State-of-the-Art of Profiling Immune Contexture in the Era of Multiplexed Staining and Digital Analysis to Study Paraffin Tumor Tissues

Fluorophore multimerization on a PEG backbone as a concept for signal amplification and lifetime modulation

Fluorescent labels have strongly contributed to many advancements in bioanalysis, molecular biology, molecular imaging, and medical diagnostics. Despite a large toolbox of molecular and nanoscale fluorophores to choose from, there is still a need for brighter labels, e.g., for flow cytometry and fluorescence microscopy, that are preferably of molecular nature. This requires versatile concepts for fluorophore multimerization, which involves the shielding of dyes from other chromophores and possible quenchers in their neighborhood. In addition, to increase the number of readout parameters for fluorescence microscopy and eventually also flow cytometry, control and tuning of the labels' fluorescence lifetimes is desired. Searching for bright multi-chromophoric or multimeric labels, we developed PEGylated dyes bearing functional groups for their bioconjugation and explored their spectroscopic properties and photostability in comparison to those of the respective monomeric dyes for two exemplarily chosen fluorophores excitable at 488 nm. Subsequently, these dyes were conjugated with anti-CD4 and anti-CD8 immunoglobulins to obtain fluorescent conjugates suitable for the labeling of cells and beads. Finally, the suitability of these novel labels for fluorescence lifetime imaging and target discrimination based upon lifetime measurements was assessed. Based upon the results of our spectroscopic studies including measurements of fluorescence quantum yields (QY) and fluorescence decay kinetics we could demonstrate the absence of significant dye-dye interactions and self-quenching in these multimeric labels. Moreover, in a first fluorescence lifetime imaging (FLIM) study, we could show the future potential of this multimerization concept for lifetime discrimination and multiplexing.

Multiplexed analysis of macrophage polarisation in pulmonary metastases of microsatellite stable colorectal cancer

Tumour-associated macrophages (TAMs) express a continuum of phenotypes ranging from an anti-tumoural M1-like phenotype to a pro-tumoural M2-like phenotype. During cancer progression, TAMs may shift to a more M2-like polarisation state, but the role of TAMs in CRC metastases is unclear. We conducted a comprehensive spatial and prognostic analysis of TAMs in CRC pulmonary metastases and corresponding primary tumours using multiplexed immunohistochemistry and machine learning-based image analysis. We obtained data from 106 resected pulmonary metastases and 74 corresponding primary tumours. TAMs in the resected pulmonary metastases were located closer to the cancer cells and presented a more M2-like polarised state in comparison to the primary tumours. Higher stromal M2-like macrophage densities in the invasive margin of pulmonary metastases were associated with worse 5-year overall survival (HR 3.19, 95% CI 1.35-7.55, p = 0.008). The results of this study highlight the value of multiplexed analysis of macrophage polarisation in cancer metastases and might have clinical implications in future cancer therapy.

Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities

Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system's complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.

Strategies for studying immune and non-immune human and canine mammary gland cancer tumour infiltrate

The tumour microenvironment (TME) is usually defined as a cell environment associated with tumours or cancerous stem cells where conditions are established affecting tumour development and progression through malignant cell interaction with non-malignant cells. The TME is made up of endothelial, immune and non-immune cells, extracellular matrix (ECM) components and signalling molecules acting specifically on tumour and non-tumour cells. Breast cancer (BC) is the commonest malignant neoplasm worldwide and the main cause of mortality in women globally; advances regarding BC study and understanding it are relevant for acquiring novel, personalised therapeutic tools. Studying canine mammary gland tumours (CMGT) is one of the most relevant options for understanding BC using animal models as they share common epidemiological, clinical, pathological, biological, environmental, genetic and molecular characteristics with human BC. In-depth, detailed investigation regarding knowledge of human BC-related TME and in its canine model is considered extremely relevant for understanding changes in TME composition during tumour development. This review addresses important aspects concerned with different methods used for studying BC- and CMGT-related TME that are important for developing new and more effective therapeutic strategies for attacking a tumour during specific evolutionary stages.

The Value of Tumor Infiltrating Lymphocytes (TIL) for Predicting the Response to Neoadjuvant Chemotherapy (NAC) in Breast Cancer according to the Molecular Subtypes

INTRODUCTION

The antitumor host immune response is an important factor in breast cancer, but its role is not fully established. The role of tumor infiltrating lymphocytes (TIL) as an immunological biomarker in breast cancer has been significantly explored in recent years. The number of patients treated with neoadjuvant chemotherapy (NAC) has increased and the identification of a biomarker to predict the probability of pCR (pathological complete response) is a high priority.

MATERIALS AND METHODS

We evaluated 334 cases of BC treated with NAC followed by surgical resection from 2020-2022 at the Ist Clinic of Oncological Surgery, Oncological Institute "Prof Dr I Chiricuta" Cluj Napoca. Of the above, 122 cases were available for histological evaluation both in pre-NAC biopsy and post-NAC resection tissue. Evaluation of biopsy fragments and resection parts were performed using hematoxylin eosin (H&E). The TIL evaluation took place according to the recommendations of the International TIL Working Group (ITILWG).

RESULTS

There was a strong association between elevated levels of pre-NAC TIL. At the same time, there is a statistically significant correlation between stromal TIL and tumor grade, the number of lymph node metastases, the molecular subtype and the number of mitoses (p < 0.005). Intratumoral TIL showed a significant correlation with tumor size, distant metastasis, molecular subtype, number of mitosis, stage and lymph node metastasis (p < 0.005). We also demonstrated that high pre-NAC STIL represents a strong predictive marker for pCR.

CONCLUSION

This study reveals the role of TIL as a predictive biomarker in breast cancer not only for the well-established TNBC (triple negative breast cancer) and HER2+ (Her2 overexpressed) subtypes but also in Luminal A and B molecular subtypes. In this scenario, the evaluation of sTIL as a novel predictive and therapy-predicting factor should become a routinely performed analysis that could guide clinicians when choosing the most appropriate therapy.

New Insights into Macrophage Polarization and its Prognostic Role in Patients with Colorectal Cancer Liver Metastasis

Background

As liver metastasis is the most common cause of mortality in patients with colorectal cancer, studying colorectal cancer liver metastasis (CLM) microenvironment is essential for improved understanding of tumor biology and to identify novel therapeutic targets.

Methods

We used multiplex immunofluorescence platform to study tumor associated macrophage (TAM) polarization and adaptive T cell subtypes in tumor samples from 105 CLM patients (49 without and 56 with preoperative chemotherapy).

Results

CLM exhibited M2 macrophage polarization, and helper T cells were the prevalent adaptive T cell subtype. The density of total, M2 and TGFβ-expressing macrophages, and regulatory T cells was lower in CLM treated with preoperative chemotherapy. CLM with right-sided primary demonstrated enrichment of TGFβ-expressing macrophages, and with left-sided primary had higher densities of helper and cytotoxic T cells. In multivariate analysis, high density of M2 macrophages correlated with longer recurrence-free survival (RFS) in the entire cohort [hazard ratio (HR) 0.425, 95% CI 0.219-0.825, p=0.011) and in patients without preoperative chemotherapy (HR 0.45, 95% CI 0.221-0.932, p=0.032). High pSMAD3-expressing macrophages were associated with shorter RFS in CLM after preoperative chemotherapy.

Conclusions

Our results highlight the significance of a multi-marker approach to define the macrophage subtypes and identify M2 macrophages as a predictor of favorable prognosis in CLM.

