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Abstract

Three-dimensional (3D) cell culture models are thought to mimic the physiological and pharmacological properties of tissues in vivo more accurately than two-dimensional cultures on plastic dishes. For the development of cancer therapies, 3D spheroid models are being created to reflect the complex histology and physiology of primary tumors with the hopes that drug responses will be more similar to and as predictive as those obtained in vivo. The effect of additional cell types in tumors, such as stromal cells, and the resulting heterotypic cell–cell crosstalk can be investigated in these heterotypic 3D cell cultures. Here, a high-throughput screening-compatible drug testing platform based on 3D multicellular spheroid models is described that enables the parallel assessment of toxicity on stromal cells and efficacy on cancer cells by drug candidates. These heterotypic microtissue tumor models incorporate NIH3T3 fibroblasts as stromal cells that are engineered with a reporter gene encoding secreted NanoLUC luciferase. By tracking the NanoLUC signal in the media over time, a time-related measurement of the cytotoxic effects of drugs on stromal cells over the cancer cells was possible, thus enabling the identification of a therapeutic window. An in vitro therapeutic index parameter is proposed to help distinguish and classify those compounds with broad cytotoxic effects versus those that are more selective at targeting cancer cells.

Keywords

3D heterotypic spheroids stroma population-bound bioluminescence reporter therapeutic index screening

Introduction

Drug discovery is a process that requires large and high-risk investments in scientific research and preclinical and clinical development.^1–3 It is therefore crucial to have compound activity data that accurately predict the effects in patients sooner early in the drug development process. Traditionally, the drug discovery process for anticancer agents has relied on in vitro cell proliferation models in which cancer cell lines are cultured as monolayers that adhere to plastic surfaces.^4,5 However, it is becoming more evident that such two-dimensional (2D) cell culture models, although very amenable to high-throughput screening (HTS) of large collections of compounds, do not reproduce the physiological complexity of a tumor in vivo. As a consequence, compounds having promising pharmacological effects in 2D cultures often fail to produce an effect in a clinical setting.^5–7 Born from this gap in predicting efficacy, three-dimensional (3D) cell assay systems are being explored to create more clinically relevant models of tumors for drug development.^4,5,8–11 In contrast to 2D cell cultures, 3D culture conditions can better mimic the multicellular interactions within the tumor and with the microenvironment of tumors, which has a significant impact on the cellular behavior and pharmacological responses.^12–16 In this regard, inclusion of stromal cells in heterotypic cell models has been shown to stimulate the proliferation of cancer cells and affects their intercellular signaling and gene expression.^5,17–19 Therefore, the use of a heterotypic 3D model may offer substantial advantages for investigating the effect of potential anticancer agents in a system that more accurately represents the in vivo tumor microenvironment.

The overall goal of this work was to develop a model system that allows for the discrimination of cell viability patterns of two cell types within a heterotypic microtissue tumor model, and to classify the cytotoxic effects of the compounds tested on each cell type in the context of the entire microtissue system. The expected benefit of such a discrimination in the differential effect between the specific case of cancer and stromal cells in vitro is to predict the therapeutic window of a drug in vivo. This 3D multicellular heterotypic spheroid model is amenable to HTS and enables the parallel assessment of drug efficacy on cancer cells and unspecific cytotoxicity on tumor-integrated stroma cells of drug candidates. Homo- and heterotypic ovarian and pancreatic microtissue models were developed and used for the screening of a focused set of anticancer compounds targeting different mechanisms considered the hallmarks of cancer.²⁰ One pancreatic and two ovarian cell lines were used for the development of the microtissue tumor models. Although pancreatic and ovarian cancers are relatively rare diseases, pancreatic cancer is the fourth leading cause of cancer-related mortality, and ovarian cancer is the most common cause for gynecological cancer death.^19,21 The introduction of a secreted NanoLUC luciferase reporter gene into a fibroblast stromal cell allowed direct discrimination of cytotoxic effects between these nonproliferative cells and the proliferating cancer cells within a heterotypic tumor spheroid. An in vitro therapeutic index parameter resulting from a 3D anticancer drug screening is proposed to help distinguish and classify those compounds with broad cytotoxic effects versus those that are more selective in targeting cancer cells.

Materials and Methods

General Cell Culture

The HEY cell line is a human ovarian carcinoma cell line and was derived from a xenografted tumor (HX-62). The ovarian cancer xenograft was originally grown from a peritoneal deposit of a moderately differentiated papillary cystadenocarcinoma of the ovary. The HEY-GFP cell line was obtained from Dr. Ernst Lengyel, University of Chicago. The HEY-GFP cells were cultivated in RPMI 1640 medium with 10% fetal bovine serum (FBS), 1% penicillin/streptomycin (pen/strep), and 25 mM HEPES. The SKOV3 cell line was obtained from the American Type Culture Collection (ATCC; Manassas, VA) and is a human ovarian cancer cell line that was established from a 64-year-old female patient, suffering from ovarian adenocarcinoma. The SKOV3 cells were cultivated in Dulbecco’s modified Eagle’s medium (DMEM) with 10% FBS, 1% pen/strep, 1% NEAA, and 25 mM HEPES. The PANC1 cell line was obtained from ATCC and is a human pancreatic cancer cell line established from a 56-year-old male patient, suffering from pancreatic epithelioid carcinoma. The PANC1 cells were cultivated in DMEM with 10% FBS, 1% pen/strep, and 25 mM HEPES. The NIH3T3 cell line is a mouse embryonic fibroblast. The NIH3T3 cells were cultivated in DMEM with 10% FBS, 1% pen/strep, and 25 mM HEPES.

