Genomic analysis: Toward a new approach in breast cancer management

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1 Critical Reviews in Oncology/Hematology 81 (2012) Genomic analysis: Toward a new approach in breast cancer management Sebastiano Cavallaro a,b,, Sabrina Paratore a,b, Femke de Snoo c, Edvige Salomone b, Loredana Villari b, Calogero Buscarino b, Francesco Ferraù b, Giuseppe Banna b, Marco Furci b, Angela Strazzanti b, Rosario Cunsolo b, Salvatore Pezzino a,b, Santi Gangi b, Francesco Basile b a Functional Genomics Center, Institute of Neurological Sciences, Italian National Research Council, Catania, Italy b University Hospital Policlinico Vittorio Emanuele, Catania, Italy c Agendia BV, Amsterdam, The Netherlands Accepted 16 March 2011 Contents 1. Introduction DNA array technology Comparative genomic hybridization array (acgh) Gene expression array Copy number variations in breast cancer Prognostic and therapeutic information from CGH data Gene expression profiles in breast cancer Diagnostic and prognostic gene signatures Gene signatures and response to therapy Genomic assays in clinical practice MammaPrint Validation Guidelines and FDA MINDACT Cost effectiveness An example of how MammaPrint can assist in breast cancer management Comparison of prognostic gene signatures Conclusion Conflict of interest statement Reviewer Acknowledgments References Biography Abstract Breast cancer is a clinically heterogeneous and complex disease that can affect differently individuals with seemingly identical clinicopathologic parameters. This heterogeneity is strictly linked to individuals and tumors genetic variability. Currently, the development of high-throughput technologies are proving novel tools to tackle this complexity. By DNA microarray technology, genomic analysis has been used successfully for breast carcinomas stratification into molecular subgroups with relevant implications for clinical outcomes, and detection of prognostic/treatment predictive signatures. Indeed, DNA microarray has rapidly improved becoming a powerful diagnostic tool. Information derived from these assays allows clinicians to estimate the risk for distant recurrence, and predict accurately which patients are likely to Corresponding author at: Istituto di Scienze Neurologiche, CNR, Via Paolo Gaifami, 18, Catania, Italy. Tel.: ; fax: address: s.cavallaro@isn.cnr.it (S. Cavallaro) /$ see front matter 2011 Elsevier Ireland Ltd. All rights reserved. doi: /j.critrevonc

2 208 S. Cavallaro et al. / Critical Reviews in Oncology/Hematology 81 (2012) benefit from adjuvant therapy. This review will describe the state-of-the-art of genomic analysis in breast cancer and introduce the clinicians to a genomic approach to cancer management, illustrating how it can help in defying a better diagnosis, prognosis and therapeutic treatment Elsevier Ireland Ltd. All rights reserved. Keywords: Breast cancer; Copy number variation; Expression profiling; Genomic assay; Prognosis; Personalized therapy 1. Introduction Breast cancer is a clinically heterogeneous and complex disease, encompassing a wide variety of pathological entities and a range of clinical behavior. This heterogeneity is strictly linked to individuals and tumors genetic variability. It is now widely acknowledged that accumulation of genetic anomalies contributes to the acquisition of an increasingly invasive or chemoresistant tumor phenotype [1]. Traditionally, decisions related to breast cancer diagnosis and treatment are based on evaluation of clinical-pathological characteristics such as patient age, tumor size, lymph node involvement, histological grade, estrogen/progesterone receptors (ER/PR), Ki-67, P53 and epidermal growth factor receptor-type 2 (HER2) status. Despite such indicators provide valuable information about clinical course, they are often not ably to accurately predict the efficacy of a given systemic therapy, so that many patients will be over-treated or under-treated. The sequencing of the human genome together with the development of modern high-throughput technologies has offered unprecedented experimental opportunities to explore and decipher the molecular complexity of human breast cancer. Using the innovative technology of whole-genome microarray, several hundred of genomic studies over the last few years have investigated various clinical and biological aspects of the disease, including tumor classification in molecular subgroups, and prediction of prognosis as well as response to different treatments. Although this plethora of studies documents the enormous potentials of genomic signatures for breast cancer management, these are hardly moving into a clinical setting. This mainly occurs for technical and analytical issues, such as those related to the handling and standardization of molecular tools, and for the lack of prospective, randomized, clinical validation studies testing the most of developed genomic assays. Although several multi-gene assays for breast cancer patients are being commercialized, only some of them have been cleared by the US Food and Drug Administration (FDA) for clinical use or endorsed by AISCO and NCCN guidelines (i.e. MammaPrint and Oncotype DX). In addition, the requirement for expertise necessary to address complex high-dimensional data analysis precludes the implementation of multi-gene tests to few research laboratories. In addressing the issue of bridging the existing gap between biomedical researchers and clinicians, this review will describe the state-of-the-art of microarray-based analysis in breast cancer research and introduce clinicians to a genomic approach to breast cancer management, illustrating how it may help in defining a better diagnosis, prognosis and therapeutic treatment. 