Background
A 70-gene microarray prognosis signature was previously discovered to improve the selection of patients with breast cancer for adjuvant therapy1,2. This diagnostic test known as “MammaPrint” was recently validated in an independent cohort and implementation was shown to be feasible in community hospitals3-7. MammaPrint was originally established on surgical resection specimens. Since most breast cancer patients will undergo core needle biopsies, we investigated whether the MammaPrint prognosis signature could be assessed in core needle biopsies.

Background
A 70-gene tumor expression profile was established as a powerful predictor of disease outcome in patients with breast cancer1,2. The test, known as “MammaPrint,” was validated in independent cohorts, and implementation was shown to be feasible in community hospitals3-7. We have shown that MammaPrint predicts risk of recurrence in T1, T2 N0 and N1-3+ breast cancer patients, independent of hormone receptor status and other traditional clinical factors. Here we investigate the performance of MammaPrint in patients with small tumors usually regarded as low risk patients.

Abstract
Background: A 70-gene tumor expression profile was established as a powerful predictor of disease outcome in patients with breast cancer1,2. The test, known as “MammaPrint,” was recently validated in independent cohorts, and implementation was shown to be feasible in community hospitals3-7. We have shown that MammaPrint predicts risk of recurrence in T1, T2 N0 and N1-3+ breast cancer patients, ER independent.

Abstract
Purpose: Most node-negative breast cancer patients are older and postmenopausal and are increasingly being offered adjuvant chemotherapy despite their lowoverall risk of distant relapse. Amolecular diagnostic test withhighnegative predictive value (NPV) for distantmetastasis in this subgroup would spare many older breast cancer patients adjuvant treatment. Experimental Design:We determined the NPVand positive predictive value of theMammaPrint assay in breast cancer patients who were consecutively diagnosed and treated at theMassachusetts General Hospital between 1985 and 1997. Primary tumors from 100 patients with nodenegative, invasive breast cancer (median age, 62.5 years; median follow-up, 11.3 years) were subjected to MammaPrint analysis and classified as being at either low or high risk for distant metastasis.

Results: The MammaPrint 70-gene signature displayed excellent NPV as in previous studies,correctly identifying 100% of women at low risk for distant metastases at 5 years. However, this assay had a lower positive predictive value (12% at 5 years) than previously observed. Conclusions:The MammaPrint assay was originally designed to identify younger breast cancer patients at low risk for distant metastasis,who might consequently be spared systemic treatment. We show here that the same signature has a very high NPV for distant recurrence after adjuvant treatment in older breast cancer patients.

Therapies for patients with cancer have changed gradually over the past decade, moving away from the administration of broadly acting cytotoxic drugs towards the use of more-specific therapies that are targeted to each tumour. To facilitate this shift, tests need to be developed to identify those individuals who require therapy and those who are most likely to benefit from certain therapies. In particular, tests that predict the clinical outcome for patients on the basis of the genes expressed by their tumours are likely to increasingly affect patient management, heralding a new era of personalized medicine.

The Human Genome program and the development of high throughput technologies have set the stage for the latest revolution in medicine, sometimes called the “-omics” revolution. Genomics, the best developed “-omics” area, is now an established and frequently used tool in medical research, and particularly in the oncology field.In breast cancer, genomics has led to a better understanding of the biology and to amolecular reclassification of the disease, with several studies1-5 showing consistently the existence of at least four major subtypes of breast cancer: luminal A and B, basal, and human epidermal growth factor (HER)-2 positive. The greatest challenge has now become the application of genomics to clinical research ultimately leading to its use in clinical practice. To this end, two major studies have recently started, aiming at validating two important new prognostication tools for breast cancer: the TAILORx (Trial Assigning Individualized Options for Treatment [Rx]) trial, run in the US, evaluates Oncotype Dx (Genomic Health, Redwood City, CA), a 21-gene recurrence score developed in tamoxifen-treated patients,6 and the MINDACT trial, run mainly in Europe, evaluates MammaPrint (Agendia, Amsterdam, the Netherlands), the 70-gene expression profile discovered at the Netherlands Cancer Institute.7,8 This article outlines the several steps in the development of MammaPrint from its discovery to its clinical validation.

The axillary lymph node status is considered to be one of the most powerful prognostic factors for operable breast cancer, with a direct relationship between number of positive nodes and disease outcome. However, approximately 30% of lymph node-positive patients will remain free of distant metastases without adjuvant chemotherapy. Identifying patients with lymph node-positive disease who are at low risk of recurrence might lead to changes in guidelines for adjuvant chemotherapy. In node-negative patients the 70-gene profile (MammaPrint) has proven to be an independent prognostic factor. In the first validation study, by van de Vijver et al., the profile was also a significant prognostic factor in node-positive patients. To further substantiate the prognostic value of the 70-gene profile in patients with 1-3 positive nodes, the TRANSBIG consortium conducted a validation study.

