Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade

ILT2 and ILT4 Drive Myeloid Suppression via Both Overlapping and Distinct Mechanisms

Solid tumors are dense three-dimensional (3D) multicellular structures that enable efficient receptor-ligand trans interactions via close cell-cell contact. Immunoglobulin-like transcript (ILT)2 and ILT4 are related immune-suppressive receptors that play a role in the inhibition of myeloid cells within the tumor microenvironment. The relative contribution of ILT2 and ILT4 to immune inhibition in the context of solid tumor tissue has not been fully explored. We present evidence that both ILT2 and ILT4 contribute to myeloid inhibition. We found that although ILT2 inhibits myeloid cell activation in the context of trans-engagement by MHC-I, ILT4 efficiently inhibits myeloid cells in the presence of either cis- or trans-engagement. In a 3D spheroid tumor model, dual ILT2/ILT4 blockade was required for the optimal activation of myeloid cells, including the secretion of CXCL9 and CCL5, upregulation of CD86 on dendritic cells, and downregulation of CD163 on macrophages. Humanized mouse tumor models showed increased immune activation and cytolytic T-cell activity with combined ILT2 and ILT4 blockade, including evidence of the generation of immune niches, which have been shown to correlate with clinical response to immune-checkpoint blockade. In a human tumor explant histoculture system, dual ILT2/ILT4 blockade increased CXCL9 secretion, downregulated CD163 expression, and increased the expression of M1 macrophage, IFNγ, and cytolytic T-cell gene signatures. Thus, we have revealed distinct contributions of ILT2 and ILT4 to myeloid cell biology and provide proof-of-concept data supporting the combined blockade of ILT2 and ILT4 to therapeutically induce optimal myeloid cell reprogramming in the tumor microenvironment.

Facts and Hopes in Using Omics to Advance Combined Immunotherapy Strategies

The field of oncology has been transformed by immune checkpoint inhibitors (ICI) and other immune-based agents; however, many patients do not receive a durable benefit. While biomarker assessments from pivotal ICI trials have uncovered certain mechanisms of resistance, results thus far have only scraped the surface. Mechanisms of resistance are as complex as the tumor microenvironment (TME) itself, and the development of effective therapeutic strategies will only be possible by building accurate models of the tumor-immune interface. With advancement of multi-omic technologies, high-resolution characterization of the TME is now possible. In addition to sequencing of bulk tumor, single-cell transcriptomic, proteomic, and epigenomic data as well as T-cell receptor profiling can now be simultaneously measured and compared between responders and nonresponders to ICI. Spatial sequencing and imaging platforms have further expanded the dimensionality of existing technologies. Rapid advancements in computation and data sharing strategies enable development of biologically interpretable machine learning models to integrate data from high-resolution, multi-omic platforms. These models catalyze the identification of resistance mechanisms and predictors of benefit in ICI-treated patients, providing scientific foundation for novel clinical trials. Moving forward, we propose a framework by which in silico screening, functional validation, and clinical trial biomarker assessment can be used for the advancement of combined immunotherapy strategies.

PDL1 immunohistochemistry in canine neoplasms: Validation of commercial antibodies, standardization of evaluation, and scoring systems

Immuno-oncology research has brought to light the paradoxical role of immune cells in the induction and elimination of cancer. Programmed cell death protein 1 (PD1), expressed by tumor-infiltrating lymphocytes, and programmed cell death ligand 1 (PDL1), expressed by tumor cells, are immune checkpoint proteins that regulate the antitumor adaptive immune response. This study aimed to validate commercially available PDL1 antibodies in canine tissue and then, applying standardized methods and scoring systems used in human pathology, evaluate PDL1 immunopositivity in different types of canine tumors. To demonstrate cross-reactivity, a monoclonal antibody (22C3) and polyclonal antibody (cod. A1645) were tested by western blot. Cross-reactivity in canine tissue cell extracts was observed for both antibodies; however, the polyclonal antibody (cod. A1645) demonstrated higher signal specificity. Canine tumor histotypes were selected based on the human counterparts known to express PDL1. Immunohistochemistry was performed on 168 tumors with the polyclonal anti-PDL1 antibody. Only membranous labeling was considered positive. PDL1 labeling was detected both in neoplastic and infiltrating immune cells. The following tumors were immunopositive: melanomas (17 of 17; 100%), renal cell carcinomas (4 of 17; 24%), squamous cell carcinomas (3 of 17; 18%), lymphomas (2 of 14; 14%), urothelial carcinomas (2 of 18; 11%), pulmonary carcinomas (2 of 20; 10%), and mammary carcinomas (1 of 31; 3%). Gastric (0 of 10; 0%) and intestinal carcinomas (0 of 24; 0%) were negative. The findings of this study suggest that PDL1 is expressed in some canine tumors, with high prevalence in melanomas.

Artificial Intelligence-Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC: Results From the LCMC3 Study

INTRODUCTION

Pathologic response (PathR) by histopathologic assessment of resected specimens may be an early clinical end point associated with long-term outcomes with neoadjuvant therapy. Digital pathology may improve the efficiency and precision of PathR assessment. LCMC3 (NCT02927301) evaluated neoadjuvant atezolizumab in patients with resectable NSCLC and reported a 20% major PathR rate.

METHODS

We determined PathR in primary tumor resection specimens using guidelines-based visual techniques and developed a convolutional neural network model using the same criteria to digitally measure the percent viable tumor on whole-slide images. Concordance was evaluated between visual determination of percent viable tumor (n = 151) performed by one of the 47 local pathologists and three central pathologists.

RESULTS

For concordance among visual determination of percent viable tumor, the interclass correlation coefficient was 0.87 (95% confidence interval [CI]: 0.84-0.90). Agreement for visually assessed 10% or less viable tumor (major PathR [MPR]) in the primary tumor was 92.1% (Fleiss kappa = 0.83). Digitally assessed percent viable tumor (n = 136) correlated with visual assessment (Pearson r = 0.73; digital/visual slope = 0.28). Digitally assessed MPR predicted visually assessed MPR with outstanding discrimination (area under receiver operating characteristic curve, 0.98) and was associated with longer disease-free survival (hazard ratio [HR] = 0.30; 95% CI: 0.09-0.97, p = 0.033) and overall survival (HR = 0.14, 95% CI: 0.02-1.06, p = 0.027) versus no MPR. Digitally assessed PathR strongly correlated with visual measurements.

CONCLUSIONS

Artificial intelligence-powered digital pathology exhibits promise in assisting pathologic assessments in neoadjuvant NSCLC clinical trials. The development of artificial intelligence-powered approaches in clinical settings may aid pathologists in clinical operations, including routine PathR assessments, and subsequently support improved patient care and long-term outcomes.

