Name: patient-derived tumor explants as a "live" preclinical platform for predicting drug resistance in patients
Uploaded: 2021-02-07T14:30:00
Description: Watch this Scientific Journal Video about Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients at JoVE.com

We thank the surgeons and pathologists at University Hospitals of Leicester NHS Trust for providing surgical resected tumor tissue. We also thank the Histology facility within Core Biotechnology Services for help with tissue processing and sectioning of FFPE tissue blocks and Kees Straatman for support with use of the Vectra Polaris. This research was supported and funded by the Explant Consortium comprising four partners: The University of Leicester, The MRC Toxicology Unit, Cancer Research UK Therapeutic Discovery Laboratories, and LifeArc. Additional support was provided by the CRUK-NIHR Leicester Experimental Cancer Medicine Centre (C10604/A25151). Funding for GM, CD, and NA was provided by Breast Cancer Now&#39;s Catalyst Programme (2017NOVPCC1066), which is supported by funding from Pfizer.

1. Tissue collection
<ol>
	<li>After surgery, transfer freshly resected human tumor specimens into a tube containing 25 mL of fresh culture medium (Dulbecco&#8217;s modified Eagle medium supplemented with 4.5 g/L glucose and L-glutamine&#160;+ 1% (v/v) fetal calf serum + 1% penicillin&#8211;streptomycin) and stored on ice. Process the explant within 2 h of surgery in a sterile class II hood.</li>
</ol>
2. Explant preparation
<ol>
	<li>Clean al...

This paper describes the methods for generation, drug treatment, and analysis of PDEs and highlights the advantages of the platform as a preclinical model system. Ex vivo&#160;culturing of a freshly resected tumor, which does not involve its deconstruction, allows for the retention of the tumor architecture13,24 and thus, the spatial interactions of cellular components in the TME as well as intratumoral heterogeneity. This method demonstrates how, by using a tumo...

The development of effective and safe anticancer agents requires appropriate preclinical models that can also provide insight into mechanisms of action that can facilitate the identification of predictive and pharmacodynamic biomarkers. Inter- and intratumor heterogeneity1,2,3,4,5 and the TME6,7,8,9,10,11,12 are known to influence anticancer drug responses, and many existing preclinical cancer models such as cell lines, organoids, and mouse models are not able to fully accommodate these crucial features. An &#34;ideal&#34; model is one that can recapitulate the complex spatial interactions of malignant with non-malignant cells within tumors as well as reflect the regional differences within tumors. This article focuses on PDEs as an emerging platform that can fulfil many of these requirements13.
The first example of the use of human PDEs, also known as histocultures, dates back to the late 1980s when Hoffman et al. generated slices of freshly resected human tumors and cultured them in a collagen matrix14,15. This involved establishing a 3D culture system that preserved tissue architecture, ensuring the maintenance of stromal components and cell interactions within the TME. Without deconstructing the original tumor, Hoffman et al.16 heralded a new approach of translational research, and since this time, many groups have optimized different explant methods with the aim of preserving the tissue integrity and generating accurate drug response data17,18,19,20,21,22,23,24, although some differences between protocols are evident. Butler et al. cultured explants in gelatin sponges to help the diffusion of nutrients and drugs through the specimen20,21,25, whereas Majumder et al. created a tumor ecosystem by culturing explants on top of a matrix composed of tumor and stromal proteins in the presence of autologous serum derived from the same patient22,23.
More recently, our group set up a protocol whereby explants are generated by fragmentation of tumors into 2 - 3 mm3-sized pieces that are then placed without additional components on permeable membranes at the air-liquid interface of a culture system24. Taken together,&#160;these numerous studies have demonstrated that PDEs allow the culture of intact, live fragments of human tumors that retain the spatial architecture and regional heterogeneity of the original tumors. In original experiments, explants or histocultures were usually subjected to homogenization following drug treatment, after which various viability assays were applied to the homogenized samples such as the histoculture drug response assay20,21, the MTT (3-(6)-2,5-diphenyltetrazolium bromide) assay, the lactate dehydrogenase assay, or the resazurin-based assay26,27,28. Recent progress in endpoint analysis techniques, particularly digital pathology, have now expanded the repertoire of endpoint tests and assays that can be performed on explants29,30. To apply these new technologies, instead of homogenization, explants are fixed in formalin, embedded in paraffin (FFPE) and then analyzed using immunostaining techniques, allowing spatial profiling. Examples of this approach have been documented for non-small cell lung cancer (NSCLC), breast cancer, colorectal cancer, and mesothelioma explants whereby immunohistochemical staining for the proliferation marker, Ki67, and the apoptotic marker, cleaved poly-ADP ribose polymerase (cPARP), was used to monitor changes in cell proliferation and cell death24,31,32,33,34.
Multiplexed immunofluorescence is particularly amenable for spatial profiling of drug responses in explants at endpoint35. For example, it is possible to measure the relocalization and spatial distribution of specific classes of immune cells, such as macrophages or T cells, within the TME upon drug treatment13,36,37,38, and investigate whether a therapeutic agent can favor the transition from &#34;cold tumor&#34; to &#34;hot tumor&#34;39. In recent years, this group has focused on the derivation of PDEs from different tumor types (NSCLC, renal cancer, breast cancer, colorectal cancer, melanoma) and the testing of a range of anticancer agents including chemotherapies, small-molecule inhibitors, and immune checkpoint inhibitors (ICIs). Endpoint analysis methods have been optimized to include multiplexed immunofluorescence to allow spatial profiling of biomarkers for viability as well as biomarkers for different constituents of the TME.

