An Evolutionary Approach for Personalized Therapy in Multiple Myeloma

Abstract

Most patients with multiple myeloma (MM) respond well to initial therapy, but invariably relapse due to evolution of resistant phenotypes. Here we examine the evolutionary dynamics of proliferation of resistant MM phenotypes during therapy. By applying computational models to data from three clinical trials for newly diagnosed MM patients, we have quantified the size and level of chemoresistance of subpopulations within the tumor burden in 124 patients, prior to and during therapy. Subsequently, we used the computational models to explore an alternative strategy of “adaptive therapy” (AT), which includes defined treatment holidays, to improve the duration of “controlled disease” (CD). Simulations showed that AT could prolong CD in all three trials: 50.0% vs. 11.1% 50-month CD for a single agent approach in older adults (P = 0.0123), 80.4% vs. 58.8% 60-month CD for a multi-agent bortezomib based therapy (P = 0.0082), and 54.0% vs. 24.0% 60-month CD for a multi-agent lenalidomide based therapy (P < 0.0001). Increases in duration of CD resulted from the stabilization of tumor burden, which in turn would delay the growth of chemoresistant sub-populations in patients with partial (PR), or very good partial response (VGPR). These computational algorithms suggest that AT may provide an alternative and feasible therapeutic management strategy in MM.