Microtissue Production Process

After expanding the cells, they were detached and the cell suspension with the desired cell density (HEY-GFP 100 cells/drop, SKOV3 200 cells/drop, PANC1 200 cells/drop, and NIH3T3 5000 cells/drop) was prepared. To produce heterotypic microtissue models, a cell suspension containing both fibroblasts and the respective cancer cells was prepared in DMEM. The cell suspension was seeded into the GravityPLUS plate (InSphero AG, Schlieren, Switzerland) by pipetting 40 µL of suspension per well. For aggregating the cells into microtissues, the loaded plate was placed into an incubator for 72 h. For longtime cultivation and compound treatments the microtissues were transferred 3 days after seeding from the GravityPLUS into the GravityTRAP (InSphero AG) plate. A medium exchange was performed twice a week to prevent the microtumors from nutrient starvation. To observe the morphology of the microtissues over time, bright field images were taken on a regular basis (Zeiss Axiovert 25, 5×; Zeiss, Oberkochen, Germany).

Histological Analysis

Microtissues were fixated and embedded in agarose. The microtissues were collected from the GravityTRAP plate into an Eppendorf tube and washed with phosphate-buffered saline (PBS). After washing, the microtissues were fixated by adding paraformaldehyde (PFA) for at least 1 h at room temperature. The PFA was then removed and 600 µL of boiled agarose solution was added for embedding. After solidification of the agarose plug, 500 µL of PBS was added into the tube. The subsequent paraffin embedding, cutting of paraffin blocks, and the immunohistochemistry (IHC) staining itself were performed on order by an external company (Sophistolab, Muttenz, Switzerland).

Transfection of the NIH3T3 Fibroblasts

In order to discriminate stromal cells from cancer cells in heterotypic microtissue models, a reporter gene was introduced into the fibroblasts in order to secrete NanoLUC luciferase. Transfection of the NIH3T3 fibroblasts by Nucleofection in order to secrete NanoLUC luciferase (pNL1.3CMV [secNluc] luciferase reporter vector; Promega, Madison, WI) was performed according to Lonza’s manual for the Amaxa 4D-Nucleofector Protocol for NIH/3T3 (Lonza Cologne GmbH, Cologne, Germany) using a Single Nucleocuvette (100 µL). The cells were trypsinized and an aliquot of 10⁶ cells was resuspended in 100 µL of 4D-Nucleofector Solution (Lonza Cologne). One microgram of pNL1.3CMV [secNluc] luciferase reporter vector was added, and the suspension was transferred into a Nucleocuvette. The Nucleocuvette was placed into the 4D-Nucleofector X Unit. The cells were transfected with program EN-158. After transfection, the cells were plated in cell culture medium into a T flask. For separation of the dead cells, the flask was incubated overnight, and vital cells were allowed to adhere to the bottom. The vital cells were subsequently used for the microtissue production process.

Drug Testing

Thirty-eight compounds targeting major cancer pathways were selected for testing in the different cancer spheroid models and were purchased commercially from different sources. The 38 compounds included 26 anticancer agents, 2 antibiotics, 2 cardiac glycosides, an antipsychotic and an antidepressant drug, an antiarrhythmic and an anticonvulsant agent, an epithelial sodium channel blocker, an L-DOPA inhibitor, an androgen receptor antagonist, and a drug acting on chemokine receptors. Supplemental Table S1 shows the list of the compounds and their respective primary mechanism of action. In addition, the table provides a potential categorization according to the respective effects of the compounds on one or more cancer hallmarks. All 38 compounds were tested on the ovarian HEY homotypic and HEY/NIH heterotypic microtissues. Twenty compounds were tested on the ovarian SKOV/NIH and pancreatic PANC/NIH heterotypic microtissues. The treatment was started after transferring microtissues into the assay plate; redosing of the microtissues was performed on days 3 and 5. Compounds were tested within a concentration range of 2 pM to 20 µM (11 doses, 1:5 dilution series). Each compound and dose were tested in one well with one sphere, for each assay condition. The vehicle control group consisted of microtissues treated with 1% DMSO in microtissue maintenance medium. A 1:5 dilution series of the compounds was prepared in a separate V-bottom plate with DMSO as the diluent. To reach a final concentration of 1% DMSO, a second dilution step was performed with microtissue maintenance medium as the diluent.

Measurement of Microtissue Size Over Time

To monitor microtissue size and morphology over time, the Dainippon SCREEN Cell3iMager was used. Ninety-six-well plates were scanned at treatment days 3, 5, and 7. The integrated analysis software enabled automated measuring of the microtissue size, including accurate discrimination of microtissues and cell debris that might occur after compound treatment.

Nano-Glo Luciferase Assay

To discriminate fibroblasts from cancer cells in the co-culture models, the fibroblasts were transfected with a reporter gene in order to secrete NanoLUC luciferase. Within a screening run the secreted NanoLUC luciferase was measured 3, 5, and 7 days after the treatment started. The Nano-Glo Luciferase Assay was performed according to Promega’s manual for the Nano-Glo Luciferase Assay System (Promega).

CellTiter-Glo Luminescent Cell Viability Assay

As a biochemical endpoint of a screening run at treatment day 7, the cell viability of the microtissues was detected by measuring the ATP content. The procedure for the CellTiter-Glo 3D Cell Viability Assay (Promega, Madison, WI) was done as follows: The complete culture medium was aspirated manually. CellTiter-Glo 3D Reagent was mixed 1:1 with microtissue maintenance medium and 40 µL of the diluted reagent was added to the microtissues and mixed. Microtissues and supernatant were transferred into a 96-well half-area white assay plate (cat. 07-200-326; Corning, Corning, NY). The assay plate was wrapped in aluminum foil and incubated for 20 min at room temperature while shaking horizontally. Luminescence was measured with the Tecan M200Pro (Männedorf, Switzerland). For analysis of the data, the values from treated microtissues were normalized to the vehicle control.

