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The Oncologist, Vol. 13, No. 4, 350-360, April 2008; doi:10.1634/theoncologist.2007-0216
© 2008 AlphaMed Press

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Breast Cancer

Current Status of Prognostic Profiling in Breast Cancer

Lajos Pusztai

Department of Breast Medical Oncology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA

Key Words. Breast cancer • Microarray • Gene profiling • Molecular analysis • Prognostic indicator • Predictive indicator • Prognostic profiling

Correspondence: Lajos Pusztai, M.D., D.Phil., Department of Breast Medical Oncology, UT M.D. Anderson Cancer Center, P.O. Box 301439, Houston, Texas 77230-1439, USA. Telephone: 713-792-2817; email: lpusztai{at}mdanderson.org

Received November 1, 2007; accepted for publication February 20, 2008.

Disclosure: L.P. owns stock in and has served as an officer or member of the Board for Nuvera Biosciences, Inc., and has acted as a consultant to Roche, Pfizer, and Bristol-Myers Squibb. No other potential conflicts of interest were reported by the author, planners, reviewers, or staff managers of this article.


   Learning Objectives
Top
 Learning Objectives
Abstract
 Introduction
 Gene-Expression Profiling and...
 Gene-Expression Profiling and...
 Effect of Genetic Classification...
 Conclusions
 Acknowledgments
 References
 
After completing this course, the reader will be able to:

  1. Assess emerging data on the use of genetic groupings of breast cancer as predictive factors, and examine the efficacy of different therapies aimed at optimizing outcomes of patients within these groups.
  2. Examine the clinical value of molecular diagnostic tests being developed to classify breast tumors, and discuss the challenges involved in validating and interpreting the results of these tests.
  3. Outline the potential uses of identifying and/or targeting breast cancer stem cells.
  4. Discuss the possible effect of genetic classification of breast tumors on the design of future clinical trials.

Access and take the CME test online and receive 1 AMA PRA Category 1 CreditTM at CME.TheOncologist.com


   ABSTRACT
Top
 Learning Objectives
 Abstract
Introduction
 Gene-Expression Profiling and...
 Gene-Expression Profiling and...
 Effect of Genetic Classification...
 Conclusions
 Acknowledgments
 References
 
Breast cancer is a clinically heterogeneous disease that can affect individuals with seemingly identical clinicopathologic parameters differently. This clinical heterogeneity is driven to a large extent by abnormal gene expression within tumors. Investigators now have the ability to identify the gene-expression fingerprint of an individual's tumor. This information may be used to rationally design therapeutic targets in the future, and also to predict the clinical course of an individual's disease, including response to a specific treatment. Genetic profiles of tumors are now being correlated with clinical outcome, and several prognostic and predictive indicators have emerged based on this research. There are at least four commercially available predictive or prognostic tests, and several more are looming on the horizon. The data gathered from these tests augment standard diagnostic and prognostic information obtained from traditional clinical pathological variables. The advent of gene-profiling technologies started to change the conduct of clinical trials. In the not too distant future, prospective tissue collection for molecular analysis may become routine in order to stratify patients for treatment arms and to optimize treatment strategies based on molecular features of the cancer. Coordinated efforts among oncologists, pathologists, surgeons, laboratory scientists, statisticians, and regulators will be essential in the quest to incorporate genetic profiling and molecular hypotheses into clinical trial planning and conduct.


   INTRODUCTION
Top
 Learning Objectives
 Abstract
 Introduction
Gene-Expression Profiling and...
 Gene-Expression Profiling and...
 Effect of Genetic Classification...
 Conclusions
 Acknowledgments
 References
 
Breast cancer is a clinically heterogeneous disease; individuals with the same stage of disease and similar pathological diagnoses can experience very different clinical courses [1]. This clinical heterogeneity is driven by the genetic variability of patients and tumors. It is widely acknowledged that a continuum of abnormal gene expression predicts the tumorigenic phenotype and the sensitivity of tumors to treatment (Fig. 1) [2]. Clinical investigators now have the capability to create a genetic blueprint of individual tumors; the genetic abnormalities identified within these tumors offer a hope to rationally select therapeutic targets for the treatment of patients with cancer [1]. Ultimately, researchers aim to use the molecular data gathered from an individual tumor for prognostication and customization of therapy for each patient. Gene-expression profiling has shown promise to distinguish between patients at low and high risk for developing distant metastases and identify those who are likely to benefit from adjuvant therapy [3]. Prognostic genetic tests for patients with breast cancer are now commercially available, and additional tests will be available in the very near future. This article reviews recent developments in molecular diagnostics and discusses the impact these developments may have on the future of breast cancer therapy.


