quantitative optical imaging of primary tumor...

Therapeutics, Targets, and Chemical Biology

Quantitative Optical Imaging of Primary Tumor OrganoidMetabolism Predicts Drug Response in Breast Cancer

Alex J. Walsh1, Rebecca S. Cook2,4, Melinda E. Sanders4,5, Luigi Aurisicchio6, Gennaro Ciliberto7,Carlos L. Arteaga2,3,4, and Melissa C. Skala1

AbstractThere is a need for technologies to predict the efficacy of cancer treatment in individual patients. Here, we show

that opticalmetabolic imaging of organoids derived fromprimary tumors can predict the therapeutic response ofxenografts and measure antitumor drug responses in human tumor–derived organoids. Optical metabolicimaging quantifies the fluorescence intensity and lifetime of NADH and FAD, coenzymes of metabolism. As earlyas 24 hours after treatment with clinically relevant anticancer drugs, the optical metabolic imaging indexof responsive organoids decreased (P < 0.001) and was further reduced when effective therapies were combined(P < 5 � 10�6), with no change in drug-resistant organoids. Drug response in xenograft-derived organoidswas validated with tumor growth measurements in vivo and staining for proliferation and apoptosis. Hetero-geneous cellular responses to drug treatment were also resolved in organoids. Optical metabolic imaging showspotential as a high-throughput screen to test the efficacy of a panel of drugs to select optimal drug combinations.Cancer Res; 74(18); 5184–94. �2014 AACR.

IntroductionWith the ever-increasing number of drugs approved to treat

cancers, selection of the optimal treatment regimen for anindividual patient is challenging. Physicians weigh the poten-tial benefits of the drugs against the side-effects to the patient.Currently, drug regimens for breast cancer are chosen on thebasis of tumor expression of several proteins, including estro-gen receptor (ER), progesterone receptor, and high levels ofhuman epidermal growth factor receptor 2 (HER2), assessed inthe diagnostic biopsy, and drug effectiveness is determinedafter weeks of treatment from tumor size measurements. Apersonalized medicine approach would identify the optimaltreatment regimen for an individual patient and reduce mor-bidity from overtreatment.

Current methods to assess therapy response include tumorsize, measured by mammography, MRI, or ultrasound. Thesemethods evaluate the regimen that the patient received.

Molecular changes induced by antitumor drugs precedechanges in tumor size and may provide proximal endpointsof drug response. Cellularmetabolismmay provide biomarkersof early treatment response, because oncogenic drivers typi-cally affect metabolic signaling (1, 2). Indeed fluoro-deoxy-glucose (FDG)–PET has been explored as a predictor ofresponse but lacks the resolution and sensitivity to accuratelypredict therapy response on a cellular level (3, 4).

Opticalmetabolic imaging (OMI) provides unique sensitivityto detect metabolic changes that occur with cellular transfor-mation (5–10) and upon treatment with anticancer drugs (11).OMI uses the intrinsic fluorescence properties of NADH andFAD, coenzymes of metabolic reactions. OMI endpointsinclude the optical redox ratio (the fluorescence intensity ofNADHdivided by thefluorescence intensity of FAD), theNADHand FAD fluorescence lifetimes, and the "OMI index" (a linearcombination of these three endpoints). The optical redox ratioprovides a dynamic readout of cellular metabolism (12), withincreased redox ratio (NADH/FAD; ref. 8) observed in malig-nant cells exhibiting the Warburg effect (increased glycolysisdespite the presence of oxygen; ref. 13). Fluorescence lifetimevalues report differences in fluorophore conformation, bind-ing, and microenvironment, such as pH, temperature, andproximity to quenchers such as free oxygen (14). OMI end-points report early, molecular changes due to anticancer drugtreatment (11), and are powerful biomarkers of drug response.

Primary tumors can be cultured ex vivo as organoids, whichcontain the malignant tumor cells and the supporting cellsfrom the tumor environment, such as fibroblasts, leukocytes,endothelial cells, and hematopoietic cells (15). Interactionsbetween cancer cells and stromal cells have been shown tomediate therapeutic resistance in tumors (16). Therefore,organoid cultures provide an attractive platform to test cancer

1Department of Biomedical Engineering, Vanderbilt University, Nashville,Tennessee. 2Department of Cancer Biology, Vanderbilt University, Nash-ville, Tennessee. 3Department ofMedicine, VanderbiltUniversity, Nashville,Tennessee. 4Breast Cancer Research Program, Vanderbilt-Ingram CancerCenter, Vanderbilt University, Nashville, Tennessee. 5Department ofPathology, Microbiology & Immunology, Vanderbilt University MedicalCenter, Nashville, Tennessee. 6TakisBiotech,Rome, Italy. 7IRCCSNationalCancer Institute "G. Pascale," Naples, Italy.

Note: Supplementary data for this article are available at Cancer ResearchOnline (http://cancerres.aacrjournals.org/).

Corresponding Author: Melissa C. Skala, Department of BiomedicalEngineering, Vanderbilt University, Station B, Box 351631, Nashville, TN37235. Phone: 615-322-2602; Fax: 615-343-7919; E-mail:[email protected]

doi: 10.1158/0008-5472.CAN-14-0663

�2014 American Association for Cancer Research.

