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Expression profiling as a prognostic and predictive factor in breast cancer

Microarray gene expression profiling combined with advanced bioinformatics is beginning to show its power in delineating disease entities that are otherwise indistinguishable. This refinement in tumor classification allows a more accurate prediction of outcome of disease for patients that present with the same stage of disease based on conventional clinical and histopathological criteria. Gene activities determining the biological behaviour of the tumor may indeed be more likely to reflect the aggressiveness of the tumor than general parameters such as tumor size, age of the patient, or even tumor grade. The immediate clinical consequences are therefore that treatment schemes can be tailored based on the gene activity patterns of the primary tumor.

Using gene expression profiling with cDNA microarrays, Perou and colleagues showed that there are several subgroups of breast cancer patients based on unsupervised cluster analysis: those of 'basal type' and those of 'luminal type'. These subgroups differ with respect to outcome of disease in patients with locally advanced breast cancer. In addition, microarray analysis has been used to identify diagnostic categories (e.g. BRCA1 and BRCA2, estrogen receptor status).

We used gene expression profiling with DNA microarrays harboring 25,000 genes on 78 primary breast cancers of young lymph-node-negative patients to establish a signature, predictive for a short interval to distant metastases. This 'poor prognosis' signature consists of genes involved in the cell cycle, invasion and angiogenesis. The prognosis signature is superior to currently available clinical and histopathological prognostic factors in predicting a short interval to distant metastases (odds ratio = 18 [95% confidence interval = 3.3-94], P < 0.001, multivariate analysis). We have validated our findings of this poor prognosis profile on a large unselected consecutive series of LN0 as well as lymph-node-positive (LN+) young breast cancer patients (n = 295). The analyses confirm that the profile is a strong independent factor in predicting outcome of disease for LN0 patients in general (10-year overall survival for the good prognosis profile 96% vs 50% for the poor prognosis profile). Furthermore, the profile is also powerful for LN+ patients. At present, the prognostic significance of the 70 genes is tested in older breast cancer patients. Nowadays, consensus guidelines in the management of breast cancer select up to 95% of lymph-node-negative young breast cancer patients for adjuvant systemic therapy (e.g. NIH and St Gallen consensus criteria). As 70–80% of these patients would have remained disease-free without this adjuvant treatment, these patients are 'overtreated'. The 'poor prognosis' signature provides a novel strategy to accurately select patients who would benefit from adjuvant systemic therapy and can greatly reduce the number of patients that receive unnecessary treatment.

Our data revealed that already small tumors display the metastatic signature, and recent results show that the molecular program established in a primary breast carcinoma is highly preserved in its distant metastasis. These findings suggest that metastatic capability in breast cancer is an inherent feature, and is not based on clonal selections. The results further imply that neo-adjuvant treatment given to patients based on (yet to be established) response expression profiles of their primary breast tumor might indeed prevent the outgrowth of micrometastases.

Currently, the EORTC breast group is preparing a 5000-patient randomized trial to compare the efficacy of guidance of breast cancer patients for adjuvant chemotherapy based on either 'conventional' St Gallen consensus criteria or the microarray prognosis test (MINDACT trial within the EU-TRANSBIG program). The aim of the study is to confirm that the microarray test will save up to 30% of the patients from unnecessary chemotherapy and to identify 5% of them who are nowadays 'undertreated'.

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van 't Veer, L. Expression profiling as a prognostic and predictive factor in breast cancer. Breast Cancer Res 7 (Suppl 2), S.28 (2005).

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