Figure 1
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  		Figure 1. Schema of tumor differentiation. This figure shows tumoral differentiation as a continuum that results from the extent of molecular oncogenic aberration and influences tumor phenotype and treatment sensitivity.

Abbreviation: HER-2, human epidermal growth factor receptor 2.

From Symmans WF. A pathologist's perspective on emerging genomic tests for breast cancer. Semin Oncol 2007;34(suppl 3):S4–S9. Reprinted with permission from Elsevier, copyright 2007.

 

   GENE-EXPRESSION PROFILING AND PROGNOSTICATION
Top
 Learning Objectives
 Abstract
 Introduction
 Gene-Expression Profiling and...
Gene-Expression Profiling and...
 Effect of Genetic Classification...
 Conclusions
 Acknowledgments
 References
 
The current standard for prognostic stratification includes 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 [4–8]. These tools integrate clinicopathologic factors into multivariate prediction models. Although these tools allow clinicians to estimate the relative risks for recurrence and mortality and estimate the potential benefits of chemotherapy for groups of patients with given disease characteristics [7], they do not address the fundamental question oncologists and patients struggle with: who as an individual (rather than as a group) will benefit from adjuvant therapy? Up to 40%–50% of patients with a poor prognosis as defined by conventional clinicopathological parameters may remain disease free without adjuvant therapy [3]. Likewise, benefit from systemic adjuvant chemotherapy for patients with lymph node–negative (LNN) disease is not uniform; some patients relapse despite therapy and others may already be cured by locoregional treatment [1]. More accurate molecular prognostic and response prediction tools could assist in minimizing overtreatment of low-risk patients and reduce undertreatment of high-risk patients, who are also sensitive to existing systemic treatment modalities.

Intrinsic Subtype Predictor
A novel molecular classification of breast cancer was proposed based on large-scale gene-expression analyses of breast cancer [9]. Four major molecular classes of breast cancer emerged from several studies: luminal-A, luminal-B, basal-like, and human epidermal growth factor receptor (HER)-positive cancers [9–11] (Fig. 2). The overall survival and chemotherapy sensitivity of the different molecular subgroups vary. Luminal-type cancers are mostly estrogen receptor (ER) positive, and patients with luminal-A cancers have the most favorable long-term survival (with endocrine therapy) compared with the other types, whereas basal-like and HER-2–positive tumors are more sensitive to chemotherapy [10, 12, 13]. The Intrinsic subtype predictor was developed to assign molecular classes to newly diagnosed breast cancers [14].


Figure 2
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  		Figure 2. (A) Hierarchical clustering of 82 breast cancers using the 689 probe sets shows four proposed molecular classes of breast cancer; (B) Correlation between molecular subclass and clinicopathological characteristics in univariate analysis is shown.

From Rouzier R, Perou CM, Symmans WF et al. Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin Cancer Res 2005;11:5678–5685. Reprinted with permission from American Association for Cancer Research.

 
Rotterdam 76-Gene Set
The Rotterdam gene set was developed to predict the prognosis of patients with LNN breast cancer [15]. Two hundred eighty-six patients who had locoregional therapy only were included in the initial marker development and validation study. Markers were selected separately from ER-negative and ER-positive tumors and were combined into a single 76-gene prognostic signature (VDX2; Veridex, LLC, Warren, NJ) that was able to predict distant metastatic recurrence with a sensitivity of 93% and a specificity of 48% [15]. This prognostic indicator performed better than standard, clinical variables in a multivariate analysis (hazard ratio [HR], 5.55; 95% confidence interval [CI], 2.46–12.5). Subsequently, this test was also evaluated on two other cohorts of patients who were not included in the original study. The first cohort included 180 patients with stage I–II breast cancer and showed 5- and 10-year distant metastasis-free survival rates of 96% (95% CI, 89%–99%) and 94% (95% CI, 83%–98%), respectively, for the good prognosis group; the corresponding rates were 74% (95% CI, 64%–81%) and 65% (95% CI, 53%–74%) for the poor prognosis group [16]. The sensitivity for 5-year metastasis-free survival was 90%, and the specificity was 50%, with positive and negative predictive values of 38% and 94%, respectively. The second validation cohort included 198 LNN cases and demonstrated similarly good 5- and 10-year distant metastasis-free survival rates: 98% (95% CI, 88%–100%) and 94% (95% CI, 83%–89%), respectively, for the genomic low-risk group [17]. The recurrence rates were significantly worse for the poor prognosis group: 76% (95% CI, 68%–82%) and 73% (95% CI, 65%–79%) at 5 and 10 years, respectively [17]. Importantly, the 76-gene signature could restratify patients within the clinical risk categories defined by the Adjuvant! Online program and the recurrence HRs remained similar after adjustments for tumor grade, size, and ER status.

