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Title: The role of memory in non-genetic inheritance and its impact on cancer treatment resistance

Journal Article · · PLoS Computational Biology (Online)

Intra-tumour heterogeneity is a leading cause of treatment failure and disease progression in cancer. While genetic mutations have long been accepted as a primary mechanism of generating this heterogeneity, the role of phenotypic plasticity is becoming increasingly apparent as a driver of intra-tumour heterogeneity. Consequently, understanding the role of this plasticity in treatment resistance and failure is a key component of improving cancer therapy. We develop a mathematical model of stochastic phenotype switching that tracks the evolution of drug-sensitive and drug-tolerant subpopulations to clarify the role of phenotype switching on population growth rates and tumour persistence. By including cytotoxic therapy in the model, we show that, depending on the strategy of the drug-tolerant subpopulation, stochastic phenotype switching can lead to either transient or permanent drug resistance. We study the role of phenotypic heterogeneity in a drug-resistant, genetically homogeneous population of non-small cell lung cancer cells to derive a rational treatment schedule that drives population extinction and avoids competitive release of the drug-tolerant sub-population. This model-informed therapeutic schedule results in increased treatment efficacy when compared against periodic therapy, and, most importantly, sustained tumour decay without the development of resistance.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE; National Institutes of Health (NIH); Natural Sciences and Engineering Research Council of Canada (NSERC)
Grant/Contract Number:
89233218CNA000001; R01-AI116868; R01-OD011095; U01CA232382; U54CA193489; RGPIN-2018-04546
OSTI ID:
1819478
Alternate ID(s):
OSTI ID: 1817793; OSTI ID: 1823752
Report Number(s):
LA-UR-20-26794; 10.1371/journal.pcbi.1009348
Journal Information:
PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Vol. 17 Journal Issue: 8; ISSN 1553-7358
Publisher:
Public Library of Science (PLoS)Copyright Statement
Country of Publication:
United States
Language:
English

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