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			<td>Sigma-Aldrich</td>
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			<td>37% Formaldehyde</td>
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			<td>Industrial methylated spirit (IMS)</td>
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			<td>99% Industrial Denatured Alcohol (IDA)</td>
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			<td>InForm</td>
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			<td>Leica ASP3000 Tissue Processor</td>
			<td>Leica Biosystems</td>
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			<td>Leica ST4040 Linear Stainer</td>
			<td>Leica Biosystems</td>
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			<td>Millicell Cell Culture Inserts, 30 mm, hydrophilic PTFE, 0.4 &#181;m</td>
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			<td>OPAL 570</td>
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			<td>OPAL 690</td>
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			<td>Akoya Biosciences</td>
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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.

Multispectral imaging of mIF-stained histological sections permits identification and phenotyping of individual cell populations and identification of tumor and stromal components in the explant TME (Figure 2). Multispectral imaging is particularly useful for the analysis of tissues with high intrinsic autofluorescence, such as tissue with a high collagen content, as it allows the autofluorescence signal to be deconvoluted from other signals and excluded from subsequent analysis. Subsequent ...

Watch this Scientific Journal Video about Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients at JoVE.com

Patient-Derived Tumor Explants As a "Live" 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 ...

The patient-derived explants are a short term method providing immediate drug response data that can accurately predict patient outcomes. The PD platforms allows drug responses to be examined in a 3D context that mirrors the pathological and architectural features of the regional tumors as closely as possible. Explants have the potential to offer novel insights into the metabolism and mechanism of action of a drug and to facilitate the identification of new pharmacodynamic biomarkers.Before starting the experiment, clean all surgical equipment with 70%industrial methylated spirits solution. Fill a 10 centimeter culture dish with 25 milliliters of fresh medium on ice. Using tweezers, transfer the specimen onto a dental wax surface And use two skin graft blades to slice the tissue into fragments of approximately two to three cubic millimeters.Transfer the explants into a 10 centimeter dish and place six to nine pieces of the tissue into a one milliliter tube containing 10%non buffered formalin solution for a 24 hour incubation at room temperature. Fill the desired number of wells of a six well plate with 1.5 milliliters of fresh medium per well. And place an organotypic culture insert dish into each well so that it floats on top of the medium.Place six to nine explants onto each insert disk and store for a 16 hour incubation in a 5%carbon dioxide incubator to allow recovery. The next day, add fresh medium and drug to each well of a new plate. Include a well for the vehicle control.When all drug treatments have been prepared, use tweezers to transfer one insert to each well of the new six well plate for a 24 to 48 hour incubation in the carbon dioxide incubator. After the drug treatment, transfer the discs into new six well plates containing 10%formalin. And add a few drops of 10%formalin to the top of each explant to ensure complete coverage with the fixative for 24 hours of incubation at room temperature.The next day, soak an appropriate number of small histology sponges in 70%industrial methylated solution. And place the sponges inside histology cassettes. When all sponges have been placed, transfer all the explants from the same drug treatment condition onto a single sponge with the drug treated side of each explant contacting