metabolism. Thus from the standpointof examining bone mass questions,well-matched normal women are ap-propriate controls for this study.

It is noteworthy that our patientsstarted the study with greater meanbone density than did the controls. Thisobservation may simply reflect normalvariation in initial bone mass in a smallseries of women. It is possible, however,that the greater initial bone mass of thepatients implied an increased degree ofendogenous estrogenization, comparedwith healthy controls, thereby predis-posing the patients to the developmentof breast carcinoma. One study of pa-tients with breast cancer has shown apositive relationship between bone min-eral content and the amount of tumortissue estrogen receptors, the latter re-ceptors providing a possible measure-ment of estrogen effect in the patients(26).

In summary, we found preserva-tion of bone mineral density inpostmenopausal patients with breastcancer during the first year of tamox-ifen treatment, whereas healthy post-menopausal controls experienced a pre-dictable and significant loss of spinalbone mineral over the same amount oftime. If larger, more long-term stud-ies bear out these preliminary find-ings, concerns about accelerated bonemineral loss in tamoxifen-treated post-menopausal breast cancer patients maybe resolved. Moreover, tamoxifen mayprove to be beneficial in the treatmentof postmenopausal osteoporosis in a se-lected population of normal women forwhom estrogen supplementation maybe contraindicated.

References(/) KIANG DT, KENNEDY BJ: Tamoxifen (anti-

estrogen) therapy in advanced breast can-cer. Ann Intern Med 87:687-690, 1977

(2) FISHER B, REDMOND C, BROWN A, ET AL:Treatment of primary breast cancer with

i chemotherapy and tamoxifen. N Engl J Med305:1-6, 1981

(3) TERENIUS L: Structure-activity relationshipsof anti-oestrogens with regard to interactionwith 17/?-oestradiol in the mouse uterus andvagina. Acta Endocrinol 66:431-447, 1971

(4) SUTHERLAND R, MESTER J, BAULIER EE: Ta-moxifen is a potent "pure" anti-estrogen inchick oviduct. Nature 267:434-437, 1977

(5) JORDAN VC, DIX CJ, NAYLOR KE, ET AL:Non-steroid antiestrogens: Their biologicaleffects and potential mechanisms of action.J Toxicol Environ Health 4:363-390, 1978

(6) KATZENELLENBOGEN BS, BHAKOO HS, FER-GUSON ER, ET AL: Estrogen and antiestrogen

action in reproductive tissues and tumors.Recent Prog Horm Res 35:259-292, 1979

(7) JORDAN VC: Biochemical pharmacologyof antiestrogen action. Pharmacol Rev36:245-276, 1984

(8) STEWART PJ, STERN PH: Effects of the anti-estrogens tamoxifen and clomiphene onbone resorption in vitro. Endocrinology118:125-131, 1986

(9) TURNER RT, WAKLEY GK, HANNON KS,ET AL: Tamoxifen prevents the skeletal ef-fects of ovarian hormone deficiency in rats.J Bone Miner Res 2:449-456, 1987

(10) TURNER RT, WAKLEY GK, HANNON KS,ET AL: Tamoxifen inhibits osteoclast-medi-ated resorption of trabecular bone in ovar-ian hormone-deficient rats. Endocrinology122:1146-1150, 1988

( / / ) JORDAN VC, PHELPS E, LINDGREN JU: Ef-fects of anti-estrogens on bone in castratedand intact female rats. Breast Cancer ResTreat 10:31-35, 1987

(12) LOVE R, MAZESS DC, TORMEY P, ET AL:Bone mineral density (BMD) in women withbreast cancer treated with tamoxifen for twoyears. Breast Cancer Res Treat 10:12, 1987

(13) BEALL PT, MISRA LK, YOUNG RL, ET AL:Clomiphene protects against osteoporosis inthe mature ovariectomized rat. Calcif Tis-sue Int 36:123-125, 1984

(14) AL-AZZAWI F, HART DM, LINDSAY R: Longterm effect of oestrogen replacement ther-apy on bone mass as measured by dual pho-ton absorptiometry. Br Med J 294:1261-1262, 1987

(15) AITKEN JM, HART DM, LINDSAY R: Oestro-gen replacement therapy for prevention ofosteoporosis after oophorectomy. Br Med J2:515-518, 1973

