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Monro2008 - chemotherapy resistance

ABSTRACT: The paper describes a model of resistance of cancer to chemotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: Modelling chemotherapy resistance in palliation and failed cure Helen C. Monro, Eamonn A. Gaffney J Theor Biol. 2009, 257 (2), pp.292 Abstract: The goal of palliative cancer chemotherapy treatment is to prolong survival and improve quality of life when tumour eradication is not feasible. Chemotherapy protocol design is considered in this context using a simple, robust, model of advanced tumour growth with Gompertzian dynamics, taking into account the effects of drug resistance. It is predicted that reduced chemotherapy protocols can readily lead to improved survival times due to the effects of competition between resistant and sensitive tumour cells. Very early palliation is also predicted to quickly yield near total tumour resistance and thus decrease survival duration. Finally, our simulations indicate that failed curative attempts using dose densification, a common protocol escalation strategy, can reduce survival times. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

SUBMITTER: Jinghao Men

PROVIDER: BIOMD0000000776 | BioModels | 2019-08-02

REPOSITORIES: BioModels

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Modelling chemotherapy resistance in palliation and failed cure.

Monro Helen C HC Gaffney Eamonn A EA

Journal of theoretical biology 20081211 2

The goal of palliative cancer chemotherapy treatment is to prolong survival and improve quality of life when tumour eradication is not feasible. Chemotherapy protocol design is considered in this context using a simple, robust, model of advanced tumour growth with Gompertzian dynamics, taking into account the effects of drug resistance. It is predicted that reduced chemotherapy protocols can readily lead to improved survival times due to the effects of competition between resistant and sensitive ...[more]

PMID: 19135065

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Project description:This mathematical model of T cell-tumour interactions considering the roles of T cell competition and stochastic extinction events in CAR T cell therapy is described by the publication: Kimmel GJ, Locke FL, Altrock PM. "The roles of T cell competition and stochastic extinction events in chimeric antigen receptor T cell therapy." Proc Biol Sci. 2021 Mar 31;288(1947):20210229. doi: 10.1098/rspb.2021.0229 Comment: Reproduction of Fig. 2(a) and (b) was simulated by using the fitted model parameter set given in Table 1 of the manuscript's Supplementary Material, however substituting the values of r_N and rho_C for those stated in Table 1 of the publication manuscript, i.e. r_N = 0.17 and rho_C = 0.0251. Abstract: Chimeric antigen receptor (CAR) T cell therapy is a remarkably effective immunotherapy that relies on in vivo expansion of engineered CAR T cells, after lymphodepletion (LD) by chemotherapy. The quantitative laws underlying this expansion and subsequent tumour eradication remain unknown. We develop a mathematical model of T cell–tumour cell interactions and demonstrate that expansion can be explained by immune reconstitution dynamics after LD and competition among T cells. CAR T cells rapidly grow and engage tumour cells but experience an emerging growth rate disadvantage compared to normal T cells. Since tumour eradication is deterministically unstable in our model, we define cure as a stochastic event, which, even when likely, can occur at variable times. However, we show that variability in timing is largely determined by patient variability. While cure events impacted by these fluctuations occur early and are narrowly distributed, progression events occur late and are more widely distributed in time. We parameterized our model using population-level CAR T cell and tumour data over time and compare our predictions with progression-free survival rates. We find that therapy could be improved by optimizing the tumour-killing rate and the CAR T cells' ability to adapt, as quantified by their carrying capacity. Our tumour extinction model can be leveraged to examine why therapy works in some patients but not others, and to better understand the interplay of deterministic and stochastic effects on outcomes. For example, our model implies that LD before a second CAR T injection is necessary.

Project description:summary Ovarian cancer is the leading cause of death among gynecologic malignancies. This is partly due to a non-durable response to chemotherapy. Prediction of resistance to chemotherapy could be a key role in more personalized treatment. In the current study we aimed to examine if microRNA based predictors could predict resistance to chemotherapy in ovarian cancer, and to investigate if the predictors could be prognostic factors for progression free and overall survival. Methods Predictors of chemotherapy-resistance were developed based on correlation between miRNA expression and differences in measured growth inhibition in a variety of human cancer cell lines in the presence of Carboplatin, Paclitaxel and Docetaxel (deposited under accession number E-MTAB-327 in the ArrayExpress database of the European Bioinformatics Institute, 2012). These predictors were then, retrospectively, blindly validated in a cohort of 170 epithelial ovarian cancer patients treated with Carboplatin and Paclitaxel or Docetaxel as first line treatment. Results In a multivariate cox proportional analysis the predictors of chemotherapy-resistance were not able to predict time to progression after end of chemotherapy (hazard ratio: 0.64, 95% CI: 0.36 – 1.12, P=0.117). However, in a multivariate logistic analysis, where time to progression was considered as either more or less than 6 months, the predictors match clinical observed chemotherapy-resistance (odds ratio: 0.19, 95% CI: 0.05-0.73, P=0.015). Neither univariate nor multivariate, time-dependent, cox analysis for progression free survival (PFS) or overall survival (OS) in all 170 patients showed to match predicted resistance to chemotherapy (PFS: hazard ratio: 0.69, 95% CI: 0.40-1.19, P=0.183, OS: hazard ratio: 0.76, 95% CI: 0.42-1.40, P=0.386). Conclusion In the current study, microRNA based predictors of chemotherapy-resistance did not demonstrate any convincing correlation to clinical observed chemotherapy-resistance, progression free survival, or overall survival, in patients with epithelial ovarian cancer. However the predictors did reflect relapse more or less than 6 months.

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Monro2008 - chemotherapy resistance

Publications

Modelling chemotherapy resistance in palliation and failed cure.

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