Digital Pathology for Better Clinical Practice

(1) Background: Digital pathology (DP) is transforming the landscape of clinical practice, offering a revolutionary approach to traditional pathology analysis and diagnosis. (2) Methods: This innovative technology involves the digitization of traditional glass slides which enables pathologists to access, analyze, and share high-resolution whole-slide images (WSI) of tissue specimens in a digital format. By integrating cutting-edge imaging technology with advanced software, DP promises to enhance clinical practice in numerous ways. DP not only improves quality assurance and standardization but also allows remote collaboration among experts for a more accurate diagnosis. Artificial intelligence (AI) in pathology significantly improves cancer diagnosis, classification, and prognosis by automating various tasks. It also enhances the spatial analysis of tumor microenvironment (TME) and enables the discovery of new biomarkers, advancing their translation for therapeutic applications. (3) Results: The AI-driven immune assays, Immunoscore (IS) and Immunoscore-Immune Checkpoint (IS-IC), have emerged as powerful tools for improving cancer diagnosis, prognosis, and treatment selection by assessing the tumor immune contexture in cancer patients. Digital IS quantitative assessment performed on hematoxylin-eosin (H&E) and CD3+/CD8+ stained slides from colon cancer patients has proven to be more reproducible, concordant, and reliable than expert pathologists' evaluation of immune response. Outperforming traditional staging systems, IS demonstrated robust potential to enhance treatment efficiency in clinical practice, ultimately advancing cancer patient care. Certainly, addressing the challenges DP has encountered is essential to ensure its successful integration into clinical guidelines and its implementation into clinical use. (4) Conclusion: The ongoing progress in DP holds the potential to revolutionize pathology practices, emphasizing the need to incorporate powerful AI technologies, including IS, into clinical settings to enhance personalized cancer therapy.

Redefining clinical practice through spatial profiling: a revolution in tissue analysis

Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.

Embracing cancer complexity: Hallmarks of systemic disease

The last 50 years have witnessed extraordinary developments in understanding mechanisms of carcinogenesis, synthesized as the hallmarks of cancer. Despite this logical framework, our understanding of the molecular basis of systemic manifestations and the underlying causes of cancer-related death remains incomplete. Looking forward, elucidating how tumors interact with distant organs and how multifaceted environmental and physiological parameters impinge on tumors and their hosts will be crucial for advances in preventing and more effectively treating human cancers. In this perspective, we discuss complexities of cancer as a systemic disease, including tumor initiation and promotion, tumor micro- and immune macro-environments, aging, metabolism and obesity, cancer cachexia, circadian rhythms, nervous system interactions, tumor-related thrombosis, and the microbiome. Model systems incorporating human genetic variation will be essential to decipher the mechanistic basis of these phenomena and unravel gene-environment interactions, providing a modern synthesis of molecular oncology that is primed to prevent cancers and improve patient quality of life and cancer outcomes.

Single-cell transcriptomes reveal the heterogeneity and microenvironment of vestibular schwannoma

BACKGROUND

Vestibular schwannoma (VS) is the most common benign tumor in the cerebellopontine angle and internal auditory canal. Illustrating the heterogeneous cellular components of VS could provide insights into its various growth patterns.

METHODS

Single-cell RNA sequencing was used to profile transcriptomes from 7 VS samples and 2 normal nerves. Multiplex immunofluorescence was employed to verify the data set results. Bulk RNA sequencing was conducted on 5 normal nerves and 44 VS samples to generate a prediction model for VS growth.

RESULTS

A total of 83 611 cells were annotated as 14 distinct cell types. We uncovered the heterogeneity in distinct VS tumors. A subset of Schwann cells with the vascular endothelial growth factor biomarker was significantly associated with fast VS growth through mRNA catabolism and peptide biosynthesis. The macrophages in the normal nerves were largely of the M2 phenotype, while no significant differences in the proportions of M1 and M2 macrophages were found between slow-growing and fast-growing VS. The normal spatial distribution of fibroblasts and vascular cells was destroyed in VS. The communications between Schwann cells and vascular cells were strengthened in VS compared with those in the normal nerve. Three cell clusters were significantly associated with fast VS growth and could refine the growth classification in bulk RNA.

CONCLUSIONS

Our findings offer novel insights into the VS microenvironment at the single-cell level. It may enhance our understanding of the different clinical phenotypes of VS and help predict growth characteristics. Molecular subtypes should be included in the treatment considerations.

A perspective on FAIR quality control in multiplexed imaging data processing

Multiplexed imaging approaches are getting increasingly adopted for imaging of large tissue areas, yielding big imaging datasets both in terms of the number of samples and the size of image data per sample. The processing and analysis of these datasets is complex owing to frequent technical artifacts and heterogeneous profiles from a high number of stained targets To streamline the analysis of multiplexed images, automated pipelines making use of state-of-the-art algorithms have been developed. In these pipelines, the output quality of one processing step is typically dependent on the output of the previous step and errors from each step, even when they appear minor, can propagate and confound the results. Thus, rigorous quality control (QC) at each of these different steps of the image processing pipeline is of paramount importance both for the proper analysis and interpretation of the analysis results and for ensuring the reusability of the data. Ideally, QC should become an integral and easily retrievable part of the imaging datasets and the analysis process. Yet, limitations of the currently available frameworks make integration of interactive QC difficult for large multiplexed imaging data. Given the increasing size and complexity of multiplexed imaging datasets, we present the different challenges for integrating QC in image analysis pipelines as well as suggest possible solutions that build on top of recent advances in bioimage analysis.

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery.

Mapping the single cell spatial immune landscapes of the melanoma microenvironment

Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.

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

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

Emerging prognostic biomarkers in advanced cutaneous melanoma: a literature update

INTRODUCTION

Over the past two years, the scientific community has witnessed an exponential growth in research focused on identifying prognostic biomarkers for melanoma, both in pre-clinical and clinical settings. This surge in studies reflects the need of developing effective prognostic indicators in the field of melanoma.

AREAS COVERED

The aim of this work is to review the scientific literature on the most recent findings on the development or validation of prognostic biomarkers in melanoma, in the attempt of providing both clinicians and researchers with an updated broad synopsis of prognostic biomarkers in cutaneous melanoma.

EXPERT OPINION

While the field of prognostic biomarkers in melanoma appears promising, there are several complexities and limitations to address. The interdependence of clinical, histological, and molecular features requires accurate classification of different biomarker families. Correlation does not imply causation, and adjustments for confounding factors are often overlooked. In this scenario, large-scale studies based on high-quality clinical trial data can provide more reliable evidence. It is essential to avoid oversimplification by focusing on a single biomarker, as the interactions among multiple factors contribute to define the disease course and patient's outcome. Furthermore, implementing well-supported evidence in real-life settings can help advance prognostic biomarker research in melanoma.

Spatial mapping of human hematopoiesis at single-cell resolution reveals aging-associated topographic remodeling

ABSTRACT

The spatial anatomy of hematopoiesis in the bone marrow (BM) has been extensively studied in mice and other preclinical models, but technical challenges have precluded a commensurate exploration in humans. Institutional pathology archives contain thousands of paraffinized BM core biopsy tissue specimens, providing a rich resource for studying the intact human BM topography in a variety of physiologic states. Thus, we developed an end-to-end pipeline involving multiparameter whole tissue staining, in situ imaging at single-cell resolution, and artificial intelligence-based digital whole slide image analysis and then applied it to a cohort of disease-free samples to survey alterations in the hematopoietic topography associated with aging. Our data indicate heterogeneity in marrow adipose tissue (MAT) content within each age group and an inverse correlation between MAT content and proportions of early myeloid and erythroid precursors, irrespective of age. We identify consistent endosteal and perivascular positioning of hematopoietic stem and progenitor cells (HSPCs) with medullary localization of more differentiated elements and, importantly, uncover new evidence of aging-associated changes in cellular and vascular morphologies, microarchitectural alterations suggestive of foci with increased lymphocytes, and diminution of a potentially active megakaryocytic niche. Overall, our findings suggest that there is topographic remodeling of human hematopoiesis associated with aging. More generally, we demonstrate the potential to deeply unravel the spatial biology of normal and pathologic human BM states using intact archival tissue specimens.

