Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA243511. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors thank Deborah Frank for her editorial comments.

The collection of human specimens for this research was approved by the Washington University School of Medicine Institutional Review Board. All eligible patients over the age of 18 years had a diagnosis or presumed diagnosis of high-grade serous ovarian cancer and were willing and able to provide informed consent. The tumor tissue from either primary or metastatic sites, in addition to ascites and pleural fluid, were obtained from consented patients at the time of care.
1. Selection of established PDOs for viability assay
NOTE: Typically, these organoids are within the first five passages. As described previously, ovarian cancer PDOs are formed by resuspending primary cell suspension in basement membrane extract (BME) such as Cultrex or Matrigel17 (see Table of Materials).
<ol>
	<li>PDOs should have a perceived doubling time of 24-72 h for the best efficiency of the assay. Estimate the perceived doubling time by visually determining the number of days it takes for organoids to form after passaging/plating. 
	NOTE: In our experience, organoids that take longer than three days to form cannot be used for this growth inhibition assay.</li>
	<li>Identify drugs and select a range of concentrations to test (e.g., Carboplatin, 0-50 &#181;M, see Table of Materials).</li>
	<li>Determine the plate setup. This assay will be performed in triplicate, limiting the number of drugs and concentrations that can be tested on one plate. 
	NOTE: Selected PDO lines must be carefully observed before performing the assay. Established ovarian cancer PDOs form multicellular solid and hollow spheres with tumor-like budding shapes (Figure 1B). PDO morphology, genomic profile, etc., must be compared to that of the patient sample (when possible) before performing the assay29-33. Ovarian cancer PDOs can be generated from primary and metastatic tumor samples and ascites17.</li>
</ol>
2. Reagent preparation for the viability assay
<ol>
	<li>Base Media: To advanced DMEM/F12, add 1% (v/v) penicillin-streptomycin, 1x glutamax, and 1% (v/v)HEPES17 (see Table of Materials).</li>
	<li>Prepare Advance Organoid Media for ovarian cancer organoids following previously published report17.</li>
</ol>
3. Plating of organoids (~Day -2)
NOTE: This step must be performed 1-3 days before adding the drugs. Before beginning, warm all reagents (Base Media, Advance Organoid Media, and organoid dissociation reagent; see Table of Materials) to 37 &#176;C in a water bath. Thaw BME in an ice water bath.
<ol>
	<li>Use a brightfield microscope to confirm whether organoids of interest are 70%-90% confluent. 
	NOTE: It is recommended to save images of the organoids for future reference.</li>
	<li>Add 1-2 mL of warmed Base Media to the well containing organoids and pipette up and down to mechanically dissociate the BME-containing organoids. Transfer the entire solution to a 15 mL conical tube17. 
	NOTE: Typically, 1-2 mL of media is enough to properly dilute the BME. Organoids of the same patient and passage can be combined.</li>
	<li>Vortex for 5-10 s to further dissociate the cells, and centrifuge at 1107 x g for 5 min at room temperature (RT).</li>
	<li>Remove the supernatant with a single-channel pipette and discard. Resuspend organoids in 1 mL organoid dissociation reagent (see Table of Materials) and transfer to a 1.5 mL microcentrifuge tube. Incubate for 7&#160;min at 37 &#176;C.
	<ol>
		<li>If the pellet is still gelatinous because of the remaining BME, add an additional 1 mL of Base Media, vortex for 5-10 s, and repeat centrifugation.</li>
	</ol>
	</li>
	<li>Centrifuge at 1107 x g for 5 min at RT. Remove and discard the supernatant and resuspend the cell pellet in 1 mL of Base Media.</li>
	<li>Count the number of cells in each PDO culture using an automatic cell counter (see Table of Materials) or a hemocytometer.</li>
	<li>Resuspend cells at 20,000 cells per 10 &#181;L of 25% Base Media and 75% BME. Do this by resuspending the organoids in the media and mixing&#160;with BME.</li>
	<li>Plate one 3 &#181;L droplets of resuspended BME + PDO cells into one well of a black opaque 96-well plate (see Table of Materials). Place the droplets in the center of each well. Plate each drug concentration in triplicate. 
	NOTE: Since the cell viability assay is luminescence-based, it is best to use black opaque plates to avoid background. Transparent plates can be used to visualize organoids during the assay,&#160;but the bottom of the plates should be covered with opaque tape before measuring luminescence.</li>