the inserted disc.Place another presoaked sponge on top of each explant and close the cassette to secure the explants in the cassette. Then submerge the cassettes in 70%industrial methylated solution before proceeding with the histological processing. After scanning, perform a training analysis and load the scans containing the stamps for training into the appropriate analysis software.In single mode view, navigate through the images and select the scans to analyze and the scan with intrinsic fluorescence. with the intrinsic fluorescence scan on a single view, click the autofluorescence picker to draw a segment on an area of unstained tissue. Click edit markers and colors and enter the marker names in the box associated with each fluorophore.Click prepare images to allow the software to subtract the intensity of intrinsic fluorescence from all the uploaded images. Click the segment tissue step on top of the screen to open the tissue segmentation training window. In the tissue categories panel, enter the name of each tissue category to be segmented.Click draw, to draw regions around groups of cells in the tissue category of interest. Then switch to the next tissue category and draw new regions. When all the training regions have been defined, use train tissue segmenter, and wait for the training accuracy to settle.Possibly to a value close to 100%Click segment all to segment the tissues. After verifying the quality of the tissue segmentation, click cell segmentation and select the cellular compartments to segment. To configure the nuclear component, use the typical intensity slider to adjust the threshold used to detect the nuclear pixels.Live feedback is provided by the preview window. To split the clusters of nuclear pixels, in the nuclear components splitting panel, select the button that best describes the staining quality of the nuclei and use the slider bar to adjust the splitting sensitivity. Then click segment all to segment all of the images.When all of the images have been segmented, in the phenotypes panel, add the list of phenotypes to be detected and enter the name of the phenotypes into the corresponding text box. Click edit phenotypes to select a particular cell and select the corresponding phenotype in the dropdown menu. After completing the selection for training, click the train classifier to evaluate the result of each phenotype.Proceed with phenotype all, save the project and click export. Select the batch analysis tab and load the project in the batch algorithm or project box. To export the images and tables, select all of the boxes in the export options, click add slides and load all the images containing the stamps for the previously prepared batches.Then click run to the start batch analysis of one stamp and export the corresponding data when it has been acquired. Multi-spectral imaging of NSCLC explant tissue stained for markers of cell viability, permits the identification and phenotyping of individual cell populations, as well as the identification of tumor and stromal components within the explant tumor micro environment. Tissue and cell segmentation of cell viability marker stains multi-spectral images as demonstrated, can be used to quantify cell death and proliferation in patient derived explants treated with drugs of interest.For example, in this analysis, in tissue samples from tumors treated with Nivolumab, a higher number of dying cells were identified compared to the control treated tumor samples. In addition, as illustrated, the ability to extract spatial information from stained dissections, allows the calculation of intercell distances. Explants can also be used for extensive spatial profiling or mass cytometry, which allows hundreds of biomarkers to be profiled simultaneously at subsidiary resolution while preserving the 3D structure of the sample.PDs could predict the efficacy of immunotherapy. For example, it could be possible to distinguish sensitive or resistant cases to immune checkpoint inhibitors and investigate the mechanisms underpinning this distinction.

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