# Different Calculation Strategies Are Congruent in Determining Chemotherapy Resistance of Brain Tumors In Vitro

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## Abstract

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## 1. Introduction

^{®}assay. The presented results are an interdisciplinary compendium of state-of-the-art disease modeling, state-of-the-art laboratory procedures, and innovative statistical modeling to investigate a biotechnological and socio-economical relevant challenge.

## 2. Materials and Methods

#### 2.1. Mathematical Background

#### 2.2. Cell Models and Experimental Setup

^{®}CX, Sigma-Aldrich, St Louis, MO, USA) and neural stem cells (H9-Derived, Gibco) were used as healthy controls to evaluate the toxicity of the drugs. Additionally, we also tested the inhibition efficiency using three different normal adult human dermal fibroblasts (NHDF-Ad, Lonza, Basel, Switzerland). Effects on cell growth were assessed 72 h after substance exposure using the CellTiterGlow

^{®}assay (Promega, Madison, WI, USA). All procedures were in consent with the local ethical commission oversights.

#### 2.3. Fitting the Curves

#### 2.4. Quantifying the Drug Effect

## 3. Results

## 4. Discussion

#### Limitations

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

GI${}_{50}$ | concentration required for 50% inhibition of growth |

PoD | point-of-departure |

AUC | area under the curve |

IC${}_{50}$ | inhibitory concentration 50% |

CI | confidence interval |

## References

- Shoemaker, R.H. The NCI60 human tumour cell line anticancer drug screen. Nat. Rev. Cancer
**2006**, 6, 813–823. [Google Scholar] [CrossRef] - Freedman, L.; Venugopalan, G.; Wisman, R. Reproducibility2020: Progress and priorities [version 1; peer review: 2 approved]. F1000Research
**2017**, 6, 604. [Google Scholar] [CrossRef][Green Version] - Haibe-Kains, B.; El-Hachem, N.; Birkbak, N.J.; Jin, A.C.; Beck, A.H.; Aerts, H.J.W.L.; Quackenbush, J. Inconsistency in large pharmacogenomic studies. Nature
**2013**, 504, 389–393. [Google Scholar] [CrossRef] [PubMed][Green Version] - Niepel, M.; Hafner, M.; Mills, C.; Subramanian, K.; Williams, E.; Chung, M.; Gaudio, B.; Barrette, A.; Stern, A.; Hu, B.; et al. A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines. Cell Syst.
**2019**, 9, 35–48. [Google Scholar] [CrossRef] [PubMed][Green Version] - Ben-David, U.; Siranosian, B.; Ha, G.; Tang, H.; Oren, Y.; Hinohara, K.; Strathdee, C.A.; Dempster, J.; Lyons, N.J. Genetic and transcriptional evolution alters cancer cell line drug response. Nature
**2018**, 560, 325–330. [Google Scholar] [CrossRef] [PubMed] - Riss, T.L.; Moravec, R.A.; Niles, A.L.; Duellman, S.; Benink, H.A.; Worzella, T.J.; Minor, L. Cell Viability Assays. In Assay Guidance Manual; Sittampalam, G.S., Grossman, A., Brimacombe, K., Arkin, M., Auld, D., Austin, C.P., Baell, J., Bejcek, B., Caaveiro, J.M.M., Chung, T.D.Y., et al., Eds.; Eli Lilly & Company and the National Center for Advancing Translational Sciences: Bethesda, MD, USA, 2004. [Google Scholar]
- Riss, T.; Moravec, R. Use of Multiple Assay Endpoints to Investigate the Effects of Incubation Time, Dose of Toxin, and Plating Density in Cell-Based Cytotoxicity Assays. Assay Drug Dev. Technol.
**2004**, 2, 51–62. [Google Scholar] [CrossRef] [PubMed] - Vargas-Toscano, A.; Khan, D.; Nickel, A.C.; Hewera, M.; Kamp, M.A.; Fischer, I.; Steiger, H.J.; Zhang, W.; Muhammad, S.; Hänggi, D.; et al. Robot technology identifies a Parkinsonian therapeutics repurpose to target stem cells of glioblastoma. CNS Oncol.
**2020**, 9, CNS58. [Google Scholar] [CrossRef] - Gadagkar, S.R.; Call, G.B. Computational tools for fitting the Hill equation to dose-response curves. J. Pharmacol. Toxicol. Methods
**2015**, 71, 68–76. [Google Scholar] [CrossRef][Green Version] - Gesztelyi, R.; Zsuga, J.; Kemeny-Beke, A.; Varga, B.; Juhasz, B.; Tosaki, A. The Hill equation and the origin of quantitative pharmacology. Arch. Hist. Exact Sci.