Invasive Gene Signature
The putative breast cancer stem cell, which can be identified by low expression of CD24 and high expression of CD44, is highly tumorigenic in experimental models [18–20]. When normal breast epithelial cells were compared with breast CD44+/CD24– cells, 186 genes that are associated with tumorigenic breast "stem" cells were discovered [21]. These genes became known as the invasive gene signature (IGS), and their presence was significantly associated with shorter overall survival and metastasis-free survival times (p < .001). The IGS was combined with prognostic criteria from the NIH and used to stratify patients with early-stage breast cancer, who are at high risk for metastasis or death, into good or poor prognostic groups. The 10-year metastasis-free survival rate was 81% among patients in the good prognosis group, but only 57% for patients in the poor prognosis group. Moreover, there was an association between the IGS and clinical outcome in patients with tumors that showed intermediate-grade differentiation.

Wound Response Indicator
Tumors have been compared to nonhealing wounds [22]. Because the response of serum-stimulated fibroblasts includes many of the processes involved in wound healing [23], the wound response indicator (WRI) was developed from genes whose expression changed following the activation of cultured fibroblasts with serum [24]. This signature was validated in patients with early-stage breast cancer (n = 295) [25]. Patients whose tumors expressed the WRI had significantly shorter overall survival and distant metastasis-free survival times relative to patients whose tumors did not express this gene signature. Moreover, the WRI signature was an independent predictor of death in a multivariate analysis of metastasis and death.

The oncotype DXTM Recurrence ScoreTM
The oncotype DXTM (Genomic Health, Inc; Redwood City, CA) is a 21-gene indicator. Two hundred fifty candidate genes were chosen from gene-expression profiling experiments, published literature, and genomic databases [9, 13, 26, 27]; these genes were correlated with breast cancer recurrence in 447 patients [28]. Sixteen cancer-related genes and five reference genes were selected from the candidate genes. The 16 cancer-related genes were then used to develop an algorithm based on the expression levels of these genes, thus allowing a Recurrence ScoreTM (RS) to be computed for each specimen. This RS correlated with the rate of distant recurrence at 10 years (Fig. 3). This assay uses fixed tumor specimens, rather than frozen tissue.


Figure 3
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  		Figure 3. Rate of distant recurrence as a continuous function of the recurrence score. The continuous function was generated with use of a piecewise log-hazard-ratio model. The dashed curves indicate the 95% confidence interval. The rug plot on top of the x-axis shows the recurrence score for individual patients in the study.

From Paik S, Shak S, Tang G et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004;351:2817–2826. Reprinted with permission from Massachusetts Medical Society, copyright 2004. All rights reserved.

 
The oncotype DXTM assay was externally validated in the National Surgical Adjuvant Breast and Bowel Project (NSABP) clinical trial B-14, which examined the effect of adjuvant tamoxifen in patients with hormone receptor–positive LNN breast cancer [28]. Approximately 25% of the tumors from the tamoxifen arm were analyzed, and their RSs were compared with patient outcomes after >10 years of follow-up. The results showed that 7% of low-risk patients (RS <18) relapsed, whereas 31% of high-risk patients (RS >31) relapsed. Subsequent studies have shown that the RS is independently associated with sensitivity to chemotherapy [29, 30] and mortality [31].

