Search

Human natural killer (NK) cells can be sub-divided into two functional subsets but the clinical significance of these CD56bright and CD56dim NK cells in anti-tumour immunity remains largely unexplored. We determined the relative abundances of gene signatures for CD56bright and CD56dim NK cells along with 3 stromal and 18 other immune cell types in the patient tumour transcriptomes from the cancer genome atlas bladder cancer dataset (TCGA-BLCA). Using this computational approach, CD56bright NK cells were predicted to be the more abundant tumour-infiltrating NK subset which was also associated with improved patient prognosis. A similar favorable survival trend was projected using gene signatures for mature myeloid dendritic cells (mDC) and CD8+ effector memory T cells (TEM) and unveiled a potential CD56bright NK-mDC-CD8+T cell crosstalk in the BLCA tumour microenvironment. Expression of transcripts encoding the activating NK cell receptors, NKG2D, NKp44, CD2, and CD160, showed positive survival trends in combination with CD56bright NK cell infiltration. Transcription factors including HOBIT, IRF3, and STAT2 were also correlated with CD56bright NK cell abundance. Additionally, a HOBIT-dependent tissue-residency program correlated with the CD56bright NK and CD8+ TEM cell signatures was found to be associated with favourable BLCA patient survival. Overall, our study highlights the significance of CD56bright NK cells in BLCA patient prognosis. Our findings facilitate a better understanding of the NK cell anti-tumour responses that may ultimately lead to the development of promising NK and T cell-based therapies for BLCA.

Proinflammatory cytokines are implicated in promoting neurodegeneration in multiple sclerosis (MS) by affecting excitatory and inhibitory transmission at central synapses. Conversely, the synaptic effects of anti-inflammatory molecules remain underexplored, despite their potential neuroprotective properties and their presence in the cerebrospinal fluid (CSF) of patients. In a study involving 184 newly diagnosed relapsing–remitting (RR)-MS patients, we investigated whether CSF levels of the anti-inflammatory interleukin (IL)-10 were linked to disease severity and neurodegeneration measures. Additionally, we examined IL-10 impact on synaptic transmission in striatal medium spiny neurons and its role in counteracting inflammatory synaptopathy induced by IL-1β in female C57BL/6 mice with experimental autoimmune encephalomyelitis (EAE). Our findings revealed a significant positive correlation between IL-10 CSF levels and changes in EDSS (Expanded Disability Status Scale) scores one year after MS diagnosis. Moreover, IL-10 levels in the CSF were positively correlated with volumes of specific subcortical brain structures, such as the nucleus caudate. In both MS patients’ CSF and EAE mice striatum, IL-10 and IL-1β expressions were upregulated, suggesting possible antagonistic effects of these cytokines. Notably, IL-10 exhibited the ability to decrease glutamate transmission, increase GABA transmission in the striatum, and reverse IL-1β-induced abnormal synaptic transmission in EAE. In conclusion, our data suggest that IL-10 exerts direct neuroprotective effects in MS patients by modulating both excitatory and inhibitory transmission and attenuating IL-1β-induced inflammatory synaptopathy. These findings underscore the potential therapeutic significance of IL-10 in mitigating neurodegeneration in MS.

BackgroundSecondary progressive multiple sclerosis (SPMS) is defined by the irreversible accumulation of disability following a relapsing-remitting MS (RRMS) course. Despite treatments advances, a reliable tool able to capture the transition from RRMS to SPMS is lacking. A T cell chimeric MS model demonstrated that T cells derived from relapsing patients exacerbate excitatory transmission of central neurons, a synaptotoxic event absent during remitting stages. We hypothesized the re-emergence of T cell synaptotoxicity during SPMS and investigated the synaptoprotective effects of siponimod, a sphingosine 1-phosphate receptor (S1PR) modulator, known to reduce grey matter damage in SPMS patients.MethodsData from healthy controls (HC), SPMS patients, and siponimod-treated SPMS patients were collected. Chimeric experiments were performed incubating human T cells on murine cortico-striatal slices, and recording spontaneous glutamatergic activity from striatal neurons. Homologous chimeric experiments were executed incubating EAE mice T cells with siponimod and specific S1PR agonists or antagonists to identify the receptor involved in siponimod-mediated synaptic recovery.ResultsSPMS patient-derived T cells significantly increased the striatal excitatory synaptic transmission (n=40 synapses) compared to HC T cells (n=55 synapses), mimicking the glutamatergic alterations observed in active RRMS-T cells. Siponimod treatment rescued SPMS T cells synaptotoxicity (n=51 synapses). Homologous chimeric experiments highlighted S1P5R involvement in the siponimod’s protective effects.ConclusionTransition from RRMS to SPMS involves the reappearance of T cell-mediated synaptotoxicity. Siponimod counteracts T cell-induced excitotoxicity, emphasizing the significance of inflammatory synaptopathy in progressive MS and its potential as a promising pharmacological target.

