We acknowledge the contributions of Mgr. Ond&#345;ej &#352;ebesta (Vinicna Microscopy Core Facility, Faculty of Science, Charles University) for whole slide scanning and the project &#34;e-Infrastruktura CZ&#34; (e-INFRA LM2018140), which supplied us with the computational resources for this study. Technicians Jana Dosoudilova and Jan Javurek are acknowledged for their excellent technical assistance.&#160;This research was funded by the European Union&#39;s Horizon 2020 research and innovation program, grant N&#176;856620, and the Ministry of Health of the Czech Republic, grant AZV NU21-03-00506, and by the Cooperatio Program (Surgical disciplines). The Vinicna Microscopy Core Facility is co-financed by the Czech-BioImaging large RI project LM2023050.

This protocol was part of a study conducted in accordance with the ethical standards set out in the Declaration of Helsinki (2013 version); it was approved by the Ethics Committee of the Faculty of Medicine and the University Hospital in Pilsen (118/2021, 11 March 2021). Figure 1 shows a summary of the employed methods.
1. Selection of FFPE tissue blocks
<ol>
	<li>Retrieve FFPE block identifiers from patient files with the help of treating physicians or pathologists.</li>
	<li>Request the FFPE blocks from the local pathology archive.</li>
	<li>For each patient, select 2-3 FFPE blocks containing viable tumor tissue, preferentially with the surrounding peritumor area and non-tumor liver. 
	NOTE: It is advised to obtain an expert pathologist&#39;s opinion for this selection. Histological assessment of the non-tumor liver, along with anamnestic, clinical, and laboratory data was used to define the underlying etiology of HCC.</li>
</ol>
2. Deparaffinization and rehydration of FFPE tissue
<ol>
	<li>Cut one or two tissue sections of 4 &#181;m thickness from each of the FFPE tissue blocks on a microtome. 
	NOTE: The thickness of the sections can be between 3 &#181;m and 5 &#181;m without noticeable staining impact; however, a thickness of 4 &#181;m is the standard.</li>
	<li>Place the sections in a water bath (42&#730;C) to remove wrinkles.</li>
	<li>Mount the slides by picking them up and placing them on a positively charged glass microscopic slide. Then, apply the slides for drying at ambient temperature (AT) for 24 h.</li>
	<li>Place the slides in a thermostat (56 &#730;C) for 1 h.</li>
	<li>Use an autostainer to deparaffinize the sections in dewax solution-1 (30 s at 72 &#176;C), dewax solution-2 (10 s at 72 &#176;C), dewax solution-3 (10 s at AT), ethanol 96%-1 (10 s), ethanol 96%-2 (10 s), and ethanol 96%-3 (10 s).</li>
	<li>Use an autostainer to rehydrate the sections in wash solution-1 (10 s, AT), wash solution-2 (10 s, AT), and wash solution-3 (5 min, AT).</li>
</ol>
3. Performing IHC in a fully automated stainer
NOTE: IHC is performed according to the manufacturer&#39;s protocol. The steps are briefly explained below.
<ol>
	<li>Antigen retrieval method
	<ol>
		<li>Retrieve antigen using the heat-induced epitope retrieval (ER) procedure in an ER solution with a high pH (e.g., Tris-EDTA): ER solution-1 (10 s, AT), ER solution-2 (10 s, AT), ER solution-3 (20 min at 100 &#730;C), ER solution-4 (12 min, AT).</li>
		<li>Rinse the slide in a wash solution for 3x (30 s each) and 1x (3 min).</li>
		<li>Block endogenous peroxidase using the Peroxide Block solution for 5 min at AT.</li>
		<li>Rinse in a wash solution for 3x (30 s each).</li>
		<li>Block nonspecific binding of antibodies with a protein block for NKp46 only for 30 min. at AT.</li>
	</ol>
	</li>
	<li>Binding with primary antibodies: Incubate with primary antibodies for 15 min at AT, then rinse with a wash solution for 3x (30 s each).</li>
	<li>For secondary antibody binding, follow the steps described below.
	<ol>
		<li>Incubate with horseradish peroxidase-conjugated secondary antibodies for 8 min at AT.</li>
		<li>Rinse with a wash solution for 2x (2 min each) and 1x with deionized water.</li>
		<li>Visualize the reaction by rinsing the slides with diaminobenzidine (DAB) solution and then incubating them with the DAB solution for 10 min at AT.</li>
		<li>Rinse for 3x (30 s) with deionized water.</li>
		<li>Counterstain the sections with Mayer&#39;s hematoxylin.</li>
		<li>Rinse with deionized water for 1x (30 s), wash solution for 1x (30 s), and deionized water for 1x (30 s).</li>
		<li>Use a fully automated slide stainer to dehydrate the sections in ethanol 70% (2 min), ethanol 80% (2 min), ethanol 96%-1 (3 min), ethanol 96%-2 (3 min), ethanol 100% (3 min), xylene-1 (4 min), xylene-2 (4 min), and xylene-3 (4 min).</li>
		<li>Use a fully automated glass coverslipper to mount the sections on the slides into a mounting medium. 
		NOTE: Negative and positive (tonsils) tissue control samples were used throughout.</li>
	</ol>
	</li>
</ol>
4. Obtaining whole-slide scans
<ol>
	<li>Scan the stained slides using the 20x objective and moderate-high density of focusing on a whole-slide scanner.</li>
</ol>
5. IHC analysis using the software
<ol>
	<li>Create a project for the IHC images in the software.
	<ol>
		<li>Download and open QuPath-0.4.3 (or higher).exe</li>
		<li>Create a new folder with a suitable name.</li>
		<li>Click the Create Project button in the upper left corner of the software and select the newly created folder.</li>
		<li>Drag scanned images into the software window. When a new window automatically pops up, select Set Image Type &#62; H-DAB and click Import (Figure 2).</li>
		<li>Observe the list of images in the left window of the menu; double-click to open the image.</li>
		<li>Select from the main menu at the top File to Save the images as a project. 
		&#8203;NOTE: The software can handle a wide range of image formats, including many whole slide formats. However, tiff format is preferred to prevent the loss of raw data and associated metadata.</li>
	</ol>
	</li>
	<li>Annotate regions of interest
	<ol>
		<li>Select the Brush tool or the Wand tool from the main menu at the top to annotate the tumor area. For discontinuous regions of the tumor, follow steps 5.2.1.1-5.2.1.3.
		<ol>
			<li>Annotate them separately.</li>
			<li>Press and hold the Ctrl key while selecting all tumor regions</li>
			<li>Right-click and select Edit Multiple &#62; Merge Selected.</li>
		</ol>
		</li>
		<li>Make sure that the tumor annotation is selected (borders have a yellow color).</li>
		<li>Click Automate in the main menu and select Show Script Editor.</li>
		<li>Copy the script available in Supplementary File 1 or at https://petebankhead.github.io/qupath/scripts/2018/08/08/three-regions.htmls and paste it in the&#160;Script Editor, then click Run. The ROIs: tumor center (TC), inner margin (IM), outer margin (OM), and peritumor area (PT) will be created and labeled automatically.</li>
		<li>For an alternative way to create regions of interest, follow steps 5.2.5.1-5.2.5.5.
		<ol>
			<li>Select the Polyline tool from the main menu at the top to draw a border separating the malignant cell nests and adjacent non-tumor tissue.</li>
			<li>Select the resulting border. Select from the main menu at the top Objects &#62; Annotation &#62; Expand annotations &#62; 500 &#181;m for Expansion Radius &#62; Flat for Line Cap. Activate Remove Interior and activate Constrain to parent.</li>
			<li>Right-click the resulting annotation, Edit Single &#62; Split. Two separate annotations will be extended automatically as 500 &#181;m-wide regions on each side of the border.</li>
			<li>Name the annotated regions appropriately for IM and OM by right-clicking the annotation on the list on the left, selecting Set Properties, and entering the name.</li>
			<li>The TC represents the remaining tumor area. Extend the 500 &#181;m-wide PT area adjacent to the OM using the same procedure. 
			NOTE: Annotated slides and QuPath AF detections were reviewed by an expert histopathologist.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Optimization of pixel classification
	<ol>
		<li>Select the Rectangle tool from the main menu. Then annotate a region, which contains all types of pixels to be distinguished (e.g., hematoxylin, DAB, and background).</li>
		<li>From the main menu, select Analyze &#62; Preprocessing &#62; Estimate Stain Vector.</li>
		<li>When the window Visual Stain Editor automatically pops up, select Auto &#62; OK, and set H- DAB estimated as the name for the stain vector.</li>
		<li>For an alternative method, follow steps 5.3.4.1-5.3.4.3.
		<ol>
			<li>Select the Rectangle tool from the main menu and annotate a small region of a typical nucleus stained with hematoxylin.</li>
			<li>Select Image from the left menu and double-click on Stain 1 &#62; Yes.</li>
			<li>Select the Rectangle tool from the main menu and annotate a small region of positive staining with DAB. Select Image from the left menu and double-click on Stain 2 &#62; Yes.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Assessment of area fraction of positive immune cells using the Pixel Classification option.
	<ol>
		<li>From the main menu, click Classify &#62; Pixel classification &#62; Create thresholder.</li>
		<li>Select High for the Resolution &#62; Channel: DAB &#62; Prefilter: Gaussian &#62; Sigma: 0.5 &#62; (Figure 3) Threshold: (0.2-0.3) &#62; Above Threshold: Positive &#62; Below Threshold: Negative.</li>
		<li>For the region, select Any Annotation, type a name for the Classifier &#62; Save &#62; Measure &#62; OK. The DAB-positive cells will be changed to red color (Figure 4).</li>
		<li>Copy the results from the left side menu to a spreadsheet.</li>
		<li>Or open Measure in the upper main menu, select Show Annotation Measurements &#62; Copy to Clipboard, and paste results into a spreadsheet.</li>
		<li>To close the image, right-click &#62; Multi View &#62; Close Viewer. 
		NOTE: Before quantification, any artifacts were eliminated. The threshold was adjusted for each case with a weak specific positive staining. The correctness of annotations and pixel classification was first crosschecked and then reviewed by a senior histopathologist. All analyses were done in a blind fashion. More information about the analysis of IHC using the software can be found at&#160;https://forum.image.sc/tag/QP.</li>
	</ol>
	</li>
</ol>
6. Statistical methods and data analysis
<ol>
	<li>The study endpoints were time to recurrence (TTR), disease-free survival (DFS), and overall survival (OS). Patients without relapse or death were censored at their last follow-up. 
	NOTE: Continuous nonnormally distributed data are expressed as median (min-max). Proportions are expressed as raw data (percentages). Area fractions of immune cells in different regions of interest (ROIs) were compared by Friedman ANOVA, followed by Wilcoxon matched pairs test with Bonferroni correction. Because of the nonparametric distribution of most of the variables, the Spearman correlation was used to evaluate the associations between pairs of ordinal or quantitative variables. To determine the prognostic value of individual predictors for endpoints, an univariable followed by multivariable Cox regression analysis was performed. Hazard ratios (HR) were calculated. HR was showing the relative risk for the combined intermediate high group compared to 1 for the low group. All survival estimates were calculated by the Kaplan-Meier method and compared between groups by the log-rank test. GraphPad Prism 9.0 (GraphPad Software LLC) was used for the statistical analyses. A 2-sided p-value &#60; 0.05 was considered statistically significant.</li>
</ol>

