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

Automated Co-in Situ Hybridization and Immunofluorescence Using Archival Tumor Tissue

In situ hybridization (ISH) and immunohistochemistry (IHC) are valuable tools for molecular pathology and cancer research. Recent advances in multiplex technology, assay automation, and digital image analysis have enabled the development of co-ISH IHC or immunofluorescence (IF) methods, which allow researchers to simultaneously view and quantify expression of mRNA and protein within the preserved tissue spatial context. These data are vital to the study of the control of gene expression in the complex tumor microenvironment.

Neoadjuvant nivolumab or nivolumab plus ipilimumab in operable non-small cell lung cancer: the phase 2 randomized NEOSTAR trial

Ipilimumab improves clinical outcomes when combined with nivolumab in metastatic non-small cell lung cancer (NSCLC), but its efficacy and impact on the immune microenvironment in operable NSCLC remain unclear. We report the results of the phase 2 randomized NEOSTAR trial (NCT03158129) of neoadjuvant nivolumab or nivolumab + ipilimumab followed by surgery in 44 patients with operable NSCLC, using major pathologic response (MPR) as the primary endpoint. The MPR rate for each treatment arm was tested against historical controls of neoadjuvant chemotherapy. The nivolumab + ipilimumab arm met the prespecified primary endpoint threshold of 6 MPRs in 21 patients, achieving a 38% MPR rate (8/21). We observed a 22% MPR rate (5/23) in the nivolumab arm. In 37 patients resected on trial, nivolumab and nivolumab + ipilimumab produced MPR rates of 24% (5/21) and 50% (8/16), respectively. Compared with nivolumab, nivolumab + ipilimumab resulted in higher pathologic complete response rates (10% versus 38%), less viable tumor (median 50% versus 9%), and greater frequencies of effector, tissue-resident memory and effector memory T cells. Increased abundance of gut Ruminococcus and Akkermansia spp. was associated with MPR to dual therapy. Our data indicate that neoadjuvant nivolumab + ipilimumab-based therapy enhances pathologic responses, tumor immune infiltrates and immunologic memory, and merits further investigation in operable NSCLC.

Identification of distinct immune landscapes using an automated nine-color multiplex immunofluorescence staining panel and image analysis in paraffin tumor tissues

Immune profiling is becoming a vital tool for identifying predictive and prognostic markers for translational studies. The study of the tumor microenvironment (TME) in paraffin tumor tissues such as malignant pleural mesothelioma (MPM) could yield insights to actionable targets to improve patient outcome. Here, we optimized and tested a new immune-profiling method to characterize immune cell phenotypes in paraffin tissues and explore the co-localization and spatial distribution between the immune cells within the TME and the stromal or tumor compartments. Tonsil tissues and tissue microarray (TMA) were used to optimize an automated nine-color multiplex immunofluorescence (mIF) panel to study the TME using eight antibodies: PD-L1, PD-1, CD3, CD8, Foxp3, CD68, KI67, and pancytokeratin. To explore the potential role of the cells into the TME with this mIF panel we applied this panel in twelve MPM cases to assess the multiple cell phenotypes obtained from the image analysis and well as their spatial distribution in this cohort. We successful optimized and applied an automated nine-color mIF panel to explore a small set of MPM cases. Image analysis showed a high degree of cell phenotype diversity with immunosuppression patterns in the TME of the MPM cases. Mapping the geographic cell phenotype distribution in the TME, we were able to identify two distinct, complex immune landscapes characterized by specific patterns of cellular distribution as well as cell phenotype interactions with malignant cells. Successful we showed the optimization and reproducibility of our mIF panel and their incorporation for comprehensive TME immune profiling into translational studies that could refine our ability to correlate immunologic phenotypes with specific patterns of cells distribution and distance analysis. Overall, this will improve our ability to understand the behavior of cells within the TME and predict new treatment strategies to improve patient outcome.

