Folate receptor alpha in ovarian cancer tissue and patient serum is associated with disease burden and treatment outcomes.

BACKGROUND: Survival rates for ovarian cancer remain poor, and monitoring and prediction of therapeutic response may benefit from additional markers. Ovarian cancers frequently overexpress Folate Receptor alpha (FRα) and the soluble receptor (sFRα) is measurable in blood. Here we investigated sFRα as a potential biomarker. METHODS: We evaluated sFRα longitudinally, before and during neo-adjuvant, adjuvant and palliative therapies, and tumour FRα expression status by immunohistrochemistry. The impact of free FRα on the efficacy of anti-FRα treatments was evaluated by an antibody-dependent cellular cytotoxicity assay. RESULTS: Membrane and/or cytoplasmic FRα staining were observed in 52.7% tumours from 316 ovarian cancer patients with diverse histotypes. Circulating sFRα levels were significantly higher in patients, compared to healthy volunteers, specifically in patients sampled prior to neoadjuvant and palliative treatments. sFRα was associated with FRα cell membrane expression in the tumour. sFRα levels decreased alongside concurrent tumour burden in patients receiving standard therapies. High concentrations of sFRα partly reduced anti-FRα antibody tumour cell killing, an effect overcome by increased antibody doses. CONCLUSIONS: sFRα may present a non-invasive marker for tumour FRα expression, with the potential for monitoring patient response to treatment. Larger, prospective studies should evaluate FRα for assessing disease burden and response to systemic treatments.

Combined anti-PD-1 and anti-CTLA-4 checkpoint blockade: Treatment of melanoma and immune mechanisms of action.

Cytotoxic T-lymphocyte associated protein-4 (CTLA-4) and the Programmed Death Receptor 1 (PD-1) are immune checkpoint molecules that are well-established targets of antibody immunotherapies for the management of malignant melanoma. The monoclonal antibodies, Ipilimumab, Pembrolizumab, and Nivolumab, designed to interfere with T cell inhibitory signals to activate immune responses against tumors, were originally approved as monotherapy. Treatment with a combination of immune checkpoint inhibitors may improve outcomes compared to monotherapy in certain patient groups and these clinical benefits may be derived from unique immune mechanisms of action. However, treatment with checkpoint inhibitor combinations also present significant clinical challenges and increased rates of immune-related adverse events. In this review, we discuss the potential mechanisms attributed to single and combined checkpoint inhibitor immunotherapies and clinical experience with their use.

Microbial and transcriptional differences elucidate atopic dermatitis heterogeneity across skin sites.

It is well established that different sites in healthy human skin are colonized by distinct microbial communities due to different physiological conditions. However, few studies have explored microbial heterogeneity between skin sites in diseased skin, such as atopic dermatitis (AD) lesions. To address this issue, we carried out deep analysis of the microbiome and transcriptome in the skin of a large cohort of AD patients and healthy volunteers, comparing two physiologically different sites: upper back and posterior thigh. Microbiome samples and biopsies were obtained from both lesional and nonlesional skin to identify changes related to the disease process. Transcriptome analysis revealed distinct disease-related gene expression profiles depending on anatomical location, with keratinization dominating the transcriptomic signatures in posterior thigh, and lipid metabolism in the upper back. Moreover, we show that relative abundance of Staphylococcus aureus is associated with disease severity in the posterior thigh, but not in the upper back. Our results suggest that AD may select for similar microbes in different anatomical locations-an "AD-like microbiome," but distinct microbial dynamics can still be observed when comparing posterior thigh to upper back. This study highlights the importance of considering the variability across skin sites when studying the development of skin inflammation.

Reactome Pengine: a web-logic API to the Homo sapiens reactome.

Summary: Existing ways of accessing data from the Reactome database are limited. Either a researcher is restricted to particular queries defined by a web application programming interface (API) or they have to download the whole database. Reactome Pengine is a web service providing a logic programming-based API to the human reactome. This gives researchers greater flexibility in data access than existing APIs, as users can send their own small programs (alongside queries) to Reactome Pengine. Availability and implementation: The server and an example notebook can be found at https://apps.nms.kcl.ac.uk/reactome-pengine. Source code is available at https://github.com/samwalrus/reactome-pengine and a Docker image is available at https://hub.docker.com/r/samneaves/rp4/. Supplementary information: Supplementary data are available at Bioinformatics online.

