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CE Update Submitted 11.12.09 | Revisions Received 12.23.09, 1.7.10, and 3.9.10 | Accepted 3.22.10 Microarray-Based Gene Expression Profiling for Molecular Classification of Breast Cancer and Identification of New Targets for Therapy Rupninder Sandhu, MBBS,1,3,4 Joel S. Parker, MS,2,4 Wendell D. Jones, PhD,5 Chad A. Livasy, MD,1,3,4 William B. Coleman, PhD1,3,4 (1Department of Pathology and Laboratory Medicine, 2Department of Genetics, 3Program in Translational Medicine, 4UNC Lineberger Comprehensive Cancer Center, University of North Carolina, School of Medicine, Chapel Hill, NC 5Expression Analysis, Durham, NC ) DOI: 10.1309/LMLIK0VIE3CJK0WD Abstract Basal-like breast cancers represent approximately 15%–20% of all breast carcinomas, are aggressive, have variable responses to chemotherapy, and associated with poor clinical outcome. The molecular mechanisms governing the biological behavior of basal-like breast cancers are not well understood. Hence, it is difficult to predict which chemotherapeutics are most likely to be effective and to determine appropriate management strategies for individual patients. Transcriptomic analysis may allow further stratification of basal-like breast cancers enabling prediction of 1) cancer recurrence after surgery; 2) likelihood of metastatic spread; 3) probable tissue sites for metastatic spread; and 4) responses to specific therapies and treatment modalities. Furthermore, careful After reading this article, readers should be able to describe the basic features of the various molecular subtypes of breast cancer, with special emphasis on traits characterizing basal-like breast cancer. Readers should also understand the basic technical attributes of gene expression microarrays, and be able to discuss the advantages and disadvantages of using microarray-based gene expression profiling for making decisions Breast cancer is a disease that is diverse in natural history, response to treatment, and patient outcomes. It remains the most common non-cutaneous female malignancy with an estimated 192,370 new cases in 2009 in the United States.1 Breast cancer-associated mortality is second only to lung cancer in the United States, with an estimated 40,170 deaths in 2009.1 Breast cancer is also the most commonly diagnosed female malignancy worldwide. According to the American Cancer Society, approximately 1.2 million women worldwide are diagnosed with breast cancer each year. Breast cancer is not a single disease, rather it represents a diverse spectrum of diseases including several distinct biological entities and subtypes. These subtypes are associated with specific morphological characteristics and examination of microarray-based gene expression profiles may identify new molecular targets (or pathways) for the development of targeted therapeutics. Targeted therapies may prove to be more efficacious in basal-like breast cancer treatment than the cytotoxic chemotherapeutics currently employed. Keywords: AP breast, molecular diagnostics, genetics related to the clinical management of basal-like breast cancers. In particular, readers should understand the molecular assays already in clinical use for breast cancer. Molecular Diagnostics exam 81002 questions and corresponding answer form are located after this CE Update on page 373. different clinical outcomes.2-10 The molecular signatures of these breast cancer subtypes reflect not only the distinct biological features of these malignant neoplasms, but also predict their clinical behavior and responses to chemotherapy,11-14 with certain subtypes having better outcomes than others. To some extent, the observed variation in disease outcome among breast cancer patients reflects the successful identification of therapeutic targets for some subtypes and the development of effective targeted therapies. The molecular mechanisms contributing to the biological/ clinical behavior of basal-like breast cancer are not well understood. These aggressive cancers constitute 15%–20% of all breast carcinomas. They resist chemotherapy and are associated Corresponding Author Abbreviations William B. Coleman, PhD [email protected] ER, estrogen receptor; PR, progesterone receptor; HER1 and HER2, human epidermal growth receptors; OS, overall survival; DFS, disease-free survival; PARP, Poly(ADP-ribosyl)ation polymerase; SSP, single sample predictor 364 LABMEDICINE ■ Volume 41 Number 6 ■ June 2010 labmedicine.com CE Update Figure 1_Differential expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth receptor 2 (HER2), among different subtypes of breast cancer. Breast cancer is classified into various subtypes based on differential immunohistochemical staining for ER, PR, HER2, HER1 (not shown), and cytokeratins (not shown). Panels A-D, luminal A breast cancer; Panels E-H, luminal B breast cancer; Panels I-L, HER2+ breast cancer; Panels M-P, basal-like breast cancer. Panels A, E, I, and M show H&E staining for each breast cancer subtype. Panels B, F, J, and N show immunostaining for ER and the results (ER+ or ER-) are indicated. Panels C, G, K, and O show immunostaining for PR and the results (PR+ or PR-) are indicated. Panels D, H, L, and P show immunostaining for HER2 and the results (HER2+ indicative of HER2 amplification or HER2-) are given. with poor clinical outcomes, contributing