Evolution of resistance to targeted anti-cancer therapies during continuous and pulsed administration strategies.
The discovery of small molecules targeted to specific oncogenic pathways has revolutionized anti-cancer therapy. However, such therapy often fails due to the evolution of acquired resistance. One long-standing question in clinical cancer research is the identification of optimum therapeutic administration strategies so that the risk of resistance is minimized. In this paper, we investigate optimal drug dosing schedules to prevent, or at least delay, the emergence of resistance. We design and analyze a stochastic mathematical model describing the evolutionary dynamics of a tumor cell population during therapy. We consider drug resistance emerging due to a single (epi)genetic alteration and calculate the probability of resistance arising during specific dosing strategies. We then optimize treatment protocols such that the risk of resistance is minimal while considering drug toxicity and side effects as constraints. Our methodology can be used to identify optimum drug administration schedules to avoid resistance conferred by one (epi)genetic alteration for any cancer and treatment type.
Foo
Jasmine
J
Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
Michor
Franziska
F
D000818:Animals; D000970:Antineoplastic Agents / Q000008:administration & dosage*; D003198:Computer Simulation; D019008:Drug Resistance, Neoplasm / Q000235:genetics*; D044127:Epigenesis, Genetic / Q000235:genetics*; D019143:Evolution, Molecular; D006801:Humans; D008957:Models, Genetic; D009363:Neoplasm Proteins / Q000378:metabolism*; D009369:Neoplasms / Q000188:drug therapy* / Q000235:genetics*; D013269:Stochastic Processes
D000970:Antineoplastic Agents; D009363:Neoplasm Proteins