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

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 h...

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Bibliographic Details
Main Authors: Sun, Daphne S., Dalin, Simona, Hemann, Michael, Lauffenburger, Douglas A, Zhao, Boyang
Other Authors: Koch Institute for Integrative Cancer Research at MIT
Format: Article
Language:en_US
Published: Nature Publishing Group 2017
Online Access:http://hdl.handle.net/1721.1/109785
https://orcid.org/0000-0001-5024-9718
https://orcid.org/0000-0003-4610-1707
Description
Summary: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.