Name: digital spatial profiling for characterization of the microenvironment in adult-type diffusely infiltrating glioma
Uploaded: 2022-09-13T12:50:00
Description: Watch this Scientific Journal Video about Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma at JoVE.com

The authors acknowledge the support of the Laboratory for Clinical Genomics and Advanced Technology in the Department of Pathology and Laboratory Medicine of the Dartmouth Hitchcock Health System. The authors also acknowledge the Pathology Shared Resource at the Dartmouth Cancer Center with NCI Cancer Center Support Grant 5P30 CA023108-37.

The protocol outlined below follows the guidelines of the Dartmouth-Hitchcock Human Research Ethics Committee. Informed consent was obtained from the patients whose tissue samples were included in this study. See the Table of Materials section for details related to all materials, reagents, equipment, and software used in this protocol.
1. Slide preparation17
<ol>
	<li>Retrieve or prepare formalin-fixed, paraffin-embedded tissue from ...

<ol>
	<li>Pedersen, P. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Migratory+patterns+of+lac-z+transfected+human+glioma+cells+in+the+rat+brain.">Migratory patterns of lac-z transfected human glioma cells in the rat brain.</a> International Journal of Cancer. 62 (6), 767-771 (1995).</li><li>Wang, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Invasion+of+white+matter+tracts+by+glioma+stem+cells+is+regulated+by+a+NOTCH1-SOX2+positive-feedback+loop.">Invasion of white matter tracts by glioma stem cells is regulated by a NOTCH1-SOX2 positive-feedback loop.</a> Nature Neuroscience. 22 (1), 91-105 (2019).</li><li>Iwadate, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Epithelial-mesenchymal+transition+in+glioblastoma+progression.">Epithelial-mesenchymal transition in glioblastoma progression.</a> Oncology Letters. 11 (3), 1615-1620 (2016).</li><li>Tao, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genomics+and+prognosis+analysis+of+epithelial-mesenchymal+transition+in+glioma.">Genomics and prognosis analysis of epithelial-mesenchymal transition in glioma.</a> Frontiers in Oncology. 10, 183 (2020).</li><li>Cuddapah, V. A., Robel, S., Watkins, S., Sontheimer, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+neurocentric+perspective+on+glioma+invasion.">A neurocentric perspective on glioma invasion.</a> Nature Reviews Neuroscience. 15 (7), 455-465 (2014).</li><li>Barthel, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glioma:+molecular+signature+and+crossroads+with+tumor+microenvironment.">Glioma: molecular signature and crossroads with tumor microenvironment.</a> Cancer and Metastasis Reviews. 1 (1), 53-75 (2021).</li><li>Ziegler, D. S., Kung, A. L., Kieran, M. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Anti-apoptosis+mechanisms+in+malignant+gliomas.">Anti-apoptosis mechanisms in malignant gliomas.</a> Journal of Clinical Oncology. 26 (3), 493-500 (2008).</li><li>Berghoff, A. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Programmed+death+ligand+1+expression+and+tumor-infiltrating+lymphocytes+in+glioblastoma.">Programmed death ligand 1 expression and tumor-infiltrating lymphocytes in glioblastoma.</a> Neuro-Oncology. 17 (8), 1064-1075 (2015).</li><li>Merritt, C. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiplex+digital+spatial+profiling+of+proteins+and+RNA+in+fixed+tissue.">Multiplex digital spatial profiling of proteins and RNA in fixed tissue.</a> Nature Biotechnology. 38 (5), 586-599 (2020).</li><li>Van, T. M., Blank, C. U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+user's+perspective+on+GeoMxTM+digital+spatial+profiling.">A user's perspective on GeoMxTM digital spatial profiling.</a> Immuno-Oncology Technology. 1, 11-18 (2019).</li><li>Garcia-Pardo, M., Calles, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ROS-1+NSCLC+therapy+resistance+mechanism.">ROS-1 NSCLC therapy resistance mechanism.</a> Precision Cancer Medicine. , (2021).</li><li>Ye, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Digital+spatial+profiling+of+individual+glomeruli+from+patients+with+anti-neutrophil+cytoplasmic+autoantibody-associated+glomerulonephritis.">Digital spatial profiling of individual glomeruli from patients with anti-neutrophil cytoplasmic autoantibody-associated glomerulonephritis.</a> Frontiers in Immunology. 13, 831253 (2022).</li><li>Bergholtz, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Best+practices+for+spatial+profiling+for+breast+cancer+research+with+the+GeoMx+digital+spatial+profiler.">Best practices for spatial profiling for breast cancer research with the GeoMx digital spatial profiler.</a> Cancers. 13 (17), 4456 (2021).</li><li>Brady, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inter-+and+intra-tumor+heterogeneity+of+metastatic+prostate+cancer+determined+by+digital+spatial+gene+expression+profiling.">Inter- and intra-tumor heterogeneity of metastatic prostate cancer determined by digital spatial gene expression profiling.</a> Nature Communications. 12 (1), 1426 (2021).</li><li>Kulasinghe, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Highly+multiplexed+digital+spatial+profiling+of+the+tumor+microenvironment+of+head+and+neck+squamous+cell+carcinoma+patients.">Highly multiplexed digital spatial profiling of the tumor microenvironment of head and neck squamous cell carcinoma patients.</a> Frontiers in Oncology. 10, 607349 (2021).</li><li>Wang, D. Y. -. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Case+study:+Digital+spatial+profiling+of+metastatic+clear+cell+carcinoma+reveals+intra-tumor+heterogeneity+in+epithelial-mesenchymal+gradient.">Case study: Digital spatial profiling of metastatic clear cell carcinoma reveals intra-tumor heterogeneity in epithelial-mesenchymal gradient.</a> bioRxiv. , (2021).</li><li>GeoMx DSP. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+slide+preparation+user+manual.">Automated slide preparation user manual.</a> GeoMx DSP. , (2022).</li><li>Allam, M., Cai, S., Coskun, A. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiplex+bioimaging+of+single-cell+spatial+profiles+for+precision+cancer+diagnostics+and+therapeutics.">Multiplex bioimaging of single-cell spatial profiles for precision cancer diagnostics and therapeutics.</a> NPJ Precision Oncology. 4, 11 (2020).</li><li>Wolchok, J. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Overall+survival+with+combined+nivolumab+and+ipilimumab+in+advanced+melanoma.">Overall survival with combined nivolumab and ipilimumab in advanced melanoma.</a> New England Journal of Medicine. 377 (14), 1345-1356 (2017).</li><li>Blank, C. U., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neoadjuvant+versus+adjuvant+ipilimumab+plus+nivolumab+in+macroscopic+stage+III+melanoma.">Neoadjuvant versus adjuvant ipilimumab plus nivolumab in macroscopic stage III melanoma.</a> Nature Medicine. 24 (11), 1655-1661 (2018).</li><li>Matthews, R. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain-enriched+hyaluronan+binding+(BEHAB)/brevican+cleavage+in+a+glioma+cell+line+is+mediated+by+a+disintegrin+and+metalloproteinase+with+thrombospondin+motifs+(ADAMTS)+family+member.">Brain-enriched hyaluronan binding (BEHAB)/brevican cleavage in a glioma cell line is mediated by a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS) family member.</a> Journal of Biological Chemistry. 275 (30), 22695-22703 (2000).</li><li>Paganetti, P. A., Caroni, P., Schwab, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glioblastoma+infiltration+into+central+nervous+system+tissue+in+vitro:+involvement+of+a+metalloprotease.">Glioblastoma infiltration into central nervous system tissue in vitro: involvement of a metalloprotease.</a> Journal of Cell Biology. 107, 2281-2291 (1988).</li><li>Beli&#235;n, A. T., Paganetti, P. A., Schwab, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Membrane-type+1+matrix+metalloprotease+(MT1-MMP)+enables+invasive+migration+of+glioma+cells+in+central+nervous+system+white+matter.">Membrane-type 1 matrix metalloprotease (MT1-MMP) enables invasive migration of glioma cells in central nervous system white matter.</a> Journal of Cell Biology. 144 (2), 373-384 (1999).</li><li>Coulie, P. G., Vanden Eynde, B. J., vander Bruggen, P., Boon, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tumour+antigens+recognized+by+T+lymphocytes:+At+the+core+of+cancer+immunotherapy.">Tumour antigens recognized by T lymphocytes: At the core of cancer immunotherapy.</a> Nature Reviews Cancer. 14 (2), 135-146 (2014).</li><li>Finn, O. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Vaccines+for+cancer+prevention:+a+practical+and+feasible+approach+to+the+cancer+epidemic.">Vaccines for cancer prevention: a practical and feasible approach to the cancer epidemic.</a> Cancer Immunology Research. 2 (8), 708-713 (2014).</li><li>Sharma, P., Allison, J. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+future+of+immune+checkpoint+therapy.">The future of immune checkpoint therapy.</a> Science. 348 (6230), 56-61 (2015).</li><li>Chen, L., Han, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Anti-PD-1/PD-L1+therapy+of+human+cancer:+past,+present,+and+future.">Anti-PD-1/PD-L1 therapy of human cancer: past, present, and future.</a> Journal of Clinical Investigation. 125 (9), 3384-3391 (2015).</li><li>Cai, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glioma-Associated+stromal+cells+stimulate+glioma+malignancy+by+regulating+the+tumor+immune+microenvironment.">Glioma-Associated stromal cells stimulate glioma malignancy by regulating the tumor immune microenvironment.</a> Frontiers in Oncology. 11, 672928 (2021).</li><li>Ishii, , et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Histological+characterization+of+the+tumorigenic+&amp;#34;peri-necrotic+niche&amp;#34;+harboring+quiescent+stem-like+tumor+cells+in+glioblastoma.">Histological characterization of the tumorigenic &amp;#34;peri-necrotic niche&amp;#34; harboring quiescent stem-like tumor cells in glioblastoma.</a> PLoS One. 11 (1), 0147366 (2016).</li><li>Lewis, C. E., Pollard, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distinct+role+of+macrophages+in+different+tumor+microenvironments.">Distinct role of macrophages in different tumor microenvironments.</a> Cancer Research. 66 (2), 605-612 (2006).</li><li>NanoString. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CosMx+Spatial+Molecular+Imager:+True+Single-Cell+In+Situ+Solution.">CosMx Spatial Molecular Imager: True Single-Cell In Situ Solution.</a> NanoString. , (2022).</li></ol>

Given the diversity of proteins that could potentially influence the aggressiveness of gliomas and the notion that several of these proteins remain undiscovered, a high-throughput protein quantification method is an ideal technologic approach. Additionally, given that spatial data in oncologic samples often correlates with differential expression18, incorporating spatial profiling into the protein quantification approach allows for more effective analysis.
The high-thro...

