Cell Panel Profiling Reveals Conserved Therapeutic Clusters and Differentiates the Mechanism of Action of Different PI3K/Mtor, Aurora Kinase and EZH2 Inhibitors
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Published OnlineFirst September 1, 2016; DOI: 10.1158/1535-7163.MCT-16-0403 Models and Technologies Molecular Cancer Therapeutics Cell Panel Profiling Reveals Conserved Therapeutic Clusters and Differentiates the Mechanism of Action of Different PI3K/mTOR, Aurora Kinase and EZH2 Inhibitors Joost C.M. Uitdehaag1, Jeroen A.D.M. de Roos1, Martine B.W. Prinsen1, Nicole Willemsen-Seegers1, Judith R.F. de Vetter1, Jelle Dylus1, Antoon M. van Doornmalen1, Jeffrey Kooijman1, Masaaki Sawa2, Suzanne J.C. van Gerwen1, Jos de Man1, Rogier C. Buijsman1, and Guido J.R. Zaman1 Abstract Cancer cell line panels are important tools to characterize which 9 were not reported before, including TTK, BET and two the in vitro activity of new investigational drugs. Here, we clusters of EZH2 inhibitors. To investigate unexpected cluster- present the inhibition profiles of 122 anticancer agents in ings, sets of BTK, Aurora and PI3K inhibitors were profiled proliferation assays with 44 or 66 genetically characterized in biochemical enzyme activity assays and surface plasmon cancer cell lines from diverse tumor tissues (Oncolines). The resonance binding assays. The BTK inhibitor ibrutinib clusters library includes 29 cytotoxics, 68 kinase inhibitors, and 11 with EGFR inhibitors, because it cross-reacts with EGFR. Aurora epigenetic modulators. For 38 compounds this is the first kinase inhibitors separate into two clusters, related to Aurora comparative profiling in a cell line panel. By strictly maintaining A or pan-Aurora selectivity. Similarly, 12 inhibitors in the optimized assay protocols, biological variation was kept to a PI3K/AKT/mTOR pathway separated into different clusters, minimum. Replicate profiles of 16 agents over three years show reflecting biochemical selectivity (pan-PI3K, PI3Kbgd-isoform a high average Pearson correlation of 0.8 using IC50 values and selective or mTOR-selective). Of these, only allosteric mTOR 0.9 using GI50 values. Good correlations were observed with inhibitors preferentially targeted PTEN-mutated cell lines. other panels. Curve fitting appears a large source of variation. This shows that cell line profiling is an excellent tool for Hierarchical clustering revealed 44 basic clusters, of which 26 the unbiased classification of antiproliferative compounds. Mol contain compounds with common mechanisms of action, of Cancer Ther; 15(12); 3097–109. Ó2016 AACR. Introduction panel of the National Cancer Institute (NCI-60), which resulted in the discovery of bortezomib and eribulin (3, 4). In parallel, a In cell panel profiling, dose–response curves are determined 39-cell line panel was set up by the Japanese Foundation for of a compound in a panel of cell line proliferation assays, and Cancer Research (JFCR-39; refs. 5, 6). this is an important tool to study the mechanism of action With the advent of genomics, attention increasingly focused and selectivity of novel cancer therapies, in addition to find to coupling cell line biology to compound response. High- drug response biomarkers before initiating expensive and throughput cell line profiling showed that cell panels could time-consuming experiments inanimalmodelsortrialsin identify patient subpopulations, for instance BRAF-mutant patients (1, 2). cell lines are in particular sensitive to BRAF inhibitors and Cell panel profiling has a long history as a tool in cancer drug EGFR-mutant cell lines are relatively sensitive to EGFR inhi- discovery. Over the period between 1989 and 2014, tens of bitors (7, 8). As a result, even larger and more fully genetically thousands of compounds have been profiled in the 60 cell line characterized cell panels were developed, encompassing up to a thousand cell lines (9–11). The Genomics of Drug Sensi- tivity in Cancer (GDSC; ref. 9) and the Cancer Cell Line 1 Netherlands Translational Research Center B.V., Kloosterstraat, the Encyclopedia (CCLE; ref. 10) analyzed 24 drugs in 1,036 cell Netherlands. 2Carna Biosciences, Inc., Kobe, Japan. lines and 138 drugs in 727 cell lines, respectively. Other Note: Supplementary data for this article are available at Molecular Cancer studies include the Cancer Therapeutics Response Portal study Therapeutics Online (http://mct.aacrjournals.org/). of 481 drugs in 860 cell lines (CTRP; refs. 11, 12) and that of Corresponding Author: Guido J.R. Zaman, Netherlands Translational Research GlaxoSmithKline, of 19 compounds in 311 cell lines (GSK; Center B.V., Kloosterstraat 9, 5349 AB, the Netherlands. Phone: 31412700500; refs. 8,13). These studies discovered, for instance, that PARP Fax: 31412700501; E-mail: [email protected] inhibitors were active in cells with the EWS–FLI1 transloca- doi: 10.1158/1535-7163.MCT-16-0403 tion, which has led to clinical evaluation of PARP inhibitors Ó2016 American Association for Cancer Research. in Ewing sarcoma (9, 14). www.aacrjournals.org 3097 Downloaded from mct.aacrjournals.org on September 29, 2021. © 2016 American Association for Cancer Research. Published