A Robust High-Content Imaging Approach for Probing the … · Therapeutic Discovery A Robust High-Content Imaging Approach for Probing the Mechanism of Action and Phenotypic Outcomes

Therapeutic Discovery

A Robust High-Content Imaging Approach for Probingthe Mechanism of Action and Phenotypic Outcomes ofCell-Cycle Modulators

Jeffrey J. Sutherland1, Jonathan Low2, Wayne Blosser2, Michele Dowless2, Thomas A. Engler3, andLouis F. Stancato2

AbstractHigh-content screening is increasingly used to elucidate changes in cellular biology arising from treat-

ment with small molecules and biological probes. We describe a cell classifier for automated analysis of

multiparametric data from immunofluorescence microscopy and characterize the phenotypes of 41 cell-cycle

modulators, including several protein kinase inhibitors in preclinical and clinical development. This method

produces a consistent assessment of treatment-induced phenotypes across experiments done by different

biologists and highlights the prevalence of nonuniform and concentration-dependent cellular response to

treatment. Contrasting cell phenotypes from high-content screening to kinase selectivity profiles from cell-free

assays highlights the limited utility of enzyme potency ratios in understanding the mechanism of action

for cell-cycle kinase inhibitors. Our cell-level approach for assessing phenotypic outcomes is reliable,

reproducible and capable of supporting medium throughput analyses of a wide range of cellular perturba-

tions. Mol Cancer Ther; 10(2); 242–54. �2011 AACR.

Introduction

The characterization of cell populations with immuno-fluorescence microscopy, or high-content screening,allows a detailed understanding of the effect of smallmolecule modulators on the mitotic cell cycle. By quan-tifying the intensity, localization, and morphology of 4 to5 markers in individual cells, such experiments typicallyproduce on the order of 105 to 106 observations pertreatment. In recognition of the heterogeneity of cellpopulations (1), several methods have been proposedfor analyzing treatment-induced perturbations by cellu-lar imaging. Such approaches assign a phenotype toindividual cells, using rules to emulate their classificationby biologists (2–4), or by applying nonsupervised (5–9) orsupervised (10, 11) multivariate analysis of cytologicalfeatures. Methods for analysis of high-content imagingexperiments have been reviewed elsewhere (12, 13).

Chemical modulation of the mitotic cell cycle hasproven to be effective in treating cancer. A number ofapproved chemotherapeutic agents disrupt DNA repli-cation [e.g., the topoisomerase inhibitors (14) topotecanand camptothecin] or microtubule dynamics (15; e.g.,the tubulin modulators paclitaxel and vinca alkaloids).The mitotic cell cycle is regulated by many kinases, andthe search for small molecule inhibitors has produced anumber of agents in preclinical and clinical development.

The roles of kinases such as CDK1, AURKA, AURKB,and PLK1 in the G2-M checkpoint are well-established(16). The CDK1-cyclin B complex regulates entry intomitosis; loss of CDK1 function results in arrest at theG2-M boundary and enrichment of cell populationshaving large nuclei with 4N DNA present as diffusechromatin. Because of their role at the G2-M checkpoint,inhibition of other G2-M kinases in addition to CDK1 isexpected to result in a phenotype consistentwith selectiveCDK1 inhibition (17). The kinase AURKA is involved incentrosome regulation, and its inhibition manifests itselfvia enrichment of cells in prometaphase. AURKA pro-motes bipolar spindle assembly (18), andmutations in thiskinase prevent centrosome separation leading to the for-mation of monopolar spindles (19). In addition to its rolein cytokinesis, AURKB activates the spindle-assemblycheckpoint, and manifestation of AURKA inhibitionrequires a functional spindle-assembly checkpoint. Forthis reason, dual aurora A/B inhibitors are expected toyield a phenotype consistent with selective AURKB inhi-bition (20). Finally, PLK1 functions in spindle formation,chromosome segregation and cytokinesis, the inhibition

Authors' Affiliation: 1Lilly Research Labs Information Technology, 2Can-cer Biology & Patient Tailoring, and 3Discovery Chemistry Research andTechnology, Eli Lilly and Company, Indianapolis, Indiana

Note: Supplementary material for this article is available at MolecularCancer Therapeutics Online (http://mct.aacrjournals.org/).

Corresponding Author: Jeffrey J. Sutherland, Eli Lilly and Company, LillyCorporate Center, Indianapolis, IN, 46285. Phone: 317-655-0833; Fax:317-276-6545. E-mail: [email protected]; or Louis F. Stancato, Can-cer Biology & Patient Tailoring, Indianapolis, IN, 46285. E-mail:[email protected]

doi: 10.1158/1535-7163.MCT-10-0720

�2011 American Association for Cancer Research.

MolecularCancer

Therapeutics

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of which results in failure to establish a bipolar spindle inprometaphase (21).Protein kinases have a high degree of structural homol-

ogy, and activity of small molecule inhibitors againstproteins other than the intended target (i.e., off-targetactivity) is frequently observed in cell-free assays. Tounderstand the relationship between selectivity andeffects on cell populations, we developed a classifier ofcellular phenotype in HCT-116 cells (a cell line for color-ectal carcinoma), and applied it to kinase and nonkinasemodulators of the cell cycle. The approach yields ahighly-reproducible assessment of changes in cell popu-lations induced by different treatments or different con-centrations of the same treatment, and highlights thelimited utility of kinase selectivity panels in selectinginhibitors that exhibit the desired phenotype.

Materials and Methods

Cell-cycle modulators and high-content imagingVarious inhibitors of proteins involved in cell-cycle

regulation were selected from the Lilly corporate collec-tion (Table 1). Where available, compounds were pur-chased from commercial vendors. Several reportedkinase inhibitors not available for purchase were synthe-sized internally, using synthetic schemes described in thepublic domain. All compounds have � 95% purity.Experiments were done by 3 biologists over a 6-monthperiod, to study phenotypes induced by cell-cycle mod-ulators. Experiments 2 and 3 were designed to specifi-cally probe reproducibility of phenotypes, whereasexperiment 1 was designed to characterize a large collec-tion of kinase inhibitors. HCT-116 cells were plated onto96-well dishes, treated with compounds in 10-point con-centration curves, and imaged on the Arrayscan VTIplatform (see Supplementary Methods). Cytological fea-tures of cells were captured with the Target Activationbio-application bundled with the imaging instrument.The selected cytological features quantify a number ofimportant changes in cells undergoing mitosis (Supple-mentary Table 1). Objects are defined from nuclei identi-fication, and mostly correspond to individual cells;clusters of nuclei from treatments causing polyploidyare captured as 1 object with �8N DNA content anddaughter nuclei in anaphase are captured as 2 objects.