**2012**, 66, 427–438. [Google Scholar] [CrossRef] - Goutelle, S.; Maurin, M.; Rougier, F.; Barbaut, X.; Bourguignon, L.; Ducher, M.; Maire, P. The Hill equation: A review of its capabilities in pharmacological modeling. Fundam. Clin. Pharmacol.
**2008**, 22, 633–648. [Google Scholar] [CrossRef] - Motulsky, H.; Christopoulos, A. Analyzing competitive binding data. In Fitting Models to Biological Data Using Linear and Nonlinear Regression; GraphPad Software, Inc.: San Diego, CA, USA, 2003; p. 211. [Google Scholar]
- Zaharevitz, D.; Holbeck, S.; Bowerman, C.; Svetlik, P. COMPARE: A web accessible tool for investigating mechanisms of cell growth inhibition. J. Mol. Graph. Model.
**2002**, 20, 297–303. [Google Scholar] [CrossRef] - Motulsky, H.; Christopoulos, A. Introduction to dose-response curves. In Fitting Models to Biological Data Using Linear and Nonlinear Regression; GraphPad Software, Inc.: San Diego CA, USA, 2003; p. 258. [Google Scholar]
- Dutta, D.; Heo, I.; Clevers, H. Disease Modeling in Stem Cell-Derived 3D Organoid Systems. Trends Mol. Med.
**2017**, 23, 393–410. [Google Scholar] [CrossRef] - Kitaeva, K.V.; Rutland, C.S.; Rizvanov, A.A.; Solovyeva, V.V. Cell Culture Based in vitro Test Systems for Anticancer Drug Screening. Front. Bioeng. Biotechnol.
**2020**, 8, 322. [Google Scholar] [CrossRef] [PubMed][Green Version] - Lathia, J.D.; Mack, S.C.; Mulkearns-Hubert, E.E.; Valentim, C.L.L.; Rich, J.N. Cancer stem cells in glioblastoma. Genes Dev.
**2015**, 29, 1203–1217. [Google Scholar] [CrossRef][Green Version] - Barretina, J.; Caponigro, G.; Stransky, N.; Venkatesan, K.; Margolin, A.A.; Kim, S.; Wilson, C.J.; Lehár, J.; Kryukov, G.V.; Sonkin, D.; et al. The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity. Nature
**2012**, 483, 603–607. [Google Scholar] [CrossRef] [PubMed] - Garnett, M.J.; Edelman, E.J.; Heidorn, S.J.; Greenman, C.D.; Dastur, A.; Lau, K.W.; Greninger, P.; Thompson, I.R.; Luo, X.; Soares, J.; et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature
**2012**, 483, 570–575. [Google Scholar] [CrossRef] [PubMed][Green Version] - Stupp, R.; Mason, W.P.; van den Bent, M.J.; Weller, M.; Fisher, B.; Taphoorn, M.J.; Belanger, K.; Brandes, A.A.; Marosi, C.; Bogdahn, U.; et al. Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma. New Engl. J. Med.
**2005**, 352, 987–996. [Google Scholar] [CrossRef] [PubMed] - May, M. Automated sample preparation. Science
**2016**, 351, 300–302. [Google Scholar] [CrossRef][Green Version] - Hewera, M.; Nickel, A.; Knipprath, N.; Muhammad, S.; Fan, X.; Steiger, H.; Hanggi, D.; Kahlert, U. Measures to increase value of preclinical research - an inexpensive and easy-to-implement approach to a QMS for an academic research lab [version 2; peer review: 2 approved, 1 approved with reservations]. F1000Research
**2020**, 9, 660. [Google Scholar] [CrossRef] - Galli, R.; Binda, E.; Orfanelli, U.; Cipelletti, B.; Gritti, A.; Vitis, S.; Fiocco, R.; Foroni, C.; Dimeco, F.; Vescovi, A. Isolation and characterization of tumorigenic, stem-like neural precursors from human glioblastoma. Cancer Res.
**2004**, 64, 7011–7021. [Google Scholar] [CrossRef][Green Version] - Binder, Z.; Wilson, K.; Salmasi, V.; Orr, B.; Eberhart, C.; Siu, I.; Lim, M.; Weingart, J.; Quinones-Hinojosa, A.; Bettegowda, C.; et al. Establishment and biological characterization of a panel of glioblastoma multiforme (GBM) and GBM variant oncosphere cell lines. PLoS ONE
**2016**, 11, e0150271. [Google Scholar] [CrossRef] [PubMed][Green Version] - Campos, B.; Wan, F.; Farhadi, M.; Ernst, A.; Zeppernick, F.; Tagscherer, K.E.; Ahmadi, R.; Lohr, J.; Dictus, C.; Gdynia, G.; et al. Differentiation Therapy Exerts Antitumor Effects on Stem-like Glioma Cells. Clin. Cancer Res.