The Prognostic Role of Macrophage Polarization in the Colorectal Cancer Microenvironment

Macrophages are among the most common cells in the colorectal cancer microenvironment, but their prognostic significance is incompletely understood. Using multiplexed immunofluorescence for CD68, CD86, IRF5, MAF, MRC1 (CD206), and KRT (cytokeratins) combined with digital image analysis and machine learning, we assessed the polarization spectrum of tumor-associated macrophages in 931 colorectal carcinomas. We then applied Cox proportional hazards regression to assess prognostic survival associations of intraepithelial and stromal densities of M1-like and M2-like macrophages while controlling for potential confounders, including stage and microsatellite instability status. We found that high tumor stromal density of M2-like macrophages was associated with worse cancer-specific survival, whereas tumor stromal density of M1-like macrophages was not significantly associated with better cancer-specific survival. High M1:M2 density ratio in tumor stroma was associated with better cancer-specific survival. Overall macrophage densities in tumor intraepithelial or stromal regions were not prognostic. These findings suggested that macrophage polarization state, rather than their overall density, was associated with cancer-specific survival, with M1- and M2-like macrophage phenotypes exhibiting distinct prognostic roles. These results highlight the utility of a multimarker strategy to assess the macrophage polarization at single-cell resolution within the tumor microenvironment.

Detection of Epstein-Barr Virus in Periodontitis: A Review of Methodological Approaches

Periodontitis, an inflammatory condition that affects the structures surrounding the tooth eventually leading to tooth loss, is one of the two biggest threats to oral health. Beyond oral health, it is associated with systemic diseases and even with cancer risk. Obviously, periodontitis represents a major global health problem with significant social and economic impact. Recently, a new paradigm was proposed in the etiopathogenesis of periodontitis involving a herpesviral–bacterial combination to promote long-term chronic inflammatory disease. Periodontitis as a risk factor for other systemic diseases can also be better explained based on viral–bacterial etiology. Significant efforts have brought numerous advances in revealing the links between periodontitis and Epstein–Barr virus (EBV), a gamma herpesvirus ubiquitous in the adult human population. The strong evidence from these studies may contribute to the advancement of periodontitis research and the ultimate control of the disease. Advancing the periodontitis research will require implementing suitable methods to establish EBV involvement in periodontitis. This review evaluates and summarizes the existing methods that allow the detection and diagnosis of EBV in periodontitis (also applicable in a more general way to other EBV-related diseases), and discusses the feasibility of the application of innovative emerging technologies.

Characterizing Immune Responses in Whole Slide Images of Cancer With Digital Pathology and Pathomics

Purpose of Review

Our goal is to show how readily available Pathomics tissue analytics can be used to study tumor immune interactions in cancer. We provide a brief overview of how Pathomics complements traditional histopathologic examination of cancer tissue samples. We highlight a novel Pathomics application, Tumor-TILs, that quantitatively measures and generates maps of tumor infiltrating lymphocytes in breast, pancreatic, and lung cancer by leveraging deep learning computer vision applications to perform automated analyses of whole slide images.

Recent Findings

Tumor-TIL maps have been generated to analyze WSIs from thousands of cases of breast, pancreatic, and lung cancer. We report the availability of these tools in an effort to promote collaborative research and motivate future development of ensemble Pathomics applications to discover novel biomarkers and perform a wide range of correlative clinicopathologic research in cancer immunopathology and beyond.

Summary

Tumor immune interactions in cancer are a fascinating aspect of cancer pathobiology with particular significance due to the emergence of immunotherapy. We present simple yet powerful specialized Pathomics methods that serve as powerful clinical research tools and potential standalone clinical screening tests to predict clinical outcomes and treatment responses for precision medicine applications in immunotherapy.

SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery

Since the publication of the Society for Immunotherapy of Cancer's (SITC) original cancer immunotherapy biomarkers resource document, there have been remarkable breakthroughs in cancer immunotherapy, in particular the development and approval of immune checkpoint inhibitors, engineered cellular therapies, and tumor vaccines to unleash antitumor immune activity. The most notable feature of these breakthroughs is the achievement of durable clinical responses in some patients, enabling long-term survival. These durable responses have been noted in tumor types that were not previously considered immunotherapy-sensitive, suggesting that all patients with cancer may have the potential to benefit from immunotherapy. However, a persistent challenge in the field is the fact that only a minority of patients respond to immunotherapy, especially those therapies that rely on endogenous immune activation such as checkpoint inhibitors and vaccination due to the complex and heterogeneous immune escape mechanisms which can develop in each patient. Therefore, the development of robust biomarkers for each immunotherapy strategy, enabling rational patient selection and the design of precise combination therapies, is key for the continued success and improvement of immunotherapy. In this document, we summarize and update established biomarkers, guidelines, and regulatory considerations for clinical immune biomarker development, discuss well-known and novel technologies for biomarker discovery and validation, and provide tools and resources that can be used by the biomarker research community to facilitate the continued development of immuno-oncology and aid in the goal of durable responses in all patients.