(16) WEISBRODE SE, CAPEN CC: The ultra-structural effect of estrogens on bone cellsin thyroparathyroidectomized rats. Am JPathol 87:311-322, 1977

(17) LINDSAY R, MACLEAN A, KRASZEWSKI A,ET AL: Bone response to termination ofoestrogen treatment. Lancet 1:1325-1327,1978

(18) GRAY TK, FLYNN TC, GRAY KM, ET AL:17/3-Estradiol acts directly on the clonal os-teoblast cell line UMR 106. Proc Natl AcadSci USA 84:6267-6271, 1987

(19) KOMM BS, TERPENING CM, BENZ DJ, ET AL:Estrogen binding, receptor mRNA, and bi-ologic response in osteoblast-like osteosar-coma cells. Science 241:81-84, 1988

(20) ERIKSEN EF, COLVARD DS, BERG NJ, ET AL:Evidence of estrogen receptors in nor-mal human osteoblast-like cells. Science241:84-86, 1988

(21) NUTIK G, CRUESS RL: Estrogen receptorsin bone. An evaluation of the uptake ofestrogen into bone cells. Proc Soc Exp BiolMed 146:265-268, 1974

(22) CAPUTO CB, MEADOWS D, RAISZ LG: Fail-ure of estrogens and androgens to in-hibit bone resorption in tissue culture. En-docrinology 98:1065-1068, 1976

(23) LISKOVA M: Influence of estrogens on boneresorption in organ culture. Calcif TissueRes 22:207-218, 1976

(24) VAN PAASSEN HC, POORTMAN J, BORGART-CREUTZBURG JHH, ET AL: Oestrogen bindingproteins in bone cell cytosol. Calcif TissueRes 25:249-254, 1978

(25) ARNETT R, LINDSAY R, DEMPSTER D: Ef-fect of estrogen and anti-estrogen on os-teoclast activity in vitro. J Bone Miner Resl(suppl):99, 1986

(26) NIELSEN HE, POULSON HS, OLSEN KJ, ET AL:Bone mineral content and estrogen recep-tors in patients with breast cancer. Eur JCancer Clin Oncol 15:703-707, 1979

Display and Analysis ofPatterns of Differential Activityof Drugs Against HumanTumor Cell Lines: Developmentof Mean Graph andCOMPARE Algorithm

K D. Paull, * R H. Shoemaker,L. Hodes, A. Monks,D. A. Scudiero, L Rubinstein,J. Plowman, M. R Boyd

The objective of this study was to de-velop and investigate an approach tooptimally detect, rank, display, and an-alyze patterns of differential growthinhibition among cultured cell lines.Such patterns of cellular responsive-ness are produced by substances testedin vitro against disease-oriented pan-els of human tumor cell lines in anew anticancer screening model un-der development by the National Can-cer Institute. In the first phase of thestudy, we developed a key methodolog-ical tool, the mean graph, which al-lowed the transformation of the nu-merical cell line response data intographic patterns. These patterns wereparticularly expressive of differentialcell growth inhibition and were con-veniently amenable to further analy-ses by an algorithm we devised andimplemented in the COMPARE com-puter program. [J Natl Cancer Inst81:1088-1092, 1989]

Received November 7,1988; revised March 23,1989; accepted April 4, 1989.

K. D. Paull, R. H. Shoemaker, L. Hodes, L. Ru-binstein, J. Plowman, M. R. Boyd, DevelopmentalTherapeutics Program, Division of Cancer Treat-ment, National Cancer Institute, National Insti-tutes of Health, Bethesda, MD.

A. Monks, D. A. Scudiero, Program ResourcesInc., NCI-Frederick Cancer Research Facility,Frederick, MD.

* Correspondence to: Dr. K. D. Paull, Develop-mental Therapeutics Program, National CancerInstitute, NIH, Executive Plaza North, Rm. 831,Bethesda, MD 20892.

1088 Journal of the National Cancer Institute

The National Cancer Institute (NCI)is implementing a new anticancer drug-screening program using a disease-oriented panel of cultured human tu-mor cell lines for the initial stages ofscreening. More detailed aspects of thisprogram and its origins, goals, meth-ods, techniques, and rationale were de-scribed previously (1-4). Criteria to de-fine activity in the new screen are beingexplored. One criterion under study, dif-ferential cell growth inhibition, was theprincipal focus of our study.