Quantitative multiplexed imaging technologies for single-cell analysis to assess predictive markers for immunotherapy in thoracic immuno-oncology: promises and challenges

The past decade has witnessed a revolution in cancer treatment by the shift from conventional drugs (chemotherapies) towards targeted molecular therapies and immune-based therapies, in particular the immune-checkpoint inhibitors (ICIs). These immunotherapies selectively release the host immune system against the tumour and have shown unprecedented durable remission for patients with cancers that were thought incurable such as advanced non-small cell lung cancer (aNSCLC). The prediction of therapy response is based since the first anti-PD-1/PD-L1 molecules FDA and EMA approvals on the level of PD-L1 tumour cells expression evaluated by immunohistochemistry, and recently more or less on tumour mutation burden in the USA. However, not all aNSCLC patients benefit from immunotherapy equally, since only around 30% of them received ICIs and among them 30% have an initial response to these treatments. Conversely, a few aNSCLC patients could have an efficacy ICIs response despite low PD-L1 tumour cells expression. In this context, there is an urgent need to look for additional robust predictive markers for ICIs efficacy in thoracic oncology. Understanding of the mechanisms that enable cancer cells to adapt to and eventually overcome therapy and identifying such mechanisms can help circumvent resistance and improve treatment. However, more than a unique universal marker, the evaluation of several molecules in the tumour at the same time, particularly by using multiplex immunostaining is a promising open room to optimise the selection of patients who benefit from ICIs. Therefore, urgent further efforts are needed to optimise to individualise immunotherapy based on both patient-specific and tumour-specific characteristics. This review aims to rethink the role of multiplex immunostaining in immuno-thoracic oncology, with the current advantages and limitations in the near-daily practice use.

A platform-independent framework for phenotyping of multiplex tissue imaging data

Multiplex imaging is a powerful tool to analyze the structural and functional states of cells in their morphological and pathological contexts. However, hypothesis testing with multiplex imaging data is a challenging task due to the extent and complexity of the information obtained. Various computational pipelines have been developed and validated to extract knowledge from specific imaging platforms. A common problem with customized pipelines is their reduced applicability across different imaging platforms: Every multiplex imaging technique exhibits platform-specific characteristics in terms of signal-to-noise ratio and acquisition artifacts that need to be accounted for to yield reliable and reproducible results. We propose a pixel classifier-based image preprocessing step that aims to minimize platform-dependency for all multiplex image analysis pipelines. Signal detection and noise reduction as well as artifact removal can be posed as a pixel classification problem in which all pixels in multiplex images can be assigned to two general classes of either I) signal of interest or II) artifacts and noise. The resulting feature representation maps contain pixel-scale representations of the input data, but exhibit significantly increased signal-to-noise ratios with normalized pixel values as output data. We demonstrate the validity of our proposed image preprocessing approach by comparing the results of two well-accepted and widely-used image analysis pipelines.

Neoadjuvant chemotherapy plus nivolumab with or without ipilimumab in operable non-small cell lung cancer: the phase 2 platform NEOSTAR trial

Neoadjuvant ipilimumab + nivolumab (Ipi+Nivo) and nivolumab + chemotherapy (Nivo+CT) induce greater pathologic response rates than CT alone in patients with operable non-small cell lung cancer (NSCLC). The impact of adding ipilimumab to neoadjuvant Nivo+CT is unknown. Here we report the results and correlates of two arms of the phase 2 platform NEOSTAR trial testing neoadjuvant Nivo+CT and Ipi+Nivo+CT with major pathologic response (MPR) as the primary endpoint. MPR rates were 32.1% (7/22, 80% confidence interval (CI) 18.7-43.1%) in the Nivo+CT arm and 50% (11/22, 80% CI 34.6-61.1%) in the Ipi+Nivo+CT arm; the primary endpoint was met in both arms. In patients without known tumor EGFR/ALK alterations, MPR rates were 41.2% (7/17) and 62.5% (10/16) in the Nivo+CT and Ipi+Nivo+CT groups, respectively. No new safety signals were observed in either arm. Single-cell sequencing and multi-platform immune profiling (exploratory endpoints) underscored immune cell populations and phenotypes, including effector memory CD8+ T, B and myeloid cells and markers of tertiary lymphoid structures, that were preferentially increased in the Ipi+Nivo+CT cohort. Baseline fecal microbiota in patients with MPR were enriched with beneficial taxa, such as Akkermansia, and displayed reduced abundance of pro-inflammatory and pathogenic microbes. Neoadjuvant Ipi+Nivo+CT enhances pathologic responses and warrants further study in operable NSCLC. (ClinicalTrials.gov registration: NCT03158129 .).

Clinical Significance of Tumour-Infiltrating B Lymphocytes (TIL-Bs) in Breast Cancer: A Systematic Literature Review

Although T lymphocytes have been considered the major players in the tumour microenvironment to induce tumour regression and contribute to anti-tumour immunity, much less is known about the role of tumour-infiltrating B lymphocytes (TIL-Bs) in solid malignancies, particularly in breast cancer, which has been regarded as heterogeneous and much less immunogenic compared to other common tumours like melanoma, colorectal cancer and non-small cell lung cancer. Such paucity of research could translate to limited opportunities for this most common type of cancer in the UK to join the immunotherapy efforts in this era of precision medicine. Here, we provide a systematic literature review assessing the clinical significance of TIL-Bs in breast cancer. Articles published between January 2000 and April 2022 were retrieved via an electronic search of two databases (PubMed and Embase) and screened against pre-specified eligibility criteria. The majority of studies reported favourable prognostic and predictive roles of TIL-Bs, indicating that they could have a profound impact on the clinical outcome of breast cancer. Further studies are, however, needed to better define the functional role of B cell subpopulations and to discover ways to harness this intrinsic mechanism in the fight against breast cancer.

Flexible Cyclic Immunofluorescence (cyCIF) Using Oligonucleotide Barcoded Antibodies

Advances in our understanding of the complex, multifaceted interactions between tumor epithelia, immune infiltrate, and tumor microenvironmental cells have been driven by highly multiplexed imaging technologies. These techniques are capable of labeling many more biomarkers than conventional immunostaining methods. However, multiplexed imaging techniques suffer from low detection sensitivity, cell loss-particularly in fragile samples-, and challenges with antibody labeling. Herein, we developed and optimized an oligonucleotide antibody barcoding strategy for cyclic immunofluorescence (cyCIF) that can be amplified to increase the detection efficiency of low-abundance antigens. Stained fluorescence signals can be readily removed using ultraviolet light treatment, preserving tissue and fragile cell sample integrity. We also extended the oligonucleotide barcoding strategy to secondary antibodies to enable the inclusion of difficult-to-label primary antibodies in a cyCIF panel. Using both the amplification oligonucleotides to label DNA barcoded antibodies and in situ hybridization of multiple fluorescently labeled oligonucleotides resulted in signal amplification and increased signal-to-background ratios. This procedure was optimized through the examination of staining parameters including staining oligonucleotide concentration, staining temperature, and oligonucleotide sequence design, resulting in a robust amplification technique. As a proof-of-concept, we demonstrate the flexibility of our cyCIF strategy by simultaneously imaging with the original oligonucleotide conjugated antibody (Ab-oligo) cyCIF strategy, the novel Ab-oligo cyCIF amplification strategy, as well as direct and indirect immunofluorescence to generate highly multiplexed images.

Evaluation of CD4+ tumor-infiltrating lymphocyte association with some clinicopathological indices of oral squamous cell carcinoma

BACKGROUND

The delayed diagnosis of oral squamous cell carcinoma (OSCC) affects therapeutic and prognostic strategies, and provides regional recurrence or distant metastasis. The tumor-infiltrating lymphocytes (TILs) are known as a critical diagnostic biomarker in antitumor immune response. We evaluated the association between CD4⁺ T-lymphocyte marker, some clinicopathological indices, and the impact of TILs on the stage and grade of OSCC.

MATERIALS AND METHODS

In this cross-sectional study, 37 OSCC specimens including 16 early and 21 advanced stages (categorized base-on recent clinical oncology references) and their related healthy surgical margin (as internal control group) were collected. Obtained histochemical data were analyzed by SPSS V.23 software. The expression of CD4⁺ marker in tumor microenvironment (TME) was compared by nonparametric Mann-Whitney and Kruskal-Wallis as well as Fisher's exact tests. P < 0.05 was remarked statistically significant.