	<li>Incubate plate in a cell culture incubator at 37 &#176;C for 15 min.</li>
	<li>Add 100 &#181;L of Advance Organoid Media to each well. Incubate plate for 24-72 h (determine according to perceived PDO doubling time). Add 100 &#181;L of Advance Organoid Media to an empty well for use as blank. 
	NOTE: The goal is to allow organoids to form.</li>
	<li>Optional: For growth rate analysis, plate additional triplicate PDOs in a separate plate. This will be used to count cells at Time = 0 and should be assayed on Day 0 using the viability assay described in Section 5.</li>
</ol>
4. Addition of drugs for the viability assay on Day 0
NOTE: Day 0 refers to the day the drugs are added to the fully formed organoids.
<ol>
	<li>Dilute selected drugs in Advance Organoid Media to desired concentrations. Dilutions can be made in 1.5 mL tubes or in a pre-set up 96-well plate, which would allow for the use of a multi-channel pipette to dispense the media. 
	NOTE: Drugs must be resuspended in the solvent suggested by the manufacturer, such as dimethyl sulfoxide. For this study, the following dilutions of carboplatin in Advance Organoid Media, were made: 1, 5, 10, 25, 50, and 75 &#181;M.</li>
	<li>Remove media from each well with a single or multi-channel pipette, being careful not to touch or disturb the PDO/BME droplet.</li>
	<li>Add 100 &#181;L of Advance Organoid Media and desired drug(s) to each well. Ensure to add fresh media to the three control wells. 
	NOTE: Drug concentrations will vary and depend on the scientific question and drug mechanism of action. When deciding what concentrations to use, we begin with previously established drug concentrations in comparable 2D cell lines (e.g., immortalized ovarian cancer cell lines). Concentrations may need to be increased if there is no observed effect on organoids.</li>
	<li>Refresh media and drugs as needed (repeat steps 4.1-4.3). Whether the media will need to be refreshed depends on the assay length, the drug&#39;s biological activity, and the drug&#39;s half-life. For assays over one week, the media will need to be refreshed at least once.</li>
</ol>
5. Ending the viability assay for readout (~Day 7)
NOTE: This step can be performed on Days 7-10. The assay length must be determined according to the half-life and pharmacodynamics of the drugs being tested.
<ol>
	<li>Allow the viability assay reagents to reach room temperature in dark. Store the reagents at 4 &#176;C overnight (in the dark) to reduce the thaw time.</li>
	<li>Remove the assay plate from cell culture incubator and allow it to acclimate to room temperature for 30 min. The plate does not have to acclimate in the dark.</li>
	<li>Add 100 &#181;L of viability assay reagent (see Table of Materials) to each well (for a total volume of 200 &#181;L), and place on a plate-shaker for 5 min (80 rpm). Remove the plate from the shaker and incubate for an additional 25 min at room temperature. Ensure the plate is always protected from light by covering it with foil or an opaque box.</li>
	<li>Turn on the bioluminescence plate reader and open the i-control software (see Table of Materials).</li>
	<li>Under Connect to: Instrument Name, select infinite 200Pro.</li>
	<li>Select Luminescence and Default Script.</li>
	<li>From the drop-down menu, choose plate type [BD96fb_Falcon-BD] Falcon 96 Flat Black.</li>
	<li>Determine which wells to read and highlight the corresponding wells under Part of Plate.</li>
	<li>Under Measurements on the left side of the screen, drag and drop Luminescence under the Part of Plate.</li>
	<li>Select the luminescence parameters: Attenuation: NONE; Integration time: 1000 ms; Settle time: 0 ms.</li>
	<li>Remove the cover from the plate and load it into the plate reader. Press Start to begin.</li>
	<li>On completion, export data and save.</li>
</ol>
6. Data analysis
<ol>
	<li>Calculate the percent cell viability.
	<ol>
		<li>Subtract the &#34;Blank&#34; from every well for the Blank Corrected readings. Next, calculate Control Average by averaging the three control wells.</li>
		<li>Use the following equation to calculate the percentage of viable cells in each well: (Experimental well / Control Average) x 100</li>
		<li>Graph the resulting data in the analysis software (see Table of Materials).</li>
	</ol>
	</li>
	<li>Examine Growth Rate (GR) metrics.
	<ol>
		<li>Manually calculate the GR metrics according to the published report34.</li>
		<li>Alternatively, use the online GR calculator (see Table of Materials) to generate the metrics35. Use the Day 0 measurements as &#34;cell_count_time0&#34;, which reflect the growth rate of the untreated PDOs.</li>
		<li>Export data and graph.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