Differential growth inhibition as de-fined here means that a cell line orgroup of cell lines can be inhibited toa given extent at a lower concentra-tion of test drug than is required to ex-ert the same effect on other cell lines.Differential growth inhibition has notbeen systematically investigated as acriterion for new drug selection. There-fore, unique data treatment and displaymethods were necessary for examina-tion of its potential for determining ac-tivity in the new NCI drug screen. Theobjective of this study was developmentof methods to optimally detect, rank,and display test results that signal dif-ferential growth inhibition. A key re-sult of the first phase of the study, themean graph, has catalyzed developmentof additional methods by transformingthe numerical cell line response datainto graphic patterns that express dif-ferential growth inhibition.

MethodsWe performed dose-response test-

ing with cells from each cell line ina pilot screening panel. This panelconsisted of =50 cell lines, includingcolon, lung, ovarian, and renal carci-noma; melanoma; central nervous sys-tem tumors; human leukemia; and amiscellaneous group of cell lines in-cluding the MCF-7 breast cancer andP388 murine leukemia cell lines andmultidrug-resistant variants of thesetwo lines. Test samples were preparedin dimethyl sulfoxide at their maximumsoluble concentrations, diluted 1:200with medium, and tested in culture atfive log10 dilutions.

Drug screening was performed withautomated assay methods describedpreviously (2,3). It is important to notethat use of a different assay method

or end point determination could yieldsubstantively different screening dataprofiles and interpretations. Neverthe-less, our general approach to data dis-play and analysis may be applicable toa variety of assays and/or end points.

Results and DiscussionThe large amount of data gener-

ated for each compound («=50 dose-response curves) required an optimalformat for the graphic presentation ofdifferential cell growth inhibition. Themean graph format developed for thispurpose not only permits the ready vi-sualization of any differential growthinhibition expressed by a test com-pound but also creates a frameworkfor logical analysis of the data. Thisframework provides the foundation forcomputer-assisted analysis of the data,a vital development, considering the un-precedented scale of the NCI in vitrodrug-screening program. However, themost intriguing property of the meangraph format is that it yields identi-fiable and characteristic "fingerprint"patterns, which appear to possess a re-markable degree of structure-functioninformation.

Mean Graph

The concept for the mean graphemerged in part from attempts to de-tect differential growth inhibition froma standard bar graph presentation.Indications of differential growth inhi-bition in a standard bar graph formatreside only in the "ragged edge," the re-gion between the tips of the bars forthe least responsive cell line and themost responsive cell line. Figure 1 illus-trates a typical horizontal bar graph andthe corresponding mean graph. In thishorizontal bar graph, the lengths of thebars are directly proportional to the po-tency of the test compound against thetumor cell line, which is expressed asthe logarithm of the concentration re-sulting in 50% growth inhibition (IC50).The region between the baseline andthe right end of the bar representingthe least potent response (fig. 1 A) tendsto defeat the perception of differentialgrowth inhibition. As an alternative, wedeveloped a graph centered at the arith-metic mean of the logarithm of the IC50

values for all cell line responses mea-sured for a compound. While choice ofthe mean as an anchor point is arbi-trary, its use has proven to be advan-tageous for the development of othermean-graph-derived analyses such asthe estimation of relative cell line sen-sitivities (5).

The mean graph (fig. 1C) is con-structed by projecting bars to the rightor left of the mean, depending onwhether cell sensitivity to a test drug ismore or less than average. The lengthof a bar is proportional to the differ-ence between the logarithm of the cellline IC50 and the mean. Differentialgrowth inhibition is depicted by the bar(delta), which projects to either side ofthe mean. A bar projecting 3 log unitsto the right of the mean, for example,would reflect a cellular response 1,000times more sensitive than the averageof all of the cellular responses to thecompound represented on the graph.

Ranking by Degree of DifferentialGrowth Inhibition

One approach to surveillance of thedrug-screening data base is to rankcompounds by their degree of differ-ential growth inhibition. A novel tech-nique for ranking follows from themean graph format.

