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An Unsupervised Approach to Identify Molecular Phenotypic Components Influencing Breast Cancer Features

¹ Department of Medicine, Division of Gastroenterology, and ² Departments of Pathology, ³ Surgery, ⁴ Epidemiology and Preventive Medicine, and University of Maryland Greenebaum Cancer Center, University of Maryland School of Medicine, Baltimore, Maryland

	ABSTRACT

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To discover a biological basis for clinical subgroupings within breast cancers, we applied principal components (PCs) analysis to cDNA microarray data from 36 breast cancers. We correlated the resulting PCs with clinical features. The 35 PCs discovered were ranked in order of their impact on gene expression patterns. Interestingly, PC 7 identified a unique subgroup consisting of estrogen receptor (ER); (+) African-American patients. This group exhibited global molecular phenotypes significantly different from both ER (-) African-American women and ER (+) or ER (-) Caucasian women (P < 0.001). Additional significant PCs included PC 4, correlating with lymph node metastasis (P = 0.04), and PC 10, with tumor stage (stage 2 versus stage 3; P = 0.007). These results provide a molecular phenotypic basis for the existence of a biologically unique subgroup comprising ER (+) breast cancers from African-American patients. Moreover, these findings illustrate the potential of PCs analysis to detect molecular phenotypic bases for relevant clinical or biological features of human tumors in general.

	Introduction

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Among women living in the United States, the three most commonly diagnosed cancers in 2003 will be cancers of the breast, lung, and colon (1) . Breast cancer alone is expected to account for 32% (211,300) of all new cancer cases and 39,800 deaths in this country (1) . Numerous clinical factors influence the prognosis of this disease, including tumor stage at time of diagnosis, age (2) , histological grade, hormone receptor status (3) , tumor size, and lymph node (LN) status (4) . High estrogen receptor (ER) mRNA levels have been associated with absent or minimal necrosis, as well as vascular invasion (3) . It has also been suggested that there is differential expression of ER isoforms between African-American (AA) and Caucasian (C) patients (5) . In tumors of AA women, the protective ERß isoform was decreased significantly relative to matched normal tissue (5) . It is believed that these and other biological differences may contribute to the higher mortality and lower survival rates observed in AA breast cancer patients (6, 7, 8) . However, comprehensive molecular approaches to understanding this specific problem (for example, applying gene microarray data) have not yet been reported. Therefore, in the current study, we analyzed global gene expression data from breast cancers using an unsupervised bioinformatics approach, principal components analysis (PCA).

We used this approach because whereas supervised analyses of gene expression data assign cases to predefined clinical groups, unsupervised strategies reveal natural groupings of patients. For example, one of the first and most widely used unsupervised techniques has been hierarchical clustering (9 , 10) . Clustering reveals groupings in data resulting from the superimposition of numerous biological characteristics or dimensions. Thus, cluster analysis may fail to distinguish among subtle categories of disease or between relevant and irrelevant genetic data (11) . PCA delineates key dimensions, or components, within a multidimensional gene dataset to explain clinical differences, such as tumor aggressiveness (12) . In contrast to hierarchical clustering, PCA reveals multiple layers of meaning designated components within the complex genetic dataset. These components are independent, allowing PCA to mine the data in a layer-oriented fashion, isolating each layer in turn from the next (13) . Moreover, PCA measures the fraction of variance contributed by each component to variance within the entire dataset (13) . Finally, PCA suggests explanations for a given component by ranking all of the genes in order of relative influence on that component. The current findings provide global molecular phenotypic evidence for the existence of an ER-positive AA breast tumor biological subgroup. This study also illustrates the potential of PCA to detect molecular phenotypic profiles of other clinical features, including age, ethnic origin, tumor size, progesterone receptor status, and LN metastasis, as well as to identify other previously uncharacterized but biologically important tumor parameters. The application of PCA to cDNA microarray data offers an analytic means to identify and explain important biological aspects of malignancy.

