Mapping the Immune Landscape in Metastatic Melanoma Reveals Localized Cell-Cell Interactions That Predict Immunotherapy Response

Dissecting the immune infiltrate of primary luminal B-like breast carcinomas in relation to age

The impact of aging on the immune landscape of luminal breast cancer (Lum-BC) is poorly characterized. Understanding the age-related dynamics of immune editing in Lum-BC is anticipated to improve the therapeutic benefit of immunotherapy in older patients. To this end, here we applied the 'multiple iterative labeling by antibody neo-deposition' (MILAN) technique, a spatially resolved single-cell multiplex immunohistochemistry method. We created tissue microarrays by sampling both the tumor center and invasive front of luminal breast tumors collected from a cohort of treatment-naïve patients enrolled in the prospective monocentric IMAGE (IMmune system and AGEing) study. Patients were subdivided into three nonoverlapping age categories (35-45 = 'young', n = 12; 55-65 = 'middle', n = 15; ≥70 = 'old', n = 26). Additionally, depending on localization and amount of cytotoxic T lymphocytes, the tumor immune types 'desert' (n = 22), 'excluded' (n = 19), and 'inflamed' (n = 12) were identified. For the MILAN technique we used 58 markers comprising phenotypic and functional markers allowing in-depth characterization of T and B lymphocytes (T&B-lym). These were compared between age groups and tumor immune types using Wilcoxon's test and Pearson's correlation. Cytometric analysis revealed a decline of the immune cell compartment with aging. T&B-lym were numerically less abundant in tumors from middle-aged and old compared to young patients, regardless of the geographical tumor zone. Likewise, desert-type tumors showed the smallest immune-cell compartment and were not represented in the group of young patients. Analysis of immune checkpoint molecules revealed a heterogeneous geographical pattern of expression, indicating higher numbers of PD-L1 and OX40-positive T&B-lym in young compared to old patients. Despite the numerical decline of immune infiltration, old patients retained higher expression levels of OX40 in T helper cells located near cancer cells, compared to middle-aged and young patients. Aging is associated with important numerical and functional changes of the immune landscape in Lum-BC. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Spatial landscapes of cancers: insights and opportunities

Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.

Tumor eradication by triplet therapy with BRAF inhibitor, TLR 7 agonist, and PD-1 antibody for BRAF-mutated melanoma

Programmed death 1 (PD-1)/programmed death-ligand 1 inhibitors are commonly used to treat various cancers, including melanoma. However, their efficacy as monotherapy is limited, and combination immunotherapies are being explored to improve outcomes. In this study, we investigated a combination immunotherapy involving an anti-PD-1 antibody that blocks the major adaptive immune-resistant mechanisms, a BRAF inhibitor that inhibits melanoma cell proliferation, and multiple primary immune-resistant mechanisms, such as cancer cell-derived immunosuppressive cytokines, and a Toll-like receptor 7 agonist that enhances innate immune responses that promote antitumor T-cell induction and functions. Using a xenogeneic nude mouse model implanted with human BRAF-mutated melanoma, a BRAF inhibitor vemurafenib was found to restore T-cell-stimulatory activity in conventional dendritic cells by reducing immunosuppressive cytokines, including interleukin 6, produced by human melanoma. Additionally, intravenous administration of the Toll-like receptor 7 agonist DSR6434 enhanced tumor growth inhibition by vemurafenib through stimulating the plasmacytoid dendritic cells/interferon-α/natural killer cell pathways and augmenting the T-cell-stimulatory activity of conventional dendritic cells. In a syngeneic mouse model implanted with murine BRAF-mutated melanoma, the vemurafenib and DSR6434 combination synergistically augmented the induction of melanoma antigen gp100-specific T cells and inhibited tumor growth. Notably, only triplet therapy with vemurafenib, DSR6434, and the anti-PD-1 antibody resulted in complete regression of SIY antigen-transduced BRAF-mutated melanoma in a CD8 T-cell-dependent manner. These findings indicate that a triple-combination strategy targeting adaptive and primary resistant mechanisms while enhancing innate immune responses that promote tumor-specific T cells may be crucial for effective tumor eradication.