CancerResearch

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Published OnlineFirst August 6, 2014; DOI: 10.1158/0008-5472.CAN-14-0663

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cell response to drugs in a relevant, "body-like" environment.Furthermore, multiple organoids can be generated from onebiopsy, enabling high-throughput tests of multiple drug com-binations with a small amount of tissue.OMI of primary tumor organoids enables high-throughput

screening of potential drugs and drug combinations to identifythemost effective treatment for an individual patient. Here, wevalidate OMI in primary tumor organoid cultures as an accu-rate, early predictor of in vivo tumor drug response in mousexenografts, and present the feasibility of this approach onprimary human tissues. The cellular resolution of this tech-nique also allows for subpopulations of cells to be tracked overtime with treatment, to identify therapies that affect all cells ina heterogeneous population.

Materials and MethodsMouse xenograftsThis study was approved by the Vanderbilt University

Animal Care andUse Committee andmeets the NIH guidelinesfor animal welfare. BT474 cells or HR6 cells (108) in 100 mLMatrigel were injected in the inguinal mammary fat pads offemale athymic nude mice (J:NU; The Jackson Laboratory).Tumors grew to�200 mm3. Tumor-bearing mice were treatedtwice weekly with the following drugs: control human IgG (10mg/kg, i.p.; R&D Systems), trastuzumab (10 mg/kg, i.p.; Van-derbilt Pharmacy), paclitaxel (2.5 mg/kg, i.p.; Vanderbilt Phar-macy), XL147 (10 mg/kg, oral gavage; Selleck Chemicals),trastuzumab þ XL147, trastuzumab þ paclitaxel, and trastu-zumab þ paclitaxel þ XL147. Tumor volume was calculatedfrom caliper measurements of tumor length (L) and width (W),(L � W2)/2, twice a week.

Primary human tissue collectionThis study was approved by the Vanderbilt University Insti-

tutional Review Board and informed consent was obtainedfrom all subjects. A primary tumor biopsy, removed from thetumormass after surgical resection, was provided by an expertbreast pathologist (M.E. Sanders). The tumorwasplaced imme-diately in sterile DMEM, transported on ice to the laboratory(�5-minute walk), and generated into organoids within 3hours of tissue resection. Pathology and receptor status ofthe tissue were obtained from the patient's medical chart.

Organoid generation and cultureBreast tumors (xenografts and primary) were washed

three times with PBS. Tumors were mechanically dissociatedinto 100 to 300 mm macrosuspensions in 0.5 mL primarymammary epithelial cell (PMEC) media [DMEM:F12 þ EGF(10 ng/mL) þ hydrocortisone (5 mg/mL) þ insulin (5 mg/mL)þ 1% penicillin:streptomycin] by cutting the tissues with ascalpel or by spinning in a C-tube (Miltenyi Biotec). Macro-suspension solutions were combined with Matrigel in a 1:2ratio, and 100 mL of the solution was placed on cover slips.The gels solidified at room temperature for 30 minutes andthen for 1 hour in the incubator. The gels were over-lainwith PMEC media supplemented with drugs. The followingin vitro drug dosages were used to replicate in vivo doses

(17–19): control (control human IgG þ DMSO), trastuzumab(25 mg/mL), paclitaxel (0.5 mmol/L), XL147 (25 nmol/L),tamoxifen (2 mmol/L), fulvestrant (1 mmol/L), and A4(10 mg/mL; Takis Biotech, Inc.).

Fluorescence lifetime instrumentationFluorescence lifetime imaging was performed on a custom-

built multiphoton microscope (Bruker), as described pre-viously (11, 20). Excitation and emission light were coupledthrough a 40� oil immersion objective (1.3 NA) within aninverted microscope (Nikon; TiE). A titanium:sapphire laser(Coherent Inc.) was tuned to 750 nm for NADH excitation(average power, 7.5–7.9 mW) and 890 nm for FAD excitation(average power, 8.4–8.6 mW). Bandpass filters, 440/80 nm forNADH and 550/100 nm for FAD, isolated emission light. A pixeldwell time of 4.8 microseconds was used to acquire 256 � 256pixel images. Each fluorescence lifetime image was collectedusing time-correlated single-photon counting electronics(SPC-150; Becker and Hickl) and a GaAsP PMT (H7422P-40;Hamamatsu). Photon count rates were maintained above 5 �105 for the entire 60-second image acquisition time, ensuringthat no photobleaching occurred. The instrument response fullwidth at halfmaximumwas 260 picoseconds asmeasured fromthe second harmonic generation of a urea crystal. Daily fluo-rescence lifetime validation was confirmed by imaging of afluorescent bead (Polysciences Inc). The measured lifetime ofthe bead (2.1 � 0.06 nanoseconds) concurs with publishedvalues (10, 20, 21).

Organoid imagingFluorescence lifetime images of organoids were acquired at

24, 48, and 72 hours after drug treatment. Organoids weregrown in 35-mm glass-bottom Petri dishes (MatTek Corp) andimaged directly through the coverslip on the bottom of thePetri dish. Six representative organoids from each treatmentgroup were imaged. The six organoids imaged containedcollectively approximately 60 to 300 cells per treatment groupfor statistical and subpopulation analyses. First, an NADHimagewas acquired and a subsequent FAD imagewas acquiredof the exact same field of view.

ImmunofluorescenceA previously reported protocol (22) was adapted for immu-

nofluorescent staining of organoids. Briefly, gels were washedwith PBS and fixed with 2 mL 4% paraformaldehyde in PBS.Gels were washed with PBS, and then 0.15mol/L glycine in PBSwas added for 10 minutes. Gels were washed in PBS, and thenadded to 0.02%Triton X-100 in PBS. Gelswerewashedwith PBSthen overlain with 1% fatty acid–free BSA, 1% donkey serum inPBS. The next day, the solution was removed and 100 mL ofantibody solution (diluted antibody in PBS with 1% donkeyserum) was added to each gel. The gels were incubated for 30minutes at room temperature, washed in PBS three times, andthen incubated in 100 mL of secondary antibody solution for 30minutes at room temperature. The gels were washed in PBSthree times, washed inwater twice, and thenmounted on slidesusing 30 mL of the ProLong Antifade Solution (MolecularProbes).