Network-based piecewise linear regression for QSAR modelling.

Quantitative Structure-Activity Relationship (QSAR) models are critical in various areas of drug discovery, for example in lead optimisation and virtual screening. Recently, the need for models that are not only predictive but also interpretable has been highlighted. In this paper, a new methodology is proposed to build interpretable QSAR models by combining elements of network analysis and piecewise linear regression. The algorithm presented, modSAR, splits data using a two-step procedure. First, compounds associated with a common target are represented as a network in terms of their structural similarity, revealing modules of similar chemical properties. Second, each module is subdivided into subsets (regions), each of which is modelled by an independent linear equation. Comparative analysis of QSAR models across five data sets of protein inhibitors obtained from ChEMBL is reported and it is shown that modSAR offers similar predictive accuracy to popular algorithms, such as Random Forest and Support Vector Machine. Moreover, we show that models built by modSAR are interpretatable, capable of evaluating the applicability domain of the compounds and serve well tasks such as virtual screening and the development of new drug leads.

The pruritus- and TH2-associated cytokine IL-31 promotes growth of sensory nerves.

BACKGROUND: Pruritus is a cardinal symptom of atopic dermatitis, and an increased cutaneous sensory network is thought to contribute to pruritus. Although the immune cell-IL-31-neuron axis has been implicated in severe pruritus during atopic skin inflammation, IL-31's neuropoietic potential remains elusive. OBJECTIVE: We sought to analyze the IL-31-related transcriptome in sensory neurons and to investigate whether IL-31 promotes sensory nerve fiber outgrowth. METHODS: In vitro primary sensory neuron culture systems were subjected to whole-transcriptome sequencing, ingenuity pathway analysis, immunofluorescence, and nerve elongation, as well as branching assays after IL-31 stimulation. In vivo we investigated the cutaneous sensory neuronal network in wild-type, Il31-transgenic, and IL-31 pump-equipped mice. RESULTS: Transgenic Il31 overexpression and subcutaneously delivered IL-31 induced an increase in the cutaneous nerve fiber density in lesional skin in vivo. Transcriptional profiling of IL-31-activated dorsal root ganglia neurons revealed enrichment for genes promoting nervous system development and neuronal outgrowth and negatively regulating cell death. Moreover, the growth cones of primary small-diameter dorsal root ganglia neurons showed abundant IL-31 receptor α expression. Indeed, IL-31 selectively promoted nerve fiber extension only in small-diameter neurons. Signal transducer and activator of transcription 3 phosphorylation mediated IL-31-induced neuronal outgrowth, and pharmacologic inhibition of signal transducer and activator of transcription 3 completely abolished this effect. In contrast, transient receptor potential cation channel vanilloid subtype 1 channels were dispensable for IL-31-induced neuronal sprouting. CONCLUSIONS: The pruritus- and TH2-associated novel cytokine IL-31 induces a distinct transcriptional program in sensory neurons, leading to nerve elongation and branching both in vitro and in vivo. This finding might help us understand the clinical observation that patients with atopic dermatitis experience increased sensitivity to minimal stimuli inducing sustained itch.

Pathway activity inference for multiclass disease classification through a mathematical programming optimisation framework.

