Evaluating Drug Response in 3D Triple Negative Breast Cancer Tumoroids with High Content Imaging and Analysis

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Two-dimensional (2D) cell culture remains the primary method of drug screening, despite being considered less physiologically relevant than three-dimensional (3D) culture. Increased complexity and technical challenges of 3D systems have limited its widespread adoption as a primary screening method. In this study, we demonstrate methods for increased throughput, imaging and automation in 3D assays that are suitable for compound screening using patient-derived samples. In addition, we show analysis approaches and descriptors that allow gain more information about disease phenotypes and compound effects. We measured responses to drug treatment in 3D tumoroids for cytotoxicity and altered morphology. Tumoroids were formed from primary cells isolated from a patient-derived tumor explant, TU-BcX-4IC, that represents metaplastic breast cancer with a triple-negative subtype and treated with 165 compounds, of approved cancer drugs, at multiple concentrations. We characterize multiple quantitative descriptors for tumor phenotypes and compound effects. Cell Painting method was used for 3D tumoroids for evaluation of phenotypic effects. Eight compounds were detected that demonstrated effects at low concentrations (10nM), including romidepsin, trametinib, bortezomib, carfilzomib, panobinostat, which will be further investigated as potential drug candidates. triple negative breast cancer 3D assays drug treatment imaging cell paint high content imaging assay automation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The current state of the drug development pipeline reflects the need for better research models. According to estimates of clinical trial success rates, only 13.8% of drugs entering Phase 1 are ultimately approved by the FDA 1,2 . This rate is even lower for oncological drugs, with an average rate of approval of 3.4% per year, based on data from 2000–2015 1,2 . For all these drugs, there was strong evidence of their efficacy in laboratory settings with tissue culture models, but roughly 96% of them failed to be both efficacious and safe in humans. This raises the question: why is there strong laboratory evidence for drugs that do not effectively treat tumors in the human body? One factor that may be contributing to this is the traditional use of two-dimensional (2D) cell culture rather than three-dimensional (3D) culture during drug discovery and screening. 2D cell culture can be faster, less costly, and relatively easier to interpret, compared to 3D. However, there is increasing evidence that 2D culture substantially alters the physiological properties of cells compared to 3D, due to the different microenvironmental cues that exist in both culture methods 3–5 . 2D culture enforces apical-basal polarity, which is not physiologically relevant for more mesenchymal cell types, and in some circumstances has been shown to alter sensitivity to apoptosis 5 . Two-dimensional culture creates single-cell monolayers, which alters diffusion of nutrients, gasses, and drugs, affecting both cell behavior and metabolic activity 4 . Furthermore, many researchers have demonstrated differences in cell proliferation rates, gene expression, and differentiation in 2D vs. 3D cultures 6 . These deviations from the true in vivo behaviors of cells may lead to Type 1 and Type 2 errors during 2D drug screens, thus ruling in drugs that are clinically futile, and ruling out drugs that would otherwise prove effective. In the context of cancer, there is increasing evidence that 2D culture is insufficient to evaluate the effects of drugs. In breast cancer specifically, there is evidence that culturing methods affect response to drug treatment. El-Feky et al 3 found that MCF7 cells cultured in 2D monolayers and 3D spheroids had differential response to FAC-based chemotherapy (5-fluorouracil, Adriamycin (doxorubicin), cyclophosphamide), with 2D cultures showing a greater reduction in viability. Additionally, Imamura et al 7 demonstrated that 3D spheroids of breast cancer cell lines (BT-549, BT-474 and T-47D) had greater resistance to doxorubicin and paclitaxel compared to 2D. However, these studies were only able to examine a limited number of chemotherapeutic agents, at limited concentrations, thus demonstrating the need for more robust, high-throughput drug screens using 3D models of breast cancer. New advanced models that include primary derived tumoroids and 3D cultures present significant technical challenges that have prevented their adoption as the predominant methods of cell culture for drug discovery and screening. 3D micro-tissues take longer to form, they are more heterogeneous and fragile, and they are more complicated to image and analyze, all of which increases the time and complexity required to perform experiments. The increased complexity of cell models requires the development of new methods that overcome these challenges and provide the best information from that complexity. It is also extremely important to develop new models for specific cancers that are derived from primary patient tumors. The use of patient-derived tumor tissues has transformed the field of drug and target discovery research, providing a translational tool and physiologically relevant system to evaluate tumor biology 1, 2 . An example of this method is the use of patient-derived organoids (PDO) for oncology research 2, 8, 10–12 . Patient-derived tumor organoids exhibit the heterogeneity of tumor tissues and presence of cancer stem cells (CSC) that can be expanded over multiple passages to produce large numbers of tumoroids (derived from isolated cells) or organoids (derived from digested tumors), that maintain molecular characteristics of the original tumor 10,12 . Primary-derived cell lines represent a variety of cell subtypes present in the tumor, as well as the different mutations involved and the degree of malignant transformation. Development of various cell lines for breast cancer, or other cancers, that represent disease subtypes is critical for finding drugs or drug combinations that would be effective against that specific cancer, or even for that specific patient. Study and characterization of patient derived tumor cells is an active area of investigation, and importance is increasing with the need to develop patient-specific therapies. However, drug testing in patient-derived tumor cells is not widely adopted because of the additional technical challenges, related to difficulties in expansion, handling, and maintenance of primary-derived tumoroids. In this study, we used a patient-derived 3D cell model representing a rare drug-resistant breast cancer subtype, which allows us to model the cellular and molecular complexity and diversity of breast cancer. In addition, we present a variety of methods for automation and high-content analysis adapted for the culture and drug screening of primary tissue-derived 3D cell models. Finally, we have tested a library of approved anti-cancer drugs and defined several potentially promising candidates for treatment of this specific cancer type. Results Cell model . In the present study we developed a compound screening method that uses primary tissue -derived tumoroids. Tumoroids were formed from TU-BcX-4IC cell line derived from primary tumor samples as previously described 12–14 . Briefly, patient-derived tumor samples were implanted into SCID mice, serially passaged, and then expanded in 2D culture 12 . TU-BcX-4IC represents metaplastic breast cancer with a triple-negative breast cancer subtype and is an example of a highly heterogeneous phenotype of breast cancer. TNBC tumors have an aggressive clinical presentation due to high rates of metastasis, recurrence and chemoresistance. The original patient’s tumor for the TU-BcX-4IC model exhibited rapid pre-operative growth despite conventional combination therapy with Adriamycin (doxorubicin), cyclophosphamide and paclitaxel. Tumoroids were formed from 2D TU-BcX-4IC cells as described in Methods. Development and Optimization of the Live Cell High-Content Assay with 3D Spheroid Cultures. In the present study, we have focused on method development for drug screening using primary-derived tumor cells in 3D culture. The goal of this study was to develop and evaluate fast, accurate, and reproducible high-content imaging methods to investigate effects of anti-cancer compounds on the morphology and viability of 3D cultures using live and fixed cells. In this study, we evaluated and optimized the workflow while characterizing several endpoint assays to test for general and mechanism-specific cytotoxicity of anti-cancer drugs (Fig. 1 ). We used low-attachment, U-shaped, black-walled, clear-bottom 384-well plates to simplify cell culture, compound addition, and imaging 15,16 . Cells aggregate at the bottom of the U-shaped wells and form tumoroids centered in the well within 48 hours. The thin plastic bottom of each well aids focusing and image acquisition with standard automated imaging systems. Entire tumoroids can be captured in one 10X or 20X image. In preliminary tests, we studied reproducibility of spheroid formation and dependence of the spheroid size on the number of plated cells. Cells were plated at different densities (500–8,000 cells/well), incubated for 48 h, and then imaged using TL imaging. We found by visual assessment that a plating density of 2,000–3000 cells/well resulted in consistent tumoroid size and shape, with sizes suitable for image acquisition and analysis (diameter ~ 200–300 mm). A plating density of 2000 cells/ well was used for subsequent assays. At this density, the average tumoroid maximum diameter after 2 days was consistent as measured by transmitted light (TL) imaging, with a value of 270 +/- 37 mm (n = 96) yielding a coefficient of variation of 13% (Fig. 2 ). Tumoroids were then treated with 168 compounds from the NCI (National Cancer Institute) library of approved anti-cancer drugs. Five concentrations were used for testing: 10nM, 100nM, 1mM, 10mM and 100mM. During screening, each compound was tested in duplicate, with one concentration per plate. In addition, positive and negative controls were included in the test plates. Positive controls included romidepsin, a compound that has shown high efficacy in previous tests 14 . Negative controls included acetaminophen, as well as multiple replicates of DMSO and media controls. Automation using a liquid handler was used for compound dilutions, cell treatments, and staining. The schematic diagram of the process is shown in Fig. 1 . During incubation with compounds, tumoroids were monitored daily using TL imaging. Image analysis allowed characterization of tumoroid size, diameter, compactness, and integrity, as well as optical density (Fig. 2 b, Supplemental Fig. 2). Images of tumoroids were taken prior to compound treatment, and during day 3 and day 5 of compound treatment. For the end point assay, tumoroids were stained with viability and nuclear dyes on day 5 of compound treatment. Cells were stained with a combination of Hoechst, calcein AM, and Ethidium Homodimer III (EthD-III) and analyzed using high content imaging for complex phenotypic analysis; a subset of plates was set aside for additional assays including Cell Painting. High Content Imaging and Automation of the Screening Workflow . The main method used for evaluation of phenotypic changes was confocal fluorescent imaging of tumoroids using an automated imaging system, the ImageXpress confocal imaging system (Fig. 2 a). The method was previously described in Sirenko 2015 16 . Images of organoids were taken with a 10X objective using Z-stack with a step size of 8 mm. Nuclei, live cells, and dead cells of stained cells were detected using DAPI, FITC, and TexasRed channels respectively. Maximum projection images were created from Z-stacks and analyzed as described in Methods. Multiple read-outs were generated characterizing spheroid area, diameter, intensities, and cell viability. In addition, cells inside each spheroid were counted using a nuclear stain. This last output was found to produce the best results. 3D culture, treatment, and end point assays are notably more complex than traditional 2D culture assays. Automated imaging and analysis of organoids are important for quantitative assessment of phenotypic changes in organoids, and for increasing throughput for experiments and tests. We built an automated, integrated system that allows monitoring, maintenance, and characterization of growth and differentiation of organoids and stem cells, as well as testing the effects of various compounds (Fig. 1 ). Suspended cells were plated into U-shape 384 well plates using a Biomek i7 automated liquid handler, incubated for 48 hours, and then treated with compounds, stained, and imaged. The protocols for compound additions, media, and staining included gentle media dispensing processes. Consecutively, tumoroids were cultured and monitored by automated imaging. Additionally, use of a collaborative robot enabled moving plates from the incubator to the imaging system, where transmitted light imaging or fluorescent imaging protocols were initiated. Observations of Phenotypic Changes and Criteria for Hit Selection. Phenotypic changes for selected compounds are shown in Fig. 2 . The phenotypic readouts included cell viability assessment and characterization of organoid and cell areas, and total cell count, along with characterization of nuclear area and fluorescence intensity. Advantages of this method included simplicity, reduced cost, and ease of workflow. In contrast to previous 3D spheroid studies performed using cell lines 16 , 3D tumoroids formed from primary cells (TU-BcX-4IC cell line) did not increase in size during culture. However, upon drug treatment we observed concentration-dependent disintegration of tumoroids (Fig. 2 ), as well as a decrease in the number of viable, Calcein AM positive cells and a increase in the number of dead, EthD-III positive cells. We have previously characterized multiple quantitative descriptors that could be used for studying tumor phenotypes and compound effects, including characterization of size and integrity, cell morphology and viability, as well as determining the presence of various cell markers 16 . However, since the phenotype of organoid disintegration was the most prominent in this study and was observed at lower concentrations than the increase in the number of dead cells, we used that read-out to characterize concentration dependencies and compare effective concentrations for different compounds. For screening the compound library, we used maximum projection images as shown in Fig. 3 . Spheroid disintegration is visible as an increase of spheroid size, in contrast to nicely shaped, round images of intact spheroids. Tumoroid disintegration can be described by several measurements: for example, increase in organoid area/diameter. Most accurate measurements were done by counting cells or nuclei inside organoids using maximum projection images. As seen in Fig. 3 , compact tumoroids had a smaller projected area and also a lower nuclear count in the projection images (yellow dots). Disintegrated tumoroids are made up of cells which are not held together in a tight aggregate but instead are loosely piled up in the well bottom resulting in 1) an increased projected area and 2) an increase in nuclear count in the projection images. It is important to note that the apparent increase in cell number represents a feature of analysis