This article describes a method that can be used to assess the therapeutic effects of conventional or novel drugs on ovarian cancer PDOs. Researchers must consider several issues before conducting the viability assay in the PDO model.
First, when selecting a PDO to use in the viability assay, one must determine the ideal organoid type (tumor vs. ascites) and passage number for their needs. In our experience, ascites-derived PDOs grow more rapidly and are easier to generate than tumor-...

Although rare, ovarian cancer is one of the most lethal gynecological cancers1,2. A challenge in developing new treatments is that ovarian cancer is heterogeneous, and the tumor microenvironment differs greatly among patients. Additionally, many ovarian cancers develop resistance to platinum-based chemotherapy and poly (ADP-ribose) polymerase inhibitors, highlighting the need for greater therapeutic options3,4,5.
One approach that may be useful in identifying new therapeutics is using patient-derived organoids (PDOs). Organoids are three-dimensional clusters of multiple cell types that self-organize and form in vitro&#160;&#34;mini-organs&#34;6,7,8,9,10. Organoids can recapitulate important tissue morphology and gene expression profiles11,12. Some of the first organoids were derived from intestinal, gastric, and colon cancer cells from both mice and humans8,9,13. Long-lived organoid cultures have been established from a wide range of benign and malignant tissues, including the bladder, colon, stomach, pancreas, brain, retina, and liver14,15,16. We previously demonstrated methods to establish PDOs from ovarian cancer tumors and ascites samples17. PDOs can be used to study molecular characteristics, cellular mechanisms, and novel drug treatments18,19,20. PDOs have several advantages over traditional two-dimensional primary cell cultures for drug screening. Although primary two-dimensional cultures are a low-cost method for drug screens, primary cell cultures are single-cell types and lack the three-dimensional architecture of tumors21,22,23. Nevertheless, PDOs are a precious resource, and cost-effective protocols are needed to optimize their use in therapeutic drug screening.
This article describes an in vitro method to use ovarian cancer PDOs to test the effects of known or candidate drugs. Whereas current medium- and high-throughput drug screens using PDOs require expensive automated dispensing instruments24,25,26, this cost-effective method uses readily available basic lab supplies and an ATP-based cell viability assay in a standard 96-well plate format (Figure 1A). This method will facilitate preliminary tests of novel ovarian cancer drugs prior to scaling up to larger screens27,28. Although ovarian cancer PDOs are used here, this method can be applied to other cancer organoid models.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>1.5 mL Plastic Tubes</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>15 mL Plastic Tubes</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>96 well Flat Black Plates</td>
			<td>MidSci</td>
			<td>781968</td>
			<td></td>
		</tr>
		<tr>
			<td>Advance Organoid Media&#160;</td>
			<td></td>
			<td></td>
			<td>see Graham et al 2022 (Jove)</td>
		</tr>
		<tr>
			<td>Advanced DMEM/F12</td>
			<td>Thermo Fisher</td>
			<td>12634028</td>
			<td></td>
		</tr>
		<tr>
			<td>Automated Cell Counter</td>
			<td>Thermo Fisher</td>
			<td>AMQAX1000</td>
			<td></td>
		</tr>
