Retrospective growth kinetics and radiosensitivity analysis of various human xenograft models

Lee, Ji Young; Kim, Misook; Kim, Eun Ho; Chung, Namhyun; Jeong, Youn Kyoung

doi:10.5625/lar.2016.32.4.187

Cited by 12 publications

(8 citation statements)

References 19 publications

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“…Thus, our data reveal that digoxin functions as a PP2A inhibitor to enhance anticancer effect in radioresistant A549 cells and xenografts. Interestingly, slowly growing tumors are particularly resistant to radiotherapy [ 31 , 32 ] and inhibition of PP2A could convert resistant tumor cells into a more sensitive phenotype by accelerating the cell cycle. Consistent with previous reports, the present study showed that PP2A inhibition by digoxin enhanced radiation response in slowly growing A549 cells and xenografts but not fast growing H460 cells ( Figure 1 ).…”

Section: Discussionmentioning

confidence: 99%

Digoxin enhances radiation response in radioresistant A549 cells by reducing protein phosphatase 2A

et al. 2017

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Protein phosphatase 2A (PP2A) is a ubiquitous multifunctional enzyme usually known as a tumor suppressor. Recent studies have reported that although inhibition of PP2A leads to acceleration of cell growth, it also induces damaged cells to pass through the cell cycle and renders them sensitive to radiotherapy. Here, we investigated the radiosensitizing effects of digoxin as a PP2A inhibitor in two non-small-cell lung cancer (NSCLC) cell types (H460 and A549) with differential sensitivity to radiation. Digoxin inhibited the proliferation of H460 and A549 cells in a dose-dependent fashion and was especially effective on radioresistant A549 cells. Interestingly, the radiosensitizing effect of digoxin was only present in the radioresistant A549 cells and xenografts. The combination of digoxin and ionizing radiation (IR) significantly reduced clonogenic survival and xenograft tumor growth (P<0.001), compared with IR alone. Digoxin suppressed PP2A protein expression and prevented IR-induced PP2A expression in A549 cells. Digoxin treatment combined with IR allowed the damaged cell to progress through the cell cycle via suppression of cell cycle-related proteins (p53, cyclin D1, cyclin B1, CDK4, and p-cdc2). Moreover, digoxin enhanced IR-induced DNA damage through reduction in levels of repair proteins and elevation of p-ATM foci formation up to 24 h (P<0.001). In conclusion, digoxin has a novel function as a PP2A inhibitor, and combined with IR produces a synergistic effect on radiosensitizing cells, thereby indicating a potentially promising therapeutic approach to radioresistant lung cancer treatment.

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Section: Discussionmentioning

confidence: 99%

Digoxin enhances radiation response in radioresistant A549 cells by reducing protein phosphatase 2A

et al. 2017

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“…Based on experimental observations, a correlation between the growth rate and the radiosensitivity was considered. The underlying biological interpretation is that faster growing tumor cells spend a higher proportion of their time in mitosis, which impairs double strand break (DSB) repair and makes them more radiosensitive compared to slower growing ones 17 – 19 . In the Small-Cell variant of lung cancer this has also been shown in-vivo , as the growth rate measured by Ki-67 in patient samples correlated with the extent of volumetric response to CRT 19 .…”

Section: Resultsmentioning

confidence: 99%

“…Considering that cells with faster growth rate have shown to be more radiation-sensitive 17 – 19 , we also introduced a possible correlation between radio-sensitivity and the growth rate parameter .…”

Section: Methodsmentioning

confidence: 99%

Prediction of Treatment Response for Combined Chemo- and Radiation Therapy for Non-Small Cell Lung Cancer Patients Using a Bio-Mathematical Model

Geng

Paganetti

Grassberger

2017

Sci Rep

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The goal of this work was to develop a mathematical model to predict Kaplan–Meier survival curves for chemotherapy combined with radiation in Non-Small Cell Lung Cancer patients for use in clinical trial design. The Gompertz model was used to describe tumor growth, radiation effect was simulated by the linear-quadratic model with an α/β-ratio of 10, and chemotherapy effect was based on the log-cell kill model. To account for repopulation during treatment, we considered two independent methods: 1) kickoff-repopulation using exponential growth with a decreased volume doubling time, or 2) Gompertz-repopulation using the gradually accelerating growth rate with tumor shrinkage. The input parameters were independently estimated by fitting to the SEER database for untreated tumors, RTOG-8808 for radiation only, and RTOG-9410 for sequential chemo-radiation. Applying the model, the benefit from concurrent chemo-radiation comparing to sequential for stage III patients was predicted to be a 6.6% and 6.2% improvement in overall survival for 3 and 5-years respectively, comparing well to the 5.3% and 4.5% observed in RTOG-9410. In summary, a mathematical model was developed to model tumor growth over extended periods of time, and can be used for the optimization of combined chemo-radiation scheduling and sequencing.

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“…The challenge is to obtain relevant parameters on tumor growth and shrinkage which greatly depends on the context. For instance, it is reported that the doubling time of cancer cells in in vitro systems is much faster as compared to the doubling time after engrafting into xenograft mice [ 43 ]. In addition, the timescale of tumor shrinkage in mice greatly differs cancer patients [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

PKPD modeling of acquired resistance to anti-cancer drug treatment

Eigenmann

Frances

Lavé

et al. 2017

J Pharmacokinet Pharmacodyn

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Non-small cell lung cancer (NSCLC) patients greatly benefit from the treatment with tyrosine kinase inhibitors (TKIs) targeting the epidermal growth factor receptor (EGFR). However, emergence of acquired resistance inevitable occurs after long-term treatment in most patients and limits clinical improvement. In the present study, resistance to drug treatment in patient-derived NSCLC xenograft mice was assessed and modeling and simulation was applied to understand the dynamics of drug resistance as a basis to explore more beneficial drug regimen. Two semi-mechanistic models were fitted to tumor growth inhibition profiles during and after treatment with erlotinib or gefitinib. The base model proposes that as a result of drug treatment, tumor cells stop proliferating and undergo several stages of damage before they eventually die. The acquired resistance model adds a resistance term to the base model which assumes that resistant cells are emerging from the pool of damaged tumor cells. As a result, tumor cells sensitive to drug treatment will either die or be converted to a drug resistant cell population which is proliferating at a slower growth rate as compared to the sensitive cells. The observed tumor growth profiles were better described by the resistance model and emergence of resistance was concluded. In simulation studies, the selection of resistant cells was explored as well as the time-variant fraction of resistant over sensitive cells. The proposed model provides insight into the dynamic processes of emerging resistance. It predicts tumor regrowth during treatment driven by the selection of resistant cells and explains why faster tumor regrowth may occur after discontinuation of TKI treatment. Finally, it is shown how the semi-mechanistic model can be used to explore different scenarios and to identify optimal treatment schedules in clinical trials.Electronic supplementary materialThe online version of this article (10.1007/s10928-017-9553-x) contains supplementary material, which is available to authorized users.

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Retrospective growth kinetics and radiosensitivity analysis of various human xenograft models

Cited by 12 publications

References 19 publications

Digoxin enhances radiation response in radioresistant A549 cells by reducing protein phosphatase 2A

Digoxin enhances radiation response in radioresistant A549 cells by reducing protein phosphatase 2A

Prediction of Treatment Response for Combined Chemo- and Radiation Therapy for Non-Small Cell Lung Cancer Patients Using a Bio-Mathematical Model

PKPD modeling of acquired resistance to anti-cancer drug treatment

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