These researchers are developing genetic databases to develop predictive genetic signatures of therapeutic response, resistance, and toxicities using cell lines and patient samples treated with chemotherapeutic drugs. They are also using genomic data to identify subclonal populations related to tumor heterogeneity and potential emerging relapses in myeloma patients. Data is being collected from model cell lines as well as primary patient samples. This includes DNA sequence, RNA expression, protein markers, and clinical outcomes. Various clustering and machine-learning approaches are used to develop predictive signatures asociated with response, resistance, and toxicities.