T cell-mediated curation and restructuring of tumor tissue coordinates an effective immune response

Antigen-specific T cells traffic to, are influenced by, and create unique cellular microenvironments. Here we characterize these microenvironments over time with multiplexed imaging in a melanoma model of adoptive T cell therapy and human patients with melanoma treated with checkpoint inhibitor therapy. Multicellular neighborhood analysis reveals dynamic immune cell infiltration and inflamed tumor cell neighborhoods associated with CD8+ T cells. T cell-focused analysis indicates T cells are found along a continuum of neighborhoods that reflect the progressive steps coordinating the anti-tumor immune response. More effective anti-tumor immune responses are characterized by inflamed tumor-T cell neighborhoods, flanked by dense immune infiltration neighborhoods. Conversely, ineffective T cell therapies express anti-inflammatory cytokines, resulting in regulatory neighborhoods, spatially disrupting productive T cell-immune and -tumor interactions. Our study provides in situ mechanistic insights into temporal tumor microenvironment changes, cell interactions critical for response, and spatial correlates of immunotherapy outcomes, informing cellular therapy evaluation and engineering.

Spatial and Compositional Biomarkers in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options, which warrants identification of novel therapeutic targets. Deciphering nuances in the tumor microenvironment (TME) may unveil insightful links between anti-tumor immunity and clinical outcomes, yet such connections remain underexplored. Here we employed a dataset derived from imaging mass cytometry of 58 TNBC patient specimens at single-cell resolution and performed in-depth quantifications with a suite of multi-scale computational algorithms. We detected distinct cell distribution patterns among clinical subgroups, potentially stemming from different infiltration related to tumor vasculature and fibroblast heterogeneity. Spatial analysis also identified ten recurrent cellular neighborhoods (CNs) - a collection of local TME characteristics with unique cell components. Coupling of the prevalence of pan-immune and perivasculature immune hotspot CNs, enrichment of inter-CN interactions was associated with improved survival. Using a deep learning model trained on engineered spatial data, we can with high accuracy (mean AUC of 5-fold cross-validation = 0.71) how a separate cohort of patients in the NeoTRIP clinical trial will respond to treatment based on baseline TME features. These data reinforce that the TME architecture is structured in cellular compositions, spatial organizations, vasculature biology, and molecular profiles, and suggest novel imaging-based biomarkers for treatment development in the context of TNBC.

Decoding the tumor microenvironment with spatial technologies

Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.

pSTAT5 is associated with improved survival in patients with thick or ulcerated primary cutaneous melanoma

Identifying prognostic biomarkers to predict clinical outcomes in stage I and II cutaneous melanomas could guide the clinical application of adjuvant and neoadjuvant therapies. We aimed to investigate the prognostic value of phosphorylated signal transducer and activator of transcription 5 (pSTAT5) as a biomarker in early-stage melanoma. This study evaluated all initially staged Ib and II melanoma patients undergoing sentinel node biopsy at a tertiary centre in Brisbane, Australia between 1994 and 2007, with survival data collected from the Queensland Cancer Registry. Primary melanoma tissue from 189 patients was analysed for pSTAT5 level through immunohistochemistry. Cox regression modelling, with adjustment for sex, age, ulceration, anatomical location, and Breslow depth, was applied to determine the association between pSTAT5 detection and melanoma-specific survival. Median duration of follow-up was 7.4 years. High pSTAT5 detection was associated with ulceration and increased tumour thickness. However, multivariate analysis indicated that high pSTAT5 detection was associated with improved melanoma-specific survival (hazard ratio: 0.15, 95% confidence interval: 0.03-0.67) as compared to low pSTAT5 detection. This association persisted when pSTAT5 detection was limited to immune infiltrate or the vasculature, as well as when sentinel node positivity was accounted for. In this cohort, staining for high-pSTAT5 tumours identified a subset of melanoma patients with increased survival outcomes as compared to low-pSTAT5 tumours, despite the former having higher-risk clinicopathological characteristics at diagnosis. pSTAT5 is likely an indicator of local immune activation, and its detection could represent a useful tool to stratify the risk of melanoma progression.

Biomaterial-Mediated Genetic Reprogramming of Merkel Cell Carcinoma and Melanoma Leads to Targeted Cancer Cell Killing In Vitro and In Vivo

Tumor immunotherapy is a promising anticancer strategy; however, tumor cells may employ resistance mechanisms, including downregulation of major histocompatibility complex (MHC) molecules to avoid immune recognition. Here, we investigate reprogramming nanoparticles (NPs) that deliver immunostimulatory genes to enhance immunotherapy and address defective antigen presentation in skin cancer in vitro and in vivo. We use a modular poly(beta-amino ester) (PBAE)-based NP to deliver DNA encoding 4-1BBL, IL-12, and IFNγ to reprogram human Merkel cell carcinoma (MCC) cells in vitro and mouse melanoma tumors in vivo to drive adaptive antitumor immune responses. Optimized NP formulations delivering 4-1BBL/IL-12 or 4-1BBL/IL-12/IFNγ DNA successfully transfect MCC and melanoma cells in vitro and in vivo, respectively, resulting in IFNγ-driven upregulation of MHC class I and II molecules on cancer cells. These NPs reprogram the tumor immune microenvironment (TIME) and elicit strong T-cell-driven immune responses, leading to cancer cell killing and T-cell proliferation in vitro and slowing tumor growth and improving survival rates in vivo. Based on expected changes to the tumor immune microenvironment, particularly the importance of IFNγ to the immune response and driving both T-cell function and exhaustion, next-generation NPs codelivering IFNγ were designed. These offered mixed benefits, exchanging improved polyfunctionality for increased T-cell exhaustion and demonstrating higher systemic toxicity in vivo. Further profiling of the immune response with these NPs provides insight into T-cell exhaustion and polyfunctionality induced by different formulations, providing a greater understanding of this immunotherapeutic strategy.

Guidelines for visualization and analysis of DC in tissues using multiparameter fluorescence microscopy imaging methods

This article is part of the Dendritic Cell Guidelines article series, which provides a collection of state-of-the-art protocols for the preparation, phenotype analysis by flow cytometry, generation, fluorescence microscopy, and functional characterization of mouse and human dendritic cells (DC) from lymphoid organs and various non-lymphoid tissues. Here, we provide detailed procedures for a variety of multiparameter fluorescence microscopy imaging methods to explore the spatial organization of DC in tissues and to dissect how DC migrate, communicate, and mediate their multiple functional roles in immunity in a variety of tissue settings. The protocols presented here entail approaches to study DC dynamics and T cell cross-talk by intravital microscopy, large-scale visualization, identification, and quantitative analysis of DC subsets and their functions by multiparameter fluorescence microscopy of fixed tissue sections, and an approach to study DC interactions with tissue cells in a 3D cell culture model. While all protocols were written by experienced scientists who routinely use them in their work, this article was also peer-reviewed by leading experts and approved by all co-authors, making it an essential resource for basic and clinical DC immunologists.

An update on methods for detection of prognostic and predictive biomarkers in melanoma

The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.

