Importance of Genomic Profiling: Applications for Breast Cancer Diagnosis, Prognosis and Prediction of Response

Medical Oncology, Hospital General Universitario Gregorio Marañón, Madrid, Spain

*Corresponding Author:

Yolanda Jerez Gilarranz
Medical Oncology
Hospital General Universitario Gregorio Marañón
Madrid, Spain
E-mail: itacayoli@hotmail.com

Received date: November 17, 2011; Accepted date: April 20, 2012; Published date: April 23, 2012

Citation: Gilarranz YJ (2012) Importance of Genomic Profiling: Applications for Breast Cancer Diagnosis, Prognosis and Prediction of Response. J Clin Exp Pathol S1:003. doi:10.4172/2161-0681.S1-003

Copyright: © 2012 Gilarranz YJ. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Breast cancer accounts for 30% of all tumors. Current incidence rates are high, and the estimated lifetime risk for women is 12.5% (i.e., 1 in 8 women will be diagnosed with cancer of the breast during their lifetime). Classically, breast cancer has been divided into two subgroups, which have different outcomes and prognoses depending on their response to hormone therapy (sensitive and insensitive). Other traditional prognostic markers include axillary lymph node status, tumor size, nuclear grade and histological grade. In the last decade, new technologies for analyzing the genomic profiles of human tumors have substantially improved our knowledge of the molecular classification of breast cancer. This development improves diagnostic accuracy and enhances the ability to individualize therapy for breast cancer, thereby leading to direct implications for patient management.

Keywords

Breast cancer subtypes; Genomic profiling platforms; Predictive and prognostic biomarkers; Individualized treatment

Introduction

Breast cancer is a highly heterogeneous disease with different molecular subtypes. A few decades ago, breast cancer classification systems were based on tumor response to endocrine therapy. Later, hormone receptor status was determined by immunohistochemistry or enzyme immunoassay techniques. Many trials attempted to determine the impact of hormone receptor status in breast cancer, and two different subgroups of breast tumors were described (hormone-receptor-positive or -negative), which have different responses to therapy and different natural histories. Current technological developments have improved our understanding of breast cancer, and new biomarkers have been described [1-3]. A key biomarker is the HER-2 receptor, belongs to the epidermal growth factor receptor (EGF-R) family. HER-2 over expression was initially described as a predictor of unresponsiveness to hormone therapy. However, new data showed that HER-2 status was an important, independent prognostic and therapeutic marker [4-6], thereby leading to further changes in the breast cancer classification system. These new data can be used in daily clinical decision-making, and have enabled the design of new targeted therapies.

New gene expression techniques have made possible to better understand intrinsic breast cancer biology. Currently, molecular classification of breast tumors is used along with classical prognostic factors (including tumor size, nuclear grade and axillary nodal status) to predict tumor evolution and behavior, and to select specific treatments in each situation.

In this review, we will evaluate the use of genomic profiling in breast cancer to establish the diagnosis, evaluate patient prognosis and predict the response to treatment.

Breast Cancer: A Heterogeneous Disease

For a long time, treatment decision-making in breast cancer was based on such features as tumor size, axillary node status, hormone receptor status, HER-2 status and grade of differentiation. However, new techniques in genomic investigation have the ability to advance our knowledge of breast cancer. Dr. Beatson was the first author to suggest the benefit of endocrine therapy as a targeted therapy. In the 19^th century, he also proposed that despite having common origins in the mammary gland, all breast cancers did not have the same natural history [7,8]. Today, we know that breast cancer is a heterogeneous group of diseases with diverse biological behaviors. Several methods for gene profiling in different tissues have been applied. DNA microarray analysis is a technique based on nucleic acid sequences represented on a small platform or chip. There are currently two available platforms, Agilent^® and Affymetrix^®. This method allows the study of thousands of DNA spots, with each spot representing a single gene [9-11]. The technology allows tumor gene expression profiles to be compared, which may lead to new molecular classifications [7].

