1. Feature (Gene) Selection MethodsSample Classification Methods Gene filtering: Variance (SD/Mean) Principal Component Analysis Regression using variable selection: LASSO Ridge regression Elastic net shrinkage Greedy algorithm Support Vector Machines (Vapnik V, et al.) Nearest Shrunken Centroids (Tibshirani R, et al) Other methods: Decision Tree: Random Forest, CART k-Nearest-Neighbor Discriminant Analysis: LDA, DLDA Naive Bayesian classifiers: BART, Markov Chain Monte Carlo Artificial Neural Networks Ensemble learning: Bootstrap Aggregating, Boosting 1 Supplemental File 1, Table 1s

2. Supplemental File 2, Figure 2s 0100 A B

3. Supplemental File 3, Table 2s Type Patients in each classCR modeling performance CRNo CRSensitivitySpecificity Sustained CR.365853%81% CR3: At the end of 3 rd cycle (early- onset or flash-point CR) 12270~0% ~100% CR8: At the end of 8 th cycle3624630%77% CR20: At the end of protocol76 20622%81%