Neoadjuvant Chemotherapy Increases Cytotoxic T Cell, Tissue Resident Memory T Cell, and B Cell Infiltration in Resectable NSCLC

INTRODUCTION

The combination of programmed cell death protein-1 or programmed death-ligand 1 immune checkpoint blockade and chemotherapy has revolutionized the treatment of advanced NSCLC, but the mechanisms underlying this synergy remain incompletely understood. In this study, we explored the relationships between neoadjuvant chemotherapy and the immune microenvironment (IME) of resectable NSCLC to identify novel mechanisms by which chemotherapy may enhance the effect of immune checkpoint blockade.

METHODS

Genomic, transcriptomic, and immune profiling data of 511 patients treated with neoadjuvant chemotherapy followed by surgery (NCT) versus upfront surgery (US) were compared with determined differential characteristics of the IMEs derived from whole-exome sequencing (NCT = 18; US = 73), RNA microarray (NCT = 45; US = 202), flow cytometry (NCT = 17; US = 39), multiplex immunofluorescence (NCT = 10; US = 72), T-cell receptor sequencing (NCT = 16 and US = 63), and circulating cytokines (NCT = 18; US = 73).

RESULTS

NCT was associated with increased infiltration of cytotoxic CD8⁺ T cells and CD20⁺ B cells. Moreover, NCT was associated with increases in CD8⁺CD103⁺ and CD4⁺CD103⁺PD-1⁺TIM3^- tissue resident memory T cells. Gene expression profiling supported memory function of CD8⁺ and CD4⁺ T cells. However, NCT did not affect T-cell receptor clonality, richness, or tumor mutational burden. Finally, NCT was associated with decreased plasma BDNF (TrkB) at baseline and week 4 after surgery.

CONCLUSIONS

Our study supports that, in the context of resectable NSCLC, neoadjuvant chemotherapy promotes antitumor immunity through T and B cell recruitment in the IME and through a phenotypic change toward cytotoxic and memory CD8⁺ and CD4⁺ memory helper T cells.

Variants in Epithelial-Mesenchymal Transition and Immune Checkpoint Genes Are Associated With Immune Cell Profiles and Predict Survival in Non-Small Cell Lung Cancer

CONTEXT.—

Identification of gene mutations that are indicative of epithelial-mesenchymal transition and a noninflammatory immune phenotype may be important for predicting response to immune checkpoint inhibitors.

OBJECTIVE.—

To evaluate the utility of multiplex immunofluorescence for immune profiling and to determine the relationships among tumor immune checkpoint and epithelial-mesenchymal transition genomic profiles and the clinical outcomes of patients with nonmetastatic non-small cell lung cancer.

DESIGN.—

Tissue microarrays containing 164 primary tumor specimens from patients with stages I to IIIA non-small cell lung carcinoma were examined by multiplex immunofluorescence and image analysis to determine the expression of programmed death ligand-1 (PD-L1) on malignant cells, CD68+ macrophages, and cells expressing the immune markers CD3, CD8, CD57, CD45RO, FOXP3, PD-1, and CD20. Immune phenotype data were tested for correlations with clinicopathologic characteristics, somatic and germline genetic variants, and outcome.

RESULTS.—

A high percentage of PD-L1+ malignant cells was associated with clinicopathologic characteristics, and high density of CD3+PD-1+ T cells was associated with metastasis, suggesting that these phenotypes may be clinically useful to identify patients who will likely benefit from immunotherapy. We also found that ZEB2 mutations were a proxy for immunologic ignorance and immune tolerance microenvironments and may predict response to checkpoint inhibitors. A multivariate Cox regression model predicted a lower risk of death for patients with a high density of CD3+CD45RO+ memory T cells, carriers of allele G of CTLA4 variant rs231775, and those whose tumors do not have ZEB2 mutations.

CONCLUSIONS.—

Genetic variants in epithelial-mesenchymal transition and immune checkpoint genes are associated with immune cell profiles and may predict patient outcomes and response to immune checkpoint blockade.