Data Analysis and Visualization

For the generation of dose–response curves and corresponding IC₅₀ values, the data obtained from size measurements, NanoLUC assays, and ATP assays were normalized to the vehicle control. The normalized data were processed using GraphPad Prism 6 software (San Diego, CA). The program uses the subsequent formula for fitting a sigmoidal dose–response curve: Y = ((Y_top))/(1 + 〖10〗^((Log IC_50-X)*Hill Slope)),where Y_top is the Y value at the top plateau. The Hill Slope variable, also called the Hill Slope coefficient, describes the steepness of the curve. Visualizations were set up via TIBCO Spotfire software (TIBCO, Palo Alto, CA).

Multiparametric Compound Classification

The multiparameter classification aims to harmonize data features derived from different tumor microtissues with diverging scales and ranges. In this study, data were generated for a set of 20 drugs from the National Center for Advancing Translational Sciences (NCATS) library describing the potency, efficacy, acute toxicity, and chronic toxicity of each drug as well as the impact of a drug on tumor growth. For each tumor microtissue (HEY/NIH, SKOV3/NIH, and PANC/NIH), ranked lists of different feature combinations were generated including a three-feature combination of potency, efficacy, and tumor growth, was well as a five-feature combination of potency, efficacy, tumor growth, acute toxicity, and chronic toxicity. For both lists, the overall rank of a drug is dependent on the biological impact of each feature; that is, a small IC₅₀ ATP value indicates a high potency of a drug and is therefore ranked lower (better). In this regard, a high efficacy, low acute and chronic toxicity, and a strong inhibition in tumor growth result in lower ranks. The final rank of a drug in each tumor tissue list (i) is determined by the equal weight rank $(\bar{R_{i}})$ over all considered feature ranks (ri,n) (n = 3 and n = 5):

\bar{R_{i}} = \frac{\sum_{1}^{n} r_{i, n}}{n}

A comparison over all drugs between the different tumor lists is enabled with the Spearman rank correlation ranging from 0 to 1, where zero indicates no correlation and 1 indicates a perfect correlation.

To compare the efficacy of a drug throughout all different tumor microtissues, the equal weight ranks from all tumor types were reranked between 1 and the number of compounds. The average rank of all tumor tissues determines the overall rank of a drug.

Results

Assessment of the morphological and growth properties of several heterotypic tumor microtissues was performed to define assay parameters for their pharmacological profiling. Three heterotypic microtissue models were established, all including a cancer cell line and NIH3T3 fibroblasts, as stromal cells: (1) ovarian cancer co-cultures with HEY-GFP tumor cells, referred to as HEY/NIH microtissues; (2) ovarian cancer co-culture systems with SKOV3 as tumor cells, referred to as SKOV/NIH microtissues; and (3) a pancreatic cancer co-culture system with PANC1 as tumor cells, referred to as PANC1/NIH microtissues. The ratio of cancer to fibroblast cells used was 1:25, based on previous work with tumor/fibroblast heterotypic microtissues.^22,23 The aggregation of the respective cells in hanging drops resulted in microtissue formation within 3 days, observed by bright field microscopy. After microtissue formation, microtissues were either harvested for histological analysis or transferred into a nonadhesive spheroid-specific 96-well plate for long-term culture. Growth profiles were obtained by measuring size over time using bright field microscope. All three models displayed fast growth characteristics, and at 10 days, HEY/NIH was six times larger and SKOV/NIH and PANC1/NIH three times larger than the size of the microtissues at 3 days ( Fig. 1A–F ). Due to the fast growth kinetics of HEY/NIH microtissues, tissues were initiated with a 1:50 ratio of cancer cells to fibroblasts. To investigate cell viability over time, the ATP content was measured regularly over a culture period of up to 13 days ( Fig. 1D–F ). The increase in size of the microtissues seen by bright field microscopy correlated with a parallel increase in ATP levels in the tissues, as measured by the reagent CellTiter-Glo, indicating a corresponding increase in the number of viable cells in the microtissue.

Figure 1.

Growth profiles and morphological appearance of 3D heterotypic multicellular tumor spheroids. (A–C) Co-culture spheroids of HEY, SKOV3, and PANC1 cancer cells and NIH3T3 fibroblasts were produced with the hanging drop technology and their growth monitored over time by bright field microscopy. (D–F) After 3 days of spheroid formation, tissue size (●) and intratissue ATP content (■) were monitored over 7 days. Each point represents the mean of eight spheroids and their corresponding standard deviation. Bright field microscopy allowed for size assessment of the microtissues, and intratissue ATP with CellTiter-Glo was used as a measure of cell viability. (G–I) All three heterotypic tumor microtissues were stained for histological characterization. Cancer and stromal cells were capable of reforming heterotypic, solid spheroids, as shown by hematoxylin and eosin staining. Eosin colors eosinophilic structures in various shades of red, pink, and orange, whereas hematoxylin colors nuclei of cells blue. After 11 days of culture, tumor microtissues formed a white necrotic area in the center of the spheres. The spheroids showed a high expression of epidermal growth factor receptor (EGRF) within the cancer cells. Heterotypic tumor spheroids exhibited a spherical geometry with a central core of nonproliferating fibroblasts and proliferating cancer cells in the periphery, as determined by IHC staining of Ki67. Scale bars = 100 µm.