disproportionately to breast cancer-related mortality.15 In this article, we discuss the possible use of microarray-based gene expression profiling to further examine basal-like breast cancers and to identify gene expression signatures that predict their clinical behaviors and responses to treatment. Transcriptomic analysis of basallike breast cancer may enable further subclassification within this molecular subtype, creating biological subgroups reflecting differences in 1) cancer recurrence after surgery; 2) likelihood of metastatic spread; 3) probable tissue sites for metastatic spread; and 4) responses to specific therapies and treatment modalities. Furthermore, careful examination of microarraybased gene expression profiles may identify new molecular targets (or pathways) for development of targeted therapeutics directed against biological subgroups of basal-like breast cancer. Targeted therapies may prove to be more efficacious in basal-like breast cancer treatment than the cytotoxic chemotherapeutics currently employed. Application of personalized and targeted therapies will improve long-term outcomes for patients with basal-like breast cancer and will lessen the burden associated with over-treatment of these patients. labmedicine.com Breast Cancer: A Heterogeneous Entity Breast cancer is a diverse disease with a number of morphologic subtypes. Invasive ductal carcinoma is the most common morphological subtype, representing 80% of the invasive breast cancers. Invasive lobular carcinoma is the next most common subtype, representing approximately 10% of invasive breast cancers. The less common subtypes of the invasive breast cancers include mucinous, cribriform, micropapillary, papillary, tubular, medullary, metaplastic, and inflammatory carcinomas. Representative examples of invasive ductal carcinomas are shown in Figure 1. Routine subclassification of invasive ductal carcinomas is accomplished by immunostaining tumor tissues for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth receptors (HER1 and HER2), and various cytokeratins. The differential expression of ER, PR, and HER2 in different subtypes of breast cancer based upon immunohistochemical staining is shown in Figure 1. The differential expression of these protein biomarkers is used as an immunohistochemical surrogate for gene expression analysis to determine molecular subtype. Approximately 70%–75% of invasive breast cancers June 2010 ■ Volume 41 Number 6 ■ LABMEDICINE 365 CE Update express the ER positive cancers (ER+). Collectively, the ER+ malignant neoplasms are classified as luminal cancers. These cancers are further subclassified into luminal A and luminal B subtypes based on their HER2 status and proliferation rate. The majority of ER+ tumors also express PR. The presence of normal PR levels suggests an intact ER signal transduction pathway in the breast cancer cells, and PR expression typically follows the ER expression pattern. The ER- breast cancers are subclassified as HER2+ and basal-like based on the HER2 overexpression/gene amplification, basal cytokeratin expression, and EGFR (HER1) expression. An immunohistochemical staining proxy based on 5 biomarkers classifies breast cancers into the major subtypes (shown schematically in Figure 2): 1) ER+ are subclassified into luminal A (ER+, PR+, HER2-) and luminal B (ER+, PR+, HER2+); 2) ER negative cancers (ER-) are subclassified into triple-negative breast cancer (ER-, PR-, HER2-) and human epidermal growth factor receptor 2-positive (ER-, PR-, HER2+); and 3) unclassified cancers (negative for all 5 markers).2-4,16 Basal-like breast cancers are distinguished from other triple-negative breast cancers (ER-, PR-, HER2-) by expression of cytokeratin 5/6 and/or EGFR. There is no international consensus on the criteria used to define cancers as basal-like in formalin-fixed, paraffin-embedded tissues. Therefore, the term basal-like is not yet routinely used in clinical practice. Rather, the basal-like breast cancers are contained in the triplenegative classification. Breast cancers, like most epithelial cancers, are associated with better treatment and survival outcomes when diagnosed at an early stage. However, outcomes of early stage breast cancers differ depending upon the molecular subtype (Figure 3). In general, the ER+ breast cancer subtypes (luminal A and luminal B) exhibit a good prognosis and excellent long-term survival (approximately 80%–85% 5-year survival), while the ER- subtypes (HER2-positive and basal-like) are difficult to treat and are associated with poor prognosis (approximately 50%–60% 5-year survival). The ability of patients with ER+ breast cancers to survive their disease reflects the availability of effective targeted therapy in the form of anti-estrogen treatment (eg, tamoxifen). However, among the ER+ breast cancers, the luminal B neoplasms are associated with a significantly worse prognosis than luminal A subtype4 (Figure 3). This difference in outcome is partly due to variations in response of ER+ subtypes (luminal A and luminal B) to anti-estrogenic treatment.17 Targeted therapy of HER2 overexpressing breast cancers, (luminal B or HER2positive [ER-] neoplasms) with trastuzumab (herceptin), either concurrent or sequential with adjuvant chemotherapy has improved survival for these breast cancer subtypes.18 Basal-like breast cancers