RESULTS

The low-grade patients represented more CD4⁺ TIL that was statistically significant (P = 0.011). However, there was no statistically significant difference in CD4⁺ TIL between various stages (P = 0.404), tumor size, and lymph node involvement (P > 0.05). Moreover, there was no significant relation between TIL infiltration, age, and tumor localization (P > 0.05), however CD4⁺ expression in women was more than men (P = 0.008). The CD4⁺ T-lymphocyte infiltration in TME was more significant than healthy surgical margin (P < 0.001). There was a statistically significant difference between healthy surgical margin and different grades and stages of OSCCs that lower grades demonstrated more CD4⁺ TIL infiltration (P < 0.001).

CONCLUSION

The CD4⁺ T-lymphocytes may play important role in differentiation and maturity of epithelial cell, tumorigenesis, and progression of OSCC.

Bioimaging Nucleic-Acid Aptamers with Different Specificities in Human Glioblastoma Tissues Highlights Tumoral Heterogeneity

Nucleic-acid aptamers are of strong interest for diagnosis and therapy. Compared with antibodies, they are smaller, stable upon variations in temperature, easy to modify, and have higher tissue-penetration abilities. However, they have been little described as detection probes in histology studies of human tissue sections. In this study, we performed fluorescence imaging with two aptamers targeting cell-surface receptors EGFR and integrin α5β1, both involved in the aggressiveness of glioblastoma. The aptamers’ cell-binding specificities were confirmed using confocal imaging. The affinities of aptamers for glioblastoma cells expressing these receptors were in the 100–300 nM range. The two aptamers were then used to detect EGFR and integrin α5β1 in human glioblastoma tissues and compared with antibody labeling. Our aptafluorescence assays proved to be able to very easily reveal, in a one-step process, not only inter-tumoral glioblastoma heterogeneity (differences observed at the population level) but also intra-tumoral heterogeneity (differences among cells within individual tumors) when aptamers with different specificities were used simultaneously in multiplexing labeling experiments. The discussion also addresses the strengths and limitations of nucleic-acid aptamers for biomarker detection in histology.

The Role of Nerve Fibers in the Tumor Immune Microenvironment of Solid Tumors

The importance of neurons and nerve fibers in the tumor microenvironment (TME) of solid tumors is now acknowledged after being unexplored for a long time; this is possible due to the development of new technologies that allow in situ characterization of the TME. Recent studies have shown that the density and types of nerves that innervate tumors can predict a patient's clinical outcome and drive several processes of tumor biology. Nowadays, several efforts in cancer research and neuroscience are taking place to elucidate the mechanisms that drive tumor-associated innervation and nerve-tumor and nerve-immune interaction. Assessment of neurons and nerves within the context of the TME can be performed in situ, in tumor tissue, using several pathology-based strategies that utilize histochemical and immunohistochemistry principles, hi-plex technologies, and computational pathology approaches to identify measurable histopathological characteristics of nerves. These features include the number and type of tumor associated nerves, topographical location and microenvironment of neural invasion of malignant cells, and investigation of neuro-related biomarker expression in nerves, tumor cells, and cells of the TME. A deeper understanding of these complex interactions and the impact of nerves in tumor biology will guide the design of better strategies for targeted therapy in clinical trials.

The Role of Pathology-Based Methods in Qualitative and Quantitative Approaches to Cancer Immunotherapy

Simple Summary

Immunotherapy has become the filar of modern oncological treatment, and programmed death-ligand 1 expression is one of the primary immune markers assessed by pathologists. However, there are still some issues concerning the evaluation of the marker and limited information about the interaction between the tumour and associated immune cells. Recent studies have focused on cancer immunology to try to understand the complex tumour microenvironment, and multiplex imaging methods are more widely used for this purpose. The presented article aims to provide an overall review of a different multiplex in situ method using spectral imaging, supported by automated image-acquisition and software-assisted marker visualisation and interpretation. Multiplex imaging methods could improve the current understanding of complex tumour-microenvironment immunology and could probably help to better match patients to appropriate treatment regimens.

Abstract

Immune checkpoint inhibitors, including those concerning programmed cell death 1 (PD-1) and its ligand (PD-L1), have revolutionised the cancer therapy approach in the past decade. However, not all patients benefit from immunotherapy equally. The prediction of patient response to this type of therapy is mainly based on conventional immunohistochemistry, which is limited by intraobserver variability, semiquantitative assessment, or single-marker-per-slide evaluation. Multiplex imaging techniques and digital image analysis are powerful tools that could overcome some issues concerning tumour-microenvironment studies. This novel approach to biomarker assessment offers a better understanding of the complicated interactions between tumour cells and their environment. Multiplex labelling enables the detection of multiple markers simultaneously and the exploration of their spatial organisation. Evaluating a variety of immune cell phenotypes and differentiating their subpopulations is possible while preserving tissue histology in most cases. Multiplexing supported by digital pathology could allow pathologists to visualise and understand every cell in a single tissue slide and provide meaning in a complex tumour-microenvironment contexture. This review aims to provide an overview of the different multiplex imaging methods and their application in PD-L1 biomarker assessment. Moreover, we discuss digital imaging techniques, with a focus on slide scanners and software.

Spatially Resolved and Highly Multiplexed Protein and RNA In Situ Detection by Combining CODEX With RNAscope In Situ Hybridization

Highly multiplexed protein and RNA in situ detection on a single tissue section concurrently is highly desirable for both basic and applied biomedical research. CO-detection by inDEXing (CODEX) is a new and powerful platform to visualize up to 60 protein biomarkers in situ, and RNAscope in situ hybridization (RNAscope) is a novel RNA detection system with high sensitivity and unprecedent specificity at a single-cell level. Nevertheless, to our knowledge, the combination of CODEX and RNAscope remained unreported until this study. Here, we report a simple and reproducible combination of CODEX and RNAscope. We also determined the cross-reactivities of CODEX anti-human antibodies to rhesus macaques, a widely used animal model of human disease.

Multiplex Immunofluorescence and the Digital Image Analysis Workflow for Evaluation of the Tumor Immune Environment in Translational Research

A robust understanding of the tumor immune environment has important implications for cancer diagnosis, prognosis, research, and immunotherapy. Traditionally, immunohistochemistry (IHC) has been regarded as the standard method for detecting proteins in situ, but this technique allows for the evaluation of only one cell marker per tissue sample at a time. However, multiplexed imaging technologies enable the multiparametric analysis of a tissue section at the same time. Also, through the curation of specific antibody panels, these technologies enable researchers to study the cell subpopulations within a single immunological cell group. Thus, multiplexed imaging gives investigators the opportunity to better understand tumor cells, immune cells, and the interactions between them. In the multiplexed imaging technology workflow, once the protocol for a tumor immune micro environment study has been defined, histological slides are digitized to produce high-resolution images in which regions of interest are selected for the interrogation of simultaneously expressed immunomarkers (including those co-expressed by the same cell) by using an image analysis software and algorithm. Most currently available image analysis software packages use similar machine learning approaches in which tissue segmentation first defines the different components that make up the regions of interest and cell segmentation, then defines the different parameters, such as the nucleus and cytoplasm, that the software must utilize to segment single cells. Image analysis tools have driven dramatic evolution in the field of digital pathology over the past several decades and provided the data necessary for translational research and the discovery of new therapeutic targets. The next step in the growth of digital pathology is optimization and standardization of the different tasks in cancer research, including image analysis algorithm creation, to increase the amount of data generated and their accuracy in a short time as described herein. The aim of this review is to describe this process, including an image analysis algorithm creation for multiplex immunofluorescence analysis, as an essential part of the optimization and standardization of the different processes in cancer research, to increase the amount of data generated and their accuracy in a short time.