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

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

Comparison of different predictive biomarker testing assays for PD-1/PD-L1 checkpoint inhibitors response: a systematic review and network meta-analysis

Background

Accurate prediction of efficacy of programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) checkpoint inhibitors is of critical importance. To address this issue, a network meta-analysis (NMA) comparing existing common measurements for curative effect of PD-1/PD-L1 monotherapy was conducted.

Methods

We searched PubMed, Embase, the Cochrane Library database, and relevant clinical trials to find out studies published before Feb 22, 2023 that use PD-L1 immunohistochemistry (IHC), tumor mutational burden (TMB), gene expression profiling (GEP), microsatellite instability (MSI), multiplex IHC/immunofluorescence (mIHC/IF), other immunohistochemistry and hematoxylin-eosin staining (other IHC&HE) and combined assays to determine objective response rates to anti-PD-1/PD-L1 monotherapy. Study-level data were extracted from the published studies. The primary goal of this study was to evaluate the predictive efficacy and rank these assays mainly by NMA, and the second objective was to compare them in subgroup analyses. Heterogeneity, quality assessment, and result validation were also conducted by meta-analysis.

Findings

144 diagnostic index tests in 49 studies covering 5322 patients were eligible for inclusion. mIHC/IF exhibited highest sensitivity (0.76, 95% CI: 0.57-0.89), the second diagnostic odds ratio (DOR) (5.09, 95% CI: 1.35-13.90), and the second superiority index (2.86). MSI had highest specificity (0.90, 95% CI: 0.85-0.94), and DOR (6.79, 95% CI: 3.48-11.91), especially in gastrointestinal tumors. Subgroup analyses by tumor types found that mIHC/IF, and other IHC&HE demonstrated high predictive efficacy for non-small cell lung cancer (NSCLC), while PD-L1 IHC and MSI were highly efficacious in predicting the effectiveness in gastrointestinal tumors. When PD-L1 IHC was combined with TMB, the sensitivity (0.89, 95% CI: 0.82-0.94) was noticeably improved revealed by meta-analysis in all studies.

Interpretation

Considering statistical results of NMA and clinical applicability, mIHC/IF appeared to have superior performance in predicting response to anti PD-1/PD-L1 therapy. Combined assays could further improve the predictive efficacy. Prospective clinical trials involving a wider range of tumor types are needed to establish a definitive gold standard in future.

The role of tumor immune microenvironment in chordoma: promising immunotherapy strategies

Chordoma is a rare malignant bone tumor with limited therapeutic options, which is resistant to conventional chemotherapy and radiotherapy, and targeted therapy is also shown with little efficacy. The long-standing delay in researching its mechanisms of occurrence and development has resulted in the dilemma of no effective treatment targets and no available drugs in clinical practice. In recent years, the role of the tumor immune microenvironment in driving tumor growth has become a hot and challenging topic in the field of cancer research. Immunotherapy has shown promising results in the treatment of various tumors. However, the study of the immune microenvironment of chordoma is still in its infancy. In this review, we aim to present a comprehensive reveal of previous exploration on the chordoma immune microenvironment and propose promising immunotherapy strategies for chordoma based on these characteristics.

Understanding virus-host interactions in tissues

Although virus-host interactions are usually studied in a single cell type using in vitro assays in immortalized cell lines or isolated cell populations, it is important to remember that what is happening inside one infected cell does not translate to understanding how an infected cell behaves in a tissue, organ or whole organism. Infections occur in complex tissue environments, which contain a host of factors that can alter the course of the infection, including immune cells, non-immune cells and extracellular-matrix components. These factors affect how the host responds to the virus and form the basis of the protective response. To understand virus infection, tools are needed that can profile the tissue environment. This Review highlights methods to study virus-host interactions in the infection microenvironment.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

CellSighter: a neural network to classify cells in highly multiplexed images

Multiplexed imaging enables measurement of multiple proteins in situ, offering an unprecedented opportunity to chart various cell types and states in tissues. However, cell classification, the task of identifying the type of individual cells, remains challenging, labor-intensive, and limiting to throughput. Here, we present CellSighter, a deep-learning based pipeline to accelerate cell classification in multiplexed images. Given a small training set of expert-labeled images, CellSighter outputs the label probabilities for all cells in new images. CellSighter achieves over 80% accuracy for major cell types across imaging platforms, which approaches inter-observer concordance. Ablation studies and simulations show that CellSighter is able to generalize its training data and learn features of protein expression levels, as well as spatial features such as subcellular expression patterns. CellSighter's design reduces overfitting, and it can be trained with only thousands or even hundreds of labeled examples. CellSighter also outputs a prediction confidence, allowing downstream experts control over the results. Altogether, CellSighter drastically reduces hands-on time for cell classification in multiplexed images, while improving accuracy and consistency across datasets.

DNA Damage Repair Pathways in Prostate Cancer: A Narrative Review of Molecular Mechanisms, Emerging Biomarkers and Therapeutic Targets in Precision Oncology

Prostate cancer (PCa) has a distinct molecular signature, including characteristic chromosomal translocations, gene deletions and defective DNA damage repair mechanisms. One crucial pathway involved is homologous recombination deficiency (HRD) and it is found in almost 20% of metastatic castrate-resistant PCa (mCRPC). Inherited/germline mutations are associated with a hereditary predisposition to early PCa development and aggressive behavior. BRCA2, ATM and CHECK2 are the most frequently HRD-mutated genes. BRCA2-mutated tumors have unfavorable clinical and pathological characteristics, such as intraductal carcinoma. PARP inhibitors, due to the induction of synthetic lethality, have been therapeutically approved for mCRPC with HRD alterations. Mutations are detected in metastatic tissue, while a liquid biopsy is utilized during follow-up, recognizing acquired resistance mechanisms. The mismatch repair (MMR) pathway is another DNA repair mechanism implicated in carcinogenesis, although only 5% of metastatic PCa is affected. It is associated with aggressive disease. PD-1 inhibitors have been used in MMR-deficient tumors; thus, the MMR status should be tested in all metastatic PCa cases. A surrogate marker of defective DNA repair mechanisms is the tumor mutational burden. PDL-1 expression and intratumoral lymphocytes have ambivalent predictive value. Few experimental molecules have been so far proposed as potential biomarkers. Future research may further elucidate the role of DNA damage pathways in PCa, revealing new therapeutic targets and predictive biomarkers.

A phase Ib trial of pembrolizumab plus paclitaxel or flat-dose capecitabine in 1st/2nd line metastatic triple-negative breast cancer

Chemoimmunotherapy with anti-programmed cell death 1/ligand 1 and cytotoxic chemotherapy is a promising therapeutic modality for women with triple-negative breast cancer, but questions remain regarding optimal chemotherapy backbone and biomarkers for patient selection. We report final outcomes from a phase Ib trial evaluating pembrolizumab (200 mg IV every 3 weeks) with either weekly paclitaxel (80 mg/m² weekly) or flat-dose capecitabine (2000 mg orally twice daily for 7 days of every 14-day cycle) in the 1st/2nd line setting. The primary endpoint is safety (receipt of 2 cycles without grade III/IV toxicities requiring discontinuation or ≥21-day delays). The secondary endpoint is efficacy (week 12 objective response). Exploratory aims are to characterize immunologic effects of treatment over time, and to evaluate novel biomarkers. The trial demonstrates that both regimens meet the pre-specified safety endpoint (paclitaxel: 87%; capecitabine: 100%). Objective response rate is 29% for pembrolizumab/paclitaxel (n = 4/13, 95% CI: 10-61%) and 43% for pembrolizumab/capecitabine (n = 6/14, 95% CI: 18-71%). Partial responses are observed in two subjects with chemo-refractory metaplastic carcinoma (both in capecitabine arm). Both regimens are associated with significant peripheral leukocyte contraction over time. Response is associated with clinical PD-L1 score, non-receipt of prior chemotherapy, and the H&E stromal tumor-infiltrating lymphocyte score, but also by a novel 27 gene IO score and spatial biomarkers (lymphocyte spatial skewness). In conclusion, pembrolizumab with paclitaxel or capecitabine is safe and clinically active. Both regimens are lymphodepleting, highlighting the competing immunostimulatory versus lymphotoxic effects of cytotoxic chemotherapy. Further exploration of the IO score and spatial TIL biomarkers is warranted. The clinical trial registration is NCT02734290.