using 2D projection images and the fact that tumoroids are falling apart and the individual cells are not tightly associated with each other. While counting cells in 3D instead of using 2D projection would better reflect the true cell count (data not shown), that would not allow quantitation of organoid disintegration. This method is better suited for screening experiments because of the reduced demand for data storage and reduction in time for 3D analysis. Simple nuclei count analysis in projection images was used as a reliable surrogate marker for organoid integrity/dis-integration. Therefore, in the analysis of projection images, we used the increase of nuclear count as a read-out for tumoroid dis-integration (Supplementary Table 1). Notably, selected drugs (e.g. doxorubicin and several others) resulted in nuclei damage, and as a result, a decrease in counted nuclei. We used AVERAGE +/-2STDEV from the DMSO samples to flag affected wells. The hits were confirmed through referencing the primary images to exclude experimental artifacts (e.g. focus failure, pipetting errors, rolling the object off center, etc) which accounted for approximately 1% of wells. Results from Library Screening and Secondary Screen. We tested the effects of 168 compounds from the NCI library with 5 concentrations listed above. Then we used assessment of tumoroid integrity by nuclear count to identify hits across different concentrations. Several drugs were identified that demonstrated efficacy of targeting tumor subtypes resistant to traditional cancer therapy. Drugs listed in Table 1 demonstrated efficacy by affecting tumoroid phenotypes at indicated concentrations (same concentrations had efficacy at higher concentrations). Interestingly, several compounds were found to have efficacy at low concentrations, which may be valuable for the development of potential therapeutics. Those include romidepsin, dactinomycin, plicamycin, bortezomib, and dasatinib. A total of 33 compounds were selected as “hits” for concentrations 10-1000 nM, while an additional 29 compounds had significant effects at 10 mM. (Supplemental Fig. 1). Because greater than 30% of compounds had some effects at a concentration of 100 mM, this data point was considered less informative. Table 1 Compounds effective in screening assay at low concentrations. Compounds were selected as described in Results section. Compound Effective concentration Mechanism of action Dactinomycin 10nM Intercalation of DNA Plicamycin 10nM DNA/RNA polymerase inhibitor, hepatotoxicity led to discontinue in 2000 Romidepsin 10nM HDAC 1/2 inhibitor Bortezomib 10nM Inhibits the catalytic site of the 26S proteasome Fulvestrant 10nM Selective Estrogen receptor degrader Dasatinib 100nM Tyrosine Kinase Inhibitor for BCR/Abl fusion Mitotane 100nM Steroidogenesis inhibitor used for adrenal carcinoma and Cushing’s Vinblastine sulfate 100nM Binds tubulin, disrupts microtubules Vincristine sulfate 100nM Binds tubulin, disrupts microtubules Carfilzomib 100nM Inhibits the 20s proteasome Trametinib 100nM Inhibits MEK1 and MEK2 Ixazomib citrate 100nM Inhibits proteasome subunit beta 5, same mechanism as bortezomib, related to carfilzomib Ponatinib 100nM Inhibitor for BCR/Abl fusion, second line after dasatinib or imatinib Belinostat 100nM Pan- HDAC inhibitor Panobinostat 100nM Pan- HDAC inhibitor Regorafenib 100nM Tyrosine Kinase Inhibitor of VEGFR-TIE2 Bosutinib 100nM Tyrosine Kinase Inhibitor of Abl and Src kinases Copanlisib tris-HCl 100nM PI3K inhibitor Mitomycin 1uM Alkylating agent- DNA crosslinking Uracil mustard 1uM Alkylating agent- DNA crosslinking Omacetaxine 1uM Inhibits translation (blocks tRNAs) Daunorubicin hydrochloride 1uM Intercalation of DNA Vandetanib 1uM Inhibits VEGFR, EGFR, RET Paclitaxel 1uM Stabilizes microtubes, inhibits spindle formation Ibrutinib 1uM Inhibits Bruton's tyrosine kinase Idarubicin hydrochloride 1uM Daunorubicin analog Venetoclax 1uM Inhibits BCL2 Epirubicin hydrochloride 1uM Intercalation of DNA Fedratinib 1uM JAK2 selective inhibitor Temsirolimus 1uM Inhibits mTOR Vorinostat 1uM Inhibits HDAC1, HDAC2, HDAC3, HDAC6 Brigatinib 1uM Inhibits ALK, EGFR Gilteritinib 1uM Inhibits FLT3 receptor To confirm our findings in the screening assay, a subset of compounds was selected for secondary follow-up analysis. 10 compounds, including panobinostat, carfilzomib, and bortezomib were tested across 7 concentrations in the 1-10000nM range. Spheroids were treated for 5 days with these compounds, then stained as described above (Fig. 4 ). Dose-dependent cell death and disintegration of microtissues was observed. EC 50 values (Table 2 ) were determined using tumoroid disintegration measurement (nuclear count in projection images). All tested compounds showed similar activities as observed in the initial compound screen. Table 2 EC 50 values of indicated compounds in 3D assay determined by 7-point concentration curves. Compound EC50, nM Bortezomib 17 Carfilzomib 16 Copanlisib 28 Romidepsin 36 Panobinostat 54 Trametinib 70 Omacetaxine < 100 Carfilzomib 1000 Sunitinib 1700 Taxol 2900 Celecoxib no effect Monitoring phenotypic changes using transmitted light. Notably, changes in tumoroid phenotypes upon compound treatments were also observed with TL imaging, and artificial intelligence (AI) tools were used to identify and classify different tumoroid phenotypes. We used a deep learning-based segmentation (IN Carta Image Analysis Software) approach to find all tumoroid objects in transmitted light, either intact or affected. Following segmentation and data extraction, we utilized a machine learning tool to classify all tumoroids into intact, intermediate or severely affected categories. Affected tumoroids were flagged by increase in the object areas (criteria were set as AVERAGE ± 2*STDEV from the DMSO-treated), allowing us to detect effective compounds (Supplemental Fig. 2, Supplemental Table 2). An advantage of this method is that compound effects could be interrogated in a label-free manner during multiple time points. However, the analysis allowed some ambiguity in determining accurate areas of the objects, especially when the phenotypic changes were weak or moderate (see Supplemental Fig. 2). The endpoint fluorescent imaging and analysis enabled more robust measurements and compound selection (Supplemental Tables 1 and 2). Evaluation of phenotypic changes by Cell Painting Assay. For a more in-depth investigation into cytotoxic mechanisms elicited by the compounds assayed, other analysis methods were performed in parallel to fully characterize phenotypic changes detected. The Cell Painting assay was adapted for 3D tumoroids for the evaluation of compound effects on tumoroid phenotype. The method uses up to six fluorescent dyes to label eight cellular components or organelles: nuclei, nucleoli, RNA, endoplasmic reticulum, mitochondria, plasma membrane, Golgi and cytoskeleton 17–20 . This method, which was developed for use in 2D assay systems has been successfully used for phenotypic profiling to provide insights into functional genomics applications and mechanism of action of novel compounds, and to reveal subtle effects of various drugs and small molecules on cell health 17–18 . Here, we adapted and further developed the Cell Painting assay for 3D cell culture models. For phenotypic profiling, the tumoroids were treated with a single 10µM concentration of compound, using the methods described above. Notable changes in the staining protocol included increased times for dye incubation, fixation, and permeabilization. A challenge of the assay is to minimize the crosstalk of multiple dyes, which we achieved by using lasers as the illumination light source, with a narrow spectrum for each wavelength and minimum overlap between stains. In the original Cell Painting assay, the Golgi and actin filament images were acquired in the same imaging channel. This created additional hurdles, as it was challenging to extract measurements from the Golgi compartment separately from the actin structures. To improve the resolution between the Golgi and actin compartments, we swapped out Alexa Fluor 568 for Alexa Fluor 750 phalloidin, which allows for the cytoskeleton to be imaged in a different channel from the Golgi compartment by using the far-red laser on our imager. Images were acquired with Z-stacks using 20x magnification, and the analysis was carried out on projection (maximum) images. Unlike the previous analysis that included cell count or live-dead evaluation, multiple read-outs were collected from the whole spheroid to form their phenotypic profile. Because the added compounds affected staining patterns, we were unable to achieve good spheroid segmentation based on the fluorescent images. Instead, the spheroid images were also acquired in transmitted light (bright field), and these images were used to identify the spheroid structures using a deep learning-based image segmentation approach which improved the analysis. Features extracted from each spheroid included object morphologies, intensities, and texture measurements for the six cell stains and bright field images. 202 measurements per spheroid were uploaded into StratoMineR software (CoreLife Analytics) for data analysis to identify hits and for cluster analysis based on similarity of their phenotypic profiles. Figure 5 presents a dendrogram showing clustering of tested compounds based on phenotype similarity of the spheroids. To determine hits from the assay, a phenotypic distance score was calculated based on the PCA (principal component analysis) components 21–22 . This score is a measure of the phenotypic effect of the compounds on the tumoroids relative to the controls. All hits were then clustered based on their phenotypic profiles using the normalized principal component scores. 24 hits (Table 3 ) were identified (p < 0.05) as being significantly different from the DMSO control tumoroids (Fig. 5 ). We obtained a reasonable correlation between the viability assay and the Cell Painting assay. From the Cell Painting assay, 67% (16), or two-thirds of the hits were also identified in the viability/spheroid disintegration assay. Hits from the viability assay were found mostly in the same clusters (clusters 1,2 and 6, Fig. 5 ), suggesting that these spheroids display phenotypic profiles that are associated with most notable cytotoxicity. For example, all compounds in clusters 1 and 2 are hits also flagged in the viability assay, and they are known to affect topoisomerase 2. The other hits from the Cell Painting assay included compounds with less obvious cytotoxic effects, but these hits were still phenotypically different from the controls and interestingly, many of these compounds are known to affect various protein kinase pathways. These results are consistent with the fact that while cytoxicity changes phenotypes very dramatically, more subtle changes can be detected using phenotypic profiling. Table 3 Compounds effective in the Cell Painting assay. Compounds selected as described in the Results section. Compound (10µM) Distance Score p-value Cluster ID Viability HIT? Dabrafenib mesylate 6.248831502 0.014165 5 yes Encorafenib 5.516036128 0.015028 5 no Avapritinib 5.386292153 0.015154 6 no Abemaciclib 5.819034729 0.006458 4 no Larotrectinib 5.666943776 0.0095 4 no Valrubicin 8.136000201 3.22E-05 1 yes Mitoxantrone 6.687853572 0.000375 2 yes Epirubicin hydrochloride 5.883205304 0.012997 2 yes Afatinib 5.046845431 0.026011 7 yes Copanlisib tris-HCl 5.146853912 0.037712 7 yes Ibrutinib 4.93823624 0.04635 7 yes Belinostat 5.348047631 0.017714 7 yes Vandetanib 5.184617965 0.0292 7 yes Entrectinib 4.84271406 0.046947 7 yes Gilteritinib 5.23882208 0.045371 7 yes Brigatinib 5.771498629 0.021974 7 yes Osimertinib 5.241373452 0.024226 7 yes Carfilzomib 5.006962745 0.036124 7 no Temsirolimus 5.699077848 0.026386 8 no Vorinostat 5.078731963 0.036114 8 no Alpelisib 5.833763084 0.003154 3 yes Plicamycin 5.134541481 0.034984 3 yes Selinexor 5.004358366 0.031059 9 no Venetoclax 4.921513854 0.034861 9 yes Evaluation of Cell Viability Using ATP Read-Outs. Cell metabolism measured by ATP level is another readout for effects of anti-cancer drugs that is related to cytotoxicity. A subset of 12 compounds were used for evaluation of compound effects, using the CellTiter-Glo 3D assay (Promega), which detects ATP levels. Compounds were tested using 7 point 5x dilutions starting from 100uM. We compared compound effects on 2D culture and 3D cultures, using imaging and CellTiter-Glo 3D methods. Cultures were set up in parallel, using same cell number (2000 ). Cultures were allowed to grow for 48 hours, then treated with compounds for 5 days. EC 50 s calculated from concentration dependencies are presented in the Table 4 . Data shows that there is significant consistency between EC 50 s obtained by imaging and CellTiter-Glo 3D readouts, while EC 50 s between 2D and 3D cultures varied for a number of compounds. Table 4 Comparison of EC 50 values obtained from 2D and 3D assays by imaging or ATP assays. Drug EC50, mM ATP 3D Imaging 3D ATP 2D Imaging 2D Plicamycin .018+/-.02 0.156 < .01 < .01 Dactinomycin 0.005 0.033 < 0.01 < .01 Idarubicin .145 +/- 0.012 3.93 .067+/-.009 ~ 1 Temsirolimus no fit 8.52 +/-10.37 no fit 10 Bortezomib .015+/-.001 0.07 0.01 .01+/-0.002 Trametinib 2.62+/-1.214 8.82 7.33+/-4.31 6.27+/-3.55 Panobinostat .084+/-.016 0.141 .046+/-.009 .006+/-.015 Ibrutinib 15.76+/-14.87 ~ 20 25+/-2.4 20.2 Carfilzomib 5.82+/-2.24 0.073 1.04+/-.10 1.05+/-.31 Romidepsin ~ .01 < 0.01 ~ .01 ~ .01 Cell Metabolism Dynamics. Tumoroids treated with four compounds, paclitaxel, romidepsin, doxorubicin, and trametinib, were analyzed for lactate secretion. Elevation of lactate typically suggests a switch to aerobic glycolysis; tumor cells metabolize glucose into lactate even in the presence of high oxygen 23 . Recent studies revealed that metabolic alterations of cancer cells play important roles in chemo-resistance in breast cancer, and exposure of cancer cells to chemotherapeutics induces metabolic reprogramming toward increased glycolysis and lactate production 24,25 . Therefore, monitoring lactate production over the course of treatment in conjunction with other response endpoints provides valuable information for understanding the dynamics of metabolic perturbations associated with drug response and resistance. Compound treatments and secretion of lactate were studied using a Pu·MA System 26 , a microfluidic-based automated organoid assay platform that allows multistep protocols to be performed without disruption of or damage to tumoroids. Five supernatant samples were collected from treated tumoroids using flowchip automation over a 12-hour period. Two to three independent samples were collected, and supernatants were analyzed for lactate concentration using the Lactate-Glo assay (Promega). The lactate secretion results for the 12hour time point are shown in Supplemental Fig. 3. Two-way ANOVA revealed statistically significant increases in lactate secretion for romidepsin and trametinib. between baseline and measured time points for three out of the four compounds. Our observations are in line with previously published reports showing increased lactate production for breast cancer cells in response to chemotherapeutic treatments 27 . Discussion In this paper, we use a patient-derived cell line representing a rare drug resistant cancer subtype for drug screening. We present results of the library testing of approved anti-cancer drugs that were tested at different compound concentrations, using high content imaging methods. We describe methods for increase of throughput