BACKGROUND: Applying machine learning methods on microarray gene expression profiles for disease classification problems is a popular method to derive biomarkers, i.e. sets of genes that can predict disease state or outcome. Traditional approaches where expression of genes were treated independently suffer from low prediction accuracy and difficulty of biological interpretation. Current research efforts focus on integrating information on protein interactions through biochemical pathway datasets with expression profiles to propose pathway-based classifiers that can enhance disease diagnosis and prognosis. As most of the pathway activity inference methods in literature are either unsupervised or applied on two-class datasets, there is good scope to address such limitations by proposing novel methodologies. RESULTS: A supervised multiclass pathway activity inference method using optimisation techniques is reported. For each pathway expression dataset, patterns of its constituent genes are summarised into one composite feature, termed pathway activity, and a novel mathematical programming model is proposed to infer this feature as a weighted linear summation of expression of its constituent genes. Gene weights are determined by the optimisation model, in a way that the resulting pathway activity has the optimal discriminative power with regards to disease phenotypes. Classification is then performed on the resulting low-dimensional pathway activity profile. CONCLUSIONS: The model was evaluated through a variety of published gene expression profiles that cover different types of disease. We show that not only does it improve classification accuracy, but it can also perform well in multiclass disease datasets, a limitation of other approaches from the literature. Desirable features of the model include the ability to control the maximum number of genes that may participate in determining pathway activity, which may be pre-specified by the user. Overall, this work highlights the potential of building pathway-based multi-phenotype classifiers for accurate disease diagnosis and prognosis problems.

Optimisation-based modelling for explainable lead discovery in malaria.

BACKGROUND: The search for new antimalarial treatments is urgent due to growing resistance to existing therapies. The Open Source Malaria (OSM) project offers a promising starting point, having extensively screened various compounds for their effectiveness. Further analysis of the chemical space surrounding these compounds could provide the means for innovative drugs. METHODS: We report an optimisation-based method for quantitative structure-activity relationship (QSAR) modelling that provides explainable modelling of ligand activity through a mathematical programming formulation. The methodology is based on piecewise regression principles and offers optimal detection of breakpoint features, efficient allocation of samples into distinct sub-groups based on breakpoint feature values, and insightful regression coefficients. Analysis of OSM antimalarial compounds yields interpretable results through rules generated by the model that reflect the contribution of individual fingerprint fragments in ligand activity prediction. Using knowledge of fragment prioritisation and screening of commercially available compound libraries, potential lead compounds for antimalarials are identified and evaluated experimentally via a Plasmodium falciparum asexual growth inhibition assay (PfGIA) and a human cell cytotoxicity assay. CONCLUSIONS: Three compounds are identified as potential leads for antimalarials using the methodology described above. This work illustrates how explainable predictive models based on mathematical optimisation can pave the way towards more efficient fragment-based lead discovery as applied in malaria.

Anti-EGFR antibody-drug conjugate carrying an inhibitor targeting CDK restricts triple-negative breast cancer growth.

PURPOSE: Anti-EGFR antibodies show limited response in breast cancer, partly due to activation of compensatory pathways. Furthermore, despite clinical success of CDK4/6 inhibitors in hormone receptor-positive tumors, aggressive triple-negative breast cancers (TNBCs) are largely resistant due to CDK2/cyclin E expression, while free CDK2 inhibitors display normal tissue toxicity, limiting their therapeutic application. A cetuximab-based antibody drug conjugate (ADC) carrying a CDK inhibitor selected based on oncogene dysregulation, alongside patient subgroup stratification, may provide EGFR-targeted delivery. EXPERIMENTAL DESIGN: Expression of G1/S-phase cell cycle regulators were evaluated alongside EGFR in breast cancer. We conjugated cetuximab with CDK inhibitor SNS-032, for specific delivery to EGFR-expressing cells. We assessed ADC internalization, and its anti-tumor functions in vitro and in orthotopically-grown basal-like/TNBC xenografts. RESULTS: Transcriptomic (6173 primary, 27 baseline and matched post-chemotherapy residual tumors), scRNA-seq (150290 cells, 27 treatment-naïve tumors) and spatial transcriptomic (43 tumor sections, 22 TNBCs) analyses confirmed expression of CDK2 and its cyclin partners in basal-like/TNBCs, associated with EGFR. Spatiotemporal live-cell imaging and super-resolution confocal microscopy demonstrated ADC colocalization with late lysosomal clusters. The ADC inhibited cell cycle progression, induced cytotoxicity against high EGFR-expressing tumor cells and bystander killing of neighboring EGFR-low tumor cells, but minimal effects on immune cells. Despite carrying a small fraction of the drug, the ADC restricted EGFR-expressing spheroid and cell line/patient-derived xenograft tumor growth. CONCLUSIONS: Exploiting EGFR overexpression, and dysregulated cell cycle in aggressive and treatment-refractory tumors, a cetuximab-CDK inhibitor ADC may provide selective and efficacious delivery of cell cycle-targeted agents to basal-like/TNBCs, including chemotherapy-resistant residual disease.