Automated detection of vascular remodeling in tumor-draining lymph nodes by the deep learning tool HEV-finder

Vascular remodeling is common in human cancer and has potential as future biomarkers for prediction of disease progression and tumor immunity status. It can also affect metastatic sites, including the tumor‐draining lymph nodes (TDLNs). Dilation of the high endothelial venules (HEVs) within TDLNs has been observed in several types of cancer. We recently demonstrated that it is a premetastatic effect that can be linked to tumor invasiveness in breast cancer. Manual visual assessment of changes in vascular morphology is a tedious and difficult task, limiting high‐throughput analysis. Here we present a fully automated approach for detection and classification of HEV dilation. By using 12,524 manually classified HEVs, we trained a deep‐learning model and created a graphical user interface for visualization of the results. The tool, named the HEV‐finder, selectively analyses HEV dilation in specific regions of the lymph nodes. We evaluated the HEV‐finder's ability to detect and classify HEV dilation in different types of breast cancer compared to manual annotations. Our results constitute a successful example of large‐scale, fully automated, and user‐independent, image‐based quantitative assessment of vascular remodeling in human pathology and lay the ground for future exploration of HEV dilation in TDLNs as a biomarker. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA

Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell's marker expression profile and, when needed, its spatial information. We demonstrate the performance of CELESTA on multiplexed immunofluorescence images of colorectal cancer and head and neck squamous cell carcinoma (HNSCC). Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, and validate our findings in an independent cohort. By coupling our spatial analysis with single-cell RNA-sequencing data on proximal sections of the same specimens, we identify cell-cell crosstalk associated with lymph node metastasis, demonstrating the power of CELESTA to facilitate identification of clinically relevant interactions.

Impact of Region-of-Interest Size on Immune Profiling Using Multiplex Immunofluorescence Tyramide Signal Amplification for Paraffin-Embedded Tumor Tissues

INTRODUCTION

Representative regions of interest (ROIs) analysis from the whole slide images (WSI) are currently being used to study immune markers by multiplex immunofluorescence (mIF) and single immunohistochemistry (IHC). However, the amount of area needed to be analyzed to be representative of the entire tumor in a WSI has not been defined.

METHODS

We labeled tumor-associated immune cells by mIF and single IHC in separate cohorts of non-small cell lung cancer (NSCLC) samples and we analyzed them as whole tumor area as well as using different number of ROIs to know how much area will be need to represent the entire tumor area.

RESULTS

For mIF using the InForm software and ROI of 0.33 mm2 each, we observed that the cell density data from five randomly selected ROIs is enough to achieve, in 90% of our samples, more than 0.9 of Spearman correlation coefficient and for single IHC using ScanScope tool box from Aperio and ROIs of 1 mm2 each, we found that the correlation value of more than 0.9 was achieved using 5 ROIs in a similar cohort. Additionally, we also observed that each cell phenotype in mIF influence differently the correlation between the areas analyzed by the ROIs and the WSI. Tumor tissue with high intratumor epithelial and immune cells phenotype, quality, and spatial distribution heterogeneity need more area analyzed to represent better the whole tumor area.

CONCLUSION

We found that at minimum 1.65 mm2 area is enough to represent the entire tumor areas in most of our NSCLC samples using mIF.

Multiplex immunohistochemical phenotyping of T cells in primary prostate cancer

BACKGROUND

Most prostate cancers are "immune cold" and poorly responsive to immune checkpoint inhibitors. However, the mechanisms responsible for the lack of a robust antitumor adaptive immune response in the prostate are poorly understood, which hinders the development of novel immunotherapeutic approaches.

AIMS

Most inflammatory infiltrates in the prostate are centered around benign glands and stroma, which can confound the molecular characterization of the antitumor immune response. We sought to analytically validate a chromogenic-based multiplex immunohistochemistry (IHC) approach applicable to whole slide digital image analysis to quantify T cell subsets from the tumor microenvironment of primary prostatic adenocarcinomas. As an initial application, we tested the hypothesis that PTEN loss leads to an altered antitumor immune response by comparing matched regions of tumors within the same individual with and without PTEN loss.

MATERIALS & METHODS

Using the HALO Image Analysis Platform (Indica Labs), we trained a classifier to quantify the densities of eight T cell phenotypes separately in the tumor epithelial and stromal subcompartments.

RESULTS

The iterative chromogenic approach using 7 different antibodies on the same slide provides highly similar findings to results using individually stained slides with single antibodies. Our main findings in carcinomas (benign removed) include the following: i) CD4+ T cells are present at higher density than CD8+ T cells; ii) all T cell subsets are present at higher densities in the stromal compartment compared to the epithelial tumor compartment; iii) most CD4+ and CD8+ T cells are PD1+; iv) cancer foci with PTEN loss harbored increased numbers of T cells compared to regions without PTEN loss, in both stromal and epithelial compartments; and v) the increases in T cells in PTEN loss regions were associated with ERG gene fusion status.

DISCUSSION

This modular approach can apply to any IHC-validated antibody combination and sets the groundwork for more detailed spatial analyses.

CONCLUSION

Iterative chromogenic IHC can be used for whole slide analysis of prostate tissue samples and can complement transcriptomic results including those using single cell and spatial genomic approaches.

Dissecting Tumor-Immune Microenvironment in Breast Cancer at a Spatial and Multiplex Resolution

The tumor immune microenvironment (TIME) is an important player in breast cancer pathophysiology. Surrogates for antitumor immune response have been explored as predictive biomarkers to immunotherapy, though with several limitations. Immunohistochemistry for programmed death ligand 1 suffers from analytical problems, immune signatures are devoid of spatial information and histopathological evaluation of tumor infiltrating lymphocytes exhibits interobserver variability. Towards improved understanding of the complex interactions in TIME, several emerging multiplex in situ methods are being developed and gaining much attention for protein detection. They enable the simultaneous evaluation of multiple targets in situ, detection of cell densities/subpopulations as well as estimations of functional states of immune infiltrate. Furthermore, they can characterize spatial organization of TIME—by cell-to-cell interaction analyses and the evaluation of distribution within different regions of interest and tissue compartments—while digital imaging and image analysis software allow for reproducibility of the various assays. In this review, we aim to provide an overview of the different multiplex in situ methods used in cancer research with special focus on breast cancer TIME at the neoadjuvant, adjuvant and metastatic setting. Spatial heterogeneity of TIME and importance of longitudinal evaluation of TIME changes under the pressure of therapy and metastatic progression are also addressed.

Application of High-Throughput Imaging Mass Cytometry Hyperion in Cancer Research

Imaging mass cytometry (IMC) enables the in situ analysis of in-depth-phenotyped cells in their native microenvironment within the preserved architecture of a single tissue section. To date, it permits the simultaneous analysis of up to 50 different protein- markers targeted by metal-conjugated antibodies. The application of IMC in the field of cancer research may notably help 1) to define biomarkers of prognostic and theragnostic significance for current and future treatments against well-established and novel therapeutic targets and 2) to improve our understanding of cancer progression and its resistance mechanisms to immune system and how to overcome them. In the present article, we not only provide a literature review on the use of the IMC in cancer-dedicated studies but we also present the IMC method and discuss its advantages and limitations among methods dedicated to deciphering the complexity of cancer tissue.

A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially-resolved tissue phenotyping at single-cell resolution

Multiplexed imaging technologies enable the study of biological tissues at single-cell resolution while preserving spatial information. Currently, high-dimension imaging data analysis is technology-specific and requires multiple tools, restricting analytical scalability and result reproducibility. Here we present SIMPLI (Single-cell Identification from MultiPLexed Images), a flexible and technology-agnostic software that unifies all steps of multiplexed imaging data analysis. After raw image processing, SIMPLI performs a spatially resolved, single-cell analysis of the tissue slide as well as cell-independent quantifications of marker expression to investigate features undetectable at the cell level. SIMPLI is highly customisable and can run on desktop computers as well as high-performance computing environments, enabling workflow parallelisation for large datasets. SIMPLI produces multiple tabular and graphical outputs at each step of the analysis. Its containerised implementation and minimum configuration requirements make SIMPLI a portable and reproducible solution for multiplexed imaging data analysis. Software is available at “SIMPLI [https://github.com/ciccalab/SIMPLI]”.

