This work is supported primarily by DOD OCRP Early Career Investigator Award W81XWH-13-1-0134(GM), DOD Pilot award W81XWH-16-1-0426 (GM), DOD Investigator Initiated award W81XWH-17-OCRP-IIRA (GM), Rivkin Center for Ovarian Cancer and Michigan Ovarian Cancer Alliance. Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award number P30CA046592. CMN is supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1256260. MEB is supported by the Department of Education Graduate Assistance in Areas of National Need (GAANN) Fellowship.

All patient samples are collected under an approved IRB protocol from consenting patients, whose samples are de-identified after tumor debulking and ascites collection.
1. Generation of Spheroids from Small Cell Numbers in 384-well Hanging Drop Plates
<ol>
	<li>Place the hanging drop plate in a sonicator filled with sterile deionized (DI) water and sonicate for 20 min.</li>
	<li>With a gloved hand, remove the plate from the sonicator and wash it with running DI water.</li>
	<li>Allow the pla...

<ol>
	<li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> All cancers Source: Globocan. , (2018).</li><li>Prasad, V., Mailankody, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Research+and+Development+Spending+to+Bring+a+Single+Cancer+Drug+to+Market+and+Revenues+After+Approval.">Research and Development Spending to Bring a Single Cancer Drug to Market and Revenues After Approval.</a> JAMA Internal Medicine. 177, 1569-1575 (2017).</li><li>Raghavan, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Personalized+Medicine+Based+Approach+to+Model+Patterns+of+Chemoresistance+and+Tumor+Recurrence+Using+Ovarian+Cancer+Stem+Cell+Spheroids.">Personalized Medicine Based Approach to Model Patterns of Chemoresistance and Tumor Recurrence Using Ovarian Cancer Stem Cell Spheroids.</a> Clinical Cancer Research. 23 (22), 6934-6945 (2017).</li><li>Jordan, C. T., Guzman, M. L., Noble, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cancer+Stem+Cells.">Cancer Stem Cells.</a> New England Journal of Medicine. 355, 1253-1261 (2006).</li><li>Ishiguro, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Establishment+and+Characterization+of+an+In+Vitro+Model+of+Ovarian+Cancer+Stem-like+Cells+with+an+Enhanced+Proliferative+Capacity.">Establishment and Characterization of an In Vitro Model of Ovarian Cancer Stem-like Cells with an Enhanced Proliferative Capacity.</a> Cancer Research. 76, 150-160 (2016).</li><li>P&#225;ez, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cancer+Dormancy:+A+Model+of+Early+Dissemination+and+Late+Cancer+Recurrence.">Cancer Dormancy: A Model of Early Dissemination and Late Cancer Recurrence.</a> Clinical Cancer Research. 18, 645-653 (2012).</li><li>Ahmed, N., Stenvers, K. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Getting+to+Know+Ovarian+Cancer+Ascites:+Opportunities+for+Targeted+Therapy-Based+Translational+Research.">Getting to Know Ovarian Cancer Ascites: Opportunities for Targeted Therapy-Based Translational Research.</a> Frontiers in Oncology. 3, (2013).</li><li>Pradhan, S., Clary, J. M., Seliktar, D., Lipke, E. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+three-dimensional+spheroidal+cancer+model+based+on+PEG-fibrinogen+hydrogel+microspheres.">A three-dimensional spheroidal cancer model based on PEG-fibrinogen hydrogel microspheres.</a> Biomaterials. 115, 141-154 (2017).</li><li>Varna, M., Bertheau, P., Legr&#232;s, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tumor+Microenvironment+in+Human+Tumor+Xenografted+Mouse+Models.">Tumor Microenvironment in Human Tumor Xenografted Mouse Models.</a> Journal of Analytical Oncology. 3, 159-166 (2014).</li><li>Huang, S. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+integrated+microfluidic+cell+culture+system+for+high-throughput+perfusion+three-dimensional+cell+culture-based+assays:+Effect+of+cell+culture+model+on+the+results+of+chemosensitivity+assays.">An integrated microfluidic cell culture system for high-throughput perfusion three-dimensional cell culture-based assays: Effect of cell culture model on the results of chemosensitivity assays.</a> Lab on a Chip. 13, 1133-1143 (2013).</li><li>Zang, R., Li, D., Tang, I. C., Wang, J., Yang, S. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell-Based+Assays+in+High-Throughput+Screening+for+Drug+Discovery.">Cell-Based Assays in High-Throughput Screening for Drug Discovery.</a> International Journal of Biotechnology for Wellness Industries. , (2012).</li><li>Murphy, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+alternative+in+vivo+drug+screening+methodology:+a+single+mouse+analysis.">Evaluation of alternative in vivo drug screening methodology: a single mouse analysis.</a> Cancer Research. 76, 5798-5809 (2016).</li><li>Hidalgo, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Patient-derived+Xenograft+models:+An+emerging+platform+for+translational+cancer+research.">Patient-derived Xenograft models: An emerging platform for translational cancer research.</a> Cancer Discovery. 4, 998-1013 (2014).</li><li>Damiati, S., Kompella, U. B., Damiati, S. A., Kodzius, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microfluidic+devices+for+drug+delivery+systems+and+drug+screening.">Microfluidic devices for drug delivery systems and drug screening.</a> Genes. 9, (2018).</li><li>Caliari, S. R., Burdick, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Practical+Guide+to+Hydrogels+for+Cell+Culture.">A Practical Guide to Hydrogels for Cell Culture.</a> Nature Methods. 14, 69-81 (2016).</li><li>Mehta, G., Hsiao, A. Y., Ingram, M., Luker, G. D., Takayama, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Opportunities+and+challenges+for+use+of+tumor+spheroids+as+models+to+test+drug+delivery+and+efficacy.">Opportunities and challenges for use of tumor spheroids as models to test drug delivery and efficacy.</a> Journal of Control Release. , (2012).</li><li>Mehta, P., Novak, C., Raghavan, S., Ward, M., Mehta, G., Papaccio, G., Desiderio, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Self-Renewal+and+CSCs+In+Vitro+Enrichment:+Growth+as+Floating+Spheres.">Self-Renewal and CSCs In Vitro Enrichment: Growth as Floating Spheres.</a> Cancer Stem Cells: Methods and Protocols. , 61-75 (2018).</li><li>Raghavan, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Formation+of+stable+small+cell+number+three-dimensional+ovarian+cancer+spheroids+using+hanging+drop+arrays+for+preclinical+drug+sensitivity+assays.">Formation of stable small cell number three-dimensional ovarian cancer spheroids using hanging drop arrays for preclinical drug sensitivity assays.</a> Gynecologic Oncology. 138, 181-189 (2015).</li><li>Raghavan, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparative+analysis+of+tumor+spheroid+generation+techniques+for+differential+in+vitro+drug+toxicity.">Comparative analysis of tumor spheroid generation techniques for differential in vitro drug toxicity.</a> Oncotarget. 7, 16948-16961 (2016).</li><li>Kim, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+Tumor+Evolution+via+Genomic+Profiling+of+Individual+Tumor+Spheroids+in+a+Malignant+Ascites.">Evaluating Tumor Evolution via Genomic Profiling of Individual Tumor Spheroids in a Malignant Ascites.</a> Scientific Reports. 8, 1-11 (2018).</li><li>Silva, I. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Aldehyde+dehydrogenase+in+combination+with+CD133+defines+angiogenic+ovarian+cancer+stem+cells+that+portend+poor+patient+survival.">Aldehyde dehydrogenase in combination with CD133 defines angiogenic ovarian cancer stem cells that portend poor patient survival.</a> Cancer Research. 71, 3991-4001 (2011).</li><li>Pulaski, H. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identifying+alemtuzumab+as+an+anti-myeloid+cell+antiangiogenic+therapy+for+the+treatment+of+ovarian+cancer.">Identifying alemtuzumab as an anti-myeloid cell antiangiogenic therapy for the treatment of ovarian cancer.</a> Journal of Translational Medicine. 7, 49 (2009).</li><li>Jager, L. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effect+of+enzymatic+and+mechanical+methods+of+dissociation+on+neural+progenitor+cells+derived+from+induced+pluripotent+stem+cells.">Effect of enzymatic and mechanical methods of dissociation on neural progenitor cells derived from induced pluripotent stem cells.</a> Advances in Medical Sciences. 61, 78-84 (2016).</li><li>Ivanov, D. P., Grabowska, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spheroid+arrays+for+high-throughput+single-cell+analysis+of+spatial+patterns+and+biomarker+expression+in+3D.">Spheroid arrays for high-throughput single-cell analysis of spatial patterns and biomarker expression in 3D.</a> Scientific Reports. 7, 41160 (2017).</li></ol>