		<tr>
			<td>Brightfield Microscope</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Carboplatin&#160;</td>
			<td>Teva&#160;Pharmaceuticals&#160;USA</td>
			<td>NDC&#160;00703-4246-01</td>
			<td></td>
		</tr>
		<tr>
			<td>CellTiter-Glo 3D Viability&#160;</td>
			<td>Promega</td>
			<td>G9681</td>
			<td></td>
		</tr>
		<tr>
			<td>Cultrex</td>
			<td>R &#38; D Systems</td>
			<td>3533-010-02</td>
			<td></td>
		</tr>
		<tr>
			<td>DMSO</td>
			<td>Sigma Aldrich</td>
			<td>D2650-100ML</td>
			<td></td>
		</tr>
		<tr>
			<td>Glutamax</td>
			<td>Life Technologies</td>
			<td>35050061</td>
			<td></td>
		</tr>
		<tr>
			<td>GR Calculator&#160;</td>
			<td>http://www.grcalculator.org</td>
			<td></td>
			<td>Online calculator</td>
		</tr>
		<tr>
			<td>GraphPad Prism</td>
			<td>GraphPad Software, Inc.</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>HEPES</td>
			<td>Life Technologies</td>
			<td>15630080</td>
			<td></td>
		</tr>
		<tr>
			<td>Matrigel</td>
			<td>Corning</td>
			<td>354230</td>
			<td></td>
		</tr>
		<tr>
			<td>Microsoft Excel</td>
			<td>Microsoft</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Penicillin-Streptomycin</td>
			<td>Thermo Fisher</td>
			<td>15140122</td>
			<td></td>
		</tr>
		<tr>
			<td>Plate Rocker</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Sterile P10, P200, and P1000 Barrier Sterile Pipette Tips</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Sterile P10, P200, and P1000 Pipettes</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Tecan Infinte 200Pro Plate Reader; i-Control Software</td>
			<td>Tecan</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>TrypLE</td>
			<td>Thermo Fisher</td>
			<td>12605010</td>
			<td>Organoid dissociation reagent</td>
		</tr>
	</tbody>
</table>

ovarian cancer patient-derived organoid models for pre-clinical drug testing

Ovarian cancer is a fatal gynecologic cancer and the fifth leading cause of cancer death among women in the United States. Developing new drug treatments is crucial to advancing healthcare and improving patient outcomes. Organoids are in-vitro three-dimensional multicellular miniature organs. Patient-derived organoid (PDO) models of ovarian cancer may be optimal for drug screening because they more accurately recapitulate tissues of interest than two-dimensional cell culture models and are inexpensive compared to patient-derived xenografts. In addition, ovarian cancer PDOs mimic the variable tumor microenvironment and genetic background typically observed in ovarian cancer. Here, a method is described that can be used to test conventional and novel drugs on PDOs derived from ovarian cancer tissue and ascites. A luminescence-based adenosine triphosphate (ATP) assay is used to measure viability, growth rate, and drug sensitivity. Drug screens in PDOs can be completed in 7-10 days, depending on the rate of organoid formation and drug treatments.

These results illustrate the response of two PDOs to the chemotherapy drug carboplatin, which is used to treat ovarian cancer. Organoids were derived from tumor biopsy (PDO #1) and from ascites (PDO #2). These organoids were selected based on their perceived doubling time (1-2 days) and morphological appearance (formation of many large organoids). Both PDO #1 and PDO #2 were plated on Day -2, at passage two, and carboplatin was added on Day 0. We tested the following carboplatin concentrations diluted in Advance Organoid...

