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

Tumor infiltrating lymphocytes as an endpoint in cancer vaccine trials

Checkpoint inhibitors have invigorated cancer immunotherapy research, including cancer vaccination. Classic early phase trial design and endpoints used in developing chemotherapy are not suited for evaluating all forms of cancer treatment. Peripheral T cell response dynamics have demonstrated inconsistency in assessing the efficacy of cancer vaccination. Tumor infiltrating lymphocytes (TILs), reflect the local tumor microenvironment and may prove a superior endpoint in cancer vaccination trials. Cancer vaccines may also promote success in combination immunotherapy treatment of weakly immunogenic tumors. This review explores the impact of TILs as an endpoint for cancer vaccination in multiple malignancies, summarizes the current literature regarding TILs analysis, and discusses the challenges of providing validity and a standardized implementation of this approach.

Detection of Ferroptosis by Immunohistochemistry and Immunofluorescence

Ferroptosis is a type of regulated cell death driven by oxidative damage, characterized by iron overload and lipid peroxidation, and regulated by a network of distinct molecules and organelles. Impaired ferroptotic response is implicated in multiple physiological and pathological processes, including tumorigenesis, neurodegeneration, and ischemia-reperfusion damage. Classical techniques of immunohistochemistry (IHC) and immunofluorescence (IF) can be employed to exhibit antigen expression and location in tissues observed with microscopy, making them powerful tools in studying the ferroptosis process. In this chapter, we introduce commonly used protocols and summarize typical markers used in IHC and IF to monitor ferroptosis.

Combination of Tissue Microarray Profiling and Multiplexed IHC Approaches to Investigate Transport Mechanism of Nucleoside Analog Drug Resistance

Nucleoside analogs (NAs) are an established class of anticancer agents being used clinically for the treatment of diverse cancers, either as monotherapy or in combination with other established anticancer or pharmacological agents. To date, nearly a dozen anticancer NAs are approved by the FDA, and several novel NAs are being tested in preclinical and clinical trials for future applications. However, improper delivery of NAs into tumor cells because of alterations in expression of one or more drug carrier proteins (e.g., solute carrier (SLC) transporters) within tumor cells or cells surrounding the tumor microenvironment stands as one of the primary reasons for therapeutic drug resistance. The combination of tissue microarray (TMA) and multiplexed immunohistochemistry (IHC) is an advanced, high-throughput approach over conventional IHC that enables researchers to effectively investigate alterations to numerous such chemosensitivity determinants simultaneously in hundreds of tumor tissues derived from patients. In this chapter, taking an example of a TMA from pancreatic cancer patients treated with gemcitabine (a NA chemotherapeutic agent), we describe the step-by-step procedure of performing multiplexed IHC, imaging of TMA slides, and quantification of expression of some relevant markers in these tissue sections as optimized in our laboratory and discuss considerations while designing and carrying out this experiment.

Prediction for recurrent non-muscle invasive bladder cancer

Non-muscle invasive bladder cancer (NMIBC) has a high recurrence rate, which places a significant burden on both patients and the healthcare system. Hence, it holds significant importance to predict the recurrence risk following treatment for individuals diagnosed with non-muscle invasive bladder cancer (NMIBC). As new generation technologies continue to emerge, an increasing number of recurrence risk prediction tools are being developed and discovered. This article provides an overview of the primary recurrence risk prediction tools currently available, including the liquid biopsy, tissue biopsy, and risk prediction tables. Each of these tools is described in detail and illustrated with relevant examples. Furthermore, we conduct an analysis of the advantages and disadvantages of these tools. This article aims to enhance the reader's understanding of the current progress in recurrence prediction tools and encourage their practical utilization in the fields of precision medicine and public health.

Development of an automated combined positive score prediction pipeline using artificial intelligence on multiplexed immunofluorescence images

Immunotherapy targeting immune checkpoint proteins, such as programmed cell death ligand 1 (PD-L1), has shown impressive outcomes in many clinical trials but only 20%-40% of patients benefit from it. Utilizing Combined Positive Score (CPS) to evaluate PD-L1 expression in tumour biopsies to identify patients with the highest likelihood of responsiveness to anti-PD-1/PD-L1 therapy has been approved by the Food and Drug Administration for several solid tumour types. Current CPS workflow requires a pathologist to manually score the two-colour PD-L1 chromogenic immunohistochemistry image. Multiplex immunofluorescence (mIF) imaging reveals the expression of an increased number of immune markers in tumour biopsies and has been used extensively in immunotherapy research. Recent rapid progress of Artificial Intelligence (AI)-based imaging analysis, particularly Deep Learning, provides cost effective and high-quality solutions to healthcare. In this article, we propose an imaging pipeline that utilizes three-colour mIF images (DAPI, PD-L1, and Pan-cytokeratin) as input and predicts the CPS using AI techniques. Our novel pipeline is composed of three modules employing algorithms of image processing, machine learning, and deep learning techniques. The first module of quality check (QC) detects and removes the image regions contaminated with sectioning and staining artefacts. The QC module ensures that only image regions free of the three common artefacts are used for downstream analysis. The second module of nuclear segmentation uses deep learning to segment and count nuclei in the DAPI images wherein our specialized method can accurately separate touching nuclei. The third module of cell phenotyping calculates CPS by identifying and counting PD-L1 positive cells and tumour cells. These modules are data-efficient and require only few manual annotations for training purposes. Using tumour biopsies from a clinical trial, we found that the CPS from the AI-based models shows a high Spearman correlation (78%, p = 0.003) to the pathologist-scored CPS.

Multiplex Fluorescent Immunohistochemistry for Preservation of Tumor Microenvironment Architecture and Spatial Relationship of Cells in Tumor Tissues

The tumor microenvironment (TME), composed of immune cells, antigens, and local soluble factors, is integral to cancer development and progression. Traditional techniques such as immunohistochemistry, immunofluorescence, or flow cytometry limit the analysis of spatial data and cellular interactions within the TME, as they are restricted to colocalization of a small number of antigens or the loss of tissue architecture. Multiplex fluorescent immunohistochemistry (mfIHC) allows for detection of multiple antigens within a single tissue sample, providing a more comprehensive description of tissue composition and spatial interactions within the TME. This technique utilizes antigen retrieval, application of primary and secondary antibodies, followed by a tyramide-based chemical reaction to covalently bind a fluorophore to an epitope of interest and, eventually, stripping of the antibodies. This allows for multiple rounds of antibody application without concern for species cross-reactivity, as well as signal amplification which abrogates the autofluorescence that frequently plagues analysis of fixed tissues. As such, mfIHC can be used to quantify multiple cellular populations and their interactions, in situ, unlocking key biologic data that was previously unavailable. This chapter provides an overview of the experimental design, staining, and imaging strategies using a manual technique in formalin-fixed paraffin-embedded tissue sections.

