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

BMC Bioinformatics

Martin Bøgsted²  Karen Dybkær¹  Hans Erik Johnsen¹  Mette Nyegaard³  Alexander Schmitz¹  Malene Krag Kjeldsen¹  Julie Støve Bødker¹  Maria Bach Laursen¹  Steffen Falgreen¹ 

[1]Department of Haematology, Aalborg University Hospital, Aalborg, Denmark

[2]Department of Mathematical Sciences, Aalborg University, Aalborg, Denmark

[3]Department of Biomedicine, Aarhus University, Aarhus, Denmark

关键词: Parametric bootstrap;    Bootstrap;    Isotonic regression;    Nonlinear regression;    Differential equation modelling;    Mathematical modelling;    Doxorubicin;    NCI60;    Dose response experiments;   

Others  :  818501
DOI  :  10.1186/1471-2105-15-168

 received in 2013-05-15, accepted in 2014-05-27,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Falgreen et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 2.

Figure 2.

Figure 2.

Figure 3.

Figure 4.

Figure 4.

Figure 4.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 7.

Figure 7.

Figure 7.

Figure 8.

Figure 9.

Figure 10.

Figure 11.

【 参考文献 】

• [1]Paull KD, Shoemaker RH, Hodes L, Monks A, Scudiero DA, Rubinstein L, Plowman J, Boyd MR: Display and analysis of patterns of differential activity of drugs against human tumor cell lines: development of mean graph and COMPARE algorithm. J Nat Cancer Inst 1989, 81(14):1088-1092.
• [2]Sharma SV, Haber DA, Settleman J: Cell line-based platforms to evaluate the therapeutic efficacy of candidate anticancer agents. Nat Rev Cancer 2010, 10(4):241-253.
• [3]Monks A, Scudiero D, Skehan P: Feasibility of a high-flux anticancer drug screen using a diverse panel of cultured human tumor cell lines. J Nat Cancer Inst 1991, 83(11):757-766.
• [4]Shoemaker RH: The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer 2006, 6(10):813-823.
• [5]Yamori T, Matsunaga A, Sato S, Yamazaki K, Komi A, Ishizu K, Mita I, Edatsugi H, Matsuba Y, Takezawa K, Nakanishi O, Kohno H, Nakajima Y, Komatsu H, Andoh T, Tsuruo T: Potent antitumor activity of MS-247, a novel DNA minor groove binder, evaluated by an in vitro and in vivo human cancer cell line panel. Cancer Res 1999, 59(16):4042-4049.
• [6]Nakatsu N, Nakamura T, Yamazaki K, Sadahiro S, Makuuchi H, Kanno J, Yamori T: Evaluation of action mechanisms of toxic chemicals using JFCR39, a panel of human cancer cell lines. Mol Pharmacol 2007, 72(5):1171-1180.
• [7]McDermott U, Sharma SV, Dowell L, Greninger P, Montagut C, Lamb J, Archibald H, Raudales R, Tam A, Lee D, Rothenberg SM, Supko JG, Sordella R, Ulkus LE, Iafrate AJ, Maheswaran S, Njauw CN, Tsao H, Drew L, Hanke JH, Ma XJ, Erlander MG, Gray NS, Haber DA, Settleman J: Identification of genotype-correlated sensitivity to selective kinase inhibitors by using high-throughput tumor cell line profiling. Proc Nat Acad Sci USA 2007, 104(50):19936-19941.
• [8]Azarenko O, Okouneva T, Singletary KW, Jordan MA, Wilson L: Suppression of microtubule dynamic instability and turnover in MCF7 breast cancer cells by sulforaphane. Carcinogenesis 2008, 29(12):2360-2368.
• [9]Liedtke C, Wang J, Tordai A, Symmans WF, Hortobagyi GN, Kiesel L, Hess K, Baggerly KA, Coombes KR, Pusztai L: Clinical evaluation of chemotherapy response predictors developed from breast cancer cell lines. Breast Cancer Res Treat 2009, 121(2):301-309.
• [10]Bøgsted M, Holst JM, Fogd K, Falgreen S, Sørensen S, Schmitz A, Bukh A, Johnsen HE, Nyegaard M, Dybkær K: Generation of a predictive melphalan resistance index by drug screen of B-cell cancer cell lines. PloS ONE 2011, 6(4):e19322.