2. DNA array technology A DNA-microarray is an orderly arrangement of DNA spots immobilized on a glass slide or other solid support. Each spot (called a probe) has a unique DNA sequence different from the others on the array and will hybridize only to its complementary strand (cdna). Currently, plethoras of different DNA microarray platforms have been developed and are commercially available. Countless detailed reviews on the technical aspects of their analysis have recently been published and the readers should refer to them for a comprehensive discussion of these technologies [2 4]. In this review, we will focus our attention on microarray platforms more commonly used to measure genomic DNA copy number variations (comparative genomic array) and gene expression levels (gene expression array) Comparative genomic hybridization array (acgh) Currently, two main CGH array platforms are used for screening of DNA copy number: the BAC (Bacterial Artificial Chromosome) based CGH array, in which the hybridization probes consist of large-insert genomic clones, long from 100 to 200 kb; and the recently developed oligonucleotide CGH array (OaCGH) comprising single strands of oligonucleotides from 25- to 85-bases in length. BAC-based CGH array has been among the first genomic arrays to be introduced and is still routinely used to detect single copy number changes in the genome, owing to its high sensitivity. This platform provides sufficiently intense signals to define accurately the boundaries of genomic aberrations and, importantly, can be readily applied to DNA extracted from archival formalin-fixed paraffin-embedded (FFPE) tissue. OaCGH platform can have of high probe density and/or tiling, which permits achieving high resolution compared to BAC arrays, detecting smaller and more candidate copy number variation regions. This type of array is widely used for research purposes, for either genome-wide screens or fine-mapping candidate region (i.e. follow-up of a collection of potentially interesting copy number alteration regions). However, the OaCGH platform has significantly lower signal intensities when compared to those obtained with BAC arrays. Lower signal intensities for each probe lead to higher levels of experimental variation/background noise, which

3 S. Cavallaro et al. / Critical Reviews in Oncology/Hematology 81 (2012) Fig. 1. Schematic representation of comparative genomic hybridization array methodology. Briefly, genomic DNA is extracted from breast tumor and normal tissue (from a core needle or at surgery) and labeled with different fluorochromes (e.g. Cy3 and Cy5). Following dye-swap labeling (tumoral Cy3-DNA vs. normal Cy5-DNA and tumoral Cy5-DNA vs. normal Cy3-DNA), labeled mixtures are co-hybridized into a microarray spotted with specific DNA probe sets. At the end of the hybridization, a laser scanner collects the image produced by fluorochromes. Tumoral and normal samples competitively bind to the spots and the resulting fluorescence intensity ratios are reflected by their relative quantities. Specialized software captures the images and converts the fluorescence intensity data to a linear red-to-green ratio profile that correlates with the hybridization intensity, which mainly depends on the extent and size of DNA tumor changes. The processed data are used to compare the clinical data available with the gene copy number changes observed. In the upper part of the figure (on the right) a diagram of chromosome 1 is reported. In the diagram, each dot represents a single clone spotted on the array and vertical lines adjacent to the chromosome indicate the thresholds for losses (red) and gain (green). render the identification of low-level gains and losses more difficult and the use of degraded DNA (such as that extracted from FFPE samples) challenging. In a typical CGH array experiment (independent of platform choice), test (tumor) and normal reference genomic DNA are differentially labeled and simultaneously hybridized in a dye-swap (Cy3 sample vs. Cy5 reference and Cy5 sample vs. Cy3 reference) on the array (see Fig. 1). The basic assumption of a CGH experiment is that the fluorescence intensity ratio test/reference (log 2 ratios) reveled by a scanner is proportional to the ratio of the concentrations of sequences in the two samples and, thereby, allows one to accurately quantify genomic DNA copy number changes resulting from amplifications

4 210 S. Cavallaro et al. / Critical Reviews in Oncology/Hematology 81 (2012) or deletions. Once an array CGH profile has been established, a wide range of computational methods may be used to estimate copy number variations. These analytical software packages use specific algorithms to decrease the experimental variation for regions with similar copy numbers such as adaptive weighted smoothing [5], maximum likelihood models, hidden Markov models [6], or Lowess methods [7] and Gaussian smoothing [8] before confidently defining genomic changes. Essentially, these methods analyze acgh data by organizing a user-defined sequence of adjacent signals into regions of constant copy number known as segments, which are then classified as a gain, a loss or no change depending on their signal intensities Gene expression array As illustrated in Fig. 2, when a microarray is hybridized to fluorescence-tagged complementary DNA or RNA derived from messenger or total RNA, each spot is a target for the mrna encoded by an individual gene. As it is previously described for a CGH array, DNA probes contain either DNA oligomers or longer DNA sequences designed to be complementary to a particular mrna of interest. The choice of using oligomers or longer cdna sequences defines two different microarray technologies; oligonucleotide and cdna microarrays, respectively. Oligonucleotide microarrays have become more popular because of technical advantages that include constant DNA concentrations across all spots, biophysically optimized sequences, reduction of secondary structures, lack of repetitive sequences and a fixed range for both T m and length. These properties allow for more uniform, stable and predictable hybridization conditions. A laser can then be used to excite the bound cdna or crna and fluorescence intensities from each spot collected by a scanner. The intensity of the fluorescence at each array element is proportional to the expression levels of mrna. It should be emphasized that expression microarrays measure steady-state levels, reflecting the equilibrium between mrna synthesis and degradation. In addition, when the immobilized cdna sequence is complementary to more than one mrna, such as in the case of alternative splice variants, the fluorescence signal represents a single consensus value for all transcripts. What makes microarray technology most remarkable for genome-wide expression