In multiple sclerosis, only immunomodulatory and immunosuppressive drugs are recognized as disease-modifying therapies. However, in recent years, several data from pre-clinical and clinical studies suggested a possible role of physical exercise as disease-modifying therapy in multiple sclerosis. Current evidence is sparse and often conflicting, and the mechanisms underlying the neuroprotective and antinflammatory role of exercise in multiple sclerosis have not been fully elucidated. Data, mainly derived from pre-clinical studies, suggest that exercise could enhance long-term potentiation and thus neuroplasticity, could reduce neuroinflammation and synaptopathy, and dampen astrogliosis and microgliosis. In humans, most trials focused on direct clinical and MRI outcomes, as investigating synaptic, neuroinflammatory, and pathological changes is not straightforward compared to animal models. The present review analyzed current evidence and limitations in research concerning the potential disease-modifying therapy effects of exercise in multiple sclerosis in animal models and human studies.

IntroductionAbout 50% of cutaneous melanoma (CM) patients present activating BRAF mutations that can be effectively targeted by BRAF inhibitors (BRAFi). However, 20% of CM patients exhibit intrinsic drug resistance to BRAFi, while most of the others develop adaptive resistance over time. The mechanisms involved in BRAFi resistance are disparate and globally seem to rewire the cellular signaling profile by up-regulating different receptor tyrosine kinases (RTKs), such as the epidermal growth factor receptor (EGFR). RTKs inhibitors have not clearly demonstrated anti-tumor activity in BRAFi resistant models. To overcome this issue, we wondered whether the shared up-regulated RTK phenotype associated with BRAFi resistance could be exploited by using immune weapons as the antibody-dependent cell cytotoxicity (ADCC)-mediated effect of anti-RTKs antibodies, and kill tumor cells independently from the mechanistic roots.Methods and resultsBy using an in vitro model of BRAFi resistance, we detected increased membrane expression of EGFR, both at mRNA and protein level in 4 out of 9 BRAFi-resistant (VR) CM cultures as compared to their parental sensitive cells. Increased EGFR phosphorylation and AKT activation were observed in the VR CM cultures. EGFR signaling appeared dispensable for maintaining resistance, since small molecule-, antibody- and CRISPR-targeting of EGFR did not restore sensitivity of VR cells to BRAFi. Importantly, immune-targeting of EGFR by the anti-EGFR antibody cetuximab efficiently and specifically killed EGFR-expressing VR CM cells, both in vitro and in humanized mouse models in vivo, triggering ADCC by healthy donors’ and patients’ peripheral blood cells. ConclusionOur data demonstrate the efficacy of immune targeting of RTKs expressed by CM relapsing on BRAFi, providing the proof-of-concept supporting the assessment of anti-RTK antibodies in combination therapies in this setting. This strategy might be expected to concomitantly trigger the crosstalk of adaptive immune response leading to a complementing T cell immune rejection of tumors.

IntroductionThe visual system is a prominent site of damage in MS since the earliest phases of the disease. Altered low-contrast visual acuity (LCVA) test has been associated with visual impairment and retinal degeneration, predicting medium- and long-term disability. However, it is unclear whether LCVA may also represent a reliable measure of neuroinflammation and a predictor of disease evolution in the very early stages of MS.MethodsWe explored in a group of 76 consecutive newly diagnosed relapsing–remitting MS (RR-MS) patients without visual impairment or altered visual evoked potentials, the association between LCVA scores at 2.5% and 1.25% and clinical characteristics, including prospective disability evaluated after 1- and 2 years of follow-up. Associations between LCVA and the CSF levels of IL-10 at diagnosis were also analyzed.ResultsA negative correlation was found between LCVA at 2.5% and Expanded Disability Status Scale (EDSS) evaluated at first (Spearman’s Rho = −0.349, p = 0.005, n = 62) and second year (Spearman’s Rho = −0.418, p