Normalization of cellular featuresNumerical values of features from the instrument soft-

ware are log2 transformed to increase the normality ofdistribution across cell populations (base 2 is convenientfor counting doublings of intensity, e.g., DNA content of2N, 4N, 8N, etc.). To account for plate-to-plate variationin cytological features (i.e., changes arising from variationin antibody staining intensity, incubator conditions, etc.),individual values for each feature are converted to Z-scores, using the mean and standard deviation obtainedby pooling dimethyl sulfoxide (DMSO)-treated cells from8 negative control wells (i.e., normalization of features on

a per-plate basis). The intensity distributions for cells in 8positive control wells containing 0.2 mmol/L of nocoda-zole are visually assessed for each plate and channel toverify for consistency across plates after feature scaling(Supplementary Fig. 1). This method of normalization isadequate to control for plate-to-plate variability within anexperiment. All further analysis uses scaled values ofcytological features.

Quantifying antiproliferative effects of compoundtreatments

For each well, the cell density is calculated by count-ing the number of objects (cells) per field of view, andaveraging across all fields for a given well. For a treat-ment compound, cell density is converted to a percent-age relative to the plate-averaged cell density fromDMSO treatment (i.e., 100% corresponds to the averagecell density for DMSO treatment). Logistic regressioncurve fits were done using TIBCO Spotfire (Version 2.1;TIBCO Software, Inc.), and the concentration at whichthe curve crosses 50% is reported as the EC50 of thecompound.

Quantifying cell phenotypes induced by compoundtreatments

A set of 8 reference compounds of reportedmechanismwere selected for the purpose of classifying cells byphenotype (designated in bold type in Table 1). Thesewere the CDK inhibitors AG-024322 and R-547, the aur-ora kinase inhibitors AZD-1152 and tozasertib, the PLK1inhibitor BI-2536, and the microtubule modulators ON-01910, nocodazole and paclitaxel. ON-01910 was origin-ally reported as a non-ATP competitive PLK1 inhibitor; insubsequent reports it has been found to not inhibit PLK1biochemically and generates a cell phenotype consistentwith microtubule modulation (22, 23). The compoundswere selected to represent a diversity of phenotypesobserved for G2-Mmodulators. Visual analysis of imagesreveals cell populations consistent with themechanism ofaction of the compounds at all concentrations above theantiproliferation EC50, allowing those wells to be pooledfor the purpose of training a classifier. Cells in these wellsare described by the cytological features in Supplemen-tary Table 1, and assigned the class label of the treatmentcompound. To our knowledge, there are no cell-cyclemodulators that arrest cells in metaphase or anaphase. Totrain the classifier in the identification of these states,images for DMSO-treated cells in experiment 3 werereviewed and �30 cells of each type identified andlabeled accordingly. The total number of cells acrosspooled wells used for training the classifier was 63,575,58,298, and 78,552 for experiments 1 to 3.

The reference compounds were used to develop aclassifier of cells for each experiment (i.e., 3 experiments,3 classifiers). The classifier was developed with therecursive partitioning algorithm in Jmp (Version 7.0.2,SAS Institute, Inc), using the compound class label asresponse variable and cytological features as factors. The

High-Content Imaging of Cell-Cycle Modulators

www.aacrjournals.org Mol Cancer Ther; 10(2) February 2011 243




Table 1. Compounds characterized by high-content imaging in HCT-116

Namea EC50, mmol/L Dominant phenotypec

Experimentb 1 Experiment 2 Experiment 3

Aurora inhibitorsAZD1152 0.026 0.023 0.019 EndoAZD1152 metabolite 0.034 0.025 0.030 EndoCYC116 0.649 EndoENMD-2076 0.519 proM#, endo"MLN-8054 0.247 >2 0.380 proM#, endo"PHA-739358 0.036 0.075 0.056 endo#, proM þ M-apopt"Tozasertib (VX-680; MK0457) 0.045 0.050 0.066 Endo

CDK inhibitorsAG-024322 0.173 0.116 0.102 G2

AG-12286 (WO 9921845 A2 223784-75-6) 0.249 G2 þ proMAlvocidib (flavopiridol) 0.153 0.256 0.266 G2 þ proMBMI-1026 (39) 0.030 G2BMS-265246 (40) 0.293 0.492 0.463 G2JNJ-7706621 (41, 42) 0.524 G2PD-171851 (43) 0.570 G2 þ proMPD-0332991 >2 G1-SAminopurvalanol 2.500 G2R-547 0.077 0.144 0.104 G2SCH-727965 0.018 0.008 0.010 G2 þ proMSNS-032 0.074 0.119 0.128 G2 þ proMWO 2001064655 A1 358788-29-1 0.292 G2 þ proMWO 2001064656 A1 358789-50-1 0.250 G2 þ endo

PLK1 inhibitorsBI-2536 0.009 0.008 0.007 proM þ M-apoptGSK-461364 0.012 0.014 0.012 proM þ M-apoptHMN-176 (28) 0.164 0.260 0.216 proM þ M-apoptWO 2006049339 A1 886856-66-2 0.101 0.072 0.070 G2 þ proM þ M-apoptWO 2006066172 A1 893440-87-4 1.861 proM þ M-apopt

Wee1 inhibitorUS 2007254892 A1 955365–24-9 2.340 proM þ M-apopt

DNA intercalators; Topoisomerase inhibitorsAclarubicin 0.238 0.255 G1-SCamptothecin 0.011 0.008 G2#, G2þproM"Doxorubicin 0.254 0.247 G2Topotecan 0.020 0.033 G2#, G2 þ proM"

DNA synthesis inhibitors5-Fluoro-20-deoxyuridine (floxuridine) >2 >2 G1-SHerboxidiene 0.010 0.011 G2 þ proMIlludin S 0.015 0.019 G1-SMitomycin 0.187 0.253 G2

Microtubule modulatorsON-01910 0.100 0.052 0.025 proM þ M-apoptAlbendazole 0.448 0.305 proM þ M-apoptCiclobendazole 0.547 0.691 proM þ M-apoptMebendazole 0.318 0.483 proM þ M-apoptNocodazole 0.055 0.072 proM þ M-apoptPaclitaxel 0.003 0.007 proM þ M-apopt

Abbreviations: G1-S, G1 or S-phase; proM, prometaphase; M-apopt, M-phase apoptotic; endo, endoreduplication.aStructures are given in Supplementary scheme 1; compounds in bold are used as references in calibrating the cell classifier; kinaseinhibitors with no reference given are described at http://www.clinicaltrials.gov; inhibitors described in patents, but not the journalliterature, are identified by the CAS number retrieved in SciFinder (Version 2007.3, American Chemical Society).bExperiments in which the compound was characterized, each of which was done by a different biologist on a different date.cPhenotype from cell-classifier for dominant cell population(s), that is, polyploidy; " and # indicate phenotypes at lower and higherconcentrations, respectively.