	Materials and Methods

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Surgical Specimens and cDNA Microarray Preparation
Thirty-six breast cancers, surgically resected between 1994 and 1999, were obtained from the University of Maryland Greenebaum Cancer Center. The clinical and molecular features of these tumors are depicted in Table 1 . In dichotomizing patient age, a cutoff value of 50 was determined a priori based on the average age of onset of menopause in our patient population. Genomic DNA and total RNA were extracted from fresh-frozen specimens. Amplified RNA (aRNA) was amplified from 20–50 µg of total RNA using a T7-based protocol (14) . Labeling was performed on 3–6 µg of aRNA by incorporating Cy3- or Cy5-labeled dCTP using random primers and Superscript reverse transcriptase (15) . A universal reference probe was prepared from an equimolar mixture containing aRNAs from eight human cancer cell lines (10 , 11 , 15 , 16) . cDNA microarray slides containing 8064 human cDNA clones were prepared according to a protocol described previously (15) . The Lawrence Livermore Laboratory cDNA library was used as a clone source (Invitrogen, Carlsbad, CA). All 8064 of the clones were independently sequence-verified and checked for correct annotation in our laboratory (10 , 11 , 15 , 16) . Microarrays were cohybridized to Cy5-labeled specimen aRNA and Cy3-labeled universal reference probe aRNA at 65°C overnight. After hybridization, each slide was scanned using a GenePix 4000A dual-laser slide scanning system (Axon Instruments, Union City, CA).

Data Analysis
Clustering and Derivation of Principal Components (PCs).
Data imported from GenePix were manipulated and clustered using average linkage clustering with centered correlation (9) . The second step in the analysis involved PCA. All of the PCA calculations were performed in MatLab (MathWorks, Inc., Natick, MA). The data, filtered as described above, were input into MatLab and normalized so that for each specimen, mean gene expression equaled 0 and SD equaled 1. Because the independent dimensions in PCA typically equal the number of specimens minus 1, and because there were 36 breast cancers in this study, 35 independent components were derived. The relative contribution of each component to the total data variance was calculated, and components were ranked in decreasing order of their relative contribution to this variance.

Associations between PCs and Clinicomolecular Data.
Beginning with the first-ranked component, attempts were made to correlate each component with known clinical data. Various statistical techniques were considered for this task, among them multivariate fitting models. For its simplicity and the ready availability of validation techniques, one-way ANOVA was used. A literature search was conducted and subgroups of breast cancers reported previously as having distinct molecular, biological, or clinical features were used in our correlative analyses. Thus, a P was calculated for the one-way ANOVA test of the association between each component and the following tumor characteristics: year of surgery, age, ethnic origin, LN status, ER status, progesterone receptor status, location (left versus right breast), stage, size, and histological type (intraductal versus lobular). One-way ANOVA calculations were performed in Statistica (StatSoft, Tulsa, OK).

Validation of Associations by Permutation Testing.
It is important to note that PCs are not observed data, but are rather summary statistics, which capture the main variance in data. Therefore, it is predictable that low Ps will be obtained when performing one-way ANOVA analyses of associations with these components. To ensure that these Ps are significant, they need to be adjusted. One method to adjust Ps in this fashion is based on permutation testing. We performed permutation-based confirmatory analyses for each observed association between a PC and a clinical feature. First, the P of the one-way ANOVA test for association between the PC and the clinical feature was calculated (see above); next, identification tags for the patients were randomly shuffled, and the Ps of the one-way ANOVA test for the association between each component and the clinical feature were recalculated. This calculation resulted in 35 different Ps, 1 for the association between each component and the given clinical or molecular feature. Among these 35 Ps, the highest P was recorded. Next, step 2 was repeated 999 times, yielding a total of 999 "highest" Ps. Finally, the P calculated at step 1 was ranked among the 999 values obtained by random permutations. The rank thus obtained constituted the "corrected" P, representing the probability of having obtained the original P by chance (18) .

Gene Loading Values in PCs.
The loading value was the number assigned by PCA to represent the influence, within a particular component, of a given gene relative to other genes. Thus, the greater the relative impact of a given gene on a particular component, the more extreme its positive or negative loading value in that component. When the loading value for a given gene was close to 0 for a given component, the gene exerted a minimal influence on that component.

	Results

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Cluster Analysis.
As a first unsupervised bioinformatics strategy, we used hierarchical agglomerative clustering. Clustering failed to recognize any clinical tumor characteristics, being influenced mainly by the age of the surgical specimen (data not shown). This result suggested that additional unsupervised approaches would be necessary if meaningful groups within the microarray data were to be discovered.