The 384-well hanging drop plate platform for 3D spheroid formation is an easily implemented tool for any cell biology or cancer biology labs. This physiologic platform enables the study of cell lines, as well as, primary patient samples within physiologically relevant 3D cultures while allowing for high throughput drug screening. The platform also ensures that the culture conditions are highly tunable, enabling tight control over plating densities, cell&#160;co-culture ratios, extracellular components, and medium composi...

Worldwide cancer-related mortality reached a toll of 9.8 million deaths in 20181, highlighting the need for the development of improved therapeutics. Unfortunately, the cost of developing cancer drugs is increasing, with the development of a single drug costing approximately 650 million USD2 indicating the need for improved strategies to develop new anti-cancer drugs. Cancer stem cells (CSCs), which are characterized by increased chemoresistance3, the capacity to self-renew, and the ability to seed new tumors4 are thought to be responsible for tumor recurrence4, metastasis5, and chemoresistance4,6, which all contribute to the malignant capacity of the tumor and thus the high death toll. In ovarian cancer, these cells are found enriched in the malignant ascites fluid in the peritoneal cavity, a condition associated with poor clinical outcomes1. As a result of the malignant capabilities of CSCs, there has been a push to develop new CSC targeting drugs to use in conjunction with traditional chemotherapies. There are several challenges that accompany the development of CSC targeting drugs including: 1) difficulty in expanding and maintaining CSCs in vitro; 2) scarcity of patient samples; 3) physiological relevance of the culture platform; and 4) heterogeneity in drug sensitivity between patients. This protocol outlines the implementation of a high throughput 3D culture platform that can overcome each of these challenges. In particular, this system allows for rapid drug screening using small numbers of patient-derived ovarian CSCs, and is highly amenable to downstream analysis techniques. While ideal for studying ovarian cancer and CSCs, our platform is also valuable in studying other cancers and differentiated cell types in complex 3D environments.
Complex 3-dimensional (3D) models are critical in studying the tumor microenvironment (TME), which is a 3D niche made up of cancer cells, non-cancer supporting cells, and extracellular matrix (ECM) proteins4. This 3D environment results in unique cell morphology, cell-cell and cell-matrix interactions, cell differentiation, cell migration, cell density, and diffusion gradients compared to traditional 2D cell culture in vitro4. All of these factors culminate in differential drug response within 3D cultures, exhibiting increased drug resistance and physiological relevance7,8. Due to the role of the 3D TME in CSC differentiation and chemoresistance, it is vital to screen for CSC targeting drugs in physiologic microenvironments. Improving the physiological relevance of CSC drug screening platforms has the potential to improve patient specific drug screening, drug development, formulation of treatment strategies, and ultimately clinical outcomes. It is equally important that the platform used for drug screening be high-throughput and compatible with downstream analysis methods to minimize cost, time, and clinical translation time of promising drugs9.
Currently, the complex TME is best maintained for drug screening applications through in vivo models such as murine syngeneic tumor models, cell line-derived xenografts, and patient-derived xenograft (PDX) models12, as they provide physiologic conditions. However, the low-throughput nature of these models, as well as, the cost, time, and technical skill sets that they require limits their utility in rapid, high throughput drug screening applications13. As alternatives to these in vivo models, many in vitro 3D models utilizing hydrogels8, culture within microfluidic devices or &#8216;organ-on-a-chip&#8217; devices10,14, and non-adherent cultures3,8 have also been developed, due to their low barrier to entry in terms of cost, time, and required skillset.
Hydrogel culture platforms are advantageous in the fine control afforded over the matrix composition, mechanical properties, and matrix structure15; however, they can inhibit high density cell culture14. Additionally, harvesting cells from hydrogels can complicate downstream analysis, due to potentially harmful effects of harvesting methods15. Microfluidic devices, on the other hand, are microscale devices that allow for output detection within the same device and for cell culture at physiologically relevant scales with minimal consumption of reagents, decreased reaction time, minimized waste, and rapid diffusion14. These characteristics make them promising platforms for investigating drug toxicity, efficacy, and pharmacokinetics. However, the challenges of efficient, quantifiable, reproducible, and user-friendly 3D cell culture, as well as, bulky and costly pumping systems have restricted microfluidic applications in high-throughput research10. Efficient detection setups and potentially difficult implementation across fields have also hindered widespread adoption of microfluidic systems10.
Contrarily, spheroids generated in non-adherent conditions in rotating mixers (nutators), ultra-low attachment plates, and hanging droplets do not include user-defined matrix components. These methodologies are especially relevant for studying ovarian cancer as the non-adherent conditions are representative of the conditions in which spheroids grow within the peritoneal cavity5. Within these non-adherent culture methods, nutator and hanging drop spheroids have been shown to exhibit higher compaction, remodeling, and chemoresistance compared to spheroids generated in ultra-low attachment plates, suggesting increased physiological relevance16,17,18,19. Due to increased capacity for high-throughput screening from smaller well sizes and minimal required cell numbers, spheroid generation in hanging drop plates is an ideal platform for drug screening. Here, we present a tunable 3D physiologic platform in 384-well hanging drop plates, that is easy to implement and highly amenable to downstream analysis, making it ideal for high throughput drug screening of ovarian cancer and ovarian CSCs.
Our 3D physiologic platform provides all of the advantages of 3D culture, including physiological cell-cell contacts, diffusion gradients, cell densities, and naturally produced ECM proteins, which may contribute to realistic drug responses16,17,18,19. Additionally, by generating these spheroids with patient-derived CSCs, we are able to determine patient specific responses to drugs1 with many technical replicates simultaneously, to overcome heterogeneity that may be found within patient tumor samples20. Furthermore, 3D culture has been shown to enhance maintenance of CSC populations3,16 and thus is representative of enriched CSC populations in the ascites7. This combined with easy downstream analysis, including flow cytometry analysis of viability and CSC proportions allows for optimal evaluation of CSC targeting drug efficacy. Finally, this physiologic platform is compatible with imaging at multiple time points during the experiment, evaluation of cell death and proliferation, cell organization and morphology with immunohistochemistry, soluble signaling with ELISA on conditioned medium, cell phenotypes with flow cytometry, and gene expression following PCR.