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Mol Cancer Ther; 10(2) February 2011 Molecular Cancer Therapeutics244




maximum significance rule was used for selecting splits.Recursive partitioning is a greedy approach, selecting thebest feature for each split in an incremental mannerregardless of its appeal from a biological perspective.To emulate the manner in which biologists analyze cells,the first splits are obtained by visual binning of DNAintensity into 2N, 4N, 8N, and >8N categories. This isdone manually via histogram plots in TIBCO Spotfire,allowing the boundaries between 2N, 4N, etc. to changefrom experiment to experiment (Supplementary Fig. 2).For experiment 3, the subsequent splits used features andrules selected by the recursive partitioning algorithm.The terminal nodes are assigned names consistent withthe values of cytological features and review of images.For the other experiments, the features selected in experi-ment 3were retained for each split, but the split valuewasallowed to change. The rules for classifying metaphaseand anaphase cells are not updated in experiments 1 to 2because of their rarity in the study of cell-cycle modula-tors, and the need to identify representative cells byreview of images. The complete decision tree as depictedin Jmp is shown in Supplementary Figure 3.

Comparing treatment wellsFor the purpose of contrasting the cell classifier to other

approaches, it is necessary to quantitatively assess thesimilarity of phenotypes observed under different treat-ment conditions (i.e., comparing cell phenotypes betweenwells). The phenotype profile for awell is represented as avector of length 9, indicating the proportion of cellsbelonging to each phenotype. For comparison, a vectorof length5 represents thewell-averagedvalues for 5DNA-related features (excludingObjectVarIntenDNAdue to itshigh correlation with ObjectAvgIntenDNA; Pearson r >0.9 for the 3 experiments).We employed theEuclidian andcosine distance metrics for comparing pairs of vectors:

p ¼ p1; p2; . . . ; pn� �

; q ¼ q1; q2; . . . ; qn� �

dis tan ce; Euclidian ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPni¼1

pi � qið Þ2s

dis tan ce; cos ine ¼Pni¼1

piqiffiffiffiffiffiffiffiffiffiPni¼1

p2i

r ffiffiffiffiffiffiffiffiffiPni¼1

q2i

r

Results

Antiproliferation effects of cell-cycle inhibitorsA total of 41 cell-cycle modulators were characterized

via high-content imaging of HCT-116 cells. Cells wereimaged on the Arrayscan VTI platform, quantifyingintensity, localization and/or morphology of Hoechststain (DNA), terminal deoxynucleotidyl transferasedUTP nick end labeling (TUNEL; amarker for apoptosis),and antibodies for cyclin B1, phospho-histone H3(pHH3), and a-tubulin (see Supplementary Methods).

To assess the reproducibility of imaging experiments,29 compounds were tested in 2 or 3 separate experiments,done by different biologists over a 6-month period. Mostcompounds inhibit cell proliferation at a concentration of1 mmol/L or less (Table 1). Using the 58 pairs of definedEC50s for the same compound (i.e., EC50s not prefixedwith the symbol ">"), the fold difference between experi-ments ranges from 1.0 to 3.9, with an average of 1.5. Theassessment of antiproliferative properties of treatments,measured by quantifying changes in cell density (i.e., cellcount per field of view), is highly reproducible acrossexperiments.

Classification of cell phenotypes fromimmunofluorescence microscopy

A classifier of cell phenotypes was developed using 8reference compounds, selected to represent a diversity ofmechanisms among G2-M modulators (see the Methodssection). A visual assessment of images from treatmentconcentrations above the antiproliferation EC50 revealscell populations consistent with published reports onstandard tubulin modulators (15) and RNAi approachesfor kinase targets (17, 20, 23). Wells containing a referencecompound at concentrations above the antiproliferationEC50 were pooled, and cells described using cytologicalfeatures (e.g., nuclear area, cyclin B1 intensity, etc.) listedin Supplementary Table 1. The classifier begins withassessment of DNA content (2N, 4N, 8N, >8N) usingboundaries defined by visual analysis of Hoechst DNAstaining intensity (Supplementary Fig. 2), and creates adecision tree by applying recursive partitioning usingcytological features as factors and the compoundmechanism of action as response variable. The rules thatconstitute the decision tree are selected incrementally in amanner that distinguishes cells treated with compoundshaving different mechanisms. A cell’s phenotype isdefined by the terminal node into which it falls(Fig. 1). As such, each cell is assigned 1 of 9 possiblephenotypes, including the phenotype "other" that corre-sponds mostly to cell debris. The model is recalibrated ineach experiment by analysis of cells treated with refer-ence compounds, without the need to examine and iden-tify cells for training as required with other approaches.

For the CDK1 inhibitors AG-024322 and R-547, thedominant cell populations are G2-arrested cells having4N DNA content, large round nuclei, and low DNAintensity (i.e., diffuse chromatin). By contrast, treatmentwith the PLK1 inhibitor BI-2536 results mostly in cellscharacteristic of prometaphase arrest with 4N DNA con-tent and high DNA intensity (i.e., condensed chromatin).The aurora kinase inhibitors AZD-1152 and tozasertibinduce polyploidy (via endoreduplication), consistentwith the dominance of the AURKB phenotype overAURKA. With a 48-hour incubation (allowing 2 cellnumber doublings), the dominant population shouldconsist of cells with 8N DNA content; smaller popula-tions with 4N and >8N DNA content arise from misseg-mentation of nuclei clusters and mostly represent






artifacts of image analysis (some >8N cells are present).Representative images from each mechanistic class areshown in Figure 2 and Supplementary Figure 4.

The classifier makes significant use of features derivedfrom DNA staining (Fig. 1). In characterizing G2-M mod-ulators, other markers such as cyclin B1 and pHH3 arefrequently examined in conjunction with DNA content.While the proportion of cells for a given phenotype varieswidely across mechanistic classes, cyclin B1 and pHH3staining intensities of cells for a given phenotype aresimilar across mechanistic classes (SupplementaryFig. 5), except for DMSO-treated cells having lower cyclin

B1 intensity for all phenotypes, and higher pHH3 inten-sity for prometaphase andmetaphase cells. TUNEL stain-ing, a measure of apoptosis through DNA end-labelingfollowing DNA fragmentation, shows greater differentia-tion across mechanistic classes for cells of a given phe-notype: cells treated with CDK1 and PLK1 inhibitorshave high induction of apoptosis compared to DMSOor aurora inhibitor treatments. Most aurora inhibitorsinduce cytokinesis defects, but cells continue cyclingbeyond 48 hours. The intensity of a-tubulin immunos-taining from treatment with paclitaxel, a microtubulestabilizer, is increased compared to destabilizers such

Figure 1. Decision tree used for classifying cells according to cytological features (described in Supplementary Table 1). DNA content is defined bymanual binning of ObjectTotalIntenDNA (left rectangles; see Supplementary Fig. 2). Other splits are obtained from recursive partitioning in Jmp, with those ingray defined by partitioning cells treated with 8 calibration compounds, using the compound mechanism of action as class label. The splits in black aredefined by partitioning examples of cells in G1, metaphase, and anaphase; approximately 30 metaphase and 30 anaphase cells were identified bymanual review of images from DMSO treatment in experiment 3. A cell is assigned the phenotype for the terminal node (rectangles) into which it falls.Cells in the nodes endo 4N, endo 8N, and endo >8N denote multinucleated cells; all are combined into 1 phenotype. Likewise, cells assigned to M-apoptoticand M-apoptotic brightest are combined, since they arise from staining variation with Hoechst 33258 in apoptotic cells (see text). With the exception of thenodes in black, numerical values used in the decision tree for each cytological feature are adjusted by recalibrating the model in each experiment; thenotation used to identify nodes [e.g., S1: 1.2, 1.0, 1.1 (0.95)] indicates the split number (S1), the numerical values of a cytological feature used forseparating cells in 3 experiments (1.2, 1.0, and 1.1 for experiments 1–3, respectively), and the range observed in 17 subsequent experiments done withcompounds from lead optimization programs (0.95).