Spatial Immunophenotyping from Whole-Slide Multiplexed Tissue Imaging Using Convolutional Neural Networks

The multiplexed immunofluorescence (mIF) platform enables biomarker discovery through the simultaneous detection of multiple markers on a single tissue slide, offering detailed insights into intratumor heterogeneity and the tumor-immune microenvironment at spatially resolved single cell resolution. However, current mIF image analyses are labor-intensive, requiring specialized pathology expertise which limits their scalability and clinical application. To address this challenge, we developed CellGate, a deep-learning (DL) computational pipeline that provides streamlined, end-to-end whole-slide mIF image analysis including nuclei detection, cell segmentation, cell classification, and combined immuno-phenotyping across stacked images. The model was trained on over 750,000 single cell images from 34 melanomas in a retrospective cohort of patients using whole tissue sections stained for CD3, CD8, CD68, CK-SOX10, PD-1, PD-L1, and FOXP3 with manual gating and extensive pathology review. When tested on new whole mIF slides, the model demonstrated high precision-recall AUC. Further validation on whole-slide mIF images of 9 primary melanomas from an independent cohort confirmed that CellGate can reproduce expert pathology analysis with high accuracy. We show that spatial immuno-phenotyping results using CellGate provide deep insights into the immune cell topography and differences in T cell functional states and interactions with tumor cells in patients with distinct histopathology and clinical characteristics. This pipeline offers a fully automated and parallelizable computing process with substantially improved consistency for cell type classification across images, potentially enabling high throughput whole-slide mIF tissue image analysis for large-scale clinical and research applications.

The current landscape of spatial biomarkers for prediction of response to immune checkpoint inhibition

Enabling the examination of cell-cell relationships in tissue, spatially resolved omics technologies have revolutionised our perspectives on cancer biology. Clinically, the development of immune checkpoint inhibitors (ICI) has advanced cancer therapeutics. However, a major challenge of effective implementation is the identification of predictive biomarkers of response. In this review we examine the potential added predictive value of spatial biomarkers of response to ICI beyond current clinical benchmarks.

Nomogram for predicting survival after first-line anti-PD-1-based immunotherapy in unresectable stage IV melanoma: a multicenter international study

BACKGROUND

The introduction of anti-programmed cell death protein 1 (PD-1) immunotherapy has revolutionized the treatment landscape for melanoma, enhancing both response rates and survival outcomes in patients with advanced stages of the disease. Despite these remarkable advances, a noteworthy subset of patients (40%-60%) does not derive advantage from this therapeutic approach. This study aims to identify key predictive factors and create a user-friendly predictive nomogram for stage IV melanoma patients receiving first-line anti-PD-1-based immunotherapy, improving treatment decisions.

MATERIALS AND METHODS

In this retrospective study, we included patients with unresectable stage IV melanoma who received first-line treatment with either anti-PD-1 monotherapy or anti-PD-1 plus anti-cytotoxic T-lymphocyte associated protein 4 between 2014 and 2018. We documented clinicopathological features and blood markers upon therapy initiation. By employing the random survival forest model and backward variable selection of the Cox model, we identified variables associated with progression-free survival (PFS) after the first-line anti-PD-1-based treatment. We developed and validated a predictive nomogram for PFS utilizing the identified variables. We assessed calibration and discrimination performance metrics as part of the evaluation process.

RESULTS

The study involved 719 patients, divided into a training cohort of 405 (56%) patients and a validation cohort of 314 (44%) patients. We combined findings from the random survival forest and the Cox model to create a nomogram that incorporates the following factors: lactate dehydrogenase (LDH), S100, melanoma subtype, neutrophil-to-lymphocyte ratio (NLR), body mass index, type of immune checkpoint inhibitor, and presence of liver or brain metastasis. The resultant model had a C-index of 0.67 in the training cohort and 0.66 in the validation cohort. Performance remained in different patient subgroups. Calibration analysis revealed a favorable correlation between predicted and actual PFS rates.

CONCLUSIONS

We developed and validated a predictive nomogram for long-term PFS in patients with unresectable stage IV melanoma undergoing first-line anti-PD-1-based immunotherapy.

