High-Throughput Multiplex Immunohistochemical Imaging of the Tumor and Its Microenvironment

The immunologic phenotype of thrombi is associated with future vascular events after cerebral infarction

BACKGROUND

Thrombi retrieved from patients with acute ischemic stroke may contain prognostic information.

OBJECTIVE

To investigate the relationship between the immunologic phenotype of thrombi and future vascular events in patients with a stroke.

METHODS

This study included patients with acute ischemic stroke who underwent endovascular thrombectomy at Chung-Ang University Hospital in Seoul, Korea, between February 2017 and January 2020. Laboratory and histological variables were compared between patients with and without recurrent vascular events (RVEs). Kaplan-Meier analysis followed by the Cox proportional hazards model was used to identify factors related to RVE. Receiver operating characteristic (ROC) analysis was conducted to evaluate the performance of the immunologic score by combining immunohistochemical phenotypes to predict RVE.

RESULTS

A total of 46 patients were included in the study with 13 RVEs (mean±SD age, 72.8±11.3 years; 26 (56.5%) men). Thrombi with a lower percentage of programmed death ligand-1 expression (HR=11.64; 95% CI 1.60 to 84.82) and a higher number of citrullinated histone H3 positive cells (HR=4.19; 95% CI 0.81 to 21.75) were associated with RVE. The presence of high-mobility group box 1 positive cell was associated with reduced risk of RVE, but the association was lost after adjustment for stroke severity. The immunologic score, which consists of the three immunohistochemical phenotypes, showed good performance in predicting RVE (area under the ROC curve, 0.858; 95% CI 0.758 to 0.958).

CONCLUSIONS

The immunological phenotype of thrombi could provide prognostic information after stroke.

HCR spectral imaging: 10-plex, quantitative, high-resolution RNA and protein imaging in highly autofluorescent samples

Signal amplification based on the mechanism of hybridization chain reaction (HCR) provides a unified framework for multiplex, quantitative, high-resolution imaging of RNA and protein targets in highly autofluorescent samples. With conventional bandpass imaging, multiplexing is typically limited to four or five targets owing to the difficulty in separating signals generated by fluorophores with overlapping spectra. Spectral imaging has offered the conceptual promise of higher levels of multiplexing, but it has been challenging to realize this potential in highly autofluorescent samples, including whole-mount vertebrate embryos. Here, we demonstrate robust HCR spectral imaging with linear unmixing, enabling simultaneous imaging of ten RNA and/or protein targets in whole-mount zebrafish embryos and mouse brain sections. Further, we demonstrate that the amplified and unmixed signal in each of the ten channels is quantitative, enabling accurate and precise relative quantitation of RNA and/or protein targets with subcellular resolution, and RNA absolute quantitation with single-molecule resolution, in the anatomical context of highly autofluorescent samples.

The tumor immune microenvironment remodeling and response to HER2-targeted therapy in HER2-positive advanced gastric cancer

Combination therapy with anti-HER2 agents and immunotherapy has demonstrated significant clinical benefits in gastric cancer (GC), but the underlying mechanism remains unclear. In this study, we used multiplex immunohistochemistry to assess the changes of the tumor microenvironment in 47 advanced GC patients receiving anti-HER2 therapy. Additionally, we performed single-cell transcriptional sequencing to investigate potential cell-to-cell communication and molecular mechanisms in four HER2-positive GC baseline samples. We observed that post-treated the infiltration of NK cells, CD8+ T cells, and B lymphocytes were significantly higher in patients who benefited from anti-HER2 treatment than baseline. Further spatial distribution analysis demonstrated that the interaction scores between NK cells and CD8+ T cells, B lymphocytes and M2 macrophages, B lymphocytes and Tregs were also significantly higher in benefited patients. Cell-cell communication analysis from scRNA sequencing showed that NK cells utilized CCL3/CCL4-CCR5 to recruit CD8+ T cell infiltration. B lymphocytes employed CD74-APP/COPA/MIF to interact with M2 macrophages, and utilized TNF-FAS/ICOS/TNFRSR1B to interact with Tregs. These cell-cell interactions contribute to inhibit the immune resistance of M2 macrophages and Tregs. Our research provides potential guidance for the use of anti-HER2 therapy in combination with immune therapy.