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as nocodazole, allowing some degree of differentiation;all mechanistic classes result in higher tubulin intensitythan DMSO treatment.In addition to phenotypes commonly observed in non-

treated cells (e.g., G2, prometaphase, etc.), the classifieridentifies "apoptotic" phenotypes for G2 and M states(Fig. 1). These cells have apparent 8N or >8N DNAcontent, and occur with higher prevalence in CDK1(G2-apoptotic cells) and PLK1 or microtubule modulators(M-phase apoptotic cells), suggesting the presence ofpolyploidy for these classes. However, this observationis inconsistent with G2-M arrest. Staining with TUNELindicates a high induction of apoptosis, and suggests thatHoechst dye has higher affinity for unwinding chromatinin apoptotic cells. DNA stainingwith propidium iodide, aDNA intercalator that binds with stoichiometry of 1 dyeper 4–5 base pairs, is not affected by DNA coiling andreveals a single 4N peak for treatments such as nocoda-zole, vs. the 2 peak population distribution for Hoechst(Supplementary Fig. 1). As such, apparent DNA intensityof 8N or >8N, in the absence of other cytological featuressuch as a high DNAperimeter-to-area ratio (due to multi-lobed nuclei) or low pHH3 intensity (consistent withAURKB inhibition), cannot be used to infer polyploidy.

In spite of this, Hoechst is preferred due to signal quench-ing from other fluorescent channels that occurs withpropidium iodide.

Summarizing concentration-dependent phenotypesThe classifier assigns a phenotype to every imaged cell.

A population of cells within a well can be summarized asthe percentage of cells exhibiting each of the 9 phenotypesreported by the classifier. Changes in populations along aconcentration-response curve can be summarized viastacked bar graphs: for the aurora inhibitor PHA-739358, the classifier reveals a mixed aurora A/B cellpopulation at the antiproliferation EC50, which becomesconsistent with AURKB inhibition at intermediate con-centrations, and exhibits a dominant AURKA profile athigher concentrations (Fig. 3). This representation isuseful for elucidating the structure-phenotype relation-ship in lead optimization programs.

Comparing phenotypes from the cell classifier toaveraged cytological features

A simple approach for quantifying treatment-inducedphenotypes consists of averaging cytological featureswithin a well (e.g., the average DNA content of cells).

Figure 2. Scatter plots showingselected cytological features, withcells colored according tophenotype from the cell classifier(left) and fields of view for DNAalone (top right) or all 4 channelscombined (bottom right; DNA,blue; CyclinB1, green; pHH3, red;a-tubulin, yellow). Representativecells treated with the CDK inhibitorR-547 at 0.25 mmol/L are identifiedusing circles colored as indicatedin the scatter plot (seeSupplementary Fig. 4 for otherinhibitors).






To understand how this approach differs from our phe-notype classifier, we describe every treatment well using3 approaches: (a) the average value for each of 12 cyto-logical features used alone, (b) a vector of length 5 con-sisting of the average values of DNA-related cytologicalfeatures (DNA well-average features), and (c) a vector oflength 9 indicating the percentage of cells for each phe-notype reported by the classifier (cell phenotype profiles).Wells for treatment concentrations below the antiproli-feration EC50 are discarded to focus on those wells inwhich cells are responding to treatment. For approach 1, 2wells are compared by taking the absolute value of thedifference between wells. For approaches 2 and 3 thatdescribe each well as a vector, the difference betweenwells is calculated using the Euclidian and cosine dis-tances (see Materials and Methods). Small differences ordistances indicate consistent assessment of phenotype.

For the purpose of assessing reproducibility betweenexperiments, we identified 16 compounds tested in all 3experiments, and set aside the 6 compounds used formodel calibration. This yielded 159 pairs of wells fromdifferent experiments that contain the same compound atthe same (or similar) concentration. Because themeasuresabove have different natural scales, the 3 approaches forcomparing wells are normalized by converting differ-ences to Z-scores. A method that yields reproducibleassessments of phenotype should produce scores withlarge negative values in the left-tail of the distribution (i.e., much more similar than the average pair of wells).Some cytological features are poorly reproduced acrossexperiments, especially total and variation for cyclin B1and pHH3 intensities (Fig. 4). Cell phenotype profiles aresignificantly more reproducible (P < 0.0001) whether

Euclidian or cosine distance metrics are used, and areless sensitive to the distance metric than the DNA well-average profiles (1-sided t tests assuming unequal var-iance, rejecting the null hypothesis that the distances aredrawn from the same distribution; N ¼ 159). This arisesby virtue of recalibration using reference compounds,and compensates for variation in experimental conditionssuch as light source intensity, Hoechst and antibodystaining, signal quenching arising from use of additionalfluorescent markers, biologist technique, etc. Over thecourse of 17 experiments in support of internal leadoptimization efforts, the variation in values used for rulesin the classifier approaches 1 Z-score unit (after normal-ization of raw data; seeMaterials andMethods) for DNA-related features, and 2 units for cyclin B1 and pHH3-related features (Fig. 1). Sources of experimental variationacross experiments cannot be fully controlled using nor-malization to DMSO control alone, or normalizationusing a signal window defined by the negative andpositive control (results not shown).

In addition, we compared the utility of the cell classifierin assessing the similarity of phenotypes from 2 treat-ments. The 16 compounds repeated across all experi-ments yielded ca. 4,000 pairs of wells per experiment;each paired well contains a different compound at aconcentration above the EC50. Although distances fromcell phenotype profiles are correlated with those fromDNA well-average profiles, some treatments appearmore similar using 1 method over the other (Fig. 4). Asan example, the aurora kinase inhibitor tozasertibappears somewhat similar to the CDK1 inhibitor R-547using the well-average measure, but is much less similarusing the cell phenotype profiles (23rd percentile for well

Figure 3. Proportion of cellsclassified into each of 9phenotypes versus concentrationfor the aurora kinase inhibitorPHA-739359. The curve indicatesinhibition of proliferationmeasured by counting cells perfield of view; representativeimages at 3 concentrations revealphenotypes consistent withlargest cell populations from theclassifier: at 0.063 mmol/L, there isevidence of multinucleated cells(aurora B) and prometaphaserosettes (aurora A); at0.125 mmol/L, the dominant cellpopulation is consistent withaurora B, whereas at higherconcentrations the phenotype isconsistent with aurora A inhibition.