The first step in this approach is toidentify a single best delta for a com-pound among all the deltas generatedfor it. This best delta is identified asDelta. The simplest definition of Deltais the highest numerical value of delta.However, a potentially better definitionof the best delta is that delta repre-senting the largest number of standarddeviations from the mean of all thedeltas observed for a cell line. Thus,Delta is the statistically rarest delta. Theadvantage of this statistical approachis that it helps to reduce the selectionbias toward the intrinsically more sen-sitive cell lines. To obtain Delta by thismethod, one calculates the number ofstandard deviations from the mean ofdeltas that each delta represents.

The cell line-specific mean of deltavalues and the corresponding standarddeviations are computed from the deltavalues for each cell line for all ofthe compounds evaluated. For a givendelta, the appropriate mean is sub-tracted from the delta, and this differ-

Vol. 81, No. 14, July 19, 1989 REPORTS 1089

JX 12312

CELL I

• LEWCCRF-CfflE - 6 0K562HOLT-4BFH-3226• ISCLCA549QtV-XBOP-18B 0 W 2HCJP-92H226H23H322H460B522* SCLCSHP-77M S - I KMS-273• COLONCOLO-205BCC2998BCT-116HCI-15BT29KH-12KH2OL2SK620•CHSSF-268SF-295SF-539SKB-I9SKB-75SIB-78TE-671D-251ff-498» HEUNOIUKMULffi-3KK19HKLRPKI-7951SK-KEL-2SK-HE-28SK-ffiL-5UACC-257UACC-62> 0VARIA1Iiawv-iOVCAR-3OVCAF-4OVCAR-5OVCAR-8SKOV-3•RBfALA498CAH-1SK12CS»12-II1)0-31• H S CACF-7/ADRP388/AOR

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Figure 1. Comparison of two display formatsfor perception of differential growth inhibition indisease-oriented human tumor cell line panel. Instandard horizontal bar graph (left), display ofdifferential drug effects is confined to "raggededge" (IB). Ready perception of differential ef-fects in section IB is effectively defeated by pres-ence of large base section, 1A. Mean graph for-mat, section 1C (right), facilitates perception ofdifferential responses of cell lines by eliminat-ing base section and projecting bars in oppositedirections depending on whether cell sensitivityto the drug is more or less than average. Re-sulting fingerprint patterns provide delta valuesused in COMPARE (pattern-recognition) analy-ses. LEUK = leukemia; NSCLC = non-small celllung cancer, SCLC = small cell lung cancer; CNS= central nervous sytem; and MISC = miscella-neous. Range = No. of log units between valuesfor most sensitive and least sensitive cell lines.

ence is divided by the correspondingstandard deviation. The quotient is thenumber of standard deviations from thecell line-specific mean. The means andstandard deviations for each cell lineare listed in table 1.

The second step in this method isto rank compounds in order of theirdifferential growth inhibition using theDeltas selected in the first step. Search-ing and sorting by Deltas provides anefficient means to identify compounds

exhibiting differential growth inhibi-tion.

Pattern-Recognition Algorithm

Another application of the meangraph format relates to pattern recog-nition. This format creates a char-acteristic fingerprint pattern for eachcompound. The possibility that thesepatterns might contain other exploitableinformation was investigated. We de-vised a simple algorithm that was im-

plemented in the COMPARE com-puter program to rank the similarity ofthe mean graph pattern of a specified"seed" compound to the patterns of allthe other compounds in the NCI screen-ing project data base. Any previouslytested compound can be used as theseed to initiate the pattern-recognitionprogram.

The COMPARE program evaluatesthe similarity of mean graph patternsby computing average differences be-

1090 Journal of the National Cancer Institute

Table 1. Cell line-specific means, standard deviations, and confidence limits of delta*

Cell line

SK-MEL-2RPMI-7951LoVoSW620NCI-H23NCI-H460DMS-114HT29H69NCI-H125WiDrSK-MEL-5NCI-H146SN12-KIDLD-1OVCAR-3Malme-3MNCI-H524U-251A549OVCAR-5NCI-H522CCRF-CEMSNB44TE671OVCAR-4A2780MCF-7SNB-19K562NCI-H82HCC2998SK-MES-1A498HL-60Caki-1OVCAR-8MOLT-4LOXUO-31NCI-H358A704EKV-XP388NCI-H520MCF-7/ADR \.P388/ADRNC1-H322SNB-75

