Differential selective pressure alters rate of drug resistance acquisition in heterogeneous tumor populations

• Main Authors: Sun, Daphne S. (Contributor), Dalin, Simona (Contributor), Hemann, Michael (Contributor), Lauffenburger, Douglas A (Contributor), Zhao, Boyang (Contributor)
• Other Authors: Koch Institute for Integrative Cancer Research at MIT (Contributor), Massachusetts Institute of Technology. Computational and Systems Biology Program (Contributor), Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Department of Biology (Contributor), Massachusetts Institute of Technology. Department of Chemical Engineering (Contributor)
• Format: Article
• Language: English
• Published: Nature Publishing Group, 2017-06-12T13:57:13Z.
• Subjects: Article
• Online Access: Get fulltext

 Recent drug discovery and development efforts have created a large arsenal of targeted and chemotherapeutic drugs for precision medicine. However, drug resistance remains a major challenge as minor pre-existing resistant subpopulations are often found to be enriched at relapse. Current drug design has been heavily focused on initial efficacy, and we do not fully understand the effects of drug selective pressure on long-term drug resistance potential. Using a minimal two-population model, taking into account subpopulation proportions and growth/kill rates, we modeled long-term drug treatment and performed parameter sweeps to analyze the effects of each parameter on therapeutic efficacy. We found that drugs with the same overall initial kill may exert differential selective pressures, affecting long-term therapeutic outcome. We validated our conclusions experimentally using a preclinical model of Burkitt's lymphoma. Furthermore, we highlighted an intrinsic tradeoff between drug-imposed overall selective pressure and rate of adaptation. A principled approach in understanding the effects of distinct drug selective pressures on short-term and long-term tumor response enables better design of therapeutics that ultimately minimize relapse.