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Exposure time independent summary statistics for assessment of drug dependent cell line growth inhibition.

Falgreen S, Laursen MB, Bødker JS, Kjeldsen MK, Schmitz A, Nyegaard M, Johnsen HE, Dybkær K, Bøgsted M - BMC Bioinformatics (2014)

Bottom Line: This may lead to suboptimal exploitation of data and biased conclusions on the potential of the drug in question.The adequacy of the mathematical model is tested for doxorubicin and found to fit real data to an acceptable degree.Variance estimates of the novel summary statistics are used to conclude that the doxorubicin screen covers a significant diverse range of responses ensuring it is useful for biological interpretations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Haematology, Aalborg University Hospital, Aalborg, Denmark. sfl@rn.dk.

ABSTRACT

Background: In vitro generated dose-response curves of human cancer cell lines are widely used to develop new therapeutics. The curves are summarised by simplified statistics that ignore the conventionally used dose-response curves' dependency on drug exposure time and growth kinetics. This may lead to suboptimal exploitation of data and biased conclusions on the potential of the drug in question. Therefore we set out to improve the dose-response assessments by eliminating the impact of time dependency.

Results: First, a mathematical model for drug induced cell growth inhibition was formulated and used to derive novel dose-response curves and improved summary statistics that are independent of time under the proposed model. Next, a statistical analysis workflow for estimating the improved statistics was suggested consisting of 1) nonlinear regression models for estimation of cell counts and doubling times, 2) isotonic regression for modelling the suggested dose-response curves, and 3) resampling based method for assessing variation of the novel summary statistics. We document that conventionally used summary statistics for dose-response experiments depend on time so that fast growing cell lines compared to slowly growing ones are considered overly sensitive. The adequacy of the mathematical model is tested for doxorubicin and found to fit real data to an acceptable degree. Dose-response data from the NCI60 drug screen were used to illustrate the time dependency and demonstrate an adjustment correcting for it. The applicability of the workflow was illustrated by simulation and application on a doxorubicin growth inhibition screen. The simulations show that under the proposed mathematical model the suggested statistical workflow results in unbiased estimates of the time independent summary statistics. Variance estimates of the novel summary statistics are used to conclude that the doxorubicin screen covers a significant diverse range of responses ensuring it is useful for biological interpretations.

Conclusion: Time independent summary statistics may aid the understanding of drugs' action mechanism on tumour cells and potentially renew previous drug sensitivity evaluation studies.

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Related in: MedlinePlus

Illustration of dose-response curves for the B-cell cancer cell line panel. Panels A and B illustrate dose-response curves obtained bythe G-model for the 14 DLBCL and 12 MM cancer cell lines. PanelsC, D, E, and F depict boxplots of thebootstrapped summary statistics GI50, TGI, LC48, and AUC0, respectively. The green and blue coloursare used for DLBCL and MM cell lines, respectively.
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Figure 11: Illustration of dose-response curves for the B-cell cancer cell line panel. Panels A and B illustrate dose-response curves obtained bythe G-model for the 14 DLBCL and 12 MM cancer cell lines. PanelsC, D, E, and F depict boxplots of thebootstrapped summary statistics GI50, TGI, LC48, and AUC0, respectively. The green and blue coloursare used for DLBCL and MM cell lines, respectively.

Mentions: The dose-response curves obtained for the 14 DLBCL and 12 MM cancer cell lines areillustrated in Panels A and B of Figure 11. The firstquadrant of the plots depicts the percentage growth for the treated cell linecompared to the same cell line un-treated, e.g. the values 75, 50, and 25 wereattained at the concentrations where the doubling time for the control was 75, 50,and 25% of that for the treated cell line, or equivalently, the growth rate of thetreated cell line was 75, 50, and 25% of that for the un-treated cell line. Thefourth quadrant depicts cell decay, e.g. the values -1/48, -1/24, and -1/16 wereattained at the concentrations where the cell line population was halved in 48, 24,and 16 hours, respectively. None of the curves contained points where the treatedcell line outgrew the controls, i.e. values greater than 100. This was an effect offorcing to be positive which was of great importance for thesummary statistic AUC0. The estimated cell line doubling time andsummary statistics GI50, TGI, LC 48,and AUC0 are shown in Table 3 withassociated 95% confidence intervals. The bootstrapped summary statisticsGI50, TGI, LC 48, andAUC0 are also illustrated by box plots in Panels C, D, E, andF of Figure 11.