Spatially Characterizing the Immune Contexture in Mouse Tissue Using Multiplex Immunohistochemistry

Multiplex immunohistochemistry (mIHC) facilitates the simultaneous detection of various immune cell markers on a single tissue section. Here, we describe a protocol for an mIHC staining workflow using specific antibodies against CD4, CD8α, FOXP3, and B220 to identify distinct lymphocyte populations including T and B cells. This staining strategy can be adapted to include other cell markers to evaluate the immune contexture in murine tissues.

In-depth mapping of protein localizations in whole tissue by micro-scaffold assisted spatial proteomics (MASP)

Accurate, in-depth mapping of proteins on whole-tissue levels provides comprehensive insights into the spatially-organized regulatory processes/networks in tissues, but is challenging. Here we describe a micro-scaffold assisted spatial proteomics (MASP) strategy, based on spatially-resolved micro-compartmentalization of tissue using a 3D-printed micro-scaffold, capable of mapping thousands of proteins across a whole-tissue slice with excellent quantitative accuracy/precision. The pipeline includes robust tissue micro-compartmentalization with precisely-preserved spatial information, reproducible procurement and preparation of the micro-specimens, followed by sensitive LC-MS analysis and map generation by a MAsP app. The mapping accuracy was validated by comparing the MASP-generated maps of spiked-in peptides and brain-region-specific markers with known patterns, and by correlating the maps of the two protein components of the same heterodimer. The MASP was applied in mapping >5000 cerebral proteins in the mouse brain, encompassing numerous important brain markers, regulators, and transporters, where many of these proteins had not previously been mapped on the whole-tissue level.

Initial characterization of immune microenvironment in pheochromocytoma and paraganglioma

Due to fewer adverse events, faster onset of action, and longer durable responses compared to chemotherapy, immunotherapy has been widely used to treat advanced solid tumors. Moreover, immunotherapy can improve the autoimmune status, thus allowing patients to benefit from the treatment in the long term. The immune microenvironment status is closely associated with the response to chemotherapies. Here, we analyzed the characteristics of the immune microenvironment in pheochromocytoma and paraganglioma (PPGL). Immunohistochemistry showed that PD-L1 is sparely expressed in PPGL with low positive rates and low expression levels, an expression pattern, that is, not correlated with tumor malignancy. Moreover, the level of intratumoral CD4⁺ and CD8⁺ lymphocyte infiltration in PPGL is low, suggesting that the immune microenvironment in PPGL may be in "immune desertification" or "immune rejection" states in which CD4⁺ and CD8⁺ lymphocyte infiltration is prevented, rendering immunotherapy less effective. In sum, our results indicate that PPGL is a microsatellite-stable tumor with low tumor mutational burden (TMB) levels, weak neoantigen production, and poor tumor antigenicity, hinting at a poor response of PPGL to chemotherapies.

Computer-assisted tumor grading, validation of PD-L1 scoring, and quantification of CD8-positive immune cell density in urothelial carcinoma, a visual guide for pathologists using QuPath

Background

Advances in digital imaging in pathology and the new capacity to scan high-quality images have change the way to practice and research in surgical pathology. QuPath is an open-source pathology software that offers a reproducible way to analyze quantified variables. We aimed to present the functionality of biomarker scoring using QuPath and provide a guide for the validation of pathologic grading using a series of cases of urothelial carcinomas.

Methods

Tissue microarrays of urothelial carcinomas were constructed and scanned. The images stained with HE, CD8 and PD-L1 immunohistochemistry were imported into QuPath and dearrayed. Training images were used to build a grade classifier and applied to all cases. Quantification of CD8 and PD-L1 was undertaken for each core using cytoplasmic and membrane color segmentation and output measurement and compared with pathologists semi-quantitative assessments.

Results

There was a good correlation between tumor grade by the pathologist and by QuPath software (Kappa agreement 0.73). For low-grade carcinomas (by the report and pathologist), the concordance was not as high. Of the 32 low-grade tumors, 22 were correctly classified as low-grade, but 11 (34%) were diagnosed as high-grade, with the high-grade to the low-grade ratio in these misclassified cases ranging from 0.41 to 0.58. The median ratio for bona fide high-grade carcinomas was 0.59. Some of the reasons the authors list as potential mimickers for high-grade cases are fulguration artifact, nuclear hyperchromasia, folded tissues, and inconsistency in staining. The correlation analysis between the software and the pathologist showed that the CD8 marker showed a moderate (r = 0.595) and statistically significant (p < 0.001) correlation. The internal consistency of this parameter showed an index of 0.470. The correlation analysis between the software and the pathologist showed that the PDL1 marker showed a robust (r = 0.834) and significant (p < 0.001) correlation. The internal consistency of this parameter showed a CCI of 0.851.

Conclusions

We were able to demonstrate the utility of QuPath in identifying and scoring tumor cells and IHC quantification of two biomarkers. The protocol we present uses a free open-source platform to help researchers deal with imaging and data processing in the surgical pathology field.

Lymphocyte Classification from Hoechst Stained Slides with Deep Learning

Multiplex immunofluorescence and immunohistochemistry benefit patients by allowing cancer pathologists to identify proteins expressed on the surface of cells. This enables cell classification, better understanding of the tumour microenvironment, and more accurate diagnoses, prognoses, and tailored immunotherapy based on the immune status of individual patients. However, these techniques are expensive. They are time consuming processes which require complex staining and imaging techniques by expert technicians. Hoechst staining is far cheaper and easier to perform, but is not typically used as it binds to DNA rather than to the proteins targeted by immunofluorescence techniques. In this work we show that through the use of deep learning it is possible to identify an immune cell subtype without immunofluorescence. We train a deep convolutional neural network to identify cells expressing the T lymphocyte marker CD3 from Hoechst 33342 stained tissue only. CD3 expressing cells are often used in key prognostic metrics such as assessment of immune cell infiltration, and by identifying them without the need for costly immunofluorescence, we present a promising new approach to cheaper prediction and improvement of patient outcomes. We also show that by using deep learning interpretability techniques, we can gain insight into the previously unknown morphological features which make this possible.

Diabetes mellitus impacts on expression of DNA mismatch repair protein PMS2 and tumor microenvironment in pancreatic ductal adenocarcinoma

AIMS/INTRODUCTION

The mismatch repair (MMR) protein recognizes DNA replication errors and plays an important role in tumorigenesis, including pancreatic ductal adenocarcinoma (PDAC). Although PMS2, a MMR protein, is degraded under oxidative stress, the effects of diabetes are still unclear. Herein, we focused on whether diabetes affected MMR protein expression in PDAC.

MATERIALS AND METHODS

Tissues from 61 surgically resected PDAC subjects were clinicopathologically analyzed. Immunohistochemical analysis was performed for MMR protein expression, oxidative stress, and immune cell infiltration. The change of MMR protein expression was assessed in PDAC cell lines under stimulation with 25 mM glucose and 500 μM palmitic acid. Survival curves were analyzed by the Kaplan-Meier method with the log-rank test.

