Clark et al. BMC Cancer (2017) 17:698 DOI 10.1186/s12885-017-3689-3 SOFTWARE Open Access GRcalculator: an online tool for calculating and mining dose–response data Nicholas A. Clark1†, Marc Hafner2†, Michal Kouril3, Elizabeth H. Williams2, Jeremy L. Muhlich2, Marcin Pilarczyk1, Mario Niepel2, Peter K. Sorger2 and Mario Medvedovic1* Abstract Background: Quantifying the response of cell lines to drugs or other perturbagens is the cornerstone of pre-clinical drug development and pharmacogenomics as well as a means to study factors that contribute to sensitivity and resistance. In dividing cells, traditional metrics derived from dose–response curves such as IC50, AUC, and Emax, are confounded by the number of cell divisions taking place during the assay, which varies widely for biological and experimental reasons. Hafner et al. (Nat Meth 13:521–627, 2016) recently proposed an alternative way to quantify drug response, normalized growth rate (GR) inhibition, that is robust to such confounders. Adoption of the GR method is expected to improve the reproducibility of dose–response assays and the reliability of pharmacogenomic associations (Hafner et al. 500–502, 2017). Results: We describe here an interactive website (www.grcalculator.org) for calculation, analysis, and visualization of dose–response data using the GR approach and for comparison of GR and traditional metrics. Data can be user-supplied or derived from published datasets. The web tools are implemented in the form of three integrated Shiny applications (grcalculator, grbrowser, and grtutorial) deployed through a Shiny server. Intuitive graphical user interfaces (GUIs) allow for interactive analysis and visualization of data. The Shiny applications make use of two R packages (shinyLi and GRmetrics) specifically developed for this purpose. The GRmetrics R package is also available via Bioconductor and can be used for offline data analysis and visualization. Source code for the Shiny applications and associated packages (shinyLi and GRmetrics) can be accessed at www.github.com/uc-bd2k/grcalculator and www.github.com/datarail/gr_metrics. Conclusions: GRcalculator is a powerful, user-friendly, and free tool to facilitate analysis of dose–response data. It generates publication-ready figures and provides a unified platform for investigators to analyze dose–response data across diverse cell types and perturbagens (including drugs, biological ligands, RNAi, etc.). GRcalculator also provides access to data collected by the NIH LINCS Program (http://www.lincsproject.org/) and other public domain datasets. The GRmetrics Bioconductor package provides computationally trained users with a platform for offline analysis of dose–response data and facilitates inclusion of GR metrics calculations within existing R analysis pipelines. These tools are therefore well suited to users in academia as well as industry. Keywords: GR metrics, GR50,GRmax, Data analysis, Web interface, Dose response, IC50,Emax, Shiny, R package, Bioconductor, NIH LINCS program * Correspondence: [email protected] †Equal contributors 1LINCS-BD2K DCIC, Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, Cincinnati, OH 45221, USA Full list of author information is available at the end of the article © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Clark et al. BMC Cancer (2017) 17:698 Page 2 of 11 Background using a fixed difference method involving the number of Measuring the relationship between the dose of a per- cells at the beginning of the treatment (x0) and the num- turbagen and cellular response is a cornerstone of pre- ber of cells at the end of the assay in an untreated (or clinical research. For simplicity, in this paper we focus vehicle-treated) control well (x(0)) and in a drug-treated specifically on drug response, but the concepts and well (x(c)). The GR value is thus: tools discussed are applicable across studies of response ðÞ= log2ðÞxc x0 to a variety of perturbagens, including small molecules, ðÞðÞ= GRðÞ¼ c 2 log2 x 0 x0 −1 antibodies, and protein ligands. In pre-clinical pharma- cology studies, response metrics are used to prioritize Alternatively, if the doubling time of untreated cells, compounds for further analysis, investigate factors that Td, is known from other data and is assumed to be ap- determine drug sensitivity and resistance, and guide plicable to the conditions of the dose–response experi- mechanism-of-action studies. In the case of cell-based ments, the GR value can be calculated as: studies using anti-cancer drugs, proliferating cells are xðcÞ typically exposed to drugs across a range of doses, and log þT=T 2 xð0Þ d = viable cell number (or a surrogate such as ATP level) is GRðcÞ¼2 T Td −1 measured at a single subsequent point in time (often following three days of drug