by using automation in 3D cancer assays and compound screening. In addition, we show advanced analysis approaches and descriptors that allow for greater information about complex compound effects. A key strength of our method is our usage of a primary tissue-derived cell model. The standard approach is to use the established immortalized cell lines as well as orthotopic xenograft models 28–35 . Although immortalized cellular models provide invaluable knowledge regarding cancer biology and drug effects on cellular systems, they are limited in their inability to re-create essential features of tumors. More specifically, these models cannot accurately reflect the tumor architecture, three-dimensional structure and alignment of tumor cells, matrix, and surrounding stroma, and cannot reproduce the cellular heterogeneity that is present in the original patient tumor 36–39 . Conversely, orthotopic xenograft models recapitulate the complexity of tumors, but the inability to scale these models limits their use in large drug screens. The methods described in this paper use primary cell-derived models to form 3D spheroids, achieving greater relevance of the results to real biology and allowing for large drug screens to be performed. Beyond the use of patient derived cells, using 3D cultures rather than 2D cultures for screening allows us to closer recapitulate properties of tumors. Cells in 3D spheroids are in close contact with each other, as they would in a human body 28 (Langhans). Also, cells in dense, multicellular micro-tissues have a higher rate of hypoxia compared to 2D monolayers, which has been shown to be associated with drug resistance 7,32 . Drug penetration into the tissue also plays role in drug efficacy, and that factor can be mimicked during screens in 3D models. In general, drug screening and discovery may result in two types of errors: type 1, false positive drugs, or type 2, false negative errors that can ultimately result in loss of drugs that may have proven clinically effective. Using more biologically relevant models would decrease both false positive and false negative types of errors. Immortalized cell lines that have been growing in cell culture for years results in the introduction of irreversible alterations in genetic information and behavioral characteristics that were not present in the original tumor 39 . As a result, those cell lines would be most responsive to anti-proliferative drugs and may not be affected by other drug classes. Cells cultured in 2D have altered morphology and organization of cell surface receptors compared to 3D, which could affect the binding efficacy of drugs and their penetration inside the tumor 10 . Here we demonstrated several assays suitable for medium- or large-scale compound testing. Various analytical methods allowed us to evaluate different aspects of compound effects: tumoroid integrity that is evaluated by measurement of spheroid area or cell count, cytotoxic effects that were evaluated by live-dead stain, and spheroid integrity that was evaluated with just nuclear stain. As expected, we observed a progressive increase in the number of effective compounds with the increase of concentration. Eight compounds were effective at a 10nM concentration, which demonstrated high efficacy and potentially can be considered for drug development. Several of those compounds were kinase inhibitors, which is a promising drug class for cancers resistant to traditional anti-cancer therapies. Interestingly, cell disintegration appeared as an effective read-out at lowest concentration, while the actual increase of dead cells appeared at higher concentrations for the same drugs. That may indicate that loss of adhesion molecules may be occurring, or that subtle changes in viability have resulted in loss of cell-cell attachment. The Cell Painting method is of particular interest due to it unbiased approach, which captures phenotypic changes, independent of cytotoxicity. Information from multiple stains that label various organelles and cellular compartments are used to represent the morphological profile of each cell. For 3D cell painting, due to the proximity of cells to each other, it is challenging to analyze the data at the cellular level. Here, we examine the effects of compounds on the phenotypic profile at the spheroid level. Interestingly, spheroid-level analysis identified not only cytotoxic compounds but also compounds that have significant phenotypic effects Cluster analysis grouped compounds with similar mechanisms of action, suggesting that spheroid-level analysis is sufficient for assessing compound effects. While Cell Painting and toxicity analysis showed the same hits for strong compounds, compounds with more subtle effects showed different results between the two assays, reflecting toxicity vs. other phenotypic changes. There is an unmet need for greater accuracy in drug screening for cancer, especially in the expanding area of personalized medicine. We describe approaches that can overcome technical challenges and make feasible drug screening that would be suitable for specific cancer subtypes or even individual patients. Through automation, large-scale drug screening using complex models, including tumoroids derived from individual patients, is feasible. In the future, this method would be suitable for screening other cancer subtypes that form tumoroids, such as colon and other cancers. Additionally, through the greater biological relevance and high-throughput nature of this system, other compound libraries can be screened. Methods 3D Cell Culture and Drug Treatment The methods for generating tumoroids and PDX organoids (PDXO) have been previously described 12 (Matossian, et al., 2021). The primary tumor sample was implanted into SCID/Beige mice and exhibited rapid tumor growth, reaching maximal tumor volume (> 1000 mm 3 ) in 14 days. Then, a cell line generated from that sample was expanded in 2D culture (TU-BcX-4IC). Tumor spheroids, which we called tumoroids in this study, were formed from TU-BcX-4IC cells expanded in 2D. To form 3D tumoroids, TU-BcX-4IC cells were dispensed at ~ 2,000 cells per well (in U-shape low attachment 384-well plates, Corning) and incubated for 48 hours until they formed tight tumoroids. 4IC cells were cultured with Advanced DMEM supplemented with glucose, non-essential amino acids (NEAA), 2mM glutamine and insulin 120µg/L, 10% FBS (Gibco 12491-015). For metabolic assays, tumoroids were cultured with DMEM + 10% dialyzed serum (2mM glutamine, 5mM glucose, without phenol red). Tumoroids were treated with compounds as follows. Compound libraries of approved anti-cancer drugs were obtained from NIH. Compound dilution plates were prepared using the Beckman liquid handling system. Five compound dilutions (20nM, 200nM, 2uM, 20uM and 200uM) were prepared in 384 well plates. Then 50ul of compound mixes were added to the tumoroid culture plates (50uL) using a liquid handler programmed for slow dispensing of liquid. The final concentrations of compounds were 10nM, 100nM, 1uM, 10uM, and 100uM. DMSO concentrations on each plate were matched for 0.05%, with the exception of 100uM plates that had 0.5% DMSO. Each plate contained 32 DMSO only wells. Oncology Drug Set and cell treatments TU-BcX-4IC cells were treated with the commercially available NCI Approved Oncology Drug set for 5 days total. On day 3, 50% of media was replaced with the fresh compound solutions. Cell cultures were treated with five concentrations, one concentration per plate, each compound in duplicates. Solution controls (DMSO) as well as a positive control (romidepsin) were included in each plate. 3D Cell Monitoring and Imaging Transmitted light (TL) and fluorescent (FL) images were acquired on the ImageXpress Confocal HT.ai High-Content Imaging System (Molecular Devices) and images analyzed using MetaXpress High-Content Image Analysis Software. Tumoroid images were acquired in TL with approximately 60 µm offset. Z-stack images were acquired with the 10X objectives using confocal mode. Best focus projection images were used for TL analysis, and maximum projection images were used for FL analysis. MetaXpress Software or IN Carta Image Analysis Software was used for analysis. Automation of 3D Cell culture and Imaging Protocols Automated imaging and analysis of organoids are important for quantitative assessment of phenotypic changes in organoids, and for increasing throughput for experiments and tests. We built an automated, integrated system that allows for automated monitoring, maintenance, and characterization of growth and differentiation of organoids and stem cells, as well as testing the effects of various compounds. The automated system includes the ImageXpress Confocal HT.ai system and MetaXpress software (Molecular Devices), automated CO 2 incubator (LiCONiC), Biomek i7 Automated Workstation (Beckman Coulter Life Sciences), collaborative robot and rail. Robotic automation was enabled by Green Button Go Scheduler (BioSero). Multiparametric live cell toxicity assay The method for imaging and high content analysis of 3D spheroids was previously described 14–16 . Briefly, following incubation with test compounds, spheroids were stained with a mixture of three dyes: 1 µM calcein AM, 3 µM EthD-III, and 33 µM Hoechst 33342 (Life Technologies, Carlsbad, CA). For selected experiments spheroids were also stained with E-cadherin (Cell Signaling Technology #3195, Danvers, MA) and CD44 (BioLegend #338,808, San Diego, CA). Automated staining of spheroids was performed for one hour. After staining, dye solution was replaced with 1X PBS. High-content imaging Images were acquired using the ImageXpress Micro Confocal High-Content Imaging System (Molecular Devices, San Jose, CA), as previously described 15 with a 10X Plan Fluor or 20X Plan Apo objective. DAPI, FITC, and Texas Red filter sets were used for imaging. A stack of 7to 15 images separated by 10to 15 µm was acquired, starting at the well bottom and covering approximately the lower half of each spheroid. Typically, a Z-stack of images covered 100–200 µm for spheroids. Image analysis was performed either in 3D or using the 2D Projection (maximum projection) images of confocal image stacks. Transmitted light images were used for cell culture monitoring or protocol optimization. To image the organoids labeled with the cell painting dyes, the ImageXpress Confocal HT.ai system, equipped with a laser light source, was used for image acquisition. The confocal mode (pinhole size 60µm) was used to image organoids with a 20x Plan Apo Lambda, NA 0.75 objective, with z-stacking enabled (5µm step size). The maximum projection image was selected in the acquisition setup. Images were captured in six fluorescent channels and in transmitted light (bright field) in order of increasing fluorophore excitation wavelength to reduce crosstalk (DAPI 405/452, FITC 467.5/520, YFP 520/562, Texas Red 555/624, Cy5 638/692 and Cy7 725/794). Organoid image analysis Images were analyzed using MetaXpress Software (Molecular Devices). Count Nuclei or Cell Scoring application modules were used for nuclear count live/dead assessment, or evaluation of cell number positive for specific markers. Output measurements included spheroid width, spheroid area, average intensity for calcein AM or EthD-III, counts of all nuclei, and evaluation of average nuclear size and average intensities. In addition, calcein AM-positive cells were counted, and their area and intensity values were recorded. In addition to cell count, areas and intensities could be determined for live (EthD-III negative) and dead (EthD-III positive) cells. EC 50 values were determined using a 4-parameter curve fit in SoftMax Pro 7 Software (Molecular Devices) (Sirenko, 2016). For the Cell Painting analysis, image analysis was done in the IN Carta image analysis software. To segment the organoids, a deep learning-based model was created based on the TL channel to segment individual organoids. Following segmentation, measurements that include intensity, shape, area, and texture were extracted from all 6 fluorescent channels. In total, 210 analytical features were obtained from each organoid. Cell Painting Assay and Data Analysis For the Cell Painting assay 17–20 , the 3D TU-BcX-4IC organoids were labeled using a protocol modified from Bray et al. The plate was first incubated with MitoTracker DeepRed (500nM) for 2 hours. The samples were fixed with 4% paraformaldehyde (PFA) in HBSS for 60 minutes. All wash steps were carried out by exchanging half the volume in each well with HBSS to minimize displacement of the organoids from the center of the well. Following fixation, samples were washed three times with HBSS. For permeabilization, samples were incubated with 0.1% Triton X-100 (in HBSS) for two hours at room temperature and washed with HBSS. The dyes were prepared in HBSS and 1% BSA (wt/vol) and incubated overnight with the following final concentrations: Hoechst (15µg/ml), Concanavalin A-488 (250µg/ml), Syto14 (7.5µM), Phalloidin 750 (15µl/ml), WGA (3.75µg/ml). Measurements from IN Carta were exported as a CSV file and uploaded into StratoMineR (Core Life Analytica), a web-based data analytics platform for hit picking and phenotypic clustering 22 . Briefly, the dataset was normalized against the median of the samples to account for plate-plate variation. Data with a significant skewness (P < 0.0001) was transformed to approximate a normal distribution. Feature scaling using robust Z-score was applied to the dataset. Data reduction was achieved using principal component analysis (PCA). The results of the first 10 principal components were used in downstream analysis. For hit selection, the unsupervised option was selected with reference to the median of the negative controls (p < 0.05). The Euclidean distance (Perlman et al., 2004) of all the vectors to the control was calculated. This approach reduces the data for each component to just one distance score which represents the phenotypic effect of the compound treatment relative to untreated samples. The median of the Euclidean distances for each well based on its replicates are calculated. Statistically significant hits were identified based on P < 0.05. Hierarchical cluster analysis was performed on statistically significant hits based on the principal components using Ward’s linkage criteria (k-means = 8). Lactate Secretion Assay The metabolic response of tumoroids to treatment was determined by measuring lactate secretion in supernatants that were collected from treated and untreated 4IC tumoroids at various timepoints of drug treatment using the microfluidic device Pu·MA System. Supernatant collection was done in the flowchip for each treatment condition in the following way: medium with drug was transferred from an adjacent reagent well of the flowchip to the sample well with tumoroid and incubated for 3 hours. After incubation, the media containing secreted lactate was transferred back to the reagent well it came from and replaced with fresh media with drugs from another well for the next 3-hour treatment. The cycle was repeated 5 times and resulted in collection of 5 supernatant samples. The first cycle was done with medium only (baseline secretion) followed by 4 treatment cycles for the total treatment duration of 12 hours. This approach allows dynamic monitoring of lactate secretion over the course of treatment. The collected supernatants were stored in the flowchips until the end of the Pu·MA protocol, then were collected and stored at -20°C until further processing. The supernatant samples were analyzed for lactate levels using the luminescent