Optical Imaging of Organoid Metabolism Predicts Drug Response

www.aacrjournals.org Cancer Res; 74(18) September 15, 2014 5185




The primary antibodies used were anti-cleaved caspase-3(Life Technologies) and anti-Ki67 (Life Technologies). Bothwere diluted at 1:100. A goat anti-rabbit IgG FITC secondaryantibody was used (Life Technologies). FITC fluorescence wasobtained by excitation at 980 nm on the multiphoton micro-scope described above, and a minimum of six organoids wereimaged. Positive staining of cleaved caspase-3 and Ki67 wasconfirmed by staining mouse thymus and mouse small intes-tine, respectively. Immunofluorescence images were quanti-fied by manual counting of the total number of cells and thenumber of positively stained cells in each field of view. Immu-nofluorescence results were presented as percentage of pos-itively stained cells, quantified from six organoids, approxi-mately 200 cells.

Generation of OMI endpoint imagesPhoton counts for nine surrounding pixels were binned

(SPCImage). Fluorescence lifetime componentswere extractedfrom the photon decay curves by deconvolving the measuredsystem response and fitting the decay to a two-component

model, IðtÞ ¼ a1 exp�t=t1 þa2 exp

�t=t2 þC, where I(t) is thefluorescence intensity at time t after the laser pulse, a1 and a2are the fractional contributions of the short and long lifetimecomponents, (i.e., a1 þ a2 ¼ 1), t1 and t2 are the fluorescencelifetimes of the short and long lifetime components, and Caccounts for background light. A two-component decay wasused to represent the lifetimes of the free and bound config-urations of NADH and FAD (10, 23, 24) and yielded the lowestx2 values (0.99–1.1), indicative of an optimal fit. Matrices of thelifetime components were exported as ascii files for furtherprocessing in Matlab.

Automated image analysis softwareTo streamline the cellular-level processing of organoid

images, an automated image analysis routine, as previouslydescribed (25), was used in Cell Profiler in Matlab. Briefly, acustomized threshold code identified pixels belonging tonuclear regions that were brighter than background but notas bright as cell cytoplasms. These nuclear pixels weresmoothed and the resulting round objects between 6 and 25pixels in diameter were segmented and saved as the nucleiwithin the image. Cells were identified by propagating out fromthe nuclei. An Otsu Global threshold was used to improvepropagation and prevent propagation into background pixels.Cell cytoplasms were defined as the cells minus the nuclei.Cytoplasm values weremeasured from each OMI image (redoxratio, NADH tm, NADH t1, NADH t2, NADHa1, FAD tm, FAD t1,FAD t2, and FAD a1).

Computation of OMI indexThe redox ratio, NADH tm, and FAD tmwere norm-centered

across cell values from all treatment groups within a sample,resulting in unitless parameters with a mean of 1. The OMIindex is the linear combination of the norm-centered redoxratio, NADH tm, and FAD tm, with the coefficients (1, 1, and�1), respectively, computed for each cell. The three endpoints,redox ratio, NADH tm, and FAD tm are independent variables(11) and are thusweighted equally. The signs of the coefficients

were chosen tomaximize difference between control and drug-responding cells.

Subpopulation analysisSubpopulation analysis was performed by generating histo-

grams of all cell values within a group as previously reported(11). Each histogramwas fit to 1, 2, and 3 component Gaussiancurves. The lowest Akaike information criterion (AIC) signifiedthe best fitting probability density function for the histogram(26). Probability density functions were normalized to have anarea under the curve equal to 1.

Statistical testsDifferences in OMI endpoints between treatment groups

were tested using a student t test with a Bonferroni correction.An a significance level less than 0.05 was used for all statisticaltests.

ResultsResponse of BT474 organoids to a panel of anticancerdrugs

Validation of an organoid-OMI screen for drug response wasfirst tested in two isogenic HER2-amplified breast cancerxenografts. BT474 xenografts are sensitive to the HER2 anti-body trastuzumab, whereas HR6 xenografts, derived as asubline of BT474, are trastuzumab resistant. The followingsingle drugs and drug combinations were tested: paclitaxel(P; chemotherapy), trastuzumab (H; anti-HER2 antibody),XL147 (X; PI3K small-molecule inhibitor; ref. 27), HþP, HþX,and HþPþX. Paclitaxel and trastuzumab are standard-of-caredrugs, and XL147 is in clinical trials and preclinical studiessupport combination therapy of XL147 with trastuzumabfor patients who have developed a resistance to trastuzumab(27, 28).

Representative redox ratio, NADH tm, and FAD tm imagesof BT474 xenograft-derived organoids demonstrate mixedmulticellular morphology and highlight the subcellular res-olution of this technique (Fig. 1A–F). A longitudinal study oftumor growth demonstrated that the BT474 xenograftsresponded to each treatment arm (Fig. 1G), with significantreduction in tumor volume, as determined from calipermeasurements, on day 7 for all treatment groups excepttrastuzumab, which had significant reduction on day 11(Fig. 1H).