analysis is the number of DNA probes that are possible to place on a single microarray. DNA microarray technology permits the quantitative and simultaneous monitoring of thousands of mrnas at different time points, under different conditions, from a variety of tissues and organisms. The knowledge of when and under what conditions a gene or a set of genes is expressed often provides important clues as to their biological role and function. The challenge is to extract relevant information from this large amount of data. A variety of analytical approaches is available to interpret microarray data (see Fig. 2). The simplest analysis involves two samples, representing a test condition and a control condition and yields a list of paired expression values, one pair for each gene. As illustrated in Fig. 2, these pairs can be represented graphically by a scatter plot, with the values of sample one plotted on the x-axis and the values of sample two plotted on the y-axis. The resulting correlation plot provides a visual image of the relationship between the two expression profiles. In this plot, mrna with similar expression levels in the two samples should have points on the identity line (y = x) and mrna that are expressed differentially lie at some distance from this line. However, the problem is that microarrays do not measure expression levels directly. Instead, they measure intensity levels, as represented by the amount of phosphorescent dye that was detected by a scanner. Many other factors, such as the overall mrna concentration of the two samples, the saturation effects in the hybridization or the quenching effect of the phosphorescent dyes can affect these intensity values. In order to correct for these differences in intensity levels, the raw data can be normalized by dividing the signal for each gene by the median gene signal or by using a normalization-constant derived from housekeeping or spiked control genes. Once normalized, a series of restrictions (or filters) can be applied to the data obtained. These restrictions include factors such as quality control, expression level constraints, sample-to-sample fold comparison and statistical group comparisons. The simplest way to identify interesting genes in DNA-microarray experiments is to search for those that are consistently either up- or down-regulated. To this end, fold-difference thresholds and/or statistical analysis of gene-expression levels can be applied. Relative differences in expression levels (fold changes) have been typically employed in group comparisons of gene expression and have great intuitive appeal for biologists. The choice of thresholds, however, is somewhat arbitrary and inherently subject to high error rates as information on sample variance is not exploited. If array experiments are replicated to an extent that permits direct estimates of the variance of each individual transcript, parametric or non-parametric statistics can be applied. In these cases, however, a high number of false positive results are expected by chance when one relies on the nominal p-value. As an example, when testing 40,000 transcripts one would expect to misidentify about 2000 mrnas as significant (p < 0.05), even when there is no real difference in gene expression. Multiple testing corrections (e.g. Bonferroni corrections, Benjamini and Hochberg false discovery rate) are needed to adjust the individual p-value to account for this effect. More complex computational methods can be used to monitor gene expression profiles and identify common patterns associated with an experimental condition. To this end, cluster analysis is a commonly used method for investigating and interpreting gene expression datasets. Finding co-regulated genes by grouping together those that have similar expression profiles may allow discovery of genes differentially expressed in particular situations that could serve for example, as potential drug targets.

5 S. Cavallaro et al. / Critical Reviews in Oncology/Hematology 81 (2012) Fig. 2. Schematic description of gene expression array methodology. RNA samples extracted from breast tumor and normal tissue (from a core needle or at surgery) can be reverse transcribed, differentially labeled and simultaneously co-hybridized to microarray. Intensity values from each spot are calculated and then analyzed by specific software (lower part of the figure). Data can be represented graphically by a scatter plot, with the values of sample one plotted on the x-axis and the values of sample two plotted on the y-axis. Data obtained under different conditions (e.g. different time points) can be analyzed with different cluster algorithms. Most cluster analysis techniques are hierarchical, the resultant classification has an increasing number of nested classes and the result resembles a phylogenetic classification. Non-hierarchical clustering techniques also exist, such as k-means clustering, which simply partition objects into different clusters without trying to specify the relationship between individual elements.

6 212 S. Cavallaro et al. / Critical Reviews in Oncology/Hematology 81 (2012) Copy number variations in breast cancer Breast cancer grows and spreads because of the accumulation of genomic changes that provide a tumor cell growth advantage [9]. As previously described, microarray-based comparative genomic hybridization (CGH) array represents a powerful tool to investigate these anomalies in a genomewide manner. In several breast cancer cases, CGH analysis has revealed differences in copy numbers of parts or entire chromosomes, or additional focal aberrations, including gene amplifications and deletions of restricted chromosomal regions. Indeed, although several alterations of chromosomal regions are widespread in the majority of breast cancers, distinct CGH profiles have been linked with different tumor histopathological types [10 20]. A genomic approach has already been used successfully to stratify breast carcinomas into molecular subgroups with relevant implications for clinical outcome. Table 1 summarizes the molecular genomic changes related to different tumor subgroups identified by CGH array technology. Recurrent unbalanced changes (i.e. gains of 1q, 8q, 16p, 17q, 20q, losses of 16q and 17p, and DNA amplifications in the