Immune contexture analysis in immuno-oncology: applications and challenges of multiplex fluorescent immunohistochemistry

The tumor microenvironment is an integral player in cancer initiation, tumor progression, response and resistance to anti-cancer therapy. Understanding the complex interactions of tumor immune architecture (referred to as 'immune contexture') has therefore become increasingly desirable to guide our approach to patient selection, clinical trial design, combination therapies, and patient management. Quantitative image analysis based on multiplexed fluorescence immunohistochemistry and deep learning technologies are rapidly developing to enable researchers to interrogate complex information from the tumor microenvironment and find predictive insights into treatment response. Herein, we discuss current developments in multiplexed fluorescence immunohistochemistry for immune contexture analysis, and their application in immuno-oncology, and discuss challenges to effectively use this technology in clinical settings. We also present a multiplexed image analysis workflow to analyse fluorescence multiplexed stained tumor sections using the Vectra Automated Digital Pathology System together with FCS express flow cytometry software. The benefit of this strategy is that the spectral unmixing accurately generates and analyses complex arrays of multiple biomarkers, which can be helpful for diagnosis, risk stratification, and guiding clinical management of oncology patients.

The Pathogenesis of Tuberculosis-The Koch Phenomenon Reinstated

Research on the pathogenesis of tuberculosis (TB) has been hamstrung for half a century by the paradigm that granulomas are the hallmark of active disease. Human TB, in fact, produces two types of granulomas, neither of which is involved in the development of adult type or post-primary TB. This disease begins as the early lesion; a prolonged subclinical stockpiling of secreted mycobacterial antigens in foamy alveolar macrophages and nearby highly sensitized T cells in preparation for a massive necrotizing hypersensitivity reaction, the Koch Phenomenon, that produces caseous pneumonia that is either coughed out to form cavities or retained to become the focus of post-primary granulomas and fibrocaseous disease. Post-primary TB progresses if the antigens are continuously released and regresses when they are depleted. This revised paradigm is supported by nearly 200 years of research and suggests new approaches and animal models to investigate long standing mysteries of human TB and vaccines that inhibit the early lesion to finally end its transmission.

New insights into the interaction of the immune system with non-small cell lung carcinomas

The basis of current and future lung cancer immunotherapy depends mainly on our knowledge of the molecular mechanisms of interactions between cancer and immune cells (ICs), as well as on interactions occurring between the different populations of intra-tumor ICs. These interactions are very complex, as virtually all immune cell types, including macrophages, neutrophils, mast cells, natural killer (NK) cells, dendritic cells and T and B lymphocytes can infiltrate lung cancer tissues at the same time. Moreover these interactions lead to progressive emergence of an imbalance in ICs. Initially ICs have an anti-tumor effect but then induce immune tolerance and eventually tumor progression and dissemination. All the cells of innate and adaptive intra-tumor immunity engage in this progressive phenotypic switch. A majority of non-small cell lung carcinoma (NSCLC) patients do not benefit from the expected positive responses associated with current immunotherapy. Thus, there is urgent need to better understand the different roles of the associated cancer ICs. This review summarizes some of the new insights into this domain, with particular focus on: the myeloid cell population associated with tumors, the tertiary lymphoid structures (TLSs), the role of the P2 purinergic receptors (P2R) and ATP, and the new concept of the “liquid microenvironment” implying blood circulating ICs.

A robust multiplex immunofluorescence and digital pathology workflow for the characterisation of the tumour immune microenvironment

Multiplex immunofluorescence is a powerful tool for the simultaneous detection of tissue-based biomarkers, revolutionising traditional immunohistochemistry. The Opal methodology allows up to eight biomarkers to be measured concomitantly without cross-reactivity, permitting identification of different cell populations within the tumour microenvironment. In this study, we aimed to validate a multiplex immunofluorescence workflow in two complementary multiplex panels and evaluate the tumour immune microenvironment in colorectal cancer (CRC) formalin-fixed paraffin-embedded tissue. We stained CRC and tonsil samples using Opal multiplex immunofluorescence on a Leica BOND RX immunostainer. We then acquired images on an Akoya Vectra Polaris and performed multispectral unmixing using inform. Antibody panels were validated on tissue microarray sections containing cores from six normal tissue types, using qupath for image analysis. Comparisons between chromogenic immunohistochemistry and multiplex immunofluorescence on consecutive sections from the same tissue microarray showed significant correlation (rs  > 0.9, P-value < 0.0001), validating both panels. We identified many factors that influenced the quality of the acquired fluorescent images, including biomarker co-expression, staining order, Opal-antibody pairing, sample thickness, multispectral unmixing and biomarker detection order during image analysis. Overall, we report the optimisation and validation of a multiplex immunofluorescence process, from staining to image analysis, ensuring assay robustness. Our multiplex immunofluorescence protocols permit the accurate detection of multiple immune markers in various tissue types, using a workflow that enables rapid processing of samples, above and beyond previous workflows.