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average vs. 84th percentile for cell phenotype profiles).This recalls the aphorism "the average cell does not exist"(1), where the tozasertib average arises from a bimodaldistribution of cells in prometaphase (small bright nuclei)and polyploidy (very large diffuse nuclei), and appears

similar to the CDK1 inhibitor with predominantly G2-arrested cells having nuclei of intermediate size andintensity (Supplementary Fig. 6). Other examples includethe CDK1 inhibitors R-547 vs. BMS-265246, where theformer has a higher proportion of G2-apoptotic cells, and

A

B

Figure 4. Reproducibility and comparison to standard approaches of population profiles from the cell classifier. A, comparing cell-level analysis to averagevalues of cytological features for cells in a given well: 159 pairs of wells are compared, each well contains the same compound at the same or similarconcentration, but from different experiments. Because the measures have different natural scales, a numerical value obtained by comparing 2 wells isconverted to a Z-score using the mean and standard deviation obtained from all pairs of wells; only wells with concentrations above the antiproliferation EC50are analyzed to avoid the dominance of cells in G1-S. Large negative values denote high reproducibility of phenotype across experiments (see text). The first 12comparisons simply take the absolute difference of means for each cytological feature, where the last 4 describe each well using a vector of well-average DNAparameters or proportion of cells belonging to each phenotype and quantify similarity using the cosine and Euclidian metrics; experiment 1 used 2-folddilutions starting at 5 mmol/L and experiments 2 to 3 used 2-fold dilutions starting at 2 mmol/L; for experiments 1 versus 2 to 3, we compared concentrationswithin 20% of each other; that is, 2.5 mmol/L from experiment 1 versus 2 mmol/L from experiments 2 to 3, etc. B, comparison of Euclidian distances fromaveraged DNA features versus cell phenotype profiles for 3,730 pairs of wells above the antiproliferation EC50 in experiment 3; R

2 ¼ 0.79; selected treatmentcomparisons which appear less similar by cell population profiles are highlighted.






the PLK1 inhibitors HMN-176 vs. BI-2536, where theformer has a higher fraction of M-phase apoptotic cells.

Cell population responses to chemotherapeuticagents

A large number of chemotherapeutic agents are knownto affect the G2-M transition of the mitotic cell cycle. Weinterrogated the connection between the mechanism ofaction of these modulators and the effect on HCT-116 cellpopulations. The phenotypic profiles were assessed in 2experiments, and yielded highly consistent results (Sup-plementary Fig. 7)

The quinoline alkaloids camptothecin and topotecanstabilize the topoisomerase I-DNA complex, resulting insingle-strand DNA breaks. Both agents produce cellpopulations arrested in G2 at concentrations near theantiproliferation EC50, consistent with their reportedcell-cycle effects (24, 25). However, an increasing popula-tion of cells in prometaphase is apparent at higher con-centrations, suggesting cellular effects unrelated totopoisomerase I inhibition. The alkylating agent mitomy-cin also results in a large enrichment of cells in G2. Whilewe observed consistent phenotypes for structurallyrelated camptothecin and topotecan, the anthracyclineantibiotics doxorubicin and aclarubicin result in distinctphenotypes, even though both stabilize the topoisome-rase II-DNA complex. Doxorubicin, at concentrationshigher than what is necessary for topoisomerase II inhibi-tion, is capable of inhibiting topoisomerase I and will alsocompete for binding sites for the various DNA stains andwill therefore elicit multiple concentration-dependentphenotypic outcomes (unpublished results). Most cellsfrom aclarubicin treatment have G1-S properties, possiblythrough more potent inhibition of topoisomerase I. Asobserved for aclarubicin, the antimetabolite 5-fluoro-20-deoxyuridine (5-FUDR or floxuridine) significantly inhi-bits proliferation without resulting in a large enrichmentof cells in G2-M. The classifier is not optimized forcharacterizing treatments that modulate the G1/S mitoticcell cycle; cells in G1 are difficult to distinguish from thosein S phase using Hoechst staining and light microscopy,necessitating additional markers such as 5-ethyl-2-deox-yuridine (EDU) or bromodeoxyuridine (BrdU) or G1-specific markers such as phosphor retinoblastoma pro-tein 1 (pRB1). The tubulin depolymerizers mebendazole,albendazole, ciclobendazole, and nocodazole, and thetubulin stabilizer paclitaxel all produce populationsenriched in prometaphase and apoptotic cells (15). Theproportion of prometaphase cells increases with concen-tration, presumably due to a diminishing proportion ofnonviable cells in the population.

Cell population responses to inhibitors of cell-cyclekinases

Inhibitors of kinases tend to exhibit varying degrees ofoff-target activity (26), making it difficult to anticipate thelevel of selectivity from cell-free assays expected to yielda phenotype consistent with modulation of the intended

target. The cell classifier was used to characterize changesin cell populations induced by treatment with several G2-M kinase inhibitors (Fig. 5 for selected inhibitors; Sup-plementary Fig. 8). Most CDK1 and pan-CDK inhibitorscause enrichment of cells in G2, consistent with the role ofCDK1 in cell-cycle regulation. Many of these moleculesinhibit interphase CDKs (CDK2/4/5/6). In particular,the inhibitors AG-024322, JNJ-7706621, and aminopurva-lanol have potencies vs. interphase CDKs that are similaror greater than that for CDK1, yet show a phenotypeconsistent with CDK1 inhibition. The residual G1-S cellsmay be nonresponders or G1/S-arrested cells arisingfrom inhibition of interphase CDKs. For example, theexquisitely selective CDK4 inhibitor PD-0332991 inhibitsproliferation, and induces mostly G1-S cells according tothe classifier (Supplementary Fig. 8). Additional markerssuch as pRB1 staining and/or EDU labeling are requiredfor effective characterization of compounds having domi-nant G1/S mechanisms.

In contract to the dominance of the CDK1 phenotypenoted above, AG-12286, alvocidib, PD-171851, SCH-727965, and SNS-032 potently inhibit CDK9 in additionto other CDKs, and producemixed populations of cells inG2, prometaphase, and advanced states of apoptosis. Therole of CDK9 in transcriptional regulation (27), coupledwith this observation, suggests that manifestation of theCDK1 phenotype is distorted byCDK9 activity.However,other inhibitors (e.g., AG-024322, BMI-1026, BMS-265246,and R-547) significantly inhibit CDK9 in vitro yet induce aCDK1 phenotype. The inhibitors JNJ-7706621, 358788-29-1, and 358789–50-1 (reported by Astrazeneca) exhibitAURKB activity in enzyme assays, which is evident inthe cell population profile for the latter despite the role ofthat kinase beyond the G2-M checkpoint. The relationshipbetween enzyme inhibition in cells and phenotype iscomplex and not fully understood from cell-free assays.

