The system analyzes gene expression data from 3,534 breast cancers with clinical annotation including survival. For each test case a case-specific training subset is selected that includes only cases with the highest molecular similarity to the tests case. Similarity is measured by Euclidean distance over all genes. Informative genes are identified in the case-specific training cohort by computing Cox regression coefficient and develop a case-specific predictor. This fixed predictor is applied to the test case. This dynamic predictor building method yields different training sets and different predictors for each new case.