**2010**, 16, 2715–2728. [Google Scholar] [CrossRef] [PubMed][Green Version] - Podergajs, N.; Brekka, N.; Radlwimmer, B.; Herold-Mende, C.; Talasila, K.; Tiemann, K.; Rajcevic, U.; Lah, T.; Bjerkvig, R.; Miletic, H. Expansive growth of two glioblastoma stem-like cell lines is mediated by bFGF and not by EGF. Radiol. Oncol.
**2013**, 47, 330–337. [Google Scholar] [CrossRef] [PubMed] - Ferrarese, R.; IV, G.R.H.; Yadav, A.K.; Bug, E.; Maticzka, D.; Reichardt, W.; Dombrowski, S.M.; Miller, T.E.; Masilamani, A.P.; Dai, F.; et al. Lineage-specific splicing of a brain-enriched alternative exon promotes glioblastoma progression. J. Clin. Investig.
**2014**, 124, 2861–2876. [Google Scholar] [CrossRef] [PubMed][Green Version] - Emmerich, C.H.; Gamboa, L.M.; Hofmann, M.C.J.; Bonin-Andresen, M.; Arbach, O.; Schendel, P.; Gerlach, B.; Hempel, K.; Bespalov, A.; Dirnagl, U.; et al. Improving target assessment in biomedical research: the GOT-IT recommendations. Nat. Rev. Drug Discov.
**2020**. [Google Scholar] [CrossRef] [PubMed] - Liston, D.R.; Davis, M. Clinically Relevant Concentrations of Anticancer Drugs: A Guide for Nonclinical Studies. Clin. Cancer Res.
**2017**, 23, 3489–3498. [Google Scholar] [CrossRef][Green Version] - Herrera-Rios, D.; Li, G.; Khan, D.; Tsiampali, J.; Nickel, A.C.; Aretz, P.; Hewera, M.; Suwala, A.K.; Jiang, T.; Steiger, H.J.; et al. A computational guided, functional validation of a novel therapeutic antibody proposes Notch signaling as a clinical relevant and druggable target in glioma. Sci. Rep.
**2020**, 10, 1–12. [Google Scholar] [CrossRef] - Kahlert, U.D.; Cheng, M.; Koch, K.; Marchionni, L.; Fan, X.; Raabe, E.H.; Maciaczyk, J.; Glunde, K.; Eberhart, C.G. Alterations in cellular metabolome after pharmacological inhibition of Notch in glioblastoma cells. Int. J. Cancer
**2016**, 138, 1246–1255. [Google Scholar] [CrossRef][Green Version] - Mehrjardi, N.Z.; Hänggi, D.; Kahlert, U.D. Current biomarker-associated procedures of cancer modeling—A reference in the context of IDH1 mutant glioma. Cell Death Dis.
**2020**, 11, 1–12. [Google Scholar] [CrossRef] - Sancho-Martinez, I.; Nivet, E.; Xia, Y.; Hishida, T.; Aguirre, A.; Ocampo, A.; Ma, L.; Morey, R.; Krause, M.N.; Zembrzycki, A.; et al. Establishment of human iPSC-based models for the study and targeting of glioma initiating cells. Nat. Commun.
**2016**, 7, 10743. [Google Scholar] [CrossRef][Green Version] - Hanaford, A.R.; Archer, T.C.; Price, A.; Kahlert, U.D.; Maciaczyk, J.; Nikkhah, G.; Kim, J.W.; Ehrenberger, T.; Clemons, P.A.; Dančík, V.; et al. DiSCoVERing Innovative Therapies for Rare Tumors: Combining Genetically Accurate Disease Models with In Silico Analysis to Identify Novel Therapeutic Targets. Clin. Cancer Res.
**2016**, 22, 3903–3914. [Google Scholar] [CrossRef] [PubMed][Green Version] - Uhlmann, C.; Kuhn, L.M.; Tigges, J.; Fritsche, E.; Kahlert, U. Efficient Modulation of TP53 Expression in Human Induced Pluripotent Stem Cells. Curr. Protoc. Stem Cell Biol.
**2020**, 52, e102. [Google Scholar] [CrossRef] [PubMed] - Dagogo-Jack, I.; Shaw, A.T. Tumour heterogeneity and resistance to cancer therapies. Nat. Rev. Clin. Oncol.
**2018**, 15, 81–94. [Google Scholar] [CrossRef] [PubMed] - Son, B.; Lee, S.; Youn, H.; Kim, E.; Kim, W.; Youn, B. The role of tumor microenvironment in therapeutic resistance. Oncotarget
**2016**, 8, 3933–3945. [Google Scholar] [CrossRef] [PubMed][Green Version] - Zuo, S.; Zhang, X.; Wang, L. A RNA sequencing-based six-gene signature for survival prediction in patients with glioblastoma. Sci. Rep.
**2019**, 9, 2615. [Google Scholar] [CrossRef] [PubMed][Green Version] - Zeng, F.; Wang, K.; Liu, X.; Zhao, Z. Comprehensive profiling identifies a novel signature with robust predictive value and reveals the potential drug resistance mechanism in glioma. Cell Commun. Signal.
**2020**, 18, 1–13. [Google Scholar] [CrossRef][Green Version]