We investigated the relationship between aurorakinase activity and cell phenotype using a number ofinhibitors at our disposal. The prodrug AZD-1152 andits metabolite differ by a phosphate group used toimprove solubility of the prodrug; both are selectiveAURKB inhibitors and induce polyploidy in treatedcells, consistent with their enzyme activity. As theyare the only selective AURKB inhibitors we have char-acterized, it is interesting to note that both retain largerpopulations of G1-S cells than the other aurora kinaseinhibitors. As cells responding to AURKB inhibitioncontinue cycling beyond 48 hours, the absence of otherarrest mechanisms may explain this observation. Theinhibitors tozasertib and CYC116 induce polyploidy asexpected for dual A/B inhibitors, unlike PHA-739358that exhibits a concentration-dependent change fromdominant polyploidy (consistent with AURKB) to do-minant prometaphase arrest (consistent with AURKA).In our hands, MLN-8054 and ENMD-2076 both inhibitAURKBmore potently in biochemical assays, yet inducecell populations consistent with AURKA at lower con-centrations and AURKB at higher concentrations. For

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Figure 5. Kinase profiling data from biochemical enzyme assays and cell phenotypes in HCT-116 cells. Right, proportion of cells classified into eachof 9 phenotypes versus treatment concentration; the curve indicates inhibition of proliferation measured by counting cells per field of view. Left and middle,results from kinase enzyme assay profiling, with insets showing detail for CDK, aurora, and PLK kinases. Changes in markers from green to red (andsmall to large) indicate increasing binding affinity, on a log10 scale. Labeled markers indicate the IC50 in mmol/L (no units) or % inhibition at 20 mmol/L (valuesfollowed by %); the absence of labels denote inactive results (i.e., IC50 > 10 mmol/L or % inhibition < 80 for single point results). See Supplementary Methodsfor details on enzyme assays. Additional inhibitors are shown in Supplementary Fig. 8. Human kinome provided courtesy of Cell Signaling Technology, Inc.www.cellsignal.com.






tozasertib, its significant binding affinity for CDK1 isnot apparent in the induced cell population.

In contrast to the variation in phenotypes observed forCDK and aurora kinase inhibitors, small molecules tar-geting PLK1 generally exhibit similar phenotypes con-sisting ofmixed populations of cells in prometaphase andadvanced apoptosis. A notable exception to the class isthe Banyu inhibitor 886856-66-2 that exhibits a small butincreasing population of G2 cells with increasing concen-tration. HMN-176 is not an ATP-competitive inhibitor,but interferes with the cellular localization of PLK1 (28).ON-01910 was originally described as a PLK1 inhibitor,but is now thought to be a tubulin modulator (22, 23;Supplementary Fig. 7).

Relationship between selectivity in cell-free assaysand phenotype in HCT-116 cells

The variation in phenotypic response among the kinaseinhibitors in Table 1 prompted us to examine the con-cordance between enzyme inhibition profiles and phe-notypic response for a larger number of internalcompounds exhibiting activity against G2-M kinases.For this purpose, we selected for analysis compoundshaving antiproliferation EC50 < 1 mmol/L in HCT-116cells and with fully determined G2-M kinase enzymeprofiles (IC50s vs. CDK1, AURKA, AURKB and PLK1,or �80 percent inhibition in single concentration testingat 20 mmol/L). For wells having concentrations above theantiproliferation EC50, we summarized the proportion ofcells belonging to non-G1-S phenotypes from the cellclassifier (Supplementary Fig. 9). While selective AURKAinhibitors induce populations dominated by prometa-phase arrest and advanced apoptosis, the dual A/Binhibitors have a larger proportion of multinucleatedcells than AURKB-selective inhibitors. For CDK inhibi-tors, a decreasing proportion of cells in G2 is apparent asselectivity vs. the aurora kinases and PLK1 increases.However, the corresponding increase in prometaphaseand M-phase apoptotic cells may be attributed to CDK9inhibition as noted above, andmost compounds were nottested vs. other CDKs. For PLK1 inhibitors, there is nodiscernable change in the dominant prometaphase andM-phase apoptotic phenotype with increasing selectivityvs. the CDKs and aurora kinases. This suggests a domi-nant role for PLK1 enzyme inhibition over other cell-cycletargets, despite a role for PLK1 in mitosis. A conventionalview holds that simultaneous inhibition of targetsinvolved earlier in the cell cycle would manifest overM-phase arrest. The lack of clear relationships betweenenzyme inhibition profiles and cell phenotype supportsthe importance of phenotype determination via high-content imaging to verify that cell death arises frommodulation of the targeted kinase (or kinases).

Discussion

The cell classifier described in this work enables theinvestigation of mechanism of action for cell-cycle mod-

ulators by summarizing cell-level results from high-con-tent imaging experiments. While an increasing body ofliterature describes approaches that distill millions ofcellular measurements for interpretation of phenotype,only a few approaches have explored the reproducibilityof phenotype across experiments (7, 10). The approachdescribed in this work has been applied to experimentsdone over 6 months by 3 different biologists, and sig-nificantly reduces variability in results that often afflictindustrial application of immunofluorescence-basedmicroscopy. The methodology is applied within leadoptimization programs at Lilly to understand changesin phenotype that arise from structural modification oflead series, and the extent to which activity at otherkinases translates to deviation from the desired effect(i.e., obtaining a phenotype consistent with inhibition of agiven kinase target).

The application of our classifier highlights the preva-lence of concentration-dependent phenotypes amonginhibitors purported to inhibit the same kinase. Whilethis work does not evaluate the effects of RNAi treat-ments, the cell morphologies that we identify as consis-tent with the intended target are informed frompublished reports using RNAi (17, 20, 22, 23, 29, 30).We postulate that departure from the expected pheno-type arises from off-target effects, rather than variableinhibition of the intended target. Although the effect cansometimes be rationalized from enzyme activity profiles(e.g., PHA-739358), there is generally no discernablequantitative relationship between enzyme selectivityand phenotype. It is noteworthy that exquisitely selectivekinase inhibitors studied in this work (PD-0332991 andthe AZD-1152 metabolite) induce the expected pheno-types.

