Cargando…
Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling
We designed a mathematical model to describe and quantify the mechanisms and dynamics of tumor growth, cell-kill and resistance as they affect durations of benefit after cancer treatment. Our aim was to explore how treatment efficacy may be related to primary tumor characteristics, with the potentia...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4729810/ https://www.ncbi.nlm.nih.gov/pubmed/26783036 http://dx.doi.org/10.1007/s10549-016-3684-4 |
_version_ | 1782412298978066432 |
---|---|
author | Gregory, Walter M. Twelves, Christopher J. Bell, Richard Smye, Stephen W. Howard, Dena R. Coleman, Robert E. Cameron, David A. |
author_facet | Gregory, Walter M. Twelves, Christopher J. Bell, Richard Smye, Stephen W. Howard, Dena R. Coleman, Robert E. Cameron, David A. |
author_sort | Gregory, Walter M. |
collection | PubMed |
description | We designed a mathematical model to describe and quantify the mechanisms and dynamics of tumor growth, cell-kill and resistance as they affect durations of benefit after cancer treatment. Our aim was to explore how treatment efficacy may be related to primary tumor characteristics, with the potential to guide future trial design and appropriate selection of therapy. Assuming a log-normal distribution of both resistant disease and tumor doubling times generates disease-free survival (DFS) or invasive DFS curves with specific shapes. Using a multivariate mathematical model, both treatment and tumor characteristics are related to quantified resistant disease and tumor regrowth rates by allowing different mean values for the influence of different treatments or clinical subtypes on these two log-normal distributions. Application of the model to the CALGB 9741 adjuvant breast cancer trial showed that dose-dense therapy was estimated to achieve an extra 3/4 log of cell-kill compared to standard therapy, but only in patients with more rapidly growing ER-negative tumors. Application of the model to the AZURE trial of adjuvant bisphosphonate treatment suggested that the 5-year duration of zoledronic acid was adequate for ER-negative tumors, but may not be so for ER-positive cases, with increased recurrences after ceasing the intervention. Mathematical models can identify different effects of treatment by subgroup and may aid in treatment design, trial analysis, and appropriate selection of therapy. They may provide a more appropriate and insightful tool than the conventional Cox model for the statistical analysis of response durations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10549-016-3684-4) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4729810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-47298102016-02-04 Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling Gregory, Walter M. Twelves, Christopher J. Bell, Richard Smye, Stephen W. Howard, Dena R. Coleman, Robert E. Cameron, David A. Breast Cancer Res Treat Clinical Trial We designed a mathematical model to describe and quantify the mechanisms and dynamics of tumor growth, cell-kill and resistance as they affect durations of benefit after cancer treatment. Our aim was to explore how treatment efficacy may be related to primary tumor characteristics, with the potential to guide future trial design and appropriate selection of therapy. Assuming a log-normal distribution of both resistant disease and tumor doubling times generates disease-free survival (DFS) or invasive DFS curves with specific shapes. Using a multivariate mathematical model, both treatment and tumor characteristics are related to quantified resistant disease and tumor regrowth rates by allowing different mean values for the influence of different treatments or clinical subtypes on these two log-normal distributions. Application of the model to the CALGB 9741 adjuvant breast cancer trial showed that dose-dense therapy was estimated to achieve an extra 3/4 log of cell-kill compared to standard therapy, but only in patients with more rapidly growing ER-negative tumors. Application of the model to the AZURE trial of adjuvant bisphosphonate treatment suggested that the 5-year duration of zoledronic acid was adequate for ER-negative tumors, but may not be so for ER-positive cases, with increased recurrences after ceasing the intervention. Mathematical models can identify different effects of treatment by subgroup and may aid in treatment design, trial analysis, and appropriate selection of therapy. They may provide a more appropriate and insightful tool than the conventional Cox model for the statistical analysis of response durations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10549-016-3684-4) contains supplementary material, which is available to authorized users. Springer US 2016-01-19 2016 /pmc/articles/PMC4729810/ /pubmed/26783036 http://dx.doi.org/10.1007/s10549-016-3684-4 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Clinical Trial Gregory, Walter M. Twelves, Christopher J. Bell, Richard Smye, Stephen W. Howard, Dena R. Coleman, Robert E. Cameron, David A. Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling |
title | Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling |
title_full | Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling |
title_fullStr | Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling |
title_full_unstemmed | Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling |
title_short | Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling |
title_sort | characterizing and quantifying the effects of breast cancer therapy using mathematical modeling |
topic | Clinical Trial |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4729810/ https://www.ncbi.nlm.nih.gov/pubmed/26783036 http://dx.doi.org/10.1007/s10549-016-3684-4 |
work_keys_str_mv | AT gregorywalterm characterizingandquantifyingtheeffectsofbreastcancertherapyusingmathematicalmodeling AT twelveschristopherj characterizingandquantifyingtheeffectsofbreastcancertherapyusingmathematicalmodeling AT bellrichard characterizingandquantifyingtheeffectsofbreastcancertherapyusingmathematicalmodeling AT smyestephenw characterizingandquantifyingtheeffectsofbreastcancertherapyusingmathematicalmodeling AT howarddenar characterizingandquantifyingtheeffectsofbreastcancertherapyusingmathematicalmodeling AT colemanroberte characterizingandquantifyingtheeffectsofbreastcancertherapyusingmathematicalmodeling AT camerondavida characterizingandquantifyingtheeffectsofbreastcancertherapyusingmathematicalmodeling |