Predicting the risk for recurrence and treatment response for patients with advanced disease remains a critical issue in clinics. Patients at high risk for recurrence after the primary treatment should be considered for more aggressive chemotherapy, whereas second-line chemotherapy may not be necessary in low-risk patients. The FDA recently approved the first gene test for cancer, MammaPrint of Agendia (Amsterdam, The Netherlands) (19
), for use in lymph node-negative women under age 61 and with a tumor size less than 5 cm. Oncotype DX of Genomic Health (Redwood City, CA) is a clinically applied multigene assay to predict recurrence of tamoxifen-treated, node-negative, and estrogen receptor-positive breast cancer (9
). Both Oncotype and MammaPrint target early stage breast cancer patients. New gene signatures are needed for predicting breast cancer recurrence in broader clinical settings.
In a previous study (21
), we presented a population-based approach to predicting recurrence and metastases of breast cancer by using gene expression patterns in tumors obtained from Sotiriou et al (12
). The external validation sets used in this study consist of completely independent patient cohorts. The prognostic prediction based on the 28-gene signature employed the “gold standard” of validation schemes, i.e., an independent training set and a validation in multiple, non-overlapping datasets. Specific cutoff values were identified for multiple experimental platforms and clinical outcomes using a nearest centroid classification method. All cutoff schemes except one were consistently validated on multiple breast cancer patient cohorts. The 28-gene signature was confirmed to predict disease-free survival and overall survival in individual breast cancer patients (n
=1,734). These results showed that the stratification scheme could be applied to predicting clinical outcomes in a new breast cancer patient based on the 28-gene expression profiles measured on various commonly used microarray platforms.
Fan et al. (36
) compared five breast cancer signatures, including Oncotype DX (9
), wound response predictor (6
), intrinsic subtypes (10
), and the “two-gene ratio” (8
) using the cohort from van de Vijver et al (13
). This comparison represents an entirely independent test set only for Oncotype DX and the “two–gene ratio”, whereas the remaining three signatures used part of the samples from van de Vijver's cohort (n
= 295) in model development. If the training samples were removed for testing these three signatures, the resulting test dataset would be greatly reduced to fewer than 147 samples and possibly as few as 72 samples (36
). In this evaluation, all five signatures except the two-gene ratio allowed for prognostic categorization with respect to disease-free survival (log-rank p
< 0.001) and overall survival (log-rank p
< 0.001). Compared with these results in consideration of the bias toward MammaPrint®
, intrinsic subtypes, and wound response predictor, our 28-gene prognostic signature is comparable as Oncotype DX and could potentially be more accurate than the other signatures in terms of predicting disease-free survival and overall survival in van de Vijver's cohort (Appendix; Suppl. Figure 1). More importantly, the 28-gene breast cancer signature showed prognostic ability beyond early stage breast cancer. The 28-gene prognostic signature quantified disease-free survival and overall survival in a broad patient population including those with advanced stage (T3/T4), tumor grade III, lymph node metastasis, or negative estrogen receptor status (ER-).
According to the REMARK guidelines (37
), cancer prognostic studies must demonstrate whether tumor markers provide information independent of traditional criteria or provide prognostic information within subgroups defined by traditional criteria. This study demonstrated that the breast cancer gene signature could refine prognosis within each subgroup defined by lymph node status (node positive or negative), tumor grade (patients with Grade II), and ER status (ER+ or ER-). These results indicated that the 28-gene signature provides independent prognostic information in addition to the traditional factors.
The prognostic categorization will address one clinically important issue, i.e., who should receive more aggressive chemotherapy? Following this, another unresolved issue is which chemotherapy should be given to a specific patient? Breast cancer patients with the same tumor stage may have remarkably different response to a chemotherapeutic agent. This study demonstrated that the 28-gene prognostic signature was also predictive of chemoresponse in cancer cell lines. Since each NCI-60 cell line was derived from a clinical tumor and the gene expression was measured in untreated cell lines, this finding has important clinical implications in predicting a patient's predisposition to certain chemotherapy based on her molecular tumor characteristics, in addition to the tumor stage. This would help physicians to design optimal treatment strategies by including drugs within the sensitive range of this patient in personalized therapy.
In summary, this study developed a scheme for applying a 28-gene signature in patient stratification based on transcriptional profiles generated on a diverse range of microarray platforms. The signature predicts a poor outcome in breast cancer patients with early stage as well as advanced disease. This is significant in the clinical management of breast cancer, because this molecular classification scheme may help physicians to identify high-risk patients who might need additional or more aggressive chemotherapy after the primary treatment. Furthermore, this prognostic gene signature is also predictive of chemoresponse to CMF, Tamoxifen, Paclitaxel, Docetaxel, and Doxorubicin (Adriamycin) in cancer cell lines, which could potentially be used to predict patient predisposition to chemotherapy.