regions of 8q12 24, 11q11 13, 17q12 21, 17q22 24, and 20q13-ter), have been observed in invasive carcinomas suggesting that there might be putative common or similar pathways for tumorigenesis [21 32]. However, CGH analyses of invasive ductal (IDCs) and lobular breast carcinomas (ILCs) have shown specific chromosomal abnormalities including gains of 4p and 5p and losses of 16q, 16q21 q23, 17q, 18q12 q21, 22q for ILC and gains of 8q and 20q for IDC [10,11,17,33,34], and an overall lower number of genetic alterations in ILCs when compared to IDCs [10,11,17,33]. Differently, when compared to low and intermediate grade ER-positive IDC, ILC showed increased genetic instability (i.e. an average 13.5% vs. 9.7% was either lost or gained) and a higher frequency of 1q gain, 11q loss and 1q32, 8p23, 11q13 14 amplifications [18]. Recently, CGH profiles from primary tumors and blood samples of IDC patients have also been matched. Seven regions of copy number gains (5p15.33, 8q24.3, 16p13.3, 17q11.2, 17q25.3, 20q13.33, and 22q13.33) have been found in more than 50% of specimens from both the primary tumor and blood. Indeed, copy number alterations identified in primary tumors were slightly in agreement with those observed in blood samples and further research is needed to confirm the use of the peripheral blood to confirm the identity of putative genomic alterations in primary breast tumors [35]. Although CGH data on breast carcinomas are dominated by common tumor histopathologic types (i.e. ductal and lobular carcinomas), the genomic profiles of rarer histological entities, such as tubular/tubulo-lobular [36,37], medullary [13,38], micropapillary [19,39] or myoepithelial/basal carcinoma [14,15,40,41], have also been reported. Additionally, recurrent chromosomal aberrations including gains at 8q and 1q and losses at 22q have been studied in a breast neoplasm, first designated as juvenile carcinoma, now classified Table 1 Genomic variations related to different breast cancer histopathological groups identified by CGH array technology. Histopathological types Histopathological subtypes Genomic alterations Reference Loss Gain ER positive, ER negative 16q, 17p 1q, 8q, 8q12 24, 11q11 13, 16p, 17q, 17q12 21, 17q22 24, 20q, 20q13-ter [21 28,30 32] Grade I 16q 1q [47,48] Grade II, Grade III 16q 8q, 17q, 20q [47,48] ER negative 5q11 35, 12q p21 25, 7p12 [18,42] IDC, ILC TP53 mutant 5q14 23 [18,42] Basal-like tumors 3q12, 4p15 32, 4q31 35, 5q11 31, 14q q12 41, 6p12 25, 7q22 36, 10p12 15, 17q25, 21q22 [42,43] luminal-a 1q12 41, 16p12 13 [42] luminal-b 7p22, 8q11 24, 19q13, 20q13 [42,43] ERBB2 subtype 17q12 21 [38,43] IDC ER positive, ER negative 5p15.33, 8q,8q24.3, 16p13.3, 17q11.2, 17q25.3, 20q, 20q13.33, 22q13.33 [10,11,17,33 35] ILC ER positive, ER negative 11q, 16q, 16q21 q23, 17q, 18q12 q21, 22q 1q, 1q32, 4p, 5p, 8p23, 11q13 14 [10,11,17,18,33,34] DCIS ER positive, ER negative 8p, 11q, 13q, 14q, 16q, 17p 1q, 5p,6p, 8q, 10q, 11q13, 17q, whole chromosome 17 [16,65,66,68] ADH 16q, 17p 1q, 6p, 10q, 11q13, 17q [16,68] ALH, LCIS 8p, 16p, 16q, 17p, 17q, 22q 1q, 6q [34,69,70] SCA ER negative/pr negative 3p, 4q, 10q, 22q, whole chromosomes 5 and 17 1q, 7q, 8q, 20q, whole chromosomes 7 and 12 [13] BRCA1 tumors 4p, 4q, 5q 3q, 3q27.1 q27.3, 8q [53 56] BRCA2 tumors 8p, 13q, 11q 8q, 17q, 17q23.3 q24.2, 20q, 20q13 [53 56]

7 S. Cavallaro et al. / Critical Reviews in Oncology/Hematology 81 (2012) as secretory carcinoma (SCA). Less frequent gains at 20q and 7q, of whole chromosomes 7 and 12, such as losses at 3p, 4q, 10q, of whole chromosomes 5 and 17 were also observed [12]. Distinct spectra of DNA copy number alterations also underlie different subtypes of breast cancer as recently defined by expression-profiling [42,43]. Higher numbers of gains/losses are associated with the basal-like tumor subtype (loss at 3q12, 4p15 32, 4q31 35, 5q11 31, and 14q22 23, and gain at 1q12 41, 6p12 25, 7q22 36, 10p12 15, 17q25, and 21q22), while high-level DNA amplifications are more frequent in luminal-b subtypes (including 7p22, 8q11 24, 19q13, and 20q13) [42,43]. Luminal-A tumors are often correlated with gain at 1q12 41 and 16p12 13 [42]. Not surprisingly, ERBB2 subtype tumors exhibit more frequent amplification at 17q12 21, a region that contains the ERBB2 gene [38,43,44]. The observation that DNA copy number alterations are associated with specific subtypes may suggest that several genomic instability mechanisms are involved in subtype pathogenesis. Although CGH studies suggest a degree of variation in the pattern of genetic aberrations among different histological subtypes, this association is not as strong as it happens to be with other clinical pathological parameters [19,20,40]. For example, the pattern of aberrations at some chromosomal loci differs significantly in invasive carcinoma stratified by grade [20,34,45,46]. Grade I (well-differentiated) carcinomas frequently demonstrated gain of 1q and loss of 16q, as well as an overall low incidence of alterations and amplifications. Higher-grade (grade II and III, intermediate and poorly differentiated) carcinomas exhibit more genetic alterations, more amplifications (8q, 17q, 20q), and a significant reduction of 16q loss as compared to grade I carcinomas [45,47,48]. Furthermore, the mechanism of 16q deletion in high-grade tumors appears to be different from that occurring in lowgrade IDC and ILC [49]. Other specific clinical pathological features, such as estrogen receptor (ER) and TP53 mutation status, have been associated with several genomic alterations. ER-negative tumors exhibit loss at 5q11 35 and 12q14 23, and gain at 6p21 25 and 7p12, while TP53 mutant tumors are more frequently correlated with loss at 5q14 23 [18,42]. With regards to hereditary breast cancer, CGH patterns of genomic aberrations have been successfully used to build a molecular classifier that allows differentiation of BRCA1/2- mutated vs. sporadic tumors [50 52]. CGH array studies on BRCA1 2 and sporadic breast cancers have shown that BRCA1 tumors have a higher frequency of copy number alterations as compared with sporadic cancers. The genetic changes more commonly found in BRCA1 tumors are gains of 8q and 3q as well as losses of 4p, 4q and 5q. BRCA2 tumors present gains of 8q, 17q and 20q and losses of 8p, 13q and 11q [53,54]. In addition, amplicons at 3q27.1 q27.3 in BRCA1 tumors and at 17q23.3 q24.2 and 20q13 in BRCA2 tumors have been described [55,56]. Based on CGH array data, a recent study identifies BRCA1-specific genomic aberrations associated with ER, PR, HER2/neu and p53 status and builds a class-predictor for BRCA1 subtypes [51]. CGH has also advanced our understanding of presumptive precursor lesions including atypical hyperplasia [57 60] and carcinoma in situ [14,61] and, in particular, have provided information about the transition from a premalignant state to invasive carcinoma [20,62 64]. The most frequent morphology both in premalignant and malignant disease is ductal. Recurrent genomic changes in low-grade ductal carcinomas in situ (DCIS) include the loss of 8p, 11q, 16q, and 17p, together with the gain of 1q. High-grade DCIS display chromosomal alterations involving the gain of 1q, 5p, 8q, and whole chromosome 17, as well as the loss of 8p, 11q, 13q, and 14q [65,66]. Interestingly, intermediategrade DCIS appear to be a heterogeneous group, inclusive of both genotypes [59,63]. Molecular analyses (i.e. Loss of Heterozigosity (LOH) have highlighted genetic similarities between atypical ductal hyperplasia (ADH) and low-grade DCIS. According to these findings, some CGH studies have identified common aberrations (loss at 16q and 17p and gain at 1q, 6p, 10q, 11q13, and 17q) in paired ADH and low-grade DCIS [16,67,68], underlining that their histo-pathological similitude is mirrored at the genetic level. However, since morphological and prognostic differences are present in ADH and DCIS, these molecular data might provide support for ADH being a precursor of low-grade DCIS. Although the second most frequent breast tumor morphology is lobular, only a small number of genomic studies have been performed in atypical lobular hyperplasia (ALH) and lobular carcinoma in situ (LCIS) [34,61,69 71]. All available data demonstrated that the more prevalent genomic variations were the losses in 8p, 16p, 16q, 17p, 17q, 22q and gains in 1q and 6q [34,69,70]. Recently, amplification at 5p12 13 locus that harbors for prolactin receptor gene (PRLr) was identified in LCIS by acgh, suggesting that this receptor may be a molecular target with a relevant role in the pathogenesis and progression of lobular neoplasia [71] Prognostic and therapeutic information from CGH data As summarized in Table 2, several attempts to correlate CGH data with patient prognosis have been made [12,19,24,40,72 85]. A pattern of chromosomal aberrations has been associated with disease recurrence and/or survival in node-negative women [24,72,74,76,84,86]. Interestingly, patients with recurrence or poor prognosis exhibited significantly more changes, specifically losses, than the no recurrence or good prognosis patients. Gain or amplification at 3q, 8q, 17q12, and 20q12 13, as well as loss at 11p, 17p13 (p53 locus), and 18q [24,72,74,76,78,87] have significantly been associated with poor outcome, whereas loss of 16q, sometimes together with gain of 16p, correlated with good prognosis [77,81]. Genome-wide analyses of patients with high-risk, stages II III breast cancer (lymph node positive) revealed that the losses of 6q and 16q, and the gain

8 214 S. Cavallaro et al. / Critical Reviews in Oncology/Hematology 81 (2012) Table 2 Genomic variations identified by CGH array correlated to breast cancer prognosis and therapeutic treatment. Samples size [Reference] Histopathological subtypes Genomic alterations Correlation to prognosis or treatment regiment Loss Gain Prognostic outcome Therapeutic outcome 48 [25]; 53[77]; 5[72]; 20[74]; 76[78]; 21[87] Lymph-node negative 11p, 7p13, 18q 3q, 8q, 17q12, 20q12 13 Poor prognosis 34 [73]; 40[77]; 70[81]; 88[80]; 43[40]; 145 [82]; 31[84] Lymph-node positive 8p, 17p13, 18p 1q, 8p11 12, 11q13 14, 17q, 20q Poor prognosis 40 [77]; 70[81] Lymph-node negative 16q 16p Good prognosis 88 [80]; 43[40]; 145 [82]; 31[84] Lymph-node positive 6q, 11q21 q25, 16q 17q Good prognosis 185 [86] Lymph-node negative 11q FAC/FEC sensitive 24 [82]; 26[88,66]; 185 [86]; 31[84]; 29[85] ER-positive 1p36, 11p15 8q21 FAC/Tamoxifen resistance 103 [89] 3p25 and 3p27 FAC/Taxane sensitive 103 [89] 11q q22.3 FAC/Taxane resistance of 17q are strictly correlated with a reduced risk of relapse [40,73,80,82,84]. These findings may be related to higher genomic instability and the possibility that the tumor might develop sets of genomic alterations that allow them to resist treatment strategies. Gene copy number anomalies that might determine breast cancer sensibility to specific therapies are also being defined. A recent study suggests that patients with lymph-node negative breast cancer and a 11q deletion may benefit from anthracycline-based chemotherapy, despite the presence of other indicators of good prognosis [86]. Different patterns of genomic alterations have been observed in ER-positive breast cancer patients of a recurrence group and a nonrecurrence group after surgery and tamoxifen treatment [66,82,84 86,88]. Chromosomal aberrations including the loss of 11p15 and 1p36 and the gain of 8q21 have significantly been associated with distant recurrence of the disease within 5 years of diagnosis and might be considered candidate markers for tumor aggressiveness or tamoxifen resistance in ER-positive breast cancers [88]. Furthermore, several DNA regions with significant differences in copy number variations have been identified comparing cases with complete response to taxane treatment (highly FAC/paclitaxel regimens sensitive) to those less sensitive [89]. In particular 11q22, encoding for a series of caspases that play an important role in executing apoptosis, was gained in 19% of sensitive tumors and lost in 29% of refractory tumors. Regions in 3p25 and 3p27, that code for components of the Rho/Ras signaling pathway, were gained in 47% of highly taxane-sensitive tumors suggesting their mediation in paclitaxel-induced apoptosis processes. Although these studies require further validation and refinement in prospective clinical trials, they reflect the importance of CGH strategies in defining biomarkers that might affect medical decisions in the near future. 4. Gene expression profiles in breast cancer 4.1. Diagnostic and prognostic gene signatures Breast cancer is phenotypically diverse in prognosis and responsiveness to treatment. One of the main observations arising from gene expression studies of breast cancer is that this diversity is reflected in the intrinsic heterogeneity of breast cancer gene expression profiles. In a seminal study, Perou et al. [44] distinguished different subtypes of breast cancer based on their gene expression profiles. Using complementary DNA (cdna) microarrays to characterize gene expression patterns in a set of 65 surgical specimens of human breast tumors, this study demonstrated that gene expression patterns in pairs of tumor samples from the same individual were almost always more similar to each other than they were to any other sample. Therefore, they defined a set of genes whose variation was significantly greater between samples from different tumors than between samples from the same tumor, both before