Purpose: To evaluate the ability of preoperative MRI-based measurements to predict the pathological T (pT) stage and cervical lymph node metastasis (CLNM) via machine learning (ML)-driven models trained in oral tongue squamous cell carcinoma (OTSCC). Materials and methods: 108 patients with a new diagnosis of OTSCC were enrolled. The preoperative MRI study included post-contrast high-resolution T1-weighted images acquired in all patients. MRI-based depth of invasion (DOI) and tumor dimension—together with shape-based and intensity-based features—were extracted from the lesion volume segmentation. The entire dataset was randomly divided into a training set and a validation set, and the performances of different types of ML algorithms were evaluated and compared. Results: MRI-based DOI and tumor dimension together with several shape-based and intensity-based signatures significantly discriminated the pT stage and LN status. The overall accuracy of the model for predicting the pT stage was 0.86 (95%CI, 0.78–0.92) and 0.81 (0.64–0.91) in the training and validation sets, respectively. There was no improvement in the model performance upon including shape-based and intensity-based features. The model for predicting CLNM based on DOI and tumor dimensions had a fair accuracy of 0.68 (0.57–0.78) and 0.69 (0.51–0.84) in the training and validation sets, respectively. The shape-based and intensity-based signatures have shown potential for improving the model sensitivity, with a comparable accuracy. Conclusion: MRI-based models driven by ML algorithms could stratify patients with OTSCC according to the pT stages. They had a moderate ability to predict cervical lymph node metastasis.

Mesenchymal stromal cells (MSCs) show immunosuppressive effects both via cell-to-cell contact (direct) with immune cells and by producing paracrine factors and extracellular vesicles (indirect). A key challenge in delivering this therapeutic effect in vivo is retaining the MSCs at the site of injection. One way to address this is by encapsulating the MSCs within suitable biomaterial scaffolds. Here, we assess the immunosuppressive effect of alginate-encapsulated murine MSCs on proliferating murine splenocytes. Our results show that MSCs are able to significantly suppress splenocyte proliferation by ∼50% via the indirect mechanism and almost completely (∼98%) via the direct mechanism. We also show for the first time that MSCs as monolayers on tissue culture plastic or encapsulated within alginate, when physically isolated from the splenocytes via transwells, are able to sustain immunosuppressive activity with repeated exposure to fresh splenocytes, for as long as 9 days. These results indicate the need to identify design strategies to simultaneously deliver both modes of MSC immunosuppression. By designing cell-biomaterial constructs with tailored degradation profiles, we can achieve a more sustained (avoiding MSCs migration and apoptosis) and controlled release of both the paracrine signals and eventually the cells themselves enabling efficient MSC-based immunosuppressive therapies for wound healing.

A plateau in treatment effect can be seen for the current ‘one-size-fits-all’ approach to oesophageal adenocarcinoma (OAC) management using neoadjuvant chemoradiotherapy (nCRT) or chemotherapy (nCT). In OAC, the tumour microenvironment (TME) is largely immunosuppressed, however a subgroup of patients with an immune-inflamed TME exist and show improved outcomes. We aimed to understand the overall immune-based mechanisms underlying treatment responses and patient outcomes in OAC, and in relation to neoadjuvant therapy modality. This study included 107 patients; 68 patients were enrolled in the Australian Gastro-Intestinal Trials Group sponsored DOCTOR Trial, and 38 patients were included from the Cancer Evolution Biobank. Matched pre-treatment and post-treatment tumour biopsies were used to perform multi-modality analysis of the OAC TME including NanoString mRNA expression analysis, multiplex and single colour immunohistochemistry (IHC), and peripheral blood mononuclear cell analysis of tumour-antigen specific T cell responses. Patients with the best clinicopathological outcomes and survival had an immune-inflamed TME enriched with anti-tumour immune cells and pathways. Those with the worst survival showed a myeloid T regulatory cell enriched TME, with decreased CD8+ cell infiltration and increased pro-tumour immune cells. Multiplex IHC analysis identified that high intra-tumoural infiltration of CD8+ cells, and low infiltration with CD163+ cells was associated with improved survival. High tumour core CD8+ T cell infiltration, and a low tumour margin infiltration of CD163+ cells was also associated with improved survival. nCRT showed improved survival compared with nCT for patients with low CD8+, or high CD163+ cell infiltration. Poly-functional T cell responses were seen with tumour-antigen specific T cells. Overall, our study supports the development of personalised therapeutic approaches based on the immune microenvironment in OAC. Patients with an immune-inflamed TME show favourable outcomes regardless of treatment modality. However, in those with an immunosuppressed TME with CD163+ cell infiltration, treatment with nCRT can improve outcomes. Our findings support previous studies into the TME of OAC and with more research, immune based biomarker selection of treatment modality may lead in improved outcomes in this deadly disease.