Breast cancer patients with similar pathological staging can have markedly different rates of disease-free and overall survival. A key challenge in breast cancer management is to accurately determine a patient’s riskof developing distant metastasis at the time of primary diagnosis. This information can then be used to tailor metastasis-preventing treatment for high-risk patients. A 70-gene microarray gene expression signature was previously discovered at the Netherlands Cancer Institute (NKI) to identify younger breast cancer patients (age < 55 years) with lymph-node negative disease who are at low risk of developing distant metastasis and might therefore be spared further adjuvant chemotherapy (1, 2). This diagnostic test known as “MammaPrintTM” was recently validated in an independent cohort (3). Many breast cancer patients, however, are older and post-menopausal with a lower overall risk of distant metastasis. A molecular diagnostic test with high negative predictive value for distant metastasis in this subgroup could spare many older women adjuvant treatment.

Background:
The increasing use of DNA microarrays in biomedical research, toxicogenomics, pharmaceutical development, and diagnostics has focused attention on the reproducibility and reliability of microarray measurements. While the reproducibility of microarray gene expression measurements has been the subject of several recent reports, there is still a need for systematic investigation into what factors most contribute to variability of measured expression levels observed among different laboratories and different experimenters.

Results:
We report the results of an interlaboratory comparison of gene expression array measurements on the same microarray platform, in which the RNA amplification and labeling, hybridization and wash, and slide scanning were each individually varied. Identical input RNA was used for all experiments. While some sources of variation have measurable influence on individual microarray signals, they showed very low influence on sample-to-reference ratios based on averaged triplicate measurements in the two-color experiments. RNA labeling was the largest contributor to interlaboratory variation.

Conclusion:
Despite this variation, measurement of one particular breast cancer gene expression signature in three different laboratories was found to be highly robust, showing a high intralaboratory and interlaboratory reproducibility when using strictly controlled standard operating procedures.

Background: A microarray-based 70-gene prognosis signature might improve the selection of patients with node-negative breast cancer for adjuvant systemic treatment. The main aims of this MicroarRAy PrognoSTics in Breast CancER (RASTER) study were to assess prospectively the feasibility of implementation of the 70-gene prognosis signature in community-based settings and its effect on adjuvant systemic treatment decisions when considered with treatment advice formulated from the Dutch Institute for Healthcare Improvement (CBO) and other guidelines.

Methods: Between January, 2004 and December, 2006, 812 women aged under 61 years with primary breast carcinoma (clinical T1–4N0M0) were enrolled. Fresh tumour samples were collected in 16 hospitals in the Netherlands within 1 h after surgery. Clinico pathological factors were collected and microarray analysis was done with a custom-designed array chip that assessed the mRNA expression index of the 70 genes previously identified for the prognostic signature. Patients with a “good” signature were deemed to have a good prognosis and, therefore, could be spared adjuvant systemic treatment with its associated adverse effects, whereas patients with a “poor” signature were judged to have a poor prognosis and should be considered for adjuvant systemic treatment. Concordance between risk predicted by the prognosis signature and risk predicted by commonly used clinicopathological guidelines (i.e. St Gallen guidelines, Nottingham Prognostic Index, and Adjuvant! Online) was assessed. Findings of 585 eligible patients: 158 patients were excluded because of sampling failure (n=128) and incorrect procedure (n=30). Prognosis signatures were assessed in 427 patients. The 70-gene prognosis signature identified 219 (51%) patients with good prognosis and 208 (49%) patients with poor prognosis. The Dutch CBO guidelines identified 184 patients (43%) with poor prognosis, which was discordant with those findings obtained with the prognosis signature in 128 (30%) patients. Oncologists recommended adjuvant treatment in 203 (48%) patients based on Dutch CBO guidelines, in 265 (62%) patients if the guidelines were used with the prognosis signature, and in 259 (61%) patients if Dutch CBO guidelines, prognosis signature, and patients’ preferences for treatment were all taken into account. Adjuvant! Online guidelines identified more patients with poor prognosis than did the signature alone (294 [69%]), and discordance with the signature occurred in 160 (37%) patients. St Gallen guidelines identified 353 (83%) patients with poor prognosis with the signature and discordance in 168 (39%) patients. Nottingham Prognostic Index recorded 179 (42%) patients with poor prognosis with the signature and discordance in 117 (27%) patients.