are characterized by autonomy of growth that is independent of expression of hormone receptors. Since these cancers lack the appropriate targets for the drugs like tamoxifen (targeting ER) and trastuzumab (targeting HER2), patients with these cancers do not derive benefit from these drugs. Basal-like breast cancers are associated with overall poor prognosis and shorter long-term survival. The poor clinical outcomes associated with basal-like breast cancer reflect the fact that these cancers show variable response to chemotherapy or recur following therapy. Lack of identification of “druggable” targets in basal-like breast cancers and poor prognosis makes the identification of molecular signatures and therapeutic targets in these cancers to be of utmost significance. No widely available targeted therapies for this breast cancer subtype have been developed to date, although phase II studies of Poly(ADP-ribosyl)ation polymerase (PARP) inhibitors have shown promising results.19 366 LABMEDICINE ■ Volume 41 Number 6 ■ June 2010 Figure 2_Schematic illustrating various breast cancer subtypes. The blue and pink rectangles group the subtypes based on the expression of ER/PR, positive in the blue (Luminal A and Luminal B) and negative (HER2+ and basal-like) in the pink. The central grey rectangle (with black outline) indicates the presence of HER2 amplification in Luminal B and HER2+ subtypes. Figure 3_Survival plot of 294 breast cancer patients. A Kaplan-Meier survival plot of overall survival corresponding to 294 breast cancers from the publicly available UNC database is shown grouped by molecular subtype. The P-value was calculated using the Log-rank test. Details of these 294 samples along with clinical annotation can be found at https://genome.unc.edu/pubsup/breastGEO/. Basal-Like Breast Cancer Discovery of Basal–Like Breast Cancers The basal breast cancer subtype was first described in studies based on immunohistochemistry.20-23 These cancers are designated basal-like because they exhibit some cellular characteristics associated with the basal myoepithelial cell layer, such as expression of cytokeratins 5/6, 14, or 17, vimentin, and laminin, but these tumors are clearly not derived from myoepithelial cells.24-26 The basal-like breast cancer subtype was rediscovered following the application of microarray-based gene expression labmedicine.com CE Update profiling to breast cancer classification.2-7,9 That the basal-like breast cancers were identified independently by 2 different methodologies indicates strongly that these cancers represent a distinct biological entity. Basal-like breast cancers are best classified through gene expression profiling.2-7,9 However, in routine clinical practice, immunohistochemistry has become the surrogate for the gene expression analysis for diagnosis of basallike breast cancers (Figure 1). Correctly classifying these cancers significantly impacts clinical decisions and research efforts. In the clinic, there is a need to correctly identify breast cancer subtypes for prognostication purposes in relation to individual patients and for decision-making related to appropriate treatment course. On the other hand, in the research environment, correct breast cancer subclassification is essential to ensure investigations expand our understanding of the biological basis for the behavior and characteristics of these cancers. Association With Risk Factors The development of basal-like breast cancer is associated with distinct risk factors, including early-onset menarche, younger age at first full-term pregnancy, high parity combined with lack of breast feeding, and abdominal adiposity (based upon waist-hip ratio).27 These breast cancers are overrepresented among patients of certain age and ethnic groups, and are frequently associated with certain genetic mutations. Specifically, basal-like breast cancer is overrepresented among premenopausal, African-American women.11 However, these cancers can and do affect women of every age and ethnic group.27 The differences in distribution of basal-like breast cancer by age and race can be partially attributed to variations in the distribution of the risk factors described and to other risk factors (eg, waist-hip ratio, use of lactation suppressants, and overexpression of leptin receptor).27 In addition, basal-like breast cancer occurs more frequently among hereditary breast cancer patients harboring germ-line BRCA1 mutation.28 Foulkes and colleagues showed that 17/72 triple-negative breast cancers harbored a BRCA1 mutation, and 88% (15/17) of these expressed the basal-like phenotype.29 Likewise, Sorlie and colleagues observed that 100% (18/18) of breast cancers from patients carrying BRCA1 mutations clustered within the basallike subgroup.4 However, the other molecular subtypes of breast cancer can be associated with BRCA1 mutations as well. Morphological Features Morphologically, basal-like breast cancers are characterized by the presence of central necrotic zones, pushing borders, and conspicuous lymphocytic infiltrate.30-34 The presence of metaplastic elements3,30-32 and medullary/atypical medullary features31,32,35 are more prevalent in basal-like breast carcinomas than in other types of breast cancer. Recent studies have shown that more than 90% of metaplastic breast carcinomas,3 as well as the majority of medullary