Conceptually, our approach is similar to methods thatextract classification rules from cells representing distinctphenotypes identified by review of images (2–4). Suchmethods can overcome variation in staining intensity, etc.by repeating the identification of representative cells andrule training in every experiment. By recalibrating thecell classifier from reference molecules selected to repre-sent the relevant cell phenotypes, the need for manualreview of images is substantially reduced. On the otherhand, the use of reference inhibitors renders the approachless effective in the identification of novel or unexpectedcell morphologies (6). The simplicity of a decision tree forcell classification is appealing, but uses artificial rectan-gular boundaries in cytological feature space. It can bedifficult to ascertain whether rarer cell populations areartifacts of classification; most PLK1 inhibitors appear toinduce small populations of multinucleated cells, butthese are M-phase apoptotic cells with irregular shapesand lower DNA staining intensity. In our hands, the useof mixture models (9) does not resolve the phenotype ofcells falling between major phenotypic classes, asexpected given the probabilistic assignment of cells tophenotype classes (unpublished results). Higher resolu-tion imaging, perhaps with additional markers, may be

Sutherland et al.





required to fully resolve distinct cell populations (10). Ascellular imaging technology continues to evolve, theability to monitor additional fluorescence channels willallow for more detailed analyses of complex cellularprocesses.The imaging assay and phenotype classifier presented

in this work are configured for medium-throughputcharacterization of G2-M modulators, and can be usedto identify the probable mechanism of action in concertwith biochemical profiling results. The classifier does notdifferentiate G1 cells from those in early S phase. Forexample, the CDK inhibitor R-547 was shown to inhibitRb1 phosphorylation in phase I studies and inducespopulations of G1- and G2-arrested HCT-116 cells whenstudied via flow cytometry (31). The cell classifier indi-cates a dominant population of G2 cells, with someresidual G1-S cells that could in fact be G1/S-arrested(i.e., blocked cell cycle). Other markers, such as antibo-dies for pRb1 and/or EDU labeling are probably neces-sary to allow full characterization of cells in G1-S.Likewise, cells responding to CDK1 and topoisomeraseinhibitors (e.g., camptothecin and doxorubicin) are notdifferentiated with the markers employed in this study,but are readily distinguished via immunostaining forgH2AX, due to chain breaks induced by topoisomeraseinhibitors (32).In a similar vein, the classifier does not differentiate

cells responding to AURKA inhibitors, PLK1 inhibitors,tubulin stabilizers (i.e., paclitaxel) or tubulin destabili-zers, all of which directly modulate cytoskeleton functionresulting in prometaphase arrest. High-resolution micro-scopy of paclitaxel reveals a metaphase-like arrangementof most chromosomes, with some lagging at the spindlepole (33). Such features are not discernable at the resolu-tion used in this assay. At best, simple intensity-derivedcytological features from a-tubulin staining provideslight differentiation of mechanistic classes, with pacli-taxel and PLK1 inhibitors having higher staining inten-sity compared with tubulin destabilizers (whetherconsidering all cells or only those in prometaphase/M-apototic states; Supplementary Fig. 5). This observationis consistent with the absence of changes in microtubulemass at lower compound concentration, despite theirimpact on microtubule dynamics as determined viahigh-resolution microscopy (34, 35). In cases where

cross-reactivity is suspected (e.g., potent PLK andAURKA enzyme activity), high-resolution microscopyof centrosomes and microtubule topology to detectmonopole spindle assemblies having reduced amountsof centrosomal g-tubulin (PLK1; refs. 22, 23, 36), circularmonopole spindles (AURKA; refs 20, 29), or the presenceof multipolar spindles (tubulin destabilizers; refs 15, 33,37) may be required to further clarify a compound’smechanism of action. Higher-resolution microscopycan provide greater cellular detail for understandingmechanism, but reduces throughput due to longer scantimes while also posing challenges with image storage.High-resolution microscopy is also not without its ambi-guities: unlike the monopolar spindles induced by treat-ment with PLK1 RNAi or BI-2536, the inhibitor GSK-461364 potently inhibits the PLK1 enzyme in biochemicaland substrate phosphorylation assays, yet induces multi-polar spindles characteristic of tubulin modulators inH460 human lung cancer cells (38).

Although the cell classifier system described herefocuses on characterization of cell-cycle modulators,the broad concept of summarizing cell-level results canbe applied across high-content screenings and phenoty-pic drug discovery. The automated classification of phe-notypic screening and structure-activity relationship datarepresents a solution to what is now the principal diffi-culty in taking raw data from a high content assay andusing it to make a statistically robust and reproducibledecision of phenotypic outcome.

Disclosure of Potential Conflicts of Interest

All the authors have Eli Lilly shares received via 401(k) and bonusplans.

Acknowledgments

The authors thank the Phenotypic DrugDiscoveryWorkingGroup andPD2Management for guidance on screen implementation, follow-up, andgeneral scientific discussion; Mark Marshall and Jake Starling for helpfuldiscussions and project guidance.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received August 2, 2010; revised November 12, 2010; acceptedNovember 24, 2010; published OnlineFirst January 7, 2011.

References1. Levsky JM, Singer RH. Gene expression and the myth of the average

cell. Trends Cell Biol 2003;13:4–6.2. Jones TR, Carpenter AE, Lamprecht MR, Moffat J, Silver SJ, Grenier

JK, et al. Scoring diverse cellular morphologies in image-basedscreens with iterative feedback and machine learning. Proc NatlAcad Sci U S A 2009;106:1826–31.

3. Tao CY, Hoyt J, Feng Y. A support vector machine classifier forrecognizing mitotic subphases using high-content screening data.J Biomol Screen 2007;12:490–6.

4. Conrad C, Erfle H, Warnat P, Daigle N, Lorch T, Ellenberg J, et al.Automatic identification of subcellular phenotypes on human cellarrays. Genome Res 2004;14:1130–6.

5. Low J, Huang S, Blosser W, Dowless M, Burch J, Neubauer B,et al. High-content imaging characterization of cell cycle ther-apeutics through in vitro and in vivo subpopulation analysis. MolCancer Ther 2008;7:2455–63.

6. Yin Z, Zhou X, Bakal C, Li F, Sun Y, Perrimon N, et al. Using iterativecluster merging with improved gap statistics to perform online phe-notype discovery in the context of high-throughput RNAi screens.BMC Bioinformatics 2008;9:264.

7. Young DW, Bender A, Hoyt J, McWhinnie E, Chirn GW, Tao CY,et al. Integrating high-content screening and ligand-targetprediction to identify mechanism of action. Nat Chem Biol 2008;4:59–68.






8. Perlman ZE, Slack MD, Feng Y, Mitchison TJ, Wu LF, Altschuler SJ.Multidimensional drug profiling by automated microscopy. Science2004;306:1194–8.

9. Slack MD, Martinez ED, Wu LF, Altschuler SJ. Characterizing hetero-geneous cellular responses to perturbations. Proc Natl Acad Sci U S A2008;105:19306–11.

10. Loo LH, Lin HJ, Steininger RJ 3rd, Wang Y, Wu LF, Altschuler SJ. Anapproach for extensibly profiling the molecular states of cellularsubpopulations. Nat Methods 2009;6:759–65.

11. Loo LH, Wu LF, Altschuler SJ. Image-based multivariate profiling ofdrug responses from single cells. Nat Methods 2007;4:445–53.

12. Feng Y, Mitchison TJ, Bender A, Young DW, Tallarico JA. Multi-parameter phenotypic profiling: using cellular effects to characterizesmall-molecule compounds. Nat Rev Drug Discov 2009;8:567–78.

13. Low J, Stancato L, Lee J, Sutherland JJ. Prioritizing hits from phe-notypic high-content screens. Curr Opin Drug Discov Devel 2008;11:338–45.