Recent advancements in tumour microenvironment landscaping for target selection and response prediction in immune checkpoint therapies achieved through spatial protein multiplexing analysis

Immune checkpoint therapies have significantly advanced cancer treatment. Nevertheless, the high costs and potential adverse effects associated with these therapies highlight the need for better predictive biomarkers to identify patients who are most likely to benefit from treatment. Unfortunately, the existing biomarkers are insufficient to identify such patients. New high-dimensional spatial technologies have emerged as a valuable tool for discovering novel biomarkers by analysing multiple protein markers at a single-cell resolution in tissue samples. These technologies provide a more comprehensive map of tissue composition, cell functionality, and interactions between different cell types in the tumour microenvironment. In this review, we provide an overview of how spatial protein-based multiplexing technologies have fuelled biomarker discovery and advanced the field of immunotherapy. In particular, we will focus on how these technologies contributed to (i) characterise the tumour microenvironment, (ii) understand the role of tumour heterogeneity, (iii) study the interplay of the immune microenvironment and tumour progression, (iv) discover biomarkers for immune checkpoint therapies (v) suggest novel therapeutic strategies.

Eganelisib, a First-in-Class PI3Kγ Inhibitor, in Patients with Advanced Solid Tumors: Results of the Phase 1/1b MARIO-1 Trial

PURPOSE

Eganelisib (IPI-549) is a first-in-class, orally administered, highly selective PI3Kγ inhibitor with antitumor activity alone and in combination with programmed cell death protein 1/ligand 1 (PD-1/PD-L1) inhibitors in preclinical studies. This phase 1/1b first-in-human, MAcrophage Reprogramming in Immuno-Oncology-1 (NCT02637531) study evaluated the safety and tolerability of once-daily eganelisib as monotherapy and in combination with nivolumab in patients with solid tumors.

PATIENTS AND METHODS

Dose-escalation cohorts received eganelisib 10-60 mg as monotherapy (n = 39) and 20-40 mg when combined with nivolumab (n = 180). Primary endpoints included incidence of dose-limiting toxicities (DLT) and adverse events (AE).

RESULTS

The most common treatment-related grade ≥3 toxicities with monotherapy were increased alanine aminotransferase (ALT; 18%), aspartate aminotransferase (AST; 18%), and alkaline phosphatase (5%). No DLTs occurred in the first 28 days; however, toxicities meeting DLT criteria (mostly grade 3 reversible hepatic enzyme elevations) occurred with eganelisib 60 mg in later treatment cycles. In combination, the most common treatment-related grade ≥3 toxicities were increased AST (13%) and increased ALT and rash (10%). Treatment-related serious AEs occurred in 5% of monotherapy patients (grade 4 bilirubin and hepatic enzyme increases in one patient each) and 13% in combination (pyrexia, rash, cytokine release syndrome, and infusion-related reaction in ≥2 patients each). Antitumor activity was observed in combination, including patients who had progressed on PD-1/PD-L1 inhibitors.

CONCLUSIONS

On the basis of the observed safety profile, eganelisib doses of 30 and 40 mg once daily in combination with PD-1/PD-L1 inhibitors were chosen for phase 2 study.

3D deconvolution of human skin immune architecture with Multiplex Annotated Tissue Imaging System

Routine clinical assays, such as conventional immunohistochemistry, often fail to resolve the regional heterogeneity of complex inflammatory skin conditions. We introduce MANTIS (Multiplex Annotated Tissue Imaging System), a flexible analytic pipeline compatible with routine practice, specifically designed for spatially resolved immune phenotyping of the skin in experimental or clinical samples. On the basis of phenotype attribution matrices coupled to α-shape algorithms, MANTIS projects a representative digital immune landscape while enabling automated detection of major inflammatory clusters and concomitant single-cell data quantification of biomarkers. We observed that severe pathological lesions from systemic lupus erythematosus, Kawasaki syndrome, or COVID-19-associated skin manifestations share common quantitative immune features while displaying a nonrandom distribution of cells with the formation of disease-specific dermal immune structures. Given its accuracy and flexibility, MANTIS is designed to solve the spatial organization of complex immune environments to better apprehend the pathophysiology of skin manifestations.

Comparing and Correcting Spectral Sensitivities between Multispectral Microscopes: A Prerequisite to Clinical Implementation

Multispectral, multiplex immunofluorescence (mIF) microscopy has been used to great effect in research to identify cellular co-expression profiles and spatial relationships within tissue, providing a myriad of diagnostic advantages. As these technologies mature, it is essential that image data from mIF microscopes is reproducible and standardizable across devices. We sought to characterize and correct differences in illumination intensity and spectral sensitivity between three multispectral microscopes. We scanned eight melanoma tissue samples twice on each microscope and calculated their average tissue region flux intensities. We found a baseline average standard deviation of 29.9% across all microscopes, scans, and samples, which was reduced to 13.9% after applying sample-specific corrections accounting for differences in the tissue shown on each slide. We used a basic calibration model to correct sample- and microscope-specific effects on overall brightness and relative brightness as a function of the image layer. We tested the generalizability of the calibration procedure and found that applying corrections to independent validation subsets of the samples reduced the variation to 2.9 ± 0.03%. Variations in the unmixed marker expressions were reduced from 15.8% to 4.4% by correcting the raw images to a single reference microscope. Our findings show that mIF microscopes can be standardized for use in clinical pathology laboratories using a relatively simple correction model.

7-UP: Generating in silico CODEX from a small set of immunofluorescence markers

Multiplex immunofluorescence (mIF) assays multiple protein biomarkers on a single tissue section. Recently, high-plex CODEX (co-detection by indexing) systems enable simultaneous imaging of 40+ protein biomarkers, unlocking more detailed molecular phenotyping, leading to richer insights into cellular interactions and disease. However, high-plex data can be slower and more costly to collect, limiting its applications, especially in clinical settings. We propose a machine learning framework, 7-UP, that can computationally generate in silico 40-plex CODEX at single-cell resolution from a standard 7-plex mIF panel by leveraging cellular morphology. We demonstrate the usefulness of the imputed biomarkers in accurately classifying cell types and predicting patient survival outcomes. Furthermore, 7-UP's imputations generalize well across samples from different clinical sites and cancer types. 7-UP opens the possibility of in silico CODEX, making insights from high-plex mIF more widely available.