Genomic and Epigenomic Biomarkers of Immune Checkpoint Immunotherapy Response in Melanoma: Current and Future Perspectives

Immune checkpoint inhibitors (ICIs) demonstrate durable responses, long-term survival benefits, and improved outcomes in cancer patients compared to chemotherapy. However, the majority of cancer patients do not respond to ICIs, and a high proportion of those patients who do respond to ICI therapy develop innate or acquired resistance to ICIs, limiting their clinical utility. The most studied predictive tissue biomarkers for ICI response are PD-L1 immunohistochemical expression, DNA mismatch repair deficiency, and tumour mutation burden, although these are weak predictors of ICI response. The identification of better predictive biomarkers remains an important goal to improve the identification of patients who would benefit from ICIs. Here, we review established and emerging biomarkers of ICI response, focusing on epigenomic and genomic alterations in cancer patients, which have the potential to help guide single-agent ICI immunotherapy or ICI immunotherapy in combination with other ICI immunotherapies or agents. We briefly review the current status of ICI response biomarkers, including investigational biomarkers, and we present insights into several emerging and promising epigenomic biomarker candidates, including current knowledge gaps in the context of ICI immunotherapy response in melanoma patients.

A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma

An important challenge in the real-world management of patients with advanced clear-cell renal cell carcinoma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB). Here we performed a comprehensive multiomics mapping of aRCC in the context of ICB treatment, involving discovery analyses in a real-world data cohort followed by validation in independent cohorts. We cross-connected bulk-tumor transcriptomes across >1,000 patients with validations at single-cell and spatial resolutions, revealing a patient-specific crosstalk between proinflammatory tumor-associated macrophages and (pre-)exhausted CD8+ T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning signature correlated with positive outcome following ICB treatment in both real-world data and independent clinical cohorts. In experiments using the RENCA-tumor mouse model, CD40 agonism combined with PD1 blockade potentiated both proinflammatory tumor-associated macrophages and CD8+ T cells, thereby achieving maximal antitumor efficacy relative to other tested regimens. Thus, we present a new multiomics and spatial map of the immune-community architecture that drives ICB response in patients with aRCC.

PI3K/mTOR inhibition induces tumour microenvironment remodelling and sensitises pS6high uterine leiomyosarcoma to PD-1 blockade

BACKGROUND

Uterine leiomyosarcomas (uLMS) are aggressive tumours with poor prognosis and limited treatment options. Although immune checkpoint blockade (ICB) has proven effective in some 'challenging-to-treat' cancers, clinical trials showed that uLMS do not respond to ICB. Emerging evidence suggests that aberrant PI3K/mTOR signalling can drive resistance to ICB. We therefore explored the relevance of the PI3K/mTOR pathway for ICB treatment in uLMS and explored pharmacological inhibition of this pathway to sensitise these tumours to ICB.

METHODS

We performed an integrated multiomics analysis based on TCGA data to explore the correlation between PI3K/mTOR dysregulation and immune infiltration in 101 LMS. We assessed response to PI3K/mTOR inhibitors in immunodeficient and humanized uLMS patient-derived xenografts (PDXs) by evaluating tumour microenvironment modulation using multiplex immunofluorescence. We explored response to single-agent and a combination of PI3K/mTOR inhibitors with PD-1 blockade in humanized uLMS PDXs. We mapped intratumoural dynamics using single-cell RNA/TCR sequencing of serially collected biopsies.

RESULTS

PI3K/mTOR over-activation (pS6high) associated with lymphocyte depletion and wound healing immune landscapes in (u)LMS, suggesting it contributes to immune evasion. In contrast, PI3K/mTOR inhibition induced profound tumour microenvironment remodelling in an ICB-resistant humanized uLMS PDX model, fostering adaptive anti-tumour immune responses. Indeed, PI3K/mTOR inhibition induced macrophage repolarisation towards an anti-tumourigenic phenotype and increased antigen presentation on dendritic and tumour cells, but also promoted infiltration of PD-1+ T cells displaying an exhausted phenotype. When combined with anti-PD-1, PI3K/mTOR inhibition led to partial or complete tumour responses, whereas no response to single-agent anti-PD-1 was observed. Combination therapy reinvigorated exhausted T cells and induced clonal hyper-expansion of a cytotoxic CD8+ T-cell population supported by a CD4+ Th1 niche.