In 2000, Perou et al. [12] proposed that phenotypic diversity could be due to different genetic profiles in breast tumors. The authors evaluated 65 surgical specimens (normal and malignant tissue) from 42 different women. Eighty-four samples with a total of 8,102 genes were analyzed using cDNA microarrays. Information regarding variation of gene expression was obtained in 1,753 genes. Based on these data, the investigators classified the breast tumors into 4 different molecular subtypes: luminal, basal, HER-2 and normal (Table 1). The two epithelial groups consisted of the luminal and basal subtypes (clinically classified as ER-positive and ER-negative). The third group was characterized by high HER-2 expression and low ER expression, and a fourth group had similar gene expression patterns to normal breast tissue [12]. Subsequent gene-profiling studies of luminal tumors further defined the Luminal A and Luminal B subgroups [13]. The role of gene expression profiling in predicting recurrence-free and diseasespecific survival rates was studied in 357 patients with invasive breast cancer. The results of this analysis demonstrated that Ki-67 levels could determine a cut-point to distinguish Luminal B from Luminal A tumors with a cut-off point of 13.25%. Luminal B tumors had higher expression levels of proliferation-related genes and were associated with more aggressive behavior than were Luminal A tumors. The HER 2 subgroup was defined by the overexpression of this EGF-R subtype. Gene profiling studies confirmed the presence of higher levels of proliferative gene expression in HER-2 enriched subgoup of patients than in the other luminal subtypes [7,14]. Tumors that showed similar gene profiling patterns to normal breast tissue were included in the normal-like subtype.

Table 1: Molecular subtypes of Perou et al. classification [7,12,13,14,19,20].

Breast cancers tumor in patients with the BRCA-1 mutation frequently is included in basal-like subtype as a consequence of its gene profile, while BRCA-2 patients’ tumors are more commonly of the luminal subtype [15,16]. “Triple negative tumors” are ER-, PRand HER-2-negative by immunohistochemistry in clinical practice, and these tumors are included in the basal-like molecular subgroup. However, several trials have demonstrated that triple-negative breast cancer is not a single disease. Rather, it is a heterogeneous pathology with biological differences and different intrinsic subtypes, suggesting basal tumors may be a possible subgroup within this pathology [17,18]. Recently, a new subgroup of triple negative tumors, known as claudinlow tumors, has been described. This subgroup is enriched in stem cells, as well as in pluripotential and undifferentiated genes [19]. These tumors, therefore, have a high proliferation capability and appear to behave more aggressively than other subtypes [19]. Some authors proposed this subtype as the most interferential subtype of tumors. Lim et al. [16] evaluated histologically normal mammary tissue from BRCA1 mutation carriers, obtained after prophylactic mastectomy, and described three different subgroups of epithelial tumors: basal stem (in BRCA-1 heterozygous women), luminal progenitors (in carriers of the mutation) and mature luminal cells (in basal breast tumors). These data suggest that the MaSC (mammary stem cell) could be the origin of these subgroups. However, the claudin-low subtype of tumors may be locked in this stem cell state, while basal-like tumors may become arrested at the luminal progenitor stage [20,21]. These data show that basal tumors are a heterogeneous group composed of different subtypes. This finding may be related with tumorgenesis and the natural history of different types of breast tumors.

The annual Saint Gallen Conference of 2011 generated evidencebased consensus therapy recommendations for early breast cancer. The international faculty concluded that molecular characterization was an ideal method for defining the heterogeneity of breast cancer. However, because genetic profiling is not routinely established in the clinic, immunohistochemical typing is still considered to be the method of choice for assessing relapse risk and estimating the probable effect of specific therapy. The intrinsic molecular breast cancer typing system for endocrine subtypes, was recognized as a valuable new taxonomic classification.

In the clinical practice, immunohistochemistry is the standard technique for diagnosing breast cancer. Molecular classification is based on basic criteria, including ER, PR and HER-2 status, Ki-67 and P-53 levels. However, significant interlaboratory variability in these results has been described [22-24], and more trials are needed to determine the true clinical applicability of this information [25] (Table 2).

Table 2: Immunohistochemistry correlation with molecular classification of breast cancer tumors.