Tumor necrosis factor (TNF) is a key player in multiple sclerosis pathology. TNF signaling is dually regulated by antagonist groups of actors: TNFR1, mediating proinflammatory effects and synaptopathy, CD40L-CD40 dyad, crucial for blood-brain barrier breakdown and facilitation of recruitment of inflammatory cells in the central nervous system, and TNFR2, promoting neuroprotective and reparative functions. A promising therapeutic approach in multiple sclerosis is represented by selective TNFR1 antagonists and TNFR2 agonists, possibly in combination. TNFR2 agonists could exert both central effects such as remyelination, reduction of glutamatergic excitotoxicity, and peripheral immunomodulation by enhancing T cells (Treg) activity. On the other side, the potential therapeutic role of platelet and CD40L-CD40 dyad inhibition could be beneficial to preserve blood-brain barrier integrity and thereby dampen neuroinflammation.

Renal transplantation (RT) is the treatment of choice for end-stage renal disease, significantly improving patients’ survival and quality of life. However, approximately 3–23% of patients encounter post-operative complications, and radiology plays a major role for their early detection and treatment or follow-up planning. CT and MRI are excellent imaging modalities to evaluate renal transplant post-operative course; nevertheless, they are both associated with a high cost and low accessibility, as well as some contraindications, making them not feasible for all patients. In particular, gadolinium-based contrast can lead to the rare condition of nephrogenic systemic fibrosis, and iodine-based contrast can lead to contrast-induced nephropathy (CIN). CT also exposes the patients who may require multiple examinations to ionizing radiation. Therefore, considering the overall advantages and disadvantages, contrast-enhanced ultrasound (CEUS) is presently considered an effective first-line imaging modality for post-operative early and long-term follow-up in RT, reducing the need for biopsies and providing adequate guidance for drainage procedures. Hence, this paper aims to review the updated knowledge on CEUS compared with CT and MRI for the evaluation of RT renal transplant complications; advantages, limitations, and possible recommendations are provided.

The recovery of the properties or geometry of a rough surface from scattered sound is of interest in many applications, including medicine, water engineering, or structural health monitoring. Existing approaches to reconstruct the roughness profile of a scattering surface based on wave scattering have no intrinsic way of predicting the uncertainty of the reconstruction. In an attempt to recover this uncertainty, a Bayesian framework, and more explicitly, an adaptive Metropolis scheme, is used to infer the properties of a rough surface, parameterised as a superposition of sinusoidal components. The Kirchhoff approximation is used in the present work as the underlying model of wave scattering, and is constrained by the assumption of surface smoothness. This implies a validity region in the parameter space, which is incorporated in the Bayesian formulation, making the resulting method physics informed compared to data-based approaches. For a three-parameter sinusoidal surface and a rough surface with a random roughness profile, physical experiments were conducted to collect scattered field data. The models were then tested on the experimental data. The recovery offers insight of the Bayesian approach results expressed in terms of confidence intervals, and could be used as a method to identify uncertainty. [ABSTRACT FROM AUTHOR]