Characterization of internal architecture of the microtissues was done by histological sectioning and IHC staining. After 3, 6, and 11 days of culture, IHC analyses of the co-culture microtissues were performed ( Fig. 1G–I ). The comparison of H&E and IHC staining with markers for proliferation (Ki67) and cancer (EGFR) cells of heterotypic spheres, at different time points, allowed for the discrimination between cancer and stromal cells. The different histological staining of the co-culture microtissues confirmed a viable microtissue, although in contrast to homotypic microtissues ( Suppl. Fig. S1 ), the lack of signal observed in the center of the spheres after 11 days of culture suggested a necrotic area. IHC staining with EGFR and Ki67 allowed the ability to clearly distinguish the cellular plasticity of these heterotypic microtissues during cultivation, in which initially a central core of nonproliferating fibroblasts (Ki67⁻/EGFR⁻) is quickly formed with proliferating cancer cells (Ki67⁺/EGFR⁺) attached to the periphery, and then, with time, the cancer cells grow and start migrating to the inside of the microtissue. In conclusion, the growth and morphological data show a picture of assembly of microtissues in which the fibroblasts formed a nonproliferating core into which the cancer proliferates to make the sphere larger; an increase in ATP levels likely reflects an increase in mostly the number of cancer cells rather than fibroblasts. Based on the growth kinetics, the rate of NanoLUC secretion ( Suppl. Fig. S2 ), and histological appearance, the treatment time of 7 days was used for the pharmacological testing. Over this time period, there was robust growth of the tissues without observing significant necrosis in the core, and robust stability of NanoLUC detects cytotoxicity on nontumor cells.

The effects of a set of drugs with different mechanisms of action (see Suppl. Table S1 ) were first tested on the different cellular models of ovarian cancer using the HEY cells grown in a monolayer, homotypic microtissues, and heterotypic HEY/NIH microtissues. The effects of the drug on the growth of the cells in each assay system were assessed by measuring the ATP content after a 2-day treatment period for the monolayer culture model, and the microtissue size at 3, 5, and 7 days by brightfield imaging and the ATP content after a 7-day treatment for the 3D microtissue cultures. Figure 2A shows a heat map of the percent cell viability at the maximum dose of compound tested (percent maximal response), which is a measure of compound efficacy, for each assay condition. Most of the 20 compounds tested were highly efficacious in the ATP monolayer assay, except for PNU-74654. The measured percent maximal responses by both ATP measurement at day 7 and tissue size at day 7 revealed a similar responsiveness to the drug treatments of homo- and heterotypic microtissues, with a tendency for homotypic microtissues to be more sensitive to the compounds. However, there is also a clear tendency that the temporal effects of the drugs on the size of heterotypic microtissues are less pronounced compared with homotypic microtissues, and in general, the time that it takes to see an effect is longer than that for heterotypic microtissues. For microtissues, of the 20 compounds, at day 7, most were efficacious in both assay readouts and tissues, except for PNU-74654, VU0483488-1, and GM-6001, especially in the heterotypic tissue. Correlation plots of percent maximal responses ( Fig. 2B,C ) confirm that efficacy of the compounds correlates between homo- and heterotypic microtissues; however, although there is a good correlation, compounds appear to be more efficacious for the homotypic microtissues by both readouts ( Fig. 2B,C ). A good correlation was also observed when evaluating the potencies (AC₅₀ values) of the compounds (Fig. 2D,E and Suppl. Table S2) comparing heterotypic and homotypic microtissues, by both ATP and tissue size measurements. The evaluation of the maximal responses (efficacy) and AC₅₀ data (potency) indicated that heterotypic microtissues show a decrease in efficacy to compounds compared with homotypic microtissues, although the potencies of the compounds remain very similar between the two cellular systems.

Figure 2.

Comparison of pharmacological effects of compounds on HEY cancer growth assays of increased complexity, as measured by cell viability with CellTiter-Glo and spheroid size by bright field microscopy. (A) The heat map illustrates the percent maximal responses at 40 µM (2D) and 20 µM (3D) drug concentrations, calculated based on ATP data for 2D HEY, 3D HEY, and 3D HEY/NIH cultures, as well as size measurements from 3D microtissues of HEY and HEY/NIH over time. Darkest red indicates 100% viability for ATP data, 100% growth for tissue size data. Darkest blue indicates 0% viability for ATP data, 0% growth for tissue size data. For percent maximal response calculations, ATP and tissue size values were normalized to the vehicle control. Correlation plots of percent maximal response (B,C) and logAC₅₀ (D,E) between homo- and heterotypic HEY microtissues. A straight line corresponds to a linear fit to the data.

To assess differences in the pharmacological responses of the different cancer types in the context of heterotypic microtissues, the efficacies and potencies of the 20 compounds were evaluated in the two ovarian (HEY/NIH, SKOV/NIH) and the pancreatic (PANC/NIH) heterotypic microtissue models. The percent maximal responses based on ATP measurements after 7 days revealed similar efficacies of the tested drugs in all cancer heterotypic microtissues ( Fig. 3A ). The percent maximal responses based on tissue size measurements at different time points showed that the efficacy of the compounds in all heterotypic tissue models generally increased with treatment time, and by day 7 most of the compounds were efficacious in all cancer microtissues ( Fig. 3 ). A comparison of AC₅₀ values of the different heterotypic tissue systems showed that, in general, the potencies of the compounds correlated between the same assay readouts (ATP) across different cancer types ( Fig. 3B–D ), and the same cancer types across different readouts ( Fig. 3E–G ). Only for SKOV3 microtissues did drugs appear to be less sensitive (>5-fold AC₅₀ ratio) by tissue size than ATP measurements ( Suppl. Table S3 ). Several compounds, including Torin-1, TAK-733, SR-3066, dasatinib, and carfilzomib, had AC₅₀ values <100 nM in all heterotypic cancer tissue types, as measured by ATP at 7 days. The NF-κB inhibitor bardoxolone methyl is an example of a compound that was very potent for SKOV3 microtissues with a >1000-fold selectivity over the other cancer microtissues by the ATP 7-day readout. These results indicate that different histological subtypes of a particular cancer type can have an influence on the responses to a drug. When measuring the size of the different cancer heterotypic spheroids, the efficacy of the compounds reported as the percent maximal response shows a trend of increasing at 7 days after treatment for the three cancer tissues. However, for HEY/NIH3T3 tissues, the efficacy of the compounds was higher at the 3-day time point than for the other two cancer heterotypic microtissues ( Fig. 3A ). When comparing the potency of the compounds as measured by logAC₅₀, there was generally a high correlation between cancerous microtissues when using the ATP value after 7 days. When comparing readouts, there was a linear correlation between the potencies by ATP and tissue size readouts for the three cancer microtissues tested. For the SKOV3 cells, however, the AC₅₀ values were about 10 times stronger in ATP than for the tissue size display ( Fig. 3E–G ). In conclusion, both the efficacy and potency data across heterotypic tissues show very similar drug effects regardless of the different cancer types at 7 days, while at 3 and 5 days, the drug effect on size was much more effective in HEY heterotypic tissues than in PANC1 and SKOV3 tissues, which were more resistant.