carcinomas,35,36 exhibit a basal-like phenotype. Basal-like breast cancers are aggressive, with high rates of cellular proliferation, high histological grade, and extremely poor clinical outcomes.3,4 These factors combine to account for the disproportionate contribution of basal-like breast cancer to breast cancer mortality. It has been suggested the high level of cellular proliferation observed in these neoplasms might account for the over-representation of basal-like breast cancers among the so-called interval breast cancers (the cancers arising between annual mammograms). labmedicine.com Clinical Behavior of Basal-Like Breast Cancers Currently, there is no consensus on the immunohistochemical criteria for the diagnostic classification of basal-like breast cancers. Studies have shown the profile constructed using ER-/ PR-, HER2-, CK5/6+, and/or EGFR+ is 100% specific but only 55% to 76% sensitive.37 Breast cancers that are ER-/PR-/HER2are broadly classified as triple-negative neoplasms (Figure 1). The triple-negative breast cancers include most (or all) basal-like breast cancers.38 Interpreting the percentage of positive cells and intensity of immunohistochemical staining is subjective. Variability in immunostaining techniques and procedures is a concern as well. Hence, standardization and/or automation of immunostaining procedures and interpretation to remove technical and subjective variation will benefit this analysis in the clinical laboratory. The low sensitivity associated with the classification of basal-like breast cancers using immunohistochemical staining may indicate that these cancers are much more heterogeneous than previously thought. Gene expression profiling-based molecular classification of breast cancers predicts the general clinical behavior of breast cancers corresponding to the different molecular subtypes. Microarray studies show the basal-like breast cancers express a common gene expression signature, and these cancers are associated with an extremely bad prognosis.3 Among the patient cohort examined in the initial study of this type, 100% of the patients with basal-like subtype succumbed to their disease within 4 years of diagnosis.3 Basal-like breast cancers respond to preoperative (neoadjuvant) chemotherapy.39,40 However, despite the observation of pathologic complete response in many patients, these individuals exhibit poor long-term survival. The poor survival outcomes among these patients, despite response to chemotherapy, may be related to a higher likelihood of relapse in individuals where pathologic complete response was not achieved.40 The malignant neoplasms constituting the basal-like breast cancer subtype are not biologically homogeneous. For example, in 1 study unsupervised hierarchical clustering within 43 cytokeratin-14 positive (basal-like phenotype) tumors revealed 4 clusters, and 1 of these displayed a worse prognosis than the other 3, strongly suggesting intra-subtype heterogeneity.41 Variable prognosis within the basal-like subtype has also been reported by other groups.42,43 Rakha and colleagues divided the basal-like breast cancers into those with a dominant basal pattern (>50% of cells are positive for cytokeratin 5/6 and 14) and the remaining basal cancers (<50% of cells are positive for cytokeratin 5/6 and 14).42 These subsets of basal-like breast cancers demonstrated differences in grade, presence of pushing margins, local spread, and long-term outcomes (disease-free survival [DFS] and overall survival [OS]).42 Likewise, Laakso and colleagues distinguished basal (uniformly positive for cytokeratins 5 and 14) and basoluminal (heterogeneously positive for cytokeratin expression) subtypes of basal-like breast cancers and showed that these subsets of basal-like cancers differ with respect to tumor size, proliferation index, expression of other markers (like vimentin), and recurrence-free survival.43 These observations underscore the necessity to further define biological subsets of basal-like breast cancer (particularly in terms of clinical behavior). The pattern of metastatic spread among basal-like breast cancers has been suggested to be different compared to other breast cancer subtypes. The basal-like breast cancers have a tendency to disseminate through hematogenous routes, involving the brain (resulting in a higher rate of cerebral metastasis) and lung, and are less likely to spread to the lymph nodes, liver, or bones.33,44-46 Prognosis of cancer is linked to various clinical parameters, including tumor size, tumor grade, lymph June 2010 ■ Volume 41 Number 6 ■ LABMEDICINE 367 CE Update node status, and the presence of distant metastasis. However, among basal-like breast cancers, prognosis has been shown to be less dependent on tumor size, tumor grade, and lymph node status reflecting the deviant nature of these cancers. Expression of basal markers like cytokeratins 5/6 was associated with poor outcome and proved to be a prognostic factor independent of the usual clinical parameters.47 These observations highlight the requirement to examine further the transcriptome of basal-like breast cancers in order to uncover the molecular basis for the biological behavior of subsets of these cancers.47 Using Microarray-Based Gene Expression Patterns to Understand the Etiology of Basal-Like Breast Cancers The phenotypic diversity among