Simple Summary: Thyroid nodules must be accurately classified as benign or malignant. The aim of this study is to develop a machine learning model based on thyroid ultrasound images in order to classify nodules into the two classes. Ultrasound (US) images were collected from 142 patients for training, validation and internal testing of three models, plus 21 images to externally test the best performing model. The random forest classifier model could perform the classification task, identifying all the malignant nodes and most of the benign. Background/Objectives: Thyroid nodules are a very common finding, mostly benign but sometimes malignant, and thus require accurate diagnosis. Ultrasound and fine needle biopsy are the most widely used and reliable diagnostic methods to date, but they are sometimes limited in addressing benign from malignant nodules, mainly with regard to ultrasound, by the operator's experience. Radiomics, quantitative feature extraction from medical images and machine learning offer promising avenues to improve diagnosis. The aim of this work was to develop a machine learning model based on thyroid ultrasound images to classify nodules into benign and malignant classes. Methods: For this purpose, images of ultrasonography from 142 subjects were collected. Among these subjects, 40 patients (28.2%) belonged to the class "malignant" and 102 patients (71.8%) belonged to the class "benign", according to histological diagnosis from fine-needle aspiration. This image set was used for the training, cross-validation and internal testing of three different machine learning models. A robust radiomic approach was applied, under the hypothesis that the radiomic feature could capture the disease heterogeneity among the two groups. Three models consisting of four ensembles of machine learning classifiers (random forests, support vector machines and k-nearest neighbor classifiers) were developed for the binary classification task of interest. The best performing model was then externally tested on a cohort of 21 new patients. Results: The best model (ensemble of random forest) showed Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) (%) of 85 (majority vote), 83.7 ** (mean) [80.2–87.2], accuracy (%) of 83, 81.2 ** [77.1–85.2], sensitivity (%) of 70, 67.5 ** [64.3–70.7], specificity (%) of 88, 86.5 ** [82–91], positive predictive value (PPV) (%) of 70, 66.5 ** [57.9–75.1] and negative predictive value (NPV) (%) of 88, 87.1 ** [85.5–88.8] (* p < 0.05, ** p < 0.005) in the internal test cohort. It achieved an accuracy of 90.5%, a sensitivity of 100%, a specificity of 86.7%, a PPV of 75% and an NPV of 100% in the external testing cohort. Conclusions: The model constituted of four ensembles of random forest classifiers could identify all the malignant nodes and the consistent majority of benign in the external testing cohort. [ABSTRACT FROM AUTHOR]

Abstract Over the past decade, immunotherapy delivered novel treatments for many cancer types. However, lung cancer still leads cancer mortality, and non-small-cell lung carcinoma patients with mutant EGFR cannot benefit from checkpoint inhibitors due to toxicity, relying only on palliative chemotherapy and the third-generation tyrosine kinase inhibitor (TKI) osimertinib. This new drug extends lifespan by 9-months vs. second-generation TKIs, but unfortunately, cancers relapse due to resistance mechanisms and the lack of antitumor immune responses. Here we explored the combination of osimertinib with anti-HER3 monoclonal antibodies and observed that the immune system contributed to eliminate tumor cells in mice and co-culture experiments using bone marrow-derived macrophages and human PBMCs. Osimertinib led to apoptosis of tumors but simultaneously, it triggered inositol-requiring-enzyme (IRE1α)-dependent HER3 upregulation, increased macrophage infiltration, and activated cGAS in cancer cells to produce cGAMP (detected by a lentivirally transduced STING activity biosensor), transactivating STING in macrophages. We sought to target osimertinib-induced HER3 upregulation with monoclonal antibodies, which engaged Fc receptor-dependent tumor elimination by macrophages, and STING agonists enhanced macrophage-mediated tumor elimination further. Thus, by engaging a tumor non-autonomous mechanism involving cGAS-STING and innate immunity, the combination of osimertinib and anti-HER3 antibodies could improve the limited therapeutic and stratification options for advanced stage lung cancer patients with mutant EGFR.

Suitable methods to assess in vivo immunogenicity and therapeutic efficacy of cancer vaccines in preclinical cancer models are critical to overcome current limitations of cancer vaccines and enhance the clinical applicability of this promising immunotherapeutic strategy. In particular, availability of methods allowing the characterization of T cell responses to endogenous tumor antigens is required to assess vaccine potency and improve the antigen formulation. Moreover, multiparametric assays to deeply characterize tumor-induced and therapy-induced immune modulation are relevant to design mechanism-based combination immunotherapies. Here we describe a versatile multiparametric flow cytometry method to assess the polyfunctionality of tumor antigen-specific CD4+ and CD8+ T cell responses based on their production of multiple cytokines after short-term ex vivo restimulation with relevant tumor epitopes of the most common mouse strains. We also report the development and application of two 21-color flow cytometry panels allowing a comprehensive characterization of T cell and natural killer cell exhaustion and memory phenotypes in mice with a particular focus on preclinical cancer models.

