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Evolutionary Modeling of Combination Treatment Strategies To Overcome Resistance to Tyrosine Kinase Inhibitors in Non-Small Cell Lung Cancer
[Image: see text] Many initially successful anticancer therapies lose effectiveness over time, and eventually, cancer cells acquire resistance to the therapy. Acquired resistance remains a major obstacle to improving remission rates and achieving prolonged disease-free survival. Consequently, novel...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical
Society
2011
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3230244/ https://www.ncbi.nlm.nih.gov/pubmed/21995722 http://dx.doi.org/10.1021/mp200270v |
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author | Mumenthaler, Shannon M. Foo, Jasmine Leder, Kevin Choi, Nathan C. Agus, David B. Pao, William Mallick, Parag Michor, Franziska |
author_facet | Mumenthaler, Shannon M. Foo, Jasmine Leder, Kevin Choi, Nathan C. Agus, David B. Pao, William Mallick, Parag Michor, Franziska |
author_sort | Mumenthaler, Shannon M. |
collection | PubMed |
description | [Image: see text] Many initially successful anticancer therapies lose effectiveness over time, and eventually, cancer cells acquire resistance to the therapy. Acquired resistance remains a major obstacle to improving remission rates and achieving prolonged disease-free survival. Consequently, novel approaches to overcome or prevent resistance are of significant clinical importance. There has been considerable interest in treating non-small cell lung cancer (NSCLC) with combinations of EGFR-targeted therapeutics (e.g., erlotinib) and cytotoxic therapeutics (e.g., paclitaxel); however, acquired resistance to erlotinib, driven by a variety of mechanisms, remains an obstacle to treatment success. In about 50% of cases, resistance is due to a T790M point mutation in EGFR, and T790M-containing cells ultimately dominate the tumor composition and lead to tumor regrowth. We employed a combined experimental and mathematical modeling-based approach to identify treatment strategies that impede the outgrowth of primary T790M-mediated resistance in NSCLC populations. Our mathematical model predicts the population dynamics of mixtures of sensitive and resistant cells, thereby describing how the tumor composition, initial fraction of resistant cells, and degree of selective pressure influence the time until progression of disease. Model development relied upon quantitative experimental measurements of cell proliferation and death using a novel microscopy approach. Using this approach, we systematically explored the space of combination treatment strategies and demonstrated that optimally timed sequential strategies yielded large improvements in survival outcome relative to monotherapies at the same concentrations. Our investigations revealed regions of the treatment space in which low-dose sequential combination strategies, after preclinical validation, may lead to a tumor reduction and improved survival outcome for patients with T790M-mediated resistance. |
format | Online Article Text |
id | pubmed-3230244 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | American Chemical
Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-32302442011-12-05 Evolutionary Modeling of Combination Treatment Strategies To Overcome Resistance to Tyrosine Kinase Inhibitors in Non-Small Cell Lung Cancer Mumenthaler, Shannon M. Foo, Jasmine Leder, Kevin Choi, Nathan C. Agus, David B. Pao, William Mallick, Parag Michor, Franziska Mol Pharm [Image: see text] Many initially successful anticancer therapies lose effectiveness over time, and eventually, cancer cells acquire resistance to the therapy. Acquired resistance remains a major obstacle to improving remission rates and achieving prolonged disease-free survival. Consequently, novel approaches to overcome or prevent resistance are of significant clinical importance. There has been considerable interest in treating non-small cell lung cancer (NSCLC) with combinations of EGFR-targeted therapeutics (e.g., erlotinib) and cytotoxic therapeutics (e.g., paclitaxel); however, acquired resistance to erlotinib, driven by a variety of mechanisms, remains an obstacle to treatment success. In about 50% of cases, resistance is due to a T790M point mutation in EGFR, and T790M-containing cells ultimately dominate the tumor composition and lead to tumor regrowth. We employed a combined experimental and mathematical modeling-based approach to identify treatment strategies that impede the outgrowth of primary T790M-mediated resistance in NSCLC populations. Our mathematical model predicts the population dynamics of mixtures of sensitive and resistant cells, thereby describing how the tumor composition, initial fraction of resistant cells, and degree of selective pressure influence the time until progression of disease. Model development relied upon quantitative experimental measurements of cell proliferation and death using a novel microscopy approach. Using this approach, we systematically explored the space of combination treatment strategies and demonstrated that optimally timed sequential strategies yielded large improvements in survival outcome relative to monotherapies at the same concentrations. Our investigations revealed regions of the treatment space in which low-dose sequential combination strategies, after preclinical validation, may lead to a tumor reduction and improved survival outcome for patients with T790M-mediated resistance. American Chemical Society 2011-10-13 2011-12-05 /pmc/articles/PMC3230244/ /pubmed/21995722 http://dx.doi.org/10.1021/mp200270v Text en Copyright © 2011 American Chemical Society http://pubs.acs.org This is an open-access article distributed under the ACS AuthorChoice Terms & Conditions. Any use of this article, must conform to the terms of that license which are available at http://pubs.acs.org. |
spellingShingle | Mumenthaler, Shannon M. Foo, Jasmine Leder, Kevin Choi, Nathan C. Agus, David B. Pao, William Mallick, Parag Michor, Franziska Evolutionary Modeling of Combination Treatment Strategies To Overcome Resistance to Tyrosine Kinase Inhibitors in Non-Small Cell Lung Cancer |
title | Evolutionary Modeling
of Combination Treatment Strategies To Overcome Resistance to Tyrosine
Kinase Inhibitors in Non-Small Cell Lung Cancer |
title_full | Evolutionary Modeling
of Combination Treatment Strategies To Overcome Resistance to Tyrosine
Kinase Inhibitors in Non-Small Cell Lung Cancer |
title_fullStr | Evolutionary Modeling
of Combination Treatment Strategies To Overcome Resistance to Tyrosine
Kinase Inhibitors in Non-Small Cell Lung Cancer |
title_full_unstemmed | Evolutionary Modeling
of Combination Treatment Strategies To Overcome Resistance to Tyrosine
Kinase Inhibitors in Non-Small Cell Lung Cancer |
title_short | Evolutionary Modeling
of Combination Treatment Strategies To Overcome Resistance to Tyrosine
Kinase Inhibitors in Non-Small Cell Lung Cancer |
title_sort | evolutionary modeling
of combination treatment strategies to overcome resistance to tyrosine
kinase inhibitors in non-small cell lung cancer |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3230244/ https://www.ncbi.nlm.nih.gov/pubmed/21995722 http://dx.doi.org/10.1021/mp200270v |
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