Figure 3.

Comparison of pharmacological effects of compounds on 3D heterotypic multicellular tumor spheroids of different cancer types. (A) The heat map illustrates size-based percent maximal responses at 20 µM drug concentrations calculated over time from HEY/NIH, PANC/NIH, and SKOV/NIH microtissues. Darkest red indicates 100% growth for tissue size data. Darkest blue indicates 0% growth for tissue size data. For percent maximal response calculations, tissue size values were normalized to the vehicle control. Correlation plots of logAC₅₀ between different tumor microtissue types using ATP at 7 days as a readout (B–D) and between ATP and tissue size at 7 days, for each cancer microtissue (E–G).

The reduction of severe side effects that are commonly associated with traditional chemotherapeutic agents is one of the main drivers for developing cancer-specific targeted therapeutics. In order to identify cytotoxic side effects of the tested compounds on stromal cells, luminescence signal from secreted NanoLUC luciferase of the transiently transfected stromal fibroblasts was measured from the heterotypic tumor microtissues, over the treatment time. The heat map in Figure 4A illustrates the percent maximal responses calculated on the basis of secreted NanoLUC data over time compared with tissue size measurements at the same times. The NanoLUC activity values at the respective measurement days of the dosed conditions were normalized to the NanoLUC activity of the untreated control for each tissue type. As a result, 100% NanoLUC signal represents 100% stromal viability. As observed when measuring the size of the tissues, the efficacy of the drugs tested increases with the length of treatment, and by day 7, most of them had reduced the NanoLUC signal by >50%. Some of the compounds, like romidepsin, an histone deacetylase (HDAC) inhibitor, have a strong effect on the NanoLUC but no effect on tumor size at day 3, which suggests transcription downregulation of NanoLUC expression by epigenetic modulation, and not necessarily cytotoxic effects on the fibroblasts, thus highlighting one of the caveats of our approach. Another set of interesting compounds are those that reduce tumor size early in the treatment without affecting NanoLUC signal, including dasatinib, TAK-733, and WAY-600 for HEY/NIH3T3, although no such compounds were observed to have an effect for SKOV3/NIH3T3 and PANC1/NIH3T3 tissues at any of the time points. These data illustrate that besides the overall trend of increased efficacy with time, by both NanoLUC and tissue size, it is clear that the time-dependent increase in efficacy for each compound is cancer tissue dependent. These varying effects of the compound on the efficacy measured through NanoLUC and ATP and tissue size readouts are also observed in correlation plots ( Fig. 4B ). The heat map in Figure 4C illustrates the logAC₅₀ for those compounds that produced enough of an efficacious response to be able to fit a dose response to calculate an AC₅₀ value (a dark cell box indicates that a curve fit was not possible because of very small efficacy). Mirroring the increased time-dependent efficacy, compounds appear more potent at day 7; also, by measuring the NanoLUC signal, and in general, there is a good correlation between the potencies of the compounds for all the cancer tissues. There are compounds like Torin 2, TAK-433, and romidepsin that are very potent at day 7 by both measures and across all cancer tissues, and other compounds, like bardoxolone methyl, which are very potent only for SKOV3 tissues, as was discussed above.

Figure 4.

Comparison of pharmacological effects of compounds on NanoLUC reporter from fibroblast cells and spheroid size on 3D heterotypic multicellular tumor spheroids of different cancer types. (A) The heat map illustrates the percent maximal responses at 20 µM drug concentrations calculated based on tissue size and NanoLUC luminescence measurements over time for HEY/NIH, PANC/NIH, and SKOV/NIH microtissues. Darkest red for tissue size indicates 100% size of the entire microtissue, and for NanoLUC data it indicates 100% viability of the fibroblasts within the microtissue. Darkest blue for tissue size data indicates 0% tissue, and for NanoLUC data it indicates 0% viability of the fibroblasts within the microtissue. (B) Correlation plots of percent maximal responses between NanoLUC and tissue size readouts in the same cancer tissues, and the same readouts across different cancer microtissues. (C) Heat map comparing the logAC₅₀ values calculated from dose responses based on tissue size and NanoLUC luminescence measurements over time for HEY/NIH, PANC/NIH, and SKOV/NIH microtissues. Those conditions for which a compound had no efficacy and IC₅₀ could be calculated are indicated in gray.

Figure 5.

Heat map based on rank changes of the individual compounds between three- and five-parameter classifications. Colored by ranking score: 1 (green) corresponds to the best therapeutic window, and 20 (red) corresponds to the smallest therapeutic window.