Accurate and Rapid Detection of Peritoneal Metastasis from Gastric Cancer by AI-Assisted Stimulated Raman Molecular Cytology

Peritoneal metastasis (PM) is the mostcommon form of distant metastasis and one of the leading causes of death in gastriccancer (GC). For locally advanced GC, clinical guidelines recommend peritoneal lavage cytology for intraoperative PM detection. Unfortunately, current peritoneal lavage cytology is limited by low sensitivity (<60%). Here the authors established the stimulated Raman molecular cytology (SRMC), a chemical microscopy-based intelligent cytology. The authors firstly imaged 53 951 exfoliated cells in ascites obtained from 80 GC patients (27 PM positive, 53 PM negative). Then, the authors revealed 12 single cell features of morphology and composition that are significantly different between PM positive and negative specimens, including cellular area, lipid protein ratio, etc. Importantly, the authors developed a single cell phenotyping algorithm to further transform the above raw features to feature matrix. Such matrix is crucial to identify the significant marker cell cluster, the divergence of which is finally used to differentiate the PM positive and negative. Compared with histopathology, the gold standard of PM detection, their SRMC method could reach 81.5% sensitivity, 84.9% specificity, and the AUC of 0.85, within 20 minutes for each patient. Together, their SRMC method shows great potential for accurate and rapid detection of PM from GC.

The "Great Debate" at Immunotherapy Bridge 2022, Naples, November 30th-December 1st, 2022

The 2022 Immunotherapy Bridge congress (November 30-December 1, Naples, Italy) featured a Great Debate session which addressed three contemporary topics in the field of immunotherapy. The debates included counterpoint views from leading experts and considered whether adoptive cell therapy (ACT) has a role in the treatment of solid tumors, the use of peripheral/blood biomarkers versus tumor microenvironment biomarkers for cancer immunotherapy and the role of chimeric antigen receptor T cell versus natural killer cell therapy. As is the tradition in the Immunotherapy Bridge Great Debates, speakers are invited by the meeting Chairs to express one side of the assigned debate and the opinions given may not fully reflect their own personal views. Audiences voted in favour of either side of the topic both before and after each debate.

Snapshot multispectral imaging using a diffractive optical network

Multispectral imaging has been used for numerous applications in e.g., environmental monitoring, aerospace, defense, and biomedicine. Here, we present a diffractive optical network-based multispectral imaging system trained using deep learning to create a virtual spectral filter array at the output image field-of-view. This diffractive multispectral imager performs spatially-coherent imaging over a large spectrum, and at the same time, routes a pre-determined set of spectral channels onto an array of pixels at the output plane, converting a monochrome focal-plane array or image sensor into a multispectral imaging device without any spectral filters or image recovery algorithms. Furthermore, the spectral responsivity of this diffractive multispectral imager is not sensitive to input polarization states. Through numerical simulations, we present different diffractive network designs that achieve snapshot multispectral imaging with 4, 9 and 16 unique spectral bands within the visible spectrum, based on passive spatially-structured diffractive surfaces, with a compact design that axially spans ~72λ_m, where λ_m is the mean wavelength of the spectral band of interest. Moreover, we experimentally demonstrate a diffractive multispectral imager based on a 3D-printed diffractive network that creates at its output image plane a spatially repeating virtual spectral filter array with 2 × 2 = 4 unique bands at terahertz spectrum. Due to their compact form factor and computation-free, power-efficient and polarization-insensitive forward operation, diffractive multispectral imagers can be transformative for various imaging and sensing applications and be used at different parts of the electromagnetic spectrum where high-density and wide-area multispectral pixel arrays are not widely available.

The localization of molecularly distinct microglia populations to Alzheimer's disease pathologies using QUIVER

New histological techniques are needed to examine protein distribution in human tissues, which can reveal cell shape and disease pathology connections. Spatial proteomics has changed the study of tumor microenvironments by identifying spatial relationships of immunomodulatory cells and proteins and contributing to the discovery of new cancer immunotherapy biomarkers. However, the fast-expanding toolkit of spatial proteomic approaches has yet to be systematically applied to investigate pathological alterations in the aging human brain in health and disease states. Moreover, post-mortem human brain tissue presents distinct technical problems due to fixation procedures and autofluorescence, which limit fluorescence methodologies. This study sought to develop a multiplex immunohistochemistry approach (visualizing the immunostain with brightfield microscopy). Quantitative multiplex Immunohistochemistry with Visual colorimetric staining to Enhance Regional protein localization (QUIVER) was developed to address these technical challenges. Using QUIVER, a ten-channel pseudo-fluorescent image was generated using chromogen removal and digital microscopy to identify unique molecular microglia phenotypes. Next, the study asked if the tissue environment, specifically the amyloid plaques and neurofibrillary tangles characteristic of Alzheimer's disease, has any bearing on microglia's cellular and molecular phenotypes. QUIVER allowed the visualization of five molecular microglia/macrophage phenotypes using digital pathology tools. The recognizable reactive and homeostatic microglia/macrophage phenotypes demonstrated spatial polarization towards and away from amyloid plaques, respectively. Yet, microglia morphology appearance did not always correspond to molecular phenotype. This research not only sheds light on the biology of microglia but also offers QUIVER, a new tool for examining pathological alterations in the brains of the elderly.

Invasive margin tissue-resident macrophages of high CD163 expression impede responses to T cell-based immunotherapy

BACKGROUND

Primary and secondary resistance is a major hurdle in cancer immunotherapy. Therefore, a better understanding of the underlying mechanisms involved in immunotherapy resistance is of pivotal importance to improve therapy outcome.

METHOD

Here, two mouse models with resistance against therapeutic vaccine-induced tumor regression were studied. Exploration of the tumor microenvironment by high dimensional flow cytometry in combination with therapeutic in vivo settings allowed for the identification of immunological factors driving immunotherapy resistance.

RESULTS

Comparison of the tumor immune infiltrate during early and late regression revealed a change from tumor-rejecting toward tumor-promoting macrophages. In concert, a rapid exhaustion of tumor-infiltrating T cells was observed. Perturbation studies identified a small but discernible CD163^hi macrophage population, with high expression of several tumor-promoting macrophage markers and a functional anti-inflammatory transcriptome profile, but not other macrophages, to be responsible. In-depth analyses revealed that they localize at the tumor invasive margins and are more resistant to Csf1r inhibition when compared with other macrophages. In vivo studies validated the activity of heme oxygenase-1 as an underlying mechanism of immunotherapy resistance. The transcriptomic profile of CD163^hi macrophages is highly similar to a human monocyte/macrophage population, indicating that they represent a target to improve immunotherapy efficacy.

CONCLUSIONS

In this study, a small population of CD163^hi tissue-resident macrophages is identified to be responsible for primary and secondary resistance against T-cell-based immunotherapies. While these CD163^hi M2 macrophages are resistant to Csf1r-targeted therapies, in-depth characterization and identification of the underlying mechanisms driving immunotherapy resistance allows the specific targeting of this subset of macrophages, thereby creating new opportunities for therapeutic intervention with the aim to overcome immunotherapy resistance.