Interpretation: Use of the prognosis signature is feasible in Dutch community hospitals. Adjuvant systemic treatment was advised less often when the more restrictive Dutch CBO guidelines were used compared with that finally given after use of the prognosis signature. For the other guidelines assessed, less adjuvant chemotherapy would be given when the data based on prognosis signature alone are used, which might spare patients from adverse effects and confirms previous findings. Future studies should assess whether use of the prognosis sign ature could improve survival or equal survival while avoiding unnecessary adjuvant systemic treatment without affecting patients’ survival, and further assess the factors that physicians use to recommend adjuvant systemic treatment.

Background: A 70-gene tumor expression profile was established as a powerful predictor of disease outcome in young breast cancer patients. This profile, however, was generated on microarrays containing 25,000 60-mer oligonucleotides that are not designed for processing of many samples on a routine basis.

Results: To facilitate its use in a diagnostic setting, the 70-gene prognosis profile was translated into a customized microarray (MammaPrint) containing a reduced set of 1,900 probes suitable for high throughput processing. RNA of 162 patient samples from two previous studies was subjected to hybridization to this custom array to validate the prognostic value. Classification results obtained from the original analysis were then compared to those generated using the algorithms based on the custom microarray and showed an extremely high correlation of prognosis prediction between the original data and those generated using the custom mini-array (p < 0.0001).

Conclusion: In this report we demonstrate for the first time that microarray technology can be used as a reliable diagnostic tool. The data clearly demonstrate the reproducibility and robustness of the small custom-made microarray. The array is therefore an excellent tool to predict outcome of disease in breast cancer patients.

Background: A 70-gene signature was previously shown to have prognostic value in patients with node-negative breast cancer. Our goal was to validate the signature in an independent group of patients.

Methods: Patients (n = 307, with 137 events after a median follow-up of 13.6 years) from five European centers were divided into high- and low-risk groups based on the gene signature classification and on clinical risk classifications. Patients were assigned to the gene signature low-risk group if their 5-year distant metastasis-free survival probability as estimated by the gene signature was greater than 90%. Patients were assigned to the clinicopathologic low-risk group if their 10-year survival probability, as estimated by Adjuvant! software, was greater than 88% (for estrogen receptor [ER]-positive patients) or 92% (for ER-negative patients). Hazard ratios (HRs) were estimated to compare time to distant metastases, disease-free survival, and overall survival in high- versus low-risk groups.

Results: The 70-gene signature out-performed the clinicopathologic risk assessment in predicting all endpoints. For time to distant metastases, the gene signature yielded HR = 2.32 (95% confidence interval [CI] = 1.35 to 4.00) without adjustment for clinical risk and hazard ratios ranging from 2.13 to 2.15 after adjustment for various estimates of clinical risk; clinicopathologic risk using Adjuvant! software yielded an unadjusted HR = 1.68 (95% CI = 0.92 to 3.07). For overall survival, the gene signature yielded an unadjusted HR = 2.79 (95% CI = 1.60 to 4.87) and adjusted hazard ratios ranging from 2.63 to 2.89; clinicopathologic risk yielded an unadjusted HR = 1.67 (95% CI = 0.93 to 2.98). For patients in the gene signature high-risk group, 10-year overall survival was 0.69 for patients in both the low- and high-clinical risk groups; for patients in the gene signature low-risk group, the 10-year survival rates were 0.88 and 0.89, respectively.

Conclusions: The 70-gene signature adds independent prognostic information to clinicopathologic risk assessment for patients with early breast cancer.

Background: The number, nature, and costs of serious adverse effects experienced by younger women receiving chemotherapy for breast cancer outside of clinical trials are unknown.

Methods: From a database of medical claims made by individuals with employer-provided health insurance between January 1998 and December 2002, we identified 12,239 women 63 years of age or younger with newly diagnosed breast cancer, of whom 4075 received chemotherapy during the 12 months after the initial breast cancer diagnosis and 8164 did not. Diagnostic codes for eight chemotherapy-related adverse effects were identified. Total hospitalizations for all causes, hospitalizations or emergency room visits for adverse effects that are typically related to chemotherapy, and health care expenditures were compared between the two groups of women. All statistical tests were two-sided.