Diffusely infiltrating gliomas are the most common type of malignant brain tumor in adults and are invariably lethal. The propensity for glioma cells to migrate extensively in the brain is a major therapeutic challenge. The mechanism by which they spread involves directed migration and unchecked invasion. Invasive glioma cells have been shown to exhibit tropism and migration along white matter tracts1, with recent research implicating demyelination of these tracts as an active, protumorigenic feature2. Invasion is mediated by an epithelial-to-mesenchymal transition, in which glioma cells acquire mesenchymal properties by reducing the expression of genes encoding extracellular matrix proteins and cell adhesion molecules, amplifying migration and facilitating propagation through the tumor microenvironment3,4,5.
At the molecular level, disruption of several proteins that confer cellular stability and interface with immunogenic components has been demonstrated6. Infiltrative gliomas are known to undergo suppression of proteins with anti-apoptotic (e.g., PTEN) properties7. They also overexpress proteins that promote evasion of the host immune response (e.g., PD1/PDL1)8. The dysregulation of these complex pathways enhances tumorigenicity and increases malignant potential.
Within samples of invasive glioma, the aim was to evaluate the differential expression of proteins key to cell growth, survival, and proliferation, and to microenvironment structural integrity between invasive and non-invasive components. Additionally, we sought to study the differential regulation of proteins with an active immunogenic role, offering insight to the mechanism by which compromised host immune defenses may enhance the proliferative and invasive potential of gliomas. This is especially relevant given the recent breadth of research demonstrating how immune markers and drivers of dysregulation in malignancy can serve as targets of immunotherapy. Identifying viable therapeutic targets among the many proteins involved in immunosurveillance and reactivity requires a highly sensitive and comprehensive approach.
Given the wide array of candidate proteins that can be studied, we sought a method akin to immunohistochemistry but with enhanced data processing efficiency. Within the field of cancer biology, DSP has emerged as a powerful technology with important advantages over alternative tools for proteomic analysis and quantification. The hallmark of DSP is its high-throughput multiplexing capability, allowing for simultaneous study of several different proteins within a sample, marking an important distinction from standard but lower-plex technologies such as immunohistochemistry (IHC)9,10. The multiplex feature of DSP does not compromise its fidelity as a quantitative and analytical tool, as demonstrated by studies comparing DSP to IHC. When used for proteomic quantification of non-small cell lung cancer specimens, for example, DSP has been shown to have similar results to IHC11. Additionally, DSP offers customizable regional specification, in which users can manually define regions within which to perform proteomic analysis. This presents an advantage over whole-section multiplex methods10,12. In a single round of processing, DSP thus offers multiple layers of analysis by surveying several protein targets across multiple regions of interest.
DSP has applications in several different pathological settings. DSP is especially advantageous in oncologic analysis, as spatial variation can correlate with cellular transformation and differential protein expression. For example, DSP has been used to compare the proteomic profile of breast cancer to the adjacent tumor microenvironment. This carries important implications for understanding the natural history of this tumor and its progression, as well as potential response to treatment13. Additional contexts illustrating the versatility of DSP include spatial quantification of protein diversity in prostate cancer14, association of immune cell marker expression with disease progression in head and neck squamous cell carcinoma15, and demonstration of an epithelial-mesenchymal gradient of protein expression distinguishing metastatic from primary clear cell ovarian cancer16. By implementing DSP, we characterize the spatial topography of proteins that could impact tumorigenesis and invasion of gliomas.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>BOND Research Detection System</td>
			<td>Leica Biosystems, Wetzlar, Germany</td>
			<td>DS9455</td>
			<td>Open detection system containing open containers in a reagent tray</td>
		</tr>
		<tr>
			<td>BOND Wash</td>
			<td>Leica Biosystems, Wetzlar, Germany</td>
			<td>AR950</td>
			<td>10X concentrated buffer solution for washing fixed tissue</td>
		</tr>
		<tr>
			<td>Buffer W</td>
			<td>NanoString, Seattle, WA</td>
			<td>contact company</td>
			<td>Blocking reagent</td>
		</tr>
		<tr>
			<td>Cy3 conjugation kit</td>
			<td>Abcam, Cambridge, UK</td>
			<td>AB188287</td>
			<td>Cy3 fluorescent antibody conjugation kit</td>
		</tr>
		<tr>
			<td>GeoMx Digital Spatial Profiler (DSP)</td>
			<td>NanoString, Seattle, WA</td>
			<td>contact company</td>
			<td>System for imaging and characterizing protein and RNA targets</td>
		</tr>
		<tr>
			<td>GeoMx DSP Instrument BufferKit</td>
			<td>NanoString, Seattle, WA</td>
			<td>100471</td>
			<td>Buffer kit for GeoMX DSP (including buffers for sample processing and preparation)</td>
		</tr>
		<tr>
			<td>GeoMx Hyb Code Pack_Protein</td>
			<td>NanoString, Seattle, WA</td>
			<td>121300401</td>
			<td>Controls for running GeoMX DSP experiemtns</td>
		</tr>
		<tr>
			<td>GeoMx Immune Cell Panel (Imm Cell Pro_Hs)</td>
			<td>NanoString, Seattle, WA</td>
			<td>121300101</td>
			<td>Protein module with targets for human immune cells and immuno-oncologic targets</td>
		</tr>
		<tr>
			<td>GeoMx Pan-Tumor Panel (Pan-Tumor_Hs)</td>
			<td>NanoString, Seattle, WA</td>
			<td>121300105</td>
			<td>Protein module with targets for multiple human tumor types and for markers of epithelial-mesenchymal transition</td>
		</tr>
		<tr>
			<td>GeoMx Protein Slide Prep FFPE</td>
			<td>NanoString, Seattle, WA</td>
			<td>121300308</td>
			<td>Sample preparation reagents for GeoMX DSP protein analysis</td>
		</tr>
		<tr>
			<td>IDH1-R132H antibody</td>
			<td>Dianova, Hamburg, Germany</td>
			<td>DIA-H09</td>
			<td>Monoclonal antibody against human IDH1 R132H</td>
		</tr>
		<tr>
			<td>LEICA Bond RX</td>
			<td>Leica Biosystems, Wetzlar, Germany</td>
			<td>contact company</td>
			<td>Fully automated IHC stainer</td>
		</tr>
		<tr>
			<td>Master Kit--12 reactions</td>
			<td>NanoString, Seattle, WA</td>
			<td>100052</td>
			<td>Materials and reagents for use with the nCounter Analysis system</td>
		</tr>
		<tr>
			<td>nCounter Analysis System</td>
			<td>NanoString, Seattle, WA</td>
			<td>contact company</td>
			<td>Automated system for multiplex target expression quantification (to be used with GeoMx DSP)</td>
		</tr>
		<tr>
			<td>TMA Master II</td>
			<td>3DHistech Ltd., Budapest, Hungary</td>
			<td></td>
			<td>To create the tissue microarray block</td>
		</tr>
	</tbody>
</table>