A composite endpoint, the OMI index, was computed as alinear combination of the mean-normalized optical redoxratio, NADH tm, and FAD tm for each cell. After 24 hours oftreatment, the OMI index was significantly reduced in alltreated BT474 organoids, compared with the control (P <0.05; Fig. 1I). By 72 hours, the OMI index decreased further inall the treatment groups (P < 5� 10�7, Fig. 1J). The redox ratio,NADH tm, and FAD tm values showed similar trends (Supple-mentary Fig. S1). Changes in short and long lifetime values andin the portion of free NADH or FAD contributed to the changesin tm (Supplementary Table S1).

The high-resolution capabilities of OMI allowed single-cellanalysis and population modeling for quantification of

Walsh et al.

Cancer Res; 74(18) September 15, 2014 Cancer Research5186




cellular subpopulations with varying OMI indices. Visualinspection of cell morphology suggested that the majorityof cells are tumor epithelial cells; stromal cells with obviousmorphologic differences were eliminated from the analysis.Population density modeling of cellular distributions of theOMI index revealed two populations with high and low OMIindex values in all of the BT474-treated organoids at 24hours (Fig. 1K and Supplementary Fig. S2). By 72 hours, theXL147-, HþP-, HþX-, and HþPþX-treated organoids have asingle population with narrower peaks (Fig. 1L and Supple-mentary Fig. S2). The trastuzumab-treated organoids havetwo populations at 72 hours, both lower than the mean OMIindex of the control organoids (Fig. 1L). Immunofluorescentstaining of cleaved caspase-3 and Ki67 of BT474 organoidstreated for 72 hours confirmed increased apoptosis anddecreased proliferation in drug-treated organoids, with thegreatest increases in cell death with combined treatments(Fig. 1M and N).

Response of HR6 organoids to a panel of anticancerdrugs

Next, the OMI-organoid screen was tested on trastuzumab-resistant HR6 xenografts (29). Representative images showHR6 organoid morphology and spatial distributions of OMIendpoints (Fig. 2A–F). These HER2-overexpressing tumors hadcontinued growth with trastuzumab treatment (Fig. 2G).Treatment with paclitaxel and XL147 initially caused HR6tumor regression (P < 0.05 on day 10 for XL147 and on day14 for paclitaxel) but then resumed growth (Fig. 2G and H).Mice treated with the HþP, HþX, and HþPþX combinationtherapies exhibited sustained HR6 tumor reduction (Fig. 2Gand H).

After 24 hours of treatment, significant reductions in theOMI index were detected in HR6 organoids treated withpaclitaxel, XL147, HþP, HþX, and HþPþX (P < 0.05; Fig. 2I).At 72 hours, the OMI index of the paclitaxel- and XL147-treated organoids was significantly greater than that of the

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Figure 1. OMI of organoids derived from trastuzumab-responsive xenografts. A, redox ratio image of a control BT474 (ERþ/HER2þ) organoid at 72 hours. Scalebar, 100 mm. B, NADH tm image of a control BT474 organoid at 72 hours. C, FAD tm image of a control BT474 organoid at 72 hours. D, redox ratio image of atrastuzumab (anti-HER2) plus paclitaxel (chemotherapy) plus XL147 (anti-PI3K; HþPþX)–treated BT474 organoid at 72 hours. E, NADH tm image of atrastuzumab plus paclitaxel plus XL147 (HþPþX)–treated BT474 organoid at 72 hours. F, FAD tm image of a trastuzumab plus paclitaxel plus XL147(HþPþX)–treated BT474 organoid at 72 hours. G, tumor growth response of BT474 tumors grown in athymic nude mice and treated with single andcombination treatments. H, table of earliest detectable (P < 0.05) reduction in tumor size for control versus treated mice. I, OMI index decreases in BT474organoids treated with single and combination therapies at 24 hours. J, OMI index of BT474 organoids treated for 72 hours. Red bars, P < 0.05 fortreated organoids versus control. K, population density modeling of the mean OMI index per cell in control, paclitaxel, trastuzumab, and HþPþX-treatedorganoids at 24 hours. L, populationdensitymodeling of theOMI index for control, paclitaxel, trastuzumab, andHþPþXBT474organoids treated for 72 hours.M, immunofluorescence staining of cleaved caspase-3 in control and treated BT474 organoids at 72 hours. N, immunofluorescence staining of Ki67 incontrol and treated BT474 organoids at 72 hours. �, P < 0.05.






control organoids (P < 0.05; ref. Fig. 2J), consistent with therecovery of HR6 tumor growth after prolonged therapy (Fig.2G). The organoids treated with drug combinations (HþP,HþX, and HþPþX) continued to have significantly lowerOMI index values (P < 10�6) at 72 hours, compared withuntreated controls. Individual OMI endpoints showed sim-ilar trends (Supplementary Fig. S3 and Supplementary TableS2). Subpopulation analysis revealed two subpopulations inthe OMI index for all treated groups except for trastuzumabat 24 hours (Fig. 2K and Supplementary Fig. S4). By 72 hours,the paclitaxel- and XL147-treated organoids had a singlepopulation (Fig. 2L and Supplementary Fig. S4). Immuno-fluorescent staining of cleaved caspase-3 of organoids trea-ted for 72 hours revealed increased cell death in HR6organoids treated with HþP, HþX, and HþPþX (P <0.05, Fig. 2M). The percentage of Ki67-positive cells at 72hours decreased with paclitaxel, HþP, HþX, and HþPþXtreatment (P < 0.005, Fig. 2N).

OMI endpoints identify breast cancer subtypesWe tested these methods on primary breast cancer biopsies

obtained from surgical resection. Tumors were obtained freshfrom deidentified mastectomy specimens not required forfurther diagnostic purposes, and dissociated into organoidswithin 1 to 3 hours postresection. Cancer drugs were addedand organoids were imaged with OMI. Representative redoxratio, NADH tm, and FAD tm images (Fig. 3) demonstrate thevarying morphology of organoids derived from ERþ, HER2þ,and triple-negative breast cancers (TNBC).