Background: Advanced head and neck squamous cell carcinoma (HNSCC) is associated with a poor prognosis, and biomarkers that predict response to treatment are highly desirable. The primary aim was to predict progression-free survival (PFS) with a multivariate risk prediction model. Methods: Experimental covariates were derived from blood samples of 56 HNSCC patients which were prospectively obtained within a Phase 2 clinical trial (NCT02633800) at baseline and after the first treatment cycle of combined platinum-based chemotherapy with cetuximab treatment. Clinical and experimental covariates were selected by Bayesian multivariate regression to form risk scores to predict PFS. Results: A ‘baseline’ and a ‘combined’ risk prediction model were generated, each of which featuring clinical and experimental covariates. The baseline risk signature has three covariates and was strongly driven by baseline percentage of CD33+CD14+HLADRhigh monocytes. The combined signature has six covariates, also featuring baseline CD33+CD14+HLADRhigh monocytes but is strongly driven by on-treatment relative change of CD8+ central memory T cells percentages. The combined model has a higher predictive power than the baseline model and was successfully validated to predict therapeutic response in an independent cohort of nine patients from an additional Phase 2 trial (NCT03494322) assessing the addition of avelumab to cetuximab treatment in HNSCC. We identified tissue counterparts for the immune cells driving the models, using imaging mass cytometry, that specifically colocalized at the tissue level and correlated with outcome. Conclusions: This immune-based combined multimodality signature, obtained through longitudinal peripheral blood monitoring and validated in an independent cohort, presents a novel means of predicting response early on during the treatment course. Funding: Daiichi Sankyo Inc, Cancer Research UK, EU IMI2 IMMUCAN, UK Medical Research Council, European Research Council (335326), Merck Serono. Cancer Research Institute, National Institute for Health Research, Guy’s and St Thomas’ NHS Foundation Trust and The Institute of Cancer Research. Clinical trial number: NCT02633800.

The biological target identification process, a pivotal phase in the drug discovery workflow, becomes particularly challenging when mutations affect proteins’ mechanisms of action. COVID-19 Spike glycoprotein mutations are known to modify the affinity toward the human angiotensin-converting enzyme ACE2 and several antibodies, compromising their neutralizing effect. Predicting new possible mutations would be an efficient way to develop specific and efficacious drugs, vaccines, and antibodies. In this work, we developed and applied a computational procedure, combining constrained logic programming and careful structural analysis based on the Structural Activity Relationship (SAR) approach, to predict and determine the structure and behavior of new future mutants. “Mutations rules” that would track statistical and functional types of substitutions for each residue or combination of residues were extracted from the GISAID database and used to define constraints for our software, having control of the process step by step. A careful molecular dynamics analysis of the predicted mutated structures was carried out after an energy evaluation of the intermolecular and intramolecular interactions using the HINT (Hydrophatic INTeraction) force field. Our approach successfully predicted, among others, known Spike mutants.

Vaccines have been hailed as one of the most remarkable medical advancements in human history, and their potential for treating cancer by generating or expanding anti-tumor T cells has garnered significant interest in recent years. However, the limited efficacy of therapeutic cancer vaccines in clinical trials can be partially attributed to the inadequacy of current preclinical mouse models in recapitulating the complexities of the human immune system. In this study, we developed two innovative humanized mouse models to assess the immunogenicity and therapeutic effectiveness of vaccines targeting human papillomavirus (HPV16) antigens and delivering tumor antigens to human CD141+ dendritic cells (DCs). Both models were based on the transference of human peripheral blood mononuclear cells (PBMCs) into immunocompromised HLA-A*02-NSG mice (NSG-A2), where the use of fresh PBMCs boosted the engraftment of human cells up to 80%. The dynamics of immune cells in the PBMC-hu-NSG-A2 mice demonstrated that T cells constituted the vast majority of engrafted cells, which progressively expanded over time and retained their responsiveness to ex vivo stimulation. Using the PBMC-hu-NSG-A2 system, we generated a hyperplastic skin graft model expressing the HPV16-E7 oncogene. Remarkably, human cells populated the skin grafts, and upon vaccination with a DNA vaccine encoding an HPV16-E6/E7 protein, rapid rejection targeted to the E7-expressing skin was detected, underscoring the capacity of the model to mount a vaccine-specific response. To overcome the decline in DC numbers observed over time in PBMC-hu-NSG-A2 animals, we augmented the abundance of CD141+ DCs, the specific targets of our tailored nanoemulsions (TNEs), by transferring additional autologous PBMCs pre-treated in vitro with the growth factor Flt3-L. The Flt3-L treatment bolstered CD141+ DC numbers, leading to potent antigen-specific CD4+ and CD8+ T cell responses in vivo, which caused the regression of pre-established triple-negative breast cancer and melanoma tumors following CD141+ DC-targeting TNE vaccination. Notably, using HLA-A*02-matching PBMCs for humanizing NSG-A2 mice resulted in a delayed onset of graft-versus-host disease and enhanced the efficacy of the TNE vaccination compared with the parental NSG strain. In conclusion, we successfully established two humanized mouse models that exhibited strong antigen-specific responses and demonstrated tumor regression following vaccination. These models serve as valuable platforms for assessing the efficacy of therapeutic cancer vaccines targeting HPV16-dysplastic skin and diverse tumor antigens specifically delivered to CD141+ DCs.

