Molecular Profiling Assays in Breast Cancer: Are We Ready for Prime Time?

April 15, 2012
Yesim Gökmen-polar, PhD
Yesim Gökmen-polar, PhD

,
Sunil Badve, MD, FRCPath
Sunil Badve, MD, FRCPath

Volume 26, Issue 4

In this review, we will present the current data on commercially available molecular profiling assays in breast cancer and discuss the challenges surrounding their incorporation into routine clinical practice as prognostic and predictive tools.

Breast cancer is a heterogeneous disease with diverse morphologies, molecular characteristics, and clinical behavior. The advances in molecular profiling technologies have changed our understanding of breast cancer and led to the identification of prognostic/predictive gene signatures. Despite the huge quantity of information gleaned from these profiling technologies and the increasing number of gene signatures, their incorporation into clinical decision making is a slow process and is limited in various aspects. The 70-gene assay (MammaPrint, Agendia, Netherlands) and the 21-gene assay (Oncotype DX, Genomic Health, USA) are the most widely used breast cancer multigene classifier assays. A 50-gene assay (PAM50, NanoString, USA) has shown promise but needs further independent validation. In this review, we will present the current data on commercially available molecular profiling assays in breast cancer and discuss the challenges surrounding their incorporation into routine clinical practice as prognostic and predictive tools.

Introduction

Breast cancer is a heterogeneous disease. Several histological subtypes are recognized but these are of limited utility, since the vast majority of the breast cancers (65% to 80%) belong to a single subtype, invasive ductal carcinoma of no special type. Thus, the prognosis of breast cancer is mostly determined using three criteria: lymph node status, tumor size, and histological grade.[1] The Nottingham Grading System, the modified version of the Scarff-Bloom-Richardson grading system, is of particular importance in small node-negative tumors.[1] It is also an independent prognostic indicator in estrogen receptor (ER)-positive breast cancer, although it may not be of significant utility in ER-negative tumors, since most of these are high histologic grade. ER, although a weak prognostic marker, is a strong predictor of response to antiestrogen therapy.[2] In addition to ER, expression of progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) are routinely used in clinical practice.[3] HER2 testing is also critical to the selection of patients most likely to obtain benefit from therapy with the anti-HER2 antibody, trastuzumab (Herceptin). However, almost half of patients with HER2-positive disease are resistant to trastuzumab.

To improve treatment decision making for the clinician, multiparametric tools, such as TNM staging, the Nottingham prognostic index (NPI), and Adjuvant! Online (http://www.adjuvantonline.com) have been introduced. TNM staging includes tumor size, nodal status, and metastases. The NPI predicts survival in patients with breast cancer by combining tumor size, lymph node stage, and histological grade.[4] Adjuvant! Online estimates the probability of survival and benefit from specific therapies in patients with early breast cancer.[5] The clinical parameters included in this tool are age, menopausal status, comorbidities, ER status, tumor grade, tumor size, and the number of involved axillary nodes. Despite the utility of all these tools, they are limited in their ability to help with clinical decision making and selection of treatment options because of the heterogeneous nature of breast cancer and the relative inefficiencies of available therapies. A considerable number of patients still develop tumor recurrence and die from breast cancer in spite of having received adjuvant anti-estrogen therapy, trastuzumab, or chemotherapeutic agents. Based on current treatment algorithms, it is estimated that up to 60% of patients will only experience toxicities and will derive little or no benefit from therapy.[6,7] There is a great need for novel predictive assays that can help identify patients likely to respond to the often toxic and very expensive therapies, so patients can avoid experiencing only toxicities and no benefits from these treatments.

Key Steps for Incorporation of Molecular Profiling Assays Into Routine Clinical Practice

Molecular profiling is an emerging concept in clinical decision making that involves arrangement of biological specimens such as tumors or other tissues into groups based on multiple changes at the genomic and transcriptomic levels. In the last decade, molecular profiling technologies have advanced our knowledge of breast cancer biology. New tests utilize gene expression assays-either microarray or real-time quantitative reverse transcription polymerase chain reaction [qRT-PCR]. A number of prognostic and predictive multigene profiling assays (mainly considered as multigene breast cancer classifiers) have had a substantial impact on clinical oncology practice and have helped make individualized management of patients a possibility. While the first-generation prognostic multigene classifiers, such as the MammaPrint assay and the Oncotype DX breast cancer assay, are the closest to clinical practice, the second-generation prognostic multigene assays, which include breast cancer microenvironment or host immune response, have not been commercialized and need further external validation studies to determine their clinical utility.[8] Despite several studies, the translation of predictive multigene classifiers into the clinic is even more challenging than that of prognostic multigene classifiers.[9] Most of the predictive assays are derived mainly from cell lines. Also, using the microarray as the assay platform is not as quantitative as using a qRT-PCR assay. Therefore, subtle changes in gene expression may not be reflected in microarray-based assays, although these subtle differences may be sufficient to cause resistance to chemotherapeutics. Furthermore, resistance may occur due to low penetrance of the drug being administered and may be unrelated to tumor tissue.