Prognostic Significance of Immune Cell Infiltration in Hepatocellular Carcinoma

There are no financial conflicts of interest to disclose.

By means of monitoring the in situ immune organization in TME, the field of immuno-oncology can add new cancer prognostic and predictive biomarkers. Our previously published paper on the role of adaptive immune cells in the TME of HCC showed positive prognostic associations of CD3+ and CD8+ T cells as well as CD20+ B cells in selected ROIs to the time to recurrence2. Here, image analysis software QuPath was used to assess the abundance of CD1a+ iDCs, CD117+ mast cells, and NKp46+ NK cells in several distinct regions of HCC and evaluated their prognostic significance. Due to the irregular shapes of some innate immune cells, their quantification can be inaccurate. This is why evaluating the area fraction of immune-positive cells was the optimal option. Among the three cell types, only CD117+ mast cells demonstrated significant prognostic impact: a higher AF of mast cells in the IM and the PT area was associated with longer survival.
Mast cells were the most abundant in all ROIs, and the association of their AF in the peritumor area and inner margin with better survival reflects the anti-tumor role of mast cells. Mast cells can be attracted into the TME by tumor-cell-released chemoattractants, such as SCF or CCL1518. Mast cells can affect anti-tumor response by direct cytotoxicity to tumor cells27 or by secreting pro-inflammatory cytokines that can inhibit tumor growth18. Increased mast cell density was protective against the recurrence of prostate cancer28, gastric cancer27, and HCC after liver transplantation29. A comprehensive investigation consisting of a larger cohort of 245 HCC patients found a positive correlation between more significant mast cell infiltration in tumor samples and more prolonged survival after tumor resection30. Rohr-Udilova et al. reported similar results of greater densities of mast cells in surrounding HCC tissue; however, only intratumor mast cell density was associated with a lower recurrence rate29.
In this study, mast cells exerted an anti-tumor effect in IM and PT liver only. The TME of different tumors is heterogeneous concerning the spatial distribution of immune cells31. The prognostic significance of immune cells in TME is also critically related to their spatial distribution32,33. Different approaches to annotating the tumor invasive margin have been proposed, including a whole margin of different widths4,34,35, inner and outer margins, again with differing widths36,37, with or without PT2,7. We followed the reproducible, standardized methodology from the International Immunooncology Biomarkers Working Group to define the invasive margin as a 1 mm region centered on the border separating the malignant cell nests from the host tissue and representing the central tumor as the remaining tumor area38. Since the earlier study highlighted significant differences in the results of inner and outer invasive margin2, those regions (each 500 &#181;m in width) have been analyzed separately. The spatial immune profiling of the peritumor area has its distinct predictive importance39,40, and therefore, the PT area was included, too.
The analysis of TC, tumor margin, and PT liver was performed in WSIs as opposed to the option of evaluating selected fields of view in the respective regions. Whole slide imaging is a potentially rich source of data41. Since we did not perform subsampling, the &#34;true expected value&#34; were obtained in a time-efficient manner10. In addition, this system was reasonable because only 1 or 2 slides per block were analyzed, which enabled us to avoid the bias of patch selection42. The slow workflow challenged us in the previous study when stereology was applied for the quantification of immune cells2.
IHC allows direct localization of CD1a+ iDCs, NKp46+NK, and CD117+ mast cell expression in liver tissue, quantification of their distributions, and, therefore, sub-classification of the cohort. IHC analysis gives this study important advantages over biochemical assays in solubilized tissue, which may lead to false negative results when only a few biomarker-positive cells are present43 and do not reflect the regional reaction of immune cells to histopathological features44. IHC is also preferable over analysis in H&#38;E-stained sections. Studies on colorectal cancer and small-cell lung cancer showed that the assessment of H&#38;E-stained sections could provide robust, quantitative tumor-immune biomarkers45,46,47; however, limitations to reflect the actual presence of specific subtypes are still present.
High quality of tissue sections and IHC staining is a prerequisite for an accurate assessment. High background, edge artifacts, and specific but undesirable staining (e.g., staining of endothelial cells of liver sinusoidal spaces with anti-CD4 antibodies) may corrupt all results. A CD56 antibody was the first choice in the current study to detect natural killer cells but was replaced with the NKp46 antibody due to the expression of CD56 in immature bile ducts. The selection of the most appropriate marker in terms of specificity, robust staining pattern, and stability in FFPE blocks represents another issue to consider. For instance, CD117 was chosen instead of tryptase because tryptase expression may be strongly downregulated, besides, cells that do not express CD117 are not mast cells48.
QuPath gives the option to smooth the borders of ROI from the outside and to remove any artifacts, large vessels, necrotic tissue, unspecific staining, background, and aggregation of erythrocytes that should not be a part of the ROI. The operator needs to select the ROI and press the &#34;Alt&#34; key while annotating those artifacts.Unspecific staining was eliminated in this study as artifacts before quantification so that it did not affect the results. In case of systematically low specific staining or high background, the protocol should be reviewed to avoid possible bias. The threshold was adjusted for each case with a weak specific positive staining. Before any routine use, the protocol should be optimized. All analyses should be done in a blind fashion and ideally, the accuracy of thresholding deserves crosschecking.
QuPath is a user-friendly, intuitive, extensible, open-source solution for digital pathology and whole slide image analysis9, tested previously for image analysis in HCC49 and different cancers50,51. The easy operation of the software for WSIs and the options for annotation of regions of interest (e.g., tumoral or peritumoral areas) are the main advantages.
Application of existing, modified, or de novo created scripts may substantially speed up the analysis. The script to create annotations for ROIs was applied, which enabled fast and accurate regional segmentation instead of manual delineation. The script is not fixed, and it can be readily customized to less than 500 &#181;m margins for regions with insufficient tissue boundaries. The width of the IM, OM, or PT area can be customized via Automate &#62; Show script editor &#62; adapting the line 30 Double Expand Margin Microns = 500 &#181;m to the available width &#62; Run. ROIs can be also added or removed. In general, the workflows in the software are not fixed, and the operator is free to develop and modify them.
A plethora of available software tools for pathological image analysis, including CellProfiler, ImageJ, Fiji, Microscopy Image Browser, and others, are available now. However, QuPath distinguishes itself through a set of advantages such as open-source architecture, user-friendly interface, algorithm customization capabilities, and integration of advanced machine-learning tools. Those features collectively position this image analysis software as a robust choice for the nuanced analysis of cancer tissues in the realm of digital pathology.