Tumor-Infiltrating Lymphocytes and Their Prognostic Value in Cutaneous Melanoma

Recent breakthroughs in tumor immunotherapy such as immune checkpoint blockade (ICB) antibodies, have demonstrated the capacity of the immune system to fight cancer in a number of malignancies such as melanoma and lung cancer. The numbers, localization and phenotypes of tumor-infiltrating lymphocytes (TIL) are not only predictive of response to immunotherapy but also key modulators of disease progression. In this review, we focus on TIL profiling in cutaneous melanoma using histopathological approaches and highlight the observed prognostic value of the primary TIL subsets. The quantification of TIL in formalin-fixed tumor samples ranges from visual scoring of lymphocytic infiltrates in H&E to multiplex immunohistochemistry and immunofluorescence followed by enumeration using image analysis software. Nevertheless, TIL enumeration in the current literature primarily relies upon single marker immunohistochemistry analyses of major lymphocyte subsets such as conventional T cells (CD3, CD4, CD8), regulatory T cells (FOXP3) and B cells (CD20). We review key studies in the literature on associations between TIL subsets and patient survival. We also cover recent findings with respect to the existence of ectopic lymphoid aggregates found in the TME which are termed tertiary lymphoid structures (TLS) and are generally a positive prognostic feature. In addition to their prognostic significance, the existence of various TIL sub-populations has also been reported to predict a patient's response to ICB. Thus, the literature on the predictive potential of TIL subsets in melanoma patients receiving ICB has also been discussed. Finally, we describe recently developed state-of-the-art profiling approaches for tumor infiltrating immune cells such as digital pathology scoring algorithms (e.g., Immunoscore) and multiplex proteomics-based immunophenotyping platforms (e.g., imaging mass cytometry). Translating these novel technologies have the potential to revolutionize tumor immunopathology leading to altering our current understanding of cancer immunology and dramatically improving outcomes for patients.

The coming 15 years in gynaecological pathology: digitisation, artificial intelligence, and new technologies

Surgical pathology forms the cornerstone of modern oncological medicine, owing to the wealth of clinically relevant information that can be obtained from tissue morphology. Although several ancillary testing modalities have been added to surgical pathology, the way in which we view and interpret tissue morphology has remained largely unchanged since the inception of our profession. In this review, we discuss new technological advances that promise to transform the way in which we access tissue morphology and how we use it to guide patient care.

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.

Genomic profile and immune contexture in colorectal cancer-relevance for prognosis and immunotherapy

Colorectal cancer (CRC) is one of the leading cancers in both genders. TNM staging system is still the most commonly used tumor classification and prognostic system. The disadvantage of TNM is that the prognostic information it provides is incomplete, and patients with the same histological tumor stages may differ significantly in the clinical outcome. Therefore, the identification of new prognostic parameters is crucial. The carcinogenic process that gives rise to an individual tumor is unique and tumor microenviroment should be taken into consideration. In CRC, T-cell infiltration is not homogenous, and recent studies are mostly focusing on memory T-cells and CD8 cells in predicting disease-free survival (DFS) and overall survival (OS). It seems that DFS and OS are not only dependent on microsatellite instable or stable status but mostly on the levels of expression of the immune signatures. Also, patients with high infiltration of cytotoxic and memory cells have significantly better outcome. This review consolidates current knowledge and recent research about importance of immune-cell-associated proteins, specific gene profiles of immune cells and immunotherapy in CRC. We also discussed cell-specific signatures in cancer treatment.

Advances in tissue-based imaging: impact on oncology research and clinical practice

INTRODUCTION

Tissue-based imaging has emerged as a critical tool in translational cancer research and is rapidly gaining traction within a clinical context. Significant progress has been made in the digital pathology arena, particularly in respect of brightfield and fluorescent imaging. Critically, the cellular context of molecular alterations occurring at DNA, RNA, or protein level within tumor tissue is now being more fully appreciated. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumor microenvironment, including the potential interplay between various cell types.

AREAS COVERED

This review summarizes the recent developments within the field of tissue-based imaging, centering on the application of these approaches in oncology research and clinical practice.