Current high-dimension imaging data analysis methods are technology-specific and require multiple tools, restricting analytical scalability and result reproducibility. Here the authors present SIMPLI, a software that overcomes these limitations for single-cell and pixel analysis of multiplexed images at spatial resolution.

Spatial Statistics for Understanding Tissue Organization

Interpreting tissue architecture plays an important role in gaining a better understanding of healthy tissue development and disease. Novel molecular detection and imaging techniques make it possible to locate many different types of objects, such as cells and/or mRNAs, and map their location across the tissue space. In this review, we present several methods that provide quantification and statistical verification of observed patterns in the tissue architecture. We categorize these methods into three main groups: Spatial statistics on a single type of object, two types of objects, and multiple types of objects. We discuss the methods in relation to four hypotheses regarding the methods' capability to distinguish random and non-random distributions of objects across a tissue sample, and present a number of openly available tools where these methods are provided. We also discuss other spatial statistics methods compatible with other types of input data.

Early Lesion of Post-Primary Tuberculosis: Subclinical Driver of Disease and Target for Vaccines and Host-Directed Therapies

The characteristic lesion of primary tuberculosis is the granuloma as is widely studied in human tissues and animal models. Post-primary tuberculosis is different. It develops only in human lungs and begins as a prolonged subclinical obstructive lobular pneumonia that slowly accumulates mycobacterial antigens and host lipids in alveolar macrophages with nearby highly sensitized T cells. After several months, the lesions undergo necrosis to produce a mass of caseous pneumonia large enough to fragment and be coughed out to produce a cavity or be retained as the focus of a post-primary granuloma. Bacteria grow massively on the cavity wall where they can be coughed out to infect new people. Here we extend these findings with the demonstration of secreted mycobacterial antigens, but not acid fast bacilli (AFB) of M. tuberculosis in the cytoplasm of ciliated bronchiolar epithelium and alveolar pneumocytes in association with elements of the programmed death ligand 1 (PD-L1), cyclo-oxygenase (COX)-2, and fatty acid synthase (FAS) pathways in the early lesion. This suggests that M. tuberculosis uses its secreted antigens to coordinate prolonged subclinical development of the early lesions in preparation for a necrotizing reaction sufficient to produce a cavity, post-primary granulomas, and fibrocaseous disease.

The tale of TILs in breast cancer: A report from The International Immuno-Oncology Biomarker Working Group

The advent of immune-checkpoint inhibitors (ICI) in modern oncology has significantly improved survival in several cancer settings. A subgroup of women with breast cancer (BC) has immunogenic infiltration of lymphocytes with expression of programmed death-ligand 1 (PD-L1). These patients may potentially benefit from ICI targeting the programmed death 1 (PD-1)/PD-L1 signaling axis. The use of tumor-infiltrating lymphocytes (TILs) as predictive and prognostic biomarkers has been under intense examination. Emerging data suggest that TILs are associated with response to both cytotoxic treatments and immunotherapy, particularly for patients with triple-negative BC. In this review from The International Immuno-Oncology Biomarker Working Group, we discuss (a) the biological understanding of TILs, (b) their analytical and clinical validity and efforts toward the clinical utility in BC, and (c) the current status of PD-L1 and TIL testing across different continents, including experiences from low-to-middle-income countries, incorporating also the view of a patient advocate. This information will help set the stage for future approaches to optimize the understanding and clinical utilization of TIL analysis in patients with BC.

Multiplexed imaging and automated signal quantification in formalin-fixed paraffin-embedded tissues by ChipCytometry

Deciphering the spatial composition of cells in tissues is essential for detailed understanding of biological processes in health and disease. Recent technological advances enabled the assessment of the enormous complexity of tissue-derived parameters by highly multiplexed tissue imaging (HMTI), but elaborate machinery and data analyses are required. This severely limits broad applicability of HMTI. Here we demonstrate for the first time the application of ChipCytometry technology, which has unique features for widespread use, on formalin-fixed paraffin-embedded samples, the most commonly used storage technique of clinically relevant patient specimens worldwide. The excellent staining quality permits workflows for automated quantification of signal intensities, which we further optimized to compensate signal spillover from neighboring cells. In combination with the high number of validated markers, the reported platform can be used from unbiased analyses of tissue composition to detection of phenotypically complex rare cells, and can be easily implemented in both routine research and clinical pathology.

A novel 5x multiplex immunohistochemical staining reveals PSMA as a helpful marker in prostate cancer with low p504s expression

The ability to combine multiple immunohistochemical (IHC) markers within a single tissue section facilitates the evaluation and detection of co-expressions, while saving tissue. A newly developed 5x multiplex (MPX) IHC staining of five different IHC markers (Basal cell cocktail (34βE12 + p63), p504s (SP116), ERG (EPR3864), Ki-67 (30-9), PSMA (EP192)) was applied on whole sections of n = 37 radical prostatectomies (RPE) including normal and cancerous tissue. Four different colors including brown, magenta, yellow and teal coded for different stainings, whereas magenta was used twice for nuclear Ki-67 and cytosolic / membranous PSMA. The staining of multiplex IHC was compared to single stains of ERG, PSMA and p504s. The proper staining of the basal cell cocktail and Ki-67 could be assessed by internal positive controls in the multiplex staining. The proportion of PSMA and p504s expression revealed a significant correlation between multiplex and single stains (p < 0.01) as well as a concordant staining pattern for ERG (n = 14 prostate cancers were identified ERG positive with both methods). Our proof of concept study demonstrates a robust staining pattern of all five different antibodies with this newly developed 5x MPX IHC. This approach facilitates the recognition of prostate cancer, in particular by adding PSMA in cases with low p504s expression.

Best Practices for Technical Reproducibility Assessment of Multiplex Immunofluorescence

Multiplex immunofluorescence (mIF) tyramide signal amplification is a new and useful tool for the study of cancer that combines the staining of multiple markers in a single slide. Several technical requirements are important to performing high-quality staining and analysis and to obtaining high internal and external reproducibility of the results. This review manuscript aimed to describe the mIF panel workflow and discuss the challenges and solutions for ensuring that mIF panels have the highest reproducibility possible. Although this platform has shown high flexibility in cancer studies, it presents several challenges in pre-analytic, analytic, and post-analytic evaluation, as well as with external comparisons. Adequate antibody selection, antibody optimization and validation, panel design, staining optimization and validation, analysis strategies, and correct data generation are important for reproducibility and to minimize or identify possible issues during the mIF staining process that sometimes are not completely under our control, such as the tissue fixation process, storage, and cutting procedures.

UltraPlex Hapten-Based Multiplexed Fluorescent Immunohistochemistry

The UltraPlex method for multiplexed two-dimensional fluorescent immunohistochemistry is described, in which hapten tags conjugated to primary antibodies facilitate multiplexed imaging of four or more antigens per tissue section at once. Anti-hapten secondary antibodies labeled with fluorophores provide amplified signal for detection, which is accomplished using a standard fluorescent microscope or digital slide scanner. The protocol is rapid and straightforward and utilizes conventionally prepared tissue samples. The resulting staining is highly sensitive and specific, enabling high-resolution imaging of multiple cellular subtypes within tissue samples. Tumor cells and tumor-infiltrating lymphocytes are presented as examples. Multiple 4-plex-stained tissue samples can be digitally overlaid to create 8-plex (or more) high-content images, enabling visualization of distribution of complex cellular subtypes across tissues.