Optimisation Models for Pathway Activity Inference in Cancer.

BACKGROUND: With advances in high-throughput technologies, there has been an enormous increase in data related to profiling the activity of molecules in disease. While such data provide more comprehensive information on cellular actions, their large volume and complexity pose difficulty in accurate classification of disease phenotypes. Therefore, novel modelling methods that can improve accuracy while offering interpretable means of analysis are required. Biological pathways can be used to incorporate a priori knowledge of biological interactions to decrease data dimensionality and increase the biological interpretability of machine learning models. METHODOLOGY: A mathematical optimisation model is proposed for pathway activity inference towards precise disease phenotype prediction and is applied to RNA-Seq datasets. The model is based on mixed-integer linear programming (MILP) mathematical optimisation principles and infers pathway activity as the linear combination of pathway member gene expression, multiplying expression values with model-determined gene weights that are optimised to maximise discrimination of phenotype classes and minimise incorrect sample allocation. RESULTS: The model is evaluated on the transcriptome of breast and colorectal cancer, and exhibits solution results of good optimality as well as good prediction performance on related cancer subtypes. Two baseline pathway activity inference methods and three advanced methods are used for comparison. Sample prediction accuracy, robustness against noise expression data, and survival analysis suggest competitive prediction performance of our model while providing interpretability and insight on key pathways and genes. Overall, our work demonstrates that the flexible nature of mathematical programming lends itself well to developing efficient computational strategies for pathway activity inference and disease subtype prediction.

Weakly supervised segmentation models as explainable radiological classifiers for lung tumour detection on CT images.

PURPOSE: Interpretability is essential for reliable convolutional neural network (CNN) image classifiers in radiological applications. We describe a weakly supervised segmentation model that learns to delineate the target object, trained with only image-level labels ("image contains object" or "image does not contain object"), presenting a different approach towards explainable object detectors for radiological imaging tasks. METHODS: A weakly supervised Unet architecture (WSUnet) was trained to learn lung tumour segmentation from image-level labelled data. WSUnet generates voxel probability maps with a Unet and then constructs an image-level prediction by global max-pooling, thereby facilitating image-level training. WSUnet's voxel-level predictions were compared to traditional model interpretation techniques (class activation mapping, integrated gradients and occlusion sensitivity) in CT data from three institutions (training/validation: n = 412; testing: n = 142). Methods were compared using voxel-level discrimination metrics and clinical value was assessed with a clinician preference survey on data from external institutions. RESULTS: Despite the absence of voxel-level labels in training, WSUnet's voxel-level predictions localised tumours precisely in both validation (precision: 0.77, 95% CI: [0.76-0.80]; dice: 0.43, 95% CI: [0.39-0.46]), and external testing (precision: 0.78, 95% CI: [0.76-0.81]; dice: 0.33, 95% CI: [0.32-0.35]). WSUnet's voxel-level discrimination outperformed the best comparator in validation (area under precision recall curve (AUPR): 0.55, 95% CI: [0.49-0.56] vs. 0.23, 95% CI: [0.21-0.25]) and testing (AUPR: 0.40, 95% CI: [0.38-0.41] vs. 0.36, 95% CI: [0.34-0.37]). Clinicians preferred WSUnet predictions in most instances (clinician preference rate: 0.72 95% CI: [0.68-0.77]). CONCLUSION: Weakly supervised segmentation is a viable approach by which explainable object detection models may be developed for medical imaging. CRITICAL RELEVANCE STATEMENT: WSUnet learns to segment images at voxel level, training only with image-level labels. A Unet backbone first generates a voxel-level probability map and then extracts the maximum voxel prediction as the image-level prediction. Thus, training uses only image-level annotations, reducing human workload. WSUnet's voxel-level predictions provide a causally verifiable explanation for its image-level prediction, improving interpretability. KEY POINTS: • Explainability and interpretability are essential for reliable medical image classifiers. • This study applies weakly supervised segmentation to generate explainable image classifiers. • The weakly supervised Unet inherently explains its image-level predictions at voxel level.