Society for Immunotherapy of Cancer (SITC) consensus definitions for resistance to combinations of immune checkpoint inhibitors

Immunotherapy is the standard of care for several cancers and the field continues to advance at a rapid pace, with novel combinations leading to indications in an increasing number of disease settings. Durable responses and long-term survival with immunotherapy have been demonstrated in some patients, though lack of initial benefit and recurrence after extended disease control remain major hurdles for the field. Many new combination regimens are in development for patients whose disease progressed on initial immunotherapy. To guide clinical trial design and support analyses of emerging molecular and cellular data surrounding mechanisms of resistance, the Society for Immunotherapy of Cancer (SITC) previously generated consensus clinical definitions for resistance to single-agent anti-PD-1 immune checkpoint inhibitors (ICIs) in three distinct scenarios: primary resistance, secondary resistance, and progression after treatment discontinuation. An unmet need still exists, however, for definitions of resistance to ICI-based combinations, which represent an expanding frontier in the immunotherapy treatment landscape. In 2021, SITC convened a workshop including stakeholders from academia, industry, and government to develop consensus definitions for resistance to ICI-based combination regimens for improved outcome assessment, trial design and drug development. This manuscript reports the minimum drug exposure requirements and time frame for progression that define resistance in both the metastatic setting and the perioperative setting, as well as key caveats and areas for future research with ICI/ICI combinations. Definitions for resistance to ICIs in combination with chemotherapy and targeted therapy will be published in companion volumes to this paper.

Combinatorial biomarker for predicting outcomes to anti-PD-1 therapy in patients with metastatic clear cell renal cell carcinoma

With a rapidly developing immunotherapeutic landscape for patients with metastatic clear cell renal cell carcinoma, biomarkers of efficacy are highly desirable to guide treatment strategy. Hematoxylin and eosin (H&E)-stained slides are inexpensive and widely available in pathology laboratories, including in resource-poor settings. Here, H&E scoring of tumor-infiltrating immune cells (TILplus) in pre-treatment tumor specimens using light microscopy is associated with improved overall survival (OS) in three independent cohorts of patients receiving immune checkpoint blockade. Necrosis score alone does not associate with OS; however, necrosis modifies the predictive effect of TILplus, a finding that has broad translational relevance for tissue-based biomarker development. PBRM1 mutational status is combined with H&E scores to further refine outcome predictions (OS, p = 0.007, and objective response, p = 0.04). These findings bring H&E assessment to the fore for biomarker development in future prospective, randomized trials, and emerging multi-omics classifiers.

Companion diagnostic requirements for spatial biology using multiplex immunofluorescence and multispectral imaging

Immunohistochemistry has long been held as the gold standard for understanding the expression patterns of therapeutically relevant proteins to identify prognostic and predictive biomarkers. Patient selection for targeted therapy in oncology has successfully relied upon standard microscopy-based methodologies, such as single-marker brightfield chromogenic immunohistochemistry. As promising as these results are, the analysis of one protein, with few exceptions, no longer provides enough information to draw effective conclusions about the probability of treatment response. More multifaceted scientific queries have driven the development of high-throughput and high-order technologies to interrogate biomarker expression patterns and spatial interactions between cell phenotypes in the tumor microenvironment. Such multi-parameter data analysis has been historically reserved for technologies that lack the spatial context that is provided by immunohistochemistry. Over the past decade, technical developments in multiplex fluorescence immunohistochemistry and discoveries made with improving image data analysis platforms have highlighted the importance of spatial relationships between certain biomarkers in understanding a patient's likelihood to respond to, typically, immune checkpoint inhibitors. At the same time, personalized medicine has instigated changes in both clinical trial design and its conduct in a push to make drug development and cancer treatment more efficient, precise, and economical. Precision medicine in immuno-oncology is being steered by data-driven approaches to gain insight into the tumor and its dynamic interaction with the immune system. This is particularly necessary given the rapid growth in the number of trials involving more than one immune checkpoint drug, and/or using those in combination with conventional cancer treatments. As multiplex methods, like immunofluorescence, push the boundaries of immunohistochemistry, it becomes critical to understand the foundation of this technology and how it can be deployed for use as a regulated test to identify the prospect of response from mono- and combination therapies. To that end, this work will focus on: 1) the scientific, clinical, and economic requirements for developing clinical multiplex immunofluorescence assays; 2) the attributes of the Akoya Phenoptics workflow to support predictive tests, including design principles, verification, and validation needs; 3) regulatory, safety and quality considerations; 4) application of multiplex immunohistochemistry through lab-developed-tests and regulated in vitro diagnostic devices.

Bypassing anti-PD-(L)1 therapy: Mechanisms and management strategies

Resistance to immune checkpoint inhibitors (ICIs) is a major issue of the current era in cancer immunotherapy. Immune evasion is a multi-factorial event, which occurs generally at a base of cold immunity. Despite advances in the field, there are still unsolved challenges about how to combat checkpoint hijacked by tumor cells and what are complementary treatment strategies to render durable anti-tumor outcomes. A point is that anti-programed death-1 receptor (PD-1)/anti-programmed death-ligand 1 (PD-L1) is not the solo path of immune escape, and responses in many types of solid tumors to the PD-1/PD-L1 inhibitors are not satisfactory. Thus, seeking mechanisms inter-connecting tumor with its immune ecosystem nearby unravel more about resistance mechanisms so as to develop methods for sustained reinvigoration of immune activity against cancer. In this review, we aimed to discuss about common and specific paths taken by tumor cells to evade immune surveillance, describing novel detection strategies, as well as suggesting some approaches to recover tumor sensitivity to the anti-PD-(L)1 therapy based on the current knowledge.

Technology meets TILs: Deciphering T cell function in the -omics era

T cells are at the center of cancer immunology because of their ability to recognize mutations in tumor cells and directly mediate cancer cell killing. Immunotherapies to rejuvenate exhausted T cell responses have transformed the clinical management of several malignancies. In parallel, the development of novel multidimensional analysis platforms, such as single-cell RNA sequencing and high-dimensional flow cytometry, has yielded unprecedented insights into immune cell biology. This convergence has revealed substantial heterogeneity of tumor-infiltrating immune cells in single tumors, across tumor types, and among individuals with cancer. Here we discuss the opportunities and challenges of studying the complex tumor microenvironment with -omics technologies that generate vast amounts of data, highlighting the opportunities and limitations of these technologies with a particular focus on interpreting high-dimensional studies of CD8+ T cells in the tumor microenvironment.

Heterogeneity of Glycan Biomarker Clusters as an Indicator of Recurrence in Pancreatic Cancer

Outcomes following tumor resection vary dramatically among patients with pancreatic cancer. A challenge in defining predictive biomarkers is to discern within the complex tumor tissue the specific subpopulations and relationships that drive recurrence. Multiplexed immunofluorescence is valuable for such studies when supplied with markers of relevant subpopulations and analysis methods to sort out the intra-tumor relationships that are informative of tumor behavior. We hypothesized that the glycan biomarkers CA19-9 and STRA, which detect separate subpopulations of cancer cells, define intra-tumoral features associated with recurrence. We probed this question using automated signal thresholding and spatial cluster analysis applied to the immunofluorescence images of the STRA and CA19-9 glycan biomarkers in whole-block tumor sections. The tumors (N = 22) displayed extreme diversity between them in the amounts of the glycans and in the levels of spatial clustering, but neither the amounts nor the clusters of the individual and combined glycans associated with recurrence. The combined glycans, however, marked divergent types of spatial clusters, alternatively only STRA, only CA19-9, or both. The co-occurrence of more than one cluster type within a tumor associated significantly with disease recurrence, in contrast to the independent occurrence of each type of cluster. In addition, intra-tumoral regions with heterogeneity in biomarker clusters spatially aligned with pathology-confirmed cancer cells, whereas regions with homogeneous biomarker clusters aligned with various non-cancer cells. Thus, the STRA and CA19-9 glycans are markers of distinct and co-occurring subpopulations of cancer cells that in combination are associated with recurrence. Furthermore, automated signal thresholding and spatial clustering provides a tool for quantifying intra-tumoral subpopulations that are informative of outcome.

