High-dimensional immune profiling identifies a biomarker to monitor dimethyl fumarate response in multiple sclerosis.

Dimethyl fumarate (DMF) is an immunomodulatory treatment for multiple sclerosis (MS). Despite its wide clinical use, the mechanisms underlying clinical response are not understood. This study aimed to reveal immune markers of therapeutic response to DMF treatment in MS. For this purpose, we prospectively collected peripheral blood mononuclear cells (PBMCs) from a highly characterized cohort of 44 individuals with MS before and at 12 and 48 wk of DMF treatment. Single cells were profiled using high-dimensional mass cytometry. To capture the heterogeneity of different immune subsets, we adopted a bioinformatic multipanel approach that allowed cell population-cluster assignment of more than 50 different parameters, including lineage and activation markers as well as chemokine receptors and cytokines. Data were further analyzed in a semiunbiased fashion implementing a supervised representation learning approach to capture subtle longitudinal immune changes characteristic for therapy response. With this approach, we identified a population of memory T helper cells expressing high levels of neuroinflammatory cytokines (granulocyte-macrophage colony-stimulating factor [GM-CSF], interferon γ [IFNγ]) as well as CXCR3, whose abundance correlated with treatment response. Using spectral flow cytometry, we confirmed these findings in a second cohort of patients. Serum neurofilament light-chain levels confirmed the correlation of this immune cell signature with axonal damage. The identified cell population is expanded in peripheral blood under natalizumab treatment, substantiating a specific role in treatment response. We propose that depletion of GM-CSF-, IFNγ-, and CXCR3-expressing T helper cells is the main mechanism of action of DMF and allows monitoring of treatment response.

Twin study reveals non-heritable immune perturbations in multiple sclerosis.

Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system underpinned by partially understood genetic risk factors and environmental triggers and their undefined interactions1,2. Here we investigated the peripheral immune signatures of 61 monozygotic twin pairs discordant for MS to dissect the influence of genetic predisposition and environmental factors. Using complementary multimodal high-throughput and high-dimensional single-cell technologies in conjunction with data-driven computational tools, we identified an inflammatory shift in a monocyte cluster of twins with MS, coupled with the emergence of a population of IL-2 hyper-responsive transitional naive helper T cells as MS-related immune alterations. By integrating data on the immune profiles of healthy monozygotic and dizygotic twin pairs, we estimated the variance in CD25 expression by helper T cells displaying a naive phenotype to be largely driven by genetic and shared early environmental influences. Nonetheless, the expanding helper T cells of twins with MS, which were also elevated in non-twin patients with MS, emerged independent of the individual genetic makeup. These cells expressed central nervous system-homing receptors, exhibited a dysregulated CD25-IL-2 axis, and their proliferative capacity positively correlated with MS severity. Together, our matched-pair analysis of the extended twin approach allowed us to discern genetically and environmentally determined features of an MS-associated immune signature.

Machine Learning Using Real-World and Translational Data to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy.

(1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients.

Introduction: Artificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. Methods: We prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. Results: Of 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. Conclusions: In this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients.

Update on treatment in multiple sclerosis.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. In recent years, many disease-modifying therapies (DMT) have been approved for MS treatment. For this reason, a profound knowledge of the characteristics and indications of the available compounds is required to tailor the therapeutic strategy to the individual patient characteristics. This should include the mechanism of action and pharmacokinetic of the drug, the safety and efficacy profile provided by clinical trials, as well as the understanding of possible side effects. Moreover, the evolving knowledge of the disease is paving the way to new and innovative therapeutic approaches, as well as the development of new biomarkers to monitor the therapeutic response and to guide the clinician's therapeutic choices. In this review we provide a comprehensive overview on currently approved therapies in MS and the emerging evidence-based strategies to adopt for initiating, monitoring, and eventually adapting a therapeutic regimen with DMT.

Single-cell profiling of myasthenia gravis identifies a pathogenic T cell signature.

Myasthenia gravis (MG) is an autoimmune disease characterized by impaired neuromuscular signaling due to autoantibodies targeting the acetylcholine receptor. Although its auto-antigens and effector mechanisms are well defined, the cellular and molecular drivers underpinning MG remain elusive. Here, we employed high-dimensional single-cell mass and spectral cytometry of blood and thymus samples from MG patients in combination with supervised and unsupervised machine-learning tools to gain insight into the immune dysregulation underlying MG. By creating a comprehensive immune map, we identified two dysregulated subsets of inflammatory circulating memory T helper (Th) cells. These signature ThCD103 and ThGM cells populated the diseased thymus, were reduced in the blood of MG patients, and were inversely correlated with disease severity. Both signature Th subsets rebounded in the blood of MG patients after surgical thymus removal, indicative of their role as cellular markers of disease activity. Together, this in-depth analysis of the immune landscape of MG provides valuable insight into disease pathogenesis, suggests novel biomarkers and identifies new potential therapeutic targets for treatment.