<table>
	<tbody>
		<tr>
			<td>0.25% trypsin-EDTA</td>
			<td>Gibco</td>
			<td>ILT25200056</td>
			<td></td>
		</tr>
		<tr>
			<td>10 mL serological pipet</td>
			<td>Fisher Scientific</td>
			<td>13-678-11E</td>
			<td></td>
		</tr>
		<tr>
			<td>10,000 cSt Si oil</td>
			<td>Millipore Sigma</td>
			<td>63148-62-9</td>
			<td>Used to coat spheroid array mold to facilitate removal of tissue processing gels, like Histogel, from the mold.</td>
		</tr>
		<tr>
			<td>100 mm tissue culture dish</td>
			<td>Thermo Scientific</td>
			<td>130182</td>
			<td></td>
		</tr>
		<tr>
			<td>15 ml conical tube</td>
			<td>Celltreat</td>
			<td>FL4021</td>
			<td></td>
		</tr>
		<tr>
			<td>1X DMEM for Serum Free Medium</td>
			<td>Gibco</td>
			<td>11965-092</td>
			<td></td>
		</tr>
		<tr>
			<td>1X F12 for Serum Free Medium</td>
			<td>Gibco</td>
			<td>11765-054</td>
			<td></td>
		</tr>
		<tr>
			<td>1X phosphate buffered saline (PBS)</td>
			<td>Gibco</td>
			<td>ILT10010023</td>
			<td></td>
		</tr>
		<tr>
			<td>4&#8217;,6-diamidino-2-phenylindole (DAPI)</td>
			<td>Thermo Fisher</td>
			<td>D1306</td>
			<td></td>
		</tr>
		<tr>
			<td>40 &#181;m filter</td>
			<td>Fisher Scientific</td>
			<td>22363547</td>
			<td></td>
		</tr>
		<tr>
			<td>6-well plate</td>
			<td>Fisher Scientific</td>
			<td>353046</td>
			<td></td>
		</tr>
		<tr>
			<td>Accutase</td>
			<td>Innovative Cell Technologies Inc.</td>
			<td>1449</td>
			<td>A gentle cell detachment enzyme composed of proteolytic and collagenolytic enzymes.</td>
		</tr>
		<tr>
			<td>ACK Lysing Buffer</td>
			<td>Thermo Scientific</td>
			<td>A1049201</td>
			<td></td>
		</tr>
		<tr>
			<td>alamarBlue</td>
			<td>Invitrogen</td>
			<td>DAL1025</td>
			<td>Resazurin dye used to measure viability and proliferation of cells based on their ability to reduce resazurin to resorufin, which is highly fluorescent.</td>
		</tr>
		<tr>
			<td>ALDEFLUOR assay kit</td>
			<td>Stem Cell Tech</td>
			<td>1700</td>
			<td>Kit to identify stem and progenitor cells that express high levels of aldehyde dehydrogenase , an indicator of cancer stem cells. The kit is composed of ALDEFLUOR Reagent, DEAB, Hydrochloric Acid, Dimethylsulphoxide, and ALDEFLUOR Assay Buffer.</td>
		</tr>
		<tr>
			<td>ALDEFLUOR Diethylaminobenzaldehyde (DEAB)</td>
			<td>Stem Cell Tech</td>
			<td>1705</td>
			<td>Diethylaminobenzaldehyde (DEAB) is an inhibitor of ALDH isozymes, used to determine non-specific ALDH staining.</td>
		</tr>
		<tr>
			<td>Andor iXon x3 CCD Camera</td>
			<td>Oxford Instruments</td>
			<td>-</td>
			<td></td>
		</tr>
		<tr>
			<td>Antibiotics and Antimycotics</td>
			<td>Gibco</td>
			<td>15240-062</td>
			<td></td>
		</tr>
		<tr>
			<td>APC-isotype IgG2b</td>
			<td>Miltenyi biotec</td>
			<td>130-092-217</td>
			<td>Isotype control to quantify non-specific staining of IgG2b antibodies.</td>
		</tr>
		<tr>
			<td>B27 Supplement</td>
			<td>Gibco</td>
			<td>17504044</td>
			<td></td>
		</tr>
		<tr>
			<td>basic Fibroblast Growth Factor</td>
			<td>Stem Cell Technologies</td>
			<td>78003.1</td>
			<td></td>
		</tr>
		<tr>
			<td>BD Lo-Dose U-100 Insulin Syringes</td>
			<td>Fisher Scientific</td>
			<td>14-826-79</td>
			<td></td>
		</tr>
		<tr>
			<td>BioTek Synergy HT Microplate Reader</td>
			<td>BioTek</td>
			<td>7091000</td>
			<td></td>
		</tr>
		<tr>
			<td>CD133-APC</td>
			<td>Miltenyi biotec</td>
			<td>130-113-184</td>
			<td>Fluorescent antibody targeting CD133, a cancer stem cell marker.</td>
		</tr>
		<tr>
			<td>cellSens Dimension Software</td>