Watch this Scientific Journal Video about Ovarian Cancer Patient-Derived Organoid Models for Pre-Clinical Drug Testing at JoVE.com

Ovarian Cancer Patient-Derived Organoid Models for Pre-Clinical Drug Testing

We present a protocol that can be used to conduct therapeutic drug testing with patient-derived ovarian cancer organoids.

Ovarian cancer is a fatal gynecologic cancer and the fifth leading cause of cancer death among women in the United States. Developing new drug treatments ...

ovarian-cancer-patient-derived-organoid-models-for-pre-clinical-drug

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JoVE Journal

Cancer Research

1.3K Views.  Washington University of Medicine in St. Louis. We present a protocol that can be used to conduct therapeutic drug testing with patient-derived ovarian cancer organoids.

Video: Ovarian Cancer Patient-Derived Organoid Models for Pre-Clinical Drug Testing

Establishment of Gastric Cancer Patient-derived Xenograft Models and Primary Cell Lines

The use of preclinical models to advance our understanding of tumor biology and investigate the efficacy of therapeutic agents is key to cancer research. Although there are many established gastric cancer cell lines and many conventional transgenic mouse models for preclinical research, the disadvantages of these in vitro and in vivo models limit their applications. Because the characteristics of these models have changed in culture, they no longer model tumor heterogeneity, and their responses have not been able to predict responses in humans. Thus, alternative models that better represent tumor heterogeneity are being developed. Patient-derived xenograft (PDX) models preserve the histologic appearance of cancer cells, retain intratumoral heterogeneity, and better reflect the relevant human components of the tumor microenvironment. However, it usually takes 4-8 months to develop a PDX model, which is longer than the expected survival of many gastric patients. For this reason, establishing primary cancer cell lines may be an effective complementary method for drug response studies. The current protocol describes methods to establish PDX models and primary cancer cell lines from surgical gastric cancer samples. These methods provide a useful tool for drug development and cancer biology research.

The current protocol describes methods to establish patient-derived xenograft (PDX) models and primary cancer cell lines from surgical gastric cancer samples. The methods provide a useful tool for drug development and cancer biology research.

The use of preclinical models to advance our understanding of tumor biology and investigate the efficacy of therapeutic agents is key to cancer research. ...

Isolation and Characterization of Patient-derived Pancreatic Ductal Adenocarcinoma Organoid Models

Pancreatic ductal adenocarcinoma (PDAC) is amongst the most lethal malignancies. Recently, next-generation organoid culture methods enabling the 3-dimensional (3D) modeling of this disease have been described. Patient-derived organoid (PDO) models can be isolated from both surgical specimens as well as small biopsies and form rapidly in culture. Importantly, organoid models preserve the pathogenic genetic alterations detected in the patient&#39;s tumor and are predictive of the patient&#39;s treatment response, thus enabling translational studies. Here, we provide comprehensive protocols for adapting tissue culture workflow to study 3D, matrix embedded, organoid models. We detail methods and considerations for isolating and propagating primary PDAC organoids. Furthermore, we describe how bespoke organoid media is prepared and quality controlled in the laboratory. Finally, we describe assays for downstream characterization of the organoid models such as isolation of nucleic acids (DNA and RNA), and drug testing. Importantly we provide critical considerations for implementing organoid methodology in a research laboratory.

Patient-derived organoid cultures of pancreatic ductal adenocarcinoma are a rapidly established 3-dimensional model that represent epithelial tumor cell compartments with high fidelity, enabling translational research into this lethal malignancy. Here, we provide detailed methods to establish and propagate organoids as well as to perform relevant biological assays using these models.

Pancreatic ductal adenocarcinoma (PDAC) is amongst the most lethal malignancies. Recently, next-generation organoid culture methods enabling the ...