QuPath showed the lowest variability compared to commercial software HALO (IndicaLab) and QuantCenter (3DHistech) for the detection of Ki67 expression in breast cancer52. The software also possesses the potential to be instructed to run a script for all project images in a reproducible batch-processing manner.
QuPath enabled us to create quantitative continuous data instead of ordinal (semi-quantitative) ones. Ordinal data obtained by visual scoring is fraught with problems due to subjectivity in interpretation, inter-observer variability, and poor reproducibility53. The promptness of the current analysis was improved by the fact that individuals without extensive histopathology background and programming expertise can perform automatic analysis, provided that the accuracy of annotations was checked by a pathologist. A study on head and neck tumors demonstrated QuPath&#39;s suitability for fast and reproducible assessment of the prognostic value of CD57+ tumor-infiltrating lymphocytes50. The study also showed a substantial concordance between human observers and QuPath. High concordance between manual observation and assessment using QuPath was also presented in oral cancer54 and breast cancer55. The aforementioned paper compared the IHC quantification of 5 clinical breast cancer biomarkers using QuPath and 2 commercial software products (Definiens Tissue Studio and inForm) with a manual scoring by a pathologist. Throughout this study, QuPath consistently exhibited the best performance as well as the least time to set up and apply, thus showing to be an excellent future alternative to visual monitoring and commercial software. Even compared to ImageJ, the best-known open-source software for biomedical image analysis, QuPath excels at dealing with large-size WSIs56.
There is an open online forum for QuPath where users post their questions about methods of digital pathology and present an active and engaged community to support the development of tools for image analysis (https://forum.image.sc/tag/qupath).
The cohort used in this study was unique and not representative of the general population with HCC because it encompassed only patients who were eligible for liver resection. Generally, a minority of patients with HCC (20%-30%) are eligible for liver resection57. This could also be a reason why fewer patients had confirmed cirrhosis because patients with this etiology of HCC probably had higher Child-Pugh scores and were not considered resectable. The cohort was also rather small, and most of the patients had an early TNM stages of the disease. Therefore, extrapolation of the results to other populations should be made with caution.
In the current study, the abundance of innate immune cells in the HCC microenvironment has been estimated through the assessment of their AF, as precise counting of irregularly shaped immune cells like dendritic cells can prove difficult. On the other hand, the area fraction of immune cells usually strongly correlates with their densities58. Immunoperoxidase labeling provides data for localization of the antigen, area fraction, or density of cells that express it, but the intensity of staining is non-linearly related to the amount of antigen. Therefore, IHC should not be used to quantitatively assess the expression level of a particular protein. The accuracy of the method also depends on the uniformity of section thickness, image quality, and selected resolution for pixel classification. Any results of image analysis should be validated and treated with caution. Since a single marker cannot fully capture the complexity of immune cells, more IHC markers and multiplex staining are needed to obtain a reliable picture of the immune TME of HCC. Using only a single IHC marker for phenotyping immune cells represents an approximation, and the results should be interpreted with caution.
Using the QuPath image analysis software to evaluate IHC-stained slides enabled us to assess the distribution and area fraction of local iDCs, mast cells, and NKs in tumor and peritumor of HCC patients and then to analyze their relationships with prognosis. The abundance of mast cells in the inner margin and peritumor liver of HCC is associated with longer DFS and OS, highlighting the anti-tumor effects of those innate immune cells. QuPath&#39;s analytical workflow is user-friendly, fast, and easy to use, with helpful default settings and easy data export. This software has been endorsed as a promising platform for digital image analysis, which could meet the need for reproducibility, consistency, and accuracy in digital pathology.

The spatial organization and abundance of tumor-infiltrating immune cells have been proven to impact survival in different cancers, hepatocellular carcinoma (HCC) included1,2,3,4. The prognostic significance of tumor-infiltrating lymphocytes in cancers was first shown on hematoxylin and eosin (H&#38;E) stained sections5,6. Then, a pioneer study by Galon et al. using immunohistochemistry (IHC) demonstrated associations of the densities of CD3+ and CD8+ T cells in colon cancer tissue with prognosis4.
IHC is a gold standard for visualizing, quantifying, and mapping immune cells within tumor tissue for further association with clinical outcomes7. IHC presents several advantages, such as low cost, widespread availability, and compatibility with formalin-fixed paraffin-embedded (FFPE) tissue8. However, accurate assessment of IHC-stained immune cells is a big challenge. Traditional scoring in selected microscopic fields of view is time-consuming and no longer sufficient to ensure the high-quality, reproducible, objective analysis essential for candidate biomarker selection and reliable clinical correlation9. Whole-slide scans can be assessed as a whole image or after subsampling10.
Computerized quantitative assessment of the abundance of immune cells in tissue samples can ensure practical, accurate, reliable, and clinically relevant data11. QuPath is a free, open-source software that enables the digital image analysis of IHC-stained slides and maximizes the amount of information yielded from individual samples12.
Since immature dendritic cells (iDCs), natural killer (NK) cells, and mast cells are involved in anti-tumor immune responses and were shown to correlate with patients&#39; outcomes13,14,15, we here present a step-by-step protocol for assessing their spatial distribution in FFPE HCC tissue and exploring their prognostic impact. CD1a is expressed mainly on the membrane of immature dendritic cells, the density of which has been associated with clinical outcomes in a variety of human tumors16,17. Mast cells can play a protumor or anti-tumor role within the tumor microenvironment (TME)18. They can support angiogenesis and facilitate metastasis19. Conversely, it has been reported that mast cells can mediate tumor cell apoptosis through the production of IL-420. Anti-CD117 staining is commonly used to visualize and quantify mast cells within the tumor tissue21. NK cells are thought to contribute to the surveillance and control of HCC22 by killing cancer cells23. NKp46 expression by NK is a crucial parameter for their anti-tumor activity24. However, NKp46+ NK cells correlated positively with tumor diameter in HCC patients25. Despite this, little is known about their correlation with patient survival.
We aimed to quantitatively assess the abundance of CD1a+ iDCs, CD117+ mast cells, and NKp46+ NK cells in different regions of HCC and highlight their prognostic significance. A total of 70 consecutive patients with pathologically confirmed stage I-IV HCC, who were eligible for resection according to BCLC guidelines and underwent curative-intent liver resection at Pilsen University Hospital between 1997 and 2019, were included in the current retrospective study. Pathology reports of the patients were reviewed. None of the patients included in this study had distant metastases nor had received neoadjuvant therapy such as chemotherapy or radiotherapy before surgery. A total of 3 patients with poor-quality histological specimens were excluded, and the remaining 67 patients were included in the study (Table 1).