digital spatial profiling for characterization of the microenvironment in adult-type diffusely infiltrating glioma

Diffusely infiltrating gliomas are associated with high morbidity and mortality due to the infiltrative nature of tumor spread. They are morphologically complex tumors, with a high degree of proteomic variability across both the tumor itself and its heterogenous microenvironment. The malignant potential of these tumors is enhanced by the dysregulation of proteins involved in several key pathways, including processes that maintain cellular stability and preserve the structural integrity of the microenvironment. Although there have been numerous bulk and single-cell glioma analyses, there is a relative paucity of spatial stratification of these proteomic data. Understanding differences in spatial distribution of tumorigenic factors and immune cell populations between the intrinsic tumor, invasive edge, and microenvironment offers valuable insight into the mechanisms underlying tumor proliferation and propagation. Digital spatial profiling (DSP) represents a powerful technology that can form the foundation for these important multilayer analyses.
DSP is a method that efficiently quantifies protein expression within user-specified spatial regions in a tissue specimen. DSP is ideal for studying the differential expression of multiple proteins within and across regions of distinction, enabling multiple levels of quantitative and qualitative analysis. The DSP protocol is systematic and user-friendly, allowing for customized spatial analysis of proteomic data. In this experiment, tissue microarrays are constructed from archived glioblastoma core biopsies. Next, a panel of antibodies is selected, targeting proteins of interest within the sample. The antibodies, which are preconjugated to UV-photocleavable DNA oligonucleotides, are then incubated with the tissue sample overnight. Under fluorescence microscopy visualization of the antibodies, regions of interest (ROIs) within which to quantify protein expression are defined with the samples. UV light is then directed at each ROI, cleaving the DNA oligonucleotides. The oligonucleotides are microaspirated and counted within each ROI, quantifying the corresponding protein on a spatial basis.

Figure 4 shows the representative results from a DSP experiment performed on samples of glioblastoma. A heat map is presented, illustrating one of the methods by which to capture data visually using the DSP software. Rows represent protein targets, and each column corresponds to a region of interest. A color range of blue to red denotes low to high expression, respectively. Variability of color within a row reflects regional protein heterogeneity and suggests a possible spatial association w...

Watch this Scientific Journal Video about Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma at JoVE.com

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Proteomic dysregulation plays an important role in the spread of diffusely infiltrating gliomas, but several relevant proteins remain unidentified. Digital spatial processing (DSP) offers an efficient, high-throughput approach for characterizing the differential expression of candidate proteins that may contribute to the invasion and migration of infiltrative gliomas.

Diffusely infiltrating gliomas are associated with high morbidity and mortality due to the infiltrative nature of tumor spread. They are morphologically ...