To model a therapeutic window between cytotoxic effects on cancer cells over stromal cells, we calculated a ratio between AC₅₀ values based on NanoLUC activity over AC₅₀ values based on ATP activity at day 7 ( Suppl. Table S4 ). We calculated two different therapeutic windows modeled based on pharmaco- and toxicokinetic concepts and taking advantage of our temporal readouts: NanoLUC data at day 3 after treatment represented acute toxicity of the compounds, whereas NanoLUC data at day 7 after treatment represented chronic toxicity. The calculated therapeutic windows for HEY/NIH spheroids are shown in Table 1 , and for SKOV/NIH and PANC/NIH microtissues they are listed in Supplemental Table S4 . For some compounds for which no significant effect was observed on the fibroblasts, an AC₅₀ value could not be calculated, and the therapeutic window of the respective compound was assumed to be infinite. For PANC1/NIH tissues, at day 3, none of the compounds had an acute toxic effect except for Torin-2. However, even Torin-2, the only compound for which an AC₅₀ value with regard to fibroblast toxicity was computable, showed a very broad therapeutic window, with the ratio of adverse to therapeutic effect being 5524-fold. In contrast, the fibroblasts within the SKOV/NIH microtissues seemed to be more sensitive to the compounds at day 3. In particular, romidepsin significantly reduced the viability of stromal cells within 3 days after treatment. An analysis of the therapeutic windows for SKOV/NIH microtissues at day 7 showed that two compounds, dasatinib and WAY-600, which had medium to high therapeutic effects, did not have chronic toxicities with respect to fibroblasts. In PANC/NIH microtissues, Torin-2 showed a broad therapeutic window even after 7 days of treatment. This suggests that those compounds specifically targeted the cancer cells. Similar to SKOV and PANC microtissues, most compounds showed no acute toxicity to stromal cells in HEY/NIH spheroids ( Table 1 ). Romidepsin, which had a narrow therapeutic window at day 3 in SKOV/NIH microtissues, did not seem to have toxic effects on fibroblasts at day 3 in HEY/NIH microtissues. However, after 7 days of treatment, the adverse effects of romidepsin outweighed the therapeutic effects in HEY/NIH microtissues. As opposed to romidepsin, dasatinib, which is a very potent compound measured in terms of ATP content, showed a large therapeutic window even after 7 days of microtissue treatment in both ovarian cancer models. The variations in therapeutic windows between the different microtissue models may be caused by different therapeutic effects of the compounds, on the one hand, or by variations in adverse effects, on the other hand. To compare the chronic toxicity with respect to stromal cells in the different heterotypic microtissues, correlation plots with NanoLUC data at day 7 were generated ( Fig. 4B ). The chronic toxic effects of the compounds on fibroblasts were comparable under the different model systems. Interestingly, within the group of compounds that generally exhibited a high toxicity, fibroblasts within PANC/NIH and SKOV/NIH microtissues were more sensitive compared with fibroblasts within HEY/NIH microtissues, and vice versa: within the group of compounds with generally lower toxicities, the compounds showed stronger effects in HEY/NIH fibroblasts than in SKOV/NIH and PANC/NIH stromal cells.

Table 1.

Therapeutic Windows, as the Ratio of Adverse Effect to Therapeutic Effect, for 3-Day and 7-Day Cytotoxicity of the Compounds in HEY/NIH, SKOV/NIH, and PANC1/NIH Spheroids.

Compound Name	HEY/NIH			SKOV/NIH			PANC/NIH			Hallmark Categorization	Primary Target
	IC₅₀ ATP (µM)	Therapeutic Window		IC₅₀ ATP (µM)	Therapeutic Window		IC₅₀ ATP (µM)	Therapeutic Window
	IC₅₀ ATP (µM)	3-Day TOX	7-Day TOX	IC₅₀ ATP (µM)	3-Day TOX	7-Day TOX	IC₅₀ ATP (µM)	3-Day TOX	7-Day TOX
HMSL10084	0.333	Infinite	1.396	0.683	14.7	1.817	0.767	Infinite	1.443	SustProlifSign	FLT3
AV-412	1.292	Infinite	6.026	0.08	Infinite	28.42	2.444	Infinite	2.338	SustProlifSign	EGFR
TAK-733	0.007	25.57	161.600	0.013	467.6	1.692	0.025	Infinite	1.800	SustProlifSign	MEK
SR-3306	0.058	21.77	2 138	0.013	24.85	3.692	0.022	Infinite	5.636	SustProlifSign	JNK 1/2/3
TAE-684	0.155	81.68	31.960	0.905	4.966	1.439	0.548	Infinite	2.473	SustProlifSign	ALK
Cantharidin	3.168	Infinite	1.045							SustProlifSign	PP-1; PP-2A
Dasatinib	0.077	Infinite	158.700	0.019	Infinite	Infinite	0.064	Infinite	3.578	ResistCellDeath	Bcr-Abl
VU0483488-1				1.042	Infinite	Infinite	ND	ND	ND	ResistCellDeath	McI-Inh
Obatoclax	0.609	71.19	5,619.000	0.959	Infinite	2.773	1.110	Infinite	3.878	ResistCellDeath	Bcl-xL
Niclosamide	1.668	Infinite	0.329	6.649	Infinite	1.404	ND	ND	ND	ResistCellDeath	STAT-3
PD-173955	0.479	Infinite	20.370							ResistCellDeath	cSRC
Sepantronium bromide	0.279	Infinite	55.190							ResistCellDeath	Survivin
PNU-74654	21.160	Infinite	0.126	2.835	Infinite	Infinite	ND	ND	ND	StPS/RCD	Wnt signaling
WAY-600	1.681	Infinite	3.486	0.507	Infinite	Infinite	7.377	Infinite	1.240	StPS/RCD	mTOR
Torin-2	0.027	9.629	2.222	0.002	Infinite	6.000	0.001	5 524	540	StPS/RCD	mTORC1
Romidepsin	0.005	Infinite	0.060	0.003	1,667	1.000	0.003	Infinite	1	StPS/RCD	HDAC
Carfilzomib	0.067	577.8	12.850	0.035	1 270	10.14	0.047	Infinite	15,47	StPS/RCD	Proteasome
Bardoxolone methyl	1.414	Infinite	0.120	0	Infinite	1.333	1.335	Infinite	Infinite	StPS/RCD	NF-κB
Cycloheximide	0.022	395.91	51.860							StPS/RCD	GSK-3β
GSK-25	6.099	Infinite	0.473	4.829	Infinite	Infinite	9.375	Infinite	2.466	activInvasMetast	ROCK
GM-6001				3.856	Infinite	3.006	9.614	Infinite	Infinite	activInvasMetast	MMP Inh
Ponatinib	0.055	106.2	29.710	0.233	9.219	0.755	0.035	Infinite	2.971	induceAngio	FGFR
Sunitinib malate	5.270	Infinite	0.023	2.595	Infinite	1.642	ND	ND	ND	induceAngio	VEGFR
HLI-373989	3.755	Infinite	1.188							evadGrowthSign	MDM2
DA-3003-1	7.359	Infinite	Infinite							evadGrowthSign	CDC25
Topotecan hydrochloride	0.025	Infinite	2.960							trad.chemo.	DNA topoisomerase I
Salinomycin	1.530	Infinite	Infinite	0.274	Infinite	5.274	0.377	Infinite	4.401	NC	Anticoccidial/antibacterial
Amiodarone	56.460	Infinite	Infinite							NC	Muscarinic M3 receptor
Benzamil	113.500	Infinite	Infinite							NC	Epithelial sodium channel
SK&F-89976A	15.280	Infinite	Infinite							NC	GAT-1
G-Strophanthin	0.070	Infinite	1.433							NC	Na+/K+-ATPase
Digoxin	0.107	Infinite	0.449							NC	Steroid
Shikonin	5.954	Infinite	1.607							NC	Chemokine receptors