			<td>Olympus</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Cisplatin</td>
			<td>Sigma-Aldrich</td>
			<td>P4394</td>
			<td>Platinum based chemotherapy agent that functions as an alkylating agent that disrupts DNA.</td>
		</tr>
		<tr>
			<td>DAPI (4&#39;,6-Diamidino-2-Phenylindole, Dihydrochloride)</td>
			<td>Invitrogen</td>
			<td>D1306</td>
			<td></td>
		</tr>
		<tr>
			<td>Epidermal Growth Factor</td>
			<td>Gibco</td>
			<td>PHG0311</td>
			<td></td>
		</tr>
		<tr>
			<td>EVOS XL Core Cell Imaging System</td>
			<td>Life Technologies</td>
			<td>AME3300</td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum - premium (FBS)</td>
			<td>Atlanta Biologicals</td>
			<td>S11150</td>
			<td></td>
		</tr>
		<tr>
			<td>Ficoll 400</td>
			<td>Sigma-Aldrich</td>
			<td>F4375</td>
			<td></td>
		</tr>
		<tr>
			<td>Hemacytometer</td>
			<td>Hausser Scientific</td>
			<td>1490</td>
			<td></td>
		</tr>
		<tr>
			<td>Histogel</td>
			<td>Thermo Scientific</td>
			<td>HG-4000-012</td>
			<td>Tissue processing gel that can penetrate and hold the specimen within the gel while preventing discoloration around the specimen upon staining.</td>
		</tr>
		<tr>
			<td>Human Adipose-Derived Mesenchymal Stem Cells</td>
			<td>Lonza</td>
			<td>PT-5006</td>
			<td></td>
		</tr>
		<tr>
			<td>Human Microvascular Endothelial Cells</td>
			<td>Lonza</td>
			<td>CC2543</td>
			<td></td>
		</tr>
		<tr>
			<td>Insulin-Transferrin-Selenium Supplement</td>
			<td>Gibco</td>
			<td>51500-056</td>
			<td></td>
		</tr>
		<tr>
			<td>Live/Dead viability kit</td>
			<td>Invitrogen</td>
			<td>L3224</td>
			<td>Kit for the fluorescence based detection of live (calcein-AM) and dead cells (Ethidium Homodimer-1).</td>
		</tr>
		<tr>
			<td>MEM Non-essential Amino Acids</td>
			<td>Gibco</td>
			<td>11140-050</td>
			<td></td>
		</tr>
		<tr>
			<td>MetaMorph 7.8 Software</td>
			<td>Molecular Devices</td>
			<td>-</td>
			<td></td>
		</tr>
		<tr>
			<td>Olympus IX81 Inverted Confocal Microscope</td>
			<td>Olympus</td>
			<td>-</td>
			<td></td>
		</tr>
		<tr>
			<td>Olympus IX83 Research Inverted Microscope</td>
			<td>Olympus</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Parafilm M</td>
			<td>Thomas Scientific</td>
			<td>7315D35</td>
			<td>Thermoplastic polymer strips that serve to limit droplet evaporation in hanging drop plates while still allowing for gas exchange.</td>
		</tr>
		<tr>
			<td>Perfecta 3D 384 Well Hanging Drop Plates</td>
			<td>3D Biomatrix</td>
			<td>HDP1384-8</td>
			<td>Available through Sigma-Aldrich</td>
		</tr>
		<tr>
			<td>phalloidin AlexaFluor488</td>
			<td>Invitrogen</td>
			<td>A12379</td>
			<td>Phalloidin is a peptide to fluorescently label F-actin in fixed cells.</td>
		</tr>
		<tr>
			<td>ProJet 3500 HD Max</td>
			<td>3D Systems</td>
			<td>-</td>
			<td>3D printer</td>
		</tr>
		<tr>
			<td>Sterile DI water</td>
			<td>Fisher Scientific</td>
			<td>353046</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypan Blue</td>
			<td>Gibco</td>
			<td>15250061</td>
			<td>Azo dye used to differentiate between live and dead cells based on its ability to pass through the damaged membrane of dead cells, but not the intact membrane of live cells.</td>
		</tr>
		<tr>
			<td>VisiJet M3 Crystal</td>
			<td>3D Systems</td>
			<td>-</td>
			<td>A biocompatible polymer material for 3D printing.</td>
		</tr>
		<tr>
			<td>Yokogawa CSU-X1 Confocal Scanner Unit</td>
			<td>Yokogawa</td>
			<td>-</td>
			<td></td>
		</tr>
	</tbody>
</table>