Prognostic Biomarkers in Breast Cancer Pathology

A prognostic factor is any factor present at the time of initial diagnosis (in the absence of systemic adjuvant treatment) that correlates with the natural history of the disease. Prognostic factors may be correlated with the disease-free interval or with overall survival. Several decades ago, oncologists evaluated the prognosis of breast cancer patients based on such clinical data as tumor size, axillary node status and nuclear grade. Subsequently, ER-positive and ERnegative tumors, which have different outcomes, were recognized. The ER pathway plays an important role in breast cancer evolution. Many studies have corroborated that ER-positive patients have a better prognosis. Moreover, the ER is the target of endocrine treatment and is therefore also considered to be an important predictive marker. ER is considered to be an independent prognostic marker and frequently is associated with other favorable risk markers such as older age, low histology grade, a favorable nuclear grade, a low phase fraction and a low proliferative index [26]. Ellis et al. [27] reported an analysis of 337 patients with stage II-III, ER-positive breast cancer who received neoadjuvant endocrine therapy for 4 months followed by surgical resection. In this analysis, ER expression, tumor size, nodal involvement, clinical response and post-treatment tumor grade were predictive factors for relapse-free survival, DFS and OS at a follow up of 62 months. The genomic classification reported by Perou et al. [12] included ER-positive patients in the luminal subgroup according to the genomic profile. Subsequent studies demonstrated two different subgroups within the luminal type: Luminal A and Luminal B. These subtypes appear to have different prognoses. The Luminal B subtype has a more aggressive profile and appears to be more highly proliferative than the Luminal A subtype [13]. It is unclear if PR status offers additional prognostic information to the ER status. Arpino et al. [28] studied a series of 31,415 patients with ER⁺/PR⁺ tumors and 13.404 patients with ER⁺/PR^- tumors. The investigators attempted to correlate hormone status with tumor aggressiveness and HER-1 or HER-2 status. In this study, 11,399 patients received adjuvant treatment with tamoxifen. The results showed that, compared with ER⁺/PR⁺ patients, ER⁺/PR^- patients had a poorer prognosis, higher expression of HER-1 and HER-2 tumors and more aggressive tumors overall. All of these patients had received adjuvant tamoxifen [28].

HER-2, a member of the EGF-R family, is overexpressed in 20-30% of all breast cancers. Traditionally, HER-2 has been considered to be a biomarker of poor prognosis. Several years ago, a number of trials showed that HER-2-positive breast cancer was associated with a higher endocrine treatment resistance rate than HER-2 negative cancer [4-6,29]. Important studies described resistance to tamoxifen therapy in HER-2 positive patients [30] but showed a possible benefit of aromatase inhibitors, such as anastrazole or letrozole [31,32]. Rasmussen et al. [33] published the results of the BIG 1-98 trial in 2008. This data suggested that HER-2-positive patients had a poor prognosis regardless of endocrine treatment [33]. Osborne and Schiff [34] described crosstalk between the ER and EGF-R pathways as a possible cause of treatment resistance in these patients, but more trials are needed to investigate this hypothesis. Luminal B-type and endocrine HER-2-positive breast cancer are associated with younger age at diagnosis, higher grade, larger tumor size, positive lymph node involvement, and lymph vascular invasion. All of these factors are associated with a higher risk of disease recurrence and worse survival, despite the use of adjuvant systemic and targeted therapies [14].

Antigen Ki-67 is a nuclear protein that is associated with and may be necessary for cellular proliferation. Ki-67 is only expressed during the G1, S, G2 and mitotic phases of the cell cycle. This biomarker may be a useful prognostic factor; it may also be able to predict response or resistance to chemotherapy or endocrine therapy. Viale and G.H.A.e.a [35] evaluated the value of Ki-67 as a prognostic marker in postmenopausal ER-positive women who received treatment with tamoxifen or letrozole as part of the Breast International Group 1-98 study. The study included 2,700 patients, and the results showed a DFS of 86% in patients expressing high levels of Ki-67 patients versus 92% in patients expressing low levels of Ki-67 (P<0.0001). Patients with high Ki-67 levels who received letrozole had a significantly improved DFS, but no difference was observed between both treatments in patients with low levels of Ki-67 expression. Dowsett and Dunbier [36] described Ki-67 as a predictive biomarker in patients receiving neoadjuvant treatment, especially those prescribed endocrine therapy. Today, Ki-67 is frequently included as an efficacy endpoint for clinical trials, and recent studies have demonstrated that its evaluation in highquality laboratories can provide interesting prognostic information [37].