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

We present here an integrative approach for testing efficacy of targeted therapies that combines the next generation sequencing technolo-gies, therapeutic target analyses and drug response monitoring using patient derived xenografts (PDX). This strategy was validated using ovarian tumors as an example. The mate-pair next generation sequencing (MPseq) protocol was used to identify structural alterations and followed by analysis of potentially targetable alterations. Human tumors grown in immunocompromised mice were treated with drugs selected based on the genomic analyses. Results demonstrated a good correlation between the predicted and the observed responses in the PDX model. The presented approach can be used to test the efficacy of combination treatments and aid personalized treatment for patients with recurrent cancer, specifically in cases when standard therapy fails and there is a need to use drugs off label.

Here we present a protocol to test the efficacy of targeted therapies selected based on the genomic makeup of a tumor. The protocol describes identification and validation of structural DNA rearrangements, engraftment of patients&#8217; tumors into mice and testing responses to corresponding drugs.

We present here an integrative approach for testing efficacy of targeted therapies that combines the next generation sequencing technolo-gies, therapeutic ...

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 ...

Generation and Culturing of High-Grade Serous Ovarian Cancer Patient-Derived Organoids

Organoids are 3D dynamic tumor models that can be grown successfully from patient-derived ovarian tumor tissue, ascites, or pleural fluid and aid in the discovery of novel therapeutics and predictive biomarkers for ovarian cancer. These models recapitulate clonal heterogeneity, the tumor microenvironment, and cell-cell and cell-matrix interactions. Additionally, they have been shown to match the primary tumor morphologically, cytologically, immunohistochemically, and genetically. Thus, organoids facilitate research on tumor cells and the tumor microenvironment and are superior to cell lines. The present protocol describes distinct methods to generate patient-derived ovarian cancer organoids from patient tumors, ascites, and pleural fluid samples with a higher than 97% success rate. The patient samples are separated into cellular suspensions by both mechanical and enzymatic digestion. The cells are then plated utilizing a basement membrane extract (BME) and are supported with optimized growth media containing supplements specific to the culturing of high-grade serous ovarian cancer (HGSOC). After forming initial organoids, the PDOs can sustain long-term culture, including passaging for expansion for subsequent experiments.

Patient-derived organoids (PDO) are a three-dimensional (3D) culture that can mimic the tumor environment in vitro. In high-grade serous ovarian cancer, PDOs represent a model to study novel biomarkers and therapeutics.

Organoids are 3D dynamic tumor models that can be grown successfully from patient-derived ovarian tumor tissue, ascites, or pleural fluid and aid in the ...

Visualizing DNA Damage Repair Proteins in Patient-Derived Ovarian Cancer Organoids via Immunofluorescence Assays

Immunofluorescence is one of the most widely used techniques to visualize target antigens with high sensitivity and specificity, allowing for the accurate identification and localization of proteins, glycans, and small molecules. While this technique is well-established in two-dimensional (2D) cell culture, less is known about its use in three-dimensional (3D) cell models. Ovarian cancer organoids are 3D tumor models that recapitulate tumor cell clonal heterogeneity, the tumor microenvironment, and cell-cell and cell-matrix interactions. Thus, they are superior to cell lines for the evaluation of drug sensitivity and functional biomarkers. Therefore, the ability to utilize immunofluorescence on primary ovarian cancer organoids is extremely beneficial in understanding the biology of this cancer. The current study describes the technique of immunofluorescence to detect DNA damage repair proteins in high-grade serous patient-derived ovarian cancer organoids (PDOs). After exposing the PDOs to ionizing radiation, immunofluorescence is performed on intact organoids to evaluate nuclear proteins as foci. Images are collected using z-stack imaging on confocal microscopy and analyzed using automated foci counting software. The described methods allow for the analysis of temporal and special recruitment of DNA damage repair proteins and colocalization of these proteins with cell-cycle markers.

Visualizing DNA Damage Repair Proteins in Patient-Derived Ovarian Cancer Organoids via Immunofluorescence Assays

The present protocol describes methods for evaluating DNA damage repair proteins in patient-derived ovarian cancer organoids. Included here are comprehensive plating and staining methods, as well as detailed, objective quantification procedures.