exposure). Relative cell with T representing assay duration. count is then determined based on the ratio of the The sign of the GR value relates directly to response number of cells in drug-treated versus vehicle-only phenotype: it lies between 0 and 1 in the case of partial control wells. Data are fitted to a sigmoidal curve, growth inhibition, equals 0 in the case of complete which is used to compute multiple metrics of sensitivity cytostasis, and lies between 0 and −1 in the case of cell such as the concentration of drug at which the re- death. Parameterization of GR dose–response curves sponse is half the control (IC50), the maximal effect at yields GR50, GEC50, GRmax, GRinf, GRAOC, and Hill coef- the highest dose tested (Emax), and the area under the ficient (hGR) values that are largely independent of cell dose–response curve (AUC)[1]. division rate. GR50, analogous to IC50, is the concentra- However, quantification of drug dose–response tion at which GR(c)=0.5; GEC50, analogous to EC50,is using relative cell counts suffers from a fundamental the concentration at which the perturbagen has half of flaw [2, 3]: for purely arithmetic reasons, when cells its maximal effect on cell growth; GRmax is the maximal undergo fewer divisions over the course of an assay measured effect of the perturbagen (in practice, we re- they appear more drug resistant than otherwise identi- port the lowest GR value measured at the two highest cal cells undergoing more divisions. The number of concentrations tested); GRinf, analogous to Einf, is the cell divisions that takes place over the course of an maximal effect of the perturbagen as extrapolated from assay varies with cell density, media composition, and the GR curve rather than directly from the data (in con- assay duration as well as with division rate, which is trast to GRmax); GRAOC (Area-Over-the-Curve is used highly variable among cell lines and also differs in a because the GR curve can dip below zero), analogous to systematic manner with tissue of origin and genotype AUC, is calculated by integrating the area between the [3]. The confounding effects of division rate on re- GR curve and the value 1 over a range of concentrations sponse as conventionally measured are sufficient to (in practice, we calculate GRAOC directly from the GR change IC50 values >100-fold following changes in ex- values using the trapezoidal rule); and hGR is the steep- perimental conditions that are largely arbitrary (e.g. ness of the sigmoidal dose–response curve. GR values plating density, serum concentration, assay duration can be estimated using both time-lapse and endpoint as- etc.). Thus, dose–response curves based on relative says; in the latter case, it is necessary only to measure cell count and their parameterization using IC50, AUC, the number of cells in each well prior to and after drug and Emax values are fundamentally unreliable. exposure. Detailed protocols for collecting the necessary These issues can be addressed by measuring the sensi- experimental data and for performing GR calculations tivity of cells to drugs on a per-division basis as computed have recently been published [4, 5]. using GR(c), the normalized growth rate inhibition value at drug concentration c: Implementation The GRcalculator web tool is implemented in the form ðÞ= ðÞ GRðÞ¼ c 2kc k 0 −1 of three integrated Shiny applications (grcalculator, grbrowser and grtutorial) (Fig. 1) deployed via the where k(c) is the growth rate of drug-treated cells and Community Edition of Shiny Server. The Shiny instance k(0) is the growth rate of untreated (or vehicle-treated) supporting GRcalculator runs on a server accessible via control cells. In practice, growth rates can be estimated the http://www.grcalculator.org domain. Clark et al. BMC Cancer (2017) 17:698 Page 3 of 11 Fig. 1 GRcalculator Shiny applications (grtutorial, grcalculator, and grbrowser) (http://www.grcalculator.org). A schematic of the GRcalculator homepage showing links to each of the Shiny applications that comprise it Shiny [6] is a web application framework for R [7] that curves. To facilitate comparison of GR and other mea- facilitates building interactive web applications using sures of perturbagen response we have implemented only R. It combines a seamless integration of analytical tools to generate dose–response curves from relative cell and visualization tools implemented in R with libraries count data and to calculate traditional response metrics of JavaScript GUI Elements. The Shiny framework also (IC50, AUC, Emax, etc.) from these curves. The shinyLi allows injection of additional JavaScript elements and package is used for easy and intuitive grid visualization modifications of underlying Cascading Style Sheets of large sets of dose–response curves within the GRcal- (CSS).