Human skin CD141+ dendritic cells regulate cutaneous immunity via the neuropeptide urocortin 2.

Skin immune homeostasis is a multi-faceted process where dermal dendritic cells (DDCs) are key in orchestrating responses to environmental stressors. We have previously identified CD141+CD14+ DDCs as a skin-resident immunoregulatory population that is vitamin-D3 (VitD3) inducible from monocyte-derived DCs (moDCs), termed CD141hi VitD3 moDCs. We demonstrate that CD141+ DDCs and CD141hi VitD3 moDCs share key immunological features including cell surface markers, reduced T cell stimulation, IL-10 production, and a common transcriptomic signature. Bioinformatic analysis identified the neuroactive ligand receptor pathway and the neuropeptide, urocortin 2 (UCN2), as a potential immunoregulatory candidate molecule. Incubation with VitD3 upregulated UCN2 in CD141+ DCs and UVB irradiation induced UCN2 in CD141+ DCs in healthy skin in vivo. Notably, CD141+ DDC generation of suppressive Tregs was dependent upon the UCN2 pathway as in vivo administration of UCN2 reversed skin inflammation in humanized mice. We propose the neuropeptide UCN2 as a novel skin DC-derived immunoregulatory mediator with a potential role in UVB and VitD3-dependent skin immune homeostasis.

Anti-cancer pro-inflammatory effects of an IgE antibody targeting the melanoma-associated antigen chondroitin sulfate proteoglycan 4.

Outcomes for half of patients with melanoma remain poor despite standard-of-care checkpoint inhibitor therapies. The prevalence of the melanoma-associated antigen chondroitin sulfate proteoglycan 4 (CSPG4) expression is ~70%, therefore effective immunotherapies directed at CSPG4 could benefit many patients. Since IgE exerts potent immune-activating functions in tissues, we engineer a monoclonal IgE antibody with human constant domains recognizing CSPG4 to target melanoma. CSPG4 IgE binds to human melanomas including metastases, mediates tumoricidal antibody-dependent cellular cytotoxicity and stimulates human IgE Fc-receptor-expressing monocytes towards pro-inflammatory phenotypes. IgE demonstrates anti-tumor activity in human melanoma xenograft models engrafted with human effector cells and is associated with enhanced macrophage infiltration, enriched monocyte and macrophage gene signatures and pro-inflammatory signaling pathways in the tumor microenvironment. IgE prolongs the survival of patient-derived xenograft-bearing mice reconstituted with autologous immune cells. No ex vivo activation of basophils in patient blood is measured in the presence of CSPG4 IgE. Our findings support a promising IgE-based immunotherapy for melanoma.

B cell profiles, antibody repertoire and reactivity reveal dysregulated responses with autoimmune features in melanoma.

B cells are known to contribute to the anti-tumor immune response, especially in immunogenic tumors such as melanoma, yet humoral immunity has not been characterized in these cancers to detail. Here we show comprehensive phenotyping in samples of circulating and tumor-resident B cells as well as serum antibodies in melanoma patients. Memory B cells are enriched in tumors compared to blood in paired samples and feature distinct antibody repertoires, linked to specific isotypes. Tumor-associated B cells undergo clonal expansion, class switch recombination, somatic hypermutation and receptor revision. Compared with blood, tumor-associated B cells produce antibodies with proportionally higher levels of unproductive sequences and distinct complementarity determining region 3 properties. The observed features are signs of affinity maturation and polyreactivity and suggest an active and aberrant autoimmune-like reaction in the tumor microenvironment. Consistent with this, tumor-derived antibodies are polyreactive and characterized by autoantigen recognition. Serum antibodies show reactivity to antigens attributed to autoimmune diseases and cancer, and their levels are higher in patients with active disease compared to post-resection state. Our findings thus reveal B cell lineage dysregulation with distinct antibody repertoire and specificity, alongside clonally-expanded tumor-infiltrating B cells with autoimmune-like features, shaping the humoral immune response in melanoma.