**Figure 1.**(

**a**) Graphical representation of the parameters of the Hill equation: ${A}_{0}$: the curve upper bound; ${b}_{\infty}$: the curve lower bound, as a fraction of ${A}_{0}$; ${\mathit{logIC}}_{50}$: the logarithm of the substance concentration at the inflection point of the curve, $I{C}_{50}$; $\beta $: the parameter controlling the slope of the curve. Note that GI${}_{50}$ is not itself a parameter of the curve; it is the point at which the curve falls to 50% of its maximum value, ${A}_{0}$. (

**b**) Definition of the point-of-departure (PoD), based on the confidence band of the curve. The existence of a drug effect can be established with 95% confidence at the lowest concentration at which the Hill curve’s confidence interval (CI, red) does not overlap with the 95% CI at $\mathit{logC}=0$ (green).

**Figure 2.**The function used to transform the data and its effect on the Hill curve: (

**a**) For low values, close to zero, the inverse softplus function (solid, red) approximates the logarithm (dashed, green). For high values, it approaches the identity line, $y=x$ (dotted, blue). (

**b**) Where the logistic (Hill) function (dashed, red) has high values, the inverse softplus (solid, blue) leaves it almost unchanged. At low values, where the logistic function becomes close to a falling exponential, the inverse softplus transforms it to an almost straight line.

**Figure 3.**Different cell lines have different growth patterns, and after 72 h of incubation, their numbers differ significantly at every drug concentration. Curves differing only in the amplitude parameter ${A}_{0}$ and sharing the remaining three parameters, ${b}_{\infty}$, $\mathit{logC}$, and $\beta $, were fitted to the empirical data. The different amplitudes were later used for normalizing the curves.

**Figure 5.**(

**a**) Correlation between the AUC and GI${}_{50}$ (dots, solid line) and between the AUC and PoD (crosses, dashed line). The three measures can be used more-or-less interchangeably for detecting substance effect. (

**b**) Correlation between GI${}_{50}$ and PoD. When both values could be computed (i.e., neither was infinite), they were very similar. Note, however, that PoD also depends on the experimental setup (see Discussion below).

**Figure 6.**(

**a**) If a drug failed to reach GI${}_{50}$, the AUC had a significantly higher value. (

**b**) The same behavior, only with slightly lower AUC values, was observed when using PoD as the criterion for the effect. (

**c**) Correlation between the rankings by GI${}_{50}$ and by PoD. For the top 20 substances (lower left corner in the figure), there is little difference between the two criteria.

**Figure 7.**(

**a**) Bortezomib showed an effect very early, at concentrations for which there were no measurements, so the confidence band was very wide. (

**b**) Itraconazole did not have an effect, and the numeric algorithm failed to fit a logistic curve to the empirical data.

**Figure 8.**(

**a**) Rigosertib sodium leveled off shortly after reaching GI${}_{50}$. (

**b**) Vinflunine tartrate leveled off before reaching GI${}_{50}$.

Substance | Rank (GI${}_{50}$) | [email protected]${}_{50}$ | Rank (PoD) | [email protected] | Rank (AUC) |
---|---|---|---|---|---|