Lactate-Glo assay (Promega). Lactate detection reagent was prepared according to the manufacturer’s protocol. Supernatant samples were diluted 1:400 in PBS. 10 mL of the diluted supernatant was transferred to a solid white 384-well assay plate and 10 mL of lactate detection reagent was added to each well. Plates were incubated for 60 minutes at room temperature. Luminescence was measured using a GloMax plate reader (Promega). Each sample was measured in duplicate. Good luminescence signal levels and signal-to-noise ratios were achieved for this assay from single tumoroid samples. The statistical significance of comparison between groups was determined using one-way or two-way ANOVA. Post hoc tests were run to confirm where the differences occurred between groups. Differences with p < 0.05 were considered statistically significant. Declarations Acknowledgements: This project received funding from the National Institutes of Health 1R01CA174785-01A1 (BC-B) and 1R41CA257425-01(MB). The project was also supported by Award Number TL1TR003106 from the National Center for Advancing Translational Sciences, as well as 1 U54 GM104940 from the National Institute of General Medical Sciences of the National Institutes of Health, which funds the Louisiana Clinical and Translational Science Center. Authors are grateful for the support of the Krewe de Pink organization in New Orleans. Finally, but most importantly, we thank the patients who donated their breast cancer tissue specimens to benefit breast cancer research; their contributions are valued and appreciated. The authors also grateful to David Egan from Core Life Analytica and Mishael Bashkurov from Molecular Devices for assistance with data analysis. Authors Contributions: OS, CB, AL, EFC and MB contributed to study design and writing the manuscript; OS, AL, PM, CB generated most of data and performed data analysis; EN, CO, EM, MW, BCB contributed to development of assay model, critical discussions, and revisions of manuscript. Data availability statement: Essential data are included into the Supplemental Tables, all other data available upon request. Oksana Sirenko (a corresponding author), [email protected] , will provide data upon request. Conflict of interest statement: Oksana Sirenko, Angeline Lim, Cathy Olsen are employed by the Molecular Devices, LLC, that manufacture automated imaging systems and image analysis software. Evan Cromwell and Ekaterina Nikolov are employed by Protein Fluidics company that manufactures Pu.Ma microfluidics system. Other authors (Emily C. McConnell, Maryl Wright, Bridgette M Collins-Burow, Matthew E Burow) claim no conflict of interests. 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Evan Cromwell and Katya Nikolov are employed by Protein Fluidics company that manufactures Pu.Ma microfluidics system. Other authors (Emily C. McConnell, Maryl Wright, Bridgette M Collins-Burow, Matthew E Burow) claim no conflict of interests. Supplementary Files SupplementalFiguresSirenkoatal2022.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1859525","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":125460581,"identity":"70ea3aca-70a0-4632-b32a-842b2d37b5cc","order_by":0,"name":"Oksana Sirenko","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBACfh4Ibc8GJBgbGGwYGNgZDPBqkeyB0IltEC1pDAzMBLQYnIHQCQwQLYeJ0XL84eeCGoYEPunDxx7OqDgfzc/MvPExD4OdnG4DDi1ne4ylZxwD+oUvLd1ww5nbuTOb2YqNeRiSjc0O4NBynodBmoeNgbGNh8dM8mHb7dwNh4GMGQwHErfh1ML++DfPP5AW/m9ALedAWsx/4tVytsFMmrcNbAub5Ma2A2BbGD7g0SLZc8bMmrdPIrGNhw3onjPJYL9IfDDA7Rd+nvTHt3m+2djL9zA/k+ypsMvtZ2/e+CGhwk4OlxYokMBwMF7lo2AUjIJRMAoIAAD8GldFVSJTfgAAAABJRU5ErkJggg==","orcid":"","institution":"Molecular Devices, LLC","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Oksana","middleName":"","lastName":"Sirenko","suffix":""},{"id":125460582,"identity":"d960afaa-d8da-438c-98aa-1896329181f2","order_by":1,"name":"Courtney K Brock","email":"","orcid":"","institution":"Tulane University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Courtney","middleName":"K","lastName":"Brock","suffix":""},{"id":125460583,"identity":"5b72b05c-ec4a-46c5-ae28-a1a9020d7f08","order_by":2,"name":"Angeline Lim","email":"","orcid":"","institution":"Molecular Devices, LLC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Angeline","middleName":"","lastName":"Lim","suffix":""},{"id":125460584,"identity":"1f630316-577d-4f3e-8a5a-13fe6bbb9fca","order_by":3,"name":"Prathyushakrishna Macha","email":"","orcid":"","institution":"Molecular Devices, LLC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Prathyushakrishna","middleName":"","lastName":"Macha","suffix":""},{"id":125460585,"identity":"20b20ef4-d32f-47ea-b1b2-ff484d8b90f8","order_by":4,"name":"Ekaterina Nikolov","email":"","orcid":"","institution":"Protein Fluidics Inc","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ekaterina","middleName":"","lastName":"Nikolov","suffix":""},{"id":125460586,"identity":"4fab8461-588f-4c9b-8247-434e233fb1b9","order_by":5,"name":"Cathy Olsen","email":"","orcid":"","institution":"Molecular Devices, LLC","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cathy","middleName":"","lastName":"Olsen","suffix":""},{"id":125460587,"identity":"14e8e1b3-78f3-4a98-b3e7-e0fea03e3850","order_by":6,"name":"Emily C. McConnell","email":"","orcid":"","institution":"Tulane University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Emily","middleName":"C.","lastName":"McConnell","suffix":""},{"id":125460589,"identity":"234b1cf2-b429-4e8c-9ec7-8ffc7d5465eb","order_by":7,"name":"Maryl Wright","email":"","orcid":"","institution":"Tulane University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maryl","middleName":"","lastName":"Wright","suffix":""},{"id":125460592,"identity":"1dcf0d79-9fce-4189-bc87-04bd8bc58919","order_by":8,"name":"Bridgette M Collins-Burow","email":"","orcid":"","institution":"Tulane University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bridgette","middleName":"M","lastName":"Collins-Burow","suffix":""},{"id":125460595,"identity":"e581d93e-8d0f-49b4-8298-3ac6fd504e18","order_by":9,"name":"Evan F Cromwell","email":"","orcid":"","institution":"Protein Fluidics Inc","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Evan","middleName":"F","lastName":"Cromwell","suffix":""},{"id":125460600,"identity":"74a6441d-b824-4907-924d-320787e4ba91","order_by":10,"name":"Matthew E Burow","email":"","orcid":"","institution":"Tulane University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"E","lastName":"Burow","suffix":""}],"badges":[],"createdAt":"2022-07-14 22:44:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1859525/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1859525/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24741679,"identity":"72606d21-8513-4803-b131-99780ff3c4a3","added_by":"auto","created_at":"2022-08-03 18:18:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1017314,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Schematic diagram of the experimental workflow including generation of patient-derived cell line, formation of tumoroids in 384 well U-shape low attachment plates, compound treatment, staining, imaging and analysis.\u0026nbsp;\u003c/p\u003e","description":"","filename":"ScientificReportsJournalfigure120220613.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1859525/v1/3b8df383ccde6f83c762785e.jpg"},{"id":24741084,"identity":"3a5b96e4-5ab9-48aa-ae62-75989b3ff947","added_by":"auto","created_at":"2022-08-03 18:13:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4451432,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Reference images of tumoroids in the following order: tumoroids formed 48h after plating, TL images (10X); composite fluorescent images of untreated tumoroid stained with calcein AM (green), EthD-1 (red) and Hoechst (blue), confocal image, 10x, maximum projection; tumoroids treated with romidepsin (10nM), bortezomib (10nM), dactinomycin (10nM), plicamycin (10nM), carfilzomib (100nM), trametinib (100nM), paclitaxel (100nM). Organoids were imaged using confocal option of the automated imaging system, Z-stack of 15 images was taken 10 µm apart, then maximum projection images created. (shown). \u003cstrong\u003eB\u003c/strong\u003e. 384 well plate with tumoroids after compound treatment, TL images (left); and fragment of test plate with stained tumoroids (right), fluorescent images, 10X magnification.\u003c/p\u003e","description":"","filename":"ScientificReportsJournalfigure220220613.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1859525/v1/e146b632d81534687b99d751.jpg"},{"id":24742380,"identity":"abe1bf77-89f5-4924-b61c-30f0685a6fde","added_by":"auto","created_at":"2022-08-03 18:23:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1917425,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e End-point analysis of fluorescent images was done using the Custom Module Editor in MetaXpress High-Content Image Acquisition and Analysis Software. Image analysis was done using the Custom Module Editor for finding organoids, nuclei, and cells. Images of nuclei of treated and untreated organoid shown. Analysis masks show organoids projection in blue and nuclei in yellow. Tumoroid area and count nuclei were used as main read-outs for phenotypic characterization of organoids and compound effects. \u003cstrong\u003e\u0026nbsp;B\u003c/strong\u003e. Automated image analysis of 3D cancer microtissues was done using transmitted light images (10X) with machine learning-based -based image analysis module In Carta software (analysis mask shown in purple). Further analysis determined the area, optical density, and other read-outs.\u003c/p\u003e","description":"","filename":"ScientificReportsJournalfigure320220613.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1859525/v1/681b3e5e82d526ab51fb1bfc.jpg"},{"id":24741089,"identity":"df6ede4a-f1ce-46bd-84be-628b152dc474","added_by":"auto","created_at":"2022-08-03 18:13:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1305917,"visible":true,"origin":"","legend":"\u003cp\u003e3D cancer microtissues were treated with indicated selected compounds for 5 days, then stained with calcein AM (green), EthD-1 (red) and Hoechst (blue). Confocal images, 10X shown. Note dose-dependent dis-integration of microtissues, also increase in cell death indicated as increase of EthD-1 staining (in red).\u003c/p\u003e","description":"","filename":"ScientificReportsJournalfigure420220613.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1859525/v1/43706188b1521556de00c959.jpg"},{"id":24741085,"identity":"f66b1a7a-e822-4c6b-81bd-cddc6e0cf9c1","added_by":"auto","created_at":"2022-08-03 18:13:23","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2539171,"visible":true,"origin":"","legend":"\u003cp\u003eThe Cell Painting assay modified for 3D spheroids. \u003cstrong\u003eA\u003c/strong\u003e. Spheroids were labeled with phalloidin, MitoTracker, WGA, SYTO 14, concanavalin A and Hoechst 33342. Shown here is an example image of a control spheroid (maximum projection). \u003cstrong\u003eB.\u003c/strong\u003e Scatterplot representing the phenotypic distance score (-log, Y-axis) for each compound is shown. Compound treated samples (blue) compared to negative controls (red) are shown. Hits are identified as those above the red dotted line (p≤ 0.05). \u003cstrong\u003eC. \u003c/strong\u003eResults from the cluster analysis are represented as a hierarchical dendrogram. Left: The cluster ID and colored bars indicate which cluster a compound treated spheroid belongs to. Rows represent the included factors, columns represent the compound treatment. Middle: Correlation matrix is shown to give an overview of the similarity/dissimilarity between the compounds. Columns and rows represent compounds used. The intensity of the color represents the similarity based on the calculated cosine vector score from the PCA factors. Right: Bar graph showing the contribution of each PCA factor to each compound hit.\u0026nbsp;\u003cstrong\u003eD.\u003c/strong\u003e Example images grouped by cluster from the Cell Painting assay. Three of the stains, Hoeschst (blue, nuclei), SYTO 14 (green, RNAP) and MitoTracker (red, mitochondria) are represented as a composite image. An example of a control DMSO treated spheroid and spheroids from cluster 7, 6 and 5 are shown.\u003c/p\u003e","description":"","filename":"ScientificReportsJournalfigure520220613.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1859525/v1/c42b98160cf5c18d4c8b1e66.jpg"},{"id":26952679,"identity":"48b49490-b237-4799-a3ae-d306ff758005","added_by":"auto","created_at":"2022-09-26 06:59:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1234122,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1859525/v1/fcd6409e-1dc8-4f66-97b4-24c0fa73efc1.pdf"},{"id":24741681,"identity":"038c97e9-9a53-4723-aa2e-7ed6bab41f5d","added_by":"auto","created_at":"2022-08-03 18:18:23","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":581412,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFiguresSirenkoatal2022.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1859525/v1/36b207355d403c8d188bf3d4.pdf"}],"financialInterests":"Competing interest reported. Oksana Sirenko, Angeline Lim, Cathy Olsen are employed by the Molecular Devices, LLC, that manufacture automated imaging systems and image analysis software. Evan Cromwell and Katya Nikolov are employed by Protein Fluidics company that manufactures Pu.Ma microfluidics system. Other authors (Emily C. McConnell, Maryl Wright, Bridgette M Collins-Burow, Matthew E Burow) claim no conflict of interests.","formattedTitle":"Evaluating Drug Response in 3D Triple Negative Breast Cancer Tumoroids with High Content Imaging and Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe current state of the drug development pipeline reflects the need for better research models. According to estimates of clinical trial success rates, only 13.8% of drugs entering Phase 1 are ultimately approved by the FDA\u003csup\u003e1,2\u003c/sup\u003e. This rate is even lower for oncological drugs, with an average rate of approval of 3.4% per year, based on data from 2000\u0026ndash;2015\u003csup\u003e1,2\u003c/sup\u003e. For all these drugs, there was strong evidence of their efficacy in laboratory settings with tissue culture models, but roughly 96% of them failed to be both efficacious and safe in humans. This raises the question: why is there strong laboratory evidence for drugs that do not effectively treat tumors in the human body?\u003c/p\u003e \u003cp\u003eOne factor that may be contributing to this is the traditional use of two-dimensional (2D) cell culture rather than three-dimensional (3D) culture during drug discovery and screening. 