SustProlifSign = sustaining proliferative signaling; ResistCellDeath = resisting cell death; activInvasMetast = activating invasion and metastasis; induceAngio = induce angiogenesis; evadGrowthSign = evading growth signalling; trad.chemo = traditional chemotherapeutic agent; NC = not categorized; ND = not detectable; Infinite = the therapeutic window of a compound was assumed to be infinite when no significant effect on the fibroblasts has been verified in reference to the vehicle control at highest drug concentration (20 µM); TOX = cytotoxicity.

To further define compound criteria to help prioritize compounds based on both high cytotoxicity effects on cancer cells and reduced toxicity on stromal cells, the compounds were ranked with respect to their efficacy, potency, acute toxicity, chronic toxicity, and effects on tumor size over time. For each category, the 10 outstanding compounds are shown ( Suppl. Table S5 ). Ranked best in terms of efficacy were the compounds that showed the lowest relative maximal responses based on ATP data. Best in terms of potency were the compounds that showed the lowest IC₅₀ values based on ATP data. Best in acute toxicity were the compounds that showed the highest NanoLUC signal at day 3 after treatment. Best in terms of chronic toxicity were the compounds that showed the highest NanoLUC signal at day 7 after treatment. In the specific case of compounds that showed infinite values, the compounds were ranked according to their respective IC₅₀ values based on ATP data. The most effective in terms of effect on tumor size over time were the compounds that resulted in the lowest tumor growth over time. A multiparameteric ranking was then calculated as described in Materials and Methods based on the different effects of the drugs described above on the tissues. For the 3-parameter ranking AC50, % maximal response, and growth constant parameters were used. For the 5-parameter ranking the therapeutic windows at 3- and 7-were also included. Figure 5 and Supplemental Table S6 show the ranking for the 20 compounds tested in all three heterotypic cancer microtissues.

For HEY/NIH tumor spheroids, the three compounds dasatinib, TAK-733, sepantronium bromide, and carfilzomib can be highlighted ( Suppl. Table S5 ). Dasatinib is listed in all categories, except for efficacy, where it was ranked 14th out of 38. The same applies to TAK-733, which was ranked 17th in terms of efficacy. Carfilzomib, a highly efficient and potent compound, ranked 10th in terms of chronic toxicity, and already showed a very broad therapeutic window, with the ratio of adverse to therapeutic effect being 578-fold at day 3. For SKOV3/NIH tumor spheroids, the proteasome inhibitor carfilzomib was also a prominent compound within the ranking ( Suppl. Table S6 ). This particular compound appeared to be effective and potent, had a significant effect on tumor growth over time, and showed low chronic toxicity to fibroblasts. In addition, the therapeutic window, with a ratio of adverse to therapeutic effect of 1270 at day 3, was very broad. Another compound, which was already outstanding in the HEY/NIH classification, is dasatinib. This potent Bcr-Abl inhibitor seems to target the cancer cells very specifically and is ranked 13th in terms of efficacy with a maximal response of 4.244. Another interesting compound was the EGFR inhibitor AV-412, which is listed in all categories, except for the effect on tumor size over time, where it was ranked 11th out of 20. Five compounds may be found within the ranking for PANC/NIH spheroids, shown in Supplemental Table S6 . These are Torin-2, ponatinib, TAE-684, carfilzomib, and bardoxolone methyl. The highly potent and efficient mTORC1 inhibitor Torin-2 is listed in all categories, except for efficacy and acute toxicity, where it nevertheless showed a very broad therapeutic window with the ratio of adverse to therapeutic effect being 5524-fold. It ranked 11th in terms of efficacy with a maximal response of 3 µM. Bardoxolone methyl, a compound with a high efficacy and an infinite therapeutic window for both acute and chronic toxicity, showed a medium potency with an IC₅₀ value of 1 µM. Supplemental Figure S3 shows the dose responses for TAK-733 and dasatinib, two of the compounds that show the overall best potency, efficacy, and therapeutic window.