Spatially Resolved Immunometabolism to Understand Infectious Disease Progression

Infectious diseases, including those of viral, bacterial, fungal, and parasitic origin are often characterized by focal inflammation occurring in one or more distinct tissues. Tissue-specific outcomes of infection are also evident in many infectious diseases, suggesting that the local microenvironment may instruct complex and diverse innate and adaptive cellular responses resulting in locally distinct molecular signatures. In turn, these molecular signatures may both drive and be responsive to local metabolic changes in immune as well as non-immune cells, ultimately shaping the outcome of infection. Given the spatial complexity of immune and inflammatory responses during infection, it is evident that understanding the spatial organization of transcripts, proteins, lipids, and metabolites is pivotal to delineating the underlying regulation of local immunity. Molecular imaging techniques like mass spectrometry imaging and spatially resolved, highly multiplexed immunohistochemistry and transcriptomics can define detailed metabolic signatures at the microenvironmental level. Moreover, a successful complementation of these two imaging techniques would allow multi-omics analyses of inflammatory microenvironments to facilitate understanding of disease pathogenesis and identify novel targets for therapeutic intervention. Here, we describe strategies for downstream data analysis of spatially resolved multi-omics data and, using leishmaniasis as an exemplar, describe how such analysis can be applied in a disease-specific context.

Nodal immune flare mimics nodal disease progression following neoadjuvant immune checkpoint inhibitors in non-small cell lung cancer

Radiographic imaging is the standard approach for evaluating the disease involvement of lymph nodes in patients with operable NSCLC although the impact of neoadjuvant immune checkpoint inhibitors (ICIs) on lymph nodes has not yet been characterized. Herein, we present an ad hoc analysis of the NEOSTAR trial (NCT03158129) where we observed a phenomenon we refer to as “nodal immune flare” (NIF) in which patients treated with neoadjuvant ICIs demonstrate radiologically abnormal nodes post-therapy that upon pathological evaluation are devoid of cancer and demonstrate de novo non-caseating granulomas. Abnormal lymph nodes are analyzed by computed tomography and ¹⁸F-fluorodeoxyglucose positron emission tomography/computer tomography to evaluate the size and the maximum standard uptake value post- and pre-therapy in NEOSTAR and an independent neoadjuvant chemotherapy cohort. NIF occurs in 16% (7/44) of patients treated with ICIs but in 0% (0/28) of patients after neoadjuvant chemotherapy. NIF is associated with an inflamed nodal immune microenvironment and with fecal abundance of genera belonging to the family Coriobacteriaceae of phylum Actinobacteria, but not with tumor responses or treatment-related toxicity. Our findings suggest that this apparent radiological cancer progression in lymph nodes may occur due to an inflammatory response after neoadjuvant immunotherapy, and such cases should be evaluated by pathological examination to distinguish NIF from true nodal progression and to ensure appropriate clinical treatment planning.

Granulomatous/sarcoid-like lesions have been reported in patients treated with immune checkpoint inhibitors (ICIs). Here the authors report the occurrence of “nodal immune flare”, an apparent radiological cancer progression in the nodes characterized by the absence of cancer and the presence of non-caseating granulomas, in patients with non-small cell lung cancer following neoadjuvant ICI treatment.

Visualization of HIV-1 reservoir: an imaging perspective

PURPOSE OF REVIEW

The persistence of HIV-1-infected cells, despite the introduction of the combinatorial antiretroviral therapy, is a major obstacle to HIV-1 eradication. Understanding the nature of HIV reservoir will lead to novel therapeutic approaches for the functional cure or eradication of the virus. In this review, we will update the recent development in imaging applications toward HIV-1/simian immunodeficiency virus (SIV) viral reservoirs research and highlight some of their limitations.

RECENT FINDINGS

CD4 T cells are the primary target of HIV-1/SIV and the predominant site for productive and latent reservoirs. This viral reservoir preferentially resides in lymphoid compartments that are difficult to access, which renders sampling and measurements problematical and a hurdle for understanding HIV-1 pathogenicity. Novel noninvasive technologies are needed to circumvent this and urgently help to find a cure for HIV-1. Recent technological advancements have had a significant impact on the development of imaging methodologies allowing the visualization of relevant biomarkers with high resolution and analytical capacity. Such methodologies have provided insights into our understanding of cellular and molecular interactions in health and disease.

SUMMARY

Imaging of the HIV-1 reservoir can provide significant insights for the nature (cell types), spatial distribution, and the role of the tissue microenvironment for its in vivo dynamics and potentially lead to novel targets for the virus elimination.

Tissue Multiplex Analyte Detection in Anatomic Pathology - Pathways to Clinical Implementation

Background: Multiplex tissue analysis has revolutionized our understanding of the tumor microenvironment (TME) with implications for biomarker development and diagnostic testing. Multiplex labeling is used for specific clinical situations, but there remain barriers to expanded use in anatomic pathology practice.

Methods: We review immunohistochemistry (IHC) and related assays used to localize molecules in tissues, with reference to United States regulatory and practice landscapes. We review multiplex methods and strategies used in clinical diagnosis and in research, particularly in immuno-oncology. Within the framework of assay design and testing phases, we examine the suitability of multiplex immunofluorescence (mIF) for clinical diagnostic workflows, considering its advantages and challenges to implementation.

Results: Multiplex labeling is poised to radically transform pathologic diagnosis because it can answer questions about tissue-level biology and single-cell phenotypes that cannot be addressed with traditional IHC biomarker panels. Widespread implementation will require improved detection chemistry, illustrated by InSituPlex technology (Ultivue, Inc., Cambridge, MA) that allows coregistration of hematoxylin and eosin (H&E) and mIF images, greater standardization and interoperability of workflow and data pipelines to facilitate consistent interpretation by pathologists, and integration of multichannel images into digital pathology whole slide imaging (WSI) systems, including interpretation aided by artificial intelligence (AI). Adoption will also be facilitated by evidence that justifies incorporation into clinical practice, an ability to navigate regulatory pathways, and adequate health care budgets and reimbursement. We expand the brightfield WSI system “pixel pathway” concept to multiplex workflows, suggesting that adoption might be accelerated by data standardization centered on cell phenotypes defined by coexpression of multiple molecules.

Conclusion: Multiplex labeling has the potential to complement next generation sequencing in cancer diagnosis by allowing pathologists to visualize and understand every cell in a tissue biopsy slide. Until mIF reagents, digital pathology systems including fluorescence scanners, and data pipelines are standardized, we propose that diagnostic labs will play a crucial role in driving adoption of multiplex tissue diagnostics by using retrospective data from tissue collections as a foundation for laboratory-developed test (LDT) implementation and use in prospective trials as companion diagnostics (CDx).

Artificial Intelligence for solid tumor diagnosis in digital pathology

Tumor diagnosis relies on the visual examination of histological slides by pathologists through a microscope eyepiece. Digital pathology, the digitalization of histological slides at high magnification with slides scanners, has raised the opportunity to extract quantitative information thanks to image analysis. In the last decade, medical image analysis has made exceptional progress due to the development of artificial intelligence (AI) algorithms. AI has been successfully used in the field of medical imaging and more recently in digital pathology. The feasibility and usefulness of AI assisted pathology tasks have been demonstrated in the very last years and we can expect those developments to be applied on routine histopathology in the future. In this review, we will describe and illustrate this technique and present the most recent applications in the field of tumor histopathology.

Retinoblastoma Is Characterized by a Cold, CD8+ Cell Poor, PD-L1- Microenvironment, Which Turns Into Hot, CD8+ Cell Rich, PD-L1+ After Chemotherapy

Purpose

To investigate the impact of chemotherapy (CHT) on human retinoblastoma (RB) tumor microenvironment (TME).