To incorporate prognostic and/or predictive multigene classifiers into clinical practice, the following key criteria need to be fulfilled:

First, the platform on which the classifier is based should be suitable for broad clinical application and ensure that the classifier is stable under a variety of operating conditions. If not, the classifier needs to be translated to a clinically applicable platform.[10] The assay protocols should be standardized to achieve satisfactory interlaboratory and intralaboratory reproducibility, thereby establishing analytic validity. Assay standardization includes preanalytic parameters, such as sample storage and preparation, and analytic performance parameters, such as the sensitivity and specificity of the system as well as assay reproducibility. The Clinical Laboratory Improvement Amendments of 1988 (CLIA) require laboratories to independently establish analytic validity and to improve assay standardization.

Second, it is critical to classify studies as developmental or validation studies in order to increase the clinical validity of the classifier. For assays that purport to elucidate predictive significance, this strategy needs to be applied to determine the clinical utility of the classifier.[10,11] Developmental studies need to include internal clinical validation; this can be accomplished either by splitting the study population into two populations (the training model and the testing model) or by cross-validation based on repeated model development and testing on random data partitions. These approaches will increase the accuracy of the classifier, which in turn makes its further development possible. Independent validation studies are critical to further evaluate the predictive accuracy and usefulness of the classifier in clinical practice; such studies should be prospectively designed, and should verify both clinical validity and clinical utility.

Third, does the classifier only assess prognosis, or does it help with selection of a certain type of therapy? What is the therapeutic relevance of the classifier? Prognostic multigene classifiers assess the likelihood of disease recurrence, whereas predictive multigene classifiers evaluate the potential benefit from certain types of chemotherapy or anti-estrogen therapy. However, a prognostic classifier may also exhibit predictive significance. If a classifier is a predictive classifier, the bar for utility is often quite low. For example, approximately half of patients with HER2 positivity respond to trastuzumab. However, if the assay assesses low likelihood for recurrence or metastases (a prognostic assay), patients classified as low risk need to have such a low risk that they can be spared from adjuvant therapy without affecting their long-term prognosis.[12]

Fourth, the incorporation of the classifier into the clinic might be more beneficial if it outperforms or adds predictive power to existing prognostic methods; this would help justify the money and time invested in its external validation in a trial of a much larger scale. In other words, it is important to determine cost-effectiveness.

In this review, we discuss the advantages and limitations of the commercially available first-generation prognostic multigene classifiers based on the key criteria described above.

First-Generation Prognostic Multigene Classifiers in Breast Cancer

The “intrinsic” classification was the first assay to use modern molecular tools to classify breast cancers. The 70-gene signature of van ’t Veer et al (MammaPrint)[13] and the 21-gene assay (Oncotype DX)[14] were the first two breast cancer multigene classifiers to become commercially available.

Both of these have been tested in more than one validation cohort and are being tested for further clinical utility in large prospective trials in Europe (MINDACT [Microarray In node Negative Disease may Avoid ChemoTherapy]; MammaPrint assay) and in the United States (TAILORx [Trial Assigning IndividuaLized Options for treatment; Oncotype DX assay). Another assay in advanced stages of development is a 50-gene assay (PAM50),[15] the clinically applicable platform of intrinsic subtype classification (Table). A number of other assays that are in various stages of development and validation are not discussed in this review.

The Intrinsic Subtypes of Breast Carcinoma

Using DNA microarrays, Perou et al[16,17] and Sorlie et al[18] classified breast carcinomas into five molecular subtypes: ER-positive (luminal A), ER-positive (luminal B), ER-negative/HER2-enriched, basal-type, and normal-like subtypes. The luminal A subtype displays high expression of ER, GATA binding protein 3 (GATA3), X-box binding protein trefoil factor 3, FOXA1, and LIV-1. The luminal B subtype, on the other hand, shows moderate expression of the genes expressed by the breast luminal cells and frequently higher proliferation and lower PR levels. These subtypes are associated with differences in both disease outcome and therapeutic response.[15,18]

The major disadvantage of the original intrinsic classification is that it was based on analysis of microarrays. The data are labile in that the addition of a single new case has the potential to change the classification rules. In order to achieve stability and permit analysis of new cases, “single sample predictors” (SSPs) were introduced. These SSPs have been modified over the different publications[15,19-22]; the agreements between these definitions have been only modest (kappa value, 0.4–0.5). The classification has also suffered from lack of validation by independent investigators. In addition, the intrinsic classification raises several important issues.