Transcriptome classification reveals molecular subtypes in psoriasis.

BACKGROUND: Psoriasis is an immune-mediated disease characterised by chronically elevated pro-inflammatory cytokine levels, leading to aberrant keratinocyte proliferation and differentiation. Although certain clinical phenotypes, such as plaque psoriasis, are well defined, it is currently unclear whether there are molecular subtypes that might impact on prognosis or treatment outcomes. RESULTS: We present a pipeline for patient stratification through a comprehensive analysis of gene expression in paired lesional and non-lesional psoriatic tissue samples, compared with controls, to establish differences in RNA expression patterns across all tissue types. Ensembles of decision tree predictors were employed to cluster psoriatic samples on the basis of gene expression patterns and reveal gene expression signatures that best discriminate molecular disease subtypes. This multi-stage procedure was applied to several published psoriasis studies and a comparison of gene expression patterns across datasets was performed. CONCLUSION: Overall, classification of psoriasis gene expression patterns revealed distinct molecular sub-groups within the clinical phenotype of plaque psoriasis. Enrichment for TGFb and ErbB signaling pathways, noted in one of the two psoriasis subgroups, suggested that this group may be more amenable to therapies targeting these pathways. Our study highlights the potential biological relevance of using ensemble decision tree predictors to determine molecular disease subtypes, in what may initially appear to be a homogenous clinical group. The R code used in this paper is available upon request.

Toward prediction of immune mechanisms and design of immunotherapies in melanoma.

Malignant melanoma, the most lethal skin cancer, is considered as a representative model for cross talk between immune responses and malignancy. Efforts to elucidate the nature of these interactions have translated into immunotherapeutic strategies. Adjuvant therapeutics such as IL-2 and IFNα2b have reached clinical application, and emerging therapies targeting key immunomodulatory molecules such as CTLA-4 have renewed excitement in the field, highlighting the potential of manipulating immune responses in the clinical setting, but also the merits for further elucidating complex underlying immunological pathways. Screening technologies have yielded new insights leading to identification of biomarkers for disease prognosis and applied clinical immunotherapies. The promise of systems biology is to integrate diverse biomedical characterizations into detailed models of underlying mechanisms and therapies through suitable computational and mathematical formalisms. In this review, we discuss recent developments in dissecting the complex and diverse immune responses associated with melanoma through both computational and experimental means. We show the significance of devising new, improved approaches that can better serve as models of immune interactions and therapies. We propose that efforts in this direction may realize the potential of personalized medicine and facilitate development of the next generation of efficacious tools to treat patients.

Radiomic assessment of oesophageal adenocarcinoma: a critical review of 18F-FDG PET/CT, PET/MRI and CT.

OBJECTIVES: Radiomic models present an avenue to improve oesophageal adenocarcinoma assessment through quantitative medical image analysis. However, model selection is complicated by the abundance of available predictors and the uncertainty of their relevance and reproducibility. This analysis reviews recent research to facilitate precedent-based model selection for prospective validation studies. METHODS: This analysis reviews research on 18F-FDG PET/CT, PET/MRI and CT radiomics in oesophageal adenocarcinoma between 2016 and 2021. Model design, testing and reporting are evaluated according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score and Radiomics Quality Score (RQS). Key results and limitations are analysed to identify opportunities for future research in the area. RESULTS: Radiomic models of stage and therapeutic response demonstrated discriminative capacity, though clinical applications require greater sensitivity. Although radiomic models predict survival within institutions, generalisability is limited. Few radiomic features have been recommended independently by multiple studies. CONCLUSIONS: Future research must prioritise prospective validation of previously proposed models to further clinical translation.

A Bayesian predictive analytics model for improving long range epidemic forecasting during an infection wave.