OnlineFirst September 1, 2016; DOI: 10.1158/1535-7163.MCT-16-0403 Uitdehaag et al. Despite the successes, the use of drug screening in large cell from ATCC who authenticated all cell lines by short tandem line panels has come under debate recently. As the shutdown repeat analysis. Cell proliferation assays were carried out as of the NCI-60 panel was announced, many important drug described (22) using ATPlite 1step (Perkin Elmer). Exposure time candidates and selective tool inhibitors remain unscreened, was 72 hours for all compounds, except for epigenetic com- making it harder to find the best tool inhibitors to probe new pounds, for which exposure time was 120 hours (Supplementary biological pathways (1, 15, 16). Second, a comparison of the Table S2). Percentage growth was calculated, relative to the growth CCLE and GDSC panels prompted a discussion about data of unexposed cells, and relative IC50s were fitted using a four- reproducibility (17–21). It was pointed out that the rank parameter logistics curve. From these and seeding cell density correlation between IC50s on the same drug in the same cell data, absolute GI50s were calculated (3, 22). For AUC calculations, lines could vary between 0.1 and 0.6, with a median of 0.28, all percent-effect data within the test range were summed. If ranges which is poor (17). One cause could be that the GDSC were adapted, AUC data were not calculated. maintains universal dose ranges and extrapolates IC50sifthey In the course of three years, 122 inhibitors were tested on fall outside the standard range. For example, for the potent 44 cell lines, and after a panel extension, on 66 cell lines (Fig. 1; compounds bortezomib and docetaxel, about 40% of the Supplementary Table S1). Compounds were dissolved in profiling IC50s consist of extrapolated values. However, also DMSO generally 48 hours before testing. Cisplatin and carbo- when the area-under-the-curve (AUC) activity measures are platin were dissolved on the day of testing. An overview of all used (as e.g. in refs. 10, 11) which are not extrapolated, rank IC50 data is presented in Supplementary Table S3A. correlations remained poor (17). A tell-tale sign is that, after When compounds were inactive, their IC50 was set to 31,600 10 data clustering by the GDSC and CTRP, the highly related nmol/L. Finally, log IC50s(innmol/L)wereusedforall taxanes docetaxel and paclitaxel fell into two different thera- subsequent analyses. When compounds had been profiled peutic subsets (9, 11). This is improbable and inconsistent twice, the data of the largest panel was chosen, and, if equal, with earlier profilings (4, 6). the earliest data set. In defense of the large screens, it has been argued that using rank correlation as measure for data consistency is not appro- Bioinformatics analysis priate, and a Pearson correlation on log IC50s should be used To compare our profiles with each other and with literature, 10 (20). In addition, the data serve their purpose of identifying Pearson correlations between log IC50swereused(r; Figs. 1 genomic markers (20, 21). Care should be taken therefore not to and 2). All further calculations were performed in R (23). Drug- dismiss these valuable data sets. Unfortunately, and in contrast sensitivity scores were calculated in the package DSS (24). to the NCI-60 and JFCR-39 screens, none of the large screens Hierarchical clustering (Fig. 3) used the Ward method and provide internal replicate data, which is essential to get insight 1 À r as distance measure. The resulting tree was validated with into the limits of the reproducibility of cell line screening. multiscale bootstrap resampling (R package pvclust; ref. 25). Here, we present a data set of inhibition profiles of 122 A cutoff for the minimally significant Pearson correlation compounds on a panel of 44 or 66 cell line proliferation assays between profiles (rmin ¼ 0.29; P < 0.05) was obtained by (Oncolines). Of these, 61 compounds are registered drugs, 42 converting the Pearson correlation to a t statistic (26). Then t are investigational drugs, while 19 are in discovery phase or are was translated into P values with the Student t distribution tool compounds. Compared with other profiles, this data set function. Affinity propagation clustering (APC) was performed contains 38 inhibitors for which no data have been reported in using the package apcluster, setting a fixed number of 43 clusters the literature, including many inhibitors with novel mechan- (Table 2; Supplementary Table S4A; ref. 27). Network trees were isms of actions, such as EZH2 inhibitors, BET inhibitors, iso- generated using the Fruchterman–Reingold algorithm, in the form-selective PI3K inhibitors and Aurora kinase inhibitors package igraph (Fig. 4; ref. 28). Principal component analysis (Table 1; Supplementary Tables S1 and S2). Compared with was performed using the function princomp (Fig. 4; ref. 23). the larger panels, the panel shows higher data quality, as evidenced by the correlations of duplicate profiles of 16 chem- Kinase activity assays ically and mechanistically different inhibitors, over a period of The inhibitory activity of compounds on Aurora A and C three years.