Exploring the Complex and Multifaceted Interplay between Melanoma Cells and the Tumor Microenvironment

Malignant melanoma is a very aggressive skin cancer, characterized by a heterogeneous nature and high metastatic potential. The incidence of melanoma is continuously increasing worldwide, and it is one of the most common cancers in young adults. In the past twenty years, our understanding of melanoma biology has increased profoundly, and disease management for patients with disseminated disease has improved due to the emergence of immunotherapy and targeted therapy. However, a significant fraction of patients relapse or do not respond adequately to treatment. This can partly be explained by the complex signaling between the tumor and its microenvironment, giving rise to melanoma phenotypes with different patterns of disease progression. This review focuses on the key aspects and complex relationship between pathogenesis, genetic abnormalities, tumor microenvironment, cellular plasticity, and metabolic reprogramming in melanoma. By acquiring a deeper understanding of the multifaceted features of melanomagenesis, we can reach a point of more individualized and patient-centered disease management and reduced costs of ineffective treatments.

Heterogeneity of the Tumor Microenvironment Across Molecular Subtypes of Breast Cancer

Breast cancer is a heterogenous disease at the molecular level thus, it can be hypothesized that different molecular subtypes differ in their tumor microenvironment (TME) also. Understanding the TME heterogeneity may provide new prognostic biomarkers and new targets for cancer therapy. For deciphering heterogeneity in the TME, immunohistochemistry for immune markers (CD3, CD4, CD8, CD68, CD163, and programmed death-ligand 1), Cancer-associated fibroblast markers [anti-fibroblast activating protein α (FAP-α), platelet-derived growth factor receptor α (PDGFR-α), S100A4, Neuron-glial antigen 2, and Caveolin-1], and angiogenesis (CD31) was performed on tissue microarrays of different molecular subtypes of breast cancer. High CD3 + T cells were noted in the Luminal B subtype ( P =0.002) of which the majority were CD8 + cytotoxic T cells. Programmed death-ligand 1 expression in immune cells was highest in the human epidermal growth factor receptor 2 (Her-2)-positive and Luminal B subtypes compared with the triple-negative breast cancer (TNBC) subtype ( P =0.003). Her-2 subtype is rich in M2 tumor-associated macrophages ( P =0.000) compared with TNBC and Luminal B subtypes. M2 immune microenvironment correlated with high tumor grade and high Ki-67. Her-2 and TNBC subtypes are rich in extracellular matrix remodeling (FAP-α, P =0.003), angiogenesis-promoting (PDGFR-α; P =0.000) and invasion markers (Neuron-glial antigen 2, P =0.000; S100A4, P =0.07) compared with Luminal subtypes. Mean Microvessel density showed an increasing trend: Luminal A>Luminal B>Her-2 positive>TNBC; however, this difference was not statistically significant. The cancer-associated fibroblasts (FAP-α, PDGFR-α, and Neuron-glial antigen 2) showed a positive correlation with lymph node metastasis in specific subtypes. Immune cells, tumor-associated macrophage, and cancer-associated fibroblast-related s tromal markers showed higher expression in Luminal B, Her-2 positive, and TNBC respectively. This differential expression of different components of TME indicates heterogeneity of the TME across molecular subtypes of breast cancer.

Nontoxic Fluorescent Nanoprobes for Multiplexed Detection and 3D Imaging of Tumor Markers in Breast Cancer

Multiplexed fluorescent immunohistochemical analysis of breast cancer (BC) markers and high-resolution 3D immunofluorescence imaging of the tumor and its microenvironment not only facilitate making the disease prognosis and selecting effective anticancer therapy (including photodynamic therapy), but also provides information on signaling and metabolic mechanisms of carcinogenesis and helps in the search for new therapeutic targets and drugs. The characteristics of imaging nanoprobe efficiency, such as sensitivity, target affinity, depth of tissue penetration, and photostability, are determined by the properties of their components, fluorophores and capture molecules, and by the method of their conjugation. Regarding individual nanoprobe components, fluorescent nanocrystals (NCs) are widely used for optical imaging in vitro and in vivo, and single-domain antibodies (sdAbs) are well established as highly specific capture molecules in diagnostic and therapeutic applications. Moreover, the technologies of obtaining functionally active sdAb–NC conjugates with the highest possible avidity, with all sdAb molecules bound to the NC in a strictly oriented manner, provide 3D-imaging nanoprobes with strong comparative advantages. This review is aimed at highlighting the importance of an integrated approach to BC diagnosis, including the detection of biomarkers of the tumor and its microenvironment, as well as the need for their quantitative profiling and imaging of their mutual location, using advanced approaches to 3D detection in thick tissue sections. The existing approaches to 3D imaging of tumors and their microenvironment using fluorescent NCs are described, and the main comparative advantages and disadvantages of nontoxic fluorescent sdAb–NC conjugates as nanoprobes for multiplexed detection and 3D imaging of BC markers are discussed.