CONCLUSIONS

Our findings indicate that aberrant PI3K/mTOR pathway activation contributes to immune escape in uLMS and provides a rationale for combining PI3K/mTOR inhibition with ICB for the treatment of this patient population.

An autophagy program that promotes T cell egress from the lymph node controls responses to immune checkpoint blockade

Lymphatic endothelial cells (LECs) of the lymph node (LN) parenchyma orchestrate leukocyte trafficking and peripheral T cell dynamics. T cell responses to immunotherapy largely rely on peripheral T cell recruitment in tumors. Yet, a systematic and molecular understanding of how LECs within the LNs control T cell dynamics under steady-state and tumor-bearing conditions is lacking. Intravital imaging combined with immune phenotyping shows that LEC-specific deletion of the essential autophagy gene Atg5 alters intranodal positioning of lymphocytes and accrues their persistence in the LNs by increasing the availability of the main egress signal sphingosine-1-phosphate. Single-cell RNA sequencing of tumor-draining LNs shows that loss of ATG5 remodels niche-specific LEC phenotypes involved in molecular pathways regulating lymphocyte trafficking and LEC-T cell interactions. Functionally, loss of LEC autophagy prevents recruitment of tumor-infiltrating T and natural killer cells and abrogates response to immunotherapy. Thus, an LEC-autophagy program boosts immune-checkpoint responses by guiding systemic T cell dynamics.

Macrophage barrier in the tumor microenvironment and potential clinical applications

The tumor microenvironment (TME) constitutes a complex microenvironment comprising a diverse array of immune cells and stromal components. Within this intricate context, tumor-associated macrophages (TAMs) exhibit notable spatial heterogeneity. This heterogeneity contributes to various facets of tumor behavior, including immune response modulation, angiogenesis, tissue remodeling, and metastatic potential. This review summarizes the spatial distribution of macrophages in both the physiological environment and the TME. Moreover, this paper explores the intricate interactions between TAMs and diverse immune cell populations (T cells, dendritic cells, neutrophils, natural killer cells, and other immune cells) within the TME. These bidirectional exchanges form a complex network of immune interactions that influence tumor immune surveillance and evasion strategies. Investigating TAM heterogeneity and its intricate interactions with different immune cell populations offers potential avenues for therapeutic interventions. Additionally, this paper discusses therapeutic strategies targeting macrophages, aiming to uncover novel approaches for immunotherapy. Video Abstract.

Rapid visualization of PD-L1 expression level in glioblastoma immune microenvironment via machine learning cascade-based Raman histopathology

INTRODUCTION

Combination immunotherapy holds promise for improving survival in responsive glioblastoma (GBM) patients. Programmed death-ligand 1 (PD-L1) expression in immune microenvironment (IME) is the most important predictive biomarker for immunotherapy. Due to the heterogeneous distribution of PD-L1, post-operative histopathology fails to accurately capture its expression in residual tumors, making intra-operative diagnosis crucial for GBM treatment strategies. However, the current methods for evaluating the expression of PD-L1 are still time-consuming.

OBJECTIVE

To overcome the PD-L1 heterogeneity and enable rapid, accurate, and label-free imaging of PD-L1 expression level in GBM IME at the tissue level.

METHODS

We proposed a novel intra-operative diagnostic method, Machine Learning Cascade (MLC)-based Raman histopathology, which uses a coordinate localization system (CLS), hierarchical clustering analysis (HCA), support vector machine (SVM), and similarity analysis (SA). This method enables visualization of PD-L1 expression in glioma cells, CD8+ T cells, macrophages, and normal cells in addition to the tumor/normal boundary. The study quantified PD-L1 expression levels using the tumor proportion, combined positive, and cellular composition scores (TPS, CPS, and CCS, respectively) based on Raman data. Furthermore, the association between Raman spectral features and biomolecules was examined biochemically.