The RS is now being prospectively validated in the Trial Assigning Individualized Options for Treatment (TAILORx) clinical trial [32]. In this phase III trial, women who have undergone surgery for ER-positive LNN breast cancer are being assigned to one of three groups based on their RS: group 1, RS <11; group 2, RS ≥11–25; group 3, RS >25. It is important to note that these cutoff values are different from those used in all previous studies (i.e., low RS group, <18; high RS group, ≥31). These more conservative thresholds to categorize patients into good, intermediate, and high risk were selected to err on the safe side and not to exclude from adjuvant chemotherapy ER-positive patients who may conceivably have a small chance of benefiting from it. In the TAILORx study, patients in group 1 receive hormone therapy alone. Patients in group 3 receive combination chemotherapy and hormone therapy. Patients in group 2 are randomized to receive either chemotherapy and hormone therapy or hormone therapy alone. The primary endpoints of this study are disease-free survival, distant recurrence-free interval, recurrence-free interval, and overall survival in group 2.

MammaPrint® 70-Gene Profile
The MammaPrint® (Agendia BV; Amsterdam, The Netherlands) 70-gene profile was developed from patients with LNN disease who were ≤55 years of age. The frozen tumors were separated into two groups: (a) those from patients who developed distant metastases within 5 years of completing treatment and (b) those from patients who remained disease free for at least 5 years [27]. When the gene-expression profiles of these two groups were compared, a 70-gene profile correlating with clinical outcome was identified. This gene set was validated internally [33] and externally [34]. The first validation used a retrospective analysis of relatively young patients (age <53 years; n = 295) with LNN and lymph node–positive disease. Within this initial group, 115 patients with a mean 5-year survival rate of 97% were classified as having a good prognosis, and 180 patients with a mean 5-year survival rate of 74% were classified as having a poor prognosis [33]. The second validation was the Translating Molecular Knowledge Into Early Breast Cancer Management: Building on the Breast International Group Network for Improved Treatment Tailoring (TRANSBIG) study [34]. This study included women (n = 307) with LNN, T1–2 breast cancer who were <61 years of age and had not previously received treatment with systemic adjuvant therapy. The duration of follow-up for patients in this study was >10 years. The MammaPrint® gene set more accurately predicted time to distant metastases (HR=2.32; 95% CI, 1.35–4.00) and overall survival (HR, 2.79; 95% CI, 1.60–4.87) than Adjuvant! Online (HR, 1.68; 95% CI, 0.92–3.07). These validation studies led to the clearance of this test by the U.S. Food and Drug Administration (FDA), allowing the test to be marketed as a prognostic marker to be used with other clinicopathologic factors.

The ongoing Microarray in Node Negative Disease May Avoid Chemotherapy (MINDACT) clinical trial is currently evaluating the usefulness of the MammaPrint® gene set in determining systemic adjuvant therapy for patients with LNN breast cancer [35, 36]. That trial will compare the prognostic information provided by the 70-gene set with prognostic information provided by Adjuvant! Online [37, 38]. Investigators plan to enroll 6,000 LNN breast cancer patients; each will have their risk assessed through both Adjuvant! Online and the 70-gene profile. If both methods classify the patient's risk for relapse as low (an estimated 13% of patients), adjuvant chemotherapy will be withheld; if both methods classify the patient's risk for relapse as high (an estimated 55% of patients), then chemotherapy will be proposed; if the methods give discordant results (an estimated 32% of patients), the patient will be randomized to either follow the clinicopathological method or follow the genomic results. It is expected that 10%–20% of women who would normally receive adjuvant chemotherapy based on their clinicopathological factors will be spared this therapy, without having any negative impact on their survival [35, 36].

Mammostrat®
Mammostrat® (Applied Genomics, Inc.; Huntsville, AL) is a five-antibody panel. These antibodies were chosen from 140 novel and 23 commercially available antisera and were selected because of their ability to distinguish tumors with a high versus low risk for recurrence [39]. Cox proportional hazard and regression tree analyses were used to identify subpanels of antibodies that were able to predict the risk for recurrence in patients with ER-positive breast cancer. The resultant five-antibody panel was validated in two cohorts of patients, with the Cox model distinguishing ER-positive patients with poor outcomes from patients with good or moderate outcomes (HRs of 2.21 and 1.88 in cohort 1 and cohort 2, respectively). In a multivariate analysis, the risk for recurrence was independent of stage, grade, and lymph node status. However, this model was not useful for ER-negative patients. The five-antibody panel was subsequently validated in tamoxifen-treated LNN breast cancer patients from the NSABP B-14 and B-20 clinical trials, which indicated that the greatest clinical utility of the antibody panel may be in postmenopausal patients [40]. A separate study showed that both high- and low-risk patients benefited from adjuvant chemotherapy, but the absolute benefit for high-risk patients appeared greater [41].

