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• W2291050268 abstract "Abstract Drug resistance significantly limits the long-term effectiveness of targeted therapeutics for cancer patients. Recent experimental studies have demonstrated that cancer cell heterogeneity and microenvironment adaptations to targeted therapy play important roles in promoting the rapid acquisition of drug resistance and in increasing cancer metastasis. The systematic development of effective therapeutics to overcome drug resistance mechanisms poses a major challenge. In this study, we used a modeling approach to connect cellular mechanisms underlying cancer drug resistance to population-level patient survival. To predict progression-free survival in cancer patients with metastatic melanoma, we developed a set of stochastic differential equations to describe the dynamics of heterogeneous cell populations while taking into account micro-environment adaptations. Clinical data on survival and circulating tumor cell DNA (ctDNA) concentrations were used to confirm the effectiveness of our model. Moreover, our model predicted distinct patterns of dose-dependent synergy when evaluating a combination of BRAF and MEK inhibitors versus a combination of BRAF and PI3K inhibitors. These predictions were consistent with the findings in previously reported studies. The impact of the drug metabolism rate on patient survival was also discussed. The proposed model might facilitate the quantitative evaluation and optimization of combination therapeutics and cancer clinical trial design." @default.
• W2291050268 created "2016-06-24" @default.
• W2291050268 creator A5004600753 @default.
• W2291050268 creator A5047111777 @default.
• W2291050268 creator A5080300349 @default.
• W2291050268 date "2016-03-01" @default.
• W2291050268 modified "2023-10-18" @default.
• W2291050268 title "Mathematical Modeling of Therapy-induced Cancer Drug Resistance: Connecting Cancer Mechanisms to Population Survival Rates" @default.
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• W2291050268 doi "https://doi.org/10.1038/srep22498" @default.
• W2291050268 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4772546" @default.
• W2291050268 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26928089" @default.
• W2291050268 hasPublicationYear "2016" @default.
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