Immunofluorescence is one of the most widely used techniques to visualize target antigens with high sensitivity and specificity, allowing for the accurate ...

Establishment of Zebrafish Patient-Derived Xenografts from Pancreatic Cancer for Chemosensitivity Testing

Cancer is one of the main causes of death worldwide, and the incidence of many types of cancer continues to increase. Much progress has been made in terms of screening, prevention, and treatment; however, preclinical models that predict the chemosensitivity profile of cancer patients are still lacking. To fill this gap, an in vivo patient-derived xenograft model was developed and validated. The model was based on zebrafish (Danio rerio) embryos at 2 days post fertilization, which were used as recipients of xenograft fragments of tumor tissue taken from a patient's surgical specimen. 
It is also worth noting that bioptic samples were not digested or disaggregated, in order to maintain the tumor microenvironment, which is crucial in terms of analyzing tumor behavior and the response to therapy. The protocol details a method for establishing zebrafish-based patient-derived xenografts (zPDXs) from primary solid tumor surgical resection. After screening by an anatomopathologist, the specimen is dissected using a scalpel blade. Necrotic tissue, vessels, or fatty tissue are removed and then chopped into 0.3 mm x 0.3 mm x 0.3 mm pieces. 
The pieces are then fluorescently labeled and xenotransplanted into the perivitelline space of zebrafish embryos. A large number of embryos can be processed at a low cost, enabling high-throughput in vivo analyses of the chemosensitivity of zPDXs to multiple anticancer drugs. Confocal images are routinely acquired to detect and quantify the apoptotic levels induced by chemotherapy treatment compared to the control group. The xenograft procedure has a significant time advantage, since it can be completed in a single day, providing a reasonable time window to carry out a therapeutic screening for co-clinical trials.

Preclinical models aim to advance the knowledge of cancer biology and predict treatment efficacy. This paper describes the generation of zebrafish-based patient-derived xenografts (zPDXs) with tumor tissue fragments. The zPDXs were treated with chemotherapy, the therapeutic effect of which was assessed in terms of cell apoptosis of the transplanted tissue.

Cancer is one of the main causes of death worldwide, and the incidence of many types of cancer continues to increase. Much progress has been made in terms ...

Optimizing HCC Organoid Formation for Target Identification and Drug Development

Development and Optimization of A Human Hepatocellular Carcinoma Patient-Derived Organoid Model for Potential Target Identification and Drug Discovery

Hepatocellular carcinoma (HCC) is a highly prevalent and lethal tumor worldwide and its late discovery and lack of effective specific therapeutic agents necessitate further research into its pathogenesis and treatment. Organoids, a novel model that closely resembles native tumor tissue and can be cultured in vitro, have garnered significant interest in recent years, with numerous reports on the development of organoid models for liver cancer. In this study, we have successfully optimized the procedure and established a culture protocol that enables the formation of larger-sized HCC organoids with stable passaging and culture conditions. We have comprehensively outlined each step of the procedure, covering the entire process of HCC tissue dissociation, organoid plating, culture, passaging, cryopreservation, and resuscitation, and provided detailed precautions in this paper. These organoids exhibit genetic similarity to the original HCC tissues and can be utilized for diverse applications, including the identification of potential therapeutic targets for tumors and subsequent drug development.

Author Spotlight: Investigating Liver Cancer Pathogenesis Using Patient-Derived Organoids 

We provide a comprehensive overview and refinement of existing protocols for hepatocellular carcinoma (HCC) organoid formation, encompassing all stages of organoid cultivation. This system serves as a valuable model for the identification of potential therapeutic targets and the assessment of drug candidate effectiveness.

Hepatocellular carcinoma (HCC) is a highly prevalent and lethal tumor worldwide and its late discovery and lack of effective specific therapeutic agents ...

