Multiple myeloma is an incurable and fatal plasma cell cancer. Most patients harbor plasma cells that resist current treatments, causing the disease to relapse following therapy. Hence, there is a pressing need to determine the best way to kill therapy-resistant multiple myeloma. In this study, we used mass cytometry (or CyTOF) to profile the molecular mechanisms that multiple myeloma cells employ to survive treatment with the standard-of-care treatments, bortezomib and dexamethasone. We developed a unique suite of 26 probes for high-throughput, simultaneous detection cell survival/death, cell cycle, signalling and tumour suppressor pathways by mass cytometry at the single cell level. These data were were analysed by purpose-built FLOWMap computational algorithms to organise the high dimensional single-cell data into an interpretable 2D graph, allowing visualization of changes in multiple markers over time. Classification analysis was then used to define the predictive power of identified markers in drug resistance. Our data reveal a simple metric involving just six death or survival proteins was a strong predictor of cell resistance or sensitivity for both drugs. We tested and confirmed these predictions using new compounds targeting distinct survival proteins, offering new combination therapies the may be effective in eradicating resistant cells. These findings provide the first time-resolved, deep profiling of multiple myeloma cells undergoing cell death following treatment. They reveal new metrics of resistance versus sensitivity and identify potential targets for salvage therapy during relapse. In addition, we demonstrate how new computational approaches like FLOWMap and statistical modeling can provide a general framework for understanding the diverse responses of tumor populations to anti-cancer drugs.