2D cell culture can be faster, less costly, and relatively easier to interpret, compared to 3D. However, there is increasing evidence that 2D culture substantially alters the physiological properties of cells compared to 3D, due to the different microenvironmental cues that exist in both culture methods\u003csup\u003e3\u0026ndash;5\u003c/sup\u003e. 2D culture enforces apical-basal polarity, which is not physiologically relevant for more mesenchymal cell types, and in some circumstances has been shown to alter sensitivity to apoptosis\u003csup\u003e5\u003c/sup\u003e. Two-dimensional culture creates single-cell monolayers, which alters diffusion of nutrients, gasses, and drugs, affecting both cell behavior and metabolic activity\u003csup\u003e4\u003c/sup\u003e. Furthermore, many researchers have demonstrated differences in cell proliferation rates, gene expression, and differentiation in 2D vs. 3D cultures\u003csup\u003e6\u003c/sup\u003e. These deviations from the true \u003cem\u003ein vivo\u003c/em\u003e behaviors of cells may lead to Type 1 and Type 2 errors during 2D drug screens, thus ruling in drugs that are clinically futile, and ruling out drugs that would otherwise prove effective. In the context of cancer, there is increasing evidence that 2D culture is insufficient to evaluate the effects of drugs. In breast cancer specifically, there is evidence that culturing methods affect response to drug treatment. El-Feky et al \u003csup\u003e3\u003c/sup\u003e found that MCF7 cells cultured in 2D monolayers and 3D spheroids had differential response to FAC-based chemotherapy (5-fluorouracil, Adriamycin (doxorubicin), cyclophosphamide), with 2D cultures showing a greater reduction in viability. Additionally, Imamura et al\u003csup\u003e7\u003c/sup\u003e demonstrated that 3D spheroids of breast cancer cell lines (BT-549, BT-474 and T-47D) had greater resistance to doxorubicin and paclitaxel compared to 2D. However, these studies were only able to examine a limited number of chemotherapeutic agents, at limited concentrations, thus demonstrating the need for more robust, high-throughput drug screens using 3D models of breast cancer.\u003c/p\u003e \u003cp\u003eNew advanced models that include primary derived tumoroids and 3D cultures present significant technical challenges that have prevented their adoption as the predominant methods of cell culture for drug discovery and screening. 3D micro-tissues take longer to form, they are more heterogeneous and fragile, and they are more complicated to image and analyze, all of which increases the time and complexity required to perform experiments. The increased complexity of cell models requires the development of new methods that overcome these challenges and provide the best information from that complexity.\u003c/p\u003e \u003cp\u003eIt is also extremely important to develop new models for specific cancers that are derived from primary patient tumors. The use of patient-derived tumor tissues has transformed the field of drug and target discovery research, providing a translational tool and physiologically relevant system to evaluate tumor biology\u003csup\u003e1, 2\u003c/sup\u003e. An example of this method is the use of patient-derived organoids (PDO) for oncology research\u003csup\u003e2, 8, 10\u0026ndash;12\u003c/sup\u003e. Patient-derived tumor organoids exhibit the heterogeneity of tumor tissues and presence of cancer stem cells (CSC) that can be expanded over multiple passages to produce large numbers of tumoroids (derived from isolated cells) or organoids (derived from digested tumors), that maintain molecular characteristics of the original tumor \u003csup\u003e10,12\u003c/sup\u003e. Primary-derived cell lines represent a variety of cell subtypes present in the tumor, as well as the different mutations involved and the degree of malignant transformation. Development of various cell lines for breast cancer, or other cancers, that represent disease subtypes is critical for finding drugs or drug combinations that would be effective against that specific cancer, or even for that specific patient. Study and characterization of patient derived tumor cells is an active area of investigation, and importance is increasing with the need to develop patient-specific therapies. However, drug testing in patient-derived tumor cells is not widely adopted because of the additional technical challenges, related to difficulties in expansion, handling, and maintenance of primary-derived tumoroids.\u003c/p\u003e \u003cp\u003eIn this study, we used a patient-derived 3D cell model representing a rare drug-resistant breast cancer subtype, which allows us to model the cellular and molecular complexity and diversity of breast cancer. In addition, we present a variety of methods for automation and high-content analysis adapted for the culture and drug screening of primary tissue-derived 3D cell models. Finally, we have tested a library of approved anti-cancer drugs and defined several potentially promising candidates for treatment of this specific cancer type.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eCell model\u003c/b\u003e. In the present study we developed a compound screening method that uses primary tissue -derived tumoroids. Tumoroids were formed from TU-BcX-4IC cell line derived from primary tumor samples as previously described\u003csup\u003e12\u0026ndash;14\u003c/sup\u003e. Briefly, patient-derived tumor samples were implanted into SCID mice, serially passaged, and then expanded in 2D culture\u003csup\u003e12\u003c/sup\u003e. TU-BcX-4IC represents metaplastic breast cancer with a triple-negative breast cancer subtype and is an example of a highly heterogeneous phenotype of breast cancer. TNBC tumors have an aggressive clinical presentation due to high rates of metastasis, recurrence and chemoresistance. The original patient\u0026rsquo;s tumor for the TU-BcX-4IC model exhibited rapid pre-operative growth despite conventional combination therapy with Adriamycin (doxorubicin), cyclophosphamide and paclitaxel. Tumoroids were formed from 2D TU-BcX-4IC cells as described in Methods.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDevelopment and Optimization of the Live Cell High-Content Assay with 3D Spheroid Cultures.\u003c/b\u003e In the present study, we have focused on method development for drug screening using primary-derived tumor cells in 3D culture. The goal of this study was to develop and evaluate fast, accurate, and reproducible high-content imaging methods to investigate effects of anti-cancer compounds on the morphology and viability of 3D cultures using live and fixed cells. In this study, we evaluated and optimized the workflow while characterizing several endpoint assays to test for general and mechanism-specific cytotoxicity of anti-cancer drugs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe used low-attachment, U-shaped, black-walled, clear-bottom 384-well plates to simplify cell culture, compound addition, and imaging\u003csup\u003e15,16\u003c/sup\u003e. Cells aggregate at the bottom of the U-shaped wells and form tumoroids centered in the well within 48 hours. The thin plastic bottom of each well aids focusing and image acquisition with standard automated imaging systems. Entire tumoroids can be captured in one 10X or 20X image. In preliminary tests, we studied reproducibility of spheroid formation and dependence of the spheroid size on the number of plated cells. Cells were plated at different densities (500\u0026ndash;8,000 cells/well), incubated for 48 h, and then imaged using TL imaging. We found by visual assessment that a plating density of 2,000\u0026ndash;3000 cells/well resulted in consistent tumoroid size and shape, with sizes suitable for image acquisition and analysis (diameter\u0026thinsp;~\u0026thinsp;200\u0026ndash;300 mm). A plating density of 2000 cells/ well was used for subsequent assays. At this density, the average tumoroid maximum diameter after 2 days was consistent as measured by transmitted light (TL) imaging, with a value of 270 +/- 37 mm (n\u0026thinsp;=\u0026thinsp;96) yielding a coefficient of variation of 13% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTumoroids were then treated with 168 compounds from the NCI (National Cancer Institute) library of approved anti-cancer drugs. Five concentrations were used for testing: 10nM, 100nM, 1mM, 10mM and 100mM. During screening, each compound was tested in duplicate, with one concentration per plate. In addition, positive and negative controls were included in the test plates. Positive controls included romidepsin, a compound that has shown high efficacy in previous tests\u003csup\u003e14\u003c/sup\u003e. Negative controls included acetaminophen, as well as multiple replicates of DMSO and media controls. Automation using a liquid handler was used for compound dilutions, cell treatments, and staining. The schematic diagram of the process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eDuring incubation with compounds, tumoroids were monitored daily using TL imaging. Image analysis allowed characterization of tumoroid size, diameter, compactness, and integrity, as well as optical density (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Supplemental Fig.\u0026nbsp;2). Images of tumoroids were taken prior to compound treatment, and during day 3 and day 5 of compound treatment. For the end point assay, tumoroids were stained with viability and nuclear dyes on day 5 of compound treatment. Cells were stained with a combination of Hoechst, calcein AM, and Ethidium Homodimer III (EthD-III) and analyzed using high content imaging for complex phenotypic analysis; a subset of plates was set aside for additional assays including Cell Painting.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHigh Content Imaging and Automation of the Screening Workflow\u003c/b\u003e. The main method used for evaluation of phenotypic changes was confocal fluorescent imaging of tumoroids using an automated imaging system, the ImageXpress confocal imaging system (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The method was previously described in Sirenko 2015\u003csup\u003e16\u003c/sup\u003e. Images of organoids were taken with a 10X objective using Z-stack with a step size of 8 mm. Nuclei, live cells, and dead cells of stained cells were detected using DAPI, FITC, and TexasRed channels respectively. Maximum projection images were created from Z-stacks and analyzed as described in Methods. Multiple read-outs were generated characterizing spheroid area, diameter, intensities, and cell viability. In addition, cells inside each spheroid were counted using a nuclear stain. This last output was found to produce the best results.\u003c/p\u003e \u003cp\u003e3D culture, treatment, and end point assays are notably more complex than traditional 2D culture assays. Automated imaging and analysis of organoids are important for quantitative assessment of phenotypic changes in organoids, and for increasing throughput for experiments and tests. We built an automated, integrated system that allows monitoring, maintenance, and characterization of growth and differentiation of organoids and stem cells, as well as testing the effects of various compounds (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Suspended cells were plated into U-shape 384 well plates using a Biomek i7 automated liquid handler, incubated for 48 hours, and then treated with compounds, stained, and imaged. The protocols for compound additions, media, and staining included gentle media dispensing processes. Consecutively, tumoroids were cultured and monitored by automated imaging. Additionally, use of a collaborative robot enabled moving plates from the incubator to the imaging system, where transmitted light imaging or fluorescent imaging protocols were initiated.\u003c/p\u003e \u003cp\u003e \u003cb\u003eObservations of Phenotypic Changes and Criteria for Hit Selection.\u003c/b\u003e Phenotypic changes for selected compounds are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The phenotypic readouts included cell viability assessment and characterization of organoid and cell areas, and total cell count, along with characterization of nuclear area and fluorescence intensity. Advantages of this method included simplicity, reduced cost, and ease of workflow.\u003c/p\u003e \u003cp\u003eIn contrast to previous 3D spheroid studies performed using cell lines\u003csup\u003e16\u003c/sup\u003e, 3D tumoroids formed from primary cells (TU-BcX-4IC cell line) did not increase in size during culture. However, upon drug treatment we observed concentration-dependent disintegration of tumoroids (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), as well as a decrease in the number of viable, Calcein AM positive cells and a increase in the number of dead, EthD-III positive cells.\u003c/p\u003e \u003cp\u003eWe have previously characterized multiple quantitative descriptors that could be used for studying tumor phenotypes and compound effects, including characterization of size and integrity, cell morphology and viability, as well as determining the presence of various cell markers\u003csup\u003e16\u003c/sup\u003e. However, since the phenotype of organoid disintegration was the most prominent in this study and was observed at lower concentrations than the increase in the number of dead cells, we used that read-out to characterize concentration dependencies and compare effective concentrations for different compounds.\u003c/p\u003e \u003cp\u003eFor screening the compound library, we used maximum projection images as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Spheroid disintegration is visible as an increase of spheroid size, in contrast to nicely shaped, round images of intact spheroids. Tumoroid disintegration can be described by several measurements: for example, increase in organoid area/diameter. Most accurate measurements were done by counting cells or nuclei inside organoids using maximum projection images. As seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, compact tumoroids had a smaller projected area and also a lower nuclear count in the projection images (yellow dots). Disintegrated tumoroids are made up of cells which are not held together in a tight aggregate but instead are loosely piled up in the well bottom resulting in 1) an increased projected area and 2) an increase in nuclear count in the projection images. It is important to note that the apparent increase in cell number represents a feature of analysis using 2D projection images and the fact that tumoroids are falling apart and the individual cells are not tightly associated with each other. While counting cells in 3D instead of using 2D projection would better reflect the true cell count (data not shown), that would not allow quantitation of organoid disintegration. This method is better suited for screening experiments because of the reduced demand for data storage and reduction in time for 3D analysis.\u003c/p\u003e \u003cp\u003eSimple nuclei count analysis in projection images was used as a reliable surrogate marker for organoid integrity/dis-integration. Therefore, in the analysis of projection images, we used the increase of nuclear count as a read-out for tumoroid dis-integration (Supplementary Table\u0026nbsp;1). Notably, selected drugs (e.g. doxorubicin and several others) resulted in nuclei damage, and as a result, a decrease in counted nuclei. We used AVERAGE +/-2STDEV from the DMSO samples to flag affected wells. The hits were confirmed through referencing the primary images to exclude experimental artifacts (e.g. focus failure, pipetting errors, rolling the object off center, etc) which accounted for approximately 1% of wells.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults from Library Screening and Secondary Screen.\u003c/b\u003e We tested the effects of 168 compounds from the NCI library with 5 concentrations listed above. Then we used assessment of tumoroid integrity by nuclear count to identify hits across different concentrations. Several drugs were identified that demonstrated efficacy of targeting tumor subtypes resistant to traditional cancer therapy. Drugs listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e demonstrated efficacy by affecting tumoroid phenotypes at indicated concentrations (same concentrations had efficacy at higher concentrations). Interestingly, several compounds were found to have efficacy at low concentrations, which may be valuable for the development of potential therapeutics. Those include romidepsin, dactinomycin, plicamycin, bortezomib, and dasatinib. A total of 33 compounds were selected as \u0026ldquo;hits\u0026rdquo; for concentrations 10-1000 nM, while an additional 29 compounds had significant effects at 10 mM. (Supplemental Fig.