Digital Spatial Profiling, or DSP, offers an efficient method for spatially stratified protein quantification. Its implementation enables us to characterize protein expression across distinct regions of a tumor and the microenvironment. A key feature of DSP is its multiplexing capability, enabling high throughput data processing.The ability to define customized regions of interest additionally provides a spatial dimension to data analysis. DSP can be applied to any sample in which spatially quantifying protein, or RNA expression may help elucidate a pathologic, or physiologic process. This is especially relevant in oncology, where regional target variability may correlate with malignant growth, or invasion.Demonstrating the procedure will be Scott Palisoul, a histotechnologist, and Rachael Barney, a genomic technologist. To prepare slides, begin by taking several two-millimeter cores from each biopsy, and putting them in a single tissue microarray block. Cut four-micron sections from the block, and mount them to glass slides.Place each slide inside the slide holder gasket, and incubate it at 60 degrees Celsius for 30 minutes. In the semi-automated IHC system, fill the container in position one with 150 microliters of wash buffer per slide, plus five milliliters of dead volume, and leave the lid open. Fill the same amount of blocking solution in the container in position two.Load the reagent container tray onto the machine. Select the Process IHC, followed by blocking solution name in the dropdown box of the Marker field. Once finished for all slides, click on Close.Then click on Print Labels, and check All slide labels not yet printed, and click on Print. Affix labels to the tops of the slides. Load the slides onto the slide tray, ensuring the sample and label face upward.Press the LED button to lower the tray, and allow the instrument to begin the scanning and recognition of slides. Click on the Start button to begin the experiment. When the run is complete, press the LED button when it blinks green.Remove the tray from the instrument, and carefully lift the cover tiles from each slide. Place the slides in the 1X phosphate buffered saline. Remove excess buffer, and outline each tissue section with a hydrophobic pen to create a hydrophobic barrier.Make an antibody PC oligo solution by adding eight microliters of each antibody per slide and using Buffer W to reach a final volume of 200 microliters. Then apply the antibody PC oligo solution to the slides and incubate the slides overnight at four degrees Celsius. After incubation, place the slides in a humidified tray, and wash thrice for 10 minutes in 1X TBST.Post-fix in 4%paraformaldehyde for 30 minutes at room temperature, followed by washing twice for five minutes in 1X TBST. Add SYTO 13 nuclear stain for 15 minutes at room temperature, followed by washing twice with 1X TBST. Hover the mouse over Data Collection in the Control Center and select New, or Continue Run.Place the slide in the slide holder with the label toward the user. Lower the slide tray clamp, ensuring that the tissue is visible in the elongated window. Add six milliliters of Buffer S.Follow the prompts in the Control Center.Zoom between different axes with the x and y sliders to delineate a region for scanning. Select Scan, and allow the scan to proceed until the entire defined target area has been imaged. Generate a 20 times image.Define the ROIs by selecting three equally sized circular ROIs of a diameter of 250 micrometers for each tissue core. Approve the ROIs by clicking on the Exit Scan Workspace button. Then wait for the UV light to cleave the oligos from the antibodies.After completing the cleaning instrument process, select New Data collection to continue with the current slides, or plate. Open the finalized plate window by double clicking on the Plate icon area. Enter the hybridization code pack lot number, and click on Update.Then click on Finalize. Detach the slide holder and collection plate by following the prompts. Store the slides in TBST, or cover them with an aqueous medium and cover slip for long-term storage.Seal the plates using a permeable seal and dry the aspirates at 65 degrees Celsius in a thermal cycler with the top open. Add seven microliters of diethyl pyrocarbonate-treated water and mix. Incubate at room temperature for 10 minutes, and then spin down quickly.Prepare the probe buffer mix by choosing appropriate probe R and probe U equations applying the equation as described in the text protocol. Add 84 microliters of probe buffer mix to each hybridization code pack. In a fresh 96-well hybridization plate, add eight microliters of each hybridization code master mix into all the 12 wells of the indicated row.Transfer seven microliters from the DSP collection plate to the corresponding well in the hybridization plate. Heat seal, spin the plate, and incubate it overnight at 67 degrees Celsius. After incubation, cool the plate on ice, and perform a quick spin.Pool the hybridization products from each well into a strip tube, gently pipetting each well five times. Spin down, and load the strip tubes into the analysis system. On the DSP instrument, save the CDF file onto a USB drive, and transfer the data to the digital analyzer.On screen, press Start Processing. Select High Sensitivity followed by Next. Press Select All for the number of wells with samples, then Finish, followed by Next on the email notification.Then click on Start. Once the prep station is done, seal the cartridge and transfer it to the digital analyzer. Press Start Counting.Select Stage Position. Press Load Existing CDF file, and select previously uploaded file. Press Done.Select Stage Position again, press Done, then Start to run the program. Save the ZIP file of the reporter count conversion files from the analysis system to a USB drive. Insert the drive into the DSP machine.In the DSP Control Center, hover over Data Collection, and then click on Upload Accounts. Select the relevant ZIP file. Click on Records in the DSP Control Center.To view scans in the queue, select Add Selected Scans to Queue, then My Analysis Queue. Select New Study from Queue by hovering over the Data Analysis option in the Control Center. DSP experiment was performed on samples of glioblastoma, and the results were visualized as a heat map.Rows represent protein targets, and each column corresponds to a region of interest. Expression level is depicted by a color range varying from blue, showing low expression, to red, which denotes high expression. Variability of color within a row reflects regional protein heterogeneity, and suggests a possible spatial association with differential protein expression.In this experiment, S100 and CD-56 were universally high, because they are neural markers. Markers with the most variability include B7-H3, KI-67, CD-44, and fibronectin, which are known to be associated with tumor proliferation, migration, and metastasis. From a vast array of candidate targets, DSP can identify those exhibiting significant regional variation.This lays the foundation for investigating factors associated with variability and their relevance to disease.