14. Pommier Y. DNA topoisomerase I inhibitors: chemistry, biology, andinterfacial inhibition. Chem Rev 2009;109:2894–902.

15. Jordan MA, Wilson L. Microtubules as a target for anticancer drugs.Nat Rev Cancer 2004;4:253–65.

16. Lapenna S, Giordano A. Cell cycle kinases as therapeutic targets forcancer. Nat Rev Drug Discov 2009;8:547–66.

17. Van Horn RD, Chu S, Fan L, Yin T, Du J, Beckmann R, et al. CDK1activity is required for mitotic activation of aurora A during G2-Mtransition of human cells. J Biol Chem 2010;285:21849–57.

18. Barr AR, Gergely F. Aurora-A: the maker and breaker of spindle poles.J Cell Sci 2007;120:2987–96.

19. Glover DM, Leibowitz MH, McLean DA, Parry H. Mutations in auroraprevent centrosome separation leading to the formation of monopolarspindles. Cell 1995;81:95–105.

20. Girdler F, Gascoigne KE, Eyers PA, Hartmuth S, Crafter C, Foote KM,et al. Validating Aurora B as an anti-cancer drug target. J Cell Sci2006;119:3664–75.

21. Lane HA, Nigg EA. Antibody microinjection reveals an essential rolefor human polo-like kinase 1 (Plk1) in the functional maturation ofmitotic centrosomes. J Cell Biol 1996;135:1701–13.

22. Peters U, Cherian J, Kim JH, Kwok BH, Kapoor TM. Probing cell-division phenotype space and Polo-like kinase function using smallmolecules. Nat Chem Biol 2006;2:618–26.

23. Steegmaier M, Hoffmann M, Baum A, Lenart P, Petronczki M, KrssakM, et al. BI 2536, a potent and selective inhibitor of polo-like kinase 1,inhibits tumor growth in vivo. Curr Biol 2007;17:316–22.

24. TaronM, Plasencia C, Abad A,Martin C, Guillot M. Cytotoxic effects oftopotecan combined with various active G2-M-phase anticancerdrugs in human tumor-derived cell lines. Investigational new drugs2000;18:139–47.

25. Tsao YP, D’Arpa P, Liu LF. The involvement of active DNA synthesis incamptothecin-induced G2 arrest: altered regulation of p34cdc2/cyclinB. Cancer research 1992;52:1823–9.

26. Fabian MA, Biggs WH 3rd, Treiber DK, Atteridge CE, Azimioara MD,Benedetti MG, et al. A small molecule-kinase interaction map forclinical kinase inhibitors. Nature Biotechnology 2005;23:329–36.

27. Garriga J, Xie H, Obradovic Z, Grana X. Selective control ofgene expression by CDK9 in human cells. J Cell Physiol 2010;222:200–8.

28. Tanaka H, Ohshima N, Ikenoya M, Komori K, Katoh F, Hidaka H.HMN-176, an active metabolite of the synthetic antitumor agent HMN-214, restores chemosensitivity to multidrug-resistant cells by target-ing the transcription factor NF-Y. Cancer research 2003;63:6942–7.

29. Manfredi MG, Ecsedy JA, Meetze KA, Balani SK, Burenkova O, ChenW, et al. Antitumor activity of MLN8054, an orally active small-molecule inhibitor of Aurora A kinase. Proc Natl Acad Sci U S A2007;104:4106–11.

30. Taylor S, Peters JM. Polo and Aurora kinases: lessons derived fromchemical biology. Curr Opin Cell Biol 2008;20:77–84.

31. DePinto W, Chu XJ, Yin X, Smith M, Packman K, Goelzer P, et al.In vitro and in vivo activity of R547: a potent and selective cyclin-dependent kinase inhibitor currently in phase I clinical trials. MolCancer Ther 2006;5:2644–58.

32. Furuta T, Hayward RL, Meng LH, Takemura H, Aune GJ, Bonner WM,et al. p21CDKN1A allows the repair of replication-mediated DNAdouble-strand breaks induced by topoisomerase I and is inactivatedby the checkpoint kinase inhibitor 7-hydroxystaurosporine. Oncogene2006;25:2839–49.

33. Kelling J, Sullivan K, Wilson L, Jordan MA. Suppression of centromeredynamics by Taxol in living osteosarcoma cells. Cancer Res 2003;63:2794–801.

34. Jordan MA, Thrower D, Wilson L. Effects of vinblastine, podophyllo-toxin and nocodazole on mitotic spindles. Implications for the role ofmicrotubule dynamics in mitosis. J Cell Sci 1992;102:401–16.

35. Jordan MA, Toso RJ, Thrower D, Wilson L. Mechanism of mitoticblock and inhibition of cell proliferation by taxol at low concentrations.Proc Natl Acad Sci U S A 1993;90:9552–6.

36. Spankuch-Schmitt B, Bereiter-Hahn J, Kaufmann M, Strebhardt K.Effect of RNA silencing of polo-like kinase-1 (PLK1) on apoptosis andspindle formation in human cancer cells. J Natl Cancer Inst 2002;94:1863–77.

37. Okouneva T, Hill BT, Wilson L, Jordan MA. The effects of vinflunine,vinorelbine, and vinblastine on centromere dynamics. Mol CancerTher 2003;2:427–36.

38. Lansing TJ, McConnell RT, Duckett DR, Spehar GM, Knick VB,Hassler DF, et al. In vitro biological activity of a novel small-moleculeinhibitor of polo-like kinase 1. Mol Cancer Ther 2007;6:450–9.

39. Seong YS, Min C, Li L, Yang JY, Kim SY, Cao X, et al. Characterizationof a novel cyclin-dependent kinase 1 inhibitor, BMI-1026. Cancerresearch 2003;63: 7384–91.

40. Misra RN, Xiao H, Rawlins DB, Shan W, Kellar KA, Mulheron JG, et al.1H-Pyrazolo[3,4-b]pyridine inhibitors of cyclin-dependent kinases:highly potent 2,6-Difluorophenacyl analogues. Bioorg Med Chem Lett2003;13:2405–8.

41. Emanuel S, Rugg CA, Gruninger RH, Lin R, Fuentes-Pesquera A,Connolly PJ, et al. The in vitro and in vivo effects of JNJ-7706621: adual inhibitor of cyclin-dependent kinases and aurora kinases. CancerRes 2005;65:9038–46.

42. Huang S, Connolly PJ, Lin R, Emanuel S, Middleton SA. Synthesis andevaluation of N-acyl sulfonamides as potential prodrugs of cyclin-dependent kinase inhibitor JNJ-7706621. Bioorg Med Chem Lett2006;16:3639–41.

43. Barvian M, Boschelli DH, Cossrow J, Dobrusin E, Fattaey A, Fritsch A,et al. Pyrido[2,3-d]pyrimidin-7-one inhibitors of cyclin-dependentkinases. J Med Chem 2000;43: 4606–16.

Sutherland et al.





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