Itraconazole | 1 | 0.000 | 64 | inf | 10 |

Bortezomib | 2 | 0.411 | 63 | inf | 1 |

Actinomycin D | 3 | 0.596 | 1 | 0.636 | 6 |

Dinaciclib | 4 | 0.586 | 2 | 0.641 | 8 |

Staurosporine | 5 | 0.472 | 4 | 0.654 | 2 |

Ganetespib | 6 | 0.566 | 3 | 0.600 | 3 |

Romidepsin | 7 | 0.000 | 12 | 0.005 | 7 |

MLN9708 | 8 | inf | 5 | inf | 4 |

Carfilzomib | 9 | 0.930 | 6 | 0.930 | 5 |

Homoharringtonine | 10 | 0.659 | 7 | 0.696 | 9 |

PF-04691502 | 11 | 0.345 | 8 | 0.329 | 14 |

BAY80-6946 | 12 | inf | 9 | inf | 13 |

INK128 | 13 | inf | 28 | inf | 12 |

Obatoclax | 14 | 0.534 | 10 | 0.434 | 11 |

Panobinostat | 15 | 0.658 | 11 | 0.639 | 16 |

Auranofin | 16 | inf | 13 | inf | 15 |

17-AAG | 17 | inf | 14 | inf | 17 |

Idarubicin hydrochloride | 18 | 0.834 | 16 | 0.790 | 19 |

Fludarabine phosphate | 19 | 0.000 | 22 | 0.000 | 24 |

Daunorubicin hydrochloride | 20 | 0.710 | 17 | 0.675 | 20 |

Substance | Rank (PoD) | [email protected] | Rank (GI${}_{50}$) | [email protected]${}_{50}$ | Rank (AUC) |
---|---|---|---|---|---|

Actinomycin D | 1 | 0.636 | 3 | 0.596 | 6 |

Dinaciclib | 2 | 0.641 | 4 | 0.586 | 8 |

Ganetespib | 3 | 0.600 | 6 | 0.566 | 3 |

Staurosporine | 4 | 0.654 | 5 | 0.472 | 2 |

MLN9708 | 5 | inf | 8 | inf | 4 |

Carfilzomib | 6 | 0.930 | 9 | 0.930 | 5 |

Homoharringtonine | 7 | 0.696 | 10 | 0.659 | 9 |

PF-04691502 | 8 | 0.329 | 11 | 0.345 | 14 |

BAY 80-6946 | 9 | inf | 12 | inf | 13 |

Obatoclax | 10 | 0.434 | 14 | 0.534 | 11 |

Panobinostat | 11 | 0.639 | 15 | 0.658 | 16 |

Romidepsin | 12 | 0.005 | 7 | 0.000 | 7 |

Auranofin | 13 | inf | 16 | inf | 15 |

17-AAG | 14 | inf | 17 | inf | 17 |

Rigosertib sodium | 15 | 0.477 | 24 | 0.477 | 25 |

Idarubicin hydrochloride | 16 | 0.790 | 18 | 0.834 | 19 |

Daunorubicin hydrochloride | 17 | 0.675 | 20 | 0.710 | 20 |

Vinflunine tartrate | 18 | 0.404 | 97 | inf | 63 |

Doxorubicin hydrochloride | 19 | 0.735 | 21 | 0.736 | 23 |

Ponatinib | 20 | 0.854 | 23 | 0.894 | 21 |

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## Share and Cite

**MDPI and ACS Style**

Fischer, I.; Nickel, A.-C.; Qin, N.; Taban, K.; Pauck, D.; Steiger, H.-J.; Kamp, M.; Muhammad, S.; Hänggi, D.; Fritsche, E.; Remke, M.; Kahlert, U.D. Different Calculation Strategies Are Congruent in Determining Chemotherapy Resistance of Brain Tumors In Vitro. *Cells* **2020**, *9*, 2689.
https://doi.org/10.3390/cells9122689

**AMA Style**

Fischer I, Nickel A-C, Qin N, Taban K, Pauck D, Steiger H-J, Kamp M, Muhammad S, Hänggi D, Fritsche E, Remke M, Kahlert UD. Different Calculation Strategies Are Congruent in Determining Chemotherapy Resistance of Brain Tumors In Vitro. *Cells*. 2020; 9(12):2689.
https://doi.org/10.3390/cells9122689

**Chicago/Turabian Style**

Fischer, Igor, Ann-Christin Nickel, Nan Qin, Kübra Taban, David Pauck, Hans-Jakob Steiger, Marcel Kamp, Sajjad Muhammad, Daniel Hänggi, Ellen Fritsche, Marc Remke, and Ulf Dietrich Kahlert. 2020. "Different Calculation Strategies Are Congruent in Determining Chemotherapy Resistance of Brain Tumors In Vitro" *Cells* 9, no. 12: 2689.
https://doi.org/10.3390/cells9122689