RESULTS

Diabetes complicated with dyslipidemia significantly decreased the expression of PMS2 in PDAC tissues with an inverse correlation with the degree of oxidative stress. Palmitic acid combined with high glucose induced degradation of PMS2 protein, enhancing oxidative stress in vitro. CD8⁺ T-cell infiltration was associated with a short duration of type 2 diabetes (≤4 years) and a low expression of PMS2 in PDAC tissues, while CD163⁺ tumor-associated macrophage infiltration was increased with a long duration of diabetes (>4 years). A short duration of diabetes exhibited a better prognosis than nondiabetic subjects with PDAC (P < 0.05), while a long duration of diabetes had a worse prognosis (P < 0.05).

CONCLUSIONS

The different phases of diabetes have a major impact on PDAC by altering PMS2 expression and the tumor immune microenvironment, which can be targeted by an immune checkpoint inhibitor.

Tumor immune microenvironment in endometrial cancer of different molecular subtypes: evidence from a retrospective observational study

Objective

Tumor immune microenvironmental features may predict survival and guide treatment. This study aimed to comprehensively decipher the immunological features of different molecular subtypes of endometrial cancer.

Methods

In this retrospective study, 26 patients with primary endometrial cancer and four with recurrent disease treated in our center from December 2018 to November 2021 were included. Next-generation sequencing was performed on tumor samples. Patients were classified into four subtypes, including POLE mutant, microsatellite instability high (MSI-H), no specific molecular profile (NSMP) and TP53 mutant subtypes. Tumor-infiltrating immune cells were quantified using multiplex immunofluorescence assays.

Results

Of the 26 primary endometrial cancer cases, three were POLE mutant, six were MSI-H, eight were NSMP and nine were TP53 mutant. Of the four recurrent cases, two belonged to the NSMP subtype and two belonged to the TP53 mutant subtype. The tumor mutation burden (TMB) levels of POLE mutant and MSI-H cases were significantly higher than that of the other two subtypes (p< 0.001). We combined POLE mutant and MSI-H subtypes into the TMB high (TMB-H) subtype. The TMB-H subtype showed a high degree of infiltration of CD8⁺ T cells. In the NSMP subtype, the overall degree of intra-tumoral infiltrating immune cells was low. In the TP53 mutant subtype, the densities of both PD-L1⁺ macrophages (p = 0.047) and PD-1⁺ T cells (p = 0.034) in tumor parenchyma were the highest among the four subtypes.

Conclusion

Endometrial cancer of TMB-H, NSMP and TP53 mutant subtypes displayed phenotypes of normal immune response, absence of immune infiltration, and suppressed immune response, respectively. These features may provide mechanistic explanations for the differences in patients' prognosis and efficacy of immune checkpoint blockade therapies among different endometrial cancer subtypes.

Advances in CAR T cell immunotherapy for paediatric brain tumours

Brain tumours are the most common solid tumour in children and the leading cause of cancer related death in children. Current treatments include surgery, chemotherapy and radiotherapy. The need for aggressive treatment means many survivors are left with permanent severe disability, physical, intellectual and social. Recent progress in immunotherapy, including genetically engineered T cells with chimeric antigen receptors (CARs) for treating cancer, may provide new avenues to improved outcomes for patients with paediatric brain cancer. In this review we discuss advances in CAR T cell immunotherapy, the major CAR T cell targets that are in clinical and pre-clinical development with a focus on paediatric brain tumours, the paediatric brain tumour microenvironment and strategies used to improve CAR T cell therapy for paediatric tumours.

Immunology of Oral Squamous Cell Carcinoma-A Comprehensive Insight with Recent Concepts

This review aims to understand the concept of oral cancer immunology through the notion of immune profiling, immunoediting and immunotherapy, and to gain knowledge regarding its application for the management of oral cancer patients. Oral cancer is an immunogenic tumor where the cells of the tumor microenvironment play an important role in tumorigenesis. Understanding the mechanism of these modulations can help design immunotherapeutic strategies in oral cancer patients. This article gives an overview of immunomodulation in the oral cancer tumor microenvironment, with concepts of immune profiling, immunoediting and immunotherapy. English literature searches via Google Scholar, Web of Science, EBSCO, Scopus, and PubMed database were performed with the key words immunology, tumor microenvironment, cells, cross talk, immune profiling, biomarkers, inflammation, gene expression, techniques, immunoediting, immunosurveillance, tumor escape, immunotherapy, immune checkpoint inhibitors, vaccines in cancer, oral cancer, and head and neck cancer. Original research articles, reviews, and case reports published from 2016-2021 (n = 81) were included to appraise different topics, and were discussed under the following subsections. Literature published on oral cancer immunology reveals that oral cancer immune profiling with appropriate markers and techniques and knowledge on immunoediting concepts can help design and play an effective role in immunotherapeutic management of oral cancer patients. An evaluation of oral cancer immunology helps to determine its role in tumorigenesis, and immunotherapy could be the emerging drift in the effective management of oral cancer.

Pooled genetic screens with image‐based profiling

Spatial structure in biology, spanning molecular, organellular, cellular, tissue, and organismal scales, is encoded through a combination of genetic and epigenetic factors in individual cells. Microscopy remains the most direct approach to exploring the intricate spatial complexity defining biological systems and the structured dynamic responses of these systems to perturbations. Genetic screens with deep single‐cell profiling via image features or gene expression programs have the capacity to show how biological systems work in detail by cataloging many cellular phenotypes with one experimental assay. Microscopy‐based cellular profiling provides information complementary to next‐generation sequencing (NGS) profiling and has only recently become compatible with large‐scale genetic screens. Optical screening now offers the scale needed for systematic characterization and is poised for further scale‐up. We discuss how these methodologies, together with emerging technologies for genetic perturbation and microscopy‐based multiplexed molecular phenotyping, are powering new approaches to reveal genotype–phenotype relationships.

Cell2Grid: an efficient, spatial, and convolutional neural network-ready representation of cell segmentation data

Purpose

Cell segmentation algorithms are commonly used to analyze large histologic images as they facilitate interpretation, but on the other hand they complicate hypothesis-free spatial analysis. Therefore, many applications train convolutional neural networks (CNNs) on high-resolution images that resolve individual cells instead, but their practical application is severely limited by computational resources. In this work, we propose and investigate an alternative spatial data representation based on cell segmentation data for direct training of CNNs.

Approach

We introduce and analyze the properties of Cell2Grid, an algorithm that generates compact images from cell segmentation data by placing individual cells into a low-resolution grid and resolves possible cell conflicts. For evaluation, we present a case study on colorectal cancer relapse prediction using fluorescent multiplex immunohistochemistry images.