RESULTS

The entire process from signal collection to visualization could be completed within 30 min. In an orthotopic glioma mouse model, the MLC-based Raman histopathology demonstrated a high average accuracy (0.990) for identifying different cells and exhibited strong concordance with multiplex immunofluorescence (84.31 %) and traditional pathologists' scoring (R2 ≥ 0.9). Moreover, the peak intensities at 837 and 874 cm-1 showed a positive linear correlation with PD-L1 expression level.

CONCLUSIONS

This study introduced a new and extendable diagnostic method to achieve rapid and accurate visualization of PD-L1 expression in GBM IMB at the tissular level, leading to great potential in GBM intraoperative diagnosis for guiding surgery and post-operative immunotherapy.

Tumor endothelial cell autophagy is a key vascular-immune checkpoint in melanoma

Tumor endothelial cells (TECs) actively repress inflammatory responses and maintain an immune-excluded tumor phenotype. However, the molecular mechanisms that sustain TEC-mediated immunosuppression remain largely elusive. Here, we show that autophagy ablation in TECs boosts antitumor immunity by supporting infiltration and effector function of T-cells, thereby restricting melanoma growth. In melanoma-bearing mice, loss of TEC autophagy leads to the transcriptional expression of an immunostimulatory/inflammatory TEC phenotype driven by heightened NF-kB and STING signaling. In line, single-cell transcriptomic datasets from melanoma patients disclose an enriched InflammatoryHigh /AutophagyLow TEC phenotype in correlation with clinical responses to immunotherapy, and responders exhibit an increased presence of inflamed vessels interfacing with infiltrating CD8+ T-cells. Mechanistically, STING-dependent immunity in TECs is not critical for the immunomodulatory effects of autophagy ablation, since NF-kB-driven inflammation remains functional in STING/ATG5 double knockout TECs. Hence, our study identifies autophagy as a principal tumor vascular anti-inflammatory mechanism dampening melanoma antitumor immunity.

Delineating spatial cell-cell interactions in the solid tumour microenvironment through the lens of highly multiplexed imaging

The growth and metastasis of solid tumours is known to be facilitated by the tumour microenvironment (TME), which is composed of a highly diverse collection of cell types that interact and communicate with one another extensively. Many of these interactions involve the immune cell population within the TME, referred to as the tumour immune microenvironment (TIME). These non-cell autonomous interactions exert substantial influence over cell behaviour and contribute to the reprogramming of immune and stromal cells into numerous pro-tumourigenic phenotypes. The study of some of these interactions, such as the PD-1/PD-L1 axis that induces CD8+ T cell exhaustion, has led to the development of breakthrough therapeutic advances. Yet many common analyses of the TME either do not retain the spatial data necessary to assess cell-cell interactions, or interrogate few (<10) markers, limiting the capacity for cell phenotyping. Recently developed digital pathology technologies, together with sophisticated bioimage analysis programs, now enable the high-resolution, highly-multiplexed analysis of diverse immune and stromal cell markers within the TME of clinical specimens. In this article, we review the tumour-promoting non-cell autonomous interactions in the TME and their impact on tumour behaviour. We additionally survey commonly used image analysis programs and highly-multiplexed spatial imaging technologies, and we discuss their relative advantages and limitations. The spatial organization of the TME varies enormously between patients, and so leveraging these technologies in future studies to further characterize how non-cell autonomous interactions impact tumour behaviour may inform the personalization of cancer treatment.​.

New-generation technologies for spatial tissue analysis, indispensable tools for deciphering intratumor heterogeneity in the development of antibody-drug conjugates and radio-immunoconjugates for cancer treatment

