Expression data of multiple myeloma patients at various stages of disease
Bone marrow plasma cell samples were obtained from 23 multiple myeloma patients at various stages of disease from the Seattle Cancer Care Institute. RNASeq gene expression profiling was applied to CD138+ purified plasma cells. We trained a machine learning algorithm on mmSYGNAL program activity to develop a risk classification scheme  for multiple myeloma and applied it to the 23 patient's gene expression profiles. Unlike other risk prediction methods we applied mmSYGNAL was able to accurately predict disease progression risk at primary diagnosis, pre- and post-transplant and even after multiple relapses, making it useful for individualized dynamic risk assessment throughout the disease trajectory. Overall design: Expression profiling by high throughput sequencing on 23 multiple myeloma patients at various stages of disease