CellSearchTM
CellSearchTM (Veridex, LLC; Warren, NJ) is not a genomic assay but a method to detect circulating tumor cells (CTCs) that are characterized by the lack of expression of CD45 cell surface antigen and by positive staining for epithelial cell adhesion molecule and cytokeratins 8, 18, and/or 19 [42]. These cells can be detected in the peripheral blood of some breast cancer patients [43]. It is believed that these cells are involved in the spread of metastases. Patients with metastatic breast cancer who had more than five CTCs at baseline and first follow-up had a worse prognosis than patients with fewer than five CTCs [44–46]. The CellSearchTM assay is now approved in the U.S. for risk stratification of patients with metastatic breast cancer based on the detection of CTCs in the peripheral blood. Changes in CTC count in response to therapy were also predictive of outcome. CTC-positive patients who became CTC negative after therapy had a significantly longer progression-free survival time (7.6 versus 2.1 months; p = .002) and overall survival time (14.6 versus 9.2 months; p = .006) than those with persistently elevated CTC counts [44]. A clinical trial is under way to examine the clinical utility of this test in switching treatment in patients who do not respond to initial treatment with a dropping CTC count.


   GENE-EXPRESSION PROFILING AND PREDICTION OF RESPONSE TO THERAPY
Top
 Learning Objectives
 Abstract
 Introduction
 Gene-Expression Profiling and...
 Gene-Expression Profiling and...
Effect of Genetic Classification...
 Conclusions
 Acknowledgments
 References
 
ER Expression and Response to Hormone Therapy and Adjuvant Chemotherapy
Once it is determined that a patient has a high risk for recurrence with surgery alone, the next step is choosing the optimal treatment for that individual's tumor. Evaluation of ER status is an essential component of the pathological analysis of breast cancer because ER status is used to determine candidacy for endocrine therapy [3]. A patient's ER status is determined by performing immunohistochemistry (IHC) on formalin-fixed, paraffin-embedded tumor tissue. However, there may be considerable variation in tissue fixation, antigen retrieval, staining techniques, and interpretation of results across laboratories [47, 48]. Some data suggest that the benefit of estrogen therapy is proportional to the level of ER expression; therefore, further standardization and improvements in quantitative ER measurements are important [49]. The expression of the ER can now be measured at the mRNA level using reverse transcription-polymerase chain reaction (RT-PCR) or DNA microarrays, which are more quantitative than IHC [50].

Subset analysis of randomized adjuvant chemotherapy trials suggests that patients with ER-positive disease derive less benefit from anthracycline- and paclitaxel-based chemotherapies than patients with ER-negative cancers [51]. For example, analysis of the Cancer and Leukemia Group B 9344 trial, which evaluated the efficacy of doxorubicin–cyclophosphamide chemotherapy with or without paclitaxel, showed that the reduction in the risk for death resulting from the inclusion of paclitaxel was 24% (95% CI, 10%–37%) for ER-negative patients compared with 11% (95% CI, –8% to 26%) for ER-positive patients [51]. A greater sensitivity to chemotherapy of ER-negative cancers relative to ER-positive disease was also observed in several neoadjuvant studies using mostly anthracycline-based or anthracycline–paclitaxel combination chemotherapies [52]. However, it is also increasingly clear that a subset of ER-positive cancers is highly sensitive to chemotherapy and these patients can derive substantial benefit from adjuvant chemotherapy. Novel molecular diagnostic tests are now available to aid in the identification of these individuals.