EXPERT OPINION

Significant advances have been made in digital pathology during the last 10 years. These include the use of quantitative image analysis algorithms, predictive artificial intelligence (AI) on large datasets of H&E images, and quantification of fluorescence multiplexed tissue imaging data. We believe that new methodologies that can integrate AI-derived histologic data with omic data, together with other forms of imaging data (such as radiologic image data), will enhance our ability to deliver better diagnostics and treatment decisions to the cancer patient.

Identification of non-cancer cells from cancer transcriptomic data

Interactions between cancer cells and non-cancer cells composing the tumour microenvironment play a primary role in determining cancer progression and shaping the response to therapy. The qualitative and quantitative characterisation of the different cell populations in the tumour microenvironment is therefore crucial to understand its role in cancer. In recent years, many experimental and computational approaches have been developed to identify the cell populations composing heterogeneous tissue samples, such as cancer. In this review, we describe the state-of-the-art approaches for the quantification of non-cancer cells from bulk and single-cell cancer transcriptomic data, with a focus on immune cells. We illustrate the main features of these approaches and highlight their applications for the analysis of the tumour microenvironment in solid cancers. We also discuss techniques that are complementary and alternative to RNA sequencing, particularly focusing on approaches that can provide spatial information on the distribution of the cells within the tumour in addition to their qualitative and quantitative measurements. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Digital spatial profiling application in breast cancer: a user's perspective

The disciplines of oncology and pathology are at present experiencing a wave of changes as precision medicine becomes embedded as standard-of-care. Consequently, the need to assess increasing numbers of biomarkers simultaneously has become more urgent and recognising the vast intra-tumoural heterogeneity, including within the microenvironment, requires a complex dimensional understanding of the localisation of the biomarker expression. Digital spatial profiling (DSP; nanoString™) technology spatially resolves and digitally quantifies proteins in a highly multiplexed assay, underpinned by the nCounter® barcoding platform. We present the application of this technology to breast cancer samples. Applying the 'off the shelf' cancer panel and a custom-conjugated E-cadherin antibody, we quantify vast intra-tumoural heterogeneity in immunological and tumour markers, and demonstrate a need for focussed selection of target cell populations. The technology offers enormous potential not only for making research advances but also for improving standard operating procedures in diagnostic applications.

A Novel Approach for Quantifying Cancer Cells Showing Hybrid Epithelial/Mesenchymal States in Large Series of Tissue Samples: Towards a New Prognostic Marker

In cancer biology, epithelial-to-mesenchymal transition (EMT) is associated with tumorigenesis, stemness, invasion, metastasis, and resistance to therapy. Evidence of co-expression of epithelial and mesenchymal markers suggests that EMT should be a stepwise process with distinct intermediate states rather than a binary switch. In the present study, we propose a morphological approach that enables the detection and quantification of cancer cells with hybrid E/M states, i.e., which combine partially epithelial (E) and partially mesenchymal (M) states. This approach is based on a sequential immunohistochemistry technique performed on the same tissue section, the digitization of whole slides, and image processing. The aim is to extract quantitative indicators able to quantify the presence of hybrid E/M states in large series of human cancer samples and to analyze their relationship with cancer aggressiveness. As a proof of concept, we applied our methodology to a series of about a hundred urothelial carcinomas and demonstrated that the presence of cancer cells with hybrid E/M phenotypes at the time of diagnosis is strongly associated with a poor prognostic value, independently of standard clinicopathological features. Although validation on a larger case series and other cancer types is required, our data support the hybrid E/M score as a promising prognostic biomarker for carcinoma patients.

Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy

Conventional immunohistochemistry (IHC) is a widely used diagnostic technique in tissue pathology. However, this technique is associated with a number of limitations, including high inter-observer variability and the capacity to label only one marker per tissue section. This review details various highly multiplexed techniques that have emerged to circumvent these constraints, allowing simultaneous detection of multiple markers on a single tissue section and the comprehensive study of cell composition, cellular functional and cell-cell interactions. Among these techniques, multiplex Immunohistochemistry/Immunofluorescence (mIHC/IF) has emerged to be particularly promising. mIHC/IF provides high-throughput multiplex staining and standardized quantitative analysis for highly reproducible, efficient and cost-effective tissue studies. This technique has immediate potential for translational research and clinical practice, particularly in the era of cancer immunotherapy.

