Signatures from 3D mammary acini predict survival in two large independent datasets of breast cancer patients.
Predicting clinical outcome and therapy responses of breast cancer patients is challenging and several groups have used transcriptional profiling of “training sets” (i.e. sets of patients with known outcome) to develop expression signatures. The supervised approach has been criticized as it is strongly dependent on the patient set used and is subject to inadequate validation. We used our 22 gene signature and unsupervised hierarchical clustering to group tumors into subclasses. The Wang and Stanford-Norway datasets were separated into two groups that accurately predicted overall survival when analyzed by the method of Kaplan-Meier (p=0.000013 and 0.045, for the Wang and Stanford datasets, respectively). Tumors with signatures most similar to differentiated acini had the best prognosis; in contrast to those with patterns like proliferating cells. This study underscores the utility of the unsupervised approach, as well as the value and potential clinical relevance of 3D culture models.