Mean

-0.0440.0340.0320.0840.0490.0000.085

-0.0880.0770.0510.0490.0630.028

-0.006-0.124-0.019

0.0140.0300.065

-0.100-0.117

0.1430.276

-0.0970.048

-0.1860.192

-0.062-0.053

0.1320.163

-0.1290.036

-0.0020.1850.001

-0.1110.2680.071

-0.200-0.140-0.026-0.303

0.230-0.089-0.174-0.138-0.061-0.329

SD

0.3320.3360.3590.3640.3650.3770.3920.3930.3940.3960.3980.3990.4010.4050.4060.4070.4080.4170.4210.4220.4320.4340.4340.4400.4430.4440.4440.4470.4470.4480.4500.4530.4600.4650.4680.4740.4790.4810.4820.4860.5030.5370.5440.5610.5700.5790.5820.5860.750

99.96%Confidence

limits

.116

.210

.288

.358

.326

.321

.457

.287

.455

.438

.443

.4601.4311.4121.299.406.443

1.4891.537.376

1.396.663.796

1.4421.599.369

1.7481.5041.5131.6981.7381.455.647

1.6271.824.658

1.5671.9501.7601.500.621.853:600

..194.906.852.900.990

2.297

No. ofdelta

values

908871855882901905833897794866461876713712912658893764824914578859734716874725800896797867767534575711693522736846875772812700867896776889376861625

*Data updated November 16, 1987. For references to test protocol and sources of cell lines, seeMethods section.

tween the deltas obtained for a specifiedseed compound and the correspondingdeltas for each of the other compoundsin the data base. For a seed compoundscreened against a panel of 50 celllines, the 50 deltas obtained for thosecell lines are subtracted from the corre-sponding deltas obtained in testing thesame 50 cell lines (or a subset of theselines if all 50 were not tested) againsteach of the other compounds in the database. In this way, the mean graph pat-

tern for the seed compound is sequen-tially compared quantitatively with allother mean graph patterns available.For each compound, two parametersare calculated: Av, which is the averagedifference between deltas (computed asthe mean of the absolute values of thedifferences), and Max, which is themaximum difference observed.

The Av and the Max values are usedby the COMPARE program to create alist of compounds ordered according to

the similarity of their mean graph pat-terns to the mean graph pattern of theseed compound. The compounds arefirst sorted by Av values; compoundswith lower Av values are ranked higher.Then compounds with the same Av val-ues are further sorted by Max values;compounds with lower Max values areranked higher. The purpose of creat-ing the ordered list was to first rankthe compounds by their similarity inmean graph fingerprints and then to in-vestigate whether this similarity corre-lates with any other significant propertycommon to the ranked compounds andthe seed compound.

Since the meaning of the similar-ity in mean graph patterns was thecentral question, it was necessary toobtain additional information aboutthe test compounds as well as rele-vant information about the seed com-pound. We constructed a data base of88 test compounds suitable for thispurpose and analyzed this data basewith the COMPARE program. Appli-cation of the algorithm to data setsrestricted to well-known or prototyp-ical seed compounds led to intrigu-ing results. Compounds known to beDNA binders (table 2), biological alkyl-ating agents, or antimetabolites weregrouped to a significant extent withagents having similar activity or struc-ture.

In table 2, the seed compoundwas doxorubicin. The three compoundswith the closest Av values were mi-toxantrone, amsacrine, and acodazole.These three drugs and the seed com-pound are thought to be DNA bindersand topoisomerase II inhibitors. Thematching sequence includes taxol, rhi-zoxin, and then three more DNAbinders (oxantrazole, bisantrene, andamonafide).

The COMPARE analysis using thealkylating agent melphalan as the seedfor comparison showed a significantclustering of the known alkylatingagents. Pipobroman, uracil mustard,and chlorambucil were ranked in thatsequence as the closest matches to mel-phalan in the test set. The antimetabo-lite cytarabine was also used as the seedcompound in a COMPARE analysis. Itsclose biological and structural analoguefazarabine ranked second to thiogua-nine in the listing.