Following the outbreak of the coronavirus epidemic in early 2020, municipalities, regional governments and policymakers worldwide had to plan their Non-Pharmaceutical Interventions (NPIs) amidst a scenario of great uncertainty. At this early stage of an epidemic, where no vaccine or medical treatment is in sight, algorithmic prediction can become a powerful tool to inform local policymaking. However, when we replicated one prominent epidemiological model to inform health authorities in a region in the south of Brazil, we found that this model relied too heavily on manually predetermined covariates and was too reactive to changes in data trends. Our four proposed models access data of both daily reported deaths and infections as well as take into account missing data (e.g., the under-reporting of cases) more explicitly, with two of the proposed versions also attempting to model the delay in test reporting. We simulated weekly forecasting of deaths from the period from 31/05/2020 until 31/01/2021, with first week data being used as a cold-start to the algorithm, after which we use a lighter variant of the model for faster forecasting. Because our models are significantly more proactive in identifying trend changes, this has improved forecasting, especially in long-range predictions and after the peak of an infection wave, as they were quicker to adapt to scenarios after these peaks in reported deaths. Assuming reported cases were under-reported greatly benefited the model in its stability, and modelling retroactively-added data (due to the "hot" nature of the data used) had a negligible impact on performance.

Influencing tumor-associated macrophages in malignant melanoma with monoclonal antibodies.

The application of monoclonal antibodies (mAbs) for the treatment of melanoma has significantly improved the clinical management of this malignancy over the last decade. Currently approved mAbs for melanoma enhance T cell effector immune responses by blocking immune checkpoint molecules PD-L1/PD-1 and CTLA-4. However, more than half of patients do not benefit from treatment. Targeting the prominent myeloid compartment within the tumor microenvironment, and in particular the ever-abundant tumor-associated macrophages (TAMs), may be a promising strategy to complement existing therapies and enhance treatment success. TAMs are a highly diverse and plastic subset of cells whose pro-tumor properties can support melanoma growth, angiogenesis and invasion. Understanding of their diversity, plasticity and multifaceted roles in cancer forms the basis for new promising TAM-centered treatment strategies. There are multiple mechanisms by which macrophages can be targeted with antibodies in a therapeutic setting, including by depletion, inhibition of specific pro-tumor properties, differential polarization to pro-inflammatory states and enhancement of antitumor immune functions. Here, we discuss TAMs in melanoma, their interactions with checkpoint inhibitor antibodies and emerging mAbs targeting different aspects of TAM biology and their potential to be translated to the clinic.

Enriched circulating and tumor-resident TGF-ß+ regulatory B cells in patients with melanoma promote FOXP3+ Tregs.

B cells are emerging as key players of anti-tumor adaptive immune responses. We investigated regulatory and pro-inflammatory cytokine-expressing B cells in patients with melanoma by flow cytometric intracellular cytokine, CyTOF, transcriptomic, immunofluorescence, single-cell RNA-seq, and B:T cell co-culture analyses. We found enhanced circulating regulatory (TGF-ß+ and PD-L1+) and reduced pro-inflammatory TNF-α+ B cell populations in patients compared with healthy volunteers (HVs), including lower IFN-γ+:IL-4+ and higher TGF-ß+:TNF-α+ B cell ratios in patients. TGF-ß-expressing B cells in the melanoma tumor microenvironment assembled in clusters and interacted with T cells via lymphoid recruitment (SELL, CXCL13, CCL4, CD74) signals and with Tregs via CD47:SIRP-γ, and FOXP3-promoting Galectin-9:CD44. While reduced in tumors compared to blood, TNF-α-expressing B cells engaged in crosstalk with Tregs via TNF-α signaling and the ICOS/ICOSL axis. Patient-derived B cells promoted FOXP3+ Treg differentiation in a TGF-ß-dependent manner, while sustaining expression of IFN-γ and TNF-α by autologous T-helper cells and promoting T-helper cell proliferation ex vivo, an effect further enhanced with anti-PD-1 checkpoint blockade. Our findings reveal cytokine-expressing B cell compartments skewed toward regulatory phenotypes in patient circulation and melanoma lesions, intratumor spatial localization, and bidirectional crosstalk between B and T cell subsets with immunosuppressive attributes.