Addressing the clinical unmet needs in primary Sjögren's Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts

Can we cure Sjogren's disease? Unmet needs and raising questions in the era of Precision medicine

The wide spectrum of Sjögren's disease (SjD) clinical manifestations coupled with its multifaceted pathogenesis has complicated drug target development, optimal clinical trial design, identification of suitable SjD subgroups, selection of appropriate outcome measures, and interpretation of treatment efficacy. Despite recent advancements in biologic treatments for autoimmune diseases, the targeted therapy(ies) for SjD remain an unmet need, with no regulatory approvals, so far. Recent large, randomized studies have suggested some patient benefits; however, reproducibility is needed, and assessment of treatment efficacy requires refinement. In this review, we explore the potential for curing SjD taking into consideration the main pathogenetic mechanisms, clinical phenotypes, and underlying endotypes. We provide an overview of current recommendations and targeted treatments, identify potential reasons for treatment failures, and propose strategies (in and out of the box) for future directions. By addressing these areas, we offer a comprehensive perspective that may inform future research and therapeutic strategies for SjD.

Neuropsychiatric manifestations in systemic lupus erythematosus and Sjogren's disease

INTRODUCTION

Autoimmune diseases often present in a systemic manner, affecting various organs and tissues. Involvement of the central and peripheral nervous system is not uncommon in these conditions and is associated with high morbidity and mortality. Therefore, early recognition of the neuropsychiatric manifestations associated with rheumatologic diseases is essential for the introduction of appropriate therapies with the objective of providing a better quality of life for individuals.

OBJECTIVE

To provide a literature review of the neuropsychiatric manifestations related to Systemic Lupus Erythematosus (SLE) and primary Sjögren's Disease (pSD), through the description of signs, symptoms, and immunological variables associated with these conditions.

METHODS

A literature review was conducted by searching for national and international articles available in the SciELO and PubMed databases related to the description of neurological and psychiatric manifestations in patients with the rheumatologic diseases of interest in this study.

RESULTS

The main NP manifestations presented in SLE and pSD are discussed, focusing on clinical presentation and etiology. Treatment option are, however, mainly based on expert opinion, since a few randomized controlled trials have been done.

CONCLUSIONS

There is a high prevalence of neuropsychiatric manifestations associated with SLE and pSD. The variety of physiopathology pathways may explain the variety of symptoms, however pathological findings are rare. Multicenter studies on attribution protocols and treatment are necessary to address the current gaps.

A graph-learning based model for automatic diagnosis of Sjögren's syndrome on digital pathological images: a multicentre cohort study

BACKGROUND

Sjögren's Syndrome (SS) is a rare chronic autoimmune disorder primarily affecting adult females, characterized by chronic inflammation and salivary and lacrimal gland dysfunction. It is often associated with systemic lupus erythematosus, rheumatoid arthritis and kidney disease, which can lead to increased mortality. Early diagnosis is critical, but traditional methods for diagnosing SS, mainly through histopathological evaluation of salivary gland tissue, have limitations.

METHODS

The study used 100 labial gland biopsy, creating whole-slide images (WSIs) for analysis. The proposed model, named Cell-tissue-graph-based pathological image analysis model (CTG-PAM) and based on graph theory, characterizes single-cell feature, cell-cell feature, and cell-tissue feature. Building upon these features, CTG-PAM achieves cellular-level classification, enabling lymphocyte recognition. Furthermore, it leverages connected component analysis techniques in the cell graph structure to perform SS diagnosis based on lymphocyte counts.

FINDINGS

CTG-PAM outperforms traditional deep learning methods in diagnosing SS. Its area under the receiver operating characteristic curve (AUC) is 1.0 for the internal validation dataset and 0.8035 for the external test dataset. This indicates high accuracy. The sensitivity of CTG-PAM for the external dataset is 98.21%, while the accuracy is 93.75%. In comparison, the sensitivity and accuracy for traditional deep learning methods (ResNet-50) are lower. The study also shows that CTG-PAM's diagnostic accuracy is closer to skilled pathologists compared to beginners.

INTERPRETATION

Our findings indicate that CTG-PAM is a reliable method for diagnosing SS. Additionally, CTG-PAM shows promise in enhancing the prognosis of SS patients and holds significant potential for the differential diagnosis of both non-neoplastic and neoplastic diseases. The AI model potentially extends its application to diagnosing immune cells in tumor microenvironments.