Urokinase-type plasminogen activator (uPA), a serine protease, and its inhibitor (PAI-1) have been evaluated as prognostic factors. Janicke et al. [38] studied 247 breast cancer patients and showed that there was a relationship between risk of recurrence and levels of uPA and PAI-1, as measured by an Enzyme-Linked Immunoassay (ELISA) technique. A low risk of relapse was seen in patients with low levels of these proteins. Look et al. [39] published similar results in a study of 8,377 patients with 79 months of follow-up, describing the special importance of these markers as prognostic factors in node-negative patients to design individualized treatment. Other studies came to similar conclusions [40]. The 2011 Saint Gallen conference evaluated the option of using uPA/PAI-1 as potential factors in clinical decisionmaking, but it was not accepted [21].

Cyclin-E is a protein that regulates the G-1 to S-phase transition in the cell cycle. It is overexpressed in 30% of breast cancers. Keyomarsi et al. [41] studied 256 patients and observed a strong correlation between levels of cyclin E and survival. Porter et al. [42] evaluated 3,122 highrisk breast cancer patients, but did not find a statistically significant correlation between cyclin E protein levels and DFS or OS. More studies are needed to evaluate the prognostic value of this protein.

Today, biomarker studies have improved our ability to predict the prognosis of breast cancer patients. However, more clinical trials are necessary to further evaluate existing markers. New studies of neoadjuvant treatment strategies may increase our knowledge base with respect to this issue.

Genomic Profiles as Prognostic Factors in Breast Cancer Pathology

The benefit of adjuvant chemotherapy in node-positive breast cancer and high-risk node-negative early-stage breast cancer is well established, but is possible to make the distinction about who early breast cancer patients present high risk of recurrence in clinical practice? And, could be possible to calculate the benefit of adjuvant treatment (chemotherapy and/or endocrine therapy)? Treatment options for early-stage breast cancer include chemotherapy, endocrine therapy and trastuzumab plus chemotherapy (in patients with HER- 2 overexpression). Overtreatment, which is associated with adverse effects and costs, is common in the adjuvant setting. Traditionally, oncologists chose adjuvant therapy based on such pathological factors as tumor size, tumor grade and nodal status, as well as patient-related factors, such as age, menopausal status and medical comorbidities. However, patients with the same clinicopathological parameters and biomarkers can have different outcomes. It is important to investigate whether new genomic advances may help to predict the natural history of breast tumors and to improve our ability to predict clinical outcomes in patients.

Adjuvant! Online is an online application that was designed to assist professionals in clinical decision-making for patients with early breast cancer. This application is based on individual patient (age, sex, comorbidities) and tumor characteristics (size, histology grade, node status) and evaluates an individual’s risk of death at 5 years [43]. However, these risk groupings do not incorporate the different molecular subtypes. The Nottingham Prognostic Index (NPI) is based on the evaluation of nodal status, tumor size and histological grade. The NPI reflects the metastatic potential, growth rate and genetic instability of breast cancers and defines 3 groups of patients: good, moderate, and poor prognosis [44,45] (Table 3).

Table 3: Genomic profiling platforms [46,48,50-53,57-61,63-66].

Currently, the use of genomics in clinical practice can provide valuable information about the potential benefit of receiving chemotherapy versus endocrine-only therapy in ER-positive, nodenegative and HER-2-negative patients. In this group of patients, genomic testing can be used with the objective of minimizing overtreatment while improving DFS and OS [46]. New technologies, such as DNA microarrays and qRT-PCR assays based on mRNA, have allowed the creation of prognostic tests that can help oncologists in clinical decision-making [47].