TABLE


Current Commercially Available Breast Cancer Classifiers: Key Steps for Their Incorporation Into Clinical Decision Making

First, does the “normal” subtype of breast cancer exist? It is becoming increasingly accepted that this was an artifact caused by a disproportionately high content of normal breast and stromal cells in the frozen samples used for microarray analysis.[15,21,23]

Second, and perhaps the more important question, what is the relevance of the “HER2-enriched” category? A fair number of cases included in the HER2-enriched category do not have increased HER2 mRNAs. In addition, it has been shown that a large number of clinically positive cases (by immunohistochemistry [IHC] and/or fluorescence in situ hybridization [FISH]) do not belong in the HER2-enriched category.[15,23-25] If one accepts the notion that the HER2-enriched category has no therapeutic relevance, then the existence of this category could be confusing for both patients and their treating physicians (see additional data below).

Third, can ER-positive tumors be subdivided into two distinct categories, luminal A and luminal B, or is this division artificial? It appears that this division is largely based on proliferation-related genes.[15,21,23] Other studies have suggested a continuous spectrum of proliferation in ER-positive tumors.[23,26]

The intrinsic classification is a major milestone in the molecular classification of breast cancer. However, it should be regarded as a “work in progress,” particularly since novel subtypes, such as molecular apocrine,[27] interferon-rich,[21] and claudin-low,[28] are still being discovered.

PAM50 Assay

The PAM50 assay is a clinically applicable updated version of the intrinsic subtyping classification based on qRT-PCR and NanoString technology. A 50-gene subtype predictor assay has been developed using a training set representing each subtype.[15] This assay measures a risk of relapse (ROR) that is prognostic of relapse-free survival for patients who have node-negative tumors and who have not received adjuvant systemic therapy. Using an independent test cohort with ER-positive breast cancer treated with adjuvant tamoxifen, the ROR score combined with tumor size performed better than the other standard clinicopathological variables.[24] The PAM50 assay is being further developed using the NanoString nCounter platform (NanoString Technologies, Seattle). Using this platform, samples from the ATAC (Arimidex, Tamoxifen, Alone or in Combination) trial were analyzed to compare PAM50 ROR scores with results from the Oncotype DX assay[14] and IHC4,[29] a four-panel set of IHC markers (ER, PR, HER2, Ki67) used to provide information on distant recurrence-free survival. Dowsett et al[30] reported that PAM50 assigned more women to the high-risk group and fewer women to the intermediate-risk group than did Oncotype DX. The PAM50 ROR score also significantly predicted 10-year distant recurrence-free survival and added prognostic information about distant recurrence to standard pathological variables in all patients. However, the current ATAC trial analysis of PAM50 does not validate the test’s ability to predict whether patients will derive benefit from specific chemotherapies. Results of the comparative analysis with IHC4 have not been presented. Cheang et al[31] emphasized that the PAM50 HER2-enriched subtype gets more treatment benefit from neoadjuvant anthracyclines/taxane and trastuzumab/taxane regimens. However, this is somewhat to be expected given the low response rates in ER-positive patients. Overall, the results with PAM50 are promising, and upon validation, the test could become part of clinical decision making in the near future.

MammaPrint

The 70-gene signature (MammaPrint) is the only microarray-based assay approved by the US Food and Drug Administration (FDA).[13] This signature was designed to predict distant disease-free survival and overall survival for lymph node–negative patients. MammaPrint has subsequently been shown to have strong prognostic value in patients with node-positive tumors. The utility of this assay has been documented in several independent studies.[32-34] The value of MammaPrint is its ability to identify patients in the “low-risk” group who show a greater than 90% chance of being disease-free for a minimum of 5 years-and who might thus be spared unnecessary adjuvant therapy. Comparative studies have shown efficacy of the signature in selecting patients for adjuvant therapy compared with the St. Gallen criteria.[34] A significant benefit of adding chemotherapy to endocrine therapy in patients classified as “high risk” by the MammaPrint assay has been documented; no significant benefit for the addition of chemotherapy in patients classified as “low risk” has also been documented.[35] The signature was shown to improve quality-adjusted survival and had the highest probability of being cost-effective.[36] It is being evaluated in a prospective, phase III clinical trial (MINDACT trial[37]). The trial has recently been expanded to allow patients with one to two positive nodes.