<table><tbody><tr><td>Anti-CD1a</td><td>Leica Biosystems</td><td>PA0235</td><td>Identifier- immature dendritic cells; RTU, Protocol F, ER2/20 min</td></tr><tr><td>Anti-CD117</td><td>Leica Biosystems</td><td>&amp;nbsp;PA0007</td><td>Identifier- mast cells; RTU, Protocol F, ER2/20 min</td></tr><tr><td>BOND Plus Microscope Slides</td><td>Leica Biosystems, Germany</td><td>S21.2113.A</td><td></td></tr><tr><td>BOND RXm</td><td>Leica Biosystems</td><td>49.1501</td><td>Fully Automated IHC Stainer&amp;nbsp;&amp;nbsp;</td></tr><tr><td>Bond Aspirating Probe Cleaning Kit</td><td>Leica Biosystems</td><td>CS9100</td><td></td></tr><tr><td>Bond&amp;nbsp;Dewax&amp;nbsp;Solution</td><td>Leica Biosystems</td><td>AR9222</td><td></td></tr><tr><td>Bond&amp;nbsp;Polymer Refine Detection Kit</td><td>Leica Biosystems</td><td>DS9800</td><td></td></tr><tr><td>BondTM Epitope Retrieval 2</td><td>Leica Biosystems</td><td>AR9640</td><td></td></tr><tr><td>BondTM Primary Antibody Diluent</td><td>Leica Biosystems</td><td>AR9352</td><td></td></tr><tr><td>BondTM Wash Solution 10X Concentrate</td><td>Leica Biosystems</td><td>AR9590</td><td></td></tr><tr><td>Computer Specifications: Intel(R) Core(TM) i5-10500 CPU @ 3.10GHz&amp;nbsp;&amp;nbsp; 3.10 GHz Installed RAM: 128 G</td><td>Intel</td><td></td><td>A 64-bit operating system that has Windows 7. Any computer with Java-based operating system and Excel available</td></tr><tr><td>Coverslips</td><td>Leica Biosystems, Germany</td><td>14071135636</td><td></td></tr><tr><td>CV Mount</td><td>Leica Biosystems, Germany</td><td>14046430011</td><td></td></tr><tr><td>CV5030 Fully Automated Glass Coverslipper</td><td>Leica Biosystems</td><td>149CVTS5025</td><td></td></tr><tr><td>Drying Oven UN30</td><td>Memmert GmbH&amp;nbsp;</td><td>UN30</td><td></td></tr><tr><td>GraphPad Prism 9.0</td><td>GraphPad Software LLC&amp;nbsp;</td><td></td><td>Version 12</td></tr><tr><td>Human NKp46/NCR1 Antibody, Monoclonal Mouse IgG2B Clone # 195314</td><td>R&amp;amp;D Systems, Inc., United States</td><td>MAB1850</td><td>Identifier- natural killer cells; Dilution 1:150, Protocol F + BLOK, ER2/20 min</td></tr><tr><td>Leica HI1210 - Water Bath</td><td>Leica Biosystems</td><td>14041521466</td><td></td></tr><tr><td>Protein Block</td><td>Agilent Dako, United States</td><td>X0909</td><td></td></tr><tr><td>QuPath 0.3.2 or higher versions</td><td></td><td></td><td>&amp;nbsp;version (QuPath v.0.3.2)&amp;nbsp;</td></tr><tr><td>RM2235 Rotary Microtome</td><td>Leica Biosystems</td><td>149AUTO00C1</td><td></td></tr><tr><td>ST5020 Multistainer Slide Stainer</td><td>Leica Biosystems</td><td>DEV-ST5010-CV5030</td><td></td></tr><tr><td>Statistica</td><td>StatSoft Inc.</td><td></td><td>version 7</td></tr><tr><td>Zeiss Axio Scan.Z1</td><td>ScienceServices GmbH, Germany</td><td>430038-9000-000&amp;nbsp;</td><td>Slide scanner&amp;nbsp;</td></tr></tbody></table>

mast cells in the microenvironment of hepatocellular carcinoma confer favorable prognosis: a retrospective study using qupath image analysis software

The insights provided by in-situ detection of immune cells within hepatocellular carcinoma (HCC) might present information on patient outcomes. Studies investigating the expression and localization of immune cells within tumor tissues are associated with several challenges, including a lack of precise annotation for tumor regions and random selection of microscopic fields of view. QuPath is an open-source, user-friendly software that could meet the growing need for digital pathology in whole-slide image (WSI) analysis.
The infiltration of HCC and adjacent tissues by CD1a+ immature dendritic cells (iDCs), CD117+ mast cells, and NKp46+ natural killer cells (NKs) cells was assessed immunohistochemically in representative specimens of 67 patients with HCC who underwent curative resection. The area fraction (AF) of positively stained cells was assessed automatically in WSIs using QuPath in the tumor center (TC), inner margin (IM), outer margin (OM), and peritumor (PT) area. The prognostic significance of immune cells was evaluated for time to recurrence (TTR), disease-free survival (DFS), and overall survival (OS).
The AF of mast cells was significantly greater than the AF of NKs, and the AF of iDCs was significantly lower compared to NKs in each region of interest. High AFs of mast cells in the IM and PT areas were associated with longer DFS. In addition, high AF of mast cells in IM was associated with longer OS.
Computer-assisted analysis using this software is a suitable tool for obtaining prognostic information for tumor-infiltrating immune cells (iDCs, mast cells, and NKs) in different regions of HCC after resection. Mast cells displayed the greatest AF in all regions of interest (ROIs). Mast cells in the peritumor region and IM showed a positive prognostic significance.