Results

We could generate Cell2Grid images at 5 - μ m resolution that were 100 times smaller than the original ones. Cell features, such as phenotype counts and nearest-neighbor cell distances, remain similar to those of original cell segmentation tables ( p < 0.0001 ). These images could be directly fed to a CNN for predicting colon cancer relapse. Our experiments showed that test set error rate was reduced by 25% compared with CNNs trained on images rescaled to 5 μ m with bilinear interpolation. Compared with images at 1 - μ m resolution (bilinear rescaling), our method reduced CNN training time by 85%.

Conclusions

Cell2Grid is an efficient spatial data representation algorithm that enables the use of conventional CNNs on cell segmentation data. Its cell-based representation additionally opens a door for simplified model interpretation and synthetic image generation.

Systematic replication enables normalization of high-throughput imaging assays

MOTIVATION

High-throughput fluorescent microscopy is a popular class of techniques for studying tissues and cells through automated imaging and feature extraction of hundreds to thousands of samples. Like other high-throughput assays, these approaches can suffer from unwanted noise and technical artifacts that obscure the biological signal. In this work, we consider how an experimental design incorporating multiple levels of replication enables the removal of technical artifacts from such image-based platforms.

RESULTS

We develop a general approach to remove technical artifacts from high-throughput image data that leverages an experimental design with multiple levels of replication. To illustrate the methods, we consider microenvironment microarrays (MEMAs), a high-throughput platform designed to study cellular responses to microenvironmental perturbations. In application to MEMAs, our approach removes unwanted spatial artifacts and thereby enhances the biological signal. This approach has broad applicability to diverse biological assays.

AVAILABILITY AND IMPLEMENTATION

Raw data are on synapse (syn2862345), analysis code is on github: gjhunt/mema_norm, a reproducible Docker image is available on dockerhub: gjhunt/mema_norm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Immune-related histologic phenotype in pretreatment tumour biopsy predicts the efficacy of neoadjuvant anti-PD-1 treatment in squamous lung cancer

BACKGROUND

Although neoadjuvant anti-PD-1 immunotherapies have shown good efficacy in non-small cell lung cancer (NSCLC) patients, there is still a lack of effective predictive markers. We aimed to develop a pretreatment histologic scoring system to predict the efficacy of neoadjuvant immunotherapy.

METHODS

One hundred forty NSCLC cases were evaluated in this study. Initially, surgical specimens from 31 squamous cell lung cancer patients treated with neoadjuvant anti-PD-1 therapy and their eligible paired pretreatment biopsies were used for pathologic evaluation and developing the pretreatment scoring system, immune-related histologic phenotype assessment criteria (irHPC). Three trained pathologists independently scored the haematoxylin-eosin (HE) slides of the pretreatment tumour biopsies according to irHPC. The follow-up was from 07 March 2018 to 31 December 2021, mainly focusing on disease-free survival (DFS) and overall survival (OS). Second, 109 biopsies of lung squamous cell carcinoma were evaluated to explore the relationship between eosinophils and PD-L1 expression.

RESULTS

Superior 2-year DFS rates and 2-year OS rates were observed in patients who achieved major pathologic response (MPR) (MPR vs. non-MPR: 92.9% vs. 78.6%; 100.0% vs. 93.3%). Whether necrosis was included in the calculation of the per cent of residual viable tumour (%RVT) or not had almost no effect on the consistency of pathologic assessment and the histological response grouping. The interpathologist variability in assessing %RVT with immune-activated phenotype was not statistically significant (P = 0.480). Four immune-related features of pretreatment biopsies were included for calculating the predictive score. The trained pathologist accurately predicted most cases according to irHPC. For interobserver reproducibility using "2 points" as the cutoff, the overall per cent agreement was 77.8%. The reliability between pathologists for a binary tumour evaluation showed "moderate" agreement (κ = 0.54). Patients with scores ≥ 2 points tended to have better 2-year DFS rates and 2-year OS rates than those with scores < 2 points (85.7% vs. 71.4%; 100.0% vs. 87.5%).

CONCLUSIONS

The irHPC scoring system reflecting the preexisting immune response could be used to predict pathologic response to neoadjuvant immunotherapy, possibly further predicting the long-term prognosis, but larger trials are needed for verification.

Development of Anti-LRRC15 Small Fragments for Imaging Purposes Using a Phage-Display ScFv Approach

The human leucine-rich repeat-containing protein 15 (LRRC15) is a membrane protein identified as a marker of CAF (cancer-associated fibroblast) cells whose overexpression is positively correlated with cancer grade and outcome. Nuclear molecular imaging (i.e., SPECT and PET) to track LRRC15 expression could be very useful in guiding further therapeutic strategies. In this study, we developed an ScFv mouse phage-display library to obtain small fragment antibodies against human LRRC15 for molecular imaging purposes. Mice were immunized with recombinant human LRRC15 (hLRRC15), and lymph node cells were harvested for ScFv (single-chain variable fragment) phage-display analysis. The built library was used for panning on cell lines with constitutive or induced expression after transfection. The choice of best candidates was performed by screening various other cell lines, using flow cytometry. The selected candidates were reformatted into Cys-ScFv or Cys-diabody by addition of cysteine, and cloned in mammalian expression vectors to obtain batches of small fragments that were further used in site-specific radiolabeling tests. The obtained library was 1.2 × 107 cfu/µg with an insertion rate >95%. The two panning rounds performed on cells permittedenrichment of 2 × 10−3. Screening with flow cytometry allowed us to identify 28 specific hLRRC15 candidates. Among these, two also recognized murine LRCC15 and were reformatted into Cys-ScFv and Cys-diabody. They were expressed transiently in a mammalian system to obtain 1.0 to 4.5 mg of Cys fragments ready for bioconjugation and radiolabeling. Thus, in this paper, we demonstrate the relevance of the phage-display ScFv library approach for the fast-track development of small antibodies for imaging and/or immunotherapy purposes.

Fluorescence lifetime image microscopy prediction with convolutional neural networks for cell detection and classification in tissues

Convolutional neural networks (CNNs) and other deep-learning models have proven to be transformative tools for the automated analysis of microscopy images, particularly in the domain of cellular and tissue imaging. These computer-vision models have primarily been applied with traditional microscopy imaging modalities (e.g. brightfield and fluorescence), likely due to the availability of large datasets in these regimes. However, more advanced microscopy imaging techniques could, potentially, allow for improved model performance in various computational histopathology tasks. In this work, we demonstrate that CNNs can achieve high accuracy in cell detection and classification without large amounts of data when applied to histology images acquired by fluorescence lifetime imaging microscopy (FLIM). This accuracy is higher than what would be achieved with regular single or dual-channel fluorescence images under the same settings, particularly for CNNs pretrained on publicly available fluorescent cell or general image datasets. Additionally, generated FLIM images could be predicted from just the fluorescence image data by using a dense U-Net CNN model trained on a subset of ground-truth FLIM images. These U-Net CNN generated FLIM images demonstrated high similarity to ground truth and improved accuracy in cell detection and classification over fluorescence alone when used as input to a variety of commonly used CNNs. This improved accuracy was maintained even when the FLIM images were generated by a U-Net CNN trained on only a few example FLIM images.