Cases and Methods

Ninety-four RBs were studied, including 44 primary RBs treated by upfront surgery (Group 1) and 50 primary RBs enucleated after CHT (CHT), either intra-arterial (IAC; Group 2, 33 cases) or systemic (S-CHT; Group 3, 17 cases). Conventional and multiplexed immunohistochemistry were performed to make quantitative comparisons among the three groups, for the following parameters: tumor-infiltrating inflammatory cells (TI-ICs); programmed cell death protein 1 (PD-1) positive TI-ICs; Ki67 proliferation index; gliosis; PD-1 ligand (PD-L1) protein expression; vessel number. We also correlated these TME factors with the presence of histological high-risk factors (HHRF+) and RB anaplasia grade (AG).

Results

After CHT, a decrease in both RB burden and Ki67 positivity was observed. In parallel, most subsets of TI-ICs, PD-1+ TI-ICs, gliosis, and PD-L1 protein expression significantly increased (P < 0.001, P = 0.02, P < 0.001, respectively). Vessel number did not significantly vary. Age, HHRFs+ and AG were significantly different between primary and chemoreduced RBs (P < 0.001, P = 0.006, P = 0.001, respectively) and were correlated with most TME factors.

Conclusions

CHT modulates host antitumor immunity by reorienting the RB TME from anergic into an active, CD8+, PD-L1+ hot state. Furthermore, some clinicopathological characteristics of RB correlate with several factors of TME. Our study adds data in favor of the possibility of a new therapeutic scenario in human RB.

Exploring Markers of Exhausted CD8 T Cells to Predict Response to Immune Checkpoint Inhibitor Therapy for Hepatocellular Carcinoma

BACKGROUND

Reversal of CD8 T-cell exhaustion was considered a major antitumor mechanism of anti-programmed cell death-1 (PD-1)/ anti-programmed death ligand-1 (PD-L1)-based immune checkpoint inhibitor (ICI) therapy.

OBJECTIVES

The aim of this study was to identify markers of T-cell exhaustion that is best associated with ICI treatment efficacy for advanced hepatocellular carcinoma (HCC).

METHODS

Immune cell composition of archival tumor samples was analyzed by transcriptomic analysis and multiplex immunofluorescence staining.

RESULTS

HCC patients with objective response after anti-PD-1/anti-PD-L1-based ICI therapy (n = 42) had higher expression of genes related to T-cell exhaustion. A 9-gene signature (LAG3, CD244, CCL5, CXCL9, CXCL13, MSR1, CSF3R, CYBB, and KLRK1) was defined, whose expression was higher in patients with response to ICI therapy, correlated with density of CD8⁺LAG3⁺ cells in tumor microenvironment, and independently predicted better progression-free and overall survival. This 9-gene signature had similar predictive values for patients who received single-agent or combination ICI therapy and was not associated with prognosis in HCC patients who received surgery, suggesting that it may outperform other T-cell signatures for predicting efficacy of ICI therapy for HCC. For HCC patients who underwent surgery for both the primary liver and metastatic lung tumors (n = 31), lung metastatic HCC was associated with a higher exhausted CD8 T-cell signature, consistent with prior observation that patients with lung metastatic HCC may have higher probability of response to ICI therapy.

CONCLUSIONS

CD8 T-cell exhaustion in tumor microenvironment may predict better efficacy of ICI therapy for HCC.

Advancing therapy for osteosarcoma

Improving the survival of patients with osteosarcoma has long proved challenging, although the treatment of this disease is on the precipice of advancement. The increasing feasibility of molecular profiling together with the creation of both robust model systems and large, well-annotated tissue banks has led to an increased understanding of osteosarcoma biology. The historical invariability of survival outcomes and the limited number of agents known to be active in the treatment of this disease facilitate clinical trials designed to identify efficacious novel therapies using small cohorts of patients. In addition, trial designs will increasingly consider the genetic background of the tumour through biomarker-based patient selection, thereby enriching for clinical activity. Indeed, osteosarcoma cells are known to express a number of surface proteins that might be of therapeutic relevance, including B7-H3, GD2 and HER2, which can be targeted using antibody-drug conjugates and/or adoptive cell therapies. In addition, immune-checkpoint inhibition might augment the latter approach by helping to overcome the immunosuppressive tumour microenvironment. In this Review, we provide a brief overview of current osteosarcoma therapy before focusing on the biological insights from the molecular profiling and preclinical modelling studies that have opened new therapeutic opportunities in this disease.

Defining T Cell Subsets in Human Tonsils Using ChipCytometry

ChipCytometry is a multiplex imaging method that can be used to analyze either cell suspensions or tissue sections. Images are acquired by iterative cycles of immunostaining with fluorescently labeled Abs, followed by photobleaching, which allows the accumulation of multiple markers on a single sample. In this study, we explored the feasibility of using ChipCytometry to identify and phenotype cell subsets, including rare cell types, using a combination of tissue sections and single-cell suspensions. Using ChipCytometry of tissue sections, we successfully demonstrated the architecture of human palatine tonsils, including the B and T cell zones, and characterized subcompartments such as the B cell mantle and germinal center zone, as well as intrafollicular PD1-expressing CD4+ T cells. Additionally, we were able to identify the rare tonsillar T cell subsets, mucosal-associated invariant T (MAIT) and γδ-T cells, within tonsil tissue. Using single-cell suspension ChipCytometry, we further dissected human tonsillar T cell subsets via unsupervised clustering analysis as well as supervised traditional manual gating. We were able to show that PD1+CD4+ T cells are comprised of CXCR5+BCL6high follicular Th cells and CXCR5-BCL6mid pre-follicular Th cells. Both supervised and unsupervised analysis approaches identified MAIT cells in single-cell suspensions, confirming a phenotype similar to that of blood-derived MAIT cells. In this study, we demonstrate that ChipCytometry is a viable method for single-cell suspension cytometry and analysis, with the additional benefit of allowing phenotyping in a spatial context using tissue sections.

Machine learning for cell classification and neighborhood analysis in glioma tissue

Multiplexed and spatially resolved single-cell analyses that intend to study tissue heterogeneity and cell organization invariably face as a first step the challenge of cell classification. Accuracy and reproducibility are important for the downstream process of counting cells, quantifying cell-cell interactions, and extracting information on disease-specific localized cell niches. Novel staining techniques make it possible to visualize and quantify large numbers of cell-specific molecular markers in parallel. However, due to variations in sample handling and artifacts from staining and scanning, cells of the same type may present different marker profiles both within and across samples. We address multiplexed immunofluorescence data from tissue microarrays of low-grade gliomas and present a methodology using two different machine learning architectures and features insensitive to illumination to perform cell classification. The fully automated cell classification provides a measure of confidence for the decision and requires a comparably small annotated data set for training, which can be created using freely available tools. Using the proposed method, we reached an accuracy of 83.1% on cell classification without the need for standardization of samples. Using our confidence measure, cells with low-confidence classifications could be excluded, pushing the classification accuracy to 94.5%. Next, we used the cell classification results to search for cell niches with an unsupervised learning approach based on graph neural networks. We show that the approach can re-detect specialized tissue niches in previously published data, and that our proposed cell classification leads to niche definitions that may be relevant for sub-groups of glioma, if applied to larger data sets.

Delineating the breast cancer immune microenvironment in the era of multiplex immunohistochemistry/immunofluorescence

Breast cancer is the most common malignancy and the leading cause of cancer death in females worldwide. Treatment is challenging, especially for those who are triple-negative. Increasing evidence suggests that diverse immune populations are present in the breast tumour microenvironment, which opens up avenues for personalised drug targets. Historically, our investigations into the immune constitution of breast tumours have been restricted to analyses of one or two markers at a given time. Recent technological advances have allowed simultaneous labelling of more than 35 markers and detailed profiling of tumour-immune infiltrates at the single-cell level, as well as determining the cellular composition and spatial analysis of the entire tumour architecture. In this review, we describe emerging technologies that have contributed to the field of breast cancer diagnosis, and discuss how to interpret the vast data sets obtained in order to effectively translate them for clinically relevant use.