Automated Kinetic Imaging for Drug Assessment in Patient-Derived Tumor Organoids

Multiplexed Live-Cell Imaging for Drug Responses in Patient-Derived Organoid Models of Cancer

Patient-derived organoid (PDO) models of cancer are a multifunctional research system that better recapitulates human disease as compared to cancer cell lines. PDO models can be generated by culturing patient tumor cells in extracellular basement membrane extracts (BME) and plating them as three-dimensional domes. However, commercially available reagents that have been optimized for phenotypic assays in monolayer cultures often are not compatible with BME. Herein, we describe a method to plate PDO models and assess drug effects using an automated live-cell imaging system. In addition, we apply fluorescent dyes that are compatible with kinetic measurements to quantify cell health and apoptosis simultaneously. Image capture can be customized to occur at regular time intervals over several days. Users can analyze drug effects in individual Z-plane images or a Z Projection of serial images from multiple focal planes. Using masking, specific parameters of interest are calculated, such as PDO number, area, and fluorescence intensity. We provide proof-of-concept data demonstrating the effect of cytotoxic agents on cell health, apoptosis, and viability. This automated kinetic imaging platform can be expanded to other phenotypic readouts to understand diverse therapeutic effects in PDO models of cancer.

Author Spotlight: Developing Multiplexed Kinetic Assays for Organoid-Based Drug Response Analysis

Patient-derived tumor organoids are a sophisticated model system for basic and translational research. This methods article details the use of multiplexed fluorescent live-cell imaging for simultaneous kinetic assessment of different organoid phenotypes.

Patient-derived organoid (PDO) models of cancer are a multifunctional research system that better recapitulates human disease as compared to cancer cell ...

Standardizing Ovarian Cancer Organoid Biobanking

Strategy for Biobanking of Ovarian Cancer Organoids: Addressing the Interpatient Heterogeneity across Histological Subtypes and Disease Stages

While the establishment of an ovarian cancer biobank from patient-derived organoids along with their clinical background information promises advances in research and patient care, standardization remains a challenge due to the heterogeneity of this lethal malignancy, combined with the inherent complexity of organoid technology. This adaptable protocol provides a systematic framework to realize the full potential of ovarian cancer organoids considering a patient-specific variability of progenitors. By implementing a structured experimental workflow to select optimal culture conditions and seeding methods, with parallel testing of direct 3D seeding versus a 2D/3D route, we obtain, in most cases, robust long-term expanding lines suitable for a broad range of downstream applications. 
Notably, the protocol has been tested and proven efficient in a great number of cases (N = 120) of highly heterogeneous starting material, including high-grade and low-grade ovarian cancer and stages of the disease with primary debulking, recurrent disease, and post-neoadjuvant surgical specimens. Within a low Wnt, high BMP exogenous signaling environment, we observed progenitors being differently susceptible to the activation of the Heregulin 1 &#223; (HER&#223;-1)-pathway, with HER&#223;-1 promoting organoid formation in some while inhibiting it in others. For a subset of the patient's samples, optimal organoid formation and long-term growth necessitate the addition of fibroblast growth factor 10 and R-Spondin 1 to the medium. 
Further, we highlight the critical steps of tissue digestion and progenitor isolation and point to examples where brief cultivation in 2D on plastic is beneficial for subsequent organoid formation in the Basement Membrane Extract type 2 matrix. Overall, optimal biobanking requires systematic testing of all main conditions in parallel to identify an adequate growth environment for individual lines. The protocol also describes the handling procedure for efficient embedding, sectioning, and staining to obtain high-resolution images of organoids, which is required for comprehensive phenotyping.

Author Spotlight: Advancing Personalized Medicine in Ovarian Cancer 

This protocol offers a systematic framework for the establishment of ovarian cancer organoids from different disease stages and addresses the challenges of patient-specific variability to increase yield and enable robust long-term expansion for subsequent applications. It includes detailed steps for tissue processing, seeding, adjusting media requirements, and immunofluorescence staining.

While the establishment of an ovarian cancer biobank from patient-derived organoids along with their clinical background information promises advances in ...