Spatial Profiling of the Prostate Cancer Tumor Microenvironment Reveals Multiple Differences in Gene Expression and Correlation with Recurrence Risk

The tumor microenvironment plays a crucial role in both the development and progression of prostate cancer. Furthermore, identifying protein and gene expression differences between different regions is valuable for treatment development. We applied Digital Spatial Profiling multiplex analysis to formalin-fixed paraffin embedded prostatectomy tissue blocks to investigate protein and transcriptome differences between tumor, tumor-adjacent stroma (TAS), CD45+ tumor, and CD45+ TAS tissue. Differential expression of an immunology/oncology protein panel (n = 58) was measured. OX40L and CTLA4 were expressed at higher levels while 22 other proteins, including CD11c, were expressed at lower levels (FDR < 0.2 and p-value < 0.05) in TAS as compared to tumor epithelia. A tissue microarray analysis of 97 patients with 1547 cores found positive correlations between high expression of CD11c and increased time to recurrence in tumor and TAS, and inverse relationships for CTLA4 and OX40L, where higher expression in tumor correlated with lower time to recurrence, but higher time to recurrence in TAS. Spatial transcriptomic analysis using a Cancer Transcriptome Atlas panel (n = 1825 genes) identified 162 genes downregulated and 69 upregulated in TAS versus tumor, 26 downregulated and 6 upregulated in CD45+ TAS versus CD45+ tumor. We utilized CIBERSORTx to estimate the relative immune cell fractions using CD45+ gene expression and found higher average fractions for memory B, naïve B, and T cells in TAS. In summary, the combination of protein expression differences, immune cell fractions, and correlations of protein expression with time to recurrence suggest that closely examining the tumor microenvironment provides valuable data that can improve prognostication and treatment techniques.

Prognostic value of FOXP3+ regulatory T cells for patients with locally advanced oropharyngeal squamous cell carcinoma

BACKGROUND

Oropharyngeal squamous cell carcinoma (OPSCC) is the most common neoplasm originating at the base of the tongue or in the tonsils or soft palate. In this study, we investigated the prognostic value of FOXP3+ regulatory T cells in OPSCC.

METHODS

Tumor tissues of patients with locally advanced OPSCC were analyzed using quantitative multiplex immunohistochemistry. Staining of CD8+ T cells, conventional CD4+FOXP3- T cells (Tconv cells), CD4+FOXP3+ regulatory T cells (Treg cells), CD20+ B cells, and CD68+ macrophages was performed, and cell density was evaluated in both the tumor and its stroma.

RESULTS

Among the 71 patients included in this study, males constituted 93.0% of the cohort, and the median age was 59 years (range: 42-80 years). A total of 56 patients (78.9%) had a smoking history, and 53 (74.6%) patients were positive for human papillomavirus (HPV). The most frequent site of OPSCC was the tonsils (70.4%), followed by the base of the tongue (25.4%). The proportion of Treg cells was lower in the tumors of patients with HPV than in those of patients without HPV. Patients with OPSCC whose tumor Treg cell levels were above the median had longer relapse-free survival (RFS) periods than those with tumor Treg cell levels below the median (HR, 0.12; 95% CI, 0.03-0.46; p = 0.02). Our multivariate analysis identified high Treg levels (HR, 0.13; 95% CI, 0.02-1.00; p = 0.05) as an RFS factor that predicted a good prognosis.

CONCLUSIONS

Our results demonstrated that high Treg cell density in locally advanced OPSCC tumors was correlated with longer RFS.

Inhibition of platelet-derived growth factor pathway suppresses tubulointerstitial injury in renal congestion

OBJECTIVE

Increased central venous pressure in congestive heart failure is responsible for renal dysfunction, which is mediated by renal venous congestion. Pericyte detachment from capillaries after renal congestion might trigger renal fibrogenesis via pericyte-myofibroblast transition (PMT). Platelet-derived growth factor receptors (PDGFRs), which are PMT indicators, were upregulated in our recently established renal congestion model. This study was designed to determine whether inhibition of the PDGFR pathway could suppress tubulointerstitial injury after renal congestion.

METHODS

The inferior vena cava between the renal veins was ligated in male Sprague-Dawley rats, inducing congestion only in the left kidney. Imatinib mesylate or vehicle were injected intraperitoneally daily from 1 day before the operation. Three days after the surgery, the effect of imatinib was assessed by physiological, morphological and molecular methods. The inhibition of PDGFRs against transforming growth factor-β1 (TGFB1)-induced fibrosis was also tested in human pericyte cell culture.

RESULTS

Increased kidney weight and renal fibrosis were observed in the congested kidneys. Upstream inferior vena cava (IVC) pressure immediately increased to around 20 mmHg after IVC ligation in both the imatinib and saline groups. Although vasa recta dilatation and pericyte detachment under renal congestion were maintained, imatinib ameliorated the increased kidney weight and suppressed renal fibrosis around the vasa recta. TGFB1-induced elevation of fibrosis markers in human pericytes was suppressed by PDGFR inhibitors at the transcriptional level.

CONCLUSION

The activation of the PDGFR pathway after renal congestion was responsible for renal congestion-induced fibrosis. This mechanism could be a candidate therapeutic target for renoprotection against renal congestion-induced tubulointerstitial injury.

New Approaches to the Diagnosis of Rejection and Prediction of Tolerance in Liver Transplantation

Immunosuppression after liver transplantation is essential for preventing allograft rejection. However, long-term drug toxicity and associated complications necessitate investigation of immunosuppression minimization and withdrawal protocols. Development of such protocols is hindered by reliance on current paradigms for monitoring allograft function and rejection status. The current standard of care for diagnosis of rejection is histopathologic assessment and grading of liver biopsies in accordance with the Banff Rejection Activity Index. However, this method is limited by cost, sampling variability, and interobserver variation. Moreover, the invasive nature of biopsy increases the risk of patient complications. Incorporating noninvasive techniques may supplement existing methods through improved understanding of rejection causes, hepatic spatial architecture, and the role of idiopathic fibroinflammatory regions. These techniques may also aid in quantification and help integrate emerging -omics analyses with current assessments. Alternatively, emerging noninvasive methods show potential to detect and distinguish between different types of rejection while minimizing risk of adverse advents. Although biomarkers have yet to replace biopsy, preliminary studies suggest that several classes of analytes may be used to detect rejection with greater sensitivity and in earlier stages than traditional methods, possibly when coupled with artificial intelligence. Here, we provide an overview of the latest efforts in optimizing the diagnosis of rejection in liver transplantation.