Virtual Microscopy Goes Global: The Images Are Virtual and the Problems Are Real

For the last two centuries, the scholarly education of histology and pathology has been based on technology, initially on the availability of low-cost, high-quality light microscopes, and more recently on the introduction of computers and e-learning approaches to biomedical education. Consequently, virtual microscopy (VM) is replacing glass slides and the traditional light microscope as the main instruments of instruction in histology and pathology laboratories. However, as with most educational changes, there are advantages and disadvantages associated with a new technology. The use of VM for the teaching of histology and pathology requires an extensive infrastructure and the availability of computing devices to all learners, both posing a considerable financial strain on schools and students. Furthermore, there may be valid reasons for practicing healthcare professionals to maintain competency in using light microscopes. In addition, some educators may be reluctant to embrace new technologies. These are some of the reasons why the introduction of VM as an integral part of histology and pathology instruction has been globally uneven. This paper compares the teaching of histology and pathology using traditional or VM in five different countries and their adjacent regions, representing developed, as well as developing areas of the globe. We identify general and local roadblocks to the introduction of this still-emerging didactic technology and outline solutions for overcoming these barriers.

New insights into the tumour immune microenvironment of nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is unique among head and neck cancers for its strong causative association with Epstein Barr-Virus and high levels of immune infiltration that play a role in pathogenesis. As such, immunotherapy for the treatment of NPC is a promising area of research in the pursuit of improving patient outcomes. Understanding the tumour immune microenvironment (TIME) of NPC is the key to developing targeted immunotherapies and stratifying patients to determine optimal treatment regimens. Recent research has uncovered distinct characteristics of the TIME in NPC as well as important differences between the different disease subtypes; however, reviewing the state of the field reveals a further need for the application of novel techniques like multiplexed hyperspectral imaging and mass cytometry. These techniques can be used to identify spatial, compositional, and functional aspects of the TIME in NPC such as immune cell sociology, novel immune populations, and differences in immune-related signalling pathways in NPC in order to identify clinically relevant characteristics for targeted immunotherapy development and biomarker discovery.

Prognostic significance of natural killer cell-associated markers in gastric cancer: quantitative analysis using multiplex immunohistochemistry

Background

Natural killer (NK) cells mediate the anti-tumoral immune response as an important component of innate immunity. The aim of this study was to investigate the prognostic significance and functional implication of NK cell-associated surface receptors in gastric cancer (GC) by using multiplex immunohistochemistry (mIHC).

Methods

We performed an mIHC on tissue microarray slides, including 55 GC tissue samples. A total of 11 antibodies including CD57, NKG2A, CD16, HLA-E, CD3, CD20, CD45, CD68, CK, SMA, and ki-67 were used. CD45 + CD3-CD57 + cells were considered as CD57 + NK cells.

Results

Among CD45 + immune cells, the proportion of CD57 + NK cell was the lowest (3.8%), whereas that of CD57 + and CD57- T cells (65.5%) was the highest, followed by macrophages (25.4%), and B cells (5.3%). CD57 + NK cells constituted 20% of CD45 + CD57 + immune cells while the remaining 80% were CD57 + T cells. The expression of HLA-E in tumor cells correlated with that in tumoral T cells, B cells, and macrophages, but not CD57 + NK cells. The higher density of tumoral CD57 + NK cells and tumoral CD57 + NKG2A + NK cells was associated with inferior survival.

Conclusions

Although the number of CD57 + NK cells was lower than that of other immune cells, CD57 + NK cells and CD57 + NKG2A + NK cells were significantly associated with poor outcomes, suggesting that NK cell subsets play a critical role in GC progression. NK cells and their inhibitory receptor, NKG2A, may be potential targets in GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03203-8.

Expression of the immune checkpoint receptors PD-1, LAG3, and TIM3 in the immune context of stage II and III gastric cancer by using single and chromogenic multiplex immunohistochemistry