\u0026nbsp;1). Because greater than 30% of compounds had some effects at a concentration of 100 mM, this data point was considered less informative.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCompounds effective in screening assay at low concentrations. Compounds were selected as described in \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eResults\u003c/span\u003e section.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffective concentration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMechanism of action\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDactinomycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntercalation of DNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlicamycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNA/RNA polymerase inhibitor, hepatotoxicity led to discontinue in 2000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRomidepsin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHDAC 1/2 inhibitor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBortezomib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibits the catalytic site of the 26S proteasome\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFulvestrant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelective Estrogen receptor degrader\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDasatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTyrosine Kinase Inhibitor for BCR/Abl fusion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMitotane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSteroidogenesis inhibitor used for adrenal carcinoma and Cushing\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVinblastine sulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinds tubulin, disrupts microtubules\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVincristine sulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinds tubulin, disrupts microtubules\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarfilzomib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibits the 20s proteasome\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrametinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibits MEK1 and MEK2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIxazomib citrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibits proteasome subunit beta 5, same mechanism as bortezomib, related to carfilzomib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePonatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibitor for BCR/Abl fusion, second line after dasatinib or imatinib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelinostat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePan- HDAC inhibitor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanobinostat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePan- HDAC inhibitor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTyrosine Kinase Inhibitor of VEGFR-TIE2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBosutinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTyrosine Kinase Inhibitor of Abl and Src kinases\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopanlisib tris-HCl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePI3K inhibitor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMitomycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlkylating agent- DNA crosslinking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUracil mustard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlkylating agent- DNA crosslinking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOmacetaxine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibits translation (blocks tRNAs)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaunorubicin hydrochloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntercalation of DNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVandetanib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibits VEGFR, EGFR, RET\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaclitaxel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStabilizes microtubes, inhibits spindle formation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIbrutinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibits Bruton's tyrosine kinase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdarubicin hydrochloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDaunorubicin analog\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenetoclax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibits BCL2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpirubicin hydrochloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntercalation of DNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFedratinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJAK2 selective inhibitor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemsirolimus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibits mTOR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVorinostat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibits HDAC1, HDAC2, HDAC3, HDAC6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrigatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibits ALK, EGFR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGilteritinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1uM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInhibits FLT3 receptor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo confirm our findings in the screening assay, a subset of compounds was selected for secondary follow-up analysis. 10 compounds, including panobinostat, carfilzomib, and bortezomib were tested across 7 concentrations in the 1-10000nM range. Spheroids were treated for 5 days with these compounds, then stained as described above (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Dose-dependent cell death and disintegration of microtissues was observed. EC\u003csub\u003e50\u003c/sub\u003e values (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were determined using tumoroid disintegration measurement (nuclear count in projection images). All tested compounds showed similar activities as observed in the initial compound screen.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEC\u003csub\u003e50\u003c/sub\u003e values of indicated compounds in 3D assay determined by 7-point concentration curves.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC50, nM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBortezomib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarfilzomib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopanlisib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRomidepsin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanobinostat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrametinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOmacetaxine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarfilzomib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrizotinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSunitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaxol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2900\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCelecoxib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e \u003cb\u003eMonitoring phenotypic changes using transmitted light.\u003c/b\u003e Notably, changes in tumoroid phenotypes upon compound treatments were also observed with TL imaging, and artificial intelligence (AI) tools were used to identify and classify different tumoroid phenotypes. We used a deep learning-based segmentation (IN Carta Image Analysis Software) approach to find all tumoroid objects in transmitted light, either intact or affected. Following segmentation and data extraction, we utilized a machine learning tool to classify all tumoroids into intact, intermediate or severely affected categories. Affected tumoroids were flagged by increase in the object areas (criteria were set as AVERAGE\u0026thinsp;\u0026plusmn;\u0026thinsp;2*STDEV from the DMSO-treated), allowing us to detect effective compounds (Supplemental Fig.\u0026nbsp;2, Supplemental Table\u0026nbsp;2). An advantage of this method is that compound effects could be interrogated in a label-free manner during multiple time points. However, the analysis allowed some ambiguity in determining accurate areas of the objects, especially when the phenotypic changes were weak or moderate (see Supplemental Fig.\u0026nbsp;2). The endpoint fluorescent imaging and analysis enabled more robust measurements and compound selection (Supplemental Tables\u0026nbsp;1 and 2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEvaluation of phenotypic changes by Cell Painting Assay.\u003c/b\u003e For a more in-depth investigation into cytotoxic mechanisms elicited by the compounds assayed, other analysis methods were performed in parallel to fully characterize phenotypic changes detected. The Cell Painting assay was adapted for 3D tumoroids for the evaluation of compound effects on tumoroid phenotype. The method uses up to six fluorescent dyes to label eight cellular components or organelles: nuclei, nucleoli, RNA, endoplasmic reticulum, mitochondria, plasma membrane, Golgi and cytoskeleton\u003csup\u003e17\u0026ndash;20\u003c/sup\u003e. This method, which was developed for use in 2D assay systems has been successfully used for phenotypic profiling to provide insights into functional genomics applications and mechanism of action of novel compounds, and to reveal subtle effects of various drugs and small molecules on cell health\u003csup\u003e17\u0026ndash;18\u003c/sup\u003e. Here, we adapted and further developed the Cell Painting assay for 3D cell culture models.\u003c/p\u003e \u003cp\u003eFor phenotypic profiling, the tumoroids were treated with a single 10\u0026micro;M concentration of compound, using the methods described above. Notable changes in the staining protocol included increased times for dye incubation, fixation, and permeabilization. A challenge of the assay is to minimize the crosstalk of multiple dyes, which we achieved by using lasers as the illumination light source, with a narrow spectrum for each wavelength and minimum overlap between stains. In the original Cell Painting assay, the Golgi and actin filament images were acquired in the same imaging channel. This created additional hurdles, as it was challenging to extract measurements from the Golgi compartment separately from the actin structures. To improve the resolution between the Golgi and actin compartments, we swapped out Alexa Fluor 568 for Alexa Fluor 750 phalloidin, which allows for the cytoskeleton to be imaged in a different channel from the Golgi compartment by using the far-red laser on our imager. Images were acquired with Z-stacks using 20x magnification, and the analysis was carried out on projection (maximum) images. Unlike the previous analysis that included cell count or live-dead evaluation, multiple read-outs were collected from the whole spheroid to form their phenotypic profile. Because the added compounds affected staining patterns, we were unable to achieve good spheroid segmentation based on the fluorescent images. Instead, the spheroid images were also acquired in transmitted light (bright field), and these images were used to identify the spheroid structures using a deep learning-based image segmentation approach which improved the analysis. Features extracted from each spheroid included object morphologies, intensities, and texture measurements for the six cell stains and bright field images. 202 measurements per spheroid were uploaded into StratoMineR software (CoreLife Analytics) for data analysis to identify hits and for cluster analysis based on similarity of their phenotypic profiles. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents a dendrogram showing clustering of tested compounds based on phenotype similarity of the spheroids.\u003c/p\u003e \u003cp\u003eTo determine hits from the assay, a phenotypic distance score was calculated based on the PCA (principal component analysis) components\u003csup\u003e21\u0026ndash;22\u003c/sup\u003e. This score is a measure of the phenotypic effect of the compounds on the tumoroids relative to the controls. All hits were then clustered based on their phenotypic profiles using the normalized principal component scores. 24 hits (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) were identified (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as being significantly different from the DMSO control tumoroids (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). We obtained a reasonable correlation between the viability assay and the Cell Painting assay. From the Cell Painting assay, 67% (16), or two-thirds of the hits were also identified in the viability/spheroid disintegration assay. Hits from the viability assay were found mostly in the same clusters (clusters 1,2 and 6, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), suggesting that these spheroids display phenotypic profiles that are associated with most notable cytotoxicity. For example, all compounds in clusters 1 and 2 are hits also flagged in the viability assay, and they are known to affect topoisomerase 2. The other hits from the Cell Painting assay included compounds with less obvious cytotoxic effects, but these hits were still phenotypically different from the controls and interestingly, many of these compounds are known to affect various protein kinase pathways. These results are consistent with the fact that while cytoxicity changes phenotypes very dramatically, more subtle changes can be detected using phenotypic profiling.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCompounds effective in the Cell Painting assay. Compounds selected as described in the \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eResults\u003c/span\u003e section.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound (10\u0026micro;M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCluster ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eViability HIT?