarker data in breast cancer could allow combinations of prognostic markers to be used simultaneously, rather than the traditional approach of assessment of prognosis based on single clinical facts. This could be done, for less cost than is currently incurred in the assessment of ER, PR, and Her-2, by batching clinical cases onto small TMAs, run once weekly. Each clinical center could have its own "training set" of cases with monitored long-term outcome. New cases, then, could be added one at a time to this training set and clustered, allowing assignment to one of the previously defined prognostic groups. Another advantage of this approach is that, if better immunohistochemical markers appear, they can be easily added to this model [for instance, more specific basal markers than are currently available (13) ] to potentially improve the training set.
We conclude that it is possible to identify prognostic cluster groups of invasive breast cancer patients with maximal differences in survival, by the clustering of immunostaining data, based on a panel of 11 prognostic markers. Our data show that the application of a small panel of markers identifies prognostic profiles for individual cancers and is independent of the major clinical indicators of prognosis, i.e., tumor size and lymph node status. From a clinical perspective, it is of critical importance to determine whether there are specific patient populations for whom it is reasonable to avoid the administration of cytotoxic chemotherapy. Limited information is available to answer this important question (18) . Classification of breast cancer cases into prognostic cluster groups may aid in the individualization of adjuvant therapy, especially for a group of patients with node-negative status. Validation of this approach will require testing a sufficiently large (population-based) series, to allow analysis of subsets of patients with unclear prognosis. Quantification and further standardization of immunohistochemical analysis will also be an asset for future studies.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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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.
Requests for reprints: Blake Gilks, Department of Pathology and Laboratory Medicine, Vancouver General Hospital, Room 1438 JPPN, 855 West 12th Avenue, Vancouver, BC, Canada V5M 1Z9. Phone: 604-875-4111, extension 63305; Fax: 604-875-4797; E-mail: bgilks{at}vanhosp.bc.ca
6 Internet address for standardized protocols: www.gpec.ubc.ca; "Research" and "Research protocols." 
7 Both GeneCluster and TreeView are available at http://rana.lbl.gov/EisenSoftware.
8 Results of immunostainings, with 11 immunomarkers per case, for all cases can be viewed online at http://gpec.bliss.ubc.ca (under "view/Clustering Breast Cancer").
Received 3/ 3/04; revised 4/22/04; accepted 4/28/04.
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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