PCA.
PCA extracted 35 PCs. To discover the biological significance of these PCs, they were correlated with clinical or biological features using one-way ANOVA (13) . These associations revealed a significant impact of several features on global molecular phenotype (Table 2A) . The following PCs were found to be associated with only one clinical characteristic: PC 1, 4, 5, 6, 9, 10, 12, 23, and 25. PC 7 was associated with LN metastatic status (P = 0.03, one-way ANOVA), ER status (P = 0.009, one-way ANOVA), and with a subgroup consisting of ER (+) AA patients (P < 0.0001, one-way ANOVA). The association between PC 7 and the ER (+) AA patients was significantly stronger than were the other two associations. Thus, the fundamental factor impacting PC 7 was the biological difference between AA patients with ER (+) tumors and all of the remaining breast cancers, rather than ER status or LN metastasis alone.

View larger version (20K): [in this window] [in a new window] [Download PPT slide]   Fig. 1. Graphic display of association of PC 7 with race and estrogen receptor (ER) status. X axis displays two categories: the first category includes ER (+) African-Americans (AA), whereas the first category includes the rest of the specimens, i.e., ER (-) AA, ER (-) C and ER (-) C patients. Y axis shows the output value for each of these four groups in PC 7; Ps were obtained by one-way ANOVA; bars, ±SE. The ER (+) AA tumors (bottom rightmost group) tended to have much lower output values in PC 7. This result demonstrates a clear separation between the 6 ER (+) AA tumors and the remaining cancers. Within the other category, there are 9 ER (-) AA patients, 11 ER (+) patients, and 6 ER (-) C patients. The represent the mean, the , ±SE, and bars ± 1.96 * SE.

Relative Contributions of Individual Genes to Each Component.
The individual contributions of genes to particular components were assessed by examining gene loading values. Loading values represent association coefficients between genes and components (13) . They range from -1 to +1, with these extremes representing perfect negative or positive associations, respectively. Genes with high loading values in a given PC are associated with high weights in the equation determining total PC output value (13) . In the current study, genes were ordered according to their loading values, representing their degree of influence on each component. Because component 7 showed the most significant P after permutation testing, attention was focused on this component. Genes with extreme positive or negative loading values in this component are displayed in Table 2B (PC 7). ER (+) AA specimens received negative values on PC 7, whereas the remaining specimens received positive values on PC 7. Thus, genes with negative loading values in PC 7 correlate with ER (+) AA specimens, whereas genes with positive loading values on PC 7 correlate with the remaining specimens. Genes with negative loading values are important in defining the biology of ER (+) AA cancers; genes with positive loading values are important in defining the biology of non-ER (+) AA cancers. The more extreme the loading value (positive or negative), the more important the gene in defining the biology of these cancer subgroups. Thus, genes with the most extreme loading values (positive or negative) are logical targets for future in-depth studies. In table 2B , proteases included NM 006103 (carboxypeptidase B1), 17472831 (cathepsin D), and NM 003064 (secretory leukocyte protease). Interestingly, NM 003064, which was printed in two separate locations on the microarray, appears twice on this list of genes with highest loading values, supporting the validity of our microarray data as well as the PCA method we used. Also of interest, a number of the most highly ranked genes have putative relationships to tumorigenicity, including topoisomerase II , IFN-stimulated protein, FBJ murine osteosarcoma viral oncogene homologue, early growth response 1. Wilms tumor homologue, trefoil factor 3, cathepsin D, and 14-3-3-.

	Discussion

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Perhaps the most interesting finding in the current study was the significant separation observed between ER (+) AA tumors and the remaining breast cancers (PC 7). This finding supports an impact of ER status on gene expression profiles. Although it was known that ER status influences gene expression (20) and that breast cancers from AA patients exhibit unique biological features (7 , 8 , 21 , 22) , the current data now suggest that ER status has a different impact on AA breast cancer patients than on Caucasion breast cancer patients. The current data identify this ER (+) AA subgroup based on an unsupervised analysis of global gene expression data and reveal genes associated with these spontaneously revealed categories.

In addition to identifying natural subgroupings among breast cancer patients, PCA identified genes influencing these groupings. Genes displaying high loading values suggested possible pathways or molecular bases underlying each PC (13) . Although other methods, such as significance analysis of microarrays (18) , may identify differentially expressed genes among predefined clinical subgroups, we have shown that PCA identifies genes relevant to natural subgroups of breast cancer patients. However, these genes still require individual verification, either by significance analysis of microarrays or using quantitative reverse transcriptase-PCR.