The demographics and clinical characteristics of the patients are presented in Table 1. The median age of the patients was 69 years, and most were males (77.6%). Regarding the etiology of HCC, chronic non-viral hepatitis was the most frequent background disease, with a prevalence of non-alcoholic steatohepatitis (NASH) (23.9%). Only 15 patients (22.4%) had cirrhosis as a background disease. A majority of patients (68.7%) had TNM stage I. More detailed clinical and pathology findings were described in our previously published work2,26.
Outcomes 
At the last follow-up, tumor recurrence was observed in 29 (41.8%) patients, of which 89.7% had local recurrence and 38 (56.7%) patients had died. At 5 years after surgery, the proportion of DFS was 33.2%, the proportion OS was 49.4%, and the recurrence-free proportion (RFP) was 48.7% (Table 2).
Distribution of immune cells in different regions of interest 
The CD117-protein was found predominantly in the cytoplasmic membrane of rounded cells (Figure 5A). In the TC and IM, they were mostly localized in the stroma and in the perivascular space. In the OM and PT areas, the CD117+ cells were observed in the tumor capsule and in the perivascular space.
The NKp46-protein was found mostly in the cytoplasmic membrane of rounded cells, which were present inside sinusoid-like spaces in the TC and IM, as well as being associated with the stroma (Figure 5B). In the OM and PT area, NKp46+ cells were observed in capsules around tumor nests and in the stroma of portal tracts.
The CD1a-protein was found in the TC and IM, mostly in the cytoplasmic membrane of rounded cells (scattered or in aggregates) in the stroma, inside and along the boundaries of sinusoid-like spaces (Figure 5C). In the PT area, CD1a+ DC was observed inside and along the boundaries of sinusoids and within the biliary epithelium in the portal tracts.
The AF of mast cells was significantly greater than the AF of NKp46+ cells (p &#60; 0.001) (Figure 6). CD1a+ iDCs in each ROI showed the lowest AF compared to mast cells and NK cells (p &#60; 0.001) (Figure 6). The AF of CD117+ and NKp46+ cells in TC or IM were significantly smaller than those in the PT area and OM (p &#60; 0.001) (Figure 6). For CD1a+ cells, the AF did not differ significantly between regions.
Significant associations for individual immune cells between all ROIs are shown in Table 3. Figure 7 shows the heat map for significant associations between different immune cells within different regions. The AF of CD117 + mast cells in TC correlated significantly with the AF of CD1a in the TC and IM.
Prognostic value of clinical and pathology variables 
Among the clinical and pathology variables, only younger age was associated with a greater risk of recurrence (HR = 0.96, CI: 0.93-0.99, p = 0.007), whereas no variable was associated with DFS and OS (Table 4).
Prognostic values of infiltrating immune cells 
A high AF of CD117+ mast cells in IM was associated with longer DFS (HR = 0.48, CI: 0.241 - 0.935, p = 0.031) and OS (HR = 0.34, CI: 0.167 - 0.703, p = 0.004) (Figure 8, Table 5). In addition, a high AF of CD117+ mast cells in the PT area was associated with longer DFS (HR = 0.48, CI: 0.247 - 0.945, p = 0.034) (Figure 8, Table 5). Contrastingly, the AFs of iDC and NK cells were not associated with any of the outcomes. CD117+ mast cells in the inner margin retained significant associations with DFS (HR = 0.46, CI: 0.23-0.91, p = 0.027) and OS (HR = 0.33, CI: 0.16-0.68, p = 0.003) after adjustment for TNM stage (Table 6). As for mast cells in the PT region, the association with DFS also remained significant (HR = 0.47, CI: 0.24-0.93, p = 0.029) (Table 6).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/66743/66743fig01.jpg" /> 
Figure 1: Representative schematic for the methods. Schematic diagram of the flow of work for identification of the prognostic role of mast cells, dendritic cells, and natural killer cells in HCC. Abbreviations: FFPE, formalin-fixed paraffin-embedded; H&#38;E, Hematoxylin and Eosin; ROIs, regions of interest.&#160;<a href="https://www.jove.com/files/ftp_upload/66743/66743fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/66743/66743fig02.jpg" /> 
Figure 2: Screenshot of the software window. A descriptive screenshot showing the step of importing images into a project in the software. <a href="https://www.jove.com/files/ftp_upload/66743/66743fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/66743/66743fig03.jpg" /> 
Figure 3: Screenshot of the option of area fraction. A screenshot from the software showing the selected parameters to quantify the area fraction of positive staining. <a href="https://www.jove.com/files/ftp_upload/66743/66743fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/66743/66743fig04.jpg" /> 
Figure 4: Screenshot of the option of pixel classification. Screenshot from the software showing the DAB positive cells before and after pixel classification. The brownish DAB staining has been converted to red color after pixel classification. <a href="https://www.jove.com/files/ftp_upload/66743/66743fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/66743/66743fig05.jpg" /> 
Figure 5: Representative immunostaining of innate immune cells in HCC patients. Representative immunostaining of (A) CD117+, (B) NKp46+, and (C) CD1a+ cells in TC (400x) and TIM (200x) of HCC. Abbreviations: HCC, hepatocellular carcinoma; TC, tumor center; TIM, tumor invasive margin, with inner margin (IM) and outer margin (OM). The dotted line represents a border between the tumor and non-tumor tissue. <a href="https://www.jove.com/files/ftp_upload/66743/66743fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/66743/66743fig06.jpg" /> 
Figure 6: Statistics describing the area fraction of innate immune cells in HCC patients. Statistics for the area fraction of CD1a+ dendritic cells, CD117+ mast cells, and NKp46+ NK cells) in the TC, IM, OM, and PT areas of HCC. Black lines are medians.Wilcoxon matched pairs test with Bonferroni correction was used for comparisons. Abbreviations: HCC, hepatocellular carcinoma; TC, tumor center; IM, inner margin; OM, outer margin; PT, peritumor area. ***: p &#60;0.001 <a href="https://www.jove.com/files/ftp_upload/66743/66743fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/66743/66743fig07.jpg" /> 
Figure 7: Heat map of significant correlations between area fractions of mast cells, DCs, and NKs in different regions of interest. Heat map of significant correlations between area fractions of CD117+ mast cells, CD1a+ DCs, and NKp46+NKs in TC, IM, OM, and PT (Spearman &#961;, p &#60; 0.05). Abbreviations: TC, tumor center; IM, inner margin; OM, outer margin; PT, peritumor area; DC, dendritic cells; NK, natural killer cells.&#160;<a href="https://www.jove.com/files/ftp_upload/66743/66743fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/66743/66743fig08.jpg" /> 
Figure 8: Kaplan-Meier DFS and OS curves for tumor-infiltrating mast cells in HCC patients. Kaplan-Meier analysis of DFS and OS according to low vs. high AF of tumor-infiltrating mast cells in the inner margin (A, C) and PT (B) of HCC. Abbreviations: HCC, hepatocellular carcinoma; DFS, disease-free survival; IM, inner invasive; PT, peritumor area. The figure has been adapted with permission from Ali et al.26. <a href="https://www.jove.com/files/ftp_upload/66743/66743fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table 1: Clinical background of enrolled hepatocellular carcinoma patients. Clinical background of enrolled cases of hepatocellular carcinoma. Abbreviations: NAFLD, non-alcoholic fatty liver disease. <a href="https://www.jove.com/files/ftp_upload/66743/Table 1.xlsx" target="_blank">Please click here to download this Table.</a>
Table 2: Estimated probability of outcomes in Kaplan-Meier analysis. The estimated probability of RFP, DFS, and OS in Kaplan-Meier analysis. Abbreviations: RFP, recurrence-free proportion; DFS, disease-free survival; OS, overall survival. <a href="https://www.jove.com/files/ftp_upload/66743/Table 2.xlsx" target="_blank">Please click here to download this Table.</a>
Table 3: Correlation between the area fraction of tumor-infiltrating mast cells, dendritic cells, and natural killer cells in different ROIs. Spearman correlation (&#961;) between the area fraction of tumor-infiltrating mast cells, dendritic cells, and natural killer cells in different ROIs, p &#60; 0.038. Abbreviations: TC, tumor center; IM, inner margin; OM, outer margin; PT, peritumor area; ROIs, regions of interest. <a href="https://www.jove.com/files/ftp_upload/66743/Table 3.xlsx" target="_blank">Please click here to download this Table.</a>
Table 4: Univariable analysis of clinical and pathology variables associated with time to recurrence (TTR), disease-free survival (DFS), and overall survival (OS). &#34;No&#34;, female gender or &#34;A&#34; were the reference categories for dichotomous variables. Bold values indicate statistical significance at the p &#60; 0.05 level. Abbreviations: HR, hazard ratio; CI, confidence interval; TTR, time to recurrence; DFS, disease-free survival. <a href="https://www.jove.com/files/ftp_upload/66743/Table 4.xlsx" target="_blank">Please click here to download this Table.</a>
Table 5: Univariable analysis of the association of area fraction of CD117+ mast cells, CD1a+denderitic cells and NKp46+natural killer cells with TTR, DFS, and OS per individual region of interest using the Cox regression (67 HCC patients). The area fraction of immune cells per area section (mm2) was converted into percentiles and then categorized into low (0-25 percentile) vs high (25-100 percentile). Hazard ratios show the relative risk compared with 1 for the low AF. Bold values indicate statistical significance at the p &#60; 0.05 level. Abbreviations: AF, area fraction; DFS, disease-free survival; OS, overall survival; HR, hazard ratio. <a href="https://www.jove.com/files/ftp_upload/66743/Table 5.xlsx" target="_blank">Please click here to download this Table.</a>
Table 6: The area fraction of CD117+ mast cells per individual ROI associated with disease-free survival and overall survival (multivariable analysis). Multivariable analysis of the association of the area fraction of CD117+ mast cells with DFS and OS in the IM and PT area using Cox regression (67 HCC patients). The area fraction of immune cells per area section (mm2) was converted into percentiles and then categorized into low (0-24 percentile) vs high (25-100 percentile). Hazard ratios show the relative risk compared with 1 for the low AF. Bold values indicate statistical significance at the p &#62; 0.05 level. Abbreviations: AF, area fraction; DFS, disease-free survival; OS, overall survival; HR, hazard ratio. <a href="https://www.jove.com/files/ftp_upload/66743/Table 6.xlsx" target="_blank">Please click here to download this Table.</a>
Supplementary File 1: Script for creating ROI. <a href="https://www.jove.com/files/ftp_upload/66743/Supplemental File (Script for ROI).zip" target="_blank">Please click here to download this File.</a>

