Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts

Abstract

Introduction: Adjuvant breast cancer therapy significantly improves survival, but overtreatment and undertreatment are major problems. Breast cancer expression profiling has so far mainly been used to identify women with a poor prognosis as candidates for adjuvant therapy but without demonstrated value for therapy prediction.

Methods: We obtained the gene expression profiles of 159 population-derived breast cancer patients, and used hierarchical clustering to identify the signature associated with prognosis and impact of adjuvant therapies, defined as distant metastasis or death within 5 years. Independent datasets of 76 treated population-derived Swedish patients, 135 untreated population-derived Swedish patients and 78 Dutch patients were used for validation. The inclusion and exclusion criteria for the studies of population-derived Swedish patients were defined.

Results: Among the 159 patients, a subset of 64 genes was found to give an optimal separation of patients with good and poor outcomes. Hierarchical clustering revealed three subgroups: patients who did well with therapy, patients who did well without therapy, and patients that failed to benefit from given therapy. The expression profile gave significantly better prognostication (odds ratio, 4.19; P = 0.007) (breast cancer end-points odds ratio, 10.64) compared with the Elston-Ellis histological grading (odds ratio of grade 2 vs 1 and grade 3 vs 1, 2.81 and 3.32 respectively; P = 0.24 and 0.16), tumor stage (odds ratio of stage 2 vs 1 and stage 3 vs 1, 1.11 and 1.28; P = 0.83 and 0.68) and age (odds ratio, 0.11; P = 0.55). The risk groups were consistent and validated in the independent Swedish and Dutch data sets used with 211 and 78 patients, respectively.

Conclusion: We have identified discriminatory gene expression signatures working both on untreated and systematically treated primary breast cancer patients with the potential to spare them from adjuvant therapy.

Figure 1

Description of exclusion criteria for all patients (pts) operated on for primary breast cancer at (a) Karolinska Hospital, 1994–1996 and (b) Uppsala University Hospital, 1987–1989.

Figure 2

Unsupervised hierarchical clustering of the Stockholm cohort (n = 159) using the 64-gene set. Each column refers to a patient and each row to a gene. Red indicates a high value of gene expression, and green indicates a low value. The list of genes is presented in Additional file 1, in the same order as they appear on the plot. Risk.score, computed by linear discriminant analysis and used here only to describe the clusters. Status.5 yr, black if the corresponding patient had distant metastasis or died within 5 years. BRCA.5 yr, black if the death was due to breast cancer. NodePos, black if the corresponding patient was lymph-node-positive; Grade3, black if the patient had Elston grade 3.

Figure 3

Supervised clustering of the node-positive treated cohort in Uppsala (n = 76) using the 64-gene set. The accompanying variables have the same meaning as in Fig. 2.

Figure 4

Supervised clustering of the node-negative untreated cohort in Uppsala (n = 135) using the 64-gene set. The accompanying variables have the same meaning as in Fig. 2.

Figure 5

Supervised clustering of the van't Veer cohort (n = 78) using 42 genes of the 64-gene set. Meta.5 yr, black if the patient had distant metastasis within 5 years.

Figure 6

Kaplan–Meier survival curves of the risk clusters found in (a) the Stockholm cohort, (b) the Uppsala treated cohort, (c) the Uppsala untreated cohort and (d) the van't Veer cohort. L, low-risk group; M, medium-risk group; H, high-risk group. The P value in each plot, computed in a Cox regression, is for simultaneous comparison of all three curves for the whole follow-up period.