Until recently, the MammaPrint assay was only able to be used with frozen tissues. The stability of the assay has been documented in tissues collected in RNA-preserving solutions such as RNAlater (Life Technologies, Carlsbad, Calif.). MammaPrint identifies patients with a high or low risk of recurrence and does not directly assess ER, PR, or HER2 mRNA levels. Nearly all ER-negative patients fall into the poor prognosis category; thus, the utility of the assay in ER-negative patients is unclear. Within the ER-positive subtypes, it is heavily dependent on proliferation-related genes, much like the other existing signatures.[38] A qRT-PCR version of the MammaPrint assay, which can be used with formalin-fixed paraffin-embedded tissue, recently became available. However, only a few independent studies of this version of the test are available as yet.

The 70-gene signature was chosen from a list of 5000 differentially expressed genes. In an attempt to identify cellular pathways for targeted therapeutics, Ein-Dor et al analyzed the importance of the 70-gene set by selecting distinct sets of 70 genes from the original 5000 genes.[39] Their analyses showed that many equally predictive lists could have been produced from the same original list. Thus, the genes included in the signature may not always have biological relevance and therefore cannot be used as candidates for targeted therapeutics.[39] In addition, a recent study reported that random gene signatures, unrelated to cancer, are highly associated with breast cancer outcome.[40] This is due to the fact that more than 50% of the breast cancer transcriptome is correlated with proliferation-related genes. Indeed, removal of the proliferation metagene, meta-PCNA, abrogated the prognostic value of the signature in breast cancer patients, thereby calling into question the prognostic relevance of published breast cancer signatures that are heavily dependent on proliferation.

Oncotype DX 21-Gene Assay

Oncotype DX is a Clinical Laboratory Improvement Amendments (CLIA)-approved RT-PCR assay that measures the expression of 21 genes, including 16 cancer-related genes associated with proliferation, invasion, and the HER2 and ER pathways, as well as 5 reference genes. The recurrence score (RS) is used to predict patient prognosis (ie, the likelihood of recurrence at 10 years[14]); scores range from 0 to 100. The assay also categorizes patients into three risk groups:

1) A low-risk group (RS, 0–18), in which the score correlates with a risk of distant recurrence of less than 10%.

2) An intermediate-risk group (18 < RS < 31), in which the risk score correlates with a risk of distant recurrence of between 10% and 20%.

3) A high-risk group (RS ≥ 31), in which the score correlates with a risk of distant recurrence of greater than 20%.

This test can be used in women of all ages with newly diagnosed ER-positive stage I or II breast cancer. In addition to providing prognostic information, it predicts benefit from hormonal therapy (tamoxifen and/or aromatase inhibitors) as well from chemotherapy.[14,41-44]

The key clinical studies for the development and validation of Oncotype DX include National Surgical Adjuvant Breast and Bowel Project (NSABP) B-14, NSABP B-20, and Southwest Oncology Group (SWOG) 8814.[41,44,45] The assay was developed using the placebo-treated arm of B-20 and validated in the B-14 study.[14] The RS predicts the likelihood of distant recurrence in tamoxifen-treated patients with node-negative, ER-positive breast cancer. The hazard ratio for poor histological grade, used as a prognostic tool, was comparable to that of RS. However, histological grade is associated with significant subjectivity.[1] Further analysis in the B-20 study showed differences in the response of patients to chemotherapy that correlated with the RS; a high RS was associated with response, while low and intermediate RS were associated with minimal response or benefit.[44] The analysis of node-positive patients in the SWOG 8814 study showed that RS was also predictive in this population, although the overall risks were higher for any given score.[41]

As with other signatures, proliferation significantly contributes to the utility of the Oncotype DX signature in ER-positive tumors. A recent study[29] showed that an algorithm based on the IHC4 combination of four markers (ER, PR, HER2, and Ki67) could provide the same prognostic information as RS; however, these data need further validation. It has been observed that there is a high degree of concordance between ER/PR on RT-PCR results and ER/PR status as determined by IHC, and between HER2 on RT-PCR results and HER2 status as determined by FISH.[46,47] This has led to single-gene scores being added to the Oncotype DX reports. Although the concordance between RT-PCR and traditional methods of determining tumor status has been high, there are a small but significant number of cases in which the results are positive with one method but not with the other. To date, there are no data to suggest that patients negative by IHC or FISH (for ER, PR, or HER2) but positive by RT-PCR respond to targeted therapeutics. However, it should be noted that this is an area of active investigation and that several studies are planned to examine this question.