Immunogradient Indicators for Antitumor Response Assessment by Automated Tumor-Stroma Interface Zone Detection

The distribution of tumor-infiltrating lymphocytes (TILs) within the tumor microenvironment provides strong prognostic value, which is increasingly important with the arrival of new immunotherapy modalities. Both visual and image analysis-based assays are developed to assess the immune contexture of the tumors. We propose an automated method based on grid subsampling of microscopy image analysis data to extract the tumor-stroma interface zone (IZ) of controlled width. The IZ is a ranking of tissue areas by their distance to the tumor edge, which is determined by a set of explicit rules. TIL density profiles across the IZ are used to compute a set of novel immunogradient indicators that reflect TIL gradient towards the tumor. We applied this method on CD8 immunohistochemistry images of surgically excised hormone receptor-positive breast and colorectal cancers to predict overall patient survival. In both cohorts, the immunogradient indicators enabled strong and independent prognostic stratification, outperforming clinical and pathologic variables. Patients with breast cancer with low immunogradient levels had a prominent decrease in survival probability 5 years after surgery. Our study provides proof of concept that data-driven, automated, operator-independent IZ sampling enables spatial immune response measurement in the tumor-host interaction frontline for prediction of disease outcomes.

Procedural Requirements and Recommendations for Multiplex Immunofluorescence Tyramide Signal Amplification Assays to Support Translational Oncology Studies

In the development of a multiplex immunofluorescence (IF) platform and the optimization and validation of new multiplex IF panels using a tyramide signal amplification system, several technical requirements are important for high-quality staining, analysis, and results. The aim of this review is to discuss the basic requirements for performing multiplex IF tyramide signal amplification (TSA) in formalin-fixed, paraffin-embedded cancer tissues to support translational oncology research. Our laboratory has stained approximately 4000 formalin-fixed, paraffin-embedded tumor samples using the multiplex IF TSA system for immune profiling of several labeled biomarkers in a single slide to elucidate cancer biology at a protein level and identify therapeutic targets and biomarkers. By analyzing several proteins in thousands of cells on a single slide, this technique provides a systems-level view of various processes in various tumor tissues. Although this technology shows high flexibility in cancer studies, it presents several challenges when applied to study different histology cancers. Our experience shows that adequate antibody validation, staining optimization, analysis strategies, and data generation are important steps for generating quality results. Tissue management, fixation procedures, storage, and cutting can also affect the results of the assay and must be standardized. Overall, this method is reliable for supporting translational research given a precise, step-by-step approach.

Digital image analysis of multiplex fluorescence IHC in colorectal cancer recognizes the prognostic value of CDX2 and its negative correlation with SOX2

Flourescence-based multiplex immunohistochemistry (mIHC) combined with multispectral imaging and digital image analysis (DIA) is a quantitative high-resolution method for determination of protein expression in tissue. We applied this method for five biomarkers (CDX2, SOX2, SOX9, E-cadherin, and β-catenin) using tissue microarrays of a Norwegian unselected series of primary colorectal cancer. The data were compared with previously obtained chromogenic IHC data of the same tissue cores, visually assessed by the Allred method. We found comparable results between the methods, although confirmed that DIA offered improved resolution to differentiate cases with high and low protein expression. However, we experienced inherent challenges with digital image analysis of membrane staining, which was better assessed visually. DIA and mIHC enabled quantitative analysis of biomarker coexpression on the same tissue section at the single-cell level, revealing a strong negative correlation between the differentiation markers CDX2 and SOX2. Both methods confirmed known prognostic associations for CDX2, but DIA improved data visualization and detection of clinicopathological and biological associations. In summary, mIHC combined with DIA is an efficient and reliable method to evaluate protein expression in tissue, here shown to recapitulate and improve detection of known clinicopathological and survival associations for the emerging biomarker CDX2, and is therefore a candidate approach to standardize CDX2 detection in pathology laboratories.

New Technologies to Image Tumors

The premise of this book is the importance of the tumor microenvironment (TME). Until recently, most research on and clinical attention to cancer biology, diagnosis, and prognosis were focused on the malignant (or premalignant) cellular compartment that could be readily appreciated using standard morphology-based imaging.

The Cancer Immunotherapy Biomarker Testing Landscape

CONTEXT.—

Cancer immunotherapy provides unprecedented rates of durable clinical benefit to late-stage cancer patients across many tumor types, but there remains a critical need for biomarkers to accurately predict clinical response. Although some cancer immunotherapy tests are associated with approved therapies and considered validated, other biomarkers are still emerging and at various states of clinical and translational exploration.