When quantified, the OMI endpoints differed between can-cer subtypes. In immortalized cell lines, the redox ratio waselevated in ERþ/HER2� cells and was greatest in HER2þ cells(P < 5 � 10�5, Fig. 4A). Similarly, NADH tm was increasedin immortalized ERþ/HER2� and HER2þ breast cancer cellsas compared with TNBC cells (P < 5 � 10�8, Fig. 4B). FAD tmwas greatest in ERþ/HER2� cells (P < 0.05, Fig. 4C). Overall,the OMI index was lowest in TNBC and greatest in HER2þ

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Figure 2. OMI of organoids derived from trastuzumab-resistant xenografts. A, redox ratio image of a control HR6 (ERþ/HER2þ) organoid at 72 hours. Scale bar,100 mm. B, NADH tm image of a control HR6 organoid at 72 hours. C, FAD tm image of a control HR6 organoid at 72 hours. D, redox ratio image of atrastuzumab (anti-HER2) plus paclitaxel (chemotherapy) plus XL147 (anti-PI3K; HþPþX)–treatedHR6 organoid at 72 hours. E, NADH tm image of anHþPþX-treated HR6 organoid at 72 hours. F, FAD tm image of an HþPþX-treated HR6 organoid at 72 hours. G, tumor growth response of HR6 tumorsgrown in athymic nude mice and treated with single and combination treatments. H, table of earliest detectable (P < 0.05) reduction in tumor size for controlversus treated mice. ^, tumors that initially shrank and then grew; NS, not significant. I, OMI index initially decreases in HR6 organoids treated withpaclitaxel, XL147, and combination therapies at 24 hours. J, OMI index of HR6 organoids treated for 72 hours. Red bars, significant reductions in OMI index;P<0.05, for treatedorganoids versuscontrol. Bluebars, significant increases inOMI index;P<0.05, for treatedorganoids versuscontrol. K, populationdensitymodeling of the mean OMI index per cell in control, paclitaxel, trastuzumab, and HþPþX organoids at 24 hours. L, population density modeling of theOMI index for HR6 control, paclitaxel, trastuzumab, and HþPþX organoids treated for 72 hours. M, immunofluorescence staining of cleaved caspase-3 incontrol and treated HR6 organoids at 72 hours. N, immunofluorescence staining of Ki67 in control and treated HR6 organoids at 72 hours. �, P < 0.05.

Walsh et al.





cells (P < 5 � 10�8, Fig. 4D), suggesting that HER2 and ERexpression influence cellular metabolism.Similar trends were observed for the OMI endpoints in

organoids derived from primary breast tumor specimenscultured under basal conditions. The redox ratio was increasedin organoids from ERþ/HER2� tumors and was greatest inHER2þ/ER� organoids (P < 5 � 10�12; Fig. 4E; SupplementaryTable S3). Likewise, NADH tm increased with ER and HER2expression (P< 5� 10�8; Fig. 4F). FAD tmwas increased in ERþ

organoids and reduced in HER2þ organoids (P < 0.05, Fig. 4G).The OMI index was lowest for TNBC, and greatest for HER2þ

organoids (P < 5 � 10�3; Fig. 4H).

Organoid response of ERþ primary human tumorsOrganoids were generated from four ERþ (HER2�) primary

human tumors and treated with the chemotherapeutic drugpaclitaxel, the selective ER modulator tamoxifen, the HER2antibody trastuzumab, and the pan-PI3K inhibitor XL147.Organoids derived from the first ERþ tumor had significantlyreduced OMI index values upon treatment with paclitaxel,tamoxifen, XL147, HþX, HþPþT, HþPþX, andHþPþTþX for72 hours (P < 5 � 10�5, Fig. 5A). Immunofluorescence ofcleaved caspase-3 showed increased cell death in parallelorganoids treated for 72 hours with paclitaxel, tamoxifen,XL147, HþX, HþPþT, HþPþX, and HþPþTþX (Fig. 5B).

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Figure 4. OMI endpoints differ among breast cancer subtypes. A, redox ratio (NADH/FAD) of TNBC cells (MDA-MB-231), ERþ (HER2�) cells (MCF7), andHER2þ cells (SKBr3, BT474, and MDA-MB-361). B, NADH tm of TNBC, ERþ, and HER2þ immortalized cell lines. C, FAD tm of TNBC, ERþ, and HER2þ

immortalized cell lines. D,OMI index increases in ERþ andHER2þ immortalized cell lines. E, redox ratio (NADH/FAD) of organoids derived from triple-negative,ERþ (HER2�), and HER2þ (ER�) primary human tumors. F, NADH tm of organoids derived from triple-negative, ERþ, and HER2þ primary humantumors. G, FAD tmof organoids derived from triple-negative, ERþ, andHER2þprimary human tumors. H, OMI index of organoids derived from triple-negative,ERþ, and HER2þ primary human tumors. �, P < 0.05.






Subpopulation analysis revealed less variability (narrowerhistogram peaks) within responsive treatment groups com-pared with the cells of control and trastuzumab-treated orga-noids (Fig. 5C and Supplementary Fig. S5). Corresponding OMIendpoints showed similar trends (Supplementary Fig. S6 andSupplementary Table S4).