physiologic patient derived 3d spheroids for anti-neoplastic drug screening to target cancer stem cells

In this protocol, we outline the procedure for generation of tumor spheroids within 384-well hanging droplets to allow for high-throughput screening of anti-cancer therapeutics in a physiologically representative microenvironment. We outline the formation of patient derived cancer stem cell spheroids, as well as, the manipulation of these spheroids for thorough analysis following drug treatment. Specifically, we describe collection of spheroid morphology, proliferation, viability, drug toxicity, cell phenotype and cell localization data. This protocol focuses heavily on analysis techniques that are easily implemented using the 384-well hanging drop platform, making it ideal for high throughput drug screening. While we emphasize the importance of this model in ovarian cancer studies and cancer stem cell research, the 384-well platform is amenable to research of other cancer types and disease models, extending the utility of the platform to many fields. By improving the speed of personalized drug screening and the quality of screening results through easily implemented physiologically representative 3D cultures, this platform is predicted to aid in the development of new therapeutics and patient-specific treatment strategies, and thus have wide-reaching clinical impact.

Spheroids formed with cell lines or patient-derived CSCs can be formed with a range of small cell numbers within hanging droplets (Figure 2A). Spheroids form reliably with as few as 10 cells per well, which allows for conservation of rare patient samples. Cells within these spheroids are surrounded by other cells in 3 dimensions as they would be in vivo, allowing for physiologic cell-cell contacts and diffusion rates. Tumor cells within the spheroids proliferate causing the spheroids to expa...

Watch this Scientific Journal Video about Physiologic Patient Derived 3D Spheroids for Anti-neoplastic Drug Screening to Target Cancer Stem Cells at JoVE.com

Physiologic Patient Derived 3D Spheroids for Anti-neoplastic Drug Screening to Target Cancer Stem Cells

This protocol describes generation of patient-derived spheroids, and downstream analysis including quantification of proliferation, cytotoxicity testing, flow cytometry, immunofluorescence staining and confocal imaging, in order to assess drug candidates&#8217; potential as anti-neoplastic therapeutics. This protocol supports precision medicine with identification of specific drugs for each patient and stage of disease.

In this protocol, we outline the procedure for generation of tumor spheroids within 384-well hanging droplets to allow for high-throughput screening of ...

Our protocol provides a simple method for 3D cell culture and is highly amenable to downstream analysis. This facilitates the evaluation of heterogeneous patient samples and their specific drug responses. The advantage of this technique is that spheroids can be formed from small cell numbers, allowing for both preservation of scarce primary samples, and high throughput screening for patient-specific drug responses.The hanging drop model creates a physiological environment with 3D drug diffusion and does responses corresponding to tumor stage. This allows for the development of targeted therapies and patient-specific treatments. This method is ideal for studying ovarian cancer, heterogeneity and the development of chemo resistance.However, it can also be used to study other cancers and drug-resistant cell populations. When plating spheroids, it is important to pipette precise volumes. This ensures consistency between spheroids.The spheroids and their respective droplets are inherently fragile and easily lost without proper handling, therefore we will show how to appropriately plate, maintain and analyze a hanging drop plate. Demonstrating this procedure will be Michael Bregenzer, a graduate student from our laboratory. Start by filling each well of the six-well plate with four to five milliliters of autoclave deionized water and sandwiching the hanging drop plate between the lid and the bottom of the plate.Add 800 to 1000 microliters of water around the rim of the hanging drop plate to provide a humid environment and minimize evaporation. Next, collect 2D grown cells by detaching them with trypsin, and then adding six or eight milliliters of medium containing FBS. Aspirate the cells with the 10 milliliters serological pipette, and deposit them in a 15 milliliter conical tube.Use a hemocytometer to count the cells. Prepare cell suspension according to manuscript directions, and mix gently with the pipette to ensure homogenous distribution. Place the tip of the pipette in the well at an angle of 45 degrees and pipette 20 microliters of suspension into each hanging drop well.Place the lid of the six-well plate back on and use a stretchy thermoplastic strip to seal the edges. Incubate in a standard carbon dioxide humidified incubator and feed the hanging drops every two to three days by adding two to three microliters of cell culture medium to each spheroid-containing well. To quantify proliferation and viability of the cells, add two microliters of filtered resazurin-based solution to the wells designated for proliferation analysis and incubate according to manuscript direction.After the incubation period, open the hanging drop sandwich in a biosafety cabinet and bring the 384 well plate with the lid still in place to the plate reader. Place it in the plate reader and read the plate. Save the experiment in the popup window and export the data into a spreadsheet.Return the plate back to the six-well base and place it in the incubator. Once all time points have been read for the day, reseal the plate before incubating. To prepare spheroids for flow cytometry analysis, use a 1000 microliter pipette to collect them from each well and deposit them into a 15 milliliter conical tube for disaggregation.Aliquot cell suspension into five micro centrifuge tubes so that each tube contains at least 50, 000 cells. Centrifuge the tubes at 400 times G for five minutes in a microcentrifuge, then aspirate the supernatant and re-suspend pellets in 100 microliters of Aldefluor buffer. Label the tubes according to manuscript directions.Add 0.5 microliters of APC isotype antibody to the APC ISO tube and one microliter of CD133 antibody to the ALDH CD133 tube. Then add five microliters of DEAB reagent and 0.5 microliters of ALDH to the DEAB tube and one microliter of ALDH to the ALDH CD133 tube. Vortex all tubes for a few seconds and incubate them at 37 degrees celsius for 45 minutes.After the incubation, vortex all tubes again and centrifuge them at 400 times G for five minutes. Label FACS tubes according to manuscript directions and fill an insulated foam container with ice. Aspirate the supernatant from the microcentrifuge tubes.Then re-suspend the unstained control in 400 microliters of FACS buffer and the rest of the cells in 400 microliters of FACS DAPI buffer. Place the tubes on the ice until they can be analyzed on a flow cytometer. Use FlowJo to analyze the data from the flow cytometer.Double click the unstained file and set the Y-axis to side scatter height or SSCH and the X-axis to forward scatter height or FSCH. Click the T button next to each axis to adjust the scale and maximize separation between different cell populations. Next, click on the Polygon Gating button, draw polygon gate around the cell population and label the population Cells.Double click the cell's population in the workspace, and then change the FSC axis to FSC width and the SSC axis to the FSC height. Choose the rectangle gate to only draw a rectangle around the leftmost dense population of cells, spanning the entire Y-axis. Label this gate Single Cells.Then right click and copy this Cells in the nested Single Cells gate and paste them under each sample in the workspace. Double click the Single Cells gate nested under the DAPI sample to view the single cell population from that sample tube. Change the FSC axis channel to the DAPI area channel and change the SSC axis channel to histogram.Click on the T button next to the DAPI axis and click on Customize Axis to adjust the scale and maximize separation between DAPI positive and DAPI negative peaks. Click Apply in the popup window and choose the Range Gate button. Spread it over the DAPI negative peak and label this gate Live Cells.Copy the Live Cells gate and paste it under the Single Cells gate, under the APC ISO DEAB and ALDH CD133 tubes, to select the same portion of live cells in each tube. Then double click the Live Cells population nested under the APC ISO sample file, and switch the X-axis to ALDH area and the Y-axis to APC area. Apply Quadrant Gate and adjust the intersection of the gate such as that approximately 0.5%of the population lies in the upper left of the plot window.Then name the quadrants as desired, then copy and paste the quadrant gates onto the live cells population nested under the DEAB file and adjust the vertical line so that approximately 0.15%of the cell population lies within the ALDH positive quadrant. Finally, copy and paste the quadrant gates to the ALDH CD133 files Live Cells population and evaluation cancer stem cell proportions based on ALDH and CD133 proportions. This protocol can be used for high throughput analysis of patient-derived cancer stem cells.As little as 10 cells per well can form reliable spheroids and as the cells proliferate, the spheroids expand in size. Proliferation capacity can be easily quantified with a resazurin-based florescence assay. The effect of drugs on spheroid morphology can be visualized with phase contrast imaging and quantified by comparing resazurin florescence in untreated and treated cells.Cell death can be validated via addition of Calcein-AM and ethidium homodimer I to spheroids, followed by imaging on a confocal microscope. Finally, spheroids can be harvested and dispersed into a single cells suspension for analysis with flow cytometry, which makes it possible to discern the effectiveness of different drug treatments on specific cell populations. To avoid droplet loss, medium evaporation and variation in spheroid size, it is important to handle your plates carefully, pipette with precision and completely seal your hanging drop plates.To evaluate changes in gene expression and soluble signaling due to drug treatment, single cell RNA sequencing and enzyme-linked immunosorbent assays, can be used to facilitate the development of therapeutics. This technique allows researches in oncology, precision medicine and drug development to explore intra and inter patient heterogeneity as well chemo resistance through high throughput drug screening of small biopsies. When performing this protocol, be sure to wear all standard personal protective equipment.Including gloves, safety glasses, lab coats, pants and close-toed shoes.