Adjuvant! Online provides information that complements RS.[42,43] Recently, Tang et al showed that the addition of clinicopathological parameters leads to an improvement in the prognostic ability of RS.[48] The Oncotype DX assay was used in the prospective, randomized TAILORx clinical trial[49] in which patients assigned an intermediate RS are being randomized to receive either chemotherapy and hormone therapy or hormone therapy alone. This trial enrolled more than 11,000 patients and was closed in October 2010. A trial for node-positive patients (RxPONDER; SWOG S1007) is being launched.

Emerging Technologies for Future Prognostic and Predictive Assays to Be Used in Clinical Decision Making

Recent advances in gene sequencing technology have led to the introduction of high-throughput, massively parallel sequencing, including cost-efficient whole-exome and whole-transcriptome sequencing. These methods allow for the detection of somatic cancer genome alterations and mRNA quantification.[50] Due to the huge amount of data involved and the complex statistical and computational challenges, this technology is still in its infancy and is not used in diagnostic laboratories.[50] However, these emerging technologies will have a tremendous near-term impact on the practice of pathology, medicine, and the entire discipline of biology. Although the whole-genome technology is expensive, two articles have demonstrated its powerful utility as a diagnostic tool in cancer patients.[51,52] The first article, by Link et al,[51] identified a novel deletion of three exons of the TP53 gene in a woman who was diagnosed with breast cancer at age 37, diagnosed with ovarian cancer at age 39, and who died of treatment-related acute myeloid leukemia (tAML) at age 42. The second paper, by Welch et al,[52] reported the use of whole-genome sequencing in a 39-year-old woman diagnosed with AML; the sequencing identified a cryptic fusion oncogene, a novel insertional translocation on chromosome 17 that created a pathogenic PML-RARA gene fusion. The patient’s diagnosis and treatment were changed based on this information. The patient remains in remission as reported in the article.

Molecular Profiling and Routine Clinical Practice: Guidelines and Challenges

FIGURE


Treatment Algorithm Using a Combinatorial Approach

One of the major challenges for clinicians is deciding which patients require adjuvant therapy. Although adjuvant therapy decreases the risk of recurrence and death in some cases (for example, in patients with ER-negative disease), not all patients benefit from it. Overtreatment can also cause adverse effects. Therefore, guidelines have been developed, including the National Institutes of Health (NIH) Consensus Development criteria,[53] the National Comprehensive Cancer Network (NCCN) guidelines,[54] the St. Gallen Expert Opinion criteria,[55] the American Society of Clinical Oncology (ASCO) guidelines, and the computer-based algorithm Adjuvant! Online. These guidelines state that the precise clinical utility and application of MammaPrint is “under investigation.” The Oncotype DX assay is included in the ASCO guidelines on the use of tumor markers in breast cancer; the assay is also included in the NCCN guidelines for breast cancer treatment-as a predictor of recurrence for ER-positive, lymph node–negative breast cancer patients treated with 5 years of hormonal therapy. Bearing in mind that these are expensive assays, it is only natural to ask whether they are ready for prime time. Alternatively, can (almost) the same information be obtained using more traditional means?

Traditionally, oncologists have used the TNM staging system to make therapeutic decisions. In the age of mammographic screening, in which the majority of the tumors diagnosed are small with little or no extramammary involvement, the utility of the TNM system is becoming limited. The topic du jour is “what is the role of biology in small tumors?” and many editorials have discussed the inadequacy of size as the sole criterion for therapeutic decision making.[56,57] This point is forcefully brought home by small triple-negative tumors and HER2-positive tumors, which have a greater propensity for recurrence than typical ER-positive tumors.