Discussion

It is expected that the use of advanced in vitro cell culture models along the drug discovery value chain will significantly reduce the failure rate in preclinical and clinical validation of drug candidates. For this purpose, models must be developed according to the requirements of each discovery stage, ranging from HTS compatibility to multiorgan models. Here, we have developed a fully scalable, cost-effective, and standardized heterotypic 3D first-line screening platform consisting of a human cancer and a mouse fibroblast cell line. Although not a complete model of human origin, it reflects an ex vivo xenotransplantation model with cell types that allows robust model development for a variety of other cancer cell lines. A mouse cell line was used to ensure comparability between cancer models for first-line drug classification and also to maintain connective tissue integrity. The fibroblasts not only reflect the stroma environment but also can be used either to assess nonspecific drug toxicity (cytotoxicity) or as a target themselves, as demonstrated by Herter and coworkers.²² Here, by integrating a cytotoxicity readout as an additional parameter in a screening setup, we can define a ranking of compounds with the highest efficacy toward cancer cells and the lowest cytotoxicity toward stromal cells representing unwanted side effects.

The 3D heterotypic multicellular tumor spheroid NanoLUC systems were developed in a 96-well format but are scalable to a 384-well high-throughput assay platform, which allows us to test compounds in dose responses to obtain both efficacy and potency measures over time using different relevant assay readouts of cell viability, size-based growth kinetics, and stromal cell viability. Comparison of percent maximal responses and AC₅₀ values of treated ovarian homotypic microtissues with those of ovarian heterotypic microtissues did show minimal differences of drug efficacies and potencies between the two systems. The addition of stromal cells to the formation of heterotypic tumor microtissues, with the resulting heterotypic cell–cell crosstalk, is expected to increase drug resistance of the cancer cells compared with homotypic microtissues.²⁴ Cell-sorting events within in the heterotypic cultures led to an inside-out configuration with fibroblasts in the core and tumor cell in the periphery.^22,23,25 The evaluation of the treatment data did reveal that heterotypic microtissues show a decrease in sensitivity to compounds compared with homotypic microtissues. However, the differences in sensitivity were minor, suggesting that in these particular cellular systems additional cell types have minimal influences on drug sensitivity. In addition, we observed that some of the drugs that showed high effects in potency or efficacy based on intratissue ATP contents after 7 days had moderate effects on tissue size development over time, and vice versa: some of the drugs that showed a moderate potency and/or efficacy had a very high influence on tumor growth over time. There was no obvious pattern, if any, that would have shown an unambiguous relationship between these variables.

To model a therapeutic window, which is defined as the ratio of adverse effect to therapeutic effect, AC₅₀ values based on NanoLUC data were set in relation to AC₅₀ values based on ATP data at the day 7 treatment. By tracking NanoLUC data over time, a time-related development of the effects on stromal cells in all microtissue models was detected for compounds that showed medium to high potencies based on ATP data. Taking into account pharmaco- and toxicokinetic aspects, two different therapeutic windows were modeled. NanoLUC data at day 3 represented acute toxicity of the compounds, whereas NanoLUC data at day 7 represented chronic toxicity. The therapeutic window classification in terms of acute toxicity revealed that all the top 10 compounds had to be considered as having an infinite therapeutic window. This means that for these compounds no significant effect on the fibroblasts has been verified in reference to the vehicle control. In general, only few compounds did have a toxic effect on the fibroblasts after 3 days of treatment. In contrast to that, the therapeutic window narrowed for most compounds due to chronic toxicity after 7 days of continuous treatment. Therefore, compounds that were listed within the top 10 compounds after 3 days of treatment did not show up anymore within the top 10 classification for chronic toxicity. Nevertheless, there are some compounds, for example, TAK-733, dasatinib, and Torin-2, which showed a broad therapeutic window even after 7 days of treatment, which indicates very specific targeting of the cancer cells. However, a high target selectivity does not necessarily imply a good clinical safety profile for a drug.²⁶ The question remains of which endpoint(s) or combinations thereof are more relevant to be considered for a transition into clinical testing and how to best mimic dosing schedules. It has been shown that continuous dosing over 7 days does not comply with clinical dosing regimens. Romidepsin, for example, was administered every 28 days on days 1 and 15 in a clinical phase II trial with pancreatic cancer patients.²⁷ The recommended dosing schedule for carfilzomib was set to intravenous infusions of 2–10 min twice a week.²⁸ However, determining the optimal dosing of targeted therapy is challenging.²⁹ It is currently being discussed whether higher drug doses with short exposure times, or lower doses with longer exposure times are beneficial.³⁰ The fact is that a continuous 7-day treatment does not reflect the in vivo procedure, and it is very likely that a temporal dosing in vitro would not affect the fibroblasts as severely as a continuous treatment. In addition, since cancer is a heterogeneous disease and tumor growth does not depend on one single target, future treatment strategies will be based on a combination of different therapies targeting the hallmarks of cancer.

Supplemental Material

Weydert_3D_TI_Supplementary_Figure_Legend_rev – Supplemental material for A 3D Heterotypic Multicellular Tumor Spheroid Assay Platform to Discriminate Drug Effects on Stroma versus Cancer Cells

Supplemental material, Weydert_3D_TI_Supplementary_Figure_Legend_rev for A 3D Heterotypic Multicellular Tumor Spheroid Assay Platform to Discriminate Drug Effects on Stroma versus Cancer Cells by Zoe Weydert, Madhu Lal-Nag, Lesley Mathews-Greiner, Christoph Thiel, Henrik Cordes, Lars Küpfer, Patrick Guye, Jens M. Kelm and Marc Ferrer in SLAS Discovery

Footnotes

Acknowledgements

We thank the R&D team of InSphero for supporting the work, Promega for providing the NanoLUC expression cassette, and Lonza Cologne for the 4D-Nucleofector transfection unit. The work at NCATS was funded by the NIH Intramural Research Program.

Supplemental material is available online with this article.

Declaration of Conflicting Interests

The authors disclosed the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Zoe Weydert, Patrick Guye, and Jens M. Kelm were employed by InSphero AG, and their research and authorship of this article was completed within the scope of their employment with InSphero AG.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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