A breast cancer prognostic signature predicts clinical outcomes in multiple tumor types.

Epidemiological studies indicate an increased risk of subsequent primary ovarian cancer from women with breast cancer. We have recently identified a 28-gene expression signature that predicts, with high accuracy, the clinical course in a large population of breast cancer patients. This prognostic gene signature also accurately predicts response to chemotherapy commonly used for treating breast cancer, including CMF, Tamoxifen, Paclitaxel, Docetaxel and Doxorubicin (Adriamycin), in a panel of 60 cancer cell lines of nine different tissue origins. This prompted us to investigate whether this prognostic gene signature could also predict clinical outcome in other cancer types of epithelial origins, including ovarian cancer (n=124), colon tumors (n=74) and lung adenocarcinomas (n=442). The results show that the gene expression signature contributes significantly more accurate (P<0.05; compared with random prediction) prognostic information in multiple cancer types independent of established clinical parameters. Furthermore, the functional pathway analysis with curated database delineated a biological network with tight connections between the signature genes and numerous well established cancer hallmarks, indicating important roles of this prognostic gene signature in tumor genesis and progression.

A population-based gene signature is predictive of breast cancer survival and chemoresponse.

It remains a critical issue to improve the survival rate in patients with recurrent or metastatic breast cancer. This study sought to develop a prognostic scheme based on a 28-gene signature in a broad patient population, including those with advanced disease. Clinically annotated transcriptional profiles of 1,734 breast cancer patients were obtained to validate the 28-gene signature in prognostic categorization. The 28-gene signature generated significant patient stratification with regard to breast cancer disease-free survival (log-rank P<0.0001; n=1,337) and overall survival (log-rank P<0.0001; n=806) in Kaplan-Meier analyses. The gene expression signature provides refined prognosis of disease-free survival (log-rank P<0.006; Kaplan-Meier analysis) within each classic clinicopathologic factor-defined subgroup, including LN-, LN+, ER-, ER+ and tumor grade II. Furthermore, it was investigated whether this gene signature predicts chemoresponse to drugs commonly used to treat breast cancer. The mRNA expression levels of this gene signature in NCI-60 cell lines were used to predict chemoresponse to CMF, tamoxifen, paclitaxel, docetaxel, and doxorubicin (adriamycin). The 28-gene prognostic signature accurately (P<0.02) predicted chemotherapeutic response to the studied drugs. This study confirmed the prognostic applicability of the breast cancer gene signature in a broad clinical setting. This prognostic signature is also predictive of drug response in cancer cell lines.

The gene expression signature of genomic instability in breast cancer is an independent predictor of clinical outcome.

Recently, expression profiling of breast carcinomas has revealed gene signatures that predict clinical outcome, and discerned prognostically relevant breast cancer subtypes. Measurement of the degree of genomic instability provides a very similar stratification of prognostic groups. We therefore hypothesized that these features are linked. We used gene expression profiling of 48 breast cancer specimens that profoundly differed in their degree of genomic instability and identified a set of 12 genes that defines the 2 groups. The biological and prognostic significance of this gene set was established through survival prediction in published datasets from patients with breast cancer. Of note, the gene expression signatures that define specific prognostic subtypes in other breast cancer datasets, such as luminal A and B, basal, normal-like, and ERBB2+, and prognostic signatures including MammaPrint and Oncotype DX, predicted genomic instability in our samples. This remarkable congruence suggests a biological interdependence of poor-prognosis gene signatures, breast cancer subtypes, genomic instability, and clinical outcome.