Positron Emission Tomography Probes for Imaging Cytotoxic Immune Cells

Non-invasive positron emission tomography (PET) imaging of immune cells is a powerful approach for monitoring the dynamics of immune cells in response to immunotherapy. Despite the clinical success of many immunotherapeutic agents, their clinical efficacy is limited to a subgroup of patients. Conventional imaging, as well as analysis of tissue biopsies and blood samples do not reflect the complex interaction between tumour and immune cells. Consequently, PET probes are being developed to capture the dynamics of such interactions, which may improve patient stratification and treatment evaluation. The clinical efficacy of cancer immunotherapy relies on both the infiltration and function of cytotoxic immune cells at the tumour site. Thus, various immune biomarkers have been investigated as potential targets for PET imaging of immune response. Herein, we provide an overview of the most recent developments in PET imaging of immune response, including the radiosynthesis approaches employed in their development.

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.

Next Generation Digital Pathology: Emerging Trends and Measurement Challenges for Molecular Pathology

Digital pathology is revolutionising the analysis of histological features and is becoming more and more widespread in both the clinic and research. Molecular pathology extends the tissue morphology information provided by conventional histopathology by providing spatially resolved molecular information to complement the structural information provided by histopathology. The multidimensional nature of the molecular data poses significant challenge for data processing, mining, and analysis. One of the key challenges faced by new and existing pathology practitioners is how to choose the most suitable molecular pathology technique for a given diagnosis. By providing a comparison of different methods, this narrative review aims to introduce the field of molecular pathology, providing a high-level overview of many different methods. Since each pixel of an image contains a wealth of molecular information, data processing in molecular pathology is more complex. The key data processing steps and variables, and their effect on the data, are also discussed.

Multiplex immunohistochemistry and high-throughput image analysis for evaluation of spatial tumor immune cell markers in human breast cancer

BACKGROUND

The clinicopathological significance of spatial tumor-infiltrating lymphocytes (TILs) subpopulations is not well studied due to lack of high-throughput scalable methodology for studies with large human sample sizes.

OBJECTIVE

Establishing a cyclic fluorescent multiplex immunohistochemistry (mIHC/IF) method coupled with computer-assisted high-throughput quantitative analysis to evaluate associations of six TIL markers (CD3, CD8, CD20, CD56, FOXP3, and PD-L1) with clinicopathological factors of breast cancer.

METHODS

Our 5-plex mIHC/IF staining was shown to be reliable and highly sensitive for labeling three biomarkers per tissue section. Through repetitive cycles of 5-plex mIHC/IF staining, more than 12 biomarkers could be detected per single tissue section. Using open-source software CellProfiler, the measurement pipelines were successfully developed for high-throughput multiplex evaluation of intratumoral and stromal TILs.

RESULTS

In analyses of 188 breast cancer samples from the Nashville Breast Health Study, high-grade tumors showed significantly increased intratumoral CD3+CD8+ CTL density (P= 0.0008, false discovery rate (FDR) adjusted P= 0.0168) and intratumoral PD-L1 expression (P= 0.0061, FDR adjusted P= 0.0602) compared with low-grade tumors.

CONCLUSIONS

The high- and low-grade breast cancers exhibit differential immune responses which may have clinical significances. The multiplexed imaging quantification strategies established in this study are reliable, cost-efficient and applicable in regular laboratory settings for high-throughput tissue biomarker studies, especially retrospective and population-based studies using archived paraffin tissues.

Spatially variant immune infiltration scoring in human cancer tissues

The Immunoscore is a method to quantify the immune cell infiltration within cancers to predict the disease prognosis. Previous immune profiling approaches relied on limited immune markers to establish patients’ tumor immunity. However, immune cells exhibit a higher-level complexity that is typically not obtained by the conventional immunohistochemistry methods. Herein, we present a spatially variant immune infiltration score, termed as SpatialVizScore, to quantify immune cells infiltration within lung tumor samples using multiplex protein imaging data. Imaging mass cytometry (IMC) was used to target 26 markers in tumors to identify stromal, immune, and cancer cell states within 26 human tissues from lung cancer patients. Unsupervised clustering methods dissected the spatial infiltration of cells in tissue using the high-dimensional analysis of 16 immune markers and other cancer and stroma enriched labels to profile alterations in the tumors’ immune infiltration patterns. Spatially resolved maps of distinct tumors determined the spatial proximity and neighborhoods of immune-cancer cell pairs. These SpatialVizScore maps provided a ranking of patients’ tumors consisting of immune inflamed, immune suppressed, and immune cold states, demonstrating the tumor’s immune continuum assigned to three distinct infiltration score ranges. Several inflammatory and suppressive immune markers were used to establish the cell-based scoring schemes at the single-cell and pixel-level, depicting the cellular spectra in diverse lung tissues. Thus, SpatialVizScore is an emerging quantitative method to deeply study tumor immunology in cancer tissues.

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.

SPOP promotes cervical cancer progression by inducing the movement of PD-1 away from PD-L1 in spatial localization

Background

Metastasis is a major obstacle in the treatment of cervical cancer (CC), and SPOP-mediated regulatory effects are involved in metastasis. However, the mechanisms have not been fully elucidated.

Methods

Proteomic sequencing and SPOP immunohistochemistry (IHC) were performed for the pelvic lymph node (pLN)-positive and non-pLN groups of CC patients. The corresponding patients were stratified by SPOP expression level for overall survival (OS) and relapse-free survival (RFS) analysis. In vitro and in vivo tests were conducted to verify the causal relationship between SPOP expression and CC metastasis. Multiplex immunofluorescence (m-IF) and the HALO system were used to analyse the mechanism, which was further verified by in vitro experiments.

Results

SPOP is upregulated in CC with pLN metastasis and negatively associated with patient outcome. In vitro and in vivo, SPOP promotes CC proliferation and metastasis. According to m-IF and HALO analysis, SPOP may promote CC metastasis by promoting the separation of PD-1 from PD-L1. Finally, it was further verified that SPOP can achieve immune tolerance by promoting the movement of PD-1 away from PD-L1 in spatial location and function.

Conclusion

This study shows that SPOP can inhibit the immune microenvironment by promoting the movement of PD-1 away from PD-L1, thereby promoting pLN metastasis of CC and resulting in worse OS and RFS.

The SPOP is associated with pelvic lymph node (pLN) metastasis and prognosis in cervical cancer (CC) patients.

This paper discusses the potential mechanism of pLN metastasis of CC from the perspective of spatial location.

This is a multi-cross study, including clinical data, tissue microarray (TMA), multicolor immunofluorescence (m-IF), spatial immunolocalization, in vitro and in vivo functional and mechanism research fusion, from clinical to basic and clinical research.

Checkpoint molecules on infiltrating immune cells in colorectal tumor microenvironment

Colorectal cancer (CRC) is one of the most prevalent cancer types worldwide, with a high mortality rate due to metastasis. The tumor microenvironment (TME) contains multiple interactions between the tumor and the host, thus determining CRC initiation and progression. Various immune cells exist within the TME, such as tumor-infiltrating lymphocytes (TILs), tumor-associated macrophages (TAMs), and tumor-associated neutrophils (TANs). The immunotherapy approach provides novel opportunities to treat solid tumors, especially toward immune checkpoints. Despite the advances in the immunotherapy of CRC, there are still obstacles to successful treatment. In this review, we highlighted the role of these immune cells in CRC, with a particular emphasis on immune checkpoint molecules involved in CRC pathogenesis.