digital-spatial-profiling-for-characterization-microenvironment-adult

Research

JoVE Journal

Cancer Research

A Protocol for Rapid Post-mortem Cell Culture of Diffuse Intrinsic Pontine Glioma (DIPG)

Diffuse Intrinsic Pontine Glioma (DIPG) is a childhood brainstem tumor that carries a universally fatal prognosis. Because surgical resection is not a viable treatment strategy and biopsy is not routinely performed, the availability of patient samples for research is limited. Consequently, efforts to study this disease have been challenged by a paucity of faithful disease models. To address this need, we describe here a protocol for the rapid processing of post-mortem autopsy tissue samples in order to generate durable patient-derived cell culture models that can be used in in vitro assays or in vivo orthotopic xenograft experiments. These models can be used to screen for potential drug targets and to study fundamental pathobiological processes within DIPG. This protocol can further be extended to analyze and isolate tumor and microenvironmental cells using Fluorescence-activated Cell Sorting (FACS), which enables subsequent analysis of gene expression, protein expression, or epigenetic modifications of DNA at the bulk cell or single cell level. Finally, this protocol can also be adapted to generate patient-derived cultures for other central nervous system tumors.

A Protocol for Rapid Post-mortem Cell Culture of Diffuse Intrinsic Pontine Glioma DIPG

This protocol describes a method for the rapid processing of post-mortem diffuse intrinsic pontine glioma samples for the establishment of patient-derived cell culture models or direct characterization of tumor and microenvironmental cells.

Diffuse Intrinsic Pontine Glioma (DIPG) is a childhood brainstem tumor that carries a universally fatal prognosis. Because surgical resection is not a ...

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

Analysis of protein expression in glioma is relevant for several aspects in the study of its pathology. Numerous proteins have been described as biomarkers with applications in diagnosis, prognosis, classification, state of tumor progression, and cell differentiation state. These analyses of biomarkers are also useful to characterize tumor neurospheres (NS) generated from glioma patients and glioma models. Tumor NS provide a valuable in vitro model to assess different features of the tumor from which they are derived and can more accurately mirror glioma biology. Here we describe a detailed method to analyze biomarkers in tumor NS using immunohistochemistry (IHC) on paraffin-embedded tumor NS.

Neurospheres grown as 3D cultures constitute a powerful tool to study glioma biology. Here we present a protocol to perform immunohistochemistry while maintaining the 3D structure of glioma neurospheres through paraffin embedding. This method enables the characterization of glioma neurosphere properties such as stemness and neural differentiation.

Analysis of protein expression in glioma is relevant for several aspects in the study of its pathology. Numerous proteins have been described as ...

Generation of Orthotopic Pancreatic Tumors and Ex vivo Characterization of Tumor-Infiltrating T Cell Cytotoxicity

In vivo models of pancreatic cancer provide invaluable tools for studying disease dynamics, immune infiltration and new therapeutic strategies. The orthotopic murine model can be performed on large cohorts of immunocompetent mice simultaneously, is relatively inexpensive and preserves the cognate tissue microenvironment. The quantification of T cell infiltration and cytotoxic activity within orthotopic tumors provides a useful indicator of an antitumoral response.
This protocol describes the methodology for surgical generation of orthotopic pancreatic tumors by injection of a low number of syngeneic tumor cells resuspended in 5 &#181;L basement membrane directly into the pancreas. Mice bearing orthotopic tumors take approximately 30 days to reach endpoint, at which point tumors can be harvested and processed for characterization of tumor-infiltrating T cell activity. Rapid enzymatic digestion using collagenase and DNase allows a single-cell suspension to be extracted from tumors. The viability and cell surface markers of immune cells extracted from the tumor are preserved; therefore, it is appropriate for multiple downstream applications, including flow-assisted cell sorting of immune cells for culture or RNA extraction, flow cytometry analysis of immune cell populations. Here, we describe the ex vivo stimulation of T cell populations for intracellular cytokine quantification (IFN&#947; and TNF&#945;) and degranulation activity (CD107a) as a measure of overall cytotoxicity. Whole-tumor digests were stimulated with phorbol myristate acetate and ionomycin for 5 h, in the presence of anti-CD107a antibody in order to upregulate cytokine production and degranulation. The addition of brefeldin A and monensin for the final 4 h was performed to block extracellular transport and maximize cytokine detection. Extra- and intra-cellular staining of cells was then performed for flow cytometry analysis, where the proportion of IFN&#947;+, TNF&#945;+ and CD107a+ CD4+ and CD8+ T cells was quantified.
This method provides a starting base to perform comprehensive analysis of the tumor microenvironment.

Generation of Orthotopic Pancreatic Tumors and Ex vivo Characterization of Tumor-Infiltrating T Cell Cytotoxicity

This protocol describes the surgical generation of orthotopic pancreatic tumors and the rapid digestion of freshly isolated murine pancreatic tumors. Following digestion, viable immune cell populations can be used for further downstream analysis, including ex vivo stimulation of T cells for intracellular cytokine detection by flow cytometry.

