A Prognostic Gene Signature for Metastasis-Free Survival of Triple Negative Breast Cancer Patients
After separating the overall training set (BrCa871) into a training set and a cross validation set, (A) a series of 24,800 potential solutions are produced by optimizing our cost function using the Nelder-Mead downhill simplex algorithm. These solutions were trained on survival data with no year-specific endpoint defined to maximize signal sensitivity (See Figure 4). Using these 24,800 potential solutions, (B) significance in both training and cross-validation sets was assessed. To control for over-fitting solutions, 556 solutions yielding significance in both sets were extracted and used to estimate the final BPMS signature.