• [11]Boik JC, Narasimhan B: An R package for assessing drug synergism/antagonism. J Stat Softw 2010, 34(6):1-18.
• [12]Malich G, Markovic B, Winder C: The sensitivity and specificity of the MTS tetrazolium assay for detecting the in vitro cytotoxicity of 20 chemicals using human cell lines. Toxicology 1997, 124(3):179-192.
• [13]Barlow RE: Statistical Inference Under Order Restrictions: The Theory and Application of Isotonic Regression. Wiley series in probability and mathematical statistics, Wiley: J. Hoboken; 1972.
• [14]Kvam PH, Vidakovic B: Nonparametric Statistics with Applications to Science and Engineering. Hoboken: Wiley; 2007.
• [15]Aguirre-Ghiso JA: Models, mechanisms and clinical evidence for cancer dormancy. Nat Rev Cancer 2007, 7(11):834-846.
• [16]Huang S, Pang L: Comparing statistical methods for quantifying drug sensitivity based on in vitro dose-response assays. Assay Drug Dev Technol 2012, 10:88-96.
• [17]Pinheiro J, Bates DM: Mixed-Effects Models in S and S-PLUS. New York: Springer Verlag; 2000.
• [18]Kondoh E, Mori S, Yamaguchi K, Baba T, Matsumura N, Cory Barnett J, Whitaker RS, Konishi I, Fujii S, Berchuck A, Murphy SK: Targeting slow-proliferating ovarian cancer cells. Int J Cancer 2010, 126(10):2448-2456.
• [19]Bracht K, Nicholls A, Liu Y, Bodmer W: 5-Fluorouracil response in a large panel of colorectal cancer cell lines is associated with mismatch repair deficiency. Br J Cancer 2010, 103(3):340-346.
• [20]Gardner SN: A mechanistic, predictive model of dose-response curves for cell cycle phase-specific and -nonspecific drugs. Cancer Res 2000, 60:1417-1425.
• [21]Kozusko F, Chen P, Grant SG, Day BW, Panetta JC: A mathematical model of in vitro cancer cell growth and treatment with the antimitotic agent curacin A. Math Biosci 2001, 170:1-16.
• [22]de Pillis LG, Gu W, Radunskaya AE: Mixed immunotherapy and chemotherapy of tumors: modeling, applications and biological interpretations. J Theor Biol 2006, 238(4):841-862.
• [23]Panetta JC, Evans WE, Cheok MH: Mechanistic mathematical modelling of mercaptopurine effects on cell cycle of human acute lymphoblastic leukaemia cells. Br J Cancer 2006, 94:93-100.
• [24]Sherer E, Hannemann RE, Rundell A, Ramkrishna D: Analysis of resonance chemotherapy in leukemia treatment via multi-staged population balance models. J Theor Biol 2006, 240(4):648-661.
• [25]Hamed SS, Roth CM: Mathematical modeling to distinguish cell cycle arrest and cell killing in chemotherapeutic concentration response curves. J Pharmacokinet Pharmacodyn 2011, 38(3):385-403.
• [26]Cao R, Francisco-Fernández M, Quinto EJ: A random effect multiplicative heteroscedastic model for bacterial growth. BMC Bioinformatics 2010, 11:77. BioMed Central Full Text
• [27]Lin D, Shkedy Z, Yekutieli D, Amaratunga D, Bijnens L: Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R. New York: Springer Verlag; 2012.
• [28]Efron B: Bootstrap methods: another look at the jackknife. Ann Stat 1979, 7:1-26.
• [29]Van Der Vaart AW: Asymptotic Statistics, Volume 3 of Cambridge Series in Statistical and Probabilistic Mathematics. New York: Cambridge University Press; 2000.
• [30]Efron B: The Jackknife, the Bootstrap and other Resampling Plans, Volume 38 of CBMS-NSF Regional Conference Series in Applied Mathematics. Philadelphia: Society for Industrial and Applied Mathematics; 1982.
• [31]Ritz C, Streibig JC: Nonlinear Regression with R. New York: Springer Verlag; 2008.
• [32]Ritz C, Streibig J: Bioassay analysis using R. J Stat Softw 2005, 12(5):1-22.