In vivo models of pancreatic cancer provide invaluable tools for studying disease dynamics, immune infiltration and new therapeutic strategies. The ...

Analysis of Side Population in Solid Tumor Cell Lines

Cancer stem cells (CSCs) are an important cause of tumor growth, metastasis, and recurrence. Isolation and identification of CSCs are of great significance for tumor research. Currently, several techniques are used for the identification and purification of CSCs from tumor tissues and tumor cell lines. Separation and analysis of side population (SP) cells are two of the commonly used methods. The methods rely on the ability of CSCs to rapidly expel fluorescent dyes, such as Hoechst 33342. The efflux of the dye is associated with the ATP-binding cassette (ABC) transporters and can be inhibited by ABC transporter inhibitors. Methods for staining cultured tumor cells with Hoechst 33342 and analyzing the proportion of their SP cells by flow cytometry are described. This assay is convenient, fast, and cost-effective. Data generated in this assay can contribute to a better understanding of the effect of genes or other extracellular and intracellular signals on the stemness properties of tumor cells.

A convenient, fast, and cost-effective method to measure the proportion of side population cells in solid tumor cell lines is presented.

Cancer stem cells (CSCs) are an important cause of tumor growth, metastasis, and recurrence. Isolation and identification of CSCs are of great ...

Tumor Transplantation for Assessing the Dynamics of Tumor-Infiltrating CD8+ T Cells in Mice

T cell-mediated immunity plays a crucial role in immune responses against tumors, with cytotoxic T lymphocytes (CTLs) playing the leading role in eradicating cancerous cells. However, the origins and replenishment of tumor antigen-specific CD8+ T cells within the tumor microenvironment (TME) remain obscure. This protocol employs the B16F10-OVA melanoma cell line, which stably expresses the surrogate neoantigen, ovalbumin (OVA), and TCR transgenic OT-I mice, in which over 90% of CD8+ T cells specifically recognize the OVA-derived peptide OVA257-264 (SIINFEKL) bound to the class I major histocompatibility complex (MHC) molecule H2-Kb. These features enable the study of antigen-specific T cell responses during tumorigenesis.
Combining this model with tumor transplantation surgery, tumor tissues from donors were transplanted into tumor-matched syngeneic recipient mice to precisely trace the influx of recipient-derived immune cells into transplanted donor tissues, allowing the analysis of the immune responses of tumor-inherent and periphery-originated antigen-specific CD8+ T cells. A dynamic transition was found to occur between these two populations. Collectively, this experimental design has provided another approach to precisely investigate the immune responses of CD8+ T cells in TME, which will shed new light on tumor immunology.

Tumor Transplantation for Assessing the Dynamics of Tumor-Infiltrating CD8+ T Cells in Mice

Here, we present a tumor transplantation protocol for the characterization of tumor-inherent and periphery-derived tumor-infiltrated lymphocytes in a mouse tumor model. Specific tracing of the influx of recipient-derived immune cells with flow cytometry reveals the dynamics of the phenotypic and functional changes of these cells during antitumor immune responses.

T cell-mediated immunity plays a crucial role in immune responses against tumors, with cytotoxic T lymphocytes (CTLs) playing the leading role in ...

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Multiplexing enables the assessment of several markers on the same tissue while providing spatial context. Spatial Omics technologies allow both protein and RNA multiplexing by leveraging photo-cleavable oligo-tagged antibodies and probes, respectively. Oligos are cleaved and quantified from specific regions across the tissue to elucidate the underlying biology. Here, the study demonstrates that automated custom antibody visualization protocols can be utilized to guide ROI selection in conjunction with spatial proteomics assays. This specific method did not show acceptable performance with spatial transcriptomics assays. The protocol describes the development of a 3-plex immunofluorescent (IF) assay for marker visualization on an automated platform, using tyramide signal amplification (TSA) to amplify the fluorescent signal from a given protein target and increase the antibody pool to choose from. The visualization protocol was automated using a thoroughly validated 3-plex assay to ensure quality and reproducibility. In addition, the exchange of DAPI for SYTO dyes was evaluated to allow imaging of TSA-based IF assays on the spatial profiling platform. Additionally, we tested the ability of selecting small ROIs using the spatial transcriptomics assay to allow the investigation of highly-specific areas of interest (e.g., areas enriched for a given cell type). ROIs of 50 &#181;m and 300 &#181;m diameter were collected, which corresponds to approximately 15 cells and 100 cells, respectively. Samples were made into libraries and sequenced to investigate the capability to detect signals from small ROIs and profile-specific regions of the tissue. We determined that spatial proteomics technologies highly benefit from automated, standardized protocols to guide ROI selection. While this automated visualization protocol was not compatible with spatial transcriptomics assays, we were able to test and confirm that specific cell populations can successfully be detected even in small ROIs with the standard manual visualization protocol.

Here, we describe a protocol for fine-tuning regions of interest (ROIs) for Spatial Omics technologies to better characterize the tumor microenvironment and identify specific cell populations. For proteomics assays, automated customized protocols can guide ROI selection, while transcriptomics assays can be fine-tuned utilizing ROIs as small as 50 &#181;m.

