Genomic aberrations and gene expression-defined subtypes in the large METABRIC 
patient cohort have been used to stratify and predict survival. The present 
study used normalized gene expression signatures of paclitaxel drug response to 
predict outcome for different survival times in METABRIC patients receiving 
hormone (HT) and, in some cases, chemotherapy (CT) agents. This machine learning 
method, which distinguishes sensitivity vs. resistance in breast cancer cell 
lines and validates predictions in patients; was also used to derive gene 
signatures of other HT  (tamoxifen) and CT agents (methotrexate, epirubicin, 
doxorubicin, and 5-fluorouracil) used in METABRIC. Paclitaxel gene signatures 
exhibited the best performance, however the other agents also predicted survival 
with acceptable accuracies. A support vector machine (SVM) model of paclitaxel 
response containing genes  ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, 
BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2, SLCO1B3, TUBB1, TUBB4A, 
and TUBB4B was 78.6% accurate in predicting survival of 84 patients treated with 
both HT and CT (median survival ≥ 4.4 yr). Accuracy was lower (73.4%) in 304 
untreated patients. The performance of other machine learning approaches was 
also evaluated at different survival thresholds. Minimum redundancy maximum 
relevance feature selection of a paclitaxel-based SVM classifier based on 
expression of genes  BCL2L1, BBC3, FGF2, FN1, and  TWIST1 was 81.1% accurate in 
53 CT patients. In addition, a random forest (RF) classifier using a gene 
signature ( ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, 
CYP3A4, MAP2, MAP4, MAPT, NR1I2,SLCO1B3, TUBB1, TUBB4A, and TUBB4B) predicted 
>3-year survival with 85.5% accuracy in 420 HT patients. A similar RF gene 
signature showed 82.7% accuracy in 504 patients treated with CT and/or HT. These 
results suggest that tumor gene expression signatures refined by machine 
learning techniques can be useful for predicting survival after drug therapies.