HER-2/neu Expression and Response to Trastuzumab and Chemotherapy
It is well established that expression of HER-2/neu is predictive of a response to trastuzumab; therefore, analysis of HER-2/neu expression on breast tissue has become the standard of care [53]. Retrospective analysis of several adjuvant randomized studies indicated that HER-2 overexpression is associated with greater benefit from adjuvant anthracycline-containing regimens than from cyclophosphamide, methotrexate, and 5-fluorouracil (CMF)-type regimens [54–58]. Retrospective analysis of results from another randomized adjuvant trial comparing the addition of paclitaxel to anthracycline-based chemotherapy with anthracycline-based chemotherapy alone also showed that the benefit from inclusion of paclitaxel was largely restricted to HER-2–overexpressing tumors [59]. These data suggest that HER-2–positive cancers may be more sensitive to anthracycline and taxane chemotherapy than HER-2 normal cancers. This is also supported by observations from preoperative clinical trials reporting higher pathological response rates to taxane-/anthracycline-based chemotherapy in HER-2–positive cancers, regardless of ER status [60]. Based on the available evidence, the American Society for Clinical Oncology Expert Panel on Tumor Markers in Breast Cancer concluded that high levels of HER-2 expression may identify patients that will particularly benefit from anthracycline-based adjuvant therapy; but the panel specified that normal HER expression should not be used alone to exclude patients from treatment with anthracyclines [61].

Topoisomerase Expression and Response to Anthracyclines
Topoisomerases are enzymes that regulate the coiling of DNA and are essential components of the cell division machinery [62]. TOP2A is the molecular target of anthracyclines, which bind to this enzyme and induce the accumulation of double-stranded breaks in cellular DNA [62, 63]. Higher than normal levels of TOP2A cause the formation of large amounts of anthracycline:enzyme complexes within the nucleus. In a retrospective analysis of two randomized studies, TOP2A amplification (and to a lesser extent TOP2A deletion) was a significant predictive factor for greater benefit from cyclophosphamide, epirubicin, and 5-fluorouracil therapy than from CMF for overall survival and was borderline significant for disease-free survival [64, 65]. These results were confirmed in a population of patients with metastatic breast cancer who participated in a randomized clinical trial of anthracycline-based chemotherapy with or without trastuzumab [66]. Another clinical trial involving patients with advanced breast cancer also showed that TOP2A overexpression was associated with a higher probability of response to single-agent doxorubicin but not to single-agent docetaxel [67]. It is important to realize that cancers expressing normal levels of TOP2A also benefit from anthracycline therapy [63]; however, TOP2A amplification indicates an above average sensitivity to these drugs. The FDA recently approved a TOP2A fluorescence in situ hybridization (FISH) assay (TOP2A FISH pharmDxTM; Dako, Glostrup, Denmark) to measure amplification of this gene in breast cancer specimens.

Predictors of Response to Taxanes
Taxanes poison the mitotic spindle through greater microtubule stabilization. Several mechanisms of resistance to taxanes have been proposed, including overexpression of P-glycoprotein, mutations in β-tubulin, and a shift in βIII-tubulin isoform expression [68]. More recently, low expression of microtubule-associated protein tau was suggested as a marker that could identify patients with a higher than average sensitivity to paclitaxel [69]. Tau expression also correlates with the expression of ER and may predict endocrine sensitivity among ER-positive cancers [70].

A 92-gene panel was developed as another potential predictor of response to taxanes. Tumors from patients (n = 24) with primary breast cancer were biopsied prior to neoadjuvant treatment with docetaxel [71]. Gene-expression patterns were then correlated with a response to docetaxel. In cross-validation experiments, the resultant 92-gene predictor detected docetaxel-sensitive and docetaxel-resistant tumors with 92% specificity and 83% sensitivity. Further validation of this predictor is needed because of the small sample size.

Predictors of Response to Tamoxifen
Tamoxifen reduces recurrence rates in patients with early-stage, ER-positive breast cancer, but more accurate identification of individuals who actually benefit from this drug is needed [72]. Several efforts were made to develop predictors of response to tamoxifen or other endocrine therapies among ER-positive patients. A comparison of ER-positive tumors obtained from tamoxifen responders and nonresponders with advanced breast cancer led to the identification of 44 genes that are differentially expressed in these tissues [73]. The predictive power of this signature was significantly superior to that of traditional predictive factors in a univariate analysis and it was associated with a longer progression-free survival time in univariate and multivariate analyses. Another group reported that a two-gene ratio (HOXB13/IL17BR) was predictive of disease-free survival in patients with early-stage, ER-positive breast cancer who received treatment with tamoxifen [72]. An RT-PCR based method to assess this ratio from paraffin-embedded tissue samples is now commercially available (AviaraDx H/ITM; AviaraDx, Carlsbad, CA). Numerous other genomic markers of endocrine sensitivity were also reported. A genomic measure of histologic grade has been reported to be prognostic in both untreated and tamoxifen-treated patients [74]. By challenging MCF-7 cells with estradiol over 24 hours, Oh et al. [75] identified genes that were induced, optimized this gene signature to separate survival curves from pilot data, and then demonstrated a modest association with survival. An index of ER-related gene expression from the microarray profiles of human tumor samples was also predictive of survival after tamoxifen treatment [76].

