Effect of cellular quiescence on the success of targeted CML therapy.
BACKGROUND
Similar to tissue stem cells, primitive tumor cells in chronic myelogenous leukemia have been observed to undergo quiescence; that is, the cells can temporarily stop dividing. Using mathematical models, we investigate the effect of cellular quiescence on the outcome of therapy with targeted small molecule inhibitors.


RESULTS
According to the models, the initiation of treatment can result in different patterns of tumor cell decline: a biphasic decline, a one-phase decline, and a reverse biphasic decline. A biphasic decline involves a fast initial phase (which roughly corresponds to the eradication of cycling cells by the drug), followed by a second and slower phase of exponential decline (corresponding to awakening and death of quiescent cells), which helps explain clinical data. We define the time when the switch to the second phase occurs, and identify parameters that determine whether therapy can drive the tumor extinct in a reasonable period of time or not. We further ask how cellular quiescence affects the evolution of drug resistance. We find that it has no effect on the probability that resistant mutants exist before therapy if treatment occurs with a single drug, but that quiescence increases the probability of having resistant mutants if patients are treated with a combination of two or more drugs with different targets. Interestingly, while quiescence prolongs the time until therapy reduces the number of cells to low levels or extinction, the therapy phase is irrelevant for the evolution of drug resistant mutants. If treatment fails as a result of resistance, the mutants will have evolved during the tumor growth phase, before the start of therapy. Thus, prevention of resistance is not promoted by reducing the quiescent cell population during therapy (e.g., by a combination of cell activation and drug-mediated killing).


CONCLUSIONS
The mathematical models provide insights into the effect of quiescence on the basic kinetics of the response to targeted treatment of CML. They identify determinants of success in the absence of drug resistant mutants, and elucidate how quiescence influences the emergence of drug resistant mutants.
Komarova
Natalia L
NL
Department of Mathematics, University of California Irvine, Irvine, California, United States of America.
Wodarz
Dominik
D
D000970:Antineoplastic Agents / Q000627:therapeutic use; D001549:Benzamides; D003131:Combined Modality Therapy / Q000379:methods; D019008:Drug Resistance, Neoplasm; D006801:Humans; D000068877:Imatinib Mesylate; D015464:Leukemia, Myelogenous, Chronic, BCR-ABL Positive / Q000473:pathology* / Q000628:therapy*; D008495:Medical Oncology / Q000379:methods; D008954:Models, Biological; D008962:Models, Theoretical; D009154:Mutation; D010879:Piperazines / Q000627:therapeutic use; D011336:Probability; D011743:Pyrimidines / Q000627:therapeutic use; D013269:Stochastic Processes
D000970:Antineoplastic Agents; D001549:Benzamides; D010879:Piperazines; D011743:Pyrimidines; D000068877:Imatinib Mesylate