Multiplex Immunofluorescence Tyramide Signal Amplification for Immune Cell Profiling of Paraffin-Embedded Tumor Tissues

Every day, more evidence is revealed regarding the importance of the relationship between the response to cancer immunotherapy and the cancer immune microenvironment. It is well established that a profound characterization of the immune microenvironment is needed to identify prognostic and predictive immune biomarkers. To this end, we find phenotyping cells by multiplex immunofluorescence (mIF) a powerful and useful tool to identify cell types in biopsy specimens. Here, we describe the use of mIF tyramide signal amplification for labeling up to eight markers on a single slide of formalin-fixed, paraffin-embedded tumor tissue to phenotype immune cells in tumor tissues. Different panels show different markers, and the different panels can be used to characterize immune cells and relevant checkpoint proteins. The panel design depends on the research hypothesis, the cell population of interest, or the treatment under investigation. To phenotype the cells, image analysis software is used to identify individual marker expression or specific co-expression markers, which can differentiate already selected phenotypes. The individual-markers approach identifies a broad number of cell phenotypes, including rare cells, which may be helpful in a tumor microenvironment study. To accurately interpret results, it is important to recognize which receptors are expressed on different cell types and their typical location (i.e., nuclear, membrane, and/or cytoplasm). Furthermore, the amplification system of mIF may allow us to see weak marker signals, such as programmed cell death ligand 1, more easily than they are seen with single-marker immunohistochemistry (IHC) labeling. Finally, mIF technologies are promising resources for discovery of novel cancer immunotherapies and related biomarkers. In contrast with conventional IHC, which permits only the labeling of one single marker per tissue sample, mIF can detect multiple markers from a single tissue sample, and at the same time, deliver extensive information about the cell phenotypes composition and their spatial localization. In this matter, the phenotyping process is critical and must be done accurately by a highly trained personal with knowledge of immune cell protein expression and tumor pathology.

Immuno-profiling and cellular spatial analysis using five immune oncology multiplex immunofluorescence panels for paraffin tumor tissue

Multiplex immunofluorescence (mIF) has arisen as an important tool for immuno-profiling tumor tissues. We updated our manual protocol into an automated protocol that allows the use of up to seven markers in five mIF panels to apply to formalin-fixed paraffin-embedded tumor tissues. Using a tyramide signal amplification system, we optimized five mIF panels that included cytokeratin to characterize malignant cells (MCs), immune checkpoint markers (i.e., PD-L1, B7-H3, B7-H4, IDO-1, VISTA, LAG3, ICOS, TIM3, and OX40), tumor-infiltrating lymphocytic markers (i.e., CD3, CD8, CD45RO, granzyme B, PD-1, and FOXP3), and markers to characterize myeloid-derived suppressor cells (i.e., CD68, CD66b, CD14, CD33, Arg-1, and CD11b). To determine analytical reproducibility and the impact of those panels for immuno-profiling tumor tissues, we performed an exploratory analysis in a set of non–small cell lung cancer (NSCLC) samples. The slides were scanned, and the different cell phenotypes were quantified by simultaneous co-localizations with the markers using image analysis software. Comparison between the time points of staining showed high analytical reproducibility. The analysis of NSCLC cases showed an immunosuppressive microenvironment with PD-L1/PD-1 expression as a predominant axis. Interestingly, high density of MCs expressing B7-H4 was correlated with recurrence. Unexpectedly, MCs expressing OX40 were also detected, and those cells were a closer distance to CD3+T-cells than were MCs expressing other immune checkpoints. Two different cellular patterns of spatial distribution were determined according the CD3 distribution, and the predominant pattern was related with active immunosuppressive interaction with MCs. Our study shows that these five mIF panels can identify multiple targets in a single cell with high reproducibility. The study of different cell populations and their spatial relationship can open new ideas for therapeutic approaches.

Next-Generation Digital Histopathology of the Tumor Microenvironment

Progress in cancer research is substantially dependent on innovative technologies that permit a concerted analysis of the tumor microenvironment and the cellular phenotypes resulting from somatic mutations and post-translational modifications. In view of a large number of genes, multiplied by differential splicing as well as post-translational protein modifications, the ability to identify and quantify the actual phenotypes of individual cell populations in situ, i.e., in their tissue environment, has become a prerequisite for understanding tumorigenesis and cancer progression. The need for quantitative analyses has led to a renaissance of optical instruments and imaging techniques. With the emergence of precision medicine, automated analysis of a constantly increasing number of cellular markers and their measurement in spatial context have become increasingly necessary to understand the molecular mechanisms that lead to different pathways of disease progression in individual patients. In this review, we summarize the joint effort that academia and industry have undertaken to establish methods and protocols for molecular profiling and immunophenotyping of cancer tissues for next-generation digital histopathology-which is characterized by the use of whole-slide imaging (brightfield, widefield fluorescence, confocal, multispectral, and/or multiplexing technologies) combined with state-of-the-art image cytometry and advanced methods for machine and deep learning.

Assessment of Immunological Features in Muscle-Invasive Bladder Cancer Prognosis Using Ensemble Learning

The clinical staging and prognosis of muscle-invasive bladder cancer (MIBC) routinely includes the assessment of patient tissue samples by a pathologist. Recent studies corroborate the importance of image analysis in identifying and quantifying immunological markers from tissue samples that can provide further insight into patient prognosis. In this paper, we apply multiplex immunofluorescence to MIBC tissue sections to capture whole-slide images and quantify potential prognostic markers related to lymphocytes, macrophages, tumour buds, and PD-L1. We propose a machine-learning-based approach for the prediction of 5 year prognosis with different combinations of image, clinical, and spatial features. An ensemble model comprising several functionally different models successfully stratifies MIBC patients into two risk groups with high statistical significance (p value < 1×10-5). Critical to improving MIBC survival rates, our method correctly classifies 71.4% of the patients who succumb to MIBC, which is significantly more than the 28.6% of the current clinical gold standard, the TNM staging system.

Detection of Programmed Cell Death Ligand 1 Expression in Lung Cancer Clinical Samples by an Automated Immunohistochemistry System

Programmed cell death 1 (PD-1) plays an important role in subsiding immune responses, in promoting self-tolerance through suppressing the activity of T-cells, and in promoting differentiation of regulatory T-cells. One of its ligands, programmed cell death ligand 1 (PD-L1) acts as a checkpoint regulator in immune cells and is also expressed in a wide range of cancer types. Anti-PD therapy modulates immune responses at the tumor site, targets tumor-induced immune defects, and repairs ongoing immune responses. Since drugs that target the PD-1/PD-L1 pathways became available as a cancer treatment, there is need for the use of different antibodies to detect the presence of these proteins in tumoral samples by immunohistochemistry or other assays. Because the detection of these antigens in tumor samples is highly clinically informative for guiding treatment decisions, especially to establish the aptness of a patient to receive anti-PD therapy, it is necessary to have a validation process that guaranties that the test results obtained when using antibodies against these proteins are specific, selective, reproducible, and conducive to quantification of antigen abundance in cancer tissue sections. Here we describe an automated immunohistochemistry staining procedure that can be applied for the validation of multiple anti-PD-L1 antibody clones when used for the staining of formalin-fixed, paraffin-embedded lung cancer tissue sections.

Tertiary lymphoid structures and B lymphocytes in cancer prognosis and response to immunotherapies

Tertiary lymphoid structures (TLS) are ectopic cellular aggregates that resemble secondary lymphoid organs in their composition and structural organization. In contrast to secondary lymphoid organs, TLS are not imprinted during embryogenesis but are formed in non-lymphoid tissues in response to local inflammation. TLS structures exhibiting a variable degree of maturation are found in solid tumors. They are composed of various immune cell types including dendritic cells and antigen-specific B and T lymphocytes, that together, actively drive the immune response against tumor development and progression. This review highlights the successive steps leading to tumor TLS formation and its association with clinical outcomes. We discuss the role played by tumor-infiltrating B lymphocytes and plasma cells, their prognostic value in solid tumors and immunotherapeutic responses and their potential for future targeting.