BackgroundMetastatic breast cancer (mBC) is a heterogeneous disease with varying responses to treatments and clinical outcomes, still requiring the identification of reliable predictive biomarkers. In this context, liquid biopsy has emerged as a powerful tool to assess in real-time the evolving landscape of cancer, which is both orchestrated by the metastatic process and immune-surveillance mechanisms. Thus, we investigated circulating tumor cells (CTCs) coupled with peripheral T-cell immunity to uncover their potential clinical relevance in mBC.MethodsA cohort of 20 mBC patients was evaluated, before and one month after starting therapy, through the following liquid biopsy approaches: CTCs enumerated by a metabolism-based assay, T-cell responses against tumor-associated antigens (TAA) characterized by interferon-γ enzyme-linked immunosorbent spot (ELISpot), and the T-cell receptor (TCR) repertoire investigated by a targeted next-generation sequencing technique. TCR repertoire features were characterized by the Morisita’s overlap and the Productive Simpson Clonality indexes, and the TCR richness. Differences between groups were calculated by Fisher’s, Mann-Whitney or Kruskal-Wallis test, as appropriate. Prognostic data analysis was estimated by Kaplan-Meier method.ResultsStratifying patients for their prognostic level of 6 CTCs before therapy, TAA specific T-cell responses were detected only in patients with a low CTC level. By analyzing the TCR repertoire, the highest TCR clonality was observed in the case of CTCs under the cut-off and a positive ELISpot response (p=0.03). Whereas, at follow-up, patients showing a good clinical response coupled with a low number of CTCs were characterized by the most elevated TCR clonality (p

Proinflammatory cytokines are implicated in promoting neurodegeneration in multiple sclerosis (MS) by affecting excitatory and inhibitory transmission at central synapses. Conversely, the synaptic effects of anti-inflammatory molecules remain underexplored, despite their potential neuroprotective properties and their presence in the cerebrospinal fluid (CSF) of patients. In a study involving 184 newly diagnosed relapsing-remitting (RR)-MS patients, we investigated whether CSF levels of the anti-inflammatory interleukin (IL)-10 were linked to disease severity and neurodegeneration measures. Additionally, we examined IL-10 impact on synaptic transmission in striatal medium spiny neurons and its role in counteracting inflammatory synaptopathy induced by IL-1β in female C57BL/6 mice with experimental autoimmune encephalomyelitis (EAE). Our findings revealed a significant positive correlation between IL-10 CSF levels and changes in EDSS (Expanded Disability Status Scale) scores one year after MS diagnosis. Moreover, IL-10 levels in the CSF were positively correlated with volumes of specific subcortical brain structures, such as the nucleus caudate. In both MS patients' CSF and EAE mice striatum, IL-10 and IL-1β expressions were upregulated, suggesting possible antagonistic effects of these cytokines. Notably, IL-10 exhibited the ability to decrease glutamate transmission, increase GABA transmission in the striatum, and reverse IL-1β-induced abnormal synaptic transmission in EAE. In conclusion, our data suggest that IL-10 exerts direct neuroprotective effects in MS patients by modulating both excitatory and inhibitory transmission and attenuating IL-1β-induced inflammatory synaptopathy. These findings underscore the potential therapeutic significance of IL-10 in mitigating neurodegeneration in MS. [ABSTRACT FROM AUTHOR]