basal-like breast cancers is a manifestation of differences in the transcriptional programs of the constituent neoplastic cells. Microarray-based gene expression analysis can be used to analyze, display, and systemically classify the molecular heterogeneity observed among basal-like breast cancers. The role of genetic changes altering the expression of oncogenes and tumor suppressor genes as causative events in development and progression of cancer has been firmly established. A variety of genetic lesions have been associated with the development of basal-like breast cancers. These include gene deletions, point mutations (TP53 gene mutation),3,48,49 as well as gene amplifications. Other anomalies associated with the development of basal-like breast cancers involve chromosomal aberrations (X chromosome isodisomy, 1q and 8q gains, X losses),48 loss of heterozygosity (on 4p and 5q),48 and aneuploidy. Basal-like breast cancers also occur frequently in hereditary breast cancer patients with germ-line BRCA1 mutations.28 Epigenetic events constitute another common molecular alteration that has been implicated in cancer initiation and progression. These changes involve 1) alteration of the DNA methylation status of the promoter regions of transcribed genes, and 2) alterations in the maintenance of chromatin structure. Cancer-associated epimutations can inactivate tumor suppressor genes (in absence of any genetic alteration) or activate protooncogenes. Promoter hypermethylation of genes including p16, retinoic acid receptor β, BRCA1, and E-cadherin have been identified to play a role in breast cancer initiation and progression.50 More specifically, promoter hypermethylation of CST6, E-cadherin, ER, CEACAM6, LCN2, and SCNN1A genes are frequently observed in basal-like breast cancers and differentiates them from other breast cancer subtypes.51,52 Single initiating mutations and a series of mutations causing transformation of normal cells into cancer cells, as well as epigenetic alterations leading to cancer development, have been identified. After these initial events occur, neoplastic cells undergo a series of changes due to the interactions between the altered cells and external signals.53 As a consequence, the incipient cancer cells arising from similar initiating events may diverge, resulting in neoplasms that are quite dissimilar from 1 another. These differences may be molecular (reflecting gene expression patterns), but otherwise they are not easily discernible (at the level of cellular morphology) and may represent the mechanisms accounting for biological subsets within the basal-like breast cancer subclass. Hence, identification of mutations and epimutations within basal-like breast cancers will be important to understanding the molecular etiology of these cancers as well as to identifying potential targets to treat these cancers. Obviously, differences in the transcriptome 368 LABMEDICINE ■ Volume 41 Number 6 ■ June 2010 of individual basal-like breast cancers can be uncovered through analysis of gene expression patterns that today are most often accomplished through DNA microarray analysis, a technology measuring the expression of thousands of genes simultaneously. The microarray enables the study of complex phenotypes at the molecular level, allowing association of gene expression with tumors having differing phenotypes. Given that the phenotype is determined by gene expression, the clinical behavior of subgroups of basal-like breast cancer will be associated with specific gene expression patterns, potentially leading to identification of therapeutic targets or biomarkers for progression. Clinical Utility of Microarray-Based Gene Expression Profiles Neoplasms representing different subsets of basal-like breast cancers may have the same clinical stage based on traditional classification criteria and may be histologically and morphologically identical, yet their biological (clinical) behavior may be remarkably different. Survival rates associated with basal-like breast cancers are dismal. Numerous studies have shown that patients with basal-like breast cancer exhibit significantly shorter OS and DFS and have high rates of tumor recurrence, highlighting the aggressive course of these cancers. A retrospective study of 49 basal-like and 49 grade and age-matched non basal-like tumors54 showed that patients with basal-like breast cancers were associated with significantly shorter OS and DFS and a higher recurrence rate. Another study based upon a cohort of 930 breast cancer patients showed that expression of basal cytokeratins (indicative of basal-like phenotype) was associated with poor progression-free survival and poor overall survival.37 Analysis of 496 primary breast cancers from the Carolina Breast Cancer Study showed that progression-free survival differed by breast cancer subtype and survival time is significantly shortened in basal-like and HER2+ subtypes.11 These studies are consistent with similar observations of poor prognosis in basal-like cancers made by numerous other groups before and after these aforementioned studies.3,4,9,14,47,55,56 In the absence of molecular targets (like ER or HER2), options for basallike breast cancer therapy are limited to aggressive cytotoxic chemotherapy. However, cytotoxic chemotherapy (whether neoadjuvant or adjuvant) has proven largely ineffective