physiologic-patient-derived-3d-spheroids-for-anti-neoplastic-drug

Research

JoVE Journal

Cancer Research

Generation of High-Throughput Three-Dimensional Tumor Spheroids for Drug Screening

Cancer cells have routinely been cultured in two dimensions (2D) on a plastic surface. This technique, however, lacks the true environment a tumor mass is exposed to in vivo. Solid tumors grow not as a sheet attached to plastic, but instead as a collection of clonal cells in a three-dimensional (3D) space interacting with their neighbors, and with distinct spatial properties such as the disruption of normal cellular polarity. These interactions cause 3D-cultured cells to acquire morphological and cellular characteristics which are more relevant to in vivo tumors. Additionally, a tumor mass is in direct contact with other cell types such as stromal and immune cells, as well as the extracellular matrix from all other cell types. The matrix deposited is comprised of macromolecules such as collagen and fibronectin.
In an attempt to increase the translation of research findings in oncology from bench to bedside, many groups have started to investigate the use of 3D model systems in their drug development strategies. These systems are thought to be more physiologically relevant because they attempt to recapitulate the complex and heterogeneous environment of a tumor. These systems, however, can be quite complex, and, although amenable to growth in 96-well formats, and some now even in 384, they offer few choices for large-scale growth and screening. This observed gap has led to the development of the methods described here in detail to culture tumor spheroids in a high-throughput capacity in 1536-well plates. These methods represent a compromise to the highly complex matrix-based systems, which are difficult to screen, and conventional 2D assays. A variety of cancer cell lines harboring different genetic mutations are successfully screened, examining compound efficacy by using a curated library of compounds targeting the Mitogen-Activated Protein Kinase or MAPK pathway. The spheroid culture responses are then compared to the response of cells grown in 2D, and differential activities are reported. These methods provide a unique protocol for testing compound activity in a high-throughput 3D setting.

Across a wide variety of disease indications, more physiologically relevant models are being developed and implemented into drug discovery programs. The new model system described here demonstrates how three-dimensional tumor spheroids can be cultured and screened in a high-throughput 1536-well plate-based system to search for new oncology drugs.

Cancer cells have routinely been cultured in two dimensions (2D) on a plastic surface. This technique, however, lacks the true environment a tumor mass is ...

Modeling Osteosarcoma Using Li-Fraumeni Syndrome Patient-derived Induced Pluripotent Stem Cells

Li-Fraumeni syndrome (LFS) is an autosomal dominant hereditary cancer disorder. Patients with LFS are predisposed to a various type of tumors, including osteosarcoma--one of the most frequent primary non-hematologic malignancies in the childhood and adolescence. Therefore, LFS provides an ideal model to study this malignancy. Taking advantage of iPSC methodologies, LFS-associated osteosarcoma can be successfully modeled by differentiating LFS patient iPSCs to mesenchymal stem cells (MSCs), and then to osteoblasts--the cells of origin for osteosarcomas. These LFS osteoblasts recapitulate oncogenic properties of osteosarcoma, providing an attractive model system for delineating the pathogenesis of osteosarcoma. This manuscript demonstrates a protocol for the generation of iPSCs from LFS patient fibroblasts, differentiation of iPSCs to MSCs, differentiation of MSCs to osteoblasts, and in vivo tumorigenesis using LFS osteoblasts. This iPSC disease model can be extended to identify potential biomarkers or therapeutic targets for LFS-associated osteosarcoma.

Here, we present a protocol for the generation of induced pluripotent Stem Cells (iPSCs) from Li-Fraumeni Syndrome (LFS) patient derived fibroblasts, differentiation of iPSCs via mesenchymal stem cells (MSCs) to osteoblasts, and modeling in vivo tumorigenesis using LFS patient-derived osteoblasts.