Read this Scientific Article Protocol about Mast Cells in the Microenvironment of Hepatocellular Carcinoma Confer Favorable Prognosis: A Retrospective Study using QuPath Image Analysis Software at JoV

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment &#8212; Challenges and Innovations in Cancer Prognosis

The presence of mast cells in the inner margin and peritumor areas of hepatocellular carcinoma after resection confers a favorable prognosis. This study endorses QuPath image analysis software as a promising platform that could meet the need for reproducibility, consistency, and accuracy in digital pathology.

Mast Cells in the Microenvironment of Hepatocellular Carcinoma Confer Favorable Prognosis: A Retrospective Study using QuPath Image Analysis Software

The insights provided by in-situ detection of immune cells within hepatocellular carcinoma (HCC) might present information on patient outcomes. Studies ...

mast-cells-microenvironment-hepatocellular-carcinoma-confer-favorable

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JoVE Journal

Cancer Research

1.2K Views.  Charles University. We richtten ons op het evalueren van de verdeling van immuuncellen binnen de micro-omgeving van de tumor. Ten eerste helpen we bij de infiltratie van immuuncellen in tumormonsters en aangrenzend niet-tumorweefsel, en vervolgens bepalen we hun prognostische impact bij hepatocellulair carcinoom. De eerste uitdaging is de complexiteit van de tumorstructuren, die de scheiding tussen tumor- en niet-tumorweefsel uitdagend maakt.Bovendien zijn handmatige raketten voor certificering tijdrovend...

Video: Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

A Portal Vein Injection Model to Study Liver Metastasis of Breast Cancer

Breast cancer is the leading cause of cancer-related mortality in women worldwide. Liver metastasis is involved in upwards of 30% of cases with breast cancer metastasis, and results in poor outcomes with median survival rates of only 4.8 - 15 months. Current rodent models of breast cancer metastasis, including primary tumor cell xenograft and spontaneous tumor models, rarely metastasize to the liver. Intracardiac and intrasplenic injection models do result in liver metastases, however these models can be confounded by concomitant secondary-site metastasis, or by compromised immunity due to removal of the spleen to avoid tumor growth at the injection site. To address the need for improved liver metastasis models, a murine portal vein injection method that delivers tumor cells firstly and directly to the liver was developed. This model delivers tumor cells to the liver without complications of concurrent metastases in other organs or removal of the spleen. The optimized portal vein protocol employs small injection volumes of 5 - 10 &#956;l, &#8805; 32 gauge needles, and hemostatic gauze at the injection site to control for blood loss. The portal vein injection approach in Balb/c female mice using three syngeneic mammary tumor lines of varying metastatic potential was tested; high-metastatic 4T1 cells, moderate-metastatic D2A1 cells, and low-metastatic D2.OR cells. Concentrations of &#8804; 10,000 cells/injection results in a latency of ~ 20 - 40 days for development of liver metastases with the higher metastatic 4T1 and D2A1 lines, and &#62; 55 days for the less aggressive D2.OR line. This model represents an important tool to study breast cancer metastasis to the liver, and may be applicable to other cancers that frequently metastasize to the liver including colorectal and pancreatic adenocarcinomas.

A surgical procedure was developed to deliver mammary tumor cells to the murine liver via portal vein injection. This model permits investigation of late stages of liver metastasis in a fully immune competent host, including tumor cell extravasation, seeding, survival, and metastatic outgrowth in the liver.

Breast cancer is the leading cause of cancer-related mortality in women worldwide. Liver metastasis is involved in upwards of 30% of cases with breast ...

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer

A hallmark of advanced tumors is a switch to aerobic glycolysis that is readily measured by [18F]-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) imaging. Co-mutations in the KRAS proto-oncogene and the LKB1 tumor suppressor gene are frequent events in lung cancer that drive hypermetabolic, glycolytic tumor growth. A critical pathway regulating the growth and metabolism of these tumors is the mechanistic target of the rapamycin (mTOR) pathway, which can be effectively targeted using selective catalytic mTOR kinase inhibitors. The mTOR inhibitor MLN0128 suppresses glycolysis in mice bearing tumors with Kras and Lkb1 co-mutations, referred to as KL mice. The therapy response in KL mice is first measured by 18F-FDG PET and computed tomography (CT) imaging before and after the delivery of MLN0128. By utilizing 18F-FDG PET/CT, researchers are able to measure dynamic changes in the glucose metabolism in genetically engineered mouse models (GEMMs) of lung cancer following a therapeutic intervention with targeted therapies. This is followed by ex vivo autoradiography and a quantitative immunohistochemical (qIHC) analysis using morphometric software. The use of qIHC enables the detection and quantification of distinct changes in the biomarker profiles following treatment as well as the characterization of distinct tumor pathologies. The coupling of PET imaging to quantitative histology is an effective strategy to identify metabolic and therapeutic responses in vivo in mouse models of disease.

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer

In this protocol, we describe how to utilize [18F]-2-fluoro-2-deoxy-D-glucose positron emission tomography and computed tomography (18F-FDG PET/CT) imaging to measure the tumor metabolic response to the targeted therapy MLN0128 in a Kras/Lkb1 mutant mouse model of lung cancer and coupled imaging with high resolution ex vivo autoradiography and quantitative histology.

A hallmark of advanced tumors is a switch to aerobic glycolysis that is readily measured by [18F]-2-fluoro-2-deoxy-D-glucose positron emission tomography ...