It has been strongly argued by some that tumor grade should be included in the decision-making process. Tumor grade is a strong indicator of biology and has been shown to predict outcomes. In addition, grade is strongly associated with ER status, with the vast majority (> 95%) of low- and intermediate-grade tumors being ER-positive. Grade has also been a significant predictor in most molecular profiling studies and remains significant in multivariate analysis. This begs the question-why is grade not used? The two most common answers are 1) that grade is subjective, and 2) that a large number of tumors are grade 2. In most studies, 30% to 60% of tumors are graded as grade 2. This is an impediment to the utility of tumor grade. However, as recently highlighted by Rakha et al, a similar percentage of cases are classified as “intermediate risk” by Oncotype DX.[58] It should also be remembered that many cases are not suitable for molecular analysis because of inadequate tumor or contamination by normal elements (as with the “normal” subtype). Also, histologic grade has limited utility in ER-negative tumors, although so do Oncotype DX and MammaPrint.

A common problem with all the gene signatures described to date is that they are dependent on proliferation.[26] More importantly, if the contribution of this pathway is excluded from the gene signatures, the gene signatures lose their value. This has revived interest in the analysis of proliferation-related markers in archival tissues using immunohistochemistry. The IHC4 (ER, PR, HER2, and Ki67) score was shown to provide information similar to that provided by Oncotype DX. Currently, a number of efforts are directed at standardizing Ki67 immunohistochemistry for routine clinical use.[59] In particular, attention is being paid to which antibodies can be recommended for routine use, how many fields need to be counted, and whether these fields should be taken from the leading edge of the tumor or from the center of the tumor. A multinational task force has been established to address these issues.

Because of the presence in the US market of the Oncotype DX breast cancer assay and the MammaPrint assay, the Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group (EWG) assessed the value of both tests. The EWG found insufficient evidence to make a recommendation for or against the use of tumor gene expression profiles to improve outcomes in defined populations of women with breast cancer.[60] For Oncotype DX, they found preliminary evidence of a potential benefit of testing results in some women with regard to decisions about treatment options (reduced adverse events in low-risk women who avoided chemotherapy), but they could not rule out the potential for harm in others (breast cancer recurrence that might have been prevented). The EWG encouraged further development and evaluation of these technologies.

It is clear that the molecular profiling tests have a great potential to improve clinical decision making, since they address the complexity of breast cancer. We highly favor the combinatorial use of these assays with the existing traditional clinicopathologic parameters, as shown in the algorithm in the Figure. Indeed, a recent study by Tang et al[48] used a similar combinatorial approach in which the Oncotype DX RS was integrated with clinicopathological parameters to develop a tool-the RS-Pathology-Clinical (RSPC) assessment. This model, although in need of validation, might have the greatest predictive and/or prognostic utility in cases classified as “intermediate risk” by Oncotype DX.[61] Studies such as these highlight the difficulties in prognostication in patients with breast cancer and the need to use anatomical, histological, and biological approaches to assist with clinical decision making. It is indisputable that multigene classifiers cannot replace, but rather strengthen, prognostication and prediction in combination with clinicopathological parameters. They do not have a role in cases in which the patient (or the clinician) has already made the decision to proceed with systemic adjuvant therapy. However, these tests do have a role to play in those patients who are undecided or for whom a definite decision cannot be made based on clinicopathological findings. No test should be ordered if its results are not going to influence clinical decisions.

Financial Disclosure:Dr. Badve is a speaker for Genomic Health. Dr. Gkmen-Polar has no significant financial interest or other relationship with the manufacturers of any products or providers of any service mentioned in this article.

References:

References

1. Rakha EA, Reis-Filho JS, Baehner F, et al. Breast cancer prognostic classification in the molecular era: the role of histological grade. Breast Cancer Res. 2010;
12:207.

2. Harvey JM, Clark GM, Osborne CK, Allred DC. Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. J Clin Oncol. 1999;17:1474-81.

3. Harris L, Fritsche H, Mennel R, et al. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol. 2007;25:5287-312.

4. D'Eredita G, Giardina C, Martellotta M, et al. Prognostic factors in breast cancer: the predictive value of the Nottingham Prognostic Index in patients with a long-term follow-up that were treated in a single institution. Eur J Cancer. 2001;37:591-6.

5. Olivotto IA, Bajdik CD, Ravdin PM, et al. Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol. 2005;23:2716-25.

6. Schmidt M, Victor A, Bratzel D, et al. Long-term outcome prediction by clinicopathological risk classification algorithms in node-negative breast cancer-comparison between Adjuvant!, St Gallen, and a novel risk algorithm used in the prospective randomized Node-Negative-Breast Cancer-3 (NNBC-3) trial. Ann Oncol. 2009;20:258-64.

7. Weigelt B, Reis-Filho JS. Molecular profiling currently offers no more than tumour morphology and basic immunohistochemistry. Breast Cancer Res. 2010;12 Suppl 4:S5.