Organoids derived from a second ERþ tumor respondedsimilarly. The OMI index decreased upon treatment withpaclitaxel, tamoxifen, HþP, PþT, and HþPþT at 72 hours(P < 5� 10�5, Fig. 5D). Subpopulation analysis revealed a single

population of control cells that shifted to lower OMI indexeswith paclitaxel, tamoxifen, HþP, PþT, andHþPþT treatments(Fig. 5E and Supplementary Fig. S7). Corresponding OMI end-points showed similar trends (Supplementary Fig. S8 andSupplementary Table S5).

The third and fourth ERþ clinical samples yielded organoidswith variable responses to treatment. Organoids derived fromthe third patient had significant reductions in OMI index after24 hours of treatment with tamoxifen, XL147, HþX, andHþPþTþX (P < 0.005, Fig. 5F). Subpopulation analysis

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Figure 5. OMI index measures drug response and heterogeneous populations in ERþ primary human tumor–derived organoids. A, OMI index of organoidsderived from an ERþ (HER2�) primary human tumor decreased with paclitaxel (chemotherapy), tamoxifen (ER antagonist), XL147 (anti-PI3K), and combinedtherapies at 72 hours. Light gray bars, significant (P < 0.05) reductions in OMI index with treatment versus control organoids. B, quantification ofimmunofluorescence staining of cleaved caspase-3 for organoids derived from the same tumor sample as in A and treated for 72 hours. C, population densitymodeling of the control, HþX-, andHþPþTþX-treatedorganoidspresented inA.D,OMI index is reducedwith paclitaxel, tamoxifen, andcombined treatmentsin organoids derived from a different ERþ patient at 72 hours. E, population density modeling of the control, tamoxifen, and HþPþT-treated organoidspresented in D. F, OMI index is reduced in organoids from a third ERþ patient treated with tamoxifen, XL147, HþX, and HþPþTþX at 24 hours. G, populationdensity modeling of the control, XL147-, and HþPþTþX-treated organoids presented in F. H, organoids derived from a fourth ERþ patient hadsignificant reductions in OMI index when treated with XL147 and combination therapies at 72 hours. I, population density modeling of the control,tamoxifen-, and HþPþX-treated organoids in H revealed multiple populations with tamoxifen treatment. �, P < 0.05.

Walsh et al.





revealed two populations with high and low OMI index valuesfor the HþP- and paclitaxel-treated organoids (Fig. 5G andSupplementary Fig. S9). Two populations, bothwithmeanOMIindex values less than that of the control organoids, wereapparent in the organoids treated with XL147 and withHþPþTþX (Fig. 5G and Supplementary Fig. S9). Organoidsfrom the fourth ERþ patient had reduced OMI indices follow-ing treatment with XL147, HþP, HþX, HþPþX, HþPþT, andHþPþTþX for 72 hours (P < 0.01, Fig. 5H). Subpopulationanalysis of cells from these organoids revealed single popula-tions with shifted mean OMI indices for all treatments excepttamoxifen, HþP, and HþX, which had two populations (Fig. 5Iand Supplementary Fig. S10). Corresponding OMI endpointsshowed similar trends (Supplementary Figs. S11–S12 andSupplementary Tables S6–S7).

Organoid Response of HERþ and TNBC primary humantumorsOMI was also performed on organoids derived from HER2þ

(ER�) and TNBC specimens. Organoids derived from theHER2þprimary tumorwere treatedwith the ER downregulatorfulvestrant, the HER2 antibody trastuzumab, and the anti-ErbB3 antibody A4 (30). The OMI index was significantlydecreased in the organoids treated for 24 hours with trastu-zumab and A4 (P < 0.005, Fig. 6A). Subpopulation analysisrevealed shifts in the mean OMI index values with thesetreatments within a single population of cells (Fig. 6B). Orga-noids derived from the TNBC specimen were treated withtamoxifen, the HER2 antibody trastuzumab, and the combi-nation of trastuzumab plus tamoxifen (HþT). No significantchanges were observed with these treatments in TNBC orga-noids after 24 hours (P > 0.3, Fig. 6C). Subpopulation analysis

revealed a single population of cells fromTNBC organoids (Fig.6D). Corresponding OMI endpoints showed similar trends(Supplementary Figs. S7 and S8 and Supplementary TablesS8 and S9).

DiscussionPrimary tumor organoids are an attractive platform for drug

screening because they are grown from intact biopsies, thusmaintaining the tumor cells within the same tumor microen-vironment (15). OMI is sensitive to early metabolic changes,achieves high resolution to allow analysis of tumor cell het-erogeneity, and uses endogenous contrast in living cells forrepeated measurements and longitudinal studies (11). TheOMI index is a holistic reporter of cellular metabolism becausethe redox ratio and NADH and FAD lifetimes are independentmeasurements (11). The mean lifetime captures not onlychanges in free-to-bound protein ratios but also preferredprotein binding and relative concentrations of NADH toNADPH (31). Cancer drugs have been shown to downregulatecertain metabolism enzymes; for example, trastuzumab down-regulates lactate dehydrogenase in breast cancer, and pacli-taxel-resistant cells have been shown to have more lactatedehydrogenase expression and activity (32). The OMI indexcaptures these drug-induced changes in metabolism enzymeactivity. Organoids remain viable with stable OMI endpoints incontrolled culture conditions (33), thus making them anattractive system to evaluate tumor response to drugs. Weused OMI to assess the response of primary breast tumororganoids to a panel of clinically relevant anticancer agentsused singly or in combination. Early OMI-measured responsein organoids (24–72 hours after treatment) corroborated with