Many of the genes identified by PCA already had been linked to other human cancers or to breast cancers in general and ER status in particular. Among the genes relevant to PC 7 [the ER (+) AA component] was Homo sapiens cathepsin D, which is overexpressed in aggressive human breast cancers (25) and induced by estrogens in hormone-responsive breast cancer cells (26) . High cathepsin D concentrations in primary breast cancers correlate with an increased risk of metastasis and are particularly useful in orienting LN-negative tumors to adjuvant therapy (25) . Topoisomerase 2 , which was also related to PC 7, is associated with mammalian cell proliferation (27) , and its overexpression is linked to cellular dedifferentiation and a biologically aggressive breast cancer phenotype (27) . Another gene linked to PC 7, 14-3-3-, interacts with cyclin-dependent kinases and controls the rate of entry of cells into mitosis (28) . The protein product of this gene has been implicated in the neoplastic transformation of breast epithelial cells by virtue of its role as a tumor suppressor; as such, it may constitute a robust biomarker with clinical utility (28) . Additional studies have implicated 14-3-3-as a target of methylation in breast cancers (29) . It has been suggested that hypermethylation and loss of expression of 14-3-3-occurs at an early stage in the progression to invasive breast cancer (29) .

The relationship between breast cancer behavior and clinical or molecular factors has been explored in numerous previous studies (21 , 30, 31, 32, 33, 34, 35) . There are studies reporting that AA women tend to develop highly aggressive breast cancers (5 , 36) , with a higher mortality rate relative to their Caucasian counterparts (7 , 37 , 38) . The basis of this disparity has not been found. Several investigators have linked this difference to coexisting variables, including socioeconomic status and limited access to health care (39) , or to stage of disease at diagnosis (40) . However, biological bases for this disparity have also been suggested. For example, cyclin Doverexpression is more prevalent in non-C breast cancers (7) , and variations in estrogen-mediated signaling due to differences in ER isoforms may account for differences in breast tumor behavior (5) . In fact, ER status per se has been correlated with ethnic origin (22 , 41) . The current study shows that based on global molecular phenotyping, ER (+) AA breast cancers constitute a distinct biological subgroup (P < 0.0001, one-way ANOVA). This finding, which suggests that ER status has disparate effects on AA and C patients, was obtained by an unsupervised bioinformatics approach (PCA) and additionally validated by permutation analysis (P = 0.006, one-way ANOVA). The unsupervised fashion by which this finding was derived supports the conclusion that this is a natural, biologically meaningful group of patients. The other associations found between PCs and clinical features were less strong than the distinction between ER (+) AA and the remaining breast cancers (i.e., at permutation analyses the other Ps were not statistically significant). Nonetheless, these associations should be additionally investigated, as they may indicate a natural biologic grouping among breast cancer patients.

It is a commonly held hope that molecular biology, by offering a more objective and scientifically based view of tumors and other human diseases, will eventually supplement our current clinicopathologic classification schemes. cDNA microarrays hold immense promise in this regard, considering the large statistical power of the data contained within them. The current study suggests that PCA can reveal multiple levels of meaning within microarray data by viewing these data from multiple viewpoints. By accomplishing this task, PCA increases the likelihood that cDNA microarrays will become part of our molecular taxonomic armamentarium. Moreover, the current study suggests that PCA can simplify complex microarray data, both by identifying and providing insight into biological and clinical categories. In some cases, PCs themselves may have more biological or clinical significance than our current clinical categories do. For example, a given component may identify cancers with a poor outcome or precancerous lesions with a higher risk of neoplastic progression. Ultimately, large-scale prospective and other clinical correlative studies are needed that apply PCA to predict the clinical behavior and natural history of human malignant disease.

	FOOTNOTES

 
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.

Note: F. M. Selaru, J. Yin, and A. Olaru contributed equally to this work.

Requests for reprints: Stephen J. Meltzer, 655 West Baltimore Street, BRB 8-009, Baltimore, MD 21201. Phone: (410) 706-3375; Fax: (410) 706-1099; E-mail: smeltzer{at}medicine.umaryland.edu

Received 10/12/03. Revised 12/12/03. Accepted 12/23/03.

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