Technologies allowing in situ tissue molecular analysis of the "high-plex" type (>20 molecules per tissue section) are the 21st century inventions that are revolutionizing our knowledge of the biology of malignant tumors and many benign alterations. These technologies are based on specific probe labeling systems for the detection of tissue components [proteins, messenger RNA (mRNA)], as well as on detailed image analysis, combined with computational tools. We are synthetically presenting technologies based on image analysis, such as multiplex immunofluorescence (mIF), imaging mass cytometry (IMC), and multiplexed ion beam imaging (MIBI), as well as the ones not based on image analysis, such as multiplex in situ hybridizations (ISHs) using various principles. All of them are supported by powerful software which enable both tissue segmentation and data analysis. In the context of cancer treatment personalization, these technologies can reveal areas of tumor tissue and/or cellular subpopulations that are responsible for good or bad responses to anticancer drugs. Thus, they represent an unprecedented aid in the exploration of intratumor heterogeneity (ITH), which has already been shown to be one of the main reasons for the therapeutic failure of targeted anticancer treatments. The arrival of antibody-drug conjugates (ADCs) and radio-immunoconjugates (RICs) in the therapeutic arsenal in oncology imposes a deep exploration of molecular ITH, where technologies of spatial tissue analysis reveal an emerging category of biomarkers-spatial biomarkers.

Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases

BACKGROUND

Recent advances in digital pathology have enabled accurate and standardised enumeration of tumour-infiltrating lymphocytes (TILs). Here, we aim to evaluate TILs as a percentage electronic TIL score (eTILs) and investigate its prognostic and predictive relevance in cutaneous melanoma.

METHODS

We included stage I to IV cutaneous melanoma patients and used hematoxylin-eosin-stained slides for TIL analysis. We assessed eTILs as a continuous and categorical variable using the published cut-off of 16.6% and applied Cox regression models to evaluate associations of eTILs with relapse-free, distant metastasis-free, and overall survival. We compared eTILs of the primaries with matched metastasis. Moreover, we assessed the predictive relevance of eTILs in therapy-naïve metastases according to the first-line therapy.

FINDINGS

We analysed 321 primary cutaneous melanomas and 191 metastatic samples. In simple Cox regression, tumour thickness (p < 0.0001), presence of ulceration (p = 0.0001) and eTILs ≤16.6% (p = 0.0012) were found to be significant unfavourable prognostic factors for RFS. In multiple Cox regression, eTILs ≤16.6% (p = 0.0161) remained significant and downgraded the current staging. Lower eTILs in the primary tissue was associated with unfavourable relapse-free (p = 0.0014) and distant metastasis-free survival (p = 0.0056). In multiple Cox regression adjusted for tumour thickness and ulceration, eTILs as continuous remained significant (p = 0.019). When comparing TILs in primary tissue and corresponding metastasis of the same patient, eTILs in metastases was lower than in primary melanomas (p < 0.0001). In therapy-naïve metastases, an eTILs >12.2% was associated with longer progression-free survival (p = 0.037) and melanoma-specific survival (p = 0.0038) in patients treated with anti-PD-1-based immunotherapy. In multiple Cox regression, lactate dehydrogenase (p < 0.0001) and eTILs ≤12.2% (p = 0.0130) were significantly associated with unfavourable melanoma-specific survival.

INTERPRETATION

Assessment of TILs is prognostic in primary melanoma samples, and the eTILs complements staging. In therapy-naïve metastases, eTILs ≤12.2% is predictive of unfavourable survival outcomes in patients receiving anti-PD-1-based therapy.

FUNDING

See a detailed list of funding bodies in the Acknowledgements section at the end of the manuscript.

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.

CD4+ T cell-induced inflammatory cell death controls immune-evasive tumours

Most clinically applied cancer immunotherapies rely on the ability of CD8+ cytolytic T cells to directly recognize and kill tumour cells1-3. These strategies are limited by the emergence of major histocompatibility complex (MHC)-deficient tumour cells and the formation of an immunosuppressive tumour microenvironment4-6. The ability of CD4+ effector cells to contribute to antitumour immunity independently of CD8+ T cells is increasingly recognized, but strategies to unleash their full potential remain to be identified7-10. Here, we describe a mechanism whereby a small number of CD4+ T cells is sufficient to eradicate MHC-deficient tumours that escape direct CD8+ T cell targeting. The CD4+ effector T cells preferentially cluster at tumour invasive margins where they interact with MHC-II+CD11c+ antigen-presenting cells. We show that T helper type 1 cell-directed CD4+ T cells and innate immune stimulation reprogramme the tumour-associated myeloid cell network towards interferon-activated antigen-presenting and iNOS-expressing tumouricidal effector phenotypes. Together, CD4+ T cells and tumouricidal myeloid cells orchestrate the induction of remote inflammatory cell death that indirectly eradicates interferon-unresponsive and MHC-deficient tumours. These results warrant the clinical exploitation of this ability of CD4+ T cells and innate immune stimulators in a strategy to complement the direct cytolytic activity of CD8+ T cells and natural killer cells and advance cancer immunotherapies.