(1) Multiple sclerosis (MS) is identified by a complex interaction between central inflammation and neurodegeneration. Genetic individual variability could play a significative role in clinical presentation. The interleukin-5 (IL-5) rs2069812 single-nucleotide polymorphism (SNP) seems to define the clinical course of Th2 autoimmune diseases, while its role in MS has never been investigated. (2) In a group of 230 patients diagnosed with relapsing–remitting MS (RR-MS) or progressive MS (P-MS) and controls (IC), rs2069812 polymorphism, cerebrospinal fluid (CSF) levels of inflammatory mediators, and clinical and demographic characteristics were determined. In RR-MS patients, No Evidence of Disease Activity (NEDA-3) at three years of follow-up was detected. (3) We identified higher levels of proinflammatory cytokines, particularly IL-2 (median [IQR], RR-MS = 0.2 [0–0.7]; P-MS = 0.1 [0–1.6]; IC = 0.1 [0.0–0.1]; p < 0.005), IL-6 (RR-MS = 0.9 [0.3–2.3]; P-MS = 0.8 [0.1–2.7]; IC = 0.1 [0.0–0.5]; p < 0.005), IL-12 (RR-MS = 0.5 [0–1.1]; P-MS = 0.5 [0–1.1]; IC = 0.0 [0.0–0.3]; p < 0.005), and GM-CSF (RR-MS = 15.6 [4.8–26.4]; P-MS = 14 [3.3–29.7]; IC = 8.9 [4.7–11.7]; p < 0.005) in MS patients compared with IC. Conversely, anti-inflammatory cytokines, specifically IL-5 (RR-MS = 0.65 [0–2.4]; P-MS = 0.1 [0–0.8]; IC = 1.7 [0.6–2.8]; p < 0.005) and IL-1ra (RR-MS = 14.7 [4.9–26.4]; P-MS = 13.1 [4.7–22.2]; IC = 27.8 [17.7–37.6]; p < 0.005) were higher in controls. According to rs2069812, in MS patients, the T-allele was associated with higher concentrations of proinflammatory mediators (IL-2, CT/TT = 0.2 [0.0–2.0]; CC = 0.1 [0.0–0.4], p = 0.015; IL-6, CT/TT = 1.2 [0.4–3.2] vs. CC = 0.7 [0.1–1.7], p = 0.007; IL-15, CT/TT = 0.1 [0.0–9.5] vs. CC = 0.0 [0.0–0.1], p = 0.019; and GM-CSF, CT/TT = 0.1 [0.0–0.6] vs. CC = 0.05 [0.0–0.1], p < 0.001), and CC was associated with anti-inflammatory mediators (IL-5, CT/TT = 0.03 [0.0–1.9] vs. CC = 1.28 [0.0–2.7], p = 0.001; IL-1ra, CT/TT = 12.1 [4.1–25.9] vs. CC = 18.1 [12.1–26.9], p = 0.006). We found the same differences in RR-MS patients (IL-2, T-allele median [IQR] = 0.3 [0.0–2.0] vs. C-allele, median [IQR] = 0.04 [0.0–0.3]; p = 0.005; IL-6, T-allele, median [IQR] = 1.3 [0.4–3.3] vs. C-allele, median [IQR] = 0.6 [0.03–1.5]; p = 0.001; IL-15, T-allele, median [IQR] = 0.1 [0.0–9.5] vs. C-allele, median [IQR] = 0.0 [0.0–0.1]; p = 0.008; GM-CSF, T-allele, median [IQR] = 0.1 [0.0–97.9] vs. C-allele, median [IQR] = 0.0 [0.0–0.001]; p < 0.001; IL-5, T-allele, median [IQR] = 0.02 [0.0–2.2] vs. C-allele, median [IQR] = 1.5 [0.0–2.9]; p = 0.016; and IL-1ra, T-allele, median [IQR] = 12.1 [4.3–26.4] vs. C-allele, median [IQR] = 18.5 [12.7–28.3]; p = 0.006) but not in P-MS, except for IL-5 (T-allele, median [IQR] = 0.1 [0–0.23] vs. C-allele, median [IQR] = 0.6 [0.0–2.5]; p = 0.022). Finally, we identified an association between CC in RR-MS patients and NEDA-3 after three years of follow-up (p = 0.007). (4) We describe, for the first time, the role of an SNP of the IL-5 gene in regulating central neuroinflammation and influencing clinical course in MS patients. [ABSTRACT FROM AUTHOR]