A Biomimetic Model for Liver Cancer to Study Tumor-Stroma Interactions in a 3D Environment with Tunable Bio-Physical Properties

Hepatocellular carcinoma (HCC) is a primary liver tumor developing in the wake of chronic liver disease. Chronic liver disease and inflammation leads to a fibrotic environment actively supporting and driving hepatocarcinogenesis. Insight into hepatocarcinogenesis in terms of the interplay between the tumor stroma micro-environment and tumor cells is thus of considerable importance. Three-dimensional (3D) cell culture models are proposed as the missing link between current in vitro 2D cell culture models and in vivo animal models. Our aim was to design a novel 3D biomimetic HCC model with accompanying fibrotic stromal compartment and vasculature. Physiologically relevant hydrogels such as collagen and fibrinogen were incorporated to mimic the bio-physical properties of the tumor ECM. In this model LX2 and HepG2 cells embedded in a hydrogel matrix were seeded onto the inverted transmembrane insert. HUVEC cells were then seeded onto the opposite side of the membrane. Three formulations consisting of ECM-hydrogels embedded with cells were prepared and the bio-physical properties were determined by rheology. Cell viability was determined by a cell viability assay over 21 days. The effect of the chemotherapeutic drug doxorubicin was evaluated in both 2D co-culture and our 3D model for a period of 72h. Rheology results show that bio-physical properties of a fibrotic, cirrhotic and HCC liver can be successfully mimicked. Overall, results indicate that this 3D model is more representative of the&#160;in vivo&#160;situation compared to traditional 2D cultures.&#160;Our 3D tumor model showed a decreased response to chemotherapeutics, mimicking drug resistance typically seen in HCC patients.&#160;

This protocol presents a 3D biomimetic model with accompanying fibrotic stromal compartment. Prepared with physiologically relevant hydrogels in ratios mimicking the bio-physical properties of the stromal extracellular matrix, an active mediator of cellular interactions, tumor growth and metastasis.

Hepatocellular carcinoma (HCC) is a primary liver tumor developing in the wake of chronic liver disease. Chronic liver disease and inflammation leads to a ...

Portal Vein Injection of Colorectal Cancer Organoids to Study the Liver Metastasis Stroma

Hepatic metastasis of colorectal cancer (CRC) is a leading cause of cancer-related death. Cancer-associated fibroblasts (CAFs), a major component of the tumor microenvironment, play a crucial role in metastatic CRC progression and predict poor patient prognosis. However, there is a lack of satisfactory mouse models to study the crosstalk between metastatic cancer cells and CAFs. Here, we present a method to investigate how liver metastasis progression is regulated by the metastatic niche and possibly could be restrained by stroma-directed therapy. Portal vein injection of CRC organoids generated a desmoplastic reaction, which faithfully recapitulated the fibroblast-rich histology of human CRC liver metastases. This model was tissue-specific with a higher tumor burden in the liver when compared to an intra-splenic injection model, simplifying mouse survival analyses. By injecting luciferase-expressing tumor organoids, tumor growth kinetics could be monitored by in vivo imaging. Moreover, this preclinical model provides a useful platform to assess the efficacy of therapeutics targeting the tumor mesenchyme. We describe methods to examine whether adeno-associated virus-mediated delivery of a tumor-inhibiting stromal gene to hepatocytes could remodel the tumor microenvironment and improve mouse survival. This approach enables the development and assessment of novel therapeutic strategies to inhibit hepatic metastasis of CRC.

Portal vein injection of colorectal cancer (CRC) organoids generates stroma-rich liver metastasis. This mouse model of CRC hepatic metastasis represents a useful tool to study tumor-stroma interactions and develop novel stroma-directed therapeutics such as adeno-associated virus-mediated gene therapies.

Hepatic metastasis of colorectal cancer (CRC) is a leading cause of cancer-related death. Cancer-associated fibroblasts (CAFs), a major component of the ...

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma

The aggressiveness and lack of well-tolerated and widely effective treatments for advanced hepatocellular carcinoma (HCC), the predominant form of liver cancer, rationalize its rank as the second most common cause of cancer-related death. Preclinical models need to be adapted to recapitulate the human conditions to select the best therapeutic candidates for clinical development and aid the delivery of personalized medicine. Three-dimensional (3D) cellular spheroid models show promise as an emerging in vitro alternative to two-dimensional (2D) monolayer cultures. Here, we describe a 3D tumor spheroid&#160;model which exploits the ability of individual cells to aggregate when maintained in hanging droplets, and is more representative of an in vivo environment than standard monolayers. Furthermore, 3D spheroids can be produced by combining homotypic or heterotypic cells, more reflective of the cellular heterogeneity in vivo, potentially enabling the study of environmental interactions that can influence progression and treatment responses. The current research optimized the cell density to form 3D homotypic and heterotypic tumor spheroids by immobilizing cell suspensions on the lids of standard 10 cm3 Petri dishes. Longitudinal analysis was performed to generate growth curves for homotypic versus heterotypic tumor/fibroblasts spheroids. Finally, the proliferative impact of fibroblasts (COS7 cells) and liver myofibroblasts (LX2) on homotypic tumor (Hep3B) spheroids was investigated. A seeding density of 3,000 cells (in 20 &#181;L media) successfully yielded Huh7/COS7 heterotypic spheroids, which displayed a steady increase in size up to culture day 8, followed by growth retardation. This finding was corroborated using Hep3B homotypic spheroids cultured in LX2 (human hepatic stellate cell line) conditioned medium (CM). LX2 CM triggered the proliferation of Hep3B spheroids compared to control tumor spheroids. In conclusion, this protocol has shown that 3D tumor spheroids can be used as a simple, economical, and prescreen in vitro tool to study tumor-stromal interactions more comprehensively.

Comprehensive in vitro models that faithfully recapitulate the relevant human disease are lacking. The current study presents three-dimensional (3D) tumor spheroid creation and culture, a reliable in vitro tool to study the tumor-stromal interaction in human hepatocellular carcinoma.

The aggressiveness and lack of well-tolerated and widely effective treatments for advanced hepatocellular carcinoma (HCC), the predominant form of liver ...

Non-Invasive PET/MR Imaging in an Orthotopic Mouse Model of Hepatocellular Carcinoma

Preclinical experimental models of hepatocellular carcinoma (HCC) that recapitulate human disease represent an important tool to study tumorigenesis and evaluate novel therapeutic approaches. Non-invasive whole-body imaging using positron emission tomography (PET) provides critical insights into the in vivo characteristics of tissues at the molecular level in real-time. We present here a protocol for orthotopic HCC xenograft creation with and without hepatic artery ligation (HAL) to induce tumor hypoxia and the assessment of their tumor metabolism in vivo using [18F]Fluoromisonidazole ([18F]FMISO) and [18F]Fluorodeoxyglucose ([18F]FDG) PET/magnetic resonance (MR) imaging. Tumor hypoxia could be readily visualized using the hypoxia marker [18F]FMISO, and it was found that the [18F]FMISO uptake was higher in HCC mice that underwent HAL than in the non-HAL group, whereas [18F]FDG could not distinguish tumor hypoxia between the two groups. HAL tumors also displayed a higher level of hypoxia-inducible factor (HIF)-1&#945; expression in response to hypoxia. Quantification of HAL tumors showed a 2.3-fold increase in [18F]FMISO uptake based on the standardized value uptake (SUV) approach.

Here, we present a protocol to create orthotopic hepatocellular carcinoma xenografts with and without hepatic artery ligation and perform non-invasive positron emission tomography (PET) imaging of tumor hypoxia using [18F]Fluoromisonidazole ([18F]FMISO) and [18F]Fluorodeoxyglucose ([18F]FDG).