Results: Women who received chemotherapy were more likely than those who did not to be hospitalized or to visit the emergency room for all causes (61% versus 42%; mean difference = 19%, 95% confidence interval [CI] = 16.7% to 21.3%, P<.001) and for chemotherapy-related serious adverse effects (16% versus 5%, mean difference = 11%, 95% CI = 9.6% to 12.4%, P<.001). The percentages of chemotherapy recipients who were hospitalized or visited the emergency room during the year after their breast cancer diagnosis were 8.4% for fever or infection; 5.5% for neutropenia or thrombocytopenia; 2.5% for dehydration or electrolyte disorders; 2.4% for nausea, emesis, or diarrhea; 2.2% for anemia; 2% for constitutional symptoms; 1.2% for deep venous thrombosis or pulmonary embolus; and 0.9% for malnutrition. Chemotherapy recipients incurred large incremental expenditures for chemotherapy-related serious adverse effects (1271 dollars per person per year) and ambulatory encounters (17,617 dollars per person per year).

Conclusions: Chemotherapy-related serious adverse effects among younger, commercially insured women with breast cancer may be more common than reported by large clinical trials and lead to more patient suffering and health care expenditures than previously estimated.

A study published in The Journal of the National Cancer Institute, found that advanced chemotherapy put patients at far greater risks of harm than adverse effect reports from clinical trial data reveal. "Researchers mined insurance claims for 3,526 women who had intravenous chemotherapy for breast cancer and tallied problems serious enough to require emergency care or a hospital stay." They found: "Overall, 16% of women in the new study had at least one of eight side effects that required emergency care or hospitalization. Side effects also included blood clots, dehydration, nausea and diarrhea."

Background
Gene-expression–profiling studies of primary breast tumors performed by different laboratories have resulted in the identification of a number of distinct prognostic profiles, or gene sets, with little overlap in terms of gene identity.

Methods
To compare the predictions derived from these gene sets for individual samples, we obtained a single data set of 295 samples and applied five gene-expression–based models: intrinsic subtypes, 70-gene profile, wound response, recurrence score, and the two-gene ratio (for patients who had been treated with tamoxifen).

Results
We found that most models had high rates of concordance in their outcome predictions for the individual samples. In particular, almost all tumors identified as having an intrinsic subtype of basal-like, HER2-positive and estrogen-receptor–negative, or luminal B (associated with a poor prognosis) were also classified as having a poor 70-gene profile, activated wound response, and high recurrence score. The 70-gene and recurrence-score models, which are beginning to be used in the clinical setting, showed 77 to 81 percent agreement in outcome classification.

Conclusions
Even though different gene sets were used for prognostication in patients with breast cancer, four of the five tested showed significant agreement in the outcome predictions for individual patients and are probably tracking a common set of biologic phenotypes.

DNA microarray analysis was first described in the mid-1990s as a means to probe the expression of thousands of genes simultaneously1,2 and was quickly adopted by the research community for the study of a wide range of biologic processes. Most of the early studies had a simple and powerful design: to compare two biologic classes in order to identify the differential expression of the genes in them — genes with potential relevance to a wide range of biologic processes, such as the progression of cancer,3,4,5,6 the causes of asthma,7,8,9 heart disease,10,11,12 and neuropsychiatric disorders,13,14,15,16,17 and the analysis of factors associated.

Microarray analysis has been shown to improve risk stratification of breast cancer. Breast tumors analyzed by hierarchical clustering of expression patterns of "intrinsic" genes have been reported to subdivide into at least four molecular subtypes that are associated with distinct patient outcomes. Using a supervised method, a 70-gene expression profile has been identified that predicts the later appearance or absence of clinical metastasis in young breast cancer patients. Here, we show that distant metastases display both the same molecular breast cancer subtype as well as the 70-gene prognosis signature as their primary tumors. Our results suggest that the capacity to metastasize is an inherent feature of most breast cancers. Furthermore, our data imply that poor prognosis breast carcinomas classified either by the intrinsic gene set or the 70 prognosis genes represent distinct disease entities that seem sustained throughout the metastatic process.

Breast cancers are routinely subcategorised on the basis of clinical stage, cellular morphology and immunohistochemical analysis of a small number of markers. The recent development of gene expression microarray and related technologies provides an opportunity to perform more detailed profiling of the disease. It is anticipated that the molecular classification arising from such studies will be more powerful than its pathological predecessor at confining treatment to those patients who are most likely to benefit. It is likely that this will result in a much less frequent use of adjuvant chemotherapy. Furthermore, of those who do receive it, a higher proportion will benefit. If adopted, this will offer considerable patient benefits in terms of reducing unnecessary toxicity and have major health economic implications.

Background: A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy.

Methods: Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses.

Results: Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (+/-SE) overall 10-year survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6+/-4.5 percent in the group with a poor-prognosis signature and 85.2+/-4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome.

Conclusions: The gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria. Copyright 2002 Massachusetts Medical Society

Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumors according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumors of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumor cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.