Mass Cytometry of CSF Identifies an MS-Associated B-cell Population.

OBJECTIVE: To identify an MS-specific immune cell population by deep immune phenotyping and relate it to soluble signaling molecules in CSF. METHODS: We analyzed surface expression of 22 markers in paired blood/CSF samples from 39 patients using mass cytometry (cytometry by time of flight). We also measured the concentrations of 296 signaling molecules in CSF using proximity extension assay. Results were analyzed using highly automated unsupervised algorithmic informatics. RESULTS: Mass cytometry objectively identified a B-cell population characterized by the expression of CD49d, CD69, CD27, CXCR3, and human leukocyte antigen (HLA)-DR as clearly associated with MS. Concentrations of the B cell-related factors, notably FCRL2, were increased in MS CSF, especially in early stages of the disease. The B-cell trophic factor B cell activating factor (BAFF) was decreased in MS. Proteins involved in neural plasticity were also reduced in MS. CONCLUSION: When analyzed without a priori assumptions, both the soluble and the cellular compartments of the CSF in MS were characterized by markers related to B cells, and the strongest candidate for an MS-specific cell type has a B-cell phenotype.

GM-CSF and CXCR4 define a T helper cell signature in multiple sclerosis.

Cytokine dysregulation is a central driver of chronic inflammatory diseases such as multiple sclerosis (MS). Here, we sought to determine the characteristic cellular and cytokine polarization profile in patients with relapsing-remitting multiple sclerosis (RRMS) by high-dimensional single-cell mass cytometry (CyTOF). Using a combination of neural network-based representation learning algorithms, we identified an expanded T helper cell subset in patients with MS, characterized by the expression of granulocyte-macrophage colony-stimulating factor and the C-X-C chemokine receptor type 4. This cellular signature, which includes expression of very late antigen 4 in peripheral blood, was also enriched in the central nervous system of patients with relapsing-remitting multiple sclerosis. In independent validation cohorts, we confirmed that this cell population is increased in patients with MS compared with other inflammatory and non-inflammatory conditions. Lastly, we also found the population to be reduced under effective disease-modifying therapy, suggesting that the identified T cell profile represents a specific therapeutic target in MS.

The end of omics? High dimensional single cell analysis in precision medicine.

High-dimensional single-cell (HDcyto) technologies, such as mass cytometry (CyTOF) and flow cytometry, are the key techniques that hold a great promise for deciphering complex biological processes. During the last decade, we witnessed an exponential increase of novel HDcyto technologies that are able to deliver an in-depth profiling in different settings, such as various autoimmune diseases and cancer. The concurrent advance of custom data-mining algorithms has provided a rich substrate for the development of novel tools in translational medicine research. HDcyto technologies have been successfully used to investigate cellular cues driving pathophysiological conditions, and to identify disease-specific signatures that may serve as diagnostic biomarkers or therapeutic targets. These technologies now also offer the possibility to describe a complete cellular environment, providing unanticipated insights into human biology. In this review, we present an update on the current cutting-edge HDcyto technologies and their applications, which are going to be fundamental in providing further insights into human immunology and pathophysiology of various diseases. Importantly, we further provide an overview of the main algorithms currently available for data mining, together with the conceptual workflow for high-dimensional cytometric data handling and analysis. Overall, this review aims to be a handy overview for immunologists on how to design, develop and read HDcyto data.

High-dimensional single-cell analysis reveals the immune signature of narcolepsy.

Narcolepsy type 1 is a devastating neurological sleep disorder resulting from the destruction of orexin-producing neurons in the central nervous system (CNS). Despite its striking association with the HLA-DQB1*06:02 allele, the autoimmune etiology of narcolepsy has remained largely hypothetical. Here, we compared peripheral mononucleated cells from narcolepsy patients with HLA-DQB1*06:02-matched healthy controls using high-dimensional mass cytometry in combination with algorithm-guided data analysis. Narcolepsy patients displayed multifaceted immune activation in CD4+ and CD8+ T cells dominated by elevated levels of B cell-supporting cytokines. Additionally, T cells from narcolepsy patients showed increased production of the proinflammatory cytokines IL-2 and TNF. Although it remains to be established whether these changes are primary to an autoimmune process in narcolepsy or secondary to orexin deficiency, these findings are indicative of inflammatory processes in the pathogenesis of this enigmatic disease.