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Figure 6. OMI index detectsresponse of HER2þ organoids totrastuzumab and resolves noresponse in TNBC. A, organoidsderived fromaHER2þ (ER�) clinicaltumor had reduced OMI indiceswith trastuzumab (anti-HER2) andA4 (anti-ErbB3) treatment, and nochange with fulvestrant treatment(ER antagonist) at 24 hours. Lightgray bars, significant reductions inOMI index due to drug treatmentcompared with control organoids(�, P < 0.05). B, population densitymodeling of the organoids derivedfrom a HER2þ tumor revealedsingle populations. C, organoidsderived from a TNBC tumor had nosignificant changes (P > 0.3) in OMIindex with treatment of targetedtherapies, tamoxifen (ERantagonist), and trastuzumab at48 hours. D, population densitymodeling revealed singlepopulations within the TNBCorganoids.






standard tumor growth curves in xenografts, and the feasibilityof this approach was confirmed in organoids derived fromprimary human breast tumors.

The OMI index was first evaluated as a reporter of tumorresponse in organoids derived from BT474 (ERþ/HER2þ)xenografts. Significant reductions in OMI index upon treat-ment with paclitaxel, trastuzumab, XL147, and combinationsthereof, at both 24 and 72 hours, correlated with reduction oftumor growth (Fig. 1). Biochemically, cellular rates of glycol-ysis, and NADH and FAD protein-binding decrease with drugtreatment in responsive cells (32), resulting in decreased redoxratios and NADH tm, and increased FAD tm in agreement withthe decreased OMI index observed in drug-treated BT474organoids. Significant reductions in tumor growth occurred7 to 11 days after treatment initiation, whereas the OMI indexdetected response 24 to 72 hours after treatment. Cellularanalysis revealed an initial heterogeneous response amongcells within organoids treated with paclitaxel and HþP at 24hours, which, by 72 hours, became a uniform response. Theheterogeneity of trastuzumab-treated BT474 organoids per-sisted over 72 hours, suggesting an intrinsic subpopulationmore susceptible to acquire drug resistance. This heterogene-ity was not seen in the combination treatments, suggesting thecombination treatments trump this drug resistance–pronesubpopulation. OMI-measured response corroborated withincreased cell death and decreased proliferation due to singleand combination drug–treated organoids, measured withdestructive postmortem techniques. The XL147-treated BT474organoids have amuch lower OMI index at 72 hours, but only amodest increase in cleaved caspase-3 activity. The samedecrease was not observed in the HR6 cells that have alterna-tive metabolism pathways activated because of their acquiredresistance to trastuzumab. The OMI index detects changes incellular metabolism that predict drug efficacy, but do notnecessarily correlate with IHC.

In the current standard of care, patients with innate drugresistance are not identified a priori. We tested the capabilitiesof OMI to predict drug resistance using trastuzumab-resistantHR6 (ERþ/HER2þ) tumors (29). XL147 is a novel PI3K inhibitorunder investigation for combined therapywith trastuzumab toimprove response of resistant tumors (27). Significant reduc-tions in the OMI index of HR6 organoids treated for 72 hoursidentified drug combinations (HþX, HþP, and HþPþX) thatinduced a sustained reduction in tumor growth in vivo (Fig. 2).The reduction in tumor growth upon treatment with HþXwasconsistent with previous reports of greater antitumor effects ofthe combination over trastuzumab and XL147 alone (27).Subpopulation analysis revealedmultiple responses within theHR6 organoids after treatment with single drugs and combi-nations, suggesting increased heterogeneity compared withthe parental BT474 organoids.

The OMI index of paclitaxel- and XL147-treated HR6 orga-noids initially decreased at 24 hours, and then increased at 72hours, mirroring the tumor growth in mice after prolongedtherapy, and indicating that the adaptations that allow HR6cells to survive trastuzumab treatment also affect response toadditional drugs. This relapse of HR6 tumors treated withpaclitaxel and XL147 was not apparent until 2 to 3 weeks of

drug treatment; yet, the OMI index identified a resistantpopulation within both paclitaxel- and XL147-treated orga-noids at 24 hours and showed a selection of this population by72 hours. Subpopulation analysis of the paclitaxel- and XL147-treated HR6 organoids revealed heterogeneous responses at 24hours, suggesting that OMI is capable of early detection ofresistant cells within a heterogeneous tumor. These resultsindicate that OMI of primary tumor organoids is able toidentify heterogeneous responses within tumors on a cellularlevel, and potentially guide therapy selection early for maximalresponse. The ability to detect innate resistance at a cellularlevel before treatment may provide leads for identification ofdrugs that target such refractory subpopulations before theyare selected by the primary therapy.

Wenext examined the feasibility of this approach using freshtumor biopsies obtained from primary tumor surgical resec-tions. OMI measurements in vivo and corresponding measure-ments from freshly excised tissues within 8 hours of surgery arestatistically identical (20), providing ample time for specimenacquisition and transport to the laboratory. Themorphology oforganoids differed among patients and within breast cancersubtypes (Fig. 3), demonstrating a greater heterogeneity withinprimary tumors compared with xenografts.

Previously published studies report differences in OMIendpoints due to the presence or absence of ER and HER2(8, 11, 34). Both ER and HER2 signaling pathways can influ-ence metabolism: ER by inducing increased glucose transport(1), and HER2 through activation of PI3K (2), among othersignal transducers. We compared OMI endpoints fromimmortalized cells and human tissue-derived organoids ofthree subtypes of breast cancer: ERþ, HER2-overexpressing,and TNBC. The OMI index of immortalized cell lines increasedwith ER expression and was highest in HER2 overexpressingcells (consistent with prior studies (11)), and these trendswere replicated in organoids derived from primary humantumors. Notably, NADH tm was significantly increased (P <0.05) in the HER2þ organoids compared with ERþ organoids,but this trend was not observed in the immortalized cell lines.This difference could be due to molecular changes induced bythe immortalization process, media components, primarytumor heterogeneity, and/or the heterogeneity within a pri-mary breast tumor. Regardless, the results shown (Fig. 4)suggest breast cancer subtypes, ERþ, HER2þ and TNBC, havedifferent OMI profiles.