Preclinical experimental models of hepatocellular carcinoma (HCC) that recapitulate human disease represent an important tool to study tumorigenesis and ...

Multiplex Immunofluorescence and Spatial Image Analysis of the Tumor Microenvironment

Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

The tumor microenvironment (TME) is composed of a plethora of different cell types, such as cytotoxic immune cells and immunomodulatory cells. Depending on its composition and the interactions between cancer cells and peri-tumoral cells, the TME may affect cancer progression. The characterization of tumors and their complex microenvironment could improve the understanding of cancer diseases and may help scientists and clinicians to discover new biomarkers.
We recently developed several multiplex immunofluorescence (mIF) panels based on tyramide signal amplification (TSA) for the characterization of the TME in colorectal cancer, head and neck squamous cell carcinoma, melanoma, and lung cancer. Once the staining and scanning of the corresponding panels are completed, the samples are analyzed on an image analysis software. The spatial position and the staining of each cell are then exported from this quantification software into R. We developed R scripts that allow us not only to analyze the density of each cell type in several tumor compartments (e.g. the center of the tumor, the margin of the tumor, and the stroma) but also to perform distance-based analyses between different cell types.
This particular workflow adds a spatial dimension to the classical density analysis already routinely performed for several markers. mIF analysis could allow scientists to have a better understanding of the complex interaction between cancer cells and the TME and to discover new predictive biomarkers of response to treatments, such as immune checkpoint inhibitors, and targeted therapies.

Author Spotlight: Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment  

In this article, a protocol for manual tyramide signal amplification (TSA) multiplex immunofluorescence (mIF) combined with image analysis and spatial analysis is described. This protocol can be used with formalin-fixed paraffin-embedded (FFPE) sections for the staining of two to six antigens per slide depending on the slide scanner available in the laboratory.

The tumor microenvironment (TME) is composed of a plethora of different cell types, such as cytotoxic immune cells and immunomodulatory cells. Depending ...

A Next-generation Tissue Microarray (ngTMA) Protocol for Biomarker Studies

Biomarker research relies on tissue microarrays (TMA). TMAs are produced by repeated transfer of small tissue cores from a &#8216;donor&#8217; block into a &#8216;recipient&#8217; block and then used for a variety of biomarker applications. The construction of conventional TMAs is labor intensive, imprecise, and time-consuming. Here, a protocol using next-generation Tissue Microarrays (ngTMA) is outlined. ngTMA is based on TMA planning and design, digital pathology, and automated tissue microarraying. The protocol is illustrated using an example of 134 metastatic colorectal cancer patients. Histological, statistical and logistical aspects are considered, such as the tissue type, specific histological regions, and cell types for inclusion in the TMA, the number of tissue spots, sample size, statistical analysis, and number of TMA copies. Histological slides for each patient are scanned and uploaded onto a web-based digital platform. There, they are viewed and annotated (marked) using a 0.6-2.0 mm diameter tool, multiple times using various colors to distinguish tissue areas. Donor blocks and 12 &#8216;recipient&#8217; blocks are loaded into the instrument. Digital slides are retrieved and matched to donor block images. Repeated arraying of annotated regions is automatically performed resulting in an ngTMA. In this example, six ngTMAs are planned containing six different tissue types/histological zones. Two copies of the ngTMAs are desired. Three to four slides for each patient are scanned; 3 scan runs are necessary and performed overnight. All slides are annotated; different colors are used to represent the different tissues/zones, namely tumor center, invasion front, tumor/stroma, lymph node metastases, liver metastases, and normal tissue. 17 annotations/case are made; time for annotation is 2-3 min/case. 12 ngTMAs are produced containing 4,556 spots. Arraying time is 15-20 hr. Due to its precision, flexibility and speed, ngTMA is a powerful tool to further improve the quality of TMAs used in clinical and translational research.

A Next-generation Tissue Microarray ngTMA Protocol for Biomarker Studies

The protocol aims at optimizing the construction and quality of tissue microarrays for biomarker research. It includes aspects of planning and design, digital pathology, virtual slide annotation, and automated tissue arraying.

Biomarker research relies on tissue microarrays (TMA). TMAs are produced by repeated transfer of small tissue cores from a &#8216;donor&#8217; block into ...

Medicine

Deciphering Tumor Microenvironment Using mIHC in Lung Squamous Carcinoma

Multiplex Immunohistochemistry Staining for Paraffin-embedded Lung Cancer Tissue

Lung cancer is the leading cause of malignant tumor-related morbidity and mortality all over the world, and the complex tumor microenvironment has been considered the leading cause of death in lung cancer patients. The complexity of the tumor microenvironment requires effective methods to understand cell-to-cell relationships in tumor tissues. The multiplex immunohistochemistry (mIHC) technique has become a key tool for inferring the relationship between the expression of proteins upstream and downstream of signaling pathways in tumor tissues and developing clinical diagnoses and treatment plans. mIHC is a multi-label immunofluorescence staining method based on Tyramine Signal Amplification (TSA) technology, which can simultaneously detect multiple target molecules on the same tissue section sample to achieve different protein co-expression and co-localization analysis. In this experimental protocol, paraffin-embedded tissue sections of lung squamous carcinoma of clinical origin were subjected to multiplex immunohistochemical staining. By optimizing the experimental protocol, multiplex immunohistochemical staining of labeled target cells and proteins was achieved, solving the problem of autofluorescence and channel crosstalk in lung tissues. In addition, multiplex immunohistochemical staining is widely used in the experimental validation of tumor-related, high-throughput sequencing, including single-cell sequencing, proteomics, and tissue space sequencing, providing intuitive and visual pathology validation results.

Author Spotlight: Enhancing Multicolor Fluorescence Localization in Lung Carcinoma Sample

This experimental protocol describes and optimizes a multiplex immunohistochemistry (IHC) staining method, mainly by optimizing single-channel antibody incubation conditions and adjusting the settings of antibodies and channels to solve the problems of autofluorescence and channel crosstalk in lung cancer tissues of clinical origin.

Lung cancer is the leading cause of malignant tumor-related morbidity and mortality all over the world, and the complex tumor microenvironment has been ...

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

The immune landscape of the tumor microenvironment (TME) is a determining factor in cancer progression and response to therapy. Specifically, the density and the location of immune cells in the TME have important diagnostic and prognostic values. Multiomic profiling of the TME has exponentially increased our understanding of the numerous cellular and molecular networks regulating tumor initiation and progression. However, these techniques do not provide information about the spatial organization of cells or cell-cell interactions. Affordable, accessible, and easy to execute multiplexing techniques that allow spatial resolution of immune cells in tissue sections are needed to complement single cell-based high-throughput technologies. Here, we describe a strategy that integrates serial imaging, sequential labeling, and image alignment to generate virtual multiparameter slides of whole tissue sections. Virtual slides are subsequently analyzed in an automated fashion using user-defined protocols that enable identification, quantification, and mapping of cell populations of interest. The image analysis is done, in this case using the analysis modules Tissuealign, Author, and HISTOmap. We present an example where we applied this strategy successfully to one clinical specimen, maximizing the information that can be obtained from limited tissue samples and providing an unbiased view of the TME in the entire tissue section.

Here, we describe a simple and accessible strategy for visualizing, quantifying, and mapping immune cells in formalin-fixed paraffin-embedded tumor tissue sections. This methodology combines existing imaging and digital analysis techniques with the purpose of expanding the multiplexing capability and multiparameter analysis of imaging assays.

The immune landscape of the tumor microenvironment (TME) is a determining factor in cancer progression and response to therapy. Specifically, the density ...

Mast Cells in the Microenvironment of Hepatocellular Carcinoma Confer Favorable Prognosis: A Retrospective Study using QuPath Image Analysis Software

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