9 S. Cavallaro et al. / Critical Reviews in Oncology/Hematology 81 (2012) and after treatment. This set of 496 intrinsic genes clustered the breast tumor profiles into three different groups designated as estrogen receptor (ER)-positive/luminal-like which, on basis of the ESR1-related genes and proliferative genes expression, was further subdivided into two groups, luminal A and B; basal-like which mostly corresponded to ER-negative, progesterone-receptor (PR)-negative, and HER2-negative tumors (hence, triple-negative tumors); HER2-positive which showed amplification and high expression of the ERBB2 gene and several other genes of the ERBB2 amplicon; and normal-like breast [44,90 92]. These subgroups correlated reasonably well with clinical characterization on the basis of ER and HER2 status, as well as with proliferation markers or histologic grade. Numerous studies have confirmed the existence of these breast cancer molecular subtypes in independent datasets, however the exact number of intrinsic subtypes remains uncertain. A meta-analysis of 599 microarrays from five separate genomic studies of breast cancer found support for some subtypes, defined by their ESR1/ERBB2 status, that partially matched the intrinsic subtypes defined by the Sorlie classification [93]. The additional value of molecular classification is in its ability to predict accurately prognosis [92,94,95]. Survival analyses have showed different outcomes for each subtype; luminal A tumors had the longest survival times, the basallike and HER2-positive subtypes had the shortest survival times, and the luminal B tumors an intermediate survival time [90,91]. While the above studies used an unsupervised correlative approach to identify clinically relevant breast cancer subtypes, supervised classification methods have been used to isolate sets of genes associated with defined clinical parameters. These strategies for prognostic markers discovery on a genome-wide scale involved two conceptually different approaches: (a) the top-down where a predictor gene signature was identified by empirical association with disease outcome, without any a priori biological assumption; and (b) the hypothesis-driven or bottom-up approach where gene expression signature was derived from the analysis of in vitro or in vivo experiments testing a predefined hypothesis or a biological process, and subsequently correlated these findings to survival [96]. In the last decade, we have witnessed the development of a multitude of prognostic gene signatures, most of which, when validated, were subjected to retrospective studies on archival material that do not provide the clinical evidence gained from prospective, randomized clinical trials [97,98]. Further in this review, we have focused on the first breast cancer prognostic signature developed, the 70-gene signature [99]. The readers are referred to excellent in-depth reviews on other commercially available tests [96, ] 4.2. Gene signatures and response to therapy As showed in Table 3, a number of genomic profiles have been focused on the need not only to identify Table 3 Neoadjuvant studies conducted to evaluate the predictive value of gene signatures. Samples size Histopathological subtypes Treatment regiment Class predictor signature Reference 31 IDC, ILC; stages II III Doxorubicin 187 gene signature [104] [105] 71 genes (AC); 17 genes (AD); 30 genes (both chemotherapies) 46 IDC, ILC Doxorubicin/cyclophosphamide (AC); doxorubicin/docetaxel (AD) 24; 44 IDC; stages II III Docetaxel 92 gene signature; 85 gene signature [106,107] 81 Inflammatory (IBC) and noninflammatory breast Anthracycline 85-Gene signature [108] cancer (NIBC) 46 ER positive Tamoxifen 44-Gene signature [109] [110,111,112] 74-Gene signature; 31-probe set predictor; 61 gene predictor for basal-like tumors 24; 33; 82 IDC, ILC; stages I III Paclitaxel and fluorouracil + doxorubicin + cyclophosphamide (T/FAC) 97 genes grade index (GGI) [113,114] 64; 229 ER-positive, histologic grade 1 3 Tamoxifen; paclitaxel and fluorouracil + doxorubicin + cyclophosphamide (T/FAC) Tamoxifen 822 estrogen-regulated genes [115] ER+ MCF-7 breast cancer cell line with 17beta-estradiol

10 216 S. Cavallaro et al. / Critical Reviews in Oncology/Hematology 81 (2012) prognosis patients but also their predicted response to a given therapy (in preoperative setting). These studies analyzed core biopsies or fine needle aspirates (FNAs) from tumors before onset of treatment, and/or core biopsies, FNAs or surgically removed tumors after completion of treatment, identifying genomic profiles associated with tumors sensitive vs. resistant to specific chemotherapeutic regimens, such as e.g. docetaxel, adriamycin/cyclophosphamide, paclitaxel/fluorouracil/doxorubicin/cyclophosphamide, and epirubicin/cyclophosphamide/paclitaxel in the neoadjuvant setting [ ]. All these studies showed promising results in identifying in preoperative setting patients who are sensitive to chemotherapeutic agents. Interestingly, an underlying feature has emerged that patients with more aggressive tumors appear to respond more favorably to chemotherapy. In particular, Rouzier et al. have shown that the rate of pathological complete response (pcr) in a neoadjuvant setting correlates with the intrinsic molecular subtypes previously described; basal-like and ERBB2+ subtypes seemed to be more sensitive to paclitaxel/doxorubicin