OBJECTIVE.—

To provide pathologists with a current and practical update on the evolving field of cancer immunotherapy testing. The scientific background, clinical data, and testing methodology for the following cancer immunotherapy biomarkers are reviewed: programmed death ligand-1 (PD-L1), mismatch repair, microsatellite instability, tumor mutational burden, polymerase δ and ε mutations, cancer neoantigens, tumor-infiltrating lymphocytes, transcriptional signatures of immune responsiveness, cancer immunotherapy resistance biomarkers, and the microbiome.

DATA SOURCES.—

Selected scientific publications and clinical trial data representing the current field of cancer immunotherapy.

CONCLUSIONS.—

The cancer immunotherapy field, including the use of biomarker testing to predict patient response, is still in evolution. PD-L1, mismatch repair, and microsatellite instability testing are helping to guide the use of US Food and Drug Administration-approved therapies, but there remains a need for better predictors of response and resistance. Several categories of tumor and patient characteristics underlying immune responsiveness are emerging and may represent the next generation of cancer immunotherapy predictive biomarkers. Pathologists have important roles and responsibilities as the field of cancer immunotherapy continues to develop, including leadership of translational studies, exploration of novel biomarkers, and the accurate and timely implementation of newly approved and validated companion diagnostics.

Single Cell Phenotypic Profiling of 27 DLBCL Cases Reveals Marked Intertumoral and Intratumoral Heterogeneity

Diffuse large B-cell lymphoma (DLBCL) is the most common histologic subtype of non-Hodgkin lymphoma and is notorious for its clinical heterogeneity. Patient outcomes can be predicted by cell-of-origin (COO) classification, demonstrating that the underlying transcriptional signature of malignant B-cells informs biological behavior in the context of standard combination chemotherapy regimens. In the current study, we used mass cytometry (CyTOF) to examine tumor phenotypes at the protein level with single cell resolution in a collection of 27 diagnostic DLBCL biopsy specimens from treatment naïve patients. We found that malignant B-cells from each patient occupied unique regions in 37-dimensional phenotypic space with no apparent clustering of samples into discrete subtypes. Interestingly, variable MHC class II expression was found to be the greatest contributor to phenotypic diversity. Within individual tumors, a subset of cases showed multiple phenotypic subpopulations, and in one case, we were able to demonstrate direct correspondence between protein-level phenotypic subsets and DNA mutation-defined subclones. In summary, CyTOF analysis can resolve both intertumoral and intratumoral heterogeneity among primary samples and reveals that each case of DLBCL is unique and may be comprised of multiple, genetically distinct subclones. © 2019 International Society for Advancement of Cytometry.

Optimisation of multiplex immunofluorescence for a non-spectral fluorescence scanning system

The use of multi-colour immunofluorescence (IF) for immunophenotyping in formalin-fixed paraffin-embedded tissue sections is gaining popularity worldwide. This technique allows for the simultaneous detection of multiple markers on the same tissue section, thereby yielding more complex information than is possible by chromogenic immunohistochemistry (IHC). However, many commercially-available multiplex IF kits are designed for use in conjunction with a multispectral imaging system, to which many research groups have limited access. Here we present two 5-colour IF panels designed for T cell characterisation in human colorectal tissue, which can be imaged using a non-spectral fluorescence slide scanner with standard band-pass filters. We describe the optimisation process and the key considerations in developing a multiplex fluorescence assay, and discuss some of the advantages and disadvantages of using multiplex IF with a non-spectral imaging system.

Multiplexed Immunohistochemistry for Molecular and Immune Profiling in Lung Cancer-Just About Ready for Prime-Time?

As targeted molecular therapies and immuno-oncology have become pivotal in the management of patients with lung cancer, the essential requirement for high throughput analyses and clinical validation of biomarkers has become even more intense, with response rates maintained in the 20%–30% range. Moreover, the list of treatment alternatives, including combination therapies, is rapidly evolving. The molecular profiling and specific tumor-associated immune contexture may be predictive of response or resistance to these therapeutic strategies. Multiplexed immunohistochemistry is an effective and proficient approach to simultaneously identify specific proteins or molecular abnormalities, to determine the spatial distribution and activation state of immune cells, as well as the presence of immunoactive molecular expression. This method is highly advantageous for investigating immune evasion mechanisms and discovering potential biomarkers to assess mechanisms of action and to predict response to a given treatment. This review provides views on the current technological status and evidence for clinical applications of multiplexing and how it could be applied to optimize clinical management of patients with lung cancer.