Multiplexing enables the assessment of several markers on the same tissue while providing spatial context. Spatial Omics technologies allow both protein ...

Live-3D-Cell Immunocytochemistry Assays of Pediatric Diffuse Midline Glioma

Cell migration and invasion are specific hallmarks of Diffuse Midline Glioma (DMG) H3K27M-mutant tumors. We have already modeled these features using three-dimensional (3D) cell-based invasion and migration assays. In this study, we have optimized these 3D assays for live-cell immunocytochemistry. An Antibody Labeling Reagent was used to detect in real-time the expression of the adhesion molecule CD44, on the plasma membrane of migrating and invading cells of a DMG H3K27M primary patient-derived cell line. CD44 is associated with cancer stem cell phenotype and tumor cell migration and invasion and is involved in the direct interactions with the central nervous system (CNS) extracellular matrix. Neurospheres (NS) from the DMG H3K27M cell line were embedded into the basal membrane matrix (BMM) or placed onto a thin coating layer of BMM, in the presence of an anti-CD44 antibody in conjunction with the antibody labeling reagent (ALR). The live-3D-cell immunocytochemistry image analysis was performed on a live-cell analysis instrument to quantitatively measure the overall CD44 expression, specifically on the migrating and invading cells. The method also allows visualizing in real-time the intermittent expression of CD44 on the plasma membrane of migrating and invading cells. Moreover, the assay also provided new insights into the potential role of CD44 in the mesenchymal to amoeboid transition in DMG H3K27M cells.

This study presents a protocol of live-3D-cell immunocytochemistry applied to a pediatric diffuse midline glioma cell line, useful to study in real-time the expression of proteins on the plasma membrane during dynamic processes like 3D cell invasion and migration.

Cell migration and invasion are specific hallmarks of Diffuse Midline Glioma (DMG) H3K27M-mutant tumors. We have already modeled these features using ...

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Multiplexed imaging technology using antibody barcoding with oligonucleotides, which sequentially detects multiple epitopes in the same tissue section, is an effective methodology for tumor evaluation that improves the understanding of the tumor microenvironment. The visualization of protein expression in formalin-fixed, paraffin-embedded&#160;tissues is achieved when a specific fluorophore is annealed to an antibody-bound barcode via complementary oligonucleotides and then sample imaging is performed; indeed, this method allows for the use of customizable panels of more than 40 antibodies in a single tissue staining reaction. This method is compatible with fresh frozen tissue, formalin-fixed, paraffin-embedded tissue, cultured cells, and peripheral blood mononuclear cells, meaning that researchers can use this technology to view a variety of sample types at single-cell resolution. This method starts with a manual staining and fixing protocol, and all the antibody barcodes are applied using an antibody cocktail. The staining fluidics instrument is fully automated and performs iterative cycles of labeling, imaging, and removing spectrally distinct fluorophores until all the biomarkers have been imaged using a standard fluorescence microscope. The images are then collected and compiled across all the imaging cycles to achieve single-cell resolution for all the markers. The single-step staining and gentle fluorophore removal not only allow for highly multiplexed biomarker analysis but also preserve the sample for additional downstream analysis if desired (e.g., hematoxylin and eosin staining). Furthermore, the image analysis software enables image processing-drift compensation, background subtraction, cell segmentation, and clustering-as well as the visualization and analysis of the images and cell phenotypes for the generation of spatial network maps. In summary, this technology employs a computerized microfluidics system and fluorescence microscope to iteratively hybridize, image, and strip fluorescently labeled DNA probes that are complementary to tissue-bound, oligonucleotide-conjugated antibodies.

Multiplexed barcoding image analysis has recently improved the characterization of the tumor microenvironment, permitting comprehensive studies of cell composition, functional state, and cell-cell interactions. Herein, we describe a staining and imaging protocol using the barcoding of oligonucleotide-conjugated antibodies and cycle imaging, which allows for the use of a high-dimensional image&#160;analysis technique.

Multiplexed imaging technology using antibody barcoding with oligonucleotides, which sequentially detects multiple epitopes in the same tissue section, is ...

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 ...

Mapping the Tumor Immune Landscape: An In-Depth Multiplex IHC Analysis Protocol

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. Multiplex immunohistochemistry is a valuable tool to visualize and identify different types of immune cells in tumor tissues while retaining its spatial information. Here we provide detailed protocols to analyze lymphocyte, myeloid, and dendritic cell populations in tissue sections. Starting from cutting formalin-fixed paraffin-embedded sections, automatic multiplex staining procedures on an automated platform, scanning of the slides on a multispectral imaging microscope, to the analysis of images using an in-house-developed machine learning algorithm ImmuNet. These protocols can be applied to a variety of tumor specimens by simply switching tumor markers to analyze immune cells in different compartments of the sample (tumor versus invasive margin) and apply nearest-neighbor analysis. This analysis is not limited to tumor samples but can also be applied to other (non-)pathogenic tissues. Improvements to the equipment and workflow over the past few years have significantly shortened throughput times, which facilitates the future application of this procedure in the diagnostic setting.

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

This protocol describes in detail how immune cell characterization of the tumor microenvironment using multiplex immunohistochemistry is carried out.

The immune cell landscape of the tumor microenvironment potentially contains information for the discovery of prognostic and predictive biomarkers. ...