The ONCOTYPE Dx assay^® evaluates the risk of recurrence in ER-positive and node-negative breast cancers in order to detect the benefit of adding chemotherapy to endocrine treatment. ONCOTYPE Dx is a 21-gene assay that performs RT-PCR on RNA obtained from paraffin-embedded breast cancer tissue. Esteban et al. [48] evaluated the utility of RT-PCR as a sensitive and precise method with the capability to be used in diagnostic and clinical research tool in patients with breast cancer. The 21-gene assay was designed by analyzing the results of three independent preliminary studies that involved 447 patients and 250 candidate genes [49]. The TAILORx (NCT00310180) trial is a multicenter study that assessed the 21-gene assay, including 16 tumor-associated genes and 5 reference genes evaluated by RT-PCR. The 21-gene assay was a genomic platform that aims to assist in clinical decision-making for treatment in ER-positive and node-negative patients. Results are expressed as a computed recurrence score, and patients are grouped in three risk groups (low-, intermediate- or high-risk) in terms of the risk of recurrence at 5 years of follow up comparing with reference of NSABP-B14 trial patients [50]. In a retrospective analysis of the NSABP-B20 trial, Paik et al. [50,51] studied this assay system in women with hormone receptor-positive, node-negative and early breast cancer. The aims of this analysis were to evaluate risk of recurrence as a continuous variable and to predict the response to tamoxifen and chemotherapy treatment. The study included 651 patients; 227 were randomized to receive tamoxifen and 424 received tamoxifen plus chemotherapy. They observed that this test for interaction between chemotherapy treatment and RS was statistically significant (p=0.038). The ONCOTYPE Dx assay^® became commercially available in January 2005. The use of ONCOTYPE Dx has been incorporated into international guidelines (such as the NCCN guidelines) as a useful method for assessing the risk of recurrence in ER-positive and node-negative tumors.

The Mammaprint^® platform has the same objective, which is to assess the risk of recurrence in ER-positive and node-negative breastcancer patients. This platform is based on microarray (Agilent^®) technology and obtains RNA from freshly frozen tumor tissue. Van´t Veer et al. [52] investigators at the Netherlands Cancer Institute, used microarray technology to analyze tumors of 117 young patients. These investigators described a 70-gene expression profile that can be used to discriminate between patients at low and high risks of distant relapse. Other retrospective trials confirmed the benefit of this 70-gene profile [53-56]. The MINDACT (Microarray in Node-Negative Disease May Avoid Chemotherapy; NCT00433589) trial described adjuvant treatment selection based on the 70-gene signature, with “low-risk” patients being offered endocrine therapy and “high-risk” patients being offered chemotherapy. This study had favorable results [56]. Other trials have demonstrated similar reproducible results [57,58].

Wang et al. [59] evaluated 115 tumors using Affymetrix^® microarray technology, described a 76-gene profile that appeared to be strongly prognostic for the risk of distant recurrence. The TRANSBIG trial evaluated 302 patients using both a 70-gene [55] and a 76-gene profile [60] independently. Only 3 genes overlapped between both platforms, but both platforms were better than Adjuvant! Online for the detection of low-risk patients. These results suggest that multi-gene assays can be used to select low risk patients in whom the intensity of systemic treatment can be reduced.

The gene expression grade index (GGI) is based on the evaluation of 97 genes (by Affymetrix^® microarray technology) that were differentially expressed consistently between low- and high-grade breast carcinomas. It was observed that intermediate grade breast tumors did not express a distinctive gene expression pattern, but in high- and low-grade tumors, showed distinct expression patterns. Comparisons of this platform with the existing 70-gene and 76-gene profiling assays, as well as with Oncotype Dx, showed consistent results with respect to survival [61,62].

Other genomic profile platforms to evaluate risk in breast cancer have been based on the p53 pathway [63], the oncogenic pathway of the activated ring-finger polycomb protein BMI1 [64], or the Invasiveness Genes Signature (IGS) [65].