containing preoperative chemotherapy than the luminal and normal-like cancers [112]. However, whether genes selected in these studies will turn out to be useful not only in the neoadjuvant setting but also for predicting long-term therapeutic response, or whether these gene signatures could be informative for patients in other cohorts at different stages of the disease, is still not known. Several additional studies to identify genomic markers of endocrine sensitivity have also been reported. A genomic study in ER-positive advanced breast tumors from patients tamoxifen responders/non-responders, led to identification of a gene signature as potential predictor of response to endocrine therapy [109]. A gene signature associated to histological grade (genome grade index, GGI) showed its ability to predict response to neoadjuvant treatment in ERnegative and ER-positive patients, and provided modest but significant additional information concerning response to chemotherapy compared to clinical variables [113,114]. By challenging ER+ MCF-7 breast cancer cell lines with 17 estradiol, estrogen-regulated genes were identified and their modest association with survival after tamoxifen treatment were demonstrated [115]. Furthermore, in a recent study patterns of deregulated pathways underlined the development of the oncogenic phenotypes and reflected clinical outcomes of specific cancers. The differential expression of those oncogenic pathways was reported as a predictor of response to different therapeutic agents that target specific components of pathways [116]. Using publicly available drug sensitivity data from in vitro experiments, multiple classifiers of response to pre-operative multidrug regimens have also been reported [117]. Although the results of these studies widely demonstrate that predictors of response to anticancer agents may be developed, they are strongly heterogeneous, with respect to endpoints, treatments regimens and patient population, and many suffer from statistical problems. Much larger studies are therefore needed to independently validate and improve the genomic tests available. 5. Genomic assays in clinical practice Currently, standard methods for prognostic stratification including Adjuvant! Online, the Nottingham Prognostic Index, and the American Joint Committee on Cancer staging system, which form the basis of treatment guidelines issued by the National Institutes of Health (NIH) Consensus Statement on Adjuvant Therapy in Breast Cancer and the St. Gallen Consensus Statement integrate clinico-pathologic features into multivariate prediction models. These tools allow to clinicians to estimate the relative risk of recurrence and death, and approximate the potential benefits of chemotherapy for a patients groups with given disease characteristics. However, they fail to resolve the fundamental question of whether an individual patient, rather than a group, will benefit from adjuvant therapy. Indeed, patients defined with poor prognosis by traditional clinicopathological parameters may remain disease free without adjuvant therapy. Likewise, benefit from systemic adjuvant therapy for breast cancer patients with lymph nodes negative is not uniform, because some patients relapse despite therapy, while others may be already cured by locoregional treatment. Actually, prognostic genomic assays that might assist clinicians in minimizing overtreatment of low risk patient are commercially available (Table 4). Among these, MammaPrint (Agendia, Amsterdam, The Netherlands) is daily the only genomic test for breast cancer management that has been approved by the Food and Drug Administration (FDA) MammaPrint A genetic profile has been designed to complement standard clinico-pathologic risk parameters with a personalized risk estimate of the likelihood of developing metastases and benefiting from adjuvant chemotherapy. The test was the world s first gene expression profile designed to predict the clinical outcome of breast cancer patients. Genes were used in the profile if they could significantly distinguish between patients who developed distant metastases within 5 years from surgery and from those who did not. No assumptions were made and the 70 most significant genes ended up in the profile, known as MammaPrint. The profile is specifically designed to recognize the patients who develop distant metastasis in the first 5 years following initial diagnosis of the disease (Fig. 3). These early recurrences are the events most affected by the cytotoxic effects of chemotherapy [118]. Thus, MammaPrint predicts those recurrences for which adjuvant chemotherapy exerts its beneficial effect Validation MammaPrint has been validated in numerous studies consisting of different patient cohorts. The first validation

11 S. Cavallaro et al. / Critical Reviews in Oncology/Hematology 81 (2012) Fig. 3. Illustration of microarray-based 70 gene profile analysis. To assess global gene expression, messenger RNA (mrna) is extracted from the fresh tumor sample (a biopsy punch of 3 mm, as shown in the left lower part of the figure) and labeled with a fluorescent dye. The labeled mrna, together with labeled mrna from a reference sample, is hybridized on a microarray. A specific algorithm is used to compare gene activity to that of a specific expression signature [99] that is strongly prognostic for the development of distant metastasis in lymph node negative patients, thereby producing a score that determines whether the patient is deemed at Low Risk or High Risk for metastasis (upper part of the figure). The Kaplan-Meier curves reported in the right lower part of the figure show how the 70 genes expression profile analyzed in the test predict 10-year disease-free survival more accurately than the St. Gallen criteria [119]. included both lymph node negative- and positive patients. There was overlap of 61 patients in the development and the first validation study. However, when the overlapping patients were excluded, the profile could still distinguish between low and high risk patients in a more sensitive way than using accepted clinical guidelines, such as St Gallen or Adjuvant! Online. The results were clinically relevant to such a degree that the pan-european TRANSBIG consortium initiated a clinical study