FHBF: Federated hybrid boosted forests with dropout rates for supervised learning tasks across highly imbalanced clinical datasets

Although several studies have deployed gradient boosting trees (GBT) as a robust classifier for federated learning tasks (federated GBT [FGBT]), even with dropout rates (federated gradient boosting trees with dropout rate [FDART]), none of them have investigated the overfitting effects of FGBT across heterogeneous and highly imbalanced datasets within federated environments nor the effect of dropouts in the loss function. In this work, we present the federated hybrid boosted forests (FHBF) algorithm, which incorporates a hybrid weight update approach to overcome ill-posed problems that arise from overfitting effects during the training across highly imbalanced datasets in the cloud. Eight case studies were conducted to stress the performance of FHBF against existing algorithms toward the development of robust AI models for lymphoma development across 18 European federated databases. Our results highlight the robustness of FHBF, yielding an average loss of 0.527 compared with FGBT (0.611) and FDART (0.584) with increased classification performance (0.938 sensitivity, 0.732 specificity).

Boosting the performance of MALT lymphoma classification in patients with primary Sjögren's Syndrome through data augmentation: a case study

Sjögren's Syndrome (SS) patients with mucosa associated lymphoid tissue lymphomas (MALTLs) and diffuse large B-cell lymphomas (DLBCLs) have 10-year survival rates of 80% and 40%, respectively. This highlights the unique biologic burden of the two histologic forms, as well as, the need for early detection and thorough monitoring of these patients. The lack of MALTL patients and the fact that most studies are single cohort and combine patients with different lymphoma subtypes narrow the understanding of MALTL progression. Here, we propose a data augmentation pipeline that utilizes an advanced synthetic data generator which is trained on a Pan European data hub with primary SS (pSS) patients to yield a high-quality synthetic data pool. The latter is used for the development of an enhanced MALTL classification model. Four scenarios were defined to assess the reliability of augmentation. Our results revealed an overall improvement in the accuracy, sensitivity, specificity, and AUC by 7%, 6.3%, 9%, and 6.3%, respectively. This is the first case study that utilizes data augmentation to reflect the progression of MALTL in pSS.

Current and future treatment in primary Sjögren's syndrome - A still challenging development

Primary Sjögren's syndrome (pSS) is a chronic autoimmune disease characterized by sicca symptoms, systemic manifestations and constitutional symptoms substantially diminishing patient's quality of life. In this review, we summarize recent recommendations for management of pSS patients and current clinical studies in pSS addressing unmet medical needs. Expanding knowledge about disease pathogenesis and the introduction of validated outcome measures, such as capturing disease activity (ESSDAI) and patient-reported outcomes (ESSPRI) have shaped recent developments. In contrast, lack of evidence for current treatment options remarkably limits the management of pSS patients as reflected by the 2019 updated EULAR recommendations for management of Sjögren's syndrome. In this context, symptomatic treatment is usually appropriate for sicca symptoms, whereas systemic treatment is reserved for moderate to severe organ manifestations including care by a multidisciplinary team in centers of expertise. Most promising targets for new treatment modalities are based on immunopathological insights and include direct B cell targeting strategies, targeting co-stimulation by CD40/CD40L blocking, inhibition of key cytokine activity (BLyS/BAFF, type I interferon) and intracellular signaling pathways.

Neuropsychiatric manifestations in primary Sjogren syndrome

INTRODUCTION

Neurologic manifestations in primary Sjogren´s Syndrome (pSS) are characterized by a heterogeneity of clinical manifestations. In clinical practice, physicians are challenged with the absence of diagnostic criteria and the lack of clinical trials to support treatment. In this article, we will review epidemiology, clinical and immunological characterization, diagnosis and treatment of neurologic events in pSS.

AREAS COVERED

This narrative review provides an overview of neurologic manifestations described in pSS, complementary investigations and treatment reported. Articles were selected from Pubmed searches conducted between December 2021 and February 2022.

EXPERT OPINION

Epidemiology and clinical features of neurologic manifestations are derived from different cohort studies. Our understanding of pathophysiology of neurologic manifestations in pSS has significantly increased in the past few years, especially regarding PNS. However, there are still many knowledge gaps on therapeutics. The few available data on therapy rely upon small case series, from experiences with other autoimmune disease, such as systemic lupus erythematosus or expert opinion. There is an urgent need for well-designed clinical trials.