Exposure time independent summary statistics for assessment of drug dependent cell line growth inhibition.

Falgreen S, Laursen MB, Bødker JS, Kjeldsen MK, Schmitz A, Nyegaard M, Johnsen HE, Dybkær K, Bøgsted M - BMC Bioinformatics (2014)

Illustration of dose-response curves for the B-cell cancer cell line panel. Panels A and B illustrate dose-response curves obtained bythe G-model for the 14 DLBCL and 12 MM cancer cell lines. PanelsC, D, E, and F depict boxplots of thebootstrapped summary statistics GI50, TGI, LC48, and AUC0, respectively. The green and blue coloursare used for DLBCL and MM cell lines, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4127655&req=5

Figure 11: Illustration of dose-response curves for the B-cell cancer cell line panel. Panels A and B illustrate dose-response curves obtained bythe G-model for the 14 DLBCL and 12 MM cancer cell lines. PanelsC, D, E, and F depict boxplots of thebootstrapped summary statistics GI50, TGI, LC48, and AUC0, respectively. The green and blue coloursare used for DLBCL and MM cell lines, respectively.
Mentions: The dose-response curves obtained for the 14 DLBCL and 12 MM cancer cell lines areillustrated in Panels A and B of Figure 11. The firstquadrant of the plots depicts the percentage growth for the treated cell linecompared to the same cell line un-treated, e.g. the values 75, 50, and 25 wereattained at the concentrations where the doubling time for the control was 75, 50,and 25% of that for the treated cell line, or equivalently, the growth rate of thetreated cell line was 75, 50, and 25% of that for the un-treated cell line. Thefourth quadrant depicts cell decay, e.g. the values -1/48, -1/24, and -1/16 wereattained at the concentrations where the cell line population was halved in 48, 24,and 16 hours, respectively. None of the curves contained points where the treatedcell line outgrew the controls, i.e. values greater than 100. This was an effect offorcing to be positive which was of great importance for thesummary statistic AUC0. The estimated cell line doubling time andsummary statistics GI50, TGI, LC 48,and AUC0 are shown in Table 3 withassociated 95% confidence intervals. The bootstrapped summary statisticsGI50, TGI, LC 48, andAUC0 are also illustrated by box plots in Panels C, D, E, andF of Figure 11.

Bottom Line: This may lead to suboptimal exploitation of data and biased conclusions on the potential of the drug in question.The adequacy of the mathematical model is tested for doxorubicin and found to fit real data to an acceptable degree.Variance estimates of the novel summary statistics are used to conclude that the doxorubicin screen covers a significant diverse range of responses ensuring it is useful for biological interpretations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Haematology, Aalborg University Hospital, Aalborg, Denmark. sfl@rn.dk.

ABSTRACT

Background: In vitro generated dose-response curves of human cancer cell lines are widely used to develop new therapeutics. The curves are summarised by simplified statistics that ignore the conventionally used dose-response curves' dependency on drug exposure time and growth kinetics. This may lead to suboptimal exploitation of data and biased conclusions on the potential of the drug in question. Therefore we set out to improve the dose-response assessments by eliminating the impact of time dependency.

Results: First, a mathematical model for drug induced cell growth inhibition was formulated and used to derive novel dose-response curves and improved summary statistics that are independent of time under the proposed model. Next, a statistical analysis workflow for estimating the improved statistics was suggested consisting of 1) nonlinear regression models for estimation of cell counts and doubling times, 2) isotonic regression for modelling the suggested dose-response curves, and 3) resampling based method for assessing variation of the novel summary statistics. We document that conventionally used summary statistics for dose-response experiments depend on time so that fast growing cell lines compared to slowly growing ones are considered overly sensitive. The adequacy of the mathematical model is tested for doxorubicin and found to fit real data to an acceptable degree. Dose-response data from the NCI60 drug screen were used to illustrate the time dependency and demonstrate an adjustment correcting for it. The applicability of the workflow was illustrated by simulation and application on a doxorubicin growth inhibition screen. The simulations show that under the proposed mathematical model the suggested statistical workflow results in unbiased estimates of the time independent summary statistics. Variance estimates of the novel summary statistics are used to conclude that the doxorubicin screen covers a significant diverse range of responses ensuring it is useful for biological interpretations.

Conclusion: Time independent summary statistics may aid the understanding of drugs' action mechanism on tumour cells and potentially renew previous drug sensitivity evaluation studies.

Show MeSH
Related in: MedlinePlus