Ambra1 modulates the tumor immune microenvironment and response to PD-1 blockade in melanoma

BACKGROUND

Loss of Ambra1 (autophagy and beclin 1 regulator 1), a multifunctional scaffold protein, promotes the formation of nevi and contributes to several phases of melanoma development. The suppressive functions of Ambra1 in melanoma are mediated by negative regulation of cell proliferation and invasion; however, evidence suggests that loss of Ambra1 may also affect the melanoma microenvironment. Here, we investigate the possible impact of Ambra1 on antitumor immunity and response to immunotherapy.

METHODS

This study was performed using an Ambra1-depleted Braf^V600E /Pten^{-/ -} genetically engineered mouse (GEM) model of melanoma, as well as GEM-derived allografts of Braf^V600E /Pten^{-/ -} and Braf^V600E /Pten^{-/ -}/Cdkn2a^{-/ -} tumors with Ambra1 knockdown. The effects of Ambra1 loss on the tumor immune microenvironment (TIME) were analyzed using NanoString technology, multiplex immunohistochemistry, and flow cytometry. Transcriptome and CIBERSORT digital cytometry analyses of murine melanoma samples and human melanoma patients (The Cancer Genome Atlas) were applied to determine the immune cell populations in null or low-expressing AMBRA1 melanoma. The contribution of Ambra1 on T-cell migration was evaluated using a cytokine array and flow cytometry. Tumor growth kinetics and overall survival analysis in Braf^V600E /Pten^{-/ -}/Cdkn2a^{-/ -} mice with Ambra1 knockdown were evaluated prior to and after administration of a programmed cell death protein-1 (PD-1) inhibitor.

RESULTS

Loss of Ambra1 was associated with altered expression of a wide range of cytokines and chemokines as well as decreased infiltration of tumors by regulatory T cells, a subpopulation of T cells with potent immune-suppressive properties. These changes in TIME composition were associated with the autophagic function of Ambra1. In the Braf^V600E /Pten^{-/ -}/Cdkn2a^{-/ -} model inherently resistant to immune checkpoint blockade, knockdown of Ambra1 led to accelerated tumor growth and reduced overall survival, but at the same time conferred sensitivity to anti-PD-1 treatment.

CONCLUSIONS

This study shows that loss of Ambra1 affects the TIME and the antitumor immune response in melanoma, highlighting new functions of Ambra1 in the regulation of melanoma biology.

The Interface of Tumour-Associated Macrophages with Dying Cancer Cells in Immuno-Oncology

Tumour-associated macrophages (TAMs) are essential players in the tumour microenvironment (TME) and modulate various pro-tumorigenic functions such as immunosuppression, angiogenesis, cancer cell proliferation, invasion and metastasis, along with resistance to anti-cancer therapies. TAMs also mediate important anti-tumour functions and can clear dying cancer cells via efferocytosis. Thus, not surprisingly, TAMs exhibit heterogeneous activities and functional plasticity depending on the type and context of cancer cell death that they are faced with. This ultimately governs both the pro-tumorigenic and anti-tumorigenic activity of TAMs, making the interface between TAMs and dying cancer cells very important for modulating cancer growth and the efficacy of chemo-radiotherapy or immunotherapy. In this review, we discuss the interface of TAMs with cancer cell death from the perspectives of cell death pathways, TME-driven variations, TAM heterogeneity and cell-death-inducing anti-cancer therapies. We believe that a better understanding of how dying cancer cells influence TAMs can lead to improved combinatorial anti-cancer therapies, especially in combination with TAM-targeting immunotherapies.