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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

The dose-response curves for the three dose-response models. Thedose-response curves for the R-model are shown in Panels A,D, and G, for the time points 25, 49, and 73 hours,respectively. Similarly for the D- and G-models PanelsB, E, and H and C, F, and I showthe dose-response curves obtained for time points 25, 49, and 73 hours,respectively.
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Figure 5: The dose-response curves for the three dose-response models. Thedose-response curves for the R-model are shown in Panels A,D, and G, for the time points 25, 49, and 73 hours,respectively. Similarly for the D- and G-models PanelsB, E, and H and C, F, and I showthe dose-response curves obtained for time points 25, 49, and 73 hours,respectively.

Mentions: Dose-response curves for the nine cell line models, described in Methods,are shown in Figure 5 for the dose-response modelsG (5), D (6), and R (7) and time pointst1 = 25, t2 = 49, and, t3= 73 hours.


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)

The dose-response curves for the three dose-response models. Thedose-response curves for the R-model are shown in Panels A,D, and G, for the time points 25, 49, and 73 hours,respectively. Similarly for the D- and G-models PanelsB, E, and H and C, F, and I showthe dose-response curves obtained for time points 25, 49, and 73 hours,respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: The dose-response curves for the three dose-response models. Thedose-response curves for the R-model are shown in Panels A,D, and G, for the time points 25, 49, and 73 hours,respectively. Similarly for the D- and G-models PanelsB, E, and H and C, F, and I showthe dose-response curves obtained for time points 25, 49, and 73 hours,respectively.
Mentions: Dose-response curves for the nine cell line models, described in Methods,are shown in Figure 5 for the dose-response modelsG (5), D (6), and R (7) and time pointst1 = 25, t2 = 49, and, t3= 73 hours.

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