We sought to determine the clinicopathological significance of PD-1, LAG3, and TIM3 in gastric cancer (GC) by examining their expression and immune context. Immunohistochemistry (IHC) for PD-1, TIM3, LAG3, and tumor-infiltrating immune cell (TIIC) markers was performed in 385 stage II/III GCs. Epstein-Barr virus (EBV) and microsatellite stability (MSI) testing were performed for molecular classification. Chromogenic multiplex IHC (mIHC) for PD1, TIM3, LAG3, CD3, CD8, FOXP3, CD68, and cytokeratin was performed in 58 of the total samples. PD-1, LAG3, and TIM3 expression in TIICs was observed in 91 (23.6%), 193 (50.1%), and 257 (66.8%) GCs by single IHC, respectively. The expression was associated with EBV⁺ and MSI-H molecular subtypes (p ≤ 0.001). A positive expression of LAG3 in the invasive margin of the tumor was associated with better prognosis in univariate (p = .020) and multivariate (p = .026) survival analyses. The expression of different immune checkpoint receptors (ICRs) was significantly positively correlated. Dual or triple ICR expression was more frequent in high PD-1 and TIM3 density groups than in low-density groups by mIHC (all p ≤ 0.05). ICRs were mainly expressed in CD3⁺CD8⁺ and CD3⁺CD8⁻ T cells. Fifty-eight GCs were classified into three groups by clustering analysis based on mIHC, and the group with the highest ICR expression in TIICs showed significantly better outcomes in progression-free survival (p = .020). In GC, PD-1, LAG3, and TIM3 expression is positively correlated and associated with better prognosis. Our study provides information for the application of effective immune checkpoint inhibitors against GC.

Programmed Death Ligand 1-Expressing Classical Dendritic Cells MitigateHelicobacter-Induced Gastritis

BACKGROUND & AIMS

Helicobacter pylori has been reported to modulate local immune responses to colonize persistently in gastric mucosa. Although the induced expression of programmed cell death ligand 1 (PD-L1) has been suggested as an immune modulatory mechanism for persistent infection of H pylori, the main immune cells expressing PD-L1 and their functions in Helicobacter-induced gastritis still remain to be elucidated.

METHODS

The blockades of PD-L1 with antibody or PD-L1-deficient bone marrow transplantation were performed in Helicobacter-infected mice. The main immune cells expressing PD-L1 in Helicobacter-infected stomach were determined by flow cytometry and immunofluorescence staining. Helicobacter felis or H pylori-infected dendritic cell (DC)-deficient mouse models including Flt3-/-, Zbtb46-diphtheria toxin receptor, and BDCA2-diphtheria toxin receptor mice were analyzed for pathologic changes and colonization levels. Finally, the location of PD-L1-expressing DCs and the correlation with H pylori infection were analyzed in human gastric tissues using multiplexed immunohistochemistry.

RESULTS

Genetic or antibody-mediated blockade of PD-L1 aggravated Helicobacter-induced gastritis with mucosal metaplasia. Gastric classical DCs expressed considerably higher levels of PD-L1 than other immune cells and co-localized with T cells in gastritis lesions from Helicobacter-infected mice and human beings. H felis- or H pylori-infected Flt3-/- or classical DC-depleted mice showed aggravated gastritis with severe T-cell and neutrophil accumulation with low bacterial loads compared with that in control mice. Finally, PD-L1-expressing DCs were co-localized with T cells and showed a positive correlation with H pylori infection in human subjects.

CONCLUSIONS

The PD-1/PD-L1 pathway may be responsible for the immune modulatory function of gastric DCs that protects the gastric mucosa from Helicobacter-induced inflammation, but allows persistent Helicobacter colonization.

Assessment of Lung Eosinophils In Situ Using Immunohistological Staining

Eosinophils are rare white blood cells that are recruited from circulation to accumulate in the lung in mouse models of allergic respiratory inflammation. In hematoxylin-eosin (HE) stained lungs, eosinophils may be difficult to detect despite their bright eosin staining in the secondary granules. For this reason, antibody-mediated detection of eosinophils is preferable for specific and clearer identification of these cells. Moreover, eosinophils may degranulate, releasing their granule proteins into surrounding tissue, and remnants of cytolysed cells cannot be detected by HE staining. The methods here demonstrate the use of eosinophil-specific anti-mouse antibodies to detect eosinophil granule proteins in formalin-fixed cells both in situ in paraffin-embedded lungs, as well as in cytospin preparations from the lung. These antibody staining techniques enable either colorimetric or fluorescence imaging of eosinophils or their granule proteins with the potential for additional antibodies to be added for detection of multiple molecules.