Predictors of Response to Multiagent Chemotherapy
The RS produced by the oncotype DXTM 21-gene assay has been used to evaluate patients who will benefit from adjuvant combination chemotherapy [29, 30]. In the NSABP B-20 clinical trial, patients with LNN ER-positive breast cancer were treated with tamoxifen alone or tamoxifen combined with MF or CMF, and the results indicated that patients with a high-risk RS (≥31) had a higher probability of relapse despite endocrine therapy relative to patients with a low-risk RS (<18) [30]. However, patients at high risk derived a greater benefit from adjuvant chemotherapy than patients with a low-risk RS. In another study, a high RS also indicated that patients treated with paclitaxel and doxorubicin in the neoadjuvant setting had a higher probability of experiencing a pathologic complete response (pCR) [29].

Several small studies have provided "proof-of-principle" that the gene-expression profile of cancers that are highly sensitive to chemotherapy is different from the gene-expression profile of tumors that are resistant to treatment [77]. The largest study included 133 patients with stage I–III breast cancer who received preoperative weekly paclitaxel and 5-fluorouracil, doxorubicin, and cyclophosphamide (T-FAC) chemotherapy [78]. The first 82 cases were used to develop a multigene signature predictive of pCR, and the remaining 51 cases were used to test the accuracy of the predictor. The overall pCR rate was 26% in both cohorts. A 30-gene predictor correctly identified all but one of the patients who achieved a pCR (12 of 13) and all but one of those who had residual cancer (27 of 28) in the validation set. It showed a significantly higher sensitivity (92% versus 61%) than a clinical variable–based predictor that included age, nuclear grade, and ER status. The high sensitivity indicates that the predictor correctly identified almost all of the patients (92%) who actually achieved a pCR. The positive predictive value (PPV) of the pharmacogenomic predictor was 52% (95% CI, 30%–73%). Because the lower bound of the 95% confidence interval did not overlap with the 26% pCR rate observed with this regimen in unselected patients, the predictor could define a patient population more likely to achieve pCR than unselected patients. The negative predictive value (NPV) of the test was 96% (95% CI, 82%–100%), indicating that <5% of test-negative patients predicted to have residual disease achieved a pCR. The NPV was similar to and the PPV was better than those seen with ER IHC analysis or HER-2/neu gene amplification as predictive markers for endocrine or trastuzumab therapies, respectively. However, to what extent this genomic predictor of sensitivity is specific to T-FAC therapy rather than being a generic marker of chemotherapy sensitivity has yet to be determined.

Comparison of Gene Expression–Based Predictors
Several of the gene expression-based predictors mentioned in this review, for example, the Intrinsic subtypes predictor, MammaPrint®, WRI, oncotype DXTM, and the two-gene ratio for patients treated with tamoxifen, have been compared [79] (Table 1). The comparison study used a single dataset of breast cancer samples from 295 women. The indicators, with the exception of the HOXB13/IL17BR two-gene ratio assay, showed high rates of concordance in predicted outcome for individual patients, despite the minimal gene overlap among the assays. These results indicate that the concordance of the predictors is a result of common cellular phenotypes (mostly proliferation) that are detected by the various predictors [80]. Although there was discordance among the involved genes, these indicators all detected common sets of biological characteristics that determined patient outcomes.