8. Reis-Filho JS, Weigelt B, Fumagalli D, Sotiriou C. Molecular profiling: moving away from tumor philately. Sci Transl Med. 2010;2:47ps43.

9. Borst P, Wessels L. Do predictive signatures really predict response to cancer chemotherapy? Cell Cycle. 2010;9:4836-40.

10. Simon R. Roadmap for developing and validating therapeutically relevant genomic classifiers. J Clin Oncol. 2005;23:7332-41.

11. Simon R. Development and validation of therapeutically relevant multi-gene biomarker classifiers.

J Natl Cancer Inst. 2005;97:866-7.

12. Sparano JA, Solin LJ. Defining the clinical utility of gene expression assays in breast cancer: the intersection of science and art in clinical decision making.
J Clin Oncol. 2010;28:1625-7.

13. van ‘t Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530-6.

14. 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;351:2817-26.

15. Parker JS, Mullins M, Cheang MC, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27:1160-7.

16. Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature. 2000;406: 747-52.

17. Perou CM, Jeffrey SS, van de Rijn M, et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci U S A. 1999;96:9212-7.

18. Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001;98:10869-74.

19. Sorlie T, Tibshirani R, Parker J, et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A. 2003;100:8418-23.

20. Chang HY, Nuyten DS, Sneddon JB, et al. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci U S A. 2005;102:3738-43.

21. Hu Z, Fan C, Oh DS, et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics. 2006;7:96.

22. Fan C, Oh DS, Wessels L, et al. Concordance among gene-expression-based predictors for breast cancer.

N Engl J Med. 2006;355:560-9.

23. Weigelt B, Mackay A, A’Hern R, et al. Breast cancer molecular profiling with single sample predictors: a retrospective analysis. Lancet Oncol. 2010;11:339-49.

24. Nielsen TO, Parker JS, Leung S, et al. A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor-positive breast cancer. Clin Cancer Res. 2010;16:5222-32.

25. de Ronde JJ, Hannemann J, Halfwerk H, et al. Concordance of clinical and molecular breast cancer subtyping in the context of preoperative chemotherapy response. Breast Cancer Res Treat. 2010;119: 119-26.

26. Wirapati P, Sotiriou C, Kunkel S, et al. Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res. 2008;10:R65.

27. Farmer P, Bonnefoi H, Becette V, et al. Identification of molecular apocrine breast tumours by microarray analysis. Oncogene. 2005;24:4660-71.

28. Prat A, Parker JS, Karginova O, et al. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res. 2010;12:R68.

29. Cuzick J, Dowsett M, Pineda S, et al. Prognostic value of a combined estrogen receptor, progesterone receptor, Ki-67, and human epidermal growth factor receptor 2 immunohistochemical score and comparison with the Genomic Health recurrence score in early breast cancer. J Clin Oncol. 2011;29:4273-8.

30. Dowsett M, Lopez-Knowles E, Sidhu K, et al. Comparison of PAM50 risk of recurrence (ROR) score with OncotypeDx and IHC4 for predicting residual risk of RFS and distant RFS after endocrine therapy: a TransATAC study. Cancer Res. 2011;71(24 Suppl):S4-5;108s.

31. Cheang M, Prat A, Fan C, Perou CM. PAM50 HER2-enriched subtype enriches for tumor response to neoadjuvant anthracyclines/taxane and trastuzumab/taxane containing regimens in HER-2 positive breast cancer. Cancer Res. 2011;71(24 Suppl):S5-2;110s.

32. van de Vijver MJ, He YD, van ’t Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347:1999-2009.

33. Bueno-de-Mesquita JM, van Harten WH, Retel VP, et al. Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study (RASTER). Lancet Oncol. 2007;8:1079-87.

34. Buyse M, Loi S, van ’t Veer L, et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst. 2006;98:1183-92.

35. Knauer M, Mook S, Rutgers EJ, et al. The predictive value of the 70-gene signature for adjuvant chemotherapy in early breast cancer. Breast Cancer Res Treat. 2010;120:655-61.

36. Retel VP, Joore MA, Knauer M, et al. Cost-effectiveness of the 70-gene signature versus St. Gallen guidelines and Adjuvant Online for early breast cancer. Eur J Cancer. 2010;46:1382-91.

37. European Organisation for Research and Treatment of Cancer (EORTC). EORTC Trial 10041 (BIG 3-04)-MINDACT. Available from: http://www.eortc.be/services/unit/mindact/MINDACT_websiteii.asp. Accessed February 17, 2012.