Vol. 81, No. 14, July 19, 1989 REPORTS 1091

Table 2. COMPARE pattern-recognition program: similarity of DNA binders and topoisomerase IIinhibitors to doxorubicin*

Drug NSCNo.

Av Max No. ofcell lines

Ranget

DoxorubicinMitoxantroneAmsacrineAcodazoleTaxolRhizoxinOxantrazoleBisantreneAmonafideDiscreetMitotaneMerbarone^10- HydroxycamptothecinDacarbazinePyrazine diazohydroxideSpiromustineBusulfanHexamethylenebisacetamideCarboplatinTeroxironeHepsulfamEbifuraminPyrazolePipobromanPhyllanthoside

123127301739249992305884125973332598349174337766308847

3872133662810712445388

361456172112

75095580

241240296934329680201047

4541025154

328426

0.0000.3430.3710.3800.3870.4210.4270.4510.4510.4550.4670.4860.4870.4910.4920.4940.5040.5070.5080.5140.5200.5250.5340.5350.537

0.001.801.491.391.571.512.431.472.342.462.242.332.232.022.232.642.162.442.692.182.492.262.123.281.82

564746364938364738274835

. 25483940484247403836 .414837

3.22.72.81.62.62.21.23.11.02.01.60.92.72.00.61.61.71.02.11.92.20.61.02.83.4

*Of 88 compounds tested, these 25 were most similar to doxorubicin in this analysis of differentialgrowth inhibition.

t Range = No. of log units between values for most sensitive and least sensitive cell lines.

(2) ALLEY MC, SCUDIERO DA, MONKS A, ET AL:Feasibility of drug screening with panels ofhuman tumor cell lines using a microculturetetrazolium assay. Cancer Res 48:589-601,1988

(5) SCUDIERO D, SHOEMAKER RH, PAULL KD,ET AL: Evaluation of a soluble tetra-zolium/formazan assay for cell growth anddrug sensitivity in culture using human andother tumor cell lines. Cancer Res 48:4827-4833, 1988

(4) SHOEMAKER RH, MONKS A, ALLEY MC, ET AL:Development of human tumor cell line pan-els for use in disease-oriented drug screen-ing. In Prediction of Response to CancerChemotherapy (Hall T, ed). New York: AlanR Liss, 1988, pp 265-286

(5) PAULL KD, HODES L, PLOWMAN J, ET AL:Reproducibility and response patterns of theIC50 values and relative cell line sensitivi-ties from the NCI human tumor cell line drugscreening project. Proc Am Assoc Cancer Res29:488, 1988

There is an important caveat that ap-plies to our algorithm. The algorithmmust give a "best match" whether ornot a meaningful one exists in the database, and thus, unrelated compoundsare sometimes given a high ranking.Despite this caveat, it seems that theobserved mean graph patterns expressvaluable information, sometimes re-flecting similarities in biological prop-erties and/or chemical structure andproperties. Expression of such similar-ities in the mean graphs appears tobe sufficiently robust that this rela-tively simple algorithm of the COM-PARE program can successfully detectand rank these similarities in an orderthat seems to have an exploitable de-gree of correlation with independentlyderived rankings of similarity based onbiochemical and/or structural consider-ations.

ConclusionsWe have developed a set of comput-

erized procedures to facilitate the de-tection, ranking, display, and analysis

of patterns of differential growth inhi-bition. These procedures are conceptu-ally centered in the mean graph, whichwas designed to graphically representscreening results for individual com-pounds tested against large numbers oftumor cell lines. Experimental applica-tions of the COMPARE program to alimited data base accrued from the pilotscreen suggest the possibility of mean-ingful clustering of mean graph patternsthat is related to biological propertiesand/or chemical structure and proper-ties. The potential wealth of informa-tion to be generated in the course ofthe new NCI drug-screening experi-ment can be subjected to a wide vari-ety of analytical procedures. Our workrepresents only the first of many poten-tial avenues to data display and analy-sis that may be explored in the courseof this project.

References(/) BOYD MR, SHOEMAKER RH, MCLEMORE TL,

ET AL: Drug development. In Thoracic Oncol-ogy (Roth JA, Ruckdeschel JC, WeisenburgerTH, eds). Philadelphia: Saunders. In press

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1092 Journal of the National Cancer Institute