Li-Fraumeni syndrome (LFS) is an autosomal dominant hereditary cancer disorder. Patients with LFS are predisposed to a various type of tumors, including ...

Drug Screening of Primary Patient Derived Tumor Xenografts in Zebrafish

Patient derived xenograft models are critical in defining how different cancers respond to drug treatment in an in vivo system. Mouse models are the standard in the field, but zebrafish have emerged as an alternative model with several advantages, including the ability for high-throughput and low-cost drug screening. Zebrafish also allow for in vivo drug screening with large replicate numbers that were previously only obtainable with in vitro systems. The ability to rapidly perform large scale drug screens may open up the possibility for personalized medicine with rapid translation of results back to clinic. Zebrafish xenograft models could also be used to rapidly screen for actionable mutations based on tumor response to targeted therapies or to identify new anti-cancer compounds from large libraries. The current major limitation in the field has been quantifying and automating the process so that drug screens can be done on a larger scale and be less labor-intensive. We have developed a workflow for xenografting primary patient samples into zebrafish larvae and performing large scale drug screens using a fluorescence microscope equipped imaging unit and automated sampler unit. This method allows for standardization and quantification of engrafted tumor area and response to drug treatment across large numbers of zebrafish larvae. Overall, this method is advantageous over traditional cell culture drug screening as it allows for growth of tumor cells in an in vivo environment throughout drug treatment, and is more practical and cost-effective than mice for large scale in vivo drug screens.

Zebrafish xenograft models allow for high-throughput drug screening and fluorescent imaging of human cancer cells in an in vivo microenvironment. We developed a workflow for large scale, automated drug screening on patient-derived leukemia samples in zebrafish using an automated fluorescence microscope equipped imaging unit.

Patient derived xenograft models are critical in defining how different cancers respond to drug treatment in an in vivo system. Mouse models are the ...

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

An understanding of drug resistance and the development of novel strategies to sensitize highly resistant cancers rely on the availability of suitable preclinical models that can accurately predict patient responses. One of the disadvantages of existing preclinical models is the inability to contextually preserve the human tumor microenvironment (TME) and accurately represent intratumoral heterogeneity, thus limiting the clinical translation of data. By contrast, by representing the culture of live fragments of human tumors, the patient-derived explant (PDE) platform allows drug responses to be examined in a three-dimensional (3D) context that mirrors the pathological and architectural features of the original tumors as closely as possible. Previous reports with PDEs have documented the ability of the platform to distinguish chemosensitive from chemoresistant tumors, and it has been shown that this segregation is predictive of patient responses to the same chemotherapies. Simultaneously, PDEs allow the opportunity to interrogate molecular, genetic, and histological features of tumors that predict drug responses, thereby identifying biomarkers for patient stratification as well as novel interventional approaches to sensitize resistant tumors. This paper reports PDE methodology in detail, from collection of patient samples through to endpoint analysis. It provides a detailed description of explant derivation and culture methods, highlighting bespoke conditions for particular tumors, where appropriate. For endpoint analysis, there is a focus on multiplexed immunofluorescence and multispectral imaging for the spatial profiling of key biomarkers within both tumoral and stromal regions. By combining these methods, it is possible to generate quantitative and qualitative drug response data that can be related to various clinicopathological parameters and thus potentially be used for biomarker identification.

Patient-Derived Tumor Explants As a &quot;Live&quot; Preclinical Platform for Predicting Drug Resistance in Patients

This paper describes methods for the generation, drug treatment, and analysis of patient-derived explants for assessing tumor drug responses in a live, patient-relevant, preclinical model system.

An understanding of drug resistance and the development of novel strategies to sensitize highly resistant cancers rely on the availability of suitable ...

A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells

The glioma stem cells (GSCs) are a small fraction of cancer cells which play essential roles in tumor initiation, angiogenesis, and drug resistance in glioblastoma (GBM), the most prevalent and devastating primary brain tumor. The presence of GSCs makes the GBM very refractory to most of individual targeted agents, so high-throughput screening methods are required to identify potential effective combination therapeutics. The protocol describes a simple workflow to enable rapid screening for potential combination therapy with synergistic interaction. The general steps of this workflow consist of establishing luciferase-tagged GSCs, preparing matrigel coated plates, combination drug screening, analyzing, and validating the results.

The glioma stem cells (GSCs) are a small fraction of cancer cells which play essential roles in tumor initiation, angiogenesis, and drug resistance in ...

Exploring Mitochondrial Energy Metabolism of Single 3D Microtissue Spheroids Using Extracellular Flux Analysis

Three-dimensional (3D) cellular aggregates, termed spheroids, have become the forefront of in vitro cell culture in recent years. In contrast to culturing cells as two-dimensional, single-cell monolayers (2D culture), spheroid cell culture promotes, regulates, and supports physiological cellular architecture and characteristics that exist in vivo, including the expression of extracellular matrix proteins, cell signaling, gene expression, protein production, differentiation, and proliferation. The importance of 3D culture has been recognized in many research fields, including oncology, diabetes, stem cell biology, and tissue engineering. Over the last decade, improved methods have been developed to produce spheroids and assess their metabolic function and fate.
Extracellular flux (XF) analyzers have been used to explore mitochondrial function in 3D microtissues such as spheroids using either an XF24 islet capture plate or an XFe96 spheroid microplate. However, distinct protocols and the optimization of probing mitochondrial energy metabolism in spheroids using XF technology have not been described in detail. This paper provides detailed protocols for probing mitochondrial energy metabolism in single 3D spheroids using spheroid microplates with the XFe96 XF analyzer. Using different cancer cell lines, XF technology is demonstrated to be capable of distinguishing between cellular respiration in 3D spheroids of not only different sizes but also different volumes, cell numbers,&#160;DNA content and type.
The optimal mitochondrial effector compound concentrations of oligomycin, BAM15, rotenone, and antimycin A are used to probe specific parameters of mitochondrial energy metabolism in 3D spheroids. This paper also discusses methods to normalize data obtained from spheroids and addresses many considerations that should be considered when exploring spheroid metabolism using XF technology. This protocol will help drive research in advanced in vitro spheroid models.