\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDabrafenib mesylate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.248831502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEncorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.516036128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvapritinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.386292153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbemaciclib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.819034729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarotrectinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.666943776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValrubicin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.136000201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.22E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMitoxantrone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.687853572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpirubicin hydrochloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.883205304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.046845431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopanlisib tris-HCl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.146853912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIbrutinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.93823624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelinostat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.348047631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVandetanib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.184617965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntrectinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.84271406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.046947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGilteritinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.23882208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrigatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.771498629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOsimertinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.241373452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarfilzomib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.006962745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.036124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemsirolimus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.699077848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVorinostat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.078731963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.036114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlpelisib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.833763084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlicamycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.134541481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelinexor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.004358366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.031059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVenetoclax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.921513854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEvaluation of Cell Viability Using ATP Read-Outs.\u003c/b\u003e Cell metabolism measured by ATP level is another readout for effects of anti-cancer drugs that is related to cytotoxicity. A subset of 12 compounds were used for evaluation of compound effects, using the CellTiter-Glo 3D assay (Promega), which detects ATP levels. Compounds were tested using 7 point 5x dilutions starting from 100uM. We compared compound effects on 2D culture and 3D cultures, using imaging and CellTiter-Glo 3D methods. Cultures were set up in parallel, using same cell number (2000 ). Cultures were allowed to grow for 48 hours, then treated with compounds for 5 days. EC\u003csub\u003e50\u003c/sub\u003es calculated from concentration dependencies are presented in the Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Data shows that there is significant consistency between EC\u003csub\u003e50\u003c/sub\u003es obtained by imaging and CellTiter-Glo 3D readouts, while EC\u003csub\u003e50\u003c/sub\u003es between 2D and 3D cultures varied for a number of compounds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of EC\u003csub\u003e50\u003c/sub\u003e values obtained from 2D and 3D assays by imaging or ATP assays.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eEC50, mM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eATP 3D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eImaging 3D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eATP 2D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eImaging 2D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlicamycin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.018+/-.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDactinomycin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIdarubicin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.145 +/- 0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.067+/-.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTemsirolimus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.52 +/-10.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eno fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBortezomib\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.015+/-.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.01+/-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrametinib\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.62+/-1.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.33+/-4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.27+/-3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePanobinostat\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.084+/-.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.046+/-.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.006+/-.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIbrutinib\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.76+/-14.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25+/-2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCarfilzomib\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.82+/-2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04+/-.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05+/-.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRomidepsin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e~\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCell Metabolism Dynamics.\u003c/b\u003e Tumoroids treated with four compounds, paclitaxel, romidepsin, doxorubicin, and trametinib, were analyzed for lactate secretion. Elevation of lactate typically suggests a switch to aerobic glycolysis; tumor cells metabolize glucose into lactate even in the presence of high oxygen\u003csup\u003e23\u003c/sup\u003e. Recent studies revealed that metabolic alterations of cancer cells play important roles in chemo-resistance in breast cancer, and exposure of cancer cells to chemotherapeutics induces metabolic reprogramming toward increased glycolysis and lactate production\u003csup\u003e24,25\u003c/sup\u003e. Therefore, monitoring lactate production over the course of treatment in conjunction with other response endpoints provides valuable information for understanding the dynamics of metabolic perturbations associated with drug response and resistance.\u003c/p\u003e \u003cp\u003eCompound treatments and secretion of lactate were studied using a Pu\u0026middot;MA System\u003csup\u003e26\u003c/sup\u003e, a microfluidic-based automated organoid assay platform that allows multistep protocols to be performed without disruption of or damage to tumoroids. Five supernatant samples were collected from treated tumoroids using flowchip automation over a 12-hour period. Two to three independent samples were collected, and supernatants were analyzed for lactate concentration using the Lactate-Glo assay (Promega). The lactate secretion results for the 12hour time point are shown in Supplemental Fig.\u0026nbsp;3. Two-way ANOVA revealed statistically significant increases in lactate secretion for romidepsin and trametinib. between baseline and measured time points for three out of the four compounds. Our observations are in line with previously published reports showing increased lactate production for breast cancer cells in response to chemotherapeutic treatments\u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this paper, we use a patient-derived cell line representing a rare drug resistant cancer subtype for drug screening. We present results of the library testing of approved anti-cancer drugs that were tested at different compound concentrations, using high content imaging methods. We describe methods for increase of throughput by using automation in 3D cancer assays and compound screening. In addition, we show advanced analysis approaches and descriptors that allow for greater information about complex compound effects.\u003c/p\u003e \u003cp\u003eA key strength of our method is our usage of a primary tissue-derived cell model. The standard approach is to use the established immortalized cell lines as well as orthotopic xenograft models\u003csup\u003e28\u0026ndash;35\u003c/sup\u003e. Although immortalized cellular models provide invaluable knowledge regarding cancer biology and drug effects on cellular systems, they are limited in their inability to re-create essential features of tumors. More specifically, these models cannot accurately reflect the tumor architecture, three-dimensional structure and alignment of tumor cells, matrix, and surrounding stroma, and cannot reproduce the cellular heterogeneity that is present in the original patient tumor\u003csup\u003e36\u0026ndash;39\u003c/sup\u003e. Conversely, orthotopic xenograft models recapitulate the complexity of tumors, but the inability to scale these models limits their use in large drug screens. The methods described in this paper use primary cell-derived models to form 3D spheroids, achieving greater relevance of the results to real biology and allowing for large drug screens to be performed.\u003c/p\u003e \u003cp\u003eBeyond the use of patient derived cells, using 3D cultures rather than 2D cultures for screening allows us to closer recapitulate properties of tumors. Cells in 3D spheroids are in close contact with each other, as they would in a human body\u003csup\u003e28\u003c/sup\u003e (Langhans). Also, cells in dense, multicellular micro-tissues have a higher rate of hypoxia compared to 2D monolayers, which has been shown to be associated with drug resistance \u003csup\u003e7,32\u003c/sup\u003e. Drug penetration into the tissue also plays role in drug efficacy, and that factor can be mimicked during screens in 3D models. In general, drug screening and discovery may result in two types of errors: type 1, false positive drugs, or type 2, false negative errors that can ultimately result in loss of drugs that may have proven clinically effective. Using more biologically relevant models would decrease both false positive and false negative types of errors. Immortalized cell lines that have been growing in cell culture for years results in the introduction of irreversible alterations in genetic information and behavioral characteristics that were not present in the original tumor\u003csup\u003e39\u003c/sup\u003e. As a result, those cell lines would be most responsive to anti-proliferative drugs and may not be affected by other drug classes. Cells cultured in 2D have altered morphology and organization of cell surface receptors compared to 3D, which could affect the binding efficacy of drugs and their penetration inside the tumor\u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHere we demonstrated several assays suitable for medium- or large-scale compound testing. Various analytical methods allowed us to evaluate different aspects of compound effects: tumoroid integrity that is evaluated by measurement of spheroid area or cell count, cytotoxic effects that were evaluated by live-dead stain, and spheroid integrity that was evaluated with just nuclear stain. As expected, we observed a progressive increase in the number of effective compounds with the increase of concentration. Eight compounds were effective at a 10nM concentration, which demonstrated high efficacy and potentially can be considered for drug development. Several of those compounds were kinase inhibitors, which is a promising drug class for cancers resistant to traditional anti-cancer therapies. Interestingly, cell disintegration appeared as an effective read-out at lowest concentration, while the actual increase of dead cells appeared at higher concentrations for the same drugs. That may indicate that loss of adhesion molecules may be occurring, or that subtle changes in viability have resulted in loss of cell-cell attachment.\u003c/p\u003e \u003cp\u003eThe Cell Painting method is of particular interest due to it unbiased approach, which captures phenotypic changes, independent of cytotoxicity. Information from multiple stains that label various organelles and cellular compartments are used to represent the morphological profile of each cell. For 3D cell painting, due to the proximity of cells to each other, it is challenging to analyze the data at the cellular level. Here, we examine the effects of compounds on the phenotypic profile at the spheroid level. Interestingly, spheroid-level analysis identified not only cytotoxic compounds but also compounds that have significant phenotypic effects Cluster analysis grouped compounds with similar mechanisms of action, suggesting that spheroid-level analysis is sufficient for assessing compound effects. While Cell Painting and toxicity analysis showed the same hits for strong compounds, compounds with more subtle effects showed different results between the two assays, reflecting toxicity vs. other phenotypic changes.\u003c/p\u003e \u003cp\u003eThere is an unmet need for greater accuracy in drug screening for cancer, especially in the expanding area of personalized medicine. We describe approaches that can overcome technical challenges and make feasible drug screening that would be suitable for specific cancer subtypes or even individual patients. Through automation, large-scale drug screening using complex models, including tumoroids derived from individual patients, is feasible. In the future, this method would be suitable for screening other cancer subtypes that form tumoroids, such as colon and other cancers. Additionally, through the greater biological relevance and high-throughput nature of this system, other compound libraries can be screened.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3D Cell Culture and Drug Treatment\u003c/h2\u003e \u003cp\u003eThe methods for generating tumoroids and PDX organoids (PDXO) have been previously described \u003csup\u003e12\u003c/sup\u003e (Matossian, et al., 2021). The primary tumor sample was implanted into SCID/Beige mice and exhibited rapid tumor growth, reaching maximal tumor volume (\u0026gt;\u0026thinsp;1000 mm\u003csup\u003e3\u003c/sup\u003e) in 14 days. Then, a cell line generated from that sample was expanded in 2D culture (TU-BcX-4IC). Tumor spheroids, which we called tumoroids in this study, were formed from TU-BcX-4IC cells expanded in 2D. To form 3D tumoroids, TU-BcX-4IC cells were dispensed at ~\u0026thinsp;2,000 cells per well (in U-shape low attachment 384-well plates, Corning) and incubated for 48 hours until they formed tight tumoroids. 4IC cells were cultured with Advanced DMEM supplemented with glucose, non-essential amino acids (NEAA), 2mM glutamine and insulin 120\u0026micro;g/L, 10% FBS (Gibco 12491-015). For metabolic assays, tumoroids were cultured with DMEM\u0026thinsp;+\u0026thinsp;10% dialyzed serum (2mM glutamine, 5mM glucose, without phenol red).