with five participating centers [119,120]. MammaPrint classified the 63% of the 302 early stage breast cancer patients accessioned as High Risk, with a 29% risk of developing distant metastasis at 10 years. Among the patients classified as Low Risk by MammaPrint (37%), the 90% remained free of distant metastasis [120]. Both validation studies indicated MammaPrint to be an independent predictor that added power to standard clinico-pathologic parameters. Additional validation studies have been published, including one in patients with 1 3 positive lymph nodes, which led to changing the eligibility criteria of the MINDACT study to include LN positive (1 3) patients [121,122]. In addition, the validation study on patients aged years led to an extension of the FDA clearance for MammaPrint to be used for patients over 55 years of age [123]. Several studies have investigated the chemotherapy benefit in the adjuvant setting for High Risk patients compared with Low Risk patients, in whom no significant benefit of chemotherapy is expected. In the first neo-adjuvant study for MammaPrint, none of the patients classified as Low Risk experienced a pathological Complete Response (pcr), a measure of chemotherapy sensitivity, whereas 20% of High Risk patients had a pcr to treatment which included either doxorubicin/cyclophosphamide (AC), doxorubicindocetaxel or docetaxel-capecitabine ( 6 cycles) if HER2 negative [124]. For HER2 positive patients, dose dense AC plus paclitaxel/trastuzumab/carboplatin (PTC) 6 cycles was the standard of care [124]. A second neo-adjuvant study was recently presented at the 2010 ASCO meeting, in which a significant association between the classification of MammaPrint as a continuous variable and the pathological response was seen [125]. A pooled meta-analysis of patients from previously reported studies contained 541 patients with 0 3 involved lymph nodes who received endocrine therapy alone or endocrine therapy plus chemotherapy as adjuvant therapy [126]. This group of patients included T1 3 and N0 1 tumors from patients of all ages with a follow-up of at least 5 years. In the MammaPrint High Risk group, a significant (HR = 0.35, p < 0.01) absolute benefit for the combined treatment of 12% with a relative benefit of 50% was observed. These results remained robust in a multivariate analysis

12 218 S. Cavallaro et al. / Critical Reviews in Oncology/Hematology 81 (2012) Table 4 Commercially available multigene prognostic classifiers of breast cancer. Parameter MammaPrint Oncotype DX Theros MapQuant Dx Bioclassifier MammoStrat Assay 70-Gene signature 21-Gene recurrence score HOXB13:IL17BR ratio Genomic grade index PAM50 Five antibody test Manufacturer Agendia Genomic Heath Biotheranostics Ipsogen ARUP Applied genomics Platform Microarray Quantitative RT-PCR Quantitative RT-PCR Microarray Quantitative RT-PCR IHC Tissue type Fresh/frozen FFPE FFPE Fresh/frozen FFPE FFPE ER positive LN negative ER positive/negative LN negative ER positive LN negative ER positive All cases on invasive breast cancer Indication Aged <61 years, Stage I II LN negative size <5 cm Prospective trial MINDACT ongoing TAILORX ongoing FDA status FDA cleared, safe and effective Not cleared Not cleared Not cleared Not cleared Not cleared Availability Europe and United States Europe and United States United States Europe United States Europe and United States ARUP, Associates in Regional and University Pathologists; ER, estrogen receptor; FDA, US Food and Drug Administration; FFPE, formalin-fixed paraffin-embedded; IHC, immunohistochemistry; LN, lymph node; RT-PCR, real time polymerase chain reaction. Information retrieved from: (HR = 0.38, p = 0.04). Conversely, there was no significant benefit for hormonal therapy plus chemotherapy vs. hormonal therapy alone in the Low Risk classified patients with an anticipated risk sufficiently low to forego chemotherapy [126] Guidelines and FDA Several guidelines (i.e. St Gallen 2009, NCCN, ASCO, Dutch BOOG) for the diagnosis and treatment of early breast cancer patients have indicated that the use of multi-gene expression arrays can be useful for selected patients. This is particularly true for women for whom clinicopathological criteria are indeterminate as to whether they would benefit from adjuvant chemotherapy. The St Gallen guidelines state that while cytotoxic chemotherapy improves outcome on average among patients with endocrine-responsive disease receiving endocrine therapy, subgroups can be defined by conventional pathology and by multigene analyses in which little or no additional benefit accrues from chemotherapy and multigene assays are widely proposed to add to the prognostic information available from classical pathological markers and in some circumstances have been shown to identify groups which do or do not benefit from the addition of chemotherapy to endocrine adjuvant therapy [127]. It has been suggested that regulation of these assays should fall in a category designated In Vitro Diagnostic Multivariate Index Assay (IVDMIA) and MammaPrint is the first and currently only breast cancer profiling test cleared in this category. While FDA is moving forward to start official oversight of laboratory developed tests (LDT), MammaPrint has obtained additional clearances including the clinical indication for use in both pre- and post-menopausal women (December 2009) ( MINDACT There is agreement that the level of evidence on multigene expression arrays is currently insufficient for full applicability in all early stage breast cancer patients. The highest possible clinical evidence is generally thought to derive from a large, prospective clinical trial. The MIN- DACT trial (Microarray in Node-negative Disease may Avoid ChemoTherapy) is being conducted by the European TRANSBIG Breast International Group and will measure the clinical utility of the MammaPrint in comparison with the Adjuvant! Online [128]. The MINDACT trial will not only be able to show the extent of chemotherapy benefit in the patient groups in which MammaPrint and the clinicopathological criteria are discordant but will also contain a wealth of information expected to help develop several response profiles by assessing full genome expression data for all 6000 patients randomized to different treatment regimen.