These protocols will help users probe mitochondrial energy metabolism in 3D cancer cell-line-derived spheroids using Seahorse extracellular flux analysis.

Three-dimensional (3D) cellular aggregates, termed spheroids, have become the forefront of in vitro cell culture in recent years. In contrast to culturing ...

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Understanding clinically relevant driver mechanisms of acquired chemo-resistance is crucial for elucidating ways to circumvent resistance and improve survival in patients with acute myeloid leukemia (AML). A small fraction of leukemic cells that survive chemotherapy have a poised epigenetic state to tolerate chemotherapeutic insult. Further exposure to chemotherapy allows these drug persister cells to attain a fixed epigenetic state, which leads to altered gene expression, resulting in the proliferation of these drug-resistant populations and eventually relapse or refractory disease. Therefore, identifying epigenetic modulations that necessitate the survival of drug-resistant leukemic cells is critical. We detail a protocol to identify epigenetic modulators that mediate resistance to the nucleoside analog cytarabine (AraC) using pooled shRNA library screening in an acquired cytarabine-resistant AML cell line. The library consists of 5,485 shRNA constructs targeting 407 human epigenetic factors, which allows high-throughput epigenetic factor screening.

High-throughput RNA interference (RNAi) screening using a pool of lentiviral shRNAs can be a tool to detect therapeutically relevant synthetic lethal targets in malignancies. We provide a pooled shRNA screening approach to investigate the epigenetic effectors in acute myeloid leukemia (AML).

Understanding clinically relevant driver mechanisms of acquired chemo-resistance is crucial for elucidating ways to circumvent resistance and improve ...

Establishing 3-Dimensional Spheroids from Patient-Derived Tumor Samples and Evaluating their Sensitivity to Drugs

Despite remarkable advances in understanding tumor biology, the vast majority of oncology drug candidates entering clinical trials fail, often due to a lack of clinical efficacy. This high failure rate illuminates the inability of the current preclinical models to predict clinical efficacy, mainly due to their inadequacy in reflecting tumor heterogeneity and the tumor microenvironment. These limitations can be addressed with 3-dimensional (3D) culture models (spheroids) established from human tumor samples derived from individual patients. These 3D cultures represent real-world biology better than established cell lines that do not reflect tumor heterogeneity. Furthermore, 3D cultures are better than 2-dimensional (2D) culture models (monolayer structures) since they replicate elements of the tumor environment, such as hypoxia, necrosis, and cell adhesion, and preserve the natural cell shape and growth. In the present study, a method was developed for preparing primary cultures of cancer cells from individual patients that are 3D and grow in multicellular spheroids. The cells can be derived directly from patient tumors or patient-derived xenografts. The method is widely applicable to solid tumors (e.g., colon, breast, and lung) and is also cost-effective, as it can be performed in its entirety in a typical cancer research/cell biology lab without relying on specialized equipment. Herein, a protocol is presented for generating 3D tumor culture models (multicellular spheroids) from primary cancer cells and evaluating their sensitivity to drugs using two complementary approaches: a cell-viability assay (MTT) and microscopic examinations. These multicellular spheroids can be used to assess potential drug candidates, identify potential biomarkers or therapeutic targets, and investigate the mechanisms of response and resistance.

The present protocol describes generating 3D tumor culture models from primary cancer cells and evaluating their sensitivity to drugs using cell-viability assays and microscopic examinations.

Despite remarkable advances in understanding tumor biology, the vast majority of oncology drug candidates entering clinical trials fail, often due to a ...

Orthotopic Implantation of Patient-Derived Cancer Cells in Mice Recapitulates Advanced Colorectal Cancer

Over the last decade, more sophisticated preclinical colorectal cancer (CRC) models have been established using patient-derived cancer cells and 3D tumoroids. Since patient derived tumor organoids can retain the characteristics of the original tumor, these reliable preclinical models enable cancer drug screening and the study of drug resistance mechanisms. However, CRC related death in patients is mostly associated with the presence of metastatic disease. It is therefore essential to evaluate the efficacy of anti-cancer therapies in relevant in vivo models that truly recapitulate the key molecular features of human cancer metastasis. We have established an orthotopic model based on the injection of CRC patient-derived cancer cells directly into the cecum wall of mice. These tumor cells develop primary tumors in the cecum that metastasize to the liver and lungs, which is frequently observed in patients with advanced CRC. This CRC mouse model can be used to evaluate drug responses monitored by microcomputed tomography (&#181;CT), a clinically relevant small-scale imaging method that can easily identify primary tumors or metastases in patients. Here, we describe the surgical procedure and the required methodology to implant patient-derived cancer cells in the cecum wall of immunodeficient mice.

This protocol describes the orthotopic implantation of patient-derived cancer cells in the cecum wall of immunodeficient mice. The model recapitulates advanced colorectal cancer metastatic disease and allows for the evaluation of new therapeutic drugs in a clinically relevant scenario of lung and liver metastases.

Over the last decade, more sophisticated preclinical colorectal cancer (CRC) models have been established using patient-derived cancer cells and 3D ...

Generation of 3D Tumor Spheroids for Drug Evaluation Studies

In recent decades, in addition to monolayer-cultured cells, three-dimensional tumor spheroids have been developed as a potentially powerful tool for the evaluation of anticancer drugs. However, the conventional culture methods lack the ability to manipulate the tumor spheroids in a homogeneous manner at the three-dimensional level. To address this limitation, in this paper, we present a convenient and effective method of constructing average-sized tumor spheroids. Additionally, we describe a method of image-based analysis using artificial intelligence-based analysis software that can scan the whole plate and obtain data on three-dimensional spheroids. Several parameters were studied. By using a standard method of tumor spheroid construction and a high-throughput imaging and analysis system, the effectiveness and accuracy of drug tests performed on three-dimensional spheroids can be dramatically increased.

This article demonstrates a standardized method for constructing three-dimensional tumor spheroids. A strategy for spheroid observation and image-based deep-learning analysis using an automated imaging system is also described.

In recent decades, in addition to monolayer-cultured cells, three-dimensional tumor spheroids have been developed as a potentially powerful tool for the ...