Organoids derived from human breast tumors were treatedwith a panel of breast cancer drugs (Figs. 5 and 6). Differencesin the drug response of these organoids suggest heterogeneityacross ERþ/HER� tumors. Organoids from one of the four ERþ

tumors did not exhibit reduced OMI indices after treatmentwith tamoxifen. Organoids derived from two of the four ERþ

samples did not have reduced OMI indices after paclitaxeltreatment. These variable responses are consistent with var-iable responses seen with these drugs in the clinic (35–38).None of the organoids had reduced OMI indices with trastu-zumab, which is expected because the organoids were derivedfrom HER2� tumors. Generally, the OMI index was reducedfurther upon treatment with drug combinations, supportingthe use of drug combinations clinically.

Walsh et al.





Subpopulation analysis revealed cells within organoid treat-ment groups that exhibit different OMI indices after treatment,suggesting that subpopulations of cells with different drugsensitivities preexist and developwithin primary tumors. Someof these cells may represent the cancer stem-like populationwith increased renewal capacity, metastatic potential, anddrug resistance (39). The populations of organoids derivedfromhuman tumors havemore variability (broader populationcurves) than those derived from xenografts, reflecting aninherent greater heterogeneity within primary tumors. Thiscorroborates previous reports (40) of greater intratumoralheterogeneity in primary tumors than in xenografts derivedfrom clonal cell lines. Thus, OMI imaging allows identificationof heterogeneous cellular response to drug treatment in adynamic population, which potentially enables drug selectionto maximize therapeutic efficacy.Organoids derived from HER2þ/ER� and TNBC primary

tumors have OMI responses consistent with their clinicalcharacteristics: reduced OMI index with trastuzumab treat-ment and no change with fulvestrant (ER antagonist) treat-ment in the HER2þ/ER� organoids (41), and no OMI indexreductions after treatment with trastuzumab or tamoxifen inthe TNBC organoids (Fig. 6a and c; refs. 42, 43). HER3 is anemerging target for breast cancer (30, 44) and the anti-HER3antibody A4 reduced the OMI index of HER2þ/ER� organoids.The results of this study support the validity of OMI for

monitoring organoid response to anticancer drugs. We dem-onstrate high selectivity of the OMI index to directly measuredrug response of organoids derived from breast cancer xeno-grafts to single anticancer drugs and their combinations, andvalidated OMI measured response with gold standard tumorgrowth in two xenograft models. We have shown that the OMIindexmeasured in primary tumor organoids resolves responseand nonresponse within 72 hours, compared with the 3 weeksrequired to resolve this response with tumor size measure-ments. Furthermore, we extend this approach and generatedrug response information from organoids derived from three

subtypes of primary human tumors, TNBC, ERþ, and HER2þ.The high resolution of OMI allows subpopulation analysis foridentification of heterogeneous tumor response to drugs indynamic tumor cell populations. Altogether, these resultssuggest that OMI of primary tumor organoids may be apowerful test to predict the action of anticancer drugs andtailor treatment decisions accordingly.

Disclosure of Potential Conflicts of InterestL. Aurisicchio has ownership interest (including patents) in Takis Biotech. No

potential conflicts of interest were disclosed by the other authors.

Authors' ContributionsConception and design: A.J. Walsh, M.E. Sanders, M.C. SkalaDevelopment of methodology: A.J. Walsh, M.C. SkalaAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): A.J. Walsh, R.S. Cook, M.E. Sanders, C.L. Arteaga,M.C. SkalaAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): A.J. Walsh, R.S. Cook, M.E. Sanders, M.C. SkalaWriting, review, and/or revision of the manuscript: A.J. Walsh, R.S. Cook,M.E. Sanders, L. Aurisicchio, G. Ciliberto, C.L. Arteaga, M.C. SkalaAdministrative, technical, or material support (i.e., reporting or orga-nizing data, constructing databases): C.L. ArteagaStudy supervision: M.C. SkalaOther (providing some key reagents): L. AurisicchioOther (providingmonoclonal antibodies used for a subset of experimentsreported in the article): G. Ciliberto

AcknowledgmentsThe authors thank C. Nixon,W. Sit, M.Madonna, and B. Stanley for assistance.

Grant SupportA.J. Walsh was supported by a grant from the National Science Foundation

(DGE-0909667). M.C. Skala received grants from the DOD BCRP (DOD-BC121998), the NIH/NCI (NIH R00-CA142888, NCI Breast Cancer SPORE P50-CA098131), and Vanderbilt. G. Ciliberto received a grant from AssociazioneItaliana per la Ricerca sul Cancro (AIRC-IG10334).

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 indicate thisfact.

Received March 6, 2014; revised July 10, 2014; accepted July 10, 2014;published OnlineFirst August 6, 2014.

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2014;74:5184-5194. Published OnlineFirst August 6, 2014.Cancer Res Alex J. Walsh, Rebecca S. Cook, Melinda E. Sanders, et al. Metabolism Predicts Drug Response in Breast CancerQuantitative Optical Imaging of Primary Tumor Organoid

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