in the treatment of basal-like breast cancer based on OS among these patients. The general failure of chemotherapy in the treatment of basal-like breast cancer may be related to the lack of targeted approaches and/or our current inability to stratify patients according to their likelihood of response to specific drugs or treatment modalities. Transcriptomic analysis may be instrumental in identifying possible targets for treatment of basal-like breast cancers. Data generated from microarray-based gene expression analysis will enable the generation of patterns associated with different biological subsets of cancerous cells (for instance, from different basal-like breast cancers). Subsequently, these gene expression patterns can be clustered based upon intrinsic differences in gene expression (unsupervised cluster analysis) to uncover gene-gene relationships or be analyzed in the context of a particular endpoint (supervised analysis) (Figure 4). The supervised analysis is frequently used to identify gene sets that differentiate neoplasms associated with a pre-identified clinical feature or outcome (Figure 4). Hence, supervised analysis will help identify gene expression patterns among basal-like breast cancers displaying response (or lack of response) to a specific labmedicine.com CE Update drug or some other biological characteristic (such as metastasis to brain). Identifying Basal-like Breast Cancer Basal-like breast cancers represent a significant fraction of the triple-negative breast cancers (ER-, PR-, HER2-). The immunohistochemical expression pattern of ER-, PR-, HER2-, cytokeratin 5/6+, and/or EGFR+ represents the signature of basal-like breast cancers. However, evaluation of breast cancers using immunohistochemistry has low specificity. Microarray analysis of basal-like breast cancers show high levels of expression of the proliferative gene cluster (including Ki-67, proliferating cell nuclear antigen, and topoisomerase II) and low expression of the ER cluster of genes (GATA binding protein 3) and the HER2+ cluster of genes (growth receptor factor bound protein 7).53,57 Hence, microarray profiling represents a more powerful technology that is better able to subclassify breast neoplasms but lacks practical utility due to batch effects and required instrumentation, in addition to currently being a more expensive method than immunohistochemistry. Nevertheless, microarray-based gene expression signatures can be mined to identify gene expression patterns specific for basal-like breast cancer that can be assayed using other (non-array based) methods. These expression patterns distinct to basal-like breast cancers will reflect the complex tumor biology associated with abnormal activation of multiple, non-linear pathways seen specifically in these cancers. Once the gene set associated with these patterns is identified, it can then be validated and henceforth used to identify basal-like breast cancers in the undiagnosed tumors. Analysis of expression of a limited number of genes is advantageous over whole genome analysis (given similar levels of sensitivity/specificity) by being less expensive, more practical, and yet still biologically based (Figure 5). The application of nonarray gene expression assays (such as real-time PCR methods) will abrogate the need for surrogate methods like immunohistochemistry for establishing breast cancer molecular subtypes. The evidence that the basal-like breast cancer subtype is heterogeneous is expanding, and there is a need to characterize the biological subsets of these breast cancers. The massively parallel nature of microarray based gene expression analysis should provide the depth of information necessary to further stratify these breast cancers. However, the established gene expression signatures used to classify basal-like breast cancers6,7,58 will not enable greater stratification. Thus, the microarray data need to be carefully mined in order to identify the gene expression patterns associated with different subsets of basal-like breast cancer. It may be useful to establish biological subgroups of basal-like breast cancer (based upon known clinical behavior) prior to a supervised analysis to identify the desired gene expression signatures. Establishment of Figure 4_Cluster analysis of gene expression data for 294 breast cancers. (A) Unsupervised cluster analysis of gene expression data from 294 breast cancers from the publicly available UNC database. Details of these 294 samples along with clinical annotation can be found at https:// genome.unc.edu/pubsup/breastGEO/. Cluster analysis was performed using the intrinsic gene list (Hu and colleagues)55 at https://genome.unc. edu/pubsup/breastTumor/data/306-genes-clid-build-161-vs-326-probes.xls. Gene expression data were generated using Agilent microarrays. Gene and sample clusters were generated from loess normalized and gene centered data with Pearson correlation and average linkage. (B) Supervised cluster analysis of gene expression data for the same 294 breast cancers and intrinsic genes, ordered by single sample predictor (SSP) subtype assignment. The color bar indicates subtype assignment by the SSP classifier with Luminal A in blue, Luminal B in light blue, HER2+ in pink, basal-like in red, and normal-like in green. In the heatmap, red indicates relatively higher expression and green