ICOSeg: Real-Time ICOS Protein Expression Segmentation from Immunohistochemistry Slides Using a Lightweight Conv-Transformer Network

Simple Summary

Inducible T-cell COStimulator (ICOS) is a biomarker of interest in checkpoint inhibitor therapy, and as a means of assessing T-cell regulation as part of a complex process of adaptive immunity. The aim of our study is to segment the ICOS positive cells using a lightweight deep-learning segmentation network. We aim to assess the potential of a convolutional neural network and transformer together that permits the capture of relevant features from immunohistochemistry images. The proposed study achieved remarkable results compared to the existing biomedical segmentation methods on our in-house dataset and surpassed our previous analysis by only utilizing the Efficient-UNet network.

Abstract

In this article, we propose ICOSeg, a lightweight deep learning model that accurately segments the immune-checkpoint biomarker, Inducible T-cell COStimulator (ICOS) protein in colon cancer from immunohistochemistry (IHC) slide patches. The proposed model relies on the MobileViT network that includes two main components: convolutional neural network (CNN) layers for extracting spatial features; and a transformer block for capturing a global feature representation from IHC patch images. The ICOSeg uses an encoder and decoder sub-network. The encoder extracts the positive cell’s salient features (i.e., shape, texture, intensity, and margin), and the decoder reconstructs important features into segmentation maps. To improve the model generalization capabilities, we adopted a channel attention mechanism that added to the bottleneck of the encoder layer. This approach highlighted the most relevant cell structures by discriminating between the targeted cell and background tissues. We performed extensive experiments on our in-house dataset. The experimental results confirm that the proposed model achieves more significant results against state-of-the-art methods, together with an 8× reduction in parameters.

Engineered Amber-Emitting Nano Luciferase and Its Use for Immunobioluminescence Imaging In Vivo

The NanoLuc luciferase (NLuc) and its furimazine (FRZ) substrate have revolutionized bioluminescence (BL) assays and imaging. However, the use of the NLuc-FRZ luciferase-luciferin pair for mammalian tissue imaging is hindered by the low tissue penetration of the emitting blue photons. Here, we present the development of an NLuc mutant, QLuc, which catalyzes the oxidation of a synthetic QTZ luciferin for bright and red-shifted emission peaking at ∼585 nm. Compared to other small single-domain NLuc mutants, this amber-light-emitting luciferase exhibited improved performance for imaging deep-tissue targets in live mice. Leveraging this novel bioluminescent reporter, we further pursued in vivo immunobioluminescence imaging (immunoBLI), which used a fusion protein of a single-chain variable antibody fragment (scFv) and QLuc for molecular imaging of tumor-associated antigens in a xenograft mouse model. As one of the most red-shifted NLuc variants, we expect QLuc to find broad applications in noninvasive mammalian imaging. Moreover, the immunoBLI method complements immunofluorescence imaging and immuno-positron emission tomography (immunoPET), serving as a convenient and nonradioactive molecular imaging tool for animal models in basic and preclinical research.

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.

Neither Tumor-Infiltrating Lymphocytes nor Cytotoxic T Cells Predict Enhanced Benefit from Chemotherapy in the DBCG77B Phase III Clinical Trial

Simple Summary

Apart from the direct killing of cancer cells, cyclophosphamide-based chemotherapy has been shown to induce an antitumor immune response, and is being used in combination with immunotherapies in cancer care. We assessed the interaction of chemotherapy with immune biomarkers expressed on primary tumor tissue from a randomized phase III clinical trial, and confirmed that the presence of tumor-infiltrating lymphocytes is linked to improved survival in premenopausal women with high-risk breast cancer, regardless of their treatment allocation. However, immune biomarkers including tumor-infiltrating lymphocytes do not predict extra benefit from cyclophosphamide chemotherapy. This finding applies across the major molecular subgroups, including non-luminal and basal breast cancers that tend to be more immunogenic, and are often considered the most suitable subsets for receiving immunotherapy.

Abstract

Recent studies have shown that immune infiltrates in the tumor microenvironment play a role in response to therapy, with some suggesting that patients with immunogenic tumors may receive increased benefit from chemotherapies. We evaluated this hypothesis in early breast cancer by testing the interaction between immune biomarkers and chemotherapy using materials from DBCG77B, a phase III clinical trial where high-risk premenopausal women were randomized to receive chemotherapy or no chemotherapy. Tissue microarrays were evaluated for tumor-infiltrating lymphocytes (TILs) assessed morphologically on hematoxylin and eosin-stained slides, and by immunohistochemistry for CD8, FOXP3, LAG-3, PD-1 and PD-L1. Following REMARK reporting guidelines, data analyses were performed according to a prespecified statistical plan, using 10-year invasive disease-free survival as the endpoint. Differences in survival probabilities between biomarker groups were evaluated by Kaplan–Meier and Cox proportional hazard ratio analyses and prediction for treatment benefit by an interaction test. Our results showed that stromal TILs were associated with an improved prognosis (HR = 0.93; p-value = 0.03), consistent with previous studies. However, none of the immune biomarkers predicted benefit from chemotherapy in the full study set nor within major breast cancer subtypes. Our study indicates that primary tumors with higher immune infiltration do not derive extra benefit from cyclophosphamide-based cytotoxic chemotherapy.

MIAMI: mutual information-based analysis of multiplex imaging data

MOTIVATION

Studying the interaction or co-expression of the proteins or markers in the tumor microenvironment of cancer subjects can be crucial in the assessment of risks, such as death or recurrence. In the conventional approach, the cells need to be declared positive or negative for a marker based on its intensity. For multiple markers, manual thresholds are required for all the markers, which can become cumbersome. The performance of the subsequent analysis relies heavily on this step and thus suffers from subjectivity and lacks robustness.

RESULTS

We present a new method where different marker intensities are viewed as dependent random variables, and the mutual information (MI) between them is considered to be a metric of co-expression. Estimation of the joint density, as required in the traditional form of MI, becomes increasingly challenging as the number of markers increases. We consider an alternative formulation of MI which is conceptually similar but has an efficient estimation technique for which we develop a new generalization. With the proposed method, we analyzed a lung cancer dataset finding the co-expression of the markers, HLA-DR and CK to be associated with survival. We also analyzed a triple negative breast cancer dataset finding the co-expression of the immuno-regulatory proteins, PD1, PD-L1, Lag3 and IDO, to be associated with disease recurrence. We demonstrated the robustness of our method through different simulation studies.

AVAILABILITY AND IMPLEMENTATION

The associated R package can be found here, https://github.com/sealx017/MIAMI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology

In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient's prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies.

