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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 result of the pre-processing procedure is illustrated for the cell lineSU-DHL-4. The circles represent absorbance measures for the particularconcentration at which it is plotted, the triangles represent the un-treatedcontrols, the plusses represent background absorbance measurements, and,finally, the crosses illustrate outliers. The figure is divided into eightpanels, where Panels A, C, E, and G show theresults for time t1 = 1 hour and Panels B, D,F, and H for time t2 = 49 hours. PanelsA and B show the raw absorbance measures for the fourreplicated experiments. The effect of the colour correction is shown in PanelsC and D. Panels E and F illustrate the result ofthe conventionally applied background correction. Finally, the result of themodel-based pre-processing procedure is illustrated in Panels G andH.
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Figure 9: The result of the pre-processing procedure is illustrated for the cell lineSU-DHL-4. The circles represent absorbance measures for the particularconcentration at which it is plotted, the triangles represent the un-treatedcontrols, the plusses represent background absorbance measurements, and,finally, the crosses illustrate outliers. The figure is divided into eightpanels, where Panels A, C, E, and G show theresults for time t1 = 1 hour and Panels B, D,F, and H for time t2 = 49 hours. PanelsA and B show the raw absorbance measures for the fourreplicated experiments. The effect of the colour correction is shown in PanelsC and D. Panels E and F illustrate the result ofthe conventionally applied background correction. Finally, the result of themodel-based pre-processing procedure is illustrated in Panels G andH.

Mentions: In Figure 9 the result of the pre-processing procedure isillustrated for the cell line SU-DHL-4. Panels A and B show the raw absorbancemeasures for the four replicated experiments whereas the effect of the colourcorrection is shown in Panels C and D. In Panels E and F the results of theconventionally applied background correction are depicted. Finally, the result of theModel-based pre-processing is illustrated in Panels G and H. Whencomparing panels E and F to G and H we noticed that the mean absorbance was estimatedwith a considerable lower variance when the model-based pre-processing was used. Across marks the measurements that are found to be outliers and for example two of theun-treated control measurements for plate 2 were deemed to be outliers as illustratedin Panel H. In panel H these measurements were clearly extreme values, however, priorto the model-based pre-processing this was not the case.For each cell line residualplots of the final pre-processing models were investigated to ensure that theabsorbance model fitted the data reasonably well. For cell line KMS-12-BM theresidual plot is illustrated in Figure 10. Panel A showsthe residual plot obtained when the heteroscedasticity of the variance was ignored,whilst Panel B shows the residual plot when the variance model was fitted. Theseplots confirm that the variance model was capable of fitting the heteroscedasticvariance observed in dose-response experiments.


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 result of the pre-processing procedure is illustrated for the cell lineSU-DHL-4. The circles represent absorbance measures for the particularconcentration at which it is plotted, the triangles represent the un-treatedcontrols, the plusses represent background absorbance measurements, and,finally, the crosses illustrate outliers. The figure is divided into eightpanels, where Panels A, C, E, and G show theresults for time t1 = 1 hour and Panels B, D,F, and H for time t2 = 49 hours. PanelsA and B show the raw absorbance measures for the fourreplicated experiments. The effect of the colour correction is shown in PanelsC and D. Panels E and F illustrate the result ofthe conventionally applied background correction. Finally, the result of themodel-based pre-processing procedure is illustrated in Panels G andH.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 9: The result of the pre-processing procedure is illustrated for the cell lineSU-DHL-4. The circles represent absorbance measures for the particularconcentration at which it is plotted, the triangles represent the un-treatedcontrols, the plusses represent background absorbance measurements, and,finally, the crosses illustrate outliers. The figure is divided into eightpanels, where Panels A, C, E, and G show theresults for time t1 = 1 hour and Panels B, D,F, and H for time t2 = 49 hours. PanelsA and B show the raw absorbance measures for the fourreplicated experiments. The effect of the colour correction is shown in PanelsC and D. Panels E and F illustrate the result ofthe conventionally applied background correction. Finally, the result of themodel-based pre-processing procedure is illustrated in Panels G andH.
Mentions: In Figure 9 the result of the pre-processing procedure isillustrated for the cell line SU-DHL-4. Panels A and B show the raw absorbancemeasures for the four replicated experiments whereas the effect of the colourcorrection is shown in Panels C and D. In Panels E and F the results of theconventionally applied background correction are depicted. Finally, the result of theModel-based pre-processing is illustrated in Panels G and H. Whencomparing panels E and F to G and H we noticed that the mean absorbance was estimatedwith a considerable lower variance when the model-based pre-processing was used. Across marks the measurements that are found to be outliers and for example two of theun-treated control measurements for plate 2 were deemed to be outliers as illustratedin Panel H. In panel H these measurements were clearly extreme values, however, priorto the model-based pre-processing this was not the case.For each cell line residualplots of the final pre-processing models were investigated to ensure that theabsorbance model fitted the data reasonably well. For cell line KMS-12-BM theresidual plot is illustrated in Figure 10. Panel A showsthe residual plot obtained when the heteroscedasticity of the variance was ignored,whilst Panel B shows the residual plot when the variance model was fitted. Theseplots confirm that the variance model was capable of fitting the heteroscedasticvariance observed in dose-response experiments.

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