indicates relatively lower expression. labmedicine.com June 2010 ■ Volume 41 Number 6 ■ LABMEDICINE 369 CE Update do not efficiently distinguish between biological subgroups of basal-like breast cancers. Hence, it will be necessary to identify molecular biomarkers for this disease to improve our ability to predict the overall severity of the disease. The ability to identify the neoplasms that will progress and spread rapidly versus those that will take a less aggressive course will improve disease management by ensuring that patients receive appropriate treatment (aggressive vs more conservative therapy). Hence, gene expression biomarkers with good predictive power will enhance and improve the stratification of patients compared to the current stratification of patients using traditional prognostic factors. Aggressive chemotherapy is not administered to all basal-like breast cancer patients with the expectation that everyone needs it or that every patient will benefit from it, but it is given to all because of our current inability to stratify patients.57 Identifying Optimal Chemotherapy Regimens Basal-like breast cancers respond differentially to standard adjuvant chemotherapy regimens. Defining the gene expression profiles specific to the various heterogeneous entities within the basal-like breast cancer class will enable Figure 5_Cluster analysis of gene expression data from 202 FFPE breast cancers. Heatmap of clinicians to optimize chemotherapy 202 FFPE samples assayed for 50 genes by qRT-PCR. Sample processing and gene expression regimens for different subsets of basaldata generation was described by Parker and colleagues.64 Cluster analysis was performed on like breast cancer. The optimal chemohousekeeper normalized, gene centered, and sample standardized expression estimates using therapy combination and schedule for Pearson correlation and average linkage clustering. The color bar indicates subtype assignan individual patient will be a function ment: Luminal A in blue, Luminal B in light blue, HER2+ in pink, basal-like in red, and normalof presence/absence of molecular targets like in green. In the heatmap, red indicates relatively higher expression and green indicates relatively lower expression. for specific chemotherapeutic agents. It is very likely that subsets of basal-like breast cancers will be identified that are resistant to anthracyclines but may gene expression signatures corresponding to biological subgroups respond to other classes of chemotherapeutic agents (like taxanes of basal-like breast cancers will enable patient stratification and or platinating agents) or vice versa. It is vital to establish not only will potentially provide insight into etiology. In addition, estabthe drug combination for chemotherapy, but also the treatment lishment of gene expression signatures associated with biological schedule. Dose-dense or accelerated chemotherapy may be indisubsets of basal-like breast cancers will enable identification of cated for patients with specific gene expression patterns, whereas molecular targets for therapy corresponding to clinical subsets, patients with a different but distinct gene expression patterns advancing personalized therapy for these cancers. may fare equally well or better with conventional chemotherapy dosing schedules, all with the same chemotherapeutic drugs. As our knowledge related to the biology of basal-like breast cancers Prognostic Parameters evolves, potential targets and drugs will be identified. The PARP Gene expression analyses can be employed to predict imporinhibitors, for example, show promising results in ongoing phase tant prognostic parameters both in terms of treatment and natuI and phase II trials.19 A paired correlative analysis of gene expresral history of disease. Identifying the gene expression patterns sion profiling on 50 patients with triple-negative breast cancer associated with shorter time to progression, lymph node invasion, in this study revealed upregulation of PARP1 gene expression hematogenous spread, and metastatic tendencies to different compared to non-triple negative breast cancer controls.19 This viscera among different subsets of stage-identical basal-like breast underscores the significance of identifying patients as potential cancers will be monumental in predicting the clinical course of responders and non-responders based on the gene expression prodisease. It is widely accepted that conventional prognostic factors filing. Knowing the likelihood of success for a specific drug and 370 LABMEDICINE ■ Volume 41 Number 6 ■ June 2010 labmedicine.com CE Update dosing-combination schedule for a particular patient subgroup at the outset of treatment will make first-line treatments more efficacious, will spare patients unnecessary treatment (and related morbidity), and will save precious time, thereby significantly increasing the chances of a successful outcome. It merits repeating that the application of personalized therapies will eliminate the unnecessary exposure and side effects associated with treatment in patients determined to be likely non-responders to a specific drug or regimen. Elimination of overtreatment will improve the quality of life for the subset of patients destined to be non-responders to certain chemotherapeutics and/or regimens but undergo chemotherapy and endure its side effects