Figure 3

Gene expression signature predicts human breast cancer. We generated a human breast cancer predictor from human PBMC samples by using a mouse model of breast cancer to first identify the most informative probes to use in a subsequent factor modeling approach. (A) We generated the predictive model from a training set of healthy cancer-free individuals (n = 10) and patients with invasive breast cancer (n = 10). Blue = normal; red = malignant. An assessment of the model fit shows that this predictor has a robust capacity to discriminate among samples based on breast tumor status with 100% sensitivity and specificity. (B) We then used this predictive model to evaluate an independent set of samples (n = 162) for the capacity to distinguish controls from patients with a diagnosis of malignant breast cancer. This represents an external validation using samples not used in either the factor generation or the model building process. (C) We were able to predict breast cancer status with a sensitivity of 89% and specificity of 100% (AUC = 0.97) as shown in the ROC curve. The optimal threshold was calculated as 0.3760 based on Youden's J-statistic. (D) We then tested the validity of our modeling strategy by swapping the training and validation sets. New factors were generated based on the original validation set and new models were generated. The model fit diagram shows the ability to generate a robust model from the original validation set. (E) This new model was validated in the original training set. (F) It demonstrated a sensitivity of 100% and a specificity of 90% (AUC = 0.98). As a negative control, we generated mock factors from a publicly available dataset that was biologically unrelated to breast tumor status. We then projected these factors into the training (G) and validation (H) sets. (I) The sensitivity was 83% and specificity was 48% (AUC = 0.63).