The Association between Blood Indexes and Immune Cell Concentrations in the Primary Tumor Microenvironment Predicting Survival of Immunotherapy in Gastric Cancer

The tumor microenvironment plays a vital role in tumor progression and treatment response. However, the association between immune cell concentrations in primary tumor and blood indexes remains unknown. Thus, we enrolled patients with gastric cancer (GC) in two cohorts. We used multiplexed immunohistochemistry to quantify in situ proteins covering rare cell types at sub-cellular resolution in 80 patients with GC in the first cohort. A high correlation between the LMR (lymphocyte-to-monocyte ratio)/NLR (neutrophil-to-lymphocyte ratio) and tumor immune microenvironment was found. The density of exhausted CD8 T cells including CD8+PD1−TIM3+, CD8+LAG3+PD1+, CD8+LAG3+PD1−, CD8+LAG3+PD1+TIM3− was negatively associated with LMR and positively associated with NLR (p < 0.05). Additionally, the higher density of macrophages in tumor core was associated with a higher platelet-to-lymphocyte ratio and systemic immune-inflammation index. Furthermore, we validated the prognostic value of LMR and NLR in an independent cohort of 357 gastric cancer patients receiving immunotherapy. Higher LMR at baseline was significantly associated with superior immune-related PFS (irPFS) and a trend of superior immune-related OS (irOS). Higher NLR was associated with inferior irOS. In conclusion, blood indexes were associated with immune cells infiltrating in primary tumors of GC. NLR and LMR are associated with the density of exhausted CD8+ T immune cells, which leads to prognostic values of immunotherapy.

Cancer vaccines: Building a bridge over troubled waters

Cancer vaccines aim to direct the immune system to eradicate cancer cells. Here we review the essential immunologic concepts underpinning natural immunity and highlight the multiple unique challenges faced by vaccines targeting cancer. Recent technological advances in mass spectrometry, neoantigen prediction, genetically and pharmacologically engineered mouse models, and single-cell omics have revealed new biology, which can help to bridge this divide. We particularly focus on translationally relevant aspects, such as antigen selection and delivery and the monitoring of human post-vaccination responses, and encourage more aggressive exploration of novel approaches.

The emerging landscape of spatial profiling technologies

Improved scale, multiplexing and resolution are establishing spatial nucleic acid and protein profiling methods as a major pillar for cellular atlas building of complex samples, from tissues to full organisms. Emerging methods yield omics measurements at resolutions covering the nano- to microscale, enabling the charting of cellular heterogeneity, complex tissue architectures and dynamic changes during development and disease. We present an overview of the developing landscape of in situ spatial genome, transcriptome and proteome technologies, exemplify their impact on cell biology and translational research, and discuss current challenges for their community-wide adoption. Among many transformative applications, we envision that spatial methods will map entire organs and enable next-generation pathology.

Challenges and Opportunities for Immunoprofiling Using a Spatial High-Plex Technology: The NanoString GeoMx® Digital Spatial Profiler

Characterization of the tumor microenvironment through immunoprofiling has become an essential resource for the understanding of the complex immune cell interactions and the assessment of biomarkers for prognosis and prediction of immunotherapy response; however, these studies are often limited by tissue heterogeneity and sample size. The nanoString GeoMx^® Digital Spatial Profiler (DSP) is a platform that allows high-plex profiling at the protein and RNA level, providing spatial and temporal assessment of tumors in frozen or formalin-fixed paraffin-embedded limited tissue sample. Recently, high-impact studies have shown the feasibility of using this technology to identify biomarkers in different settings, including predictive biomarkers for immunotherapy in different tumor types. These studies showed that compared to other multiplex and high-plex platforms, the DSP can interrogate a higher number of biomarkers with higher throughput; however, it does not provide single-cell resolution, including co-expression of biomarker or spatial information at the single-cell level. In this review, we will describe the technical overview of the platform, present current evidence of the advantages and limitations of the applications of this technology, and provide important considerations for the experimental design for translational immune-oncology research using this tissue-based high-plex profiling approach.

Mistic: An open-source multiplexed image t-SNE viewer

Understanding the complex ecology of a tumor tissue and the spatiotemporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immuno-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. Here, we present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D images rendered using multiple layout options to provide an overall visual preview of the entire dataset. In particular, the positions of the images can be t-SNE or UMAP coordinates. This grouped view of all images allows an exploratory understanding of the specific expression pattern of a given biomarker or collection of biomarkers across all images, helps to identify images expressing a particular phenotype, and can help select images for subsequent downstream analysis. Currently, there is no freely available tool to generate such image t-SNEs.

Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms

Purpose: Validation of artificial intelligence (AI) algorithms in digital pathology with a reference standard is necessary before widespread clinical use, but few examples focus on creating a reference standard based on pathologist annotations. This work assesses the results of a pilot study that collects density estimates of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer biopsy specimens. This work will inform the creation of a validation dataset for the evaluation of AI algorithms fit for a regulatory purpose. Approach: Collaborators and crowdsourced pathologists contributed glass slides, digital images, and annotations. Here, "annotations" refer to any marks, segmentations, measurements, or labels a pathologist adds to a report, image, region of interest (ROI), or biological feature. Pathologists estimated sTILs density in 640 ROIs from hematoxylin and eosin stained slides of 64 patients via two modalities: an optical light microscope and two digital image viewing platforms. Results: The pilot study generated 7373 sTILs density estimates from 29 pathologists. Analysis of annotations found the variability of density estimates per ROI increases with the mean; the root mean square differences were 4.46, 14.25, and 26.25 as the mean density ranged from 0% to 10%, 11% to 40%, and 41% to 100%, respectively. The pilot study informs three areas of improvement for future work: technical workflows, annotation platforms, and agreement analysis methods. Upgrades to the workflows and platforms will improve operability and increase annotation speed and consistency. Conclusions: Exploratory data analysis demonstrates the need to develop new statistical approaches for agreement. The pilot study dataset and analysis methods are publicly available to allow community feedback. The development and results of the validation dataset will be publicly available to serve as an instructive tool that can be replicated by developers and researchers.

Integrated Analytical System for Clinical Single-Cell Analysis

High-dimensional analyses of cancers can potentially be used to better define cancer subtypes, analyze the complex tumor microenvironment, and perform cancer cell pathway analyses for drug trials. Unfortunately, integrated systems that allow such analyses in serial fine needle aspirates within a day or at point-of-care currently do not exist. To achieve this, an integrated immunofluorescence single-cell analyzer (i2SCAN) for deep profiling of directly harvested cells is developed. By combining a novel cellular imaging system, highly cyclable bioorthogonal FAST antibody panels, and integrated computational analysis, it is shown that same-day analysis is possible in thousands of harvested cells. It is demonstrated that the i2SCAN approach allows comprehensive analysis of breast cancer samples obtained by fine needle aspiration or core tissues. The method is a rapid, robust, and low-cost solution to high-dimensional analysis of scant clinical specimens.