Parker et al. [66] proposed a risk model incorporating the gene expression-based “intrinsic subtypes” (luminal A, luminal B, HER-2 enriched, basal like). This model was based on microarray technology and RT-PCR. The investigators described a 50-gene subtype predictor of prognosis in untreated patients and of pathological complete response in patients receiving neoadjuvant treatment. The study population included 761 patients who did not receive chemotherapy and 133 patients who received anthracycline- or taxane-based treatment. A risk of relapse (ROR) was proposed for each patient; ROR was based on the tumor subtype (ROR-S) or was associated with tumor size (ROR-C) as a predictive factor for pathological response with neoadjuvant treatment. This method allows evaluation of the relationship between the genomic or intrinsic classification and clinical response. For this reason, the PAM-50 platform is currently being used in clinical trials [25,67-69].

In the 2011 Saint Gallen conference, only the multiparameter gene assay, Oncotype Dx, was considered to be potentially useful for decision-making regarding adjuvant chemotherapy in cases in which other factors do not help. The NCCN [70] and ASCO 2011 [71] guidelines have also accepted this genomic tool [21].

Predictive Factors and Value of Genomic Profiling in Breast Cancer Management

Breast cancer is a heterogeneous disease, and genomic studies have demonstrated the existence of different tumor subtypes [12,20] that express different pathways and therefore, respond differently to chemotherapy. New technologies may identify predictive biomarkers that could help to select the optimal treatment in patients with different molecular subtypes of breast cancer, thereby allowing individualized treatment for each patient.

Classic prognostic factors can also aid in treatment decisionmaking. Martin et al. [72] evaluated the relationship between tumor size and various responses to chemotherapy, observing that small tumors had a better response to taxanes than do larger tumors, which appeared to respond better to doxorubicin-based regimens. ER status is an approved predictor of response to endocrine therapy and chemotherapy. The implication of PR status is unclear. The ATAC trial observed better results in terms of time to recurrence with aromatase inhibitors than with tamoxifen in ER⁺/PR⁺ patients compared with ER⁺/PR^- patients. However, the Breast International Group 1-98 trial showed a benefit to letrozole compared with tamoxifen regardless of PR status [31,32]. Other trials showed that PR status did not affect DFS or OS in patients who received adjuvant chemotherapy [73]. HER-2 status is another approved prognostic factor. A new targeted therapy, trastuzumab, has improved DFS and OS in both early-stage and metastatic breast-cancer patients. Trastuzumab, a monoclonal antibody against HER-2, can improve both DFS and OS in neoadjuvant, adjuvant and metastatic breast cancer patients [74-80] when used with chemotherapy. Lapatinib, a tyrosine kinase inhibitor, has been approved as an effective treatment in metastatic breast cancer. Ongoing trials are attempting to evaluate whether lapatinib synergizes with trastuzumab in neoadjuvant and adjuvant treatment. Other new drugs, such as T-DM1 (a potent microtubule disrupting drug) [81], neratinib, pertuzumab and PI3K pathway inhibitors, are being evaluated in ongoing trials. However, there is still a 30% risk of recurrence in HER- 2 positive subgroup of patients, despite the use of adjuvant treatment. Therefore, new trials based on this target are necessary to improve treatment of this group of patients. Recent trials have also attempted to investigate whether there is an advantage to administering more than one anti-HER-2 therapy in neoadjuvant treatment in an attempt to avoid resistance to trastuzumab [82-84].

Important predictive biomarkers have also been described. The TOPO2A gene maps to chromosome 17q21 and is a potential molecular target of anthracyclines [85]. This enzyme plays an important role in cell division, acting on double-stranded DNA, and the protein is amplified in 24% to 54% of HER2-amplified tumors [86,87]. Arriola et al. [88] described that amplification of TOPO2A was frequently associated with HER-2 and ER overexpression. These authors showed that TOPO2A was a proliferation marker and predictor of better disease-free survival and overall survival in patients receiving anthracycline-based treatment [88]. Romero et al. [89] evaluated published microarray studies involving TOPO2A and observed higher expression of TOPO2A in more proliferative molecular subtypes, such as basal-like, luminal B, and Her2-enriched tumors, compared with luminal A, claudin-low, or normal-like tumors. The authors concluded that TOPO2A expression varies significantly, depending on the molecular subtype of the tumor. TOPO2A may be a biomarker for proliferation; nevertheless, further investigation of its function as a predictor of response to chemotherapy is needed.