\u003c/p\u003e \u003cp\u003eTumoroids were treated with compounds as follows. Compound libraries of approved anti-cancer drugs were obtained from NIH. Compound dilution plates were prepared using the Beckman liquid handling system. Five compound dilutions (20nM, 200nM, 2uM, 20uM and 200uM) were prepared in 384 well plates. Then 50ul of compound mixes were added to the tumoroid culture plates (50uL) using a liquid handler programmed for slow dispensing of liquid. The final concentrations of compounds were 10nM, 100nM, 1uM, 10uM, and 100uM. DMSO concentrations on each plate were matched for 0.05%, with the exception of 100uM plates that had 0.5% DMSO. Each plate contained 32 DMSO only wells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOncology Drug Set and cell treatments\u003c/h2\u003e \u003cp\u003eTU-BcX-4IC cells were treated with the commercially available NCI Approved Oncology Drug set for 5 days total. On day 3, 50% of media was replaced with the fresh compound solutions. Cell cultures were treated with five concentrations, one concentration per plate, each compound in duplicates. Solution controls (DMSO) as well as a positive control (romidepsin) were included in each plate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3D Cell Monitoring and Imaging\u003c/h2\u003e \u003cp\u003eTransmitted light (TL) and fluorescent (FL) images were acquired on the ImageXpress Confocal HT.ai High-Content Imaging System (Molecular Devices) and images analyzed using MetaXpress High-Content Image Analysis Software. Tumoroid images were acquired in TL with approximately 60 \u0026micro;m offset. Z-stack images were acquired with the 10X objectives using confocal mode. Best focus projection images were used for TL analysis, and maximum projection images were used for FL analysis. MetaXpress Software or IN Carta Image Analysis Software was used for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAutomation of 3D Cell culture and Imaging Protocols\u003c/h2\u003e \u003cp\u003eAutomated imaging and analysis of organoids are important for quantitative assessment of phenotypic changes in organoids, and for increasing throughput for experiments and tests. We built an automated, integrated system that allows for automated monitoring, maintenance, and characterization of growth and differentiation of organoids and stem cells, as well as testing the effects of various compounds. The automated system includes the ImageXpress Confocal HT.ai system and MetaXpress software (Molecular Devices), automated CO\u003csub\u003e2\u003c/sub\u003e incubator (LiCONiC), Biomek i7 Automated Workstation (Beckman Coulter Life Sciences), collaborative robot and rail. Robotic automation was enabled by Green Button Go Scheduler (BioSero).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMultiparametric live cell toxicity assay\u003c/h2\u003e \u003cp\u003eThe method for imaging and high content analysis of 3D spheroids was previously described\u003csup\u003e14\u0026ndash;16\u003c/sup\u003e. Briefly, following incubation with test compounds, spheroids were stained with a mixture of three dyes: 1 \u0026micro;M calcein AM, 3 \u0026micro;M EthD-III, and 33 \u0026micro;M Hoechst 33342 (Life Technologies, Carlsbad, CA). For selected experiments spheroids were also stained with E-cadherin (Cell Signaling Technology #3195, Danvers, MA) and CD44 (BioLegend #338,808, San Diego, CA). Automated staining of spheroids was performed for one hour. After staining, dye solution was replaced with 1X PBS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eHigh-content imaging\u003c/h2\u003e \u003cp\u003eImages were acquired using the ImageXpress Micro Confocal High-Content Imaging System (Molecular Devices, San Jose, CA), as previously described\u003csup\u003e15\u003c/sup\u003e with a 10X Plan Fluor or 20X Plan Apo objective. DAPI, FITC, and Texas Red filter sets were used for imaging. A stack of 7to 15 images separated by 10to 15 \u0026micro;m was acquired, starting at the well bottom and covering approximately the lower half of each spheroid. Typically, a Z-stack of images covered 100\u0026ndash;200 \u0026micro;m for spheroids. Image analysis was performed either in 3D or using the 2D Projection (maximum projection) images of confocal image stacks. Transmitted light images were used for cell culture monitoring or protocol optimization.\u003c/p\u003e \u003cp\u003eTo image the organoids labeled with the cell painting dyes, the ImageXpress Confocal HT.ai system, equipped with a laser light source, was used for image acquisition. The confocal mode (pinhole size 60\u0026micro;m) was used to image organoids with a 20x Plan Apo Lambda, NA 0.75 objective, with z-stacking enabled (5\u0026micro;m step size). The maximum projection image was selected in the acquisition setup. Images were captured in six fluorescent channels and in transmitted light (bright field) in order of increasing fluorophore excitation wavelength to reduce crosstalk (DAPI 405/452, FITC 467.5/520, YFP 520/562, Texas Red 555/624, Cy5 638/692 and Cy7 725/794).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eOrganoid image analysis\u003c/h2\u003e \u003cp\u003eImages were analyzed using MetaXpress Software (Molecular Devices). Count Nuclei or Cell Scoring application modules were used for nuclear count live/dead assessment, or evaluation of cell number positive for specific markers. Output measurements included spheroid width, spheroid area, average intensity for calcein AM or EthD-III, counts of all nuclei, and evaluation of average nuclear size and average intensities. In addition, calcein AM-positive cells were counted, and their area and intensity values were recorded. In addition to cell count, areas and intensities could be determined for live (EthD-III negative) and dead (EthD-III positive) cells. EC\u003csub\u003e50\u003c/sub\u003e values were determined using a 4-parameter curve fit in SoftMax Pro 7 Software (Molecular Devices) (Sirenko, 2016).\u003c/p\u003e \u003cp\u003eFor the Cell Painting analysis, image analysis was done in the IN Carta image analysis software. To segment the organoids, a deep learning-based model was created based on the TL channel to segment individual organoids. Following segmentation, measurements that include intensity, shape, area, and texture were extracted from all 6 fluorescent channels. In total, 210 analytical features were obtained from each organoid.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell Painting Assay and Data Analysis\u003c/h2\u003e \u003cp\u003eFor the Cell Painting assay\u003csup\u003e17\u0026ndash;20\u003c/sup\u003e, the 3D TU-BcX-4IC organoids were labeled using a protocol modified from Bray et al. The plate was first incubated with MitoTracker DeepRed (500nM) for 2 hours. The samples were fixed with 4% paraformaldehyde (PFA) in HBSS for 60 minutes. All wash steps were carried out by exchanging half the volume in each well with HBSS to minimize displacement of the organoids from the center of the well. Following fixation, samples were washed three times with HBSS. For permeabilization, samples were incubated with 0.1% Triton X-100 (in HBSS) for two hours at room temperature and washed with HBSS. The dyes were prepared in HBSS and 1% BSA (wt/vol) and incubated overnight with the following final concentrations: Hoechst (15\u0026micro;g/ml), Concanavalin A-488 (250\u0026micro;g/ml), Syto14 (7.5\u0026micro;M), Phalloidin 750 (15\u0026micro;l/ml), WGA (3.75\u0026micro;g/ml).\u003c/p\u003e \u003cp\u003eMeasurements from IN Carta were exported as a CSV file and uploaded into StratoMineR (Core Life Analytica), a web-based data analytics platform for hit picking and phenotypic clustering\u003csup\u003e22\u003c/sup\u003e. Briefly, the dataset was normalized against the median of the samples to account for plate-plate variation. Data with a significant skewness (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) was transformed to approximate a normal distribution. Feature scaling using robust Z-score was applied to the dataset. Data reduction was achieved using principal component analysis (PCA). The results of the first 10 principal components were used in downstream analysis. For hit selection, the unsupervised option was selected with reference to the median of the negative controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Euclidean distance (Perlman et al., 2004) of all the vectors to the control was calculated. This approach reduces the data for each component to just one distance score which represents the phenotypic effect of the compound treatment relative to untreated samples. The median of the Euclidean distances for each well based on its replicates are calculated. Statistically significant hits were identified based on P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Hierarchical cluster analysis was performed on statistically significant hits based on the principal components using Ward\u0026rsquo;s linkage criteria (k-means\u0026thinsp;=\u0026thinsp;8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLactate Secretion Assay\u003c/h2\u003e \u003cp\u003eThe metabolic response of tumoroids to treatment was determined by measuring lactate secretion in supernatants that were collected from treated and untreated 4IC tumoroids at various timepoints of drug treatment using the microfluidic device Pu\u0026middot;MA System. Supernatant collection was done in the flowchip for each treatment condition in the following way: medium with drug was transferred from an adjacent reagent well of the flowchip to the sample well with tumoroid and incubated for 3 hours. After incubation, the media containing secreted lactate was transferred back to the reagent well it came from and replaced with fresh media with drugs from another well for the next 3-hour treatment. The cycle was repeated 5 times and resulted in collection of 5 supernatant samples. The first cycle was done with medium only (baseline secretion) followed by 4 treatment cycles for the total treatment duration of 12 hours. This approach allows dynamic monitoring of lactate secretion over the course of treatment.\u003c/p\u003e \u003cp\u003eThe collected supernatants were stored in the flowchips until the end of the Pu\u0026middot;MA protocol, then were collected and stored at -20\u0026deg;C until further processing. The supernatant samples were analyzed for lactate levels using the luminescent Lactate-Glo assay (Promega). Lactate detection reagent was prepared according to the manufacturer\u0026rsquo;s protocol. Supernatant samples were diluted 1:400 in PBS. 10 mL of the diluted supernatant was transferred to a solid white 384-well assay plate and 10 mL of lactate detection reagent was added to each well. Plates were incubated for 60 minutes at room temperature. Luminescence was measured using a GloMax plate reader (Promega). Each sample was measured in duplicate. Good luminescence signal levels and signal-to-noise ratios were achieved for this assay from single tumoroid samples. The statistical significance of comparison between groups was determined using one-way or two-way ANOVA. Post hoc tests were run to confirm where the differences occurred between groups. Differences with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements:\u003c/p\u003e\n\u003cp\u003eThis project received funding from the National Institutes of Health 1R01CA174785-01A1 (BC-B) and 1R41CA257425-01(MB). The project was also supported by Award Number TL1TR003106 from the National Center for Advancing Translational Sciences, as well as 1 U54 GM104940 from the National Institute of General Medical Sciences of the National Institutes of Health, which funds the Louisiana Clinical and Translational Science Center. Authors are grateful for the support of the Krewe de Pink organization in New Orleans. Finally, but most importantly, we thank the patients who donated their breast cancer tissue specimens to benefit breast cancer research; their contributions are valued and appreciated. The authors also grateful to \u0026nbsp;David Egan from Core Life Analytica and Mishael Bashkurov from Molecular Devices for assistance with data analysis.\u003c/p\u003e\n\u003cp\u003eAuthors Contributions: OS, CB, AL, EFC and MB contributed to study design and writing the manuscript; OS, AL, PM, CB generated most of data and performed data analysis; EN, CO, EM, MW, BCB contributed to development of assay model, critical discussions, and revisions of manuscript.\u003c/p\u003e\n\u003cp\u003eData availability statement:\u003c/p\u003e\n\u003cp\u003eEssential data are included into the Supplemental Tables, all other data available upon request. Oksana Sirenko (a corresponding author), [email protected], will provide data upon request.\u003c/p\u003e\n\u003cp\u003eConflict of interest statement:\u003c/p\u003e\n\u003cp\u003eOksana Sirenko, Angeline Lim, Cathy Olsen are employed by the Molecular Devices, LLC, that manufacture automated imaging systems and image analysis software. Evan Cromwell and Ekaterina Nikolov are employed by Protein Fluidics company that manufactures Pu.Ma microfluidics system. Other authors (Emily C. McConnell, Maryl Wright, Bridgette M Collins-Burow, Matthew E Burow) claim no conflict of interests.\u003c/p\u003e\n\u003cp\u003eIn addition: \u0026nbsp; all experimental protocols were approved by Tulane University, as well as Molecular Devices and Protein Fluidics. All methods were carried out in accordance with the appropriate guidelines and regulations, including ARRIVE guidelines.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChi Heem Wong, Kien Wei Siah, Andrew W Lo, Estimation of clinical trial success rates and related parameters, \u003cem\u003eBiostatistics\u003c/em\u003e, Volume 20, Issue 2, April 2019, Pages 273–286, https://doi.org/10.1093/biostatistics/kxx069\u003c/li\u003e\n\u003cli\u003eUnited States Food and Drug Administration. Novel Drug Approvals for 2021. FDA.gov. 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Proc Natl Acad Sci U S A 2011;108:18709–13\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"triple negative breast cancer, 3D assays, drug treatment, imaging, cell paint, high content imaging, assay automation","lastPublishedDoi":"10.21203/rs.3.rs-1859525/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1859525/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThere is a critical need to develop methods for efficient testing of drug efficacy in patient-derived tumor samples to discover new therapeutics. Two-dimensional (2D) cell culture remains the primary method of drug screening, despite being considered less physiologically relevant than three-dimensional (3D) culture. Increased complexity and technical challenges of 3D systems have limited its widespread adoption as a primary screening method. In this study, we demonstrate methods for increased throughput, imaging and automation in 3D assays that are suitable for compound screening using patient-derived samples. In addition, we show analysis approaches and descriptors that allow gain more information about disease phenotypes and compound effects.\u003c/p\u003e \u003cp\u003eWe measured responses to drug treatment in 3D tumoroids for cytotoxicity and altered morphology. Tumoroids were formed from primary cells isolated from a patient-derived tumor explant, TU-BcX-4IC, that represents metaplastic breast cancer with a triple-negative subtype and treated with 165 compounds, of approved cancer drugs, at multiple concentrations. We characterize multiple quantitative descriptors for tumor phenotypes and compound effects. Cell Painting method was used for 3D tumoroids for evaluation of phenotypic effects. Eight compounds were detected that demonstrated effects at low concentrations (10nM), including romidepsin, trametinib, bortezomib, carfilzomib, panobinostat, which will be further investigated as potential drug candidates.\u003c/p\u003e","manuscriptTitle":"Evaluating Drug Response in 3D Triple Negative Breast Cancer Tumoroids with High Content Imaging and Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-03 18:13:20","doi":"10.21203/rs.3.rs-1859525/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56b99f1c-0335-4e65-93df-39e468171b03","owner":[],"postedDate":"August 3rd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-09-26T06:59:19+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-03 18:13:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1859525","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1859525","identity":"rs-1859525","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}