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Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling.

ABSTRACT: 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.

SUBMITTER: Gregory WM

PROVIDER: S-EPMC4729810 | biostudies-other | 2016 Jan

REPOSITORIES: biostudies-other

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Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling.

Gregory Walter M WM Twelves Christopher J CJ Bell Richard R Smye Stephen W SW Howard Dena R DR Coleman Robert E RE Cameron David A DA

Breast cancer research and treatment 20160119 2

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 inva ...[more]

PMID: 26783036

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Project description:The transferrin (Tf) trafficking pathway is a promising mechanism for use in targeted cancer therapy due to the overexpression of transferrin receptors (TfRs) on cancerous cells. We have previously developed a mathematical model of the Tf/TfR trafficking pathway to improve the efficiency of Tf as a drug carrier. By using diphtheria toxin (DT) as a model toxin, we found that mutating the Tf protein to change its iron release rate improves cellular association and efficacy of the drug. Though this is an improvement upon using wild-type Tf as the targeting ligand, conjugated toxins like DT are unfortunately still highly cytotoxic at off-target sites. In this work, we address this hurdle in cancer research by developing a mathematical model to predict the efficacy and selectivity of Tf conjugates that use an alternative toxin. For this purpose, we have chosen to study a mutant of DT, cross-reacting material 107 (CRM107). First, we developed a mathematical model of the Tf-DT trafficking pathway by extending our Tf/TfR model to include intracellular trafficking via DT and DT receptors. Using this mathematical model, we subsequently investigated the efficacy of several conjugates in cancer cells: DT and CRM107 conjugated to wild-type Tf, as well as to our engineered mutant Tf proteins (K206E/R632A Tf and K206E/R534A Tf). We also investigated the selectivity of mutant Tf-CRM107 against non-neoplastic cells. Through the use of our mathematical model, we predicted that (i) mutant Tf-CRM107 exhibits a greater cytotoxicity than wild-type Tf-CRM107 against cancerous cells, (ii) this improvement was more drastic with CRM107 conjugates than with DT conjugates, and (iii) mutant Tf-CRM107 conjugates were selective against non-neoplastic cells. These predictions were validated with in vitro cytotoxicity experiments, demonstrating that mutant Tf-CRM107 conjugates is indeed a more suitable therapeutic agent. Validation from in vitro experiments also confirmed that such whole-cell kinetic models can be useful in cancer therapeutic design.

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Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling.

Publications

Characterizing and quantifying the effects of breast cancer therapy using mathematical modeling.

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