Rouzier et al. studied tissue from 122 patients treated with taxanebased regimens and described an association between low tau protein expression and sensitivity to paclitaxel. The authors concluded that low tau expression may be used as a marker to select patients for paclitaxel therapy [90].

Other biomarkers of response, including Ki-67, p27, p-glycoprotein overexpression, and enzymes involved in drug metabolism, such as CYP3A4, have been studied as predictive factors [91], although none has been validated to date.

The new genomic classifications of breast cancer have also been studied as predictors of response to different treatments. Several authors have described more resistance to chemotherapy in Luminal A type patients, who appear to benefit more from endocrine therapy [92]. Also, higher sensitivity to chemotherapy treatment in basal-like and HER-2 positive tumors has been described. Carey et al. [93] studied 107 patients who received neoadjuvant treatment with doxorubicin and cyclophosphamide along with hormonal treatment in ER positive patients. A better clinical response was observed in basal-like and HER- 2 positive tumors, despite the fact that this group of patients had a poor prognosis, especially in cases where pathological complete response was not achieved. An evaluation of predictors of response to paclitaxel treatment in the GEICAM 9906 trial concluded that the basal subgroup of patients obtained significant benefit from the use of taxanes with the FEC regimen. Patients in the Luminal A subgroup also appeared to benefit from this regimen [94].

Genomic profiling platforms may help to predict treatment response and resistance. The benefits of adjuvant tamoxifen treatment have been confirmed by the Oncotype Dx^® platform. The NSABP-B14 trial randomized patients to receive tamoxifen vs. placebo. The results of this study showed a statistically significant benefit with tamoxifen treatment in the low- and intermediate-risk subgroups of patients, while high-risk patients obtained little benefit [95]. The NSABP-B20 trial evaluated the benefit of adding chemotherapy treatment (CMF or MF) before 5 years of adjuvant tamoxifen. No benefit with this regimen was seen in the low- and intermediate-risk subgroups, but the high-risk subgroup did obtain a statistically significant benefit to treatment [51]. The SWOG 8814/INT 0100 trial obtained similar results with FAC chemotherapy followed by tamoxifen treatment for 5 years [96]. The Mammaprint platform has been evaluated as a predictor of neoadjuvant treatment response, and it was concluded that the low-risk subgroup obtained a poorer response with chemotherapy treatment than did high-risk patients, who rarely obtained pathological complete response with this treatment [97]. Other platforms have been used to predict treatment response. Using microarray technology, Ayers et al. [98] evaluated a 74-marker platform to predict response to neoadjuvant treatment with T/FAC chemotherapy. Subsequent studies appear to have obtained more precise markers than previous investigations [99]. Chang et al. [100] evaluated 24 patients who received neoadjuvant docetaxel chemotherapy for 3 months in an attempt to identify patterns of gene expression associated with resistance and/or incomplete response to docetaxel. These investigators identified 92 genes that may predict clinical response.

Conclusion

New molecular techniques have enabled the classification of breast cancers based on gene profiling. These assays can generate data about the intrinsic characteristics of a tumor, thereby providing useful diagnostic, prognostic and predictive information.

Human genome sequencing has improved our knowledge regarding the underlying biology of breast cancer. Studying breast cancer genomic profiles has led to a new taxonomy of breast cancer and allows tumors to be distinguished in terms of their natural history, treatment response and aggressiveness. For these reasons, adequate diagnosis is vital for designing a good treatment strategy. More data and validation about new genomic profiling techniques and gene profile are needed. This approach allows physicians to select and tailor breast cancer treatment, trying to individualize treatment for each patient with breast cancer. Ultimately, this information can maximize the chance of cancer response and minimize overtreatment and associated toxicity. However, the genomic profiling platforms described in this paper have different biological backgrounds, and results from studies using different platforms are not necessarily directly comparable. Prospective trials are needed to improve our understanding of the role of genomic profiling platforms and biomarkers, thereby allowing accurate prediction of the prognosis and treatment response in each clinical situation.

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