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  		Table 1. Prognostic gene expression indicators

 

   EFFECT OF GENETIC CLASSIFICATION ON CLINICAL TRIAL DESIGN
Top
 Learning Objectives
 Abstract
 Introduction
 Gene-Expression Profiling and...
 Gene-Expression Profiling and...
 Effect of Genetic Classification...
Conclusions
 Acknowledgments
 References
 
Perhaps the most important contribution of genomic studies to breast cancer research has been the realization that breast cancer is not a single disease with heterogeneous ER or HER-2 expression, but a collection of molecularly distinct neoplastic diseases of the breast. To conduct clinical trials that include all the different molecular classes of breast cancer may be as na|fkve as combining all lymphomas into the same study. The existing and emerging diagnostic assays make it possible to molecularly stratify breast cancer patients for therapy and have important implications for clinical trial design. First and foremost, the incorporation of prospective tissue collection into future clinical trials is essential to maximize the information that can be gained from these studies [37, 38].

The incorporation of tissue collection into clinical trial design will allow prospective correlative studies to be performed and will also allow molecular stratification of patients for treatment arms [30]. Serial biopsies could make it possible to search for pharmacodynamic changes during therapy that may act as surrogate markers of response. This is important because novel agents may not cause rapid tumor regression and the use of validated molecular surrogate markers could also allow investigators to move away from the maximum-tolerated dose model of clinical trials and toward finding the biologically most relevant dose.

The integration of genetic profiling into clinical trial design necessitates that clinical trials become collaborative efforts among clinicians, scientists, pathologists, surgeons, and statisticians at the planning stages and during the trial [37, 38]. The inclusion of basic scientists will allow the clinical trial team to incorporate molecular hypotheses into the trial design at the earliest planning stages. Statisticians with special expertise in the analysis of high-dimensional datasets are also needed on the team, to ensure that the trial has adequate statistical power and that the conclusions are supported by the data. Without rigorous statistical scrutiny, it is very easy to generate misleading results from high-dimensional molecular datasets.


   CONCLUSIONS
Top
 Learning Objectives
 Abstract
 Introduction
 Gene-Expression Profiling and...
 Gene-Expression Profiling and...
 Effect of Genetic Classification...
 Conclusions
Acknowledgments
 References
 
It is clear that genomic profiling has the potential to change the prognostication and treatment options for patients with breast cancer. Although a few genetic tests are already approved by the FDA and several others are commercially available, clinicians wonder if these tests are ready for practical application in the clinic. At this time, strong claims cannot be made about the clinical value of these assays and their potential superiority to standard clinicopathological parameters [37, 38]. However, several of these novel gene expression–based assays seem to augment existing prognostic tools. An important potential of microarray-based tests is that multiple predictions, including prognosis and sensitivity to various treatment modalities, may be generated from a single experiment. These assays would use information from different sets of genes measured from the same tissue for different predictions, which could substantially improve the cost-effectiveness of these emerging tests. In order to provide a truly personalized treatment recommendation, it is important to understand the risk for relapse and the probability of benefit from endocrine therapy and chemotherapy separately and to consider patient preferences in the light of these results.

Many of the currently available tests are based on retrospective studies performed on archival material, and thus, they do not provide the level I evidence that can only be gained from prospective, randomized, high-powered clinical trials [37, 38]. The MammaPrint® 70-gene set is currently being validated in the MINDACT clinical trial, which is being performed by the Breast International Group in conjunction with the European Organization for Research and Treatment of Cancer [35]. Likewise, oncotype DXTM is being evaluated in the TAILORx clinical trial, which is part of the National Cancer Institute's Program for the Assessment of Clinical Tests. These trials should provide clinicians with definitive information regarding the clinical utility of these tests, and these studies will serve as models for future efforts addressing the clinical utility of gene-expression profiling. However, survival results from these studies will not be available for several years. It is also important to remember that some forms of clinical benefit from these novel tests may be more subtle than improvements in survival. It may be argued that additional information that helps patients and physicians feel more comfortable with a particular treatment recommendation has value on its own.


   ACKNOWLEDGMENTS
Top
 Learning Objectives
 Abstract
 Introduction
 Gene-Expression Profiling and...
 Gene-Expression Profiling and...
 Effect of Genetic Classification...
 Conclusions
 Acknowledgments
References
 
This manuscript describes data presented at the Second Annual Biological Basis of Breast Cancer conference, June 30, 2007, supported by the Center for Biomedical Continuing Education (CBCE), Inc., and was commissioned by the CBCE.


   REFERENCES
Top
 Learning Objectives
 Abstract
 Introduction
 Gene-Expression Profiling and...
 Gene-Expression Profiling and...
 Effect of Genetic Classification...
 Conclusions
 Acknowledgments
 References
 

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