because of our current lack of stratification capability. In addition, stratification in terms of responders and non-responders to a specific treatment regimen will relieve a huge economic burden incurred by inappropriate use of drugs deemed to be ineffective in some subsets. Developing Resistance to Chemotherapeutics Changes in the biological properties of a given neoplasm over time resulting in resistance to chemotherapy are not well understood. Identifying gene expression patterns associated with neoplasms that respond to drug treatment (responders) and responders that have become chemo-resistant (following treatment) will benefit our understanding of the biology of the process but may also lead to the development of targeted chemotherapy to treat the resistant phenotype. For example, if the resistant phenotype is associated with methylation-dependent silencing of gene(s) whose expression is vital for the chemotherapeutic drugs to be effective, demethylating agents could be employed to sensitize the cancer cells, resulting in the re-establishment of chemosensitivity. Comparing the gene-expression profile in cancers before and after the development of chemotherapy resistance will define the molecular determinants of drug resistance. Microarray-Based Assays in Clinical Use in Breast Cancers Several molecular assays currently employed in the clinical assessment of breast cancer are derived from microarray-based gene expression profiling. One example of a microarray-based assay is MammaPrint (Agendia, Amsterdam, The Netherlands) or the 70-gene prognosis profile that was approved by the Food and Drug Administration (FDA) in February 2007. This assay is offered as a prognostic test for breast cancer patients that are ER+ or ER-, lymph node-negative (stage I-II), and under age 61. This test differentiates between patients at low risk or at high risk for metastasis on the basis of a score yielded by the assay.59,60 High-risk patients are recommended for more aggressive chemotherapy compared to patients with a low score. Another good example is the Oncotype Dx Recurrence Score (Genomic Health, Redwood City, CA), which is based upon a gene expression signature identified through supervised analysis of microarray data.61 This is a 21-gene prognostic and predictor assay based on a continuous variable algorithm used to predict the likelihood of relapse among patients with ER+, lymph node-negative, early stage breast cancer.62,63 The score reflects the potential for recurrence in early stage breast cancer, stratifying the patients that need to be aggressively treated versus those where conservative treatment will suffice. A number of studies have been conducted or are in progress based on the microarray-based assays in all subtypes of breast cancer, including basal-like breast cancer. For example, Rouzier and labmedicine.com colleagues conducted a study on 82 breast cancers to study the response to preoperative chemotherapy.39 Basal-like and HER2+ subtypes showed higher rates of pathological complete response when treated with doxorubicin-containing and paclitaxel-containing regimens compared to the luminal tumors.39 Gene expression analysis showed that none of the genes associated with pathological complete response in basal-like cancers were associated with pathological complete response in HER2+ tumors. These studies suggest that gene expression profiling can be clinically predictive for a variety of outcomes, such as disease progression, response to particular therapy, and likelihood of relapse. Summary in Perspective The basal-like breast cancer subtype represents 15%–20% of invasive breast cancers, is associated with poor prognosis, and contributes disproportionately to breast cancer mortality. At present, the genes responsible for the aggressive behavior of this breast cancer subtype are largely unknown, and no widely available targeted therapies exist for this form of breast cancer. The generally poor prognosis associated with basal-like breast cancer partially reflects the current inability to stratify these neoplasms relative to their clinical behavior. The detailed characterization of the transcriptome of basal-like breast cancers and careful mining of the gene expression profiles corresponding to subsets of neoplasms within the basal-like breast cancer subtype holds promise for 1) identifying new targets for therapy; 2) determining which therapies will be most effective; and 3) optimizing diagnostic and predictive testing. New investigations are needed to examine the relationship between gene expression patterns among basal-like breast cancers and their response to specific therapies. Studies are needed to differentiate the gene expression signature of the subset of basal-like cancers metastasizing to the brain vs the subset undergoing lymphatic or visceral spread. Identification of the more clinically aggressive subset tending to metastasize to the brain at the outset will enable clinicians to treat these patients aggressively to alter the clinical course of the disease, thus improving the outcome for individual patients. Additional studies are also needed to stratify the gene expression signatures predicting sensitivity or resistance to specific chemotherapeutic regimens in order to optimize and personalize chemotherapy. 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