Towards a Systems Immunology Approach to Unravel Responses to Cancer Immunotherapy

Immunotherapy, particularly immune checkpoint blockade and chimeric antigen receptor (CAR)-T cells, holds a great promise against cancer. These treatments have markedly improved survival in solid as well as in hematologic tumors previously considered incurable. However, durable responses occur in a fraction of patients, and existing biomarkers (e.g. PD-L1) have shown limited prediction power. This scenario highlights the need to dissect the complex interplay between immune and tumor cells to identify reliable biomarkers of response to be used for patients’ selection. In this context, systems immunology represents indeed the new frontier to address important clinical challenges in biomarker discovery. Through the integration of multiple layers of data obtained with several high-throughput approaches, systems immunology may give insights on the vast range of inter-individual differences and on the influences of genes and factors that cooperatively shape the individual immune response to a given treatment. In this Mini Review, we give an overview of the current high-throughput methodologies, including genomics, epigenomics, transcriptomics, metabolomics, proteomics, and multi-parametric phenotyping suitable for systems immunology as well as on the key steps of data integration and biological interpretation. Additionally, we review recent studies in which multi-omics technologies have been used to characterize mechanisms of response and to identify powerful biomarkers of response to checkpoint inhibitors, CAR-T cell therapy, dendritic cell-based and peptide-based cancer vaccines. We also highlight the need of favoring the collaboration of researchers with complementary expertise and of integrating multi-omics data into biological networks with the final goal of developing accurate markers of therapeutic response.

Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images

BACKGROUND

Multiplex immunohistochemistry (mIHC) permits the labeling of six or more distinct cell types within a single histologic tissue section. The classification of each cell type requires detection of the unique colored chromogens localized to cells expressing biomarkers of interest. The most comprehensive and reproducible method to evaluate such slides is to employ digital pathology and image analysis pipelines to whole-slide images (WSIs). Our suite of deep learning tools quantitatively evaluates the expression of six biomarkers in mIHC WSIs. These methods address the current lack of readily available methods to evaluate more than four biomarkers and circumvent the need for specialized instrumentation to spectrally separate different colors. The use case application for our methods is a study that investigates tumor immune interactions in pancreatic ductal adenocarcinoma (PDAC) with a customized mIHC panel.

METHODS

Six different colored chromogens were utilized to label T-cells (CD3, CD4, CD8), B-cells (CD20), macrophages (CD16), and tumor cells (K17) in formalin-fixed paraffin-embedded (FFPE) PDAC tissue sections. We leveraged pathologist annotations to develop complementary deep learning-based methods: (1) ColorAE is a deep autoencoder which segments stained objects based on color; (2) U-Net is a convolutional neural network (CNN) trained to segment cells based on color, texture and shape; and ensemble methods that employ both ColorAE and U-Net, collectively referred to as (3) ColorAE:U-Net. We assessed the performance of our methods using: structural similarity and DICE score to evaluate segmentation results of ColorAE against traditional color deconvolution; F1 score, sensitivity, positive predictive value, and DICE score to evaluate the predictions from ColorAE, U-Net, and ColorAE:U-Net ensemble methods against pathologist-generated ground truth. We then used prediction results for spatial analysis (nearest neighbor).

RESULTS

We observed that (1) the performance of ColorAE is comparable to traditional color deconvolution for single-stain IHC images (note: traditional color deconvolution cannot be used for mIHC); (2) ColorAE and U-Net are complementary methods that detect 6 different classes of cells with comparable performance; (3) combinations of ColorAE and U-Net into ensemble methods outperform using either ColorAE and U-Net alone; and (4) ColorAE:U-Net ensemble methods can be employed for detailed analysis of the tumor microenvironment (TME). We developed a suite of scalable deep learning methods to analyze 6 distinctly labeled cell populations in mIHC WSIs. We evaluated our methods and found that they reliably detected and classified cells in the PDAC tumor microenvironment. We also present a use case, wherein we apply the ColorAE:U-Net ensemble method across 3 mIHC WSIs and use the predictions to quantify all stained cell populations and perform nearest neighbor spatial analysis. Thus, we provide proof of concept that these methods can be employed to quantitatively describe the spatial distribution immune cells within the tumor microenvironment. These complementary deep learning methods are readily deployable for use in clinical research studies.

Cellular and Extracellular Components in Tumor Microenvironment and Their Application in Early Diagnosis of Cancers

Tumors are surrounded by complex environmental components, including blood and lymph vessels, fibroblasts, endothelial cells, immune cells, cytokines, extracellular vesicles, and extracellular matrix. All the stromal components together with the tumor cells form the tumor microenvironment (TME). In addition, extracellular physical and chemical factors, including extracellular pH, hypoxia, elevated interstitial fluid pressure, and fibrosis, are closely associated with tumor progression, metastasis, immunosuppression, and drug resistance. Cellular and extracellular components in TME contribute to nearly all procedures of carcinogenesis. By summarizing the recent work in this field, we make a comprehensive review on the role of cellular and extracellular components in the process of carcinogenesis and their potential application in early diagnosis of cancer. We hope that a systematic review of the diverse aspects of TME will help both research scientists and clinicians in this field.