38. Sotiriou C, Wirapati P, Loi S, et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis.

J Natl Cancer Inst. 2006;98:262-72.

39. Ein-Dor L, Kela I, Getz G, et al. Outcome signature genes in breast cancer: is there a unique set? Bioinformatics. 2005;21:171-8.

40. Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput Biol. 2011;7:e1002240.

41. Albain KS, Barlow WE, Shak S, et al. Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. Lancet Oncol. 2010;11:55-65.

42. Dowsett M, Cuzick J, Wale C, et al. Prediction of risk of distant recurrence using the 21-gene recurrence score in node-negative and node-positive postmenopausal patients with breast cancer treated with anastrozole or tamoxifen: a TransATAC study.

J Clin Oncol. 2010;28:1829-34.

43. Goldstein LJ, Gray R, Badve S, et al. Prognostic utility of the 21-gene assay in hormone receptor-positive operable breast cancer compared with classical clinicopathologic features. J Clin Oncol. 2008;26:4063-71.

44. Paik S, Tang G, Shak S, et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol. 2006;24:3726-34.

45. Habel LA, Shak S, Jacobs MK, et al. A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients. Breast Cancer Res. 2006;8:R25.

46. Badve SS, Baehner FL, Gray RP, et al. Estrogen- and progesterone-receptor status in ECOG 2197: comparison of immunohistochemistry by local and central laboratories and quantitative reverse transcription polymerase chain reaction by central laboratory. J Clin Oncol. 2008;26:2473-81.

47. Baehner FL, Achacoso N, Maddala T, et al. Human epidermal growth factor receptor 2 assessment in a case-control study: comparison of fluorescence in situ hybridization and quantitative reverse transcription polymerase chain reaction performed by central laboratories. J Clin Oncol. 2010;28:4300-6.

48. Tang G, Cuzick J, Costantino JP, et al. Risk of recurrence and chemotherapy benefit for patients with node-negative, estrogen receptor-positive breast cancer: recurrence score alone and integrated with pathologic and clinical factors.

J Clin Oncol. 2011;29:4365-72.

49. National Cancer Institute. Personalized treatment trial for breast cancer launced. Available from: http://www.cancer.gov/newscenter/pressreleases/TAILORxRelease. Accessed February 17, 2012.

50. Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet. 2010;11:685-96.

51. Link DC, Schuettpelz LG, Shen D, et al. Identification of a novel TP53 cancer susceptibility mutation through whole-genome sequencing of a patient with therapy-related AML. JAMA. 2011;305:1568-76.

52. Welch JS, Westervelt P, Ding L, et al. Use of whole-genome sequencing to diagnose a cryptic fusion oncogene. JAMA. 2011;305:1577-84.

53. National Institutes of Health (NIH). Adjuvant therapy for breast cancer. NIH Consensus Development Conference Statement November 1-3, 2000. Available from: http://consensus.nih.gov/2000/2000AdjuvantTherapyBreastCancer114html.htm. Accessed February 17, 2012.

54. Carlson RW, Anderson BO, Burstein HJ, et al. Invasive breast cancer. J Natl Compr Canc Netw. 2007;5:246-312.

55. Goldhirsch A, Glick JH, Gelber RD, et al. Meeting highlights: international expert consensus on the primary therapy of early breast cancer 2005. Ann Oncol. 2005;16:1569-83.

56. Badve S. Does tumor size trump biology? Clin Breast Cancer. 2010;10:111-2.

57. Lin NU, Dieras V, Paul D, et al. Multicenter phase II study of lapatinib in patients with brain metastases from HER2-positive breast cancer. Clin Cancer Res. 2009;15:1452-9.

58. Rakha EA Ellis IO. Modern classification of breast cancer: should we stick with morphology or convert to molecular profile characteristics. Adv Anat Pathol. 2011;18:255-67.

59. Dowsett M, Nielsen TO, A’Hern R, et al. Assessment of Ki67 in breast cancer: recommendations from the International Ki67 in Breast Cancer Working Group.

J Natl Cancer Inst. 2011;103:1656-64.

60. Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group. Recommendations from the EGAPP Working Group: can tumor gene expression profiling improve outcomes in patients with breast cancer? Available from: http://www.egappreviews.org/docs/EGAPPWG-BrCaGEPRec.pdf. Accessed February 17, 2012.

61. Pusztai L. Anatomy and biology: two complementary sides of breast cancer prognostication. J Clin Oncol. 2011;29:4347-8.