FOXP3 expression diversifies the metabolic capacity and enhances the efficacy of CD8 T cells in adoptive immunotherapy of melanoma

Regulatory T cells overwhelm conventional T cells in the tumor microenvironment (TME) thanks to a FOXP3-driven metabolic program that allows them to engage different metabolic pathways. Using a melanoma model of adoptive T cell therapy (ACT), we show that FOXP3 overexpression in mature CD8 T cells improved their antitumor efficacy, favoring their tumor recruitment, proliferation, and cytotoxicity. FOXP3-overexpressing (Foxp3UP) CD8 T cells exhibited features of tissue-resident memory-like and effector T cells, but not suppressor activity. Transcriptomic analysis of tumor-infiltrating Foxp3UP CD8 T cells showed positive enrichment in a wide variety of metabolic pathways, such as glycolysis, fatty acid (FA) metabolism, and oxidative phosphorylation (OXPHOS). Intratumoral Foxp3UP CD8 T cells exhibited an enhanced capacity for glucose and FA uptake as well as accumulation of intracellular lipids. Interestingly, Foxp3UP CD8 T cells compensated for the loss of mitochondrial respiration-driven ATP production by activating aerobic glycolysis. Moreover, in limiting nutrient conditions these cells engaged FA oxidation to drive OXPHOS for their energy demands. Importantly, their ability to couple glycolysis and OXPHOS allowed them to sustain proliferation under glucose restriction. Our findings demonstrate a hitherto unknown role for FOXP3 in the adaptation of CD8 T cells to TME that may enhance their efficacy in ACT.

Heterogeneity of glycan biomarker clusters as an indicator of recurrence in pancreatic cancer

Introduction

Outcomes following tumor resection vary dramatically among patients with pancreatic ductal adenocarcinoma (PDAC). A challenge in defining predictive biomarkers is to discern within the complex tumor tissue the specific subpopulations and relationships that drive recurrence. Multiplexed immunofluorescence is valuable for such studies when supplied with markers of relevant subpopulations and analysis methods to sort out the intra-tumor relationships that are informative of tumor behavior. We hypothesized that the glycan biomarkers CA19-9 and STRA, which detect separate subpopulations of cancer cells, define intra-tumoral features associated with recurrence.

Methods

We probed this question using automated signal thresholding and spatial cluster analysis applied to the immunofluorescence images of the STRA and CA19-9 glycan biomarkers in whole-block sections of PDAC tumors collected from curative resections.

Results

The tumors (N = 22) displayed extreme diversity between them in the amounts of the glycans and in the levels of spatial clustering, but neither the amounts nor the clusters of the individual and combined glycans associated with recurrence. The combined glycans, however, marked divergent types of spatial clusters, alternatively only STRA, only CA19-9, or both. The co-occurrence of more than one cluster type within a tumor associated significantly with disease recurrence, in contrast to the independent occurrence of each type of cluster. In addition, intra-tumoral regions with heterogeneity in biomarker clusters spatially aligned with pathology-confirmed cancer cells, whereas regions with homogeneous biomarker clusters aligned with various non-cancer cells.

Conclusion

Thus, the STRA and CA19-9 glycans are markers of distinct and co-occurring subpopulations of cancer cells that in combination are associated with recurrence. Furthermore, automated signal thresholding and spatial clustering provides a tool for quantifying intra-tumoral subpopulations that are informative of outcome.

Multiparameter single-cell proteomic technologies give new insights into the biology of ovarian tumors

High-grade serous ovarian cancer (HGSOC) is the most lethal gynecological malignancy. Its diagnosis at advanced stage compounded with its excessive genomic and cellular heterogeneity make curative treatment challenging. Two critical therapeutic challenges to overcome are carboplatin resistance and lack of response to immunotherapy. Carboplatin resistance results from diverse cell autonomous mechanisms which operate in different combinations within and across tumors. The lack of response to immunotherapy is highly likely to be related to an immunosuppressive HGSOC tumor microenvironment which overrides any clinical benefit. Results from a number of studies, mainly using transcriptomics, indicate that the immune tumor microenvironment (iTME) plays a role in carboplatin response. However, in patients receiving treatment, the exact mechanistic details are unclear. During the past decade, multiplex single-cell proteomic technologies have come to the forefront of biomedical research. Mass cytometry or cytometry by time-of-flight, measures up to 60 parameters in single cells that are in suspension. Multiplex cellular imaging technologies allow simultaneous measurement of up to 60 proteins in single cells with spatial resolution and interrogation of cell-cell interactions. This review suggests that functional interplay between cell autonomous responses to carboplatin and the HGSOC immune tumor microenvironment could be clarified through the application of multiplex single-cell proteomic technologies. We conclude that for better clinical care, multiplex single-cell proteomic technologies could be an integral component of multimodal biomarker development that also includes genomics and radiomics. Collection of matched samples from patients before and on treatment will be critical to the success of these efforts.

Pan-cancer Landscape of Programmed Death Ligand-1 and Programmed Death Ligand-2 Structural Variations

PURPOSE

Programmed cell death protein-1 (PD-1) receptor and ligand interactions are the target of immunotherapies for more than 20 cancer types. Biomarkers that predict response to immunotherapy are microsatellite instability, tumor mutational burden, and programmed death ligand-1 (PD-L1) immunohistochemistry. Structural variations (SVs) in PD-L1 (CD274) and PD-L2 (PDCD1LG2) have been observed in cancer, but the comprehensive landscape is unknown. Here, we describe the genomic landscape of PD-L1 and PD-L2 SVs, their potential impact on the tumor microenvironment, and evidence that patients with these alterations can benefit from immunotherapy.

METHODS

We analyzed sequencing data from cancer cases with PD-L1 and PD-L2 SVs across 22 publications and four data sets, including Foundation Medicine Inc, The Cancer Genome Atlas, International Cancer Genome Consortium, and the Oncology Research Information Exchange Network. We leveraged RNA sequencing to evaluate immune signatures. We curated literature reporting clinical outcomes of patients harboring PD-L1 or PD-L2 SVs.

RESULTS

Using data sets encompassing 300,000 tumors, we curated 486 cases with SVs in PD-L1 and PD-L2 and observed consistent breakpoint patterns, or hotspots. Leveraging The Cancer Genome Atlas, we observed significant upregulation in PD-L1 expression and signatures for interferon signaling, macrophages, T cells, and immune cell proliferation in samples harboring PD-L1 or PD-L2 SVs. Retrospective review of 12 studies that identified patients with SVs in PD-L1 or PD-L2 revealed > 50% (52/71) response rate to PD-1 immunotherapy with durable responses.

CONCLUSION

Our findings show that the 3'-UTR is frequently affected, and that SVs are associated with increased expression of ligands and immune signatures. Retrospective evidence from curated studies suggests this genomic alteration could help identify candidates for PD-1/PD-L1